{"meta":{"query_hash":"b5236ad0ed3f","filters":{"venue":"Sensors"},"cohort_total":2265,"direct_labels_cover":3,"predictions_cover":2265,"exported":2265,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/b5236ad0ed3f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Sensors"},"results":[{"id":"W1499855463","doi":"10.3390/s150614045","title":"A Review of Membrane-Based Biosensors for Pathogen Detection","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Biosensor; Transduction (biophysics); Nanotechnology; Membrane; Biochemical engineering; Biology; Materials science; Engineering; Biophysics; Biochemistry","score_opus":0.04527705267802797,"score_gpt":0.30032061015745254,"score_spread":0.2550435574794246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499855463","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031994982,0.9943567,0.0010365569,0.0002666767,0.00041152933,0.000014886762,0.000073031246,0.000031884214,0.0034886978],"genre_scores_gemma":[0.0011588904,0.99542135,0.0010372086,0.0001901338,0.00016834258,0.0000147402225,0.00008500536,0.0000043931473,0.0019199225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964833,0.00003822376,0.000046005367,0.00007093246,0.0001686637,0.000027890952],"domain_scores_gemma":[0.99963546,0.00015121057,0.000058297195,0.000014178491,0.000113961774,0.000026983858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004997908,0.0012717448,0.0012851481,0.0032112158,0.00042517023,0.0010693948,0.001016752,0.0011223776,0.0068985904],"category_scores_gemma":[0.00072035595,0.00048596002,0.00066035154,0.0034293905,0.000359791,0.0019024205,0.00067215035,0.001464336,0.00434076],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052435702,0.00012193782,0.00018925009,0.03785139,0.00008459728,0.0003029795,0.0001036212,0.00053151057,0.017193194,0.006478392,0.039880645,0.89721006],"study_design_scores_gemma":[0.000003872229,0.00008406475,0.00035808905,0.0019619144,0.00006779445,0.0008996054,0.00003637635,0.00010998596,0.00245452,0.0010817721,0.9929168,0.00002526256],"about_ca_topic_score_codex":0.0008712408,"about_ca_topic_score_gemma":0.0014113971,"teacher_disagreement_score":0.0068985904,"about_ca_system_score_codex":0.00056745275,"about_ca_system_score_gemma":0.001048658,"threshold_uncertainty_score":0.023078084},"labels":[],"label_agreement":null},{"id":"W1500650360","doi":"10.3390/s150614788","title":"Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasound energy; Artificial neural network; Acoustics; Ultrasound; Sound intensity; Ultrasonic sensor; Therapeutic ultrasound; Transducer; Computer science; Sound power; Materials science; Artificial intelligence; Physics","score_opus":0.06410335381451737,"score_gpt":0.2506919504855417,"score_spread":0.18658859667102434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500650360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055913247,0.00040103166,0.9380471,0.00034431124,0.00021878345,0.00020932882,0.000080980615,0.0014519066,0.0033333383],"genre_scores_gemma":[0.6225109,0.00037359446,0.37230688,0.0003615553,0.0000574227,0.00044352774,0.00011225944,0.00005700855,0.0037768136],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994267,0.00006859885,0.000053322874,0.00018781293,0.00022223545,0.000041372194],"domain_scores_gemma":[0.99963236,0.000070995746,0.000061603525,0.000028035949,0.00018442262,0.000022465045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237792,0.00068873505,0.00054878177,0.00042447794,0.00034147926,0.00051697006,0.0014719231,0.0010039934,0.0010764042],"category_scores_gemma":[0.00063271855,0.00053222803,0.00047858598,0.0003799336,0.00033097272,0.0010725938,0.00050000264,0.00060610427,0.00045747115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040272388,0.0003977994,0.0040662466,0.0006146395,0.00015317486,0.0003964061,0.00019739673,0.13762774,0.6625623,0.0031971626,0.002238251,0.18814611],"study_design_scores_gemma":[0.000023308217,0.00026760454,0.0012493094,0.000019334066,0.000053269963,0.00014089736,0.00001476874,0.8923016,0.10300524,0.0003574605,0.0025139146,0.000053179865],"about_ca_topic_score_codex":0.001598649,"about_ca_topic_score_gemma":0.001882823,"teacher_disagreement_score":0.001598649,"about_ca_system_score_codex":0.000608741,"about_ca_system_score_gemma":0.00065704406,"threshold_uncertainty_score":0.004416764},"labels":[],"label_agreement":null},{"id":"W1527750937","doi":"10.3390/s150614458","title":"DEADS: Depth and Energy Aware Dominating Set Based Algorithm for Cooperative Routing along with Sink Mobility in Underwater WSNs","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"King Saud University","keywords":"Computer science; Computer network; Wireless sensor network; Routing protocol; Sink (geography); Maximization; Network performance; Routing (electronic design automation); Network layer; Physical layer; Geographic routing; Distributed computing; Link-state routing protocol; Wireless; Layer (electronics); Mathematical optimization; Telecommunications; Mathematics","score_opus":0.02907986546285019,"score_gpt":0.2438437981807115,"score_spread":0.21476393271786132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527750937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067920156,0.0006210016,0.92845553,0.0003457932,0.000104362465,0.00012965704,0.00005353763,0.00031277255,0.0020571954],"genre_scores_gemma":[0.77341276,0.00041145482,0.2219071,0.00018561629,0.00003624124,0.00023146761,0.00019258473,0.00005607246,0.0035666868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996984,0.00010477647,0.00001991909,0.000047176487,0.000095114534,0.000034682278],"domain_scores_gemma":[0.99957997,0.0001821583,0.00005937629,0.000038143586,0.00009093,0.000049505157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084810983,0.00069055165,0.00082189514,0.00061091804,0.0006457154,0.0004544909,0.0020371694,0.00059602223,0.00047021866],"category_scores_gemma":[0.0013642593,0.00028367285,0.00054203975,0.0004889281,0.00044575112,0.00078657275,0.0011484984,0.00042384153,0.00013958495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015905303,0.000089080975,0.0014639529,0.00011098916,0.000088201225,0.00013820888,0.000332821,0.8931816,0.010534491,0.007212522,0.0015342783,0.08515479],"study_design_scores_gemma":[0.000013073641,0.00008589859,0.00012328626,0.000005325643,0.000012184336,0.000040815157,0.00003859101,0.9964185,0.0011207847,0.0014327858,0.0007024388,0.0000063711886],"about_ca_topic_score_codex":0.0019554426,"about_ca_topic_score_gemma":0.002880752,"teacher_disagreement_score":0.0020371694,"about_ca_system_score_codex":0.0005998356,"about_ca_system_score_gemma":0.0008266923,"threshold_uncertainty_score":0.004485309},"labels":[],"label_agreement":null},{"id":"W1529613181","doi":"10.3390/s150614701","title":"Performance Analysis of Several GPS/Galileo Precise Point Positioning Models","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Galileo (satellite navigation); Global Positioning System; Precise Point Positioning; Geodesy; Satellite; Computer science; GNSS applications; Geography; Telecommunications; Engineering; Aerospace engineering","score_opus":0.023710932796784448,"score_gpt":0.21856718914223086,"score_spread":0.1948562563454464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529613181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72693473,0.0012803179,0.25169703,0.00075300504,0.00017845049,0.00018435389,0.0009597733,0.003643636,0.014368647],"genre_scores_gemma":[0.97515327,0.0002450852,0.02269691,0.00006746454,0.000018182978,0.00005064579,0.00068962795,0.0000970257,0.0009818049],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982765,0.0006079318,0.00009916976,0.00026934652,0.0005453708,0.00020169638],"domain_scores_gemma":[0.9967745,0.0016988941,0.00026181407,0.00038818613,0.00074753014,0.00012910663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032516227,0.0010977712,0.0009875845,0.0010546613,0.00066441187,0.001343529,0.001378958,0.0009950716,0.0013275804],"category_scores_gemma":[0.007521312,0.00070157746,0.0008995656,0.0017712344,0.000626507,0.0015034955,0.0013488586,0.0009870958,0.0003721813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022740933,0.00003670988,0.0050368453,0.000045633453,0.00008324044,0.000031515097,0.000038832753,0.9749716,0.0005893848,0.00075658667,0.00036647078,0.01781567],"study_design_scores_gemma":[0.000025370302,0.00006454508,0.0014892466,0.0000061120268,0.000025253854,0.000012434336,0.00002604238,0.9971763,0.0006489537,0.0002468143,0.00026633186,0.00001258218],"about_ca_topic_score_codex":0.05676906,"about_ca_topic_score_gemma":0.022731185,"teacher_disagreement_score":0.05676906,"about_ca_system_score_codex":0.0016909868,"about_ca_system_score_gemma":0.0015746895,"threshold_uncertainty_score":0.11287731},"labels":[],"label_agreement":null},{"id":"W1529660842","doi":"10.3390/s150510616","title":"Tracking Diurnal Variation in Photosynthetic Down-Regulation Using Low Cost Spectroscopic Instrumentation","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia; National Stroke Foundation; National Science Foundation","keywords":"Radiance; Remote sensing; Environmental science; Photochemical Reflectance Index; Calibration; Eddy covariance; Computer science; Optics; Chlorophyll fluorescence; Mathematics; Physics; Geography; Ecology; Statistics","score_opus":0.020577798654581793,"score_gpt":0.2484991814833596,"score_spread":0.2279213828287778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529660842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95591474,0.00031494338,0.041336536,0.000038957707,0.000026746602,0.000047559857,0.00028862845,0.00047783376,0.0015539526],"genre_scores_gemma":[0.96178466,0.0002835352,0.03653516,0.00004769204,0.000017902943,0.00009150468,0.00035887322,0.00006583328,0.00081489666],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997447,0.000042143893,0.000008609608,0.00008111557,0.0001043985,0.000019042247],"domain_scores_gemma":[0.99974173,0.00008504252,0.000056641507,0.000040357398,0.00006177406,0.000014542304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033163602,0.0002488678,0.00026606736,0.0003088111,0.00023968938,0.00033066963,0.0003484627,0.0002935785,0.0005197005],"category_scores_gemma":[0.0005222482,0.00018395779,0.00015652229,0.00039425507,0.00014681969,0.00029592073,0.00015409029,0.0003521956,0.00020552476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000217185,0.00019161594,0.022692101,0.000100688325,0.000032587086,0.000026918278,0.00012860044,0.0021077332,0.9298918,0.00011403826,0.00025490345,0.044241834],"study_design_scores_gemma":[0.00004694412,0.0009667274,0.36513582,0.000018527251,0.00009727594,0.00028360446,0.00014310451,0.06388093,0.56585693,0.00044724703,0.003062135,0.000060814138],"about_ca_topic_score_codex":0.0009877373,"about_ca_topic_score_gemma":0.0021886555,"teacher_disagreement_score":0.0009877373,"about_ca_system_score_codex":0.00022904327,"about_ca_system_score_gemma":0.00014973301,"threshold_uncertainty_score":0.0019639134},"labels":[],"label_agreement":null},{"id":"W1543656953","doi":"10.3390/s150612218","title":"Microfabrication and Integration of a Sol-Gel PZT Folded Spring Energy Harvester","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Energy harvesting; Microfabrication; Voltage; Battery (electricity); Electrical engineering; Rectification; Power management; Electronic engineering; Power (physics); Engineering; Automotive engineering; Fabrication","score_opus":0.027100001048358295,"score_gpt":0.22153583892482345,"score_spread":0.19443583787646515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543656953","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7081992,0.0009634497,0.28301987,0.0003460047,0.00023882011,0.0002220851,0.0002486438,0.001489425,0.0052725417],"genre_scores_gemma":[0.6613842,0.0005462021,0.33028203,0.00011905243,0.00003792587,0.00018977156,0.00021565835,0.00016045698,0.007064634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997645,0.000009374176,0.000012933833,0.00006124395,0.0001247525,0.000027070446],"domain_scores_gemma":[0.99987996,0.000027995226,0.000033901044,0.000028100721,0.000021952757,0.000008168836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023148792,0.0002571567,0.00027696582,0.00024918452,0.00022359216,0.0003090936,0.0006141672,0.0004030658,0.0009097006],"category_scores_gemma":[0.0002971729,0.00028068956,0.00039264452,0.0001779169,0.0002567424,0.00035285196,0.00033920296,0.00046878168,0.00047738015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007914007,0.000014804668,0.00006399202,0.000031681724,0.0000043258774,0.00007731847,0.00004287106,0.0006972454,0.993598,0.0002356564,0.00006020156,0.00516597],"study_design_scores_gemma":[0.000007778897,0.00016776392,0.0009842399,0.0000038731123,0.000009862995,0.00024605388,0.000017900706,0.0045673796,0.99041545,0.00009874274,0.0034714318,0.00000951837],"about_ca_topic_score_codex":0.00023903417,"about_ca_topic_score_gemma":0.0006808346,"teacher_disagreement_score":0.0009097006,"about_ca_system_score_codex":0.00025125121,"about_ca_system_score_gemma":0.00027060453,"threshold_uncertainty_score":0.0030431747},"labels":[],"label_agreement":null},{"id":"W1544808012","doi":"10.3390/s150717715","title":"Electrochemical Impedance Sensors for Monitoring Trace Amounts of NO3 in Selected Growing Media","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Canada Research Chairs","keywords":"Nitrate; Dielectric spectroscopy; Sensitivity (control systems); Microelectronics; Data acquisition; Computer science; Computer data storage; Environmental science; Precision agriculture; Electrical engineering; Process engineering; Remote sensing; Electronic engineering; Materials science; Engineering; Computer hardware; Chemistry; Electrochemistry; Agriculture; Geography","score_opus":0.01989395988490249,"score_gpt":0.2580301890410311,"score_spread":0.2381362291561286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544808012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.340786,0.012869254,0.632091,0.001045801,0.000512918,0.0004277522,0.0011762418,0.0015118042,0.009579176],"genre_scores_gemma":[0.6524831,0.009295439,0.32798532,0.00031435437,0.000082960185,0.00023626578,0.0005827904,0.00006750626,0.008952232],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933296,0.00008583763,0.000030607633,0.000106336425,0.00042200947,0.000022251563],"domain_scores_gemma":[0.999665,0.00011351229,0.00005837425,0.00002870842,0.000114568975,0.000019873192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054631213,0.00045380712,0.00035482092,0.0004242769,0.00017927002,0.00033350414,0.0006987459,0.00058348896,0.0006443817],"category_scores_gemma":[0.00093946303,0.0002581661,0.00028073802,0.000425088,0.00019707845,0.000738081,0.00037301567,0.00050273957,0.00038682178],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047838625,0.00002022291,0.00035649736,0.00010247833,0.0000052206433,0.00003194121,0.000022007875,0.0003538378,0.9835857,0.00024482643,0.00018960699,0.015039755],"study_design_scores_gemma":[0.000013355269,0.00031393688,0.0024396896,0.000021118343,0.000021821004,0.00035109,0.000049114078,0.012104916,0.9758136,0.00026533505,0.008580667,0.000025338531],"about_ca_topic_score_codex":0.00064457546,"about_ca_topic_score_gemma":0.002362863,"teacher_disagreement_score":0.0006987459,"about_ca_system_score_codex":0.00039088566,"about_ca_system_score_gemma":0.00025900707,"threshold_uncertainty_score":0.0028891563},"labels":[],"label_agreement":null},{"id":"W1569031853","doi":"10.3390/s150612180","title":"GNSS Space-Time Interference Mitigation and Attitude Determination in the Presence of Interference Signals","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"European Space Agency","keywords":"GNSS applications; Pseudorange; Interference (communication); Computer science; Distortion (music); Wideband; Antenna (radio); Electronic engineering; Satellite system; GPS signals; Narrowband; Global Positioning System; Telecommunications; Assisted GPS; Engineering; Bandwidth (computing); Channel (broadcasting); Amplifier","score_opus":0.02023083020922153,"score_gpt":0.24568038266983805,"score_spread":0.22544955246061651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569031853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20391725,0.00020607412,0.7879394,0.00009988804,0.000054819757,0.000035437053,0.00007046626,0.00086184836,0.006814775],"genre_scores_gemma":[0.7971487,0.00020738448,0.19878462,0.00008214815,0.000035607944,0.000046302208,0.00024013953,0.00008029502,0.0033746797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995316,0.00008104934,0.000016325088,0.00006905701,0.0002518551,0.000050013066],"domain_scores_gemma":[0.9997328,0.000050825958,0.000045673507,0.00003891929,0.00012042929,0.000011277204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028088476,0.00057941565,0.0003416103,0.00048755607,0.00028564155,0.0004551716,0.00036906626,0.0004971059,0.00058117945],"category_scores_gemma":[0.0012060553,0.00020735849,0.0003007855,0.0005502499,0.00030662608,0.0004794766,0.000513538,0.00033250346,0.0005770048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055367785,0.00009386782,0.013726781,0.0001882309,0.00010562593,0.00040400974,0.00033174275,0.14611238,0.40855327,0.006219154,0.0013080139,0.42240328],"study_design_scores_gemma":[0.000051982857,0.00036243864,0.02426656,0.000030013727,0.00009141068,0.0006783215,0.00015503874,0.6729688,0.29147893,0.002406864,0.0074596484,0.000050006824],"about_ca_topic_score_codex":0.0019039161,"about_ca_topic_score_gemma":0.0030492146,"teacher_disagreement_score":0.0019039161,"about_ca_system_score_codex":0.00022753877,"about_ca_system_score_gemma":0.00051698973,"threshold_uncertainty_score":0.0037856102},"labels":[],"label_agreement":null},{"id":"W1590784329","doi":"10.3390/s150510547","title":"A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"China Scholarship Council; Queen's University; National Natural Science Foundation of China","keywords":"Swarm behaviour; Fibre optic gyroscope; Computer science; Algorithm; Gyroscope; Calibration; Inertial navigation system; Artificial neural network; Simulation; Inertial frame of reference; Optical fiber; Artificial intelligence; Engineering; Mathematics; Statistics","score_opus":0.04273353641588182,"score_gpt":0.2621689103930439,"score_spread":0.2194353739771621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590784329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011744166,0.0001985498,0.9859282,0.000066929606,0.000054317625,0.000036753005,0.000016588265,0.00032589922,0.001628745],"genre_scores_gemma":[0.55935514,0.0003702801,0.43510088,0.0001405686,0.000065462744,0.00032660173,0.00019557193,0.000093792245,0.004351636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997063,0.000059238082,0.000023227054,0.00006767912,0.00010935139,0.000034032106],"domain_scores_gemma":[0.99963856,0.000117935,0.000055782126,0.000026588712,0.00013838135,0.000022805647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005139359,0.0009284711,0.0007139879,0.00054824667,0.0004641644,0.00054952,0.0009812382,0.0008093698,0.0010805741],"category_scores_gemma":[0.0014556645,0.000308563,0.0007357134,0.00042407753,0.00041364366,0.0006253005,0.00076345634,0.0006917976,0.00028941128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009034436,0.000039358398,0.0017071238,0.000093114904,0.00008572488,0.00008614424,0.00015585606,0.8161304,0.009068007,0.00434375,0.0015122254,0.16668797],"study_design_scores_gemma":[0.000011351945,0.00002504637,0.00012687493,0.0000049054943,0.000008422336,0.000018042821,0.000007742963,0.99776554,0.0007338855,0.00044524187,0.0008466815,0.000006221307],"about_ca_topic_score_codex":0.00882931,"about_ca_topic_score_gemma":0.0052107,"teacher_disagreement_score":0.00882931,"about_ca_system_score_codex":0.00037976,"about_ca_system_score_gemma":0.0010047465,"threshold_uncertainty_score":0.017555833},"labels":[],"label_agreement":null},{"id":"W1604023377","doi":"10.3390/s150614513","title":"The Role of Infrared Thermography as a Non-Invasive Tool for the Detection of Lameness in Cattle","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Thermography; Lameness; Skin temperature; Biomedical engineering; Infrared; Medicine; Materials science; Surgery; Optics","score_opus":0.05438394816381693,"score_gpt":0.35148734571153617,"score_spread":0.29710339754771925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604023377","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013543403,0.9993217,0.00007876537,0.000111623645,0.0000493362,0.0000023954833,0.000010834251,0.0000028017068,0.0002871415],"genre_scores_gemma":[0.0012325267,0.99823123,0.00017711041,0.00008424775,0.000064639484,0.000003730691,0.00001752447,0.0000011050706,0.00018802026],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940157,0.00012597619,0.00009703987,0.00010620153,0.00022995056,0.000039272167],"domain_scores_gemma":[0.9981893,0.0010691154,0.00028181556,0.000031494572,0.00037835585,0.000049972437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014662218,0.00077904825,0.0016940662,0.0045650303,0.00023708196,0.0012831044,0.00082496746,0.001164854,0.002706435],"category_scores_gemma":[0.0018630853,0.00034170476,0.00085945526,0.00283248,0.0006897328,0.0015653961,0.0004820224,0.001265275,0.0008759993],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009168939,0.00006895851,0.000688043,0.041070033,0.00019924314,0.00018987106,0.00010194936,0.00029204012,0.0028039298,0.0011487224,0.0077844504,0.94556105],"study_design_scores_gemma":[0.000024043014,0.00040563077,0.007660365,0.018813271,0.00075396424,0.00402832,0.000333284,0.0002075097,0.0027287968,0.0020958097,0.9628735,0.00007549863],"about_ca_topic_score_codex":0.0018365644,"about_ca_topic_score_gemma":0.0026824714,"teacher_disagreement_score":0.0045650303,"about_ca_system_score_codex":0.0005616527,"about_ca_system_score_gemma":0.0011746399,"threshold_uncertainty_score":0.009053946},"labels":[],"label_agreement":null},{"id":"W1632941276","doi":"10.3390/s150511189","title":"Modification of an RBF ANN-Based Temperature Compensation Model of Interferometric Fiber Optical Gyroscopes","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Compensation (psychology); Artificial neural network; Interferometry; Gyroscope; Fibre optic gyroscope; Radial basis function; Fiber; Optical fiber; Temperature measurement; Computer science; Materials science; Control theory (sociology); Engineering; Artificial intelligence; Optics; Physics; Telecommunications; Aerospace engineering","score_opus":0.03875352385591572,"score_gpt":0.26742615535492575,"score_spread":0.22867263149901004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1632941276","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03032962,0.0003903026,0.9655115,0.00011739984,0.00008350705,0.00004031223,0.000053170093,0.0009737109,0.0025005026],"genre_scores_gemma":[0.8909223,0.0003704466,0.10399424,0.00009225674,0.00004310018,0.00010323569,0.00015640391,0.000070083166,0.004247874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948287,0.000089385496,0.00003397573,0.0001472383,0.00020176072,0.00004483313],"domain_scores_gemma":[0.999705,0.00004815414,0.00004135582,0.000034195542,0.00016141181,0.000009834209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005268671,0.0006737223,0.0005404936,0.00034530228,0.0002571545,0.00048478646,0.0009558266,0.00064465654,0.0008498488],"category_scores_gemma":[0.0010292282,0.0002737462,0.00060144713,0.00027650487,0.00025851044,0.00088610547,0.0003662626,0.00065661845,0.00035747926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020037727,0.00007307409,0.0024175956,0.00016643938,0.00008279747,0.00012426481,0.00009498609,0.794355,0.043496422,0.0024297626,0.0009320972,0.15562718],"study_design_scores_gemma":[0.0000027877204,0.000021854821,0.00035728602,0.00000392029,0.000007688903,0.000020968526,0.0000027106514,0.9950321,0.0038434314,0.0001618794,0.00053855387,0.0000067528586],"about_ca_topic_score_codex":0.007636686,"about_ca_topic_score_gemma":0.0059587806,"teacher_disagreement_score":0.007636686,"about_ca_system_score_codex":0.00058453955,"about_ca_system_score_gemma":0.00051598204,"threshold_uncertainty_score":0.015184462},"labels":[],"label_agreement":null},{"id":"W1683817273","doi":"10.3390/s140815084","title":"Stability Analysis for a Multi-Camera Photogrammetric System","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NovAtel (Canada); University of Calgary","funders":"","keywords":"Photogrammetry; Collinearity; Orientation (vector space); Computer science; Calibration; Computer vision; Digital camera; Artificial intelligence; Stability (learning theory); Camera resectioning; Ground truth; Machine learning; Mathematics","score_opus":0.1428522358457093,"score_gpt":0.3157084360700478,"score_spread":0.17285620022433848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1683817273","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009254954,0.4094017,0.5679946,0.00059289485,0.00053602754,0.00009211612,0.0000894157,0.0003684223,0.011669856],"genre_scores_gemma":[0.27172273,0.52972263,0.18351692,0.00039711499,0.0008531956,0.000259616,0.00033405342,0.00014006959,0.013053631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988979,0.00017699393,0.00009819174,0.00022166397,0.00056982745,0.000035341338],"domain_scores_gemma":[0.9986941,0.00054865656,0.00020342835,0.00007339145,0.000464123,0.000016363387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011735271,0.0010100646,0.00096107187,0.002894691,0.0003640375,0.0010520057,0.0009964933,0.001106925,0.0018787205],"category_scores_gemma":[0.0023057745,0.00049193355,0.0010389818,0.002162149,0.00071369635,0.0013436818,0.0005833516,0.0007242879,0.0010337747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039082675,0.000032128213,0.0013694526,0.0060745482,0.00013247374,0.00025034047,0.00017739255,0.029548848,0.0184565,0.011559536,0.003057876,0.92930186],"study_design_scores_gemma":[0.000030265543,0.0007472678,0.015991187,0.00419594,0.0007851082,0.005822936,0.0009835646,0.31202373,0.10797377,0.045558184,0.5054672,0.00042092078],"about_ca_topic_score_codex":0.0015603817,"about_ca_topic_score_gemma":0.00127526,"teacher_disagreement_score":0.002894691,"about_ca_system_score_codex":0.0006659127,"about_ca_system_score_gemma":0.0006872467,"threshold_uncertainty_score":0.0062848926},"labels":[],"label_agreement":null},{"id":"W1694175521","doi":"10.3390/s150717693","title":"Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; University of Alberta; University of British Columbia","funders":"Charles Darwin University; Australian Government","keywords":"Computer science; Compensation (psychology); Node (physics); Filter (signal processing); Detector; Reset (finance); Sensitivity (control systems); Annotation; P wave; Artificial intelligence; Real-time computing; Electronic engineering; Computer vision; Engineering; Acoustics; Physics; Telecommunications; Atrial fibrillation; Medicine","score_opus":0.05220403879660943,"score_gpt":0.3318739242509128,"score_spread":0.2796698854543033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1694175521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08095051,0.0010195556,0.91192883,0.00014984403,0.00016455028,0.00015045948,0.00055380416,0.00339627,0.0016861594],"genre_scores_gemma":[0.52190655,0.00084899965,0.47119656,0.0002198327,0.00027536723,0.00023883763,0.0020539807,0.00033388968,0.002925927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901867,0.00016587427,0.00009478834,0.00035287923,0.00030982835,0.00005789335],"domain_scores_gemma":[0.9976488,0.0010830576,0.0002570903,0.00032480544,0.0006082203,0.000078028876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013415682,0.0010086361,0.0006609506,0.0018145398,0.00024190535,0.0010581954,0.000673547,0.00089372916,0.002975433],"category_scores_gemma":[0.0057022567,0.00022776624,0.0006205811,0.0008917901,0.0002617083,0.0008173135,0.00084637007,0.00061269855,0.0022666513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012136734,0.00022303048,0.015808057,0.00036507563,0.00018485998,0.00040154046,0.000114154034,0.020696295,0.093298875,0.0016529508,0.00591967,0.8601218],"study_design_scores_gemma":[0.00022552097,0.00096907624,0.05168913,0.00014035685,0.00027956557,0.00358989,0.00014673432,0.83145857,0.09397712,0.0062689963,0.011121847,0.0001330905],"about_ca_topic_score_codex":0.0006479044,"about_ca_topic_score_gemma":0.0006929467,"teacher_disagreement_score":0.002975433,"about_ca_system_score_codex":0.00020482675,"about_ca_system_score_gemma":0.00051778206,"threshold_uncertainty_score":0.009953797},"labels":[],"label_agreement":null},{"id":"W1770625086","doi":"10.3390/s151024716","title":"On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; University of Alberta; University of British Columbia","funders":"National Critical Care and Trauma Response Centre","keywords":"Photoplethysmogram; Waveform; Heat stress; Diastole; Root mean square; SIGNAL (programming language); Stress (linguistics); Energy (signal processing); Feature (linguistics); Time domain; Mathematics; Biomedical engineering; Computer science; Statistics; Medicine; Physics; Internal medicine; Telecommunications; Blood pressure","score_opus":0.01952084039235092,"score_gpt":0.2552539019095248,"score_spread":0.23573306151717388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1770625086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5677296,0.004587245,0.42091566,0.00021865702,0.00021366548,0.00022786931,0.00062312494,0.0008471549,0.004637051],"genre_scores_gemma":[0.90155035,0.0031499092,0.091589145,0.00017744527,0.00013171317,0.00015803712,0.00069919025,0.000058764264,0.0024854762],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995689,0.00012690971,0.00002804765,0.000104529965,0.00014742774,0.000024268404],"domain_scores_gemma":[0.99951434,0.00025006317,0.000057964844,0.00004553702,0.000116403346,0.00001572568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062121276,0.00048170413,0.00036501992,0.0008400945,0.00013016543,0.0004262107,0.00022685123,0.00039875341,0.0008966453],"category_scores_gemma":[0.0013790388,0.000085939006,0.0003660172,0.00064024195,0.00019406025,0.0003334593,0.0002228101,0.00030405592,0.00047289298],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061921205,0.00021717531,0.016438574,0.0004994044,0.00017250495,0.00031745966,0.0001669657,0.011692651,0.42580694,0.00056244305,0.0009870387,0.5425197],"study_design_scores_gemma":[0.000044055858,0.0012675098,0.36010242,0.00014321884,0.00027272213,0.002736696,0.00024695872,0.4243625,0.2022014,0.0016881123,0.006802357,0.00013206282],"about_ca_topic_score_codex":0.00079271663,"about_ca_topic_score_gemma":0.0014417177,"teacher_disagreement_score":0.0008966453,"about_ca_system_score_codex":0.00010756177,"about_ca_system_score_gemma":0.00018400725,"threshold_uncertainty_score":0.0032853484},"labels":[],"label_agreement":null},{"id":"W1787408038","doi":"10.3390/s150922490","title":"Carbon Nanomaterials Based Electrochemical Sensors/Biosensors for the Sensitive Detection of Pharmaceutical and Biological Compounds","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanomaterials; Graphene; Nanotechnology; Carbon nanotube; Materials science; Biosensor; Nanocomposite; Electrochemical gas sensor; Electrochemistry; Chemistry; Electrode","score_opus":0.02653658529043478,"score_gpt":0.24476248064208594,"score_spread":0.21822589535165116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1787408038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16300228,0.22355776,0.542488,0.0050803632,0.004563888,0.0010570715,0.0023784926,0.0065460564,0.051326137],"genre_scores_gemma":[0.45092547,0.08634578,0.4199106,0.0025973376,0.00088325894,0.000618081,0.0016072415,0.00028030295,0.036831934],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990711,0.00011854895,0.000045806977,0.00016697608,0.0005512352,0.000046331643],"domain_scores_gemma":[0.99982053,0.000050857037,0.000025166553,0.000016972024,0.00006841357,0.000018150331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005722272,0.0009391332,0.00056784967,0.0011587992,0.00031155453,0.0006156476,0.0008904762,0.0012366351,0.0023032497],"category_scores_gemma":[0.00057603,0.0004988998,0.0003884316,0.0007912597,0.00043936147,0.00088498986,0.0003703497,0.0007705259,0.0013734428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004774294,0.000056285888,0.00016119005,0.0005371124,0.00002372722,0.00013892536,0.00001975342,0.00058601174,0.94382405,0.0028517696,0.0020129206,0.049740497],"study_design_scores_gemma":[0.000009751848,0.00013248359,0.0004207757,0.000038800175,0.000026811813,0.0003278894,0.000016572829,0.006864587,0.95027965,0.00086677837,0.040986627,0.0000292219],"about_ca_topic_score_codex":0.0005300814,"about_ca_topic_score_gemma":0.0012660724,"teacher_disagreement_score":0.0023032497,"about_ca_system_score_codex":0.00056685816,"about_ca_system_score_gemma":0.00042961782,"threshold_uncertainty_score":0.0077050924},"labels":[],"label_agreement":null},{"id":"W1834676530","doi":"10.3390/s151026236","title":"Protein Adsorption in Microengraving Immunoassays","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Adsorption; Chemistry; Chromatography; Computational biology; Biology; Organic chemistry","score_opus":0.016992736288196864,"score_gpt":0.27241196499092496,"score_spread":0.2554192287027281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834676530","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68325156,0.0062569175,0.30539784,0.00047579582,0.00020823698,0.00024436667,0.0002510899,0.00087738625,0.0030368064],"genre_scores_gemma":[0.85475916,0.0039741723,0.13535656,0.00028560657,0.000058511607,0.0003003955,0.00027304332,0.00008775687,0.004904809],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988049,0.00026261224,0.000057563993,0.00021877153,0.0005405773,0.00011558956],"domain_scores_gemma":[0.99922943,0.00047567472,0.0000867159,0.00004827581,0.00012101737,0.00003887403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008763583,0.000577136,0.00060388865,0.00045385907,0.0003397438,0.0006522537,0.0006427073,0.00072515255,0.00043002918],"category_scores_gemma":[0.0011018682,0.0004087314,0.00034395192,0.0005074264,0.00039503723,0.00075335824,0.00037879695,0.000884648,0.00039063962],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030671898,0.000024168852,0.0002733689,0.000053134754,0.0000046226633,0.000021389804,0.000025224917,0.0003070818,0.9953785,0.00019802334,0.00005510643,0.003628739],"study_design_scores_gemma":[0.0000037535938,0.00008955542,0.0012488014,0.0000049736377,0.000006383781,0.00008996125,0.00002226608,0.0095182005,0.98762995,0.00014054931,0.0012331364,0.00001243288],"about_ca_topic_score_codex":0.000673598,"about_ca_topic_score_gemma":0.0008651979,"teacher_disagreement_score":0.0008763583,"about_ca_system_score_codex":0.00064524694,"about_ca_system_score_gemma":0.0002701199,"threshold_uncertainty_score":0.004681647},"labels":[],"label_agreement":null},{"id":"W1840576954","doi":"10.3390/s150923262","title":"Workload Model Based Dynamic Adaptation of Social Internet of Vehicles","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Scalability; Cyber-physical system; Cloud computing; Adaptation (eye); Software deployment; Process (computing); Abstraction; Workload; The Internet; Key (lock); Distributed computing; Computer security; World Wide Web; Software engineering; Database","score_opus":0.017942904319143305,"score_gpt":0.2242299191672521,"score_spread":0.2062870148481088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1840576954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.764153,0.000349499,0.22279182,0.00073964533,0.00013299829,0.0001709829,0.00036058953,0.00051105197,0.010790423],"genre_scores_gemma":[0.9973635,0.000042888125,0.0018112645,0.000015224987,0.000006879982,0.000023785165,0.000056851466,0.000012255728,0.0006673308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936694,0.00021330461,0.0000246841,0.00012375748,0.00011763643,0.00015381286],"domain_scores_gemma":[0.99886835,0.0005381713,0.00016117202,0.00008048344,0.0002538275,0.00009799369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000837348,0.0006767768,0.00052041083,0.000566665,0.0004191613,0.0009367691,0.0010617868,0.00073136325,0.0011191203],"category_scores_gemma":[0.0030418178,0.00028783208,0.0004006113,0.00041243449,0.000497478,0.00095138693,0.00058394513,0.00045797642,0.00016605145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003996065,0.000028491204,0.0018155602,0.000014074529,0.0000122717265,0.00006182247,0.00004369541,0.991879,0.0011301272,0.0025446797,0.00030426058,0.0021260907],"study_design_scores_gemma":[7.841694e-7,0.0000059191566,0.0002045208,4.3674623e-7,0.0000010950049,0.0000040690848,0.000010652714,0.99934965,0.000070723574,0.00030164677,0.00004878217,0.0000016866941],"about_ca_topic_score_codex":0.0136287175,"about_ca_topic_score_gemma":0.006866776,"teacher_disagreement_score":0.0136287175,"about_ca_system_score_codex":0.0015324426,"about_ca_system_score_gemma":0.0006505626,"threshold_uncertainty_score":0.027098775},"labels":[],"label_agreement":null},{"id":"W1843335441","doi":"10.3390/s151027060","title":"An Accurate and Fault-Tolerant Target Positioning System for Buildings Using Laser Rangefinders and Low-Cost MEMS-Based MARG Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"China Scholarship Council","keywords":"Microelectromechanical systems; Laser scanning; Computer science; Fault tolerance; Fault (geology); Remote sensing; Laser; Geology; Engineering; Artificial intelligence; Materials science; Seismology; Physics; Optics; Nanotechnology; Distributed computing","score_opus":0.020501324291942326,"score_gpt":0.24763543339136562,"score_spread":0.2271341090994233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843335441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1266635,0.00053003576,0.8645636,0.00018972902,0.00011317758,0.00007329943,0.00015968073,0.0055446317,0.0021624076],"genre_scores_gemma":[0.8659211,0.00022960632,0.13096544,0.00007009392,0.000047371323,0.00006967458,0.00022296718,0.000034175453,0.0024395671],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976736,0.000025978683,0.000013110114,0.000051244584,0.00011687704,0.000025441295],"domain_scores_gemma":[0.9998147,0.000019212865,0.000041725554,0.000031327207,0.00007790181,0.000015136457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019780833,0.0005854925,0.0004299593,0.00038280632,0.00044418834,0.0003010658,0.0005937452,0.0004195093,0.0007348536],"category_scores_gemma":[0.00032134124,0.00020744946,0.00023708308,0.0003138007,0.00017433625,0.00059598987,0.0004612008,0.00031130065,0.00051242905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035770368,0.00011689362,0.01204899,0.00043321768,0.00009824195,0.00044297735,0.00035255923,0.054403022,0.38179213,0.0027280701,0.005416797,0.54180944],"study_design_scores_gemma":[0.00016956643,0.0013099798,0.023885163,0.00006422576,0.00028009724,0.0011544479,0.0002073486,0.7263595,0.22086649,0.0016631202,0.023883931,0.0001562371],"about_ca_topic_score_codex":0.0026823182,"about_ca_topic_score_gemma":0.0041208146,"teacher_disagreement_score":0.0026823182,"about_ca_system_score_codex":0.00033609523,"about_ca_system_score_gemma":0.0005707124,"threshold_uncertainty_score":0.0053334236},"labels":[],"label_agreement":null},{"id":"W1866712053","doi":"10.3390/s151127493","title":"Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec; York University; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photogrammetry; Calibration; Software; Computer science; Georeference; Data acquisition; 3D modeling; Inertial measurement unit; Inertial navigation system; Remote sensing; Computer vision; Artificial intelligence; Inertial frame of reference; Geography","score_opus":0.09327739155812548,"score_gpt":0.2518902898279136,"score_spread":0.15861289826978814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866712053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2627177,0.0002191437,0.7271148,0.00012670357,0.00009953437,0.0009023162,0.00057834433,0.0040673353,0.0041741026],"genre_scores_gemma":[0.5841516,0.0001141292,0.4132935,0.00003712229,0.0000135831015,0.00029597542,0.000589054,0.0001066373,0.0013983157],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939716,0.00012511408,0.000031544834,0.00010256271,0.0002919144,0.000051738865],"domain_scores_gemma":[0.9995338,0.00006526769,0.00003003612,0.0001217035,0.00020340743,0.00004583632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088159443,0.00049761054,0.0004063441,0.00055921794,0.00031026924,0.00043764513,0.0007861291,0.0005488183,0.0018430066],"category_scores_gemma":[0.00086521846,0.0002104763,0.00025793855,0.00039444378,0.00025179583,0.00048935914,0.00055416947,0.0003333706,0.0007412113],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045871479,0.00039546305,0.012456797,0.0005939353,0.00009961044,0.0005370975,0.00072250905,0.07960808,0.55100536,0.0027788237,0.0021726743,0.34917098],"study_design_scores_gemma":[0.00027649806,0.0024073673,0.0376158,0.00009948956,0.00015242465,0.00084584474,0.00045675552,0.46151185,0.4627357,0.0011455952,0.032642867,0.000109923545],"about_ca_topic_score_codex":0.0014012506,"about_ca_topic_score_gemma":0.0014366782,"teacher_disagreement_score":0.0018430066,"about_ca_system_score_codex":0.00031073284,"about_ca_system_score_gemma":0.0008084055,"threshold_uncertainty_score":0.006165445},"labels":[],"label_agreement":null},{"id":"W1866765583","doi":"10.3390/s150922776","title":"A Wireless Optogenetic Headstage with Multichannel Electrophysiological Recording Capability","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Photoreceptor and optogenetics research","field":"Neuroscience","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire en Santé Mentale de Québec; Doric Lenses (Canada); Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optogenetics; Electrophysiology; Optical recording; Duty cycle; Materials science; Computer science; Electrical engineering; Optoelectronics; Neuroscience; Voltage; Engineering","score_opus":0.08334940268549386,"score_gpt":0.3160675347069631,"score_spread":0.23271813202146924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866765583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16700612,0.0009984752,0.8200294,0.0004074591,0.00042605508,0.0008503125,0.0018907522,0.0042656655,0.004125732],"genre_scores_gemma":[0.4225912,0.001421301,0.5593988,0.0007401174,0.0002567128,0.0014721706,0.0013093782,0.00037481164,0.0124355005],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978656,0.00001357963,0.00002064969,0.000062539024,0.000093378396,0.000023459179],"domain_scores_gemma":[0.99972564,0.000068556896,0.000057167807,0.000046526944,0.00005050635,0.000051575636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025376462,0.00048122043,0.00035811015,0.0003454748,0.00018124605,0.00033443797,0.0014115471,0.00041156766,0.0030483494],"category_scores_gemma":[0.00035113818,0.0003263076,0.00042937626,0.00027891534,0.000275646,0.00074210187,0.0008205065,0.00040890658,0.0009588621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005996503,0.000020676298,0.00015366076,0.00008855523,0.00000977145,0.00010418606,0.000013140625,0.00015957038,0.9847119,0.0003116006,0.00043176266,0.013935262],"study_design_scores_gemma":[0.00004851475,0.0006145989,0.004298427,0.000018406661,0.0000671774,0.0016677855,0.000019145467,0.0044847764,0.96661705,0.00035282059,0.021761883,0.00004937735],"about_ca_topic_score_codex":0.00029525592,"about_ca_topic_score_gemma":0.0005275287,"teacher_disagreement_score":0.0030483494,"about_ca_system_score_codex":0.00024423117,"about_ca_system_score_gemma":0.00048030296,"threshold_uncertainty_score":0.010197759},"labels":[],"label_agreement":null},{"id":"W1867318178","doi":"10.3390/s150818724","title":"Diazonium Chemistry for the Bio-Functionalization of Glassy Nanostring Resonator Arrays","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; China Scholarship Council; University of Alberta","keywords":"Surface modification; Biomolecule; Nanotechnology; X-ray photoelectron spectroscopy; Chemistry; Materials science; Biosensor; Diazonium Compounds; Covalent bond; Linker; Grafting; Combinatorial chemistry; Chemical engineering; Polymer; Organic chemistry; Computer science; Physical chemistry","score_opus":0.01774552676010622,"score_gpt":0.2178582977884242,"score_spread":0.20011277102831798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1867318178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9290899,0.0019036187,0.06334978,0.00025944193,0.00014111721,0.0001496481,0.0001995547,0.00037588287,0.00453105],"genre_scores_gemma":[0.9604488,0.0011366312,0.034581523,0.00012988177,0.000016229069,0.0000893546,0.00017304107,0.000042685177,0.0033818674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999838,0.000030479054,0.000011118264,0.000037849728,0.00005480828,0.000027782],"domain_scores_gemma":[0.9999342,0.000025177576,0.000013165732,0.000009069885,0.000010350966,0.000008046117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028416605,0.0004062527,0.0001743449,0.00017694732,0.000109323395,0.00017479049,0.00027371958,0.0002732109,0.0009559022],"category_scores_gemma":[0.00021270901,0.00019934861,0.0001746001,0.00007792905,0.00019373855,0.00017433494,0.00023164997,0.0003121161,0.00045606997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008840425,0.0000047558037,0.000017053893,0.00002096169,0.00000158848,0.000022380691,0.0000073340257,0.00007106297,0.99894744,0.00014831928,0.000017749382,0.00073264027],"study_design_scores_gemma":[0.000002799117,0.000033242224,0.00012874606,0.000001150141,0.0000019220683,0.000046194178,0.0000029497542,0.0005501095,0.99820757,0.000026314927,0.0009963632,0.0000026177797],"about_ca_topic_score_codex":0.00017910532,"about_ca_topic_score_gemma":0.00057788735,"teacher_disagreement_score":0.0009559022,"about_ca_system_score_codex":0.00029664213,"about_ca_system_score_gemma":0.0001394881,"threshold_uncertainty_score":0.0031978488},"labels":[],"label_agreement":null},{"id":"W1894708002","doi":"10.3390/s150820543","title":"DNA-Redox Cation Interaction Improves the Sensitivity of an Electrochemical Immunosensor for Protein Detection","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Biogate Laboratories (Canada); Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detection limit; Redox; Chemistry; Electrochemistry; Biosensor; Human chorionic gonadotropin; Selectivity; DNA; Linear range; Hormone; Biophysics; Chromatography; Electrode; Biochemistry; Inorganic chemistry; Biology","score_opus":0.010099839344109707,"score_gpt":0.27800094409089343,"score_spread":0.26790110474678375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1894708002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.805405,0.008924841,0.17761917,0.000945227,0.00052361935,0.00015822433,0.00023446498,0.0011625631,0.005026899],"genre_scores_gemma":[0.88913065,0.0027717205,0.10156734,0.0005095821,0.00009366885,0.00008127698,0.00025049737,0.00005040758,0.005544861],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993401,0.000135253,0.000044488104,0.00017694787,0.00023947508,0.00006377344],"domain_scores_gemma":[0.9997025,0.00011917769,0.00003524485,0.000016678016,0.0000914154,0.00003497818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005387073,0.0006582318,0.00039079532,0.00036800298,0.00016520795,0.00042951028,0.00066326605,0.0010683632,0.0008949289],"category_scores_gemma":[0.0008877804,0.00033571143,0.00023118992,0.00029078478,0.0003310895,0.00056372664,0.0004256658,0.0006235276,0.00066446187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010996175,0.000007446254,0.0000485698,0.000022832934,0.000002204604,0.000020106112,0.0000051223346,0.000041453008,0.997965,0.000051096766,0.000017752705,0.0018073488],"study_design_scores_gemma":[0.0000036242527,0.000037679096,0.00024243694,0.000002200872,0.0000054801926,0.000102238344,0.0000041488156,0.0010954519,0.9973544,0.00003094082,0.0011162623,0.000005225936],"about_ca_topic_score_codex":0.00039070836,"about_ca_topic_score_gemma":0.0008239756,"teacher_disagreement_score":0.0010683632,"about_ca_system_score_codex":0.00028114195,"about_ca_system_score_gemma":0.00025277192,"threshold_uncertainty_score":0.0029938817},"labels":[],"label_agreement":null},{"id":"W1902631842","doi":"10.3390/s151026331","title":"Using Polynomials to Simplify Fixed Pattern Noise and Photometric Correction of Logarithmic CMOS Image Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Fixed-pattern noise; Pixel; Monotonic function; Computer science; CMOS; Logarithm; Image sensor; Noise (video); Nonlinear system; Algorithm; Calibration; Lookup table; Electronic engineering; Artificial intelligence; Image (mathematics); Mathematics; Physics; Engineering; Statistics","score_opus":0.028310464608816917,"score_gpt":0.26598889571388873,"score_spread":0.2376784311050718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902631842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028407825,0.00022239813,0.9682105,0.000077142264,0.000044578446,0.000032620323,0.000034724086,0.00064019853,0.002329906],"genre_scores_gemma":[0.47315896,0.0005239226,0.5225637,0.00007089149,0.000048423808,0.000048292695,0.000087431676,0.00016141808,0.0033368864],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945575,0.00008174747,0.00002302708,0.00012092928,0.00027258284,0.000045957746],"domain_scores_gemma":[0.9995264,0.00017310944,0.000082949264,0.000112327354,0.00009368275,0.0000116358415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003424165,0.00068342977,0.0002704299,0.00031323038,0.00026552024,0.0005618591,0.0007690566,0.00036631114,0.00123252],"category_scores_gemma":[0.001866488,0.0002298378,0.00046260888,0.00050706347,0.00049735664,0.00072313607,0.0005214543,0.0007732526,0.0005516573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004272165,0.000102173675,0.0011229606,0.00027136697,0.00003347943,0.0003191635,0.0002398707,0.14119287,0.5026559,0.045075573,0.0011675257,0.30739182],"study_design_scores_gemma":[0.000021780159,0.00023445283,0.0006573217,0.000022181624,0.00003146859,0.0005724575,0.000034882753,0.694252,0.28675103,0.0063903527,0.010983587,0.00004845587],"about_ca_topic_score_codex":0.0015542033,"about_ca_topic_score_gemma":0.0020663904,"teacher_disagreement_score":0.0015542033,"about_ca_system_score_codex":0.0006954594,"about_ca_system_score_gemma":0.000629659,"threshold_uncertainty_score":0.005045891},"labels":[],"label_agreement":null},{"id":"W1908900982","doi":"10.3390/s150922192","title":"Simulations of Interdigitated Electrode Interactions with Gold Nanoparticles for Impedance-Based Biosensing Applications","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; Athabasca University; University of Alberta","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures","keywords":"Multiphysics; Biosensor; Electrode; Materials science; Nanotechnology; Nanoparticle; Electrical impedance; Electric field; Optoelectronics; Finite element method; Chemistry; Electrical engineering; Engineering; Physics","score_opus":0.019272568880893043,"score_gpt":0.3028943671638721,"score_spread":0.28362179828297907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1908900982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7782454,0.00064824,0.16243759,0.0015820166,0.00020124982,0.00018593599,0.0016015124,0.0006736542,0.05442443],"genre_scores_gemma":[0.95477986,0.00032927477,0.034494143,0.00031378862,0.000028835575,0.00029920766,0.0005975654,0.00017822481,0.008979018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998592,0.000035680117,0.000006326044,0.000023159777,0.00004624198,0.00002941369],"domain_scores_gemma":[0.99941874,0.00037312898,0.000046506404,0.00003967935,0.0000808981,0.000041109764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002488061,0.0006040838,0.000604461,0.00047824372,0.00060329837,0.0007482059,0.0011574015,0.002095863,0.0059290873],"category_scores_gemma":[0.0012972573,0.0004778051,0.0006226971,0.0006463748,0.00047924492,0.000861542,0.0006217258,0.00077175384,0.0005136883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033520537,0.000033837285,0.0004337733,0.000035011723,0.000015233302,0.0000857679,0.000047232596,0.9929517,0.0031766936,0.0020877505,0.00023787681,0.0008616528],"study_design_scores_gemma":[0.000012457331,0.000011554818,0.00011064432,0.0000028354837,0.0000029651185,0.000007893344,0.000014242072,0.9981633,0.0007330352,0.00059738534,0.00033941897,0.00000432259],"about_ca_topic_score_codex":0.006513359,"about_ca_topic_score_gemma":0.0045019323,"teacher_disagreement_score":0.006513359,"about_ca_system_score_codex":0.0011196326,"about_ca_system_score_gemma":0.00077310565,"threshold_uncertainty_score":0.019834757},"labels":[],"label_agreement":null},{"id":"W1964073021","doi":"10.3390/s140202036","title":"Energy-Efficient Data Reduction Techniques for Wireless Seizure Detection Systems","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg","keywords":"Computer science; Wireless sensor network; Real-time computing; Wireless; Reduction (mathematics); Electroencephalography; Energy consumption; Feature (linguistics); Epileptic seizure; Feature extraction; Battery (electricity); Energy (signal processing); Power (physics); Embedded system; Artificial intelligence; Engineering; Electrical engineering; Computer network; Telecommunications; Medicine","score_opus":0.036053612009837366,"score_gpt":0.27603960996125526,"score_spread":0.2399859979514179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964073021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034600634,0.0012442408,0.9612031,0.0003577448,0.000051143976,0.00006104664,0.00006084774,0.00036891928,0.0020522908],"genre_scores_gemma":[0.5592401,0.0020332062,0.4331797,0.00019479172,0.00012712272,0.00018610549,0.00030178935,0.000054577282,0.0046826825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997701,0.000037136375,0.000016614687,0.000027294158,0.00013455453,0.0000143827365],"domain_scores_gemma":[0.9997242,0.00014312753,0.00003611019,0.00003215332,0.000058718135,0.0000056447816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018803177,0.00049665215,0.00030983,0.00040816958,0.0002293374,0.00032833908,0.00040176322,0.00031101558,0.0012969441],"category_scores_gemma":[0.0008482287,0.00014386946,0.00022898869,0.00054529926,0.00025572392,0.00053790305,0.00035568402,0.00041670795,0.00044201408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002898038,0.00017315522,0.0006684027,0.00027329824,0.00004770464,0.0002027605,0.00017196313,0.10729309,0.18829827,0.01045006,0.0027878068,0.6893437],"study_design_scores_gemma":[0.000038631413,0.00032395017,0.0013107852,0.0000456379,0.000032919932,0.00040051027,0.00009443244,0.8886418,0.09098948,0.008220195,0.009873414,0.000028243274],"about_ca_topic_score_codex":0.0006200685,"about_ca_topic_score_gemma":0.0009538893,"teacher_disagreement_score":0.0012969441,"about_ca_system_score_codex":0.000210232,"about_ca_system_score_gemma":0.00024171929,"threshold_uncertainty_score":0.0043386817},"labels":[],"label_agreement":null},{"id":"W1965634225","doi":"10.3390/s150407349","title":"A Wafer Level Vacuum Encapsulated Capacitive Accelerometer Fabricated in an Unmodified Commercial MEMS Process","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CMC Microsystems (Canada); Queen's University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Microelectromechanical systems; Accelerometer; Microfabrication; Bulk micromachining; Materials science; Wafer; Capacitive sensing; Surface micromachining; Gyroscope; Optoelectronics; Electronic engineering; Capacitance; Fabrication; Electrical engineering; Engineering; Computer science; Electrode","score_opus":0.12222524175134984,"score_gpt":0.3049669636465435,"score_spread":0.18274172189519367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965634225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7306108,0.0021604432,0.2417264,0.000852253,0.0007042331,0.0006059471,0.0020091764,0.0046569277,0.016673876],"genre_scores_gemma":[0.7251636,0.00062108855,0.26635385,0.00018311755,0.00006619712,0.00021846288,0.00062824466,0.000122153,0.006643279],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964523,0.000015137486,0.000015638734,0.000105240455,0.00018892997,0.000029887451],"domain_scores_gemma":[0.99982625,0.000025775551,0.000035787583,0.00002710467,0.00006591062,0.000019222949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015192898,0.00045515358,0.00046379995,0.00025418328,0.000266406,0.00033996152,0.0007737347,0.00067286054,0.0014860216],"category_scores_gemma":[0.00033778325,0.00039947825,0.00020425611,0.00028665617,0.00020440281,0.00041720542,0.00023063578,0.00035412394,0.0009451543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037100148,0.000021922933,0.0003701143,0.000074202886,0.0000070497267,0.00012998629,0.000028521486,0.00020483659,0.99029374,0.00039415603,0.00047751135,0.007960822],"study_design_scores_gemma":[0.000043207943,0.0009992996,0.006372561,0.000014532499,0.00003518353,0.0010346017,0.000037773894,0.008176847,0.9690364,0.00011965395,0.014090321,0.000039529925],"about_ca_topic_score_codex":0.0006770829,"about_ca_topic_score_gemma":0.0012756311,"teacher_disagreement_score":0.0014860216,"about_ca_system_score_codex":0.00035955885,"about_ca_system_score_gemma":0.0005105822,"threshold_uncertainty_score":0.004971266},"labels":[],"label_agreement":null},{"id":"W1965810992","doi":"10.3390/s130810765","title":"Digital Pixel Sensor Array with Logarithmic Delta-Sigma Architecture","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Pixel; Delta-sigma modulation; Dynamic range; Image sensor; Logarithm; Noise (video); Computer science; Electronic engineering; CMOS; Fixed-pattern noise; Distortion (music); Scalability; Artificial intelligence; Human eye; Computer vision; Computer hardware; Engineering; Image (mathematics); Mathematics","score_opus":0.0034499671916495655,"score_gpt":0.16275991108734778,"score_spread":0.15930994389569822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965810992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25990498,0.0018964926,0.70266706,0.00068717264,0.0002888684,0.0003320976,0.0007484871,0.008400921,0.025073908],"genre_scores_gemma":[0.6614455,0.00045642338,0.32330957,0.00064936455,0.0000682671,0.00013817531,0.00031083636,0.00006194906,0.013559946],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964464,0.00004099779,0.000016255202,0.00009815958,0.00017702779,0.0000228955],"domain_scores_gemma":[0.99975115,0.000043699965,0.000025922009,0.00002794233,0.00013016362,0.000021034408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001982065,0.0002482805,0.0003348345,0.00027936537,0.00021795904,0.00055189105,0.0008898901,0.0005410985,0.0028483775],"category_scores_gemma":[0.00041348752,0.00020785061,0.00017453336,0.0004391912,0.00018677134,0.0006084481,0.0003538526,0.00028288233,0.0008877225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034983567,0.00012907237,0.0021368086,0.0002697915,0.000041816238,0.00015811382,0.00008853468,0.006092948,0.8740791,0.0026568435,0.0033679518,0.11062926],"study_design_scores_gemma":[0.00008331001,0.0011508525,0.004365033,0.000026882377,0.00008015965,0.0011552346,0.00004921773,0.14093836,0.8255811,0.00096651213,0.025532939,0.00007039915],"about_ca_topic_score_codex":0.0005855003,"about_ca_topic_score_gemma":0.00090141967,"teacher_disagreement_score":0.0028483775,"about_ca_system_score_codex":0.00047668442,"about_ca_system_score_gemma":0.00036201638,"threshold_uncertainty_score":0.009528816},"labels":[],"label_agreement":null},{"id":"W1966233527","doi":"10.3390/s140508162","title":"Spectral Imaging at the Microscale and Beyond","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Microscale chemistry; Context (archaeology); Spectral imaging; Microfluidics; Nanotechnology; Computer science; Focus (optics); Data science; Remote sensing; Materials science; Physics; Psychology; Geography; Optics","score_opus":0.003995015228293797,"score_gpt":0.2703622678171192,"score_spread":0.2663672525888254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966233527","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00872341,0.52178913,0.3080628,0.031661797,0.032786638,0.00011850074,0.00038716488,0.0012335691,0.095237054],"genre_scores_gemma":[0.16652365,0.47043297,0.20935239,0.025919784,0.05074468,0.00039770285,0.0008162768,0.0014017295,0.07441087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990552,0.00016147805,0.000050869312,0.00023721367,0.00040291302,0.00009229946],"domain_scores_gemma":[0.9989926,0.00049853587,0.000081717466,0.000108984525,0.00024276177,0.00007549886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011362723,0.0006285863,0.0008362896,0.0010950159,0.00071194716,0.0024624423,0.0009837586,0.0021556304,0.0058149206],"category_scores_gemma":[0.0012036912,0.00036184592,0.00058669405,0.0006946321,0.0021736347,0.0041188113,0.0018639482,0.0031811965,0.002702159],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012996406,0.00008191808,0.000528176,0.003460769,0.000074301926,0.0006182637,0.0005784571,0.0012085487,0.15234259,0.46049145,0.091133215,0.2893523],"study_design_scores_gemma":[0.0000039343245,0.000067619505,0.00035718762,0.00027424135,0.000018171932,0.001244623,0.00008890128,0.0017934035,0.04885912,0.061395954,0.8858461,0.000050770686],"about_ca_topic_score_codex":0.00021590557,"about_ca_topic_score_gemma":0.00021274226,"teacher_disagreement_score":0.0058149206,"about_ca_system_score_codex":0.00083618076,"about_ca_system_score_gemma":0.0005244671,"threshold_uncertainty_score":0.01945281},"labels":[],"label_agreement":null},{"id":"W1966568420","doi":"10.3390/s110201744","title":"Sensing Phosphatidylserine in Cellular Membranes","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Lipid Membrane Structure and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"Canadian Institutes of Health Research; Hospital for Sick Children; University of Toronto","keywords":"Phosphatidylserine; Phospholipid scramblase; Cell biology; Phospholipid; Intracellular; Transmembrane protein; Biology; Membrane; Annexin A5; Cell; Annexin; Chemistry; Biochemistry","score_opus":0.027108618860551097,"score_gpt":0.2823189768344183,"score_spread":0.2552103579738672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966568420","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018182043,0.98945516,0.0025478352,0.00032175038,0.00024390282,0.000013395594,0.00007275634,0.000050169492,0.005476717],"genre_scores_gemma":[0.0056034513,0.98856074,0.0018652084,0.00014455638,0.00010066527,0.000014477265,0.00008036695,0.000005409851,0.0036250905],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99983716,0.000015636366,0.000012510943,0.000036403035,0.000083391285,0.0000148222125],"domain_scores_gemma":[0.99992585,0.000023315966,0.000012663862,0.0000035806763,0.000027173162,0.000007475571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025555282,0.00079360127,0.0008579661,0.0012479804,0.00024434508,0.00073690095,0.0005700823,0.0009505203,0.0013528547],"category_scores_gemma":[0.00027694204,0.00025163527,0.00024872454,0.001463379,0.00034506104,0.0012405525,0.00056462275,0.0009020723,0.003278008],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007806592,0.00005665562,0.00027366617,0.011921269,0.000042908072,0.00032882515,0.00010965992,0.00066757627,0.10801552,0.007993893,0.010506436,0.86000544],"study_design_scores_gemma":[0.000008886947,0.00011688999,0.001182885,0.0012943246,0.00006240075,0.0022125647,0.000121607234,0.0004913393,0.051904365,0.0037285045,0.93883204,0.00004425568],"about_ca_topic_score_codex":0.00053884415,"about_ca_topic_score_gemma":0.0006150353,"teacher_disagreement_score":0.0013528547,"about_ca_system_score_codex":0.0005104267,"about_ca_system_score_gemma":0.00045419746,"threshold_uncertainty_score":0.004525721},"labels":[],"label_agreement":null},{"id":"W1966580307","doi":"10.3390/s110505112","title":"Amorphous and Polycrystalline Photoconductors for Direct Conversion Flat Panel X-Ray Image Sensors","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":511,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; University of Waterloo; Thunder Bay Regional Research Institute; Lakehead University; University of Saskatchewan","funders":"","keywords":"Detective quantum efficiency; X-ray detector; Dark current; Amorphous solid; Optoelectronics; Crystallite; Photoconductivity; Materials science; Detector; Quantum efficiency; Optics; Optical transfer function; Semiconductor; Physics; Photodetector; Computer science; Image quality; Chemistry; Image (mathematics)","score_opus":0.048986284859761926,"score_gpt":0.27026568493196457,"score_spread":0.22127940007220265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966580307","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52425164,0.03871725,0.35698518,0.0017320534,0.0013637102,0.0008077315,0.0027500468,0.0046729706,0.06871936],"genre_scores_gemma":[0.7770388,0.008153554,0.16927138,0.0006664494,0.00021381132,0.00026767046,0.0010095721,0.00030646546,0.04307233],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996227,0.000040402,0.000021462965,0.000095550706,0.00019855954,0.000021297485],"domain_scores_gemma":[0.9995826,0.00012741533,0.00007754443,0.00007106057,0.000116460935,0.00002499149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028274834,0.0003583912,0.00023381259,0.00039124506,0.00018901685,0.00082552765,0.0006578777,0.0005468717,0.0052696033],"category_scores_gemma":[0.00064492784,0.00023531052,0.00018636556,0.0005897251,0.00026166264,0.00067531975,0.00025185297,0.0006791879,0.0019453363],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009674804,0.000054489272,0.0007373421,0.00036415897,0.000024115403,0.00031189425,0.00008620111,0.0008268404,0.9408549,0.0062774844,0.0034137173,0.046952102],"study_design_scores_gemma":[0.000049167662,0.00078151544,0.0062307874,0.00013306183,0.000053568587,0.0012433346,0.00015602117,0.013646534,0.8723714,0.0022098909,0.103081204,0.000043534674],"about_ca_topic_score_codex":0.00070336054,"about_ca_topic_score_gemma":0.002224212,"teacher_disagreement_score":0.0052696033,"about_ca_system_score_codex":0.00042871162,"about_ca_system_score_gemma":0.0002189624,"threshold_uncertainty_score":0.01762861},"labels":[],"label_agreement":null},{"id":"W1966921058","doi":"10.3390/s141121117","title":"Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Ecosystem health; Ecosystem services; Environmental resource management; Ecosystem; Hyperspectral imaging; Environmental science; Computer science; Lidar; Resilience (materials science); Radar; Geography; Ecology","score_opus":0.06403514250655645,"score_gpt":0.2836487556206685,"score_spread":0.21961361311411204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966921058","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001378461,0.99775773,0.00023496739,0.00088711764,0.0001462936,0.0000027138315,0.000007880332,0.0000047536246,0.0008207768],"genre_scores_gemma":[0.0011644999,0.9976853,0.00033449178,0.00028281292,0.00024621293,0.0000039631823,0.0000110764395,0.0000013243562,0.00027037645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960023,0.00009609347,0.000046995923,0.000071716146,0.00015252054,0.000032556913],"domain_scores_gemma":[0.9983481,0.00089849555,0.00013681463,0.000040882733,0.0004949522,0.00008064523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018424287,0.0007832737,0.0013639292,0.0028695166,0.00033727274,0.0015101538,0.0011661252,0.0017138477,0.0021884379],"category_scores_gemma":[0.0015397283,0.00030232244,0.0006706502,0.0037574104,0.0011169326,0.0030526286,0.0008224671,0.0019946804,0.00090552896],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003158162,0.000076202676,0.0005398684,0.011561364,0.00007623336,0.00017289408,0.0001850471,0.0007499302,0.0010258496,0.009299443,0.0135458065,0.96273583],"study_design_scores_gemma":[0.00000896091,0.00013568476,0.0035423653,0.00970926,0.00013258208,0.0016327803,0.00057762733,0.00065910234,0.0006678944,0.013363565,0.9694939,0.00007621234],"about_ca_topic_score_codex":0.0025675467,"about_ca_topic_score_gemma":0.0031218124,"teacher_disagreement_score":0.0028695166,"about_ca_system_score_codex":0.0007950504,"about_ca_system_score_gemma":0.0015910645,"threshold_uncertainty_score":0.00974381},"labels":[],"label_agreement":null},{"id":"W1967001512","doi":"10.3390/s100404053","title":"Fluorescence-Based Comparative Binding Studies of the Supramolecular Host Properties of PAMAM Dendrimers Using Anilinonaphthalene Sulfonates: Unusual Host-Dependent Fluorescence Titration Behavior","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Dendrimers and Hyperbranched Polymers","field":"Materials Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dendrimer; Titration; Fluorescence; Chemistry; Supramolecular chemistry; Titration curve; Substituent; Crystallography; Stereochemistry; Physical chemistry; Organic chemistry; Crystal structure","score_opus":0.06731258624092357,"score_gpt":0.3057953988729226,"score_spread":0.23848281263199905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967001512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967715,0.00024451345,0.0024236275,0.000019816443,0.000004485058,0.000017777293,0.000059720016,0.000059732603,0.0003987203],"genre_scores_gemma":[0.99429274,0.00029685904,0.004176759,0.000028017828,0.0000059668077,0.000036943347,0.00013323428,0.000015996295,0.0010135929],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997565,0.00006549925,0.000013628185,0.00004269839,0.00007240618,0.00004918972],"domain_scores_gemma":[0.9996768,0.00015676297,0.000051012637,0.000022384434,0.000047612353,0.00004536189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038494254,0.00046814597,0.00040117782,0.00020133177,0.00017691072,0.0002374507,0.00025315807,0.0003990341,0.0007815638],"category_scores_gemma":[0.0004915028,0.00017820146,0.00022979792,0.00016571517,0.00022327773,0.00026455722,0.00023921495,0.00043629133,0.0002618895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026497488,0.000008267807,0.000060241924,0.0000146467055,0.0000021539581,0.000008693674,0.000020576914,0.00009963283,0.99943095,0.0000128838155,0.000004338382,0.00031117376],"study_design_scores_gemma":[0.0000019536647,0.00012854622,0.0006843364,9.549774e-7,0.000003708939,0.000030184434,0.000013210467,0.0008929478,0.99809057,0.000009154707,0.00013980786,0.000004641531],"about_ca_topic_score_codex":0.0004786496,"about_ca_topic_score_gemma":0.0005292459,"teacher_disagreement_score":0.0007815638,"about_ca_system_score_codex":0.0003334469,"about_ca_system_score_gemma":0.00010475204,"threshold_uncertainty_score":0.0026146173},"labels":[],"label_agreement":null},{"id":"W1968671573","doi":"10.3390/s140407156","title":"Angular Rate Optimal Design for the Rotary Strapdown Inertial Navigation System","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Harbin Engineering University; National Natural Science Foundation of China; York University","keywords":"Angular velocity; Inertial navigation system; Angular acceleration; Control theory (sociology); Laplace transform; Inertial frame of reference; Word error rate; Inverse; Computer science; Physics; Mathematics; Mathematical analysis; Classical mechanics; Artificial intelligence; Geometry","score_opus":0.010308478444793231,"score_gpt":0.20452883561417742,"score_spread":0.19422035716938418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968671573","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00867158,0.00044969202,0.9843188,0.0001097086,0.0000818968,0.00008800743,0.000028617234,0.00015869735,0.0060929237],"genre_scores_gemma":[0.8420689,0.0010013847,0.15152292,0.000109205874,0.00008761693,0.00043342696,0.00010099372,0.00006431071,0.004611286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989857,0.00023358436,0.00008069442,0.00023015955,0.00038028587,0.0000895823],"domain_scores_gemma":[0.99930525,0.00016006066,0.00017085898,0.000037843707,0.00029985255,0.000026159729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011925703,0.0012132423,0.0008931482,0.00069310685,0.00048328371,0.0011660685,0.0006985079,0.00089828076,0.0025165014],"category_scores_gemma":[0.0020898548,0.0005718613,0.00055106025,0.0004597343,0.00056643225,0.00077180204,0.00055744185,0.0006556857,0.00073590304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004420367,0.000091558206,0.0010946484,0.0006908892,0.00007345684,0.00018016873,0.00029297336,0.82753277,0.03811795,0.024384316,0.0018894786,0.105209604],"study_design_scores_gemma":[0.00006114869,0.00028421343,0.00045893417,0.000038267044,0.00003919541,0.000058760885,0.000034343797,0.9888228,0.0047635217,0.00198804,0.0034212857,0.000029566983],"about_ca_topic_score_codex":0.002438445,"about_ca_topic_score_gemma":0.0014916075,"teacher_disagreement_score":0.0025165014,"about_ca_system_score_codex":0.0006384806,"about_ca_system_score_gemma":0.0011118106,"threshold_uncertainty_score":0.00841856},"labels":[],"label_agreement":null},{"id":"W1972399486","doi":"10.3390/s140407248","title":"Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":340,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Food safety; Quality (philosophy); Food quality; Remote sensing; Computer science; Spectral imaging; Environmental science; Artificial intelligence; Geography; Medicine; Physics","score_opus":0.09448051066697802,"score_gpt":0.4226196049282389,"score_spread":0.32813909426126087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972399486","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030388104,0.99444026,0.0016562144,0.00033677166,0.00022153572,0.000011006938,0.000019114545,0.000019907817,0.002991253],"genre_scores_gemma":[0.0017584023,0.9948638,0.0016580049,0.0002153041,0.0002931301,0.000012015364,0.000034742152,0.000004827103,0.0011598088],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995252,0.00006752246,0.000039181476,0.00009722047,0.00023089959,0.0000399994],"domain_scores_gemma":[0.99898547,0.0004473876,0.0001158821,0.000036570487,0.0003578732,0.00005676835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013031357,0.0013385995,0.0011693805,0.00354003,0.00034292694,0.00090972095,0.001083661,0.0013308605,0.004127879],"category_scores_gemma":[0.0010588726,0.00051954994,0.0007752606,0.003935753,0.00075041055,0.0019802568,0.0010290679,0.00208296,0.0022996508],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004897465,0.000096266675,0.0002610787,0.012970693,0.000077904435,0.00016228009,0.00007038522,0.0006982952,0.010785795,0.005711061,0.014375348,0.95474195],"study_design_scores_gemma":[0.000010962631,0.00015625168,0.0013298034,0.0019797818,0.00011510009,0.0015261836,0.000089480665,0.00068319956,0.007419428,0.0047573135,0.9818687,0.00006388707],"about_ca_topic_score_codex":0.00088990957,"about_ca_topic_score_gemma":0.0010979024,"teacher_disagreement_score":0.004127879,"about_ca_system_score_codex":0.0006737203,"about_ca_system_score_gemma":0.00083296676,"threshold_uncertainty_score":0.0138091445},"labels":[],"label_agreement":null},{"id":"W1972709171","doi":"10.3390/s141017981","title":"Microelectronics-Based Biosensors Dedicated to the Detection of Neurotransmitters: A Review","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Microtechnology; Miniaturization; Microelectronics; Nanotechnology; Neuroscience; Nanoelectronics; Medicine; Computer science; Biology; Materials science","score_opus":0.047185045045467666,"score_gpt":0.31764984793646833,"score_spread":0.27046480289100067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972709171","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028244112,0.99622643,0.0006959378,0.00020661253,0.00031405446,0.000011302656,0.000029179484,0.000017241726,0.002216875],"genre_scores_gemma":[0.0010152332,0.9963954,0.00084177195,0.00014568608,0.00017973401,0.000012914513,0.0000400059,0.0000029721145,0.0013662174],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99979144,0.000022920429,0.000030072186,0.000043844917,0.0000940123,0.000017743216],"domain_scores_gemma":[0.99968815,0.00013124294,0.000046033932,0.000009913007,0.00010194284,0.000022866623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041446544,0.0011381318,0.001156143,0.0027642236,0.0002787351,0.0008401373,0.00096495386,0.0011605346,0.003881725],"category_scores_gemma":[0.00056740735,0.00037141342,0.00044820856,0.0029767177,0.00036335806,0.0015762052,0.00056285335,0.0011980833,0.0031053603],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003717217,0.00011052713,0.00022732743,0.021481292,0.000060389517,0.00027370368,0.00007946839,0.00045594166,0.0077924426,0.0043068016,0.021557841,0.9436171],"study_design_scores_gemma":[0.0000059013682,0.00012099663,0.0006430273,0.0026204793,0.00008365109,0.0017993106,0.000067606146,0.00015327838,0.0021361995,0.0017599073,0.990585,0.000024638684],"about_ca_topic_score_codex":0.0006159437,"about_ca_topic_score_gemma":0.0009959518,"teacher_disagreement_score":0.003881725,"about_ca_system_score_codex":0.00039322532,"about_ca_system_score_gemma":0.0009095437,"threshold_uncertainty_score":0.012985647},"labels":[],"label_agreement":null},{"id":"W1972935550","doi":"10.3390/s110606454","title":"Enviro-Net: From Networks of Ground-Based Sensor Systems to a Web Platform for Sensor Data Management","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"University of Alberta; Inter-American Institute for Global Change Research; Universidade Federal de Minas Gerais; Universidade de São Paulo; Instituto Tecnológico de Costa Rica; Universidad de Buenos Aires; Canarie; Universidad Nacional de San Luis; Universidade Estadual de Montes Claros; Natural Sciences and Engineering Research Council of Canada; Universidade Estadual Paulista; National Science Foundation","keywords":"Sensor web; Visualization; Data management; Computer science; Data processing; Wireless sensor network; Remote sensing; Management system; Real-time computing; Systems engineering; Data science; Database; Engineering; Telecommunications; Data mining; Geography; Operating system","score_opus":0.07962817965866022,"score_gpt":0.2505405111366671,"score_spread":0.17091233147800688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972935550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024700673,0.0010679844,0.7564133,0.002157231,0.00041696226,0.0011648139,0.0080151055,0.18146434,0.024599668],"genre_scores_gemma":[0.21138063,0.0026765738,0.6728778,0.0018236047,0.00035061073,0.0014944054,0.0492176,0.01666952,0.043509282],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986583,0.00022558955,0.00012424329,0.0002140076,0.000659809,0.00011805284],"domain_scores_gemma":[0.99795413,0.00049111975,0.00016261882,0.00052785466,0.00034988407,0.0005143549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035599405,0.0012045107,0.00093008345,0.0017556772,0.00065351714,0.0033461796,0.0028138517,0.00087566505,0.009422571],"category_scores_gemma":[0.00343037,0.00089989306,0.0006723118,0.0017344508,0.00080083683,0.005228035,0.0039211228,0.0019010359,0.0043094736],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001645296,0.0012972603,0.014972685,0.0013762716,0.00052234513,0.0019254967,0.001276602,0.02442577,0.04456736,0.027896993,0.20586158,0.6742324],"study_design_scores_gemma":[0.0004594707,0.00058582483,0.01765467,0.0005452474,0.00020994796,0.0011572896,0.0006656123,0.33364674,0.03442621,0.047746453,0.56255525,0.0003472937],"about_ca_topic_score_codex":0.0046840385,"about_ca_topic_score_gemma":0.0054943273,"teacher_disagreement_score":0.009422571,"about_ca_system_score_codex":0.0006170898,"about_ca_system_score_gemma":0.0010724401,"threshold_uncertainty_score":0.03152162},"labels":[],"label_agreement":null},{"id":"W1973892800","doi":"10.3390/s130811032","title":"Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fractional programming; Mathematical optimization; Cognitive radio; Optimization problem; Wireless sensor network; Nonlinear programming; Geometric programming; Computer science; Convex optimization; Parametric programming; Iterative method; Convergence (economics); Transformation (genetics); Parametric statistics; Efficient energy use; Energy (signal processing); Nonlinear system; Wireless; Mathematics; Regular polygon; Engineering; Telecommunications; Computer network; Electrical engineering","score_opus":0.005804259776851552,"score_gpt":0.19004503292704433,"score_spread":0.18424077315019277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973892800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010569852,0.00025203606,0.9846213,0.00016739276,0.000013542602,0.000019384419,0.000016613802,0.000048275317,0.0042915735],"genre_scores_gemma":[0.7966907,0.0013160253,0.19731319,0.00010549101,0.00006615084,0.00022527747,0.00008138229,0.00008874581,0.0041130204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996259,0.00017140055,0.000010116227,0.000044739976,0.0001181145,0.000029644734],"domain_scores_gemma":[0.9994024,0.0003998951,0.000069485184,0.00005253527,0.00006441251,0.000011258371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083754514,0.00073486665,0.0005147444,0.00029349004,0.00024301844,0.0008825857,0.00053690013,0.0005248597,0.00072693004],"category_scores_gemma":[0.0035875838,0.0002982549,0.00050353975,0.0006083408,0.00095279975,0.0011611682,0.00084610074,0.0010748059,0.00019041022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016474687,0.000018249963,0.000102921054,0.000030630865,0.000008539174,0.00003262004,0.000044701686,0.9395608,0.0015045727,0.042117674,0.0003135915,0.016249325],"study_design_scores_gemma":[0.0000016338935,0.000007412057,0.00002511298,0.0000024610943,0.0000010680219,0.000008964913,0.000006661846,0.989794,0.000301686,0.009585509,0.00026312316,0.0000023655452],"about_ca_topic_score_codex":0.0018174362,"about_ca_topic_score_gemma":0.0013328095,"teacher_disagreement_score":0.0018174362,"about_ca_system_score_codex":0.0006197397,"about_ca_system_score_gemma":0.0006180368,"threshold_uncertainty_score":0.004496515},"labels":[],"label_agreement":null},{"id":"W1974166894","doi":"10.3390/s110504875","title":"A Survey of System Architecture Requirements for Health Care-Based Wireless Sensor Networks","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Wireless sensor network; Routing protocol; Node (physics); Efficient energy use; Routing (electronic design automation); Engineering","score_opus":0.06147384999697498,"score_gpt":0.31940464708444183,"score_spread":0.25793079708746686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974166894","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021381274,0.34274876,0.53083736,0.006928602,0.0016840004,0.001329944,0.0017766362,0.0017625685,0.09155096],"genre_scores_gemma":[0.15386906,0.44887727,0.3624201,0.0025996442,0.0014448853,0.002242914,0.004813571,0.0005928076,0.02313976],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972886,0.0005718625,0.0004935516,0.00029726772,0.0011929885,0.00015575158],"domain_scores_gemma":[0.9961414,0.0015762664,0.00031804002,0.00020137118,0.001683361,0.00007959338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030474598,0.0012532001,0.0010068062,0.0024167658,0.00082766183,0.0023135485,0.001795814,0.0018263289,0.005361376],"category_scores_gemma":[0.0069043217,0.0009498671,0.0008832668,0.0028029373,0.00047217318,0.0038202167,0.0009772009,0.0014518353,0.0025241855],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015900527,0.00015658404,0.004239526,0.014931436,0.00012303791,0.00074827444,0.00066633656,0.027802667,0.02362248,0.10415505,0.025749978,0.7976455],"study_design_scores_gemma":[0.000029245235,0.0006008575,0.0054701553,0.005212438,0.0002551227,0.00276077,0.00078559946,0.077779494,0.010156708,0.04134506,0.85545194,0.00015255094],"about_ca_topic_score_codex":0.0024663575,"about_ca_topic_score_gemma":0.0031926576,"teacher_disagreement_score":0.005361376,"about_ca_system_score_codex":0.0017699383,"about_ca_system_score_gemma":0.0017952729,"threshold_uncertainty_score":0.017935574},"labels":[],"label_agreement":null},{"id":"W1974853808","doi":"10.3390/s131115513","title":"Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; RSS; Kalman filter; Cross-correlation; Algorithm; Matching (statistics); Pattern recognition (psychology); Artificial intelligence; Map matching; Data mining; Global Positioning System; Mathematics; Statistics","score_opus":0.006698961570399669,"score_gpt":0.20897777991644004,"score_spread":0.20227881834604036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974853808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018831095,0.00019227527,0.9771993,0.0000529769,0.000065932705,0.000035139234,0.00007725756,0.0020802119,0.001465882],"genre_scores_gemma":[0.62438023,0.00028159985,0.3706552,0.000120684694,0.00007323835,0.00012364319,0.00037367287,0.00008023831,0.0039115385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993019,0.00008987649,0.0000369797,0.00017189182,0.00032570315,0.00007364047],"domain_scores_gemma":[0.9991221,0.0001703,0.00010932136,0.00015259378,0.00041954787,0.000026069718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005133256,0.0007145069,0.000802947,0.00092588493,0.0005396656,0.00065071206,0.001117534,0.00056902645,0.0012352619],"category_scores_gemma":[0.0017129423,0.0003626103,0.00039989775,0.0007837315,0.00029922667,0.0010730722,0.0005513277,0.00045395942,0.0008410111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046017184,0.00015060448,0.006364077,0.00018385539,0.00011337075,0.00021908728,0.00019229128,0.119501874,0.080255605,0.004575755,0.0044779046,0.7835055],"study_design_scores_gemma":[0.000033473723,0.00014003202,0.0041251085,0.000015592044,0.000057609064,0.00045264058,0.000027019987,0.938495,0.050928496,0.00080335786,0.004858925,0.00006268595],"about_ca_topic_score_codex":0.007948485,"about_ca_topic_score_gemma":0.008396327,"teacher_disagreement_score":0.007948485,"about_ca_system_score_codex":0.0006096425,"about_ca_system_score_gemma":0.00091158046,"threshold_uncertainty_score":0.01580447},"labels":[],"label_agreement":null},{"id":"W1974864272","doi":"10.3390/s130303394","title":"Using a Standing-Tree Acoustic Tool to Identify Forest Stands for the Production of Mechanically-Graded Lumber","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Tree Root and Stability Studies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chronosequence; Black spruce; Stress wave; Forestry; Young's modulus; Environmental science; Logging; Tree (set theory); Wood production; Engineering; Mathematics; Forest management; Materials science; Structural engineering; Geography; Soil science; Composite material","score_opus":0.04021160529694832,"score_gpt":0.2866500343566627,"score_spread":0.2464384290597144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974864272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937581,0.000112596244,0.0044384133,0.000009887019,0.0000038469316,0.000028156312,0.00021358517,0.000043731627,0.0013917644],"genre_scores_gemma":[0.98340803,0.000113797505,0.014841988,0.000018525678,0.0000035802764,0.00001829173,0.0003988336,0.000012324508,0.0011846551],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997751,0.00002577551,0.000008553889,0.000063960455,0.00009507023,0.00003142558],"domain_scores_gemma":[0.9995196,0.00012362833,0.00011393813,0.00001716901,0.00016540868,0.0000603124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005063192,0.00031841153,0.00019237309,0.000996208,0.0003195766,0.0005902987,0.00038883413,0.0002796849,0.0013243904],"category_scores_gemma":[0.0006356639,0.00013316568,0.00016030291,0.00055437395,0.00014757692,0.00034613084,0.00015880194,0.00013306325,0.00045801964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026686973,0.0001414631,0.70854944,0.00015085231,0.00005592048,0.00031479984,0.0005986133,0.0022347881,0.19814116,0.00010561462,0.00029155865,0.08914897],"study_design_scores_gemma":[0.000004469235,0.00027064842,0.9804043,0.000018554505,0.000026889395,0.00017082204,0.00054449396,0.0056570754,0.012085686,0.000033295102,0.00076607446,0.000017547762],"about_ca_topic_score_codex":0.044978086,"about_ca_topic_score_gemma":0.30719018,"teacher_disagreement_score":0.044978086,"about_ca_system_score_codex":0.0005312134,"about_ca_system_score_gemma":0.0007551404,"threshold_uncertainty_score":0.08943254},"labels":[],"label_agreement":null},{"id":"W1975168546","doi":"10.3390/s141120149","title":"A Study of the Effect of the Fringe Fields on the Electrostatic Force in Vertical Comb Drives","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Electrostatics; Voltage; Order (exchange); Materials science; Physics; Engineering; Electrical engineering; Quantum mechanics","score_opus":0.005517895040255807,"score_gpt":0.22953662205817207,"score_spread":0.22401872701791628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975168546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98537517,0.00036710274,0.011816507,0.0000832024,0.000013159737,0.000017221477,0.00004338328,0.00004414828,0.0022400941],"genre_scores_gemma":[0.9963174,0.00014279794,0.0030921965,0.00000638122,0.0000031916918,0.0000052183623,0.000012742173,0.0000051083266,0.00041503203],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983346,0.0000140981,0.0000065329014,0.00002835388,0.00009375525,0.000023681225],"domain_scores_gemma":[0.9990233,0.0007281622,0.00008667388,0.000053940254,0.00009088335,0.000016988794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025204156,0.00017666662,0.00019531159,0.00015465479,0.0003079036,0.000295798,0.00021875415,0.00032583726,0.00079164887],"category_scores_gemma":[0.0015634998,0.00018489754,0.00019832919,0.00017409987,0.0003652307,0.00043509208,0.00019786436,0.00022772088,0.00012746142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016747156,0.00005699242,0.0028985485,0.00014550805,0.00001644895,0.00033515703,0.00019438863,0.027211538,0.94574356,0.0022947236,0.00014042876,0.020795371],"study_design_scores_gemma":[0.000030138403,0.00095650944,0.016922684,0.000024286319,0.00002221828,0.0005251336,0.00016372728,0.22313821,0.75473,0.00055744225,0.0028934139,0.000036261405],"about_ca_topic_score_codex":0.001402978,"about_ca_topic_score_gemma":0.0013363003,"teacher_disagreement_score":0.001402978,"about_ca_system_score_codex":0.00034168622,"about_ca_system_score_gemma":0.0002567662,"threshold_uncertainty_score":0.0027896166},"labels":[],"label_agreement":null},{"id":"W1976823810","doi":"10.3390/s140303939","title":"Ground Testing Strategies for Verifying the Slew Rate Tolerance of Star Trackers","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Star tracker; Slew rate; Star (game theory); Computer science; A* search algorithm; Physics; Control theory (sociology); Algorithm; Artificial intelligence; Astrophysics; Astronomy; Spacecraft","score_opus":0.020902844817967274,"score_gpt":0.2298581762761073,"score_spread":0.20895533145814002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976823810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7379684,0.00011268963,0.25798613,0.00009645273,0.000030516707,0.000094366515,0.00019248086,0.0010896083,0.0024292402],"genre_scores_gemma":[0.97605544,0.000029337842,0.023442334,0.000018997562,0.0000026650434,0.000035701272,0.00009950917,0.00004245647,0.00027353034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855596,0.0004052409,0.00012994194,0.00021234978,0.00055704056,0.000139372],"domain_scores_gemma":[0.99375385,0.0029579366,0.00094910996,0.0010282521,0.0011286007,0.00018223119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019026471,0.0004917615,0.0003587575,0.0009356033,0.00045965737,0.0006496871,0.0013998543,0.0008023229,0.0020457415],"category_scores_gemma":[0.012057516,0.00031306082,0.0004032562,0.00046439562,0.00052808033,0.0013779268,0.00094301114,0.00045653997,0.00041780237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016106397,0.00060698047,0.08334685,0.00039745035,0.00012803092,0.0007432045,0.0010554505,0.5363427,0.2580853,0.006997498,0.0009867565,0.10969908],"study_design_scores_gemma":[0.000062845116,0.001054207,0.0114485165,0.000035120098,0.000026359085,0.00025435167,0.00027323523,0.85339135,0.13047485,0.0017068336,0.001234226,0.000038236984],"about_ca_topic_score_codex":0.0018937323,"about_ca_topic_score_gemma":0.0021981846,"teacher_disagreement_score":0.0020457415,"about_ca_system_score_codex":0.00069154485,"about_ca_system_score_gemma":0.00053409464,"threshold_uncertainty_score":0.010062218},"labels":[],"label_agreement":null},{"id":"W1977684457","doi":"10.3390/s130607021","title":"Improved Adhesion of Gold Thin Films Evaporated on Polymer Resin: Applications for Sensing Surfaces and MEMS","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Microfabrication; Photoresist; Substrate (aquarium); Adhesion; Thin film; Layer (electronics); Nanotechnology; Deposition (geology); Shrinkage; Polymer; Surface finish; Adhesive; Polymer substrate; Composite material; Microelectromechanical systems; Fabrication","score_opus":0.01024773917022873,"score_gpt":0.2614180298012322,"score_spread":0.25117029063100343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977684457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9517084,0.004047283,0.040543996,0.00027798762,0.00011742683,0.000078295016,0.0001396573,0.00042439258,0.0026625826],"genre_scores_gemma":[0.9360352,0.0026352212,0.05563884,0.0001286961,0.000055111635,0.000071618284,0.00017686319,0.00010285943,0.0051556146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978715,0.000023029226,0.000015072934,0.000048165122,0.00008775374,0.000038955193],"domain_scores_gemma":[0.99986124,0.00003297764,0.00003377192,0.000021803044,0.000034157958,0.000015969135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022000013,0.0005394344,0.00030273866,0.00029918787,0.00023296697,0.00029706015,0.00052341836,0.00049150607,0.0013083341],"category_scores_gemma":[0.00028556612,0.00031635034,0.00022883218,0.00017299003,0.00022233877,0.00029972263,0.00032262498,0.00049319793,0.0005183245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041650205,0.000004875852,0.000030668045,0.000030381481,0.0000018785146,0.000017059734,0.0000068696436,0.000045502824,0.99843043,0.00003346543,0.000021941263,0.0013727323],"study_design_scores_gemma":[0.0000017487515,0.000040328214,0.00043866405,0.000001669549,0.000005151335,0.000047745514,0.000004353682,0.00042318864,0.9983016,0.0000139993335,0.0007184104,0.0000030798476],"about_ca_topic_score_codex":0.00047898965,"about_ca_topic_score_gemma":0.00077478937,"teacher_disagreement_score":0.0013083341,"about_ca_system_score_codex":0.00023826737,"about_ca_system_score_gemma":0.00016687205,"threshold_uncertainty_score":0.004376769},"labels":[],"label_agreement":null},{"id":"W1977794869","doi":"10.3390/s130404961","title":"Compressive Sensing Image Sensors-Hardware Implementation","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; CMC Microsystems","keywords":"Compressed sensing; Computer science; Image sensor; CMOS; Computer hardware; Implementation; Coding (social sciences); Chip; Encoding (memory); Data acquisition; Electronic engineering; Artificial intelligence; Engineering; Telecommunications","score_opus":0.011671316095405204,"score_gpt":0.251402855512459,"score_spread":0.2397315394170538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977794869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006452229,0.0014687417,0.97348166,0.00051176565,0.0001716578,0.00016406826,0.0001280859,0.001455142,0.01616671],"genre_scores_gemma":[0.23992884,0.0027973522,0.74566555,0.0005063527,0.00028307003,0.00033363124,0.00033849254,0.00008438898,0.010062266],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995498,0.000072673094,0.000022862194,0.00006164722,0.00026020716,0.000032753112],"domain_scores_gemma":[0.99974936,0.00007904881,0.000017853508,0.00004601669,0.00009599761,0.0000117841655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029997018,0.0005135522,0.00035969686,0.00032951127,0.00020397216,0.0006875087,0.0008971752,0.00069558405,0.0058942693],"category_scores_gemma":[0.0010123479,0.00022476907,0.00023411216,0.00035646156,0.00029882498,0.00076147425,0.00054513296,0.0007126222,0.0025163624],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025102036,0.00014412083,0.000741112,0.0008842711,0.00006899927,0.00024071403,0.00012879714,0.06725451,0.15895428,0.094982184,0.013019978,0.66332996],"study_design_scores_gemma":[0.00008573211,0.0007103156,0.000844741,0.00021637158,0.00006210257,0.001323333,0.00009538548,0.67832243,0.17956582,0.026145678,0.11255209,0.00007601879],"about_ca_topic_score_codex":0.0006238401,"about_ca_topic_score_gemma":0.0007988441,"teacher_disagreement_score":0.0058942693,"about_ca_system_score_codex":0.00031572726,"about_ca_system_score_gemma":0.00049846363,"threshold_uncertainty_score":0.01971829},"labels":[],"label_agreement":null},{"id":"W1978095935","doi":"10.3390/s100807674","title":"Flat-Cladding Fiber Bragg Grating Sensors for Large Strain Amplitude Fatigue Tests","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Extensometer; Cladding (metalworking); Fiber Bragg grating; Amplitude; Composite material; Optical fiber; Optics; Optoelectronics; Wavelength","score_opus":0.018457784422159746,"score_gpt":0.2719632282520028,"score_spread":0.2535054438298431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978095935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7621612,0.003026658,0.22992484,0.00018933813,0.00014605488,0.00034736798,0.0006786061,0.0011610418,0.0023648823],"genre_scores_gemma":[0.84809005,0.00065205176,0.14924005,0.00012644747,0.000029820934,0.00012121964,0.0003115325,0.000034556193,0.001394281],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930644,0.00007783792,0.00003371451,0.00011193257,0.00040319696,0.00006683328],"domain_scores_gemma":[0.99946517,0.00012023722,0.00007215179,0.000063418964,0.00022144556,0.000057502373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077624153,0.0006176585,0.00035650065,0.00044972036,0.0001383795,0.00016361159,0.00072259153,0.00041148785,0.0007043285],"category_scores_gemma":[0.0006802603,0.00029139008,0.00017917807,0.00021113757,0.00026936253,0.0003731859,0.00024049608,0.00037772467,0.00030150844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025522126,0.000018590155,0.00051070034,0.000032005628,0.000003742342,0.000021013077,0.000014987795,0.00035538484,0.99332774,0.000038943806,0.000057518562,0.0055937804],"study_design_scores_gemma":[0.000012069891,0.00031728728,0.008368456,0.00000605601,0.000013230445,0.00015516396,0.000016649765,0.015467368,0.9746523,0.000059706694,0.0009129159,0.000018786937],"about_ca_topic_score_codex":0.0022243962,"about_ca_topic_score_gemma":0.0075967507,"teacher_disagreement_score":0.0022243962,"about_ca_system_score_codex":0.0003770298,"about_ca_system_score_gemma":0.0002965282,"threshold_uncertainty_score":0.004422903},"labels":[],"label_agreement":null},{"id":"W1978615060","doi":"10.3390/s140813556","title":"A Comprehensive Review of Sensors and Instrumentation Methods in Devices for Musical Expression","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Instrumentation (computer programming); Computer science; Signal conditioning; Human–computer interaction; Musical expression; Musical instrument; Task (project management); SIGNAL (programming language); New Interfaces for Musical Expression; Expression (computer science); Musical; Systems engineering; Engineering; Musical composition","score_opus":0.07592104227759539,"score_gpt":0.4138358901796347,"score_spread":0.3379148479020393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978615060","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003518551,0.9858818,0.006201222,0.00044490022,0.00097032066,0.00003093552,0.000086429565,0.000060749753,0.005971887],"genre_scores_gemma":[0.0033898205,0.9792197,0.0084945485,0.0008353233,0.00119281,0.00008168719,0.00021744416,0.00005696706,0.00651169],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790275,0.00031410894,0.0003133463,0.00039451008,0.0009781558,0.00009713637],"domain_scores_gemma":[0.99782443,0.0012918378,0.00018072364,0.00012700638,0.0005261653,0.000049909842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016703136,0.0016364042,0.00176417,0.0037726618,0.0006280096,0.0022037788,0.0019774523,0.0024393816,0.010322047],"category_scores_gemma":[0.0024738184,0.0009801072,0.0011928917,0.0048042526,0.00095739565,0.00368132,0.0012307515,0.0023274224,0.0069476566],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007937833,0.00010210212,0.00042064764,0.02755421,0.000093750394,0.00030832933,0.00019593038,0.0008352878,0.011335613,0.012122985,0.037851337,0.9091005],"study_design_scores_gemma":[0.000004420064,0.00010493148,0.00060555927,0.0032588607,0.0000717436,0.0011287546,0.000077772806,0.00037848897,0.0032649455,0.0026787298,0.9883741,0.000051564606],"about_ca_topic_score_codex":0.0010229729,"about_ca_topic_score_gemma":0.0011465711,"teacher_disagreement_score":0.010322047,"about_ca_system_score_codex":0.00076208945,"about_ca_system_score_gemma":0.0014092461,"threshold_uncertainty_score":0.03453064},"labels":[],"label_agreement":null},{"id":"W1980856039","doi":"10.3390/s110606214","title":"Interfacial Chemistry and the Design of Solid-Phase Nucleic Acid Hybridization Assays Using Immobilized Quantum Dots as Donors in Fluorescence Resonance Energy Transfer","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto Mississauga; Bộ Giáo dục và Ðào tạo; Ministère de l’Éducation, Gouvernement de l’Ontario; University of Toronto","keywords":"Förster resonance energy transfer; Quantum dot; Biosensor; Chemistry; Nucleic acid; Fluorescence; Nanotechnology; Acceptor; Nucleic acid detection; Peptide nucleic acid; Materials science; Biochemistry; Physics","score_opus":0.01912788125020746,"score_gpt":0.2843010092948866,"score_spread":0.26517312804467913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980856039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4343574,0.00884445,0.5517854,0.00047199332,0.00012169431,0.00041434207,0.00014059995,0.00065484835,0.0032093676],"genre_scores_gemma":[0.73273486,0.0057293493,0.2579007,0.0002297328,0.00003301715,0.0004015689,0.0002748535,0.000091576294,0.0026043635],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994186,0.00016500757,0.000049632403,0.00011524317,0.00020986272,0.000041643445],"domain_scores_gemma":[0.9996495,0.00015044917,0.000065885404,0.00002426476,0.000073756346,0.000036060723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009627366,0.00063321623,0.00042614894,0.00047502358,0.0002341731,0.0011044303,0.00061110465,0.0006163671,0.00058690045],"category_scores_gemma":[0.0010507517,0.00060388946,0.0003350721,0.00031656455,0.00040997763,0.0006791351,0.00032686832,0.0006857083,0.00047493645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050077695,0.000039619474,0.00022152028,0.00012416141,0.00001071643,0.00006353347,0.000022526325,0.0034383973,0.9856753,0.0012009131,0.000028211476,0.009125172],"study_design_scores_gemma":[0.00001933512,0.00014229312,0.00034807023,0.000011964679,0.000022167214,0.00014741313,0.00001508995,0.019099819,0.9772775,0.0005533058,0.002349464,0.000013636691],"about_ca_topic_score_codex":0.00025186257,"about_ca_topic_score_gemma":0.00037493356,"teacher_disagreement_score":0.0011044303,"about_ca_system_score_codex":0.0005534819,"about_ca_system_score_gemma":0.00034136648,"threshold_uncertainty_score":0.0050914884},"labels":[],"label_agreement":null},{"id":"W1981137979","doi":"10.3390/s8106704","title":"NeuroMEMS: Neural Probe Microtechnologies","year":2008,"lang":"en","type":"review","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":189,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; CMC Microsystems","keywords":"Microfabrication; Neural Prosthesis; Neural activity; Neuroscience; Nanotechnology; Computer science; Materials science; Medicine; Biology; Pathology","score_opus":0.10902366968413868,"score_gpt":0.31781558901162577,"score_spread":0.20879191932748709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981137979","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023971396,0.8600189,0.07812286,0.0022287404,0.0036886123,0.00024831368,0.00025118873,0.0009928739,0.052051365],"genre_scores_gemma":[0.020426018,0.80298555,0.07821904,0.0022084143,0.0012623894,0.0007434305,0.0004402929,0.00016868617,0.09354623],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991786,0.00007196634,0.00005312095,0.00012535923,0.00052752905,0.00004338079],"domain_scores_gemma":[0.99965215,0.00011383485,0.000044722765,0.000035202902,0.00012691108,0.000027215228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007905083,0.0013729131,0.0013469465,0.002238428,0.0005265295,0.001391289,0.0016892047,0.002070276,0.008620787],"category_scores_gemma":[0.0008720846,0.0006823032,0.0004546766,0.0019080872,0.00095389015,0.0023777075,0.001034349,0.0019366181,0.011190835],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055755947,0.00007991775,0.00018547938,0.0059664883,0.000034137447,0.000480521,0.00016080799,0.00056361506,0.08295852,0.023446763,0.028217478,0.85785055],"study_design_scores_gemma":[0.000011352433,0.00006508131,0.00028128203,0.0005020756,0.000017149609,0.0018396109,0.000042147516,0.00047096057,0.042830516,0.004391785,0.9495176,0.000030535855],"about_ca_topic_score_codex":0.0004222697,"about_ca_topic_score_gemma":0.0007295871,"teacher_disagreement_score":0.008620787,"about_ca_system_score_codex":0.0008501905,"about_ca_system_score_gemma":0.0008670374,"threshold_uncertainty_score":0.028839469},"labels":[],"label_agreement":null},{"id":"W1981196755","doi":"10.3390/s110404244","title":"Tightly Coupled Low Cost 3D RISS/GPS Integration Using a Mixture Particle Filter for Vehicular Navigation","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University; Trusted Positioning (Canada)","funders":"","keywords":"Global Positioning System; Particle filter; Particle (ecology); Computer science; Filter (signal processing); Aerospace engineering; Real-time computing; Environmental science; Engineering; Telecommunications; Computer vision; Geology","score_opus":0.040427926858330625,"score_gpt":0.26051047434238594,"score_spread":0.2200825474840553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981196755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015364862,0.000098682605,0.9832978,0.00003220518,0.000040713836,0.000020373438,0.000015145854,0.0003827886,0.0007474426],"genre_scores_gemma":[0.72572875,0.00025140247,0.2703256,0.00007038928,0.00006368956,0.00010704074,0.00019374146,0.000056785455,0.0032026495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954456,0.00006886304,0.000021229856,0.00010216565,0.00021720742,0.00004591806],"domain_scores_gemma":[0.99984634,0.000036244848,0.000023911784,0.000026465179,0.00005365543,0.000013386049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039131535,0.0006718059,0.00074671855,0.00036726135,0.00030204156,0.00046616571,0.0006733885,0.00059596344,0.00085517426],"category_scores_gemma":[0.00057830696,0.00041550866,0.0009575201,0.000414702,0.00030478265,0.0006328549,0.00087347254,0.0006945262,0.00048194703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005574869,0.0001379594,0.006154139,0.00019813281,0.00026393152,0.000337679,0.00024946296,0.5680671,0.09299714,0.011784386,0.0016949394,0.3175577],"study_design_scores_gemma":[0.000010463665,0.00008009526,0.00082546746,0.0000043539335,0.000025425023,0.00003652864,0.000008930387,0.99113804,0.0059189927,0.0005840321,0.0013513493,0.000016319942],"about_ca_topic_score_codex":0.0066445046,"about_ca_topic_score_gemma":0.0050541684,"teacher_disagreement_score":0.0066445046,"about_ca_system_score_codex":0.00035699073,"about_ca_system_score_gemma":0.00067107956,"threshold_uncertainty_score":0.013211668},"labels":[],"label_agreement":null},{"id":"W1981823922","doi":"10.3390/s91108907","title":"Editorial: Nanotechnological Advances in Biosensors","year":2009,"lang":"en","type":"editorial","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fowler Kennedy Sport Medicine Clinic","funders":"","keywords":"Biosensor; Biological fluids; Nanotechnology; Biochemical engineering; Computer science; Computational biology; Biology; Chemistry; Materials science; Chromatography; Engineering","score_opus":0.004044902652316649,"score_gpt":0.2723194689673483,"score_spread":0.26827456631503166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981823922","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000018893621,0.01127788,0.00018614871,0.027499445,0.9587291,0.00001651455,0.000029683044,0.00006122982,0.0021811312],"genre_scores_gemma":[0.0005172708,0.014696317,0.00031827568,0.04804311,0.91102827,0.00003202612,0.00005069692,0.00006439477,0.025249615],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960705,0.00060472393,0.00036550107,0.00044102353,0.0023187317,0.0001995119],"domain_scores_gemma":[0.99046296,0.003474995,0.00053880195,0.00027307548,0.003932473,0.0013176801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041852836,0.0033191103,0.0025475575,0.0028531759,0.0023490812,0.0049470984,0.0034788158,0.0120704,0.012423012],"category_scores_gemma":[0.0131116435,0.0010738049,0.0022478343,0.0014106628,0.0024001775,0.0046175187,0.0014365906,0.01767464,0.018522026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020360367,0.0000072055664,0.0000091962265,0.000123571,0.0000068872223,0.000053687203,0.000005012936,0.00001948996,0.000055995635,0.00031757343,0.99361813,0.005762835],"study_design_scores_gemma":[0.000016978902,0.00001407657,0.000056027417,0.00012506828,0.00001076337,0.00018343408,0.00000977546,0.000059291084,0.00008373871,0.0005210564,0.99891126,0.000008470799],"about_ca_topic_score_codex":0.0013399592,"about_ca_topic_score_gemma":0.0038061263,"teacher_disagreement_score":0.012423012,"about_ca_system_score_codex":0.0022893832,"about_ca_system_score_gemma":0.0021656405,"threshold_uncertainty_score":0.0415591},"labels":[],"label_agreement":null},{"id":"W1982632174","doi":"10.3390/s100505014","title":"Low Light CMOS Contact Imager with an Integrated Poly-Acrylic Emission Filter for Fluorescence Detection","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Asylum, Migration and Integration Fund; University of Calgary","keywords":"Materials science; Quantum yield; Fluorophore; Fluorescence; Optoelectronics; Visible spectrum; Absorbance; Filter (signal processing); Quantum efficiency; Photochemistry; Optics; Chemistry; Physics; Computer science","score_opus":0.00664533231778968,"score_gpt":0.22086248343317916,"score_spread":0.21421715111538947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982632174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2945448,0.00232424,0.6864741,0.0003798943,0.00033720606,0.00035879313,0.00083239435,0.0038531285,0.010895453],"genre_scores_gemma":[0.42294756,0.0010160598,0.55989987,0.00033191033,0.0000999867,0.00036211332,0.000484426,0.00024226197,0.014615773],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936694,0.000032640764,0.000026558731,0.00015660502,0.00037582606,0.00004143242],"domain_scores_gemma":[0.99965703,0.00007819519,0.00007926752,0.000046440637,0.0001121501,0.000026952444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027708968,0.0005151456,0.0004897899,0.0003900039,0.0002468812,0.0005628223,0.0009169154,0.00077301654,0.002489944],"category_scores_gemma":[0.00048942165,0.00028811267,0.00041649668,0.00034526092,0.00020567415,0.00065339135,0.00027570673,0.00052322407,0.002086277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032117467,0.000019536266,0.00010831027,0.00006944972,0.0000062537247,0.00007490459,0.00001747136,0.00013064302,0.9917269,0.00028216554,0.00017742813,0.0073547917],"study_design_scores_gemma":[0.000008384382,0.00018014245,0.00100027,0.000005797455,0.000018566228,0.00043869842,0.000009845076,0.0035274301,0.989416,0.000058152553,0.0053207623,0.000016129927],"about_ca_topic_score_codex":0.0006791345,"about_ca_topic_score_gemma":0.0016164012,"teacher_disagreement_score":0.002489944,"about_ca_system_score_codex":0.0006175496,"about_ca_system_score_gemma":0.00049443473,"threshold_uncertainty_score":0.008329749},"labels":[],"label_agreement":null},{"id":"W1983193593","doi":"10.3390/s140304416","title":"Experiments and Identification of the Unbalance of Aerostatic Guideways on the Micro-Scale","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Tribology and Lubrication Engineering","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Displacement (psychology); Vibration; Spectral density; Acceleration; Scale (ratio); Finite element method; Engineering; Structural engineering; Acoustics; Power (physics); Physics; Classical mechanics","score_opus":0.006842525609236834,"score_gpt":0.19725049041894807,"score_spread":0.19040796480971123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983193593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99341303,0.00006824931,0.0056685824,0.000032879907,0.00002150907,0.000024082354,0.000079777295,0.00010821821,0.0005837353],"genre_scores_gemma":[0.9960622,0.00003515947,0.0033537056,0.000010240521,0.0000058242936,0.000017106622,0.000041785017,0.000009116104,0.00046482641],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969685,0.00001899998,0.000014172373,0.000089401045,0.000111062946,0.00006950022],"domain_scores_gemma":[0.9995028,0.00011289463,0.000067932764,0.000091176706,0.00013659612,0.00008872753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027162762,0.0003613783,0.00036223404,0.00028573253,0.00035809953,0.00021332929,0.00035027994,0.00044783336,0.0015683874],"category_scores_gemma":[0.000745051,0.00024257628,0.00017059587,0.0002731872,0.0005527157,0.0004086015,0.00045006475,0.0003137534,0.0002140279],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022377014,0.000063786414,0.0015496121,0.000039828712,0.000005252877,0.0001665936,0.00015171399,0.0015333906,0.9921062,0.00013656101,0.00008245243,0.003940915],"study_design_scores_gemma":[0.00004456831,0.0013192913,0.025997492,0.000006610049,0.000021051314,0.00018038775,0.0004385628,0.023700077,0.94618845,0.00021710557,0.0018593798,0.00002698033],"about_ca_topic_score_codex":0.0010267147,"about_ca_topic_score_gemma":0.0014943385,"teacher_disagreement_score":0.0015683874,"about_ca_system_score_codex":0.00017407049,"about_ca_system_score_gemma":0.00015776823,"threshold_uncertainty_score":0.0052467585},"labels":[],"label_agreement":null},{"id":"W1983600558","doi":"10.3390/s111211295","title":"Protein Binding Detection Using On-Chip Silicon Gratings","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streptavidin; Silicon; Substrate (aquarium); Materials science; Grating; Biosensor; Refractive index; Biotin; Wavelength; Nanometre; Optoelectronics; Optics; Nanotechnology; Chemistry; Composite material","score_opus":0.02805630948396085,"score_gpt":0.21787543649331506,"score_spread":0.18981912700935422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983600558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9191999,0.002311584,0.07064717,0.00030873236,0.00024119727,0.00009095128,0.00066363264,0.0011615093,0.005375314],"genre_scores_gemma":[0.9093863,0.0013801934,0.083738655,0.00025730027,0.00004739967,0.00006215293,0.0008686883,0.000049614442,0.0042097615],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997265,0.00003729891,0.000013771712,0.000068788,0.00010913907,0.00004446195],"domain_scores_gemma":[0.9999012,0.00002818409,0.00001470158,0.000015240381,0.000025330302,0.000015390164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001619681,0.00032127477,0.0003091077,0.00023279201,0.0001128148,0.0002674675,0.0005810058,0.00044875193,0.00089506706],"category_scores_gemma":[0.00018138022,0.00023528944,0.00025247253,0.0002008019,0.00018160051,0.00016556971,0.0002543753,0.00023886716,0.0005512126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025289277,0.00002008319,0.0002496022,0.00004166703,0.000008320342,0.00002472358,0.00001001359,0.00021841645,0.99563676,0.000071713555,0.00013499099,0.0035583696],"study_design_scores_gemma":[0.000007835844,0.00010260773,0.0016315556,0.000002965903,0.000013737863,0.00012603548,0.000013946459,0.0052179988,0.9908957,0.00005302455,0.0019268916,0.0000076931865],"about_ca_topic_score_codex":0.0009443487,"about_ca_topic_score_gemma":0.0012398467,"teacher_disagreement_score":0.0009443487,"about_ca_system_score_codex":0.0002933345,"about_ca_system_score_gemma":0.00017442372,"threshold_uncertainty_score":0.0029942393},"labels":[],"label_agreement":null},{"id":"W1984946028","doi":"10.3390/s130708103","title":"A Robust Self-Alignment Method for Ship’s Strapdown INS Under Mooring Conditions","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; National Natural Science Foundation of China; University of Calgary","keywords":"Inertial navigation system; Mooring; Kalman filter; Control theory (sociology); Inertial frame of reference; Inertial measurement unit; Engineering; Process (computing); Accelerometer; Noise (video); Frame (networking); Gyroscope; Computer science; Artificial intelligence; Computer vision; Marine engineering; Aerospace engineering; Physics","score_opus":0.022786859436729773,"score_gpt":0.2555982983126598,"score_spread":0.23281143887593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984946028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005928832,0.00008223863,0.9931867,0.000015432533,0.000033013977,0.000011740374,0.000014071207,0.0004197381,0.0003081942],"genre_scores_gemma":[0.31203735,0.0002998472,0.6822825,0.000057801706,0.00010455165,0.00010327151,0.0002230488,0.00016904973,0.0047226083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996118,0.000040533196,0.000021468546,0.00013427329,0.00016614934,0.000025837006],"domain_scores_gemma":[0.99977034,0.00003557346,0.00003934931,0.00003958557,0.00010188794,0.000013288496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028363257,0.000598673,0.0005921896,0.00043344035,0.00039731155,0.00040889825,0.00063651265,0.0004908583,0.0013142676],"category_scores_gemma":[0.0009320986,0.00037017497,0.00046879836,0.00043368092,0.00026882478,0.00074437365,0.0004693956,0.0005773019,0.0006815123],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015967568,0.000057648216,0.0012113376,0.00012708415,0.0000705751,0.0001348333,0.00016143559,0.12506278,0.10023074,0.0048056883,0.002276737,0.7657015],"study_design_scores_gemma":[0.000012703696,0.00009348212,0.0014770173,0.0000073900983,0.000020228634,0.000115491166,0.000022812852,0.97574496,0.018315991,0.00092213537,0.0032371434,0.000030735777],"about_ca_topic_score_codex":0.0038263844,"about_ca_topic_score_gemma":0.003852233,"teacher_disagreement_score":0.0038263844,"about_ca_system_score_codex":0.00027090756,"about_ca_system_score_gemma":0.00080862164,"threshold_uncertainty_score":0.007608235},"labels":[],"label_agreement":null},{"id":"W1985962510","doi":"10.3390/s150408852","title":"A Homogenous Fluorescence Quenching Based Assay for Specific and Sensitive Detection of Influenza Virus A Hemagglutinin Antigen","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Poultry Industry Council","keywords":"Hemagglutinin (influenza); Detection limit; Chemistry; Quenching (fluorescence); Fluorescence; Glycan; Virus; Influenza A virus; Virology; Biophysics; Conjugated system; Nanoparticle; Nanotechnology; Chromatography; Biology; Biochemistry; Materials science; Glycoprotein","score_opus":0.10381745392658474,"score_gpt":0.3540997794664052,"score_spread":0.25028232553982044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985962510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39333594,0.011465185,0.5873608,0.00045608202,0.00030369766,0.00059136766,0.00077296613,0.0010100843,0.004703918],"genre_scores_gemma":[0.7112719,0.0037432702,0.27740812,0.0005114865,0.00009092343,0.000550069,0.0010250174,0.00005532099,0.005343943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987785,0.00030302774,0.000079855105,0.00037157314,0.00037984498,0.00008722515],"domain_scores_gemma":[0.999529,0.00016544841,0.00011153953,0.00004999119,0.000113474656,0.000030557374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097228226,0.00090319285,0.00057992944,0.00050397794,0.00024911255,0.00037799196,0.0008815461,0.0013945192,0.0006503952],"category_scores_gemma":[0.00089802494,0.0003486516,0.0005816441,0.00026548203,0.00036489536,0.0005307237,0.0005053342,0.00077157805,0.0006049457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001761495,0.000025411942,0.00030427007,0.000101456106,0.0000130263215,0.00003027252,0.000019507923,0.00007586069,0.99591005,0.00012487435,0.00007753233,0.003300225],"study_design_scores_gemma":[0.0000054743914,0.0001817152,0.001221569,0.000009435008,0.000034930363,0.0002259238,0.000014686855,0.002355549,0.9938313,0.00004977861,0.0020562368,0.000013340014],"about_ca_topic_score_codex":0.00042604416,"about_ca_topic_score_gemma":0.00073050684,"teacher_disagreement_score":0.0013945192,"about_ca_system_score_codex":0.00045647862,"about_ca_system_score_gemma":0.0003019955,"threshold_uncertainty_score":0.0051419735},"labels":[],"label_agreement":null},{"id":"W1986718632","doi":"10.3390/s100605569","title":"Modeling and Analysis of Energy Conservation Scheme Based on Duty Cycling in Wireless Ad Hoc Sensor Network","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Timer; Energy conservation; Wireless sensor network; Energy consumption; Duty cycle; Computer science; Node (physics); Real-time computing; Sensor node; Computer network; Engineering; Key distribution in wireless sensor networks; Voltage; Wireless; Electrical engineering; Telecommunications; Wireless network","score_opus":0.010846365265056383,"score_gpt":0.22399386103805444,"score_spread":0.21314749577299805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986718632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06434039,0.0016003748,0.9226678,0.0003693591,0.000101326325,0.00009452022,0.00006481682,0.00021720285,0.010544207],"genre_scores_gemma":[0.96951586,0.0017450658,0.024210488,0.00007887235,0.000049632803,0.00011858018,0.00006119664,0.000050056537,0.004170322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952257,0.00012270684,0.000025465526,0.000082647064,0.00018700874,0.000059691607],"domain_scores_gemma":[0.9993298,0.00030744963,0.000095351425,0.00005632111,0.00018867129,0.00002240404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008047862,0.0005299016,0.00053647836,0.0006611742,0.0004323359,0.0007495968,0.0014468542,0.00075339805,0.0009853406],"category_scores_gemma":[0.0022519855,0.00035334475,0.0005881635,0.00067108433,0.0006506488,0.0017612502,0.0004468334,0.0005892223,0.00020968546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035471952,0.000030268662,0.0009954624,0.00007388556,0.000022448028,0.000114500275,0.00013115784,0.94476944,0.0042231986,0.036825605,0.00048519982,0.012293392],"study_design_scores_gemma":[0.0000011852809,0.000009091033,0.00008250152,0.0000030516933,0.0000035422554,0.00001992054,0.0000058557403,0.99693394,0.00017125226,0.0025568923,0.00021022264,0.0000025751172],"about_ca_topic_score_codex":0.003820233,"about_ca_topic_score_gemma":0.0022511522,"teacher_disagreement_score":0.003820233,"about_ca_system_score_codex":0.0010365404,"about_ca_system_score_gemma":0.00060723675,"threshold_uncertainty_score":0.007596016},"labels":[],"label_agreement":null},{"id":"W1987308251","doi":"10.3390/s110505360","title":"Microfabrication and Applications of Opto-Microfluidic Sensors","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Canada Research Chairs","keywords":"Microfabrication; Microfluidics; Software portability; Nanotechnology; Bioanalysis; Fabrication; Materials science; Computer science","score_opus":0.01559107779802941,"score_gpt":0.24271655988145477,"score_spread":0.22712548208342537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987308251","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079225,0.98980755,0.004399194,0.00015556507,0.00021885429,0.000020575746,0.000030708685,0.000040738036,0.0045345365],"genre_scores_gemma":[0.0056784893,0.9835835,0.005827917,0.00020943665,0.00021075283,0.00004176559,0.0000665527,0.00000748795,0.0043740757],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996456,0.000029767052,0.000031470117,0.000082250706,0.00018519156,0.000025754038],"domain_scores_gemma":[0.9998479,0.000060055747,0.000025552697,0.000010277473,0.000047418427,0.000008867909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003996548,0.001039021,0.00090779446,0.002305407,0.00032482954,0.00068577327,0.0010250405,0.0009106802,0.0015965506],"category_scores_gemma":[0.00045846956,0.0004946504,0.00049192837,0.0019474687,0.00048823463,0.00084797124,0.00045725555,0.00097056205,0.001996919],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029835135,0.00012134815,0.00027657681,0.00911362,0.00003881225,0.00048624928,0.00011024615,0.0014585531,0.04937935,0.0105281975,0.010334828,0.9181225],"study_design_scores_gemma":[0.000007745403,0.00009723593,0.001114427,0.001143094,0.000034077733,0.0035687706,0.0000515823,0.0006680994,0.031466097,0.003547757,0.95825636,0.000044658267],"about_ca_topic_score_codex":0.0008269945,"about_ca_topic_score_gemma":0.0010167937,"teacher_disagreement_score":0.002305407,"about_ca_system_score_codex":0.0005676208,"about_ca_system_score_gemma":0.00050296605,"threshold_uncertainty_score":0.005340934},"labels":[],"label_agreement":null},{"id":"W1987691393","doi":"10.3390/s110605716","title":"Using Acoustic Sensors to Improve the Efficiency of the Forest Value Chain in Canada: A Case Study with Laminated Veneer Lumber","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laminated veneer lumber; Veneer; Stiffness; Materials science; Ultrasonic sensor; Young's modulus; Acoustic emission; Population; Composite material; Acoustics; Environmental science","score_opus":0.019331268019895303,"score_gpt":0.197209177937536,"score_spread":0.17787790991764071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987691393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987287,0.00003104169,0.0007851908,0.000018613055,5.1794666e-7,0.00002505217,0.000018106526,0.000007887797,0.00038487103],"genre_scores_gemma":[0.99149513,0.000079651225,0.007148417,0.000016342443,8.772479e-7,0.0000113421565,0.000036604622,0.000005454569,0.001206326],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992304,0.00014968596,0.00002973256,0.00010920545,0.0003248073,0.00015606242],"domain_scores_gemma":[0.9992073,0.0003141071,0.00009776665,0.000051824092,0.00026743917,0.00006160634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000758631,0.00045522497,0.00038495977,0.00048993045,0.0017152759,0.00079591037,0.0008510975,0.0006871501,0.00041582884],"category_scores_gemma":[0.0015614422,0.00023095684,0.00028206743,0.0007681136,0.0008192838,0.00030911923,0.00035364841,0.00032836036,0.000093572366],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000871923,0.0015110587,0.66670156,0.0002566947,0.00010911679,0.012740246,0.011816169,0.014120149,0.15893187,0.0006983171,0.0004039129,0.13183893],"study_design_scores_gemma":[0.000070201386,0.0029193545,0.7564111,0.00005662993,0.0001993963,0.0054254774,0.02555165,0.048878845,0.15461047,0.00045429956,0.005272362,0.00015022067],"about_ca_topic_score_codex":0.5131586,"about_ca_topic_score_gemma":0.7964594,"teacher_disagreement_score":0.48684138,"about_ca_system_score_codex":0.003719623,"about_ca_system_score_gemma":0.0025160667,"threshold_uncertainty_score":0.97941697},"labels":[],"label_agreement":null},{"id":"W1987752998","doi":"10.3390/s130911507","title":"Mutational Analysis of a Red Fluorescent Protein-Based Calcium Ion Indicator","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Alberta","keywords":"mCherry; Calmodulin; Chromophore; Fluorescence; Chemistry; Biophysics; Calcium; Conformational change; Biochemistry; EF hand; Fluorescent protein; Green fluorescent protein; Stereochemistry; Biology; Enzyme; Gene","score_opus":0.008338926459303028,"score_gpt":0.2706489934581344,"score_spread":0.2623100669988314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987752998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922348,0.00017185742,0.006366006,0.00007735,0.000019699686,0.000052053943,0.00033969764,0.00006258274,0.0006759064],"genre_scores_gemma":[0.9933302,0.00016928515,0.0052408925,0.000034890585,0.000004428409,0.000024883057,0.00053814333,0.000041188738,0.00061606226],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984217,0.00004278292,0.00001702878,0.000024018369,0.000046734986,0.000027287224],"domain_scores_gemma":[0.99972075,0.000093822666,0.00007141874,0.000025174219,0.000033764085,0.000055103766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022533942,0.00038580975,0.0002652179,0.00017019326,0.00012548544,0.0002590315,0.00041731776,0.00040976095,0.0005679644],"category_scores_gemma":[0.0004983572,0.000099039906,0.0002744539,0.00018271663,0.00025507426,0.00013497501,0.0002754235,0.0005128261,0.0002863251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093158604,0.0000384938,0.00039342407,0.00002502275,0.000009588172,0.00023646069,0.00001922928,0.0004889571,0.99739134,0.00019784519,0.000023887009,0.0010824406],"study_design_scores_gemma":[0.000024562249,0.00022277408,0.005519683,0.000013286909,0.000029808658,0.0008998916,0.000041253454,0.0063167373,0.9851726,0.00008185012,0.0016596739,0.000017918017],"about_ca_topic_score_codex":0.0014193694,"about_ca_topic_score_gemma":0.0010044904,"teacher_disagreement_score":0.0014193694,"about_ca_system_score_codex":0.0003770016,"about_ca_system_score_gemma":0.00031689368,"threshold_uncertainty_score":0.0028222203},"labels":[],"label_agreement":null},{"id":"W1988035201","doi":"10.3390/s120100429","title":"Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Jinan University","keywords":"Simultaneous localization and mapping; Computer vision; Artificial intelligence; Extended Kalman filter; Homography; Computer science; Monocular; Sensor fusion; Kalman filter; Line (geometry); Fuse (electrical); Robot; Mobile robot; Engineering; Mathematics","score_opus":0.011794730070112038,"score_gpt":0.2194794050942123,"score_spread":0.20768467502410026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988035201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04341205,0.00077158975,0.95393926,0.000092716866,0.000066702974,0.00002460375,0.00003823094,0.0004984322,0.0011564485],"genre_scores_gemma":[0.7095903,0.00053607573,0.2882268,0.00007786311,0.000043798205,0.00005112731,0.0001401278,0.000035840247,0.0012979475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994654,0.00011330934,0.000026743186,0.00009006462,0.00026311263,0.00004152329],"domain_scores_gemma":[0.9997675,0.0000498001,0.000042529067,0.000053595915,0.00007322815,0.00001343997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049372425,0.00032629515,0.00038128992,0.00049815257,0.0002853753,0.00043169013,0.0004514285,0.0004171732,0.00070709834],"category_scores_gemma":[0.00086430926,0.00030040662,0.00035699207,0.0006523168,0.00023392138,0.0010753358,0.00082029664,0.00036969237,0.0002521817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045508766,0.00010933254,0.002343433,0.0002696102,0.00011683305,0.00025175844,0.00032342967,0.09820947,0.20494586,0.0077222744,0.0023207269,0.68293214],"study_design_scores_gemma":[0.000044780012,0.00040972218,0.0051192692,0.00004027479,0.000086937056,0.00043110704,0.00015856359,0.8473114,0.12923755,0.005658752,0.0114485305,0.000053064403],"about_ca_topic_score_codex":0.0010746169,"about_ca_topic_score_gemma":0.0014740153,"teacher_disagreement_score":0.0010746169,"about_ca_system_score_codex":0.00023276782,"about_ca_system_score_gemma":0.00040682667,"threshold_uncertainty_score":0.0026111007},"labels":[],"label_agreement":null},{"id":"W1990961313","doi":"10.3390/s131115673","title":"Building Kinetic Models for Determining Vitamin C Content in Fresh Jujube and Predicting Its Shelf Life Based on Near-Infrared Spectroscopy","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Shelf life; Ziziphus; Chemistry; Vitamin C; Calibration; Food science; Analytical Chemistry (journal); Botany; Chromatography; Mathematics; Statistics; Biology","score_opus":0.03246688765576662,"score_gpt":0.2672232564851352,"score_spread":0.23475636882936857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990961313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5362221,0.0018641207,0.45639575,0.00020151472,0.000053258616,0.00017663182,0.0012661834,0.0011189493,0.0027014283],"genre_scores_gemma":[0.9443849,0.0015142922,0.050181564,0.00004713512,0.000010343681,0.00029597324,0.0009095612,0.00008362876,0.0025727027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998529,0.000021806729,0.000011786082,0.000052767886,0.00003863763,0.000022074726],"domain_scores_gemma":[0.9996431,0.00020049098,0.00005138355,0.000019260637,0.00007604367,0.000009805656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051132,0.0009109431,0.0007087941,0.0005783604,0.00022876021,0.00074975967,0.0006244316,0.00080926914,0.000711333],"category_scores_gemma":[0.0011324266,0.00045912797,0.0011379133,0.00041828697,0.00020054281,0.00083626166,0.0002353545,0.000730549,0.0005221183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002103296,0.00015350402,0.0070205578,0.00039617214,0.000109382505,0.00010460121,0.00009388838,0.87385267,0.09074624,0.0013133483,0.00041873078,0.025580639],"study_design_scores_gemma":[0.000005185301,0.000054190557,0.0010963833,0.0000070432716,0.000030297486,0.00001964575,0.000013512026,0.98530036,0.01265241,0.000354276,0.0004514547,0.00001517265],"about_ca_topic_score_codex":0.010579214,"about_ca_topic_score_gemma":0.0068946937,"teacher_disagreement_score":0.010579214,"about_ca_system_score_codex":0.00092399813,"about_ca_system_score_gemma":0.000718015,"threshold_uncertainty_score":0.021035314},"labels":[],"label_agreement":null},{"id":"W1992246527","doi":"10.3390/s110605900","title":"Operating Systems for Wireless Sensor Networks: A Survey","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wireless sensor network; Software deployment; Computer science; Scheduling (production processes); Process (computing); Embedded system; Computer network; Engineering; Operating system","score_opus":0.04437768160148756,"score_gpt":0.23882040093675477,"score_spread":0.19444271933526722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992246527","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008380623,0.79591936,0.15400133,0.0024916434,0.0029207414,0.00058309466,0.0010910567,0.0037395563,0.030872596],"genre_scores_gemma":[0.04560377,0.840873,0.088547796,0.0019211369,0.0037625083,0.0008264192,0.003684571,0.0012204474,0.013560341],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968076,0.000526545,0.00063983304,0.00031449282,0.0015026925,0.0002089658],"domain_scores_gemma":[0.99605346,0.0021184972,0.00031656137,0.00038438008,0.0009375473,0.00018948081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002052111,0.0014279044,0.0012659072,0.0037143605,0.000841298,0.0030430688,0.0024445772,0.0016146471,0.0060795965],"category_scores_gemma":[0.00620143,0.00083172275,0.0008208127,0.006069019,0.00085793016,0.0066622505,0.0019004659,0.0026787452,0.003669654],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014514993,0.00016236528,0.0018188787,0.009164109,0.00006544991,0.00029261087,0.00045616517,0.0036703357,0.0044149356,0.021235215,0.05262881,0.90594614],"study_design_scores_gemma":[0.00002620559,0.00023509555,0.0019402821,0.0032518257,0.000077714234,0.0017988154,0.00035593318,0.010232943,0.0033753,0.019562278,0.9590363,0.0001074879],"about_ca_topic_score_codex":0.0013083789,"about_ca_topic_score_gemma":0.0010168444,"teacher_disagreement_score":0.0060795965,"about_ca_system_score_codex":0.0008550676,"about_ca_system_score_gemma":0.0019970483,"threshold_uncertainty_score":0.020338237},"labels":[],"label_agreement":null},{"id":"W1992558361","doi":"10.3390/s131115221","title":"A New Approach for Improving Reliability of Personal Navigation Devices under Harsh GNSS Signal Conditions","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Unavailability; Computer science; Reliability (semiconductor); GNSS augmentation; Multipath propagation; SIGNAL (programming language); Real-time computing; Satellite navigation; Multipath mitigation; Satellite system; Global Positioning System; Remote sensing; Reliability engineering; Engineering; Telecommunications; Channel (broadcasting)","score_opus":0.01506919788207783,"score_gpt":0.23174647009639865,"score_spread":0.21667727221432082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992558361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019176029,0.00050214486,0.9779719,0.00010533843,0.000077546385,0.00003904926,0.000029196717,0.0009390001,0.0011597609],"genre_scores_gemma":[0.48439497,0.00066757546,0.50913334,0.00018155211,0.00019084077,0.00007804078,0.0001240396,0.00008546363,0.0051442143],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921966,0.00008547935,0.000051353654,0.00021845366,0.00036733245,0.000057753386],"domain_scores_gemma":[0.9993316,0.00010966918,0.00012382405,0.00013658342,0.00027595265,0.000022524415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004414629,0.0008554038,0.000562796,0.00090432353,0.0004032045,0.0005966389,0.0009309652,0.00088691333,0.00094741007],"category_scores_gemma":[0.0012464782,0.0002575095,0.0005321849,0.000518667,0.0004098379,0.00088994094,0.00059018115,0.00058516394,0.00059993454],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026673338,0.00012949137,0.0020165576,0.00030666057,0.00011266327,0.00022614622,0.00023494841,0.05204596,0.38446987,0.0067184432,0.0018656132,0.55160683],"study_design_scores_gemma":[0.000058098783,0.0012197577,0.0061123595,0.000054844022,0.0002127834,0.0013786609,0.00008021249,0.73542863,0.23390602,0.0040045213,0.017431784,0.00011235087],"about_ca_topic_score_codex":0.0012317284,"about_ca_topic_score_gemma":0.0016860347,"teacher_disagreement_score":0.0012317284,"about_ca_system_score_codex":0.0003890785,"about_ca_system_score_gemma":0.000466988,"threshold_uncertainty_score":0.0031694174},"labels":[],"label_agreement":null},{"id":"W1992850481","doi":"10.3390/s141224156","title":"Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Clutter; Point cloud; Computer science; Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Feature (linguistics); Scale (ratio); Cognitive neuroscience of visual object recognition; Point (geometry); Object (grammar); Computer vision; Task (project management); Mathematics; Radar","score_opus":0.013221487387874611,"score_gpt":0.20333120082580347,"score_spread":0.19010971343792887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992850481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01825476,0.00032468035,0.9747847,0.00006295332,0.000034259654,0.0001053525,0.00030815348,0.005560491,0.0005645745],"genre_scores_gemma":[0.2543139,0.00060126616,0.7412878,0.000125259,0.000044988305,0.00014177714,0.0019975505,0.00030737036,0.0011800895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986405,0.0000893018,0.000069169175,0.00038071012,0.00068923726,0.00013102865],"domain_scores_gemma":[0.9986714,0.00023866254,0.00020068404,0.0005744578,0.00024299913,0.00007178767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007017854,0.001373565,0.0015639509,0.0036224083,0.00066734315,0.0020012923,0.0023546976,0.0015281622,0.001716064],"category_scores_gemma":[0.001974042,0.0009859796,0.0019291615,0.0034137536,0.0007622485,0.002860549,0.0021994538,0.0014032685,0.0028003743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015909664,0.00015680521,0.0035985084,0.00025151158,0.00015911256,0.000241982,0.00016837865,0.07063241,0.058850773,0.0012245689,0.0032954162,0.8612614],"study_design_scores_gemma":[0.000018503983,0.00010387196,0.0066413763,0.00004207767,0.00005074428,0.0007084243,0.00022725247,0.9410552,0.041900337,0.0046710926,0.004516663,0.000064534506],"about_ca_topic_score_codex":0.0066856905,"about_ca_topic_score_gemma":0.013027792,"teacher_disagreement_score":0.0066856905,"about_ca_system_score_codex":0.0006733779,"about_ca_system_score_gemma":0.00073146785,"threshold_uncertainty_score":0.013293564},"labels":[],"label_agreement":null},{"id":"W1994982992","doi":"10.3390/s7123442","title":"An Overview of Label-free Electrochemical Protein Sensors","year":2007,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Redox; Electrochemistry; Chemistry; Molecular recognition; Protein detection; Combinatorial chemistry; Enzyme; Electrode; Nanotechnology; Selectivity; Molecule; Biochemistry; Materials science; Inorganic chemistry; Organic chemistry","score_opus":0.05319687566173492,"score_gpt":0.38332767214513513,"score_spread":0.3301307964834002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994982992","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004799778,0.9839236,0.007686085,0.00038363613,0.00067058305,0.00004418013,0.00006942455,0.000099185985,0.006643289],"genre_scores_gemma":[0.0023938667,0.9799998,0.0086960755,0.0005582104,0.0003953257,0.00007417714,0.00016766129,0.000015566251,0.0076994407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936885,0.0000637312,0.000049031663,0.00013170573,0.00034370163,0.00004295711],"domain_scores_gemma":[0.9996897,0.00011310176,0.000036409227,0.000015282912,0.00012212293,0.000023333234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075734634,0.0016169263,0.001694717,0.0023871663,0.00037914448,0.0010462985,0.002173127,0.0021408987,0.0035404442],"category_scores_gemma":[0.0006345768,0.0007476093,0.000674733,0.0030839324,0.000557512,0.0023620808,0.00070201984,0.0021027545,0.0060720635],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008187325,0.000213301,0.0001814635,0.012098635,0.00007708985,0.00035071047,0.00006530537,0.0010800583,0.024309125,0.011935864,0.030648025,0.9189585],"study_design_scores_gemma":[0.000011079182,0.00010098216,0.0002712653,0.0008493987,0.000037256483,0.0009427751,0.000022619104,0.00045506726,0.007708757,0.0023987265,0.9871672,0.000034942528],"about_ca_topic_score_codex":0.00084544683,"about_ca_topic_score_gemma":0.0008755149,"teacher_disagreement_score":0.0035404442,"about_ca_system_score_codex":0.0009503863,"about_ca_system_score_gemma":0.00067666033,"threshold_uncertainty_score":0.011843979},"labels":[],"label_agreement":null},{"id":"W1996479774","doi":"10.3390/s101211301","title":"Pressure Sensing in High-Refractive-Index Liquids Using Long-Period Gratings Nanocoated with Silicon Nitride","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Materials science; Refractive index; Cladding (metalworking); Silicon nitride; Optoelectronics; Plasma-enhanced chemical vapor deposition; Grating; Nitride; Silicon; Optics; Nanotechnology; Composite material; Layer (electronics)","score_opus":0.00585506579476222,"score_gpt":0.21761865938633035,"score_spread":0.21176359359156813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996479774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9779847,0.0014069815,0.019099297,0.00010334443,0.00007589245,0.000020115463,0.00008250123,0.00019520083,0.0010319478],"genre_scores_gemma":[0.9740623,0.00077937084,0.023982983,0.00007305226,0.000024239434,0.000018759563,0.000072085175,0.000029021317,0.00095806166],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983287,0.000012779197,0.0000097529255,0.00005384257,0.00007203846,0.000018668743],"domain_scores_gemma":[0.9998293,0.000039314833,0.000065720145,0.00001546251,0.000032349126,0.000017964026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012372271,0.000339213,0.00023105151,0.00014711509,0.00010137875,0.0002072403,0.000334277,0.00036845895,0.00018063687],"category_scores_gemma":[0.00019372489,0.00015053725,0.00017160394,0.00014862788,0.00029777415,0.0003779608,0.0002168577,0.00023980916,0.00012970452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011927971,0.00000372212,0.00010092893,0.000017825618,0.000002205523,0.000022528407,0.0000061340234,0.00009155908,0.9987627,0.0000195945,0.000009870348,0.0009509454],"study_design_scores_gemma":[0.0000023343594,0.000049487644,0.00063722325,0.0000014459919,0.000005042543,0.00004427059,0.0000061358405,0.0023392031,0.99657685,0.000009063014,0.00032495594,0.000003978133],"about_ca_topic_score_codex":0.0010235606,"about_ca_topic_score_gemma":0.0020340919,"teacher_disagreement_score":0.0010235606,"about_ca_system_score_codex":0.0004631975,"about_ca_system_score_gemma":0.00017857124,"threshold_uncertainty_score":0.003360808},"labels":[],"label_agreement":null},{"id":"W1998115190","doi":"10.3390/s140100848","title":"NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Real-time computing; Novelty detection; Probabilistic logic; Energy consumption; Power consumption; Electricity meter; Markov chain; Entropy (arrow of time); Smart grid; Wireless sensor network; Smart meter; Novelty; Embedded system; Artificial intelligence; Machine learning; Power (physics); Engineering; Computer network; Electrical engineering","score_opus":0.022362742521795397,"score_gpt":0.2330455566000565,"score_spread":0.2106828140782611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998115190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024133923,0.00038153815,0.9607978,0.00016155031,0.000098829674,0.00011468161,0.00022237313,0.012396881,0.0016923606],"genre_scores_gemma":[0.6534887,0.000531209,0.3414251,0.00021813635,0.00015677288,0.00021387846,0.0006634609,0.00036760172,0.002935259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999579,0.000077130746,0.000024362424,0.00010480992,0.00019559384,0.000019190538],"domain_scores_gemma":[0.9993807,0.00026542906,0.000105180305,0.000097876364,0.00011618584,0.000034567984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051072333,0.00061784836,0.00062721554,0.0009132195,0.00017696233,0.00046902924,0.001220339,0.0005536463,0.001471336],"category_scores_gemma":[0.0018020748,0.00023931691,0.00027673325,0.0005682641,0.00019019611,0.0013514837,0.00071583455,0.0005296503,0.00064474286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007370248,0.00033878125,0.009150214,0.0005582462,0.00021296776,0.00044942062,0.00026623762,0.04248572,0.077244736,0.0062852493,0.0100341635,0.8522373],"study_design_scores_gemma":[0.0000811751,0.00039838176,0.0067112166,0.00003563829,0.00010502715,0.00068930234,0.000047655525,0.9315582,0.03858164,0.005891366,0.015826566,0.00007384518],"about_ca_topic_score_codex":0.0004416723,"about_ca_topic_score_gemma":0.0005679614,"teacher_disagreement_score":0.001471336,"about_ca_system_score_codex":0.00022890267,"about_ca_system_score_gemma":0.00027685307,"threshold_uncertainty_score":0.0049221516},"labels":[],"label_agreement":null},{"id":"W2001095938","doi":"10.3390/s140203156","title":"Smart Materials Based on DNA Aptamers: Taking Aptasensing to the Next Level","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Nanotechnology; Smart material; Computer science; Drug delivery; Molecular recognition; Biochemical engineering; Materials science; Engineering; Chemistry; Biology; Molecule","score_opus":0.07694546265464386,"score_gpt":0.3462495352887189,"score_spread":0.269304072634075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001095938","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024047865,0.99690527,0.0004937311,0.00029732386,0.0002803253,0.0000069203606,0.000010937198,0.0000142168665,0.0017507162],"genre_scores_gemma":[0.0010888147,0.996216,0.00066673756,0.00024149303,0.00013768127,0.000011435222,0.000020619276,0.0000026871621,0.0016145916],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998373,0.000018843622,0.000015036617,0.000028033355,0.00008094095,0.00001981745],"domain_scores_gemma":[0.99984205,0.00006865609,0.000020375552,0.0000055601486,0.000046183322,0.000017188378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048748174,0.0007779722,0.000979618,0.0020130791,0.00026552178,0.000861212,0.00081246416,0.0011588747,0.0022332454],"category_scores_gemma":[0.0003996944,0.00040324082,0.00038577587,0.0017301027,0.00054950087,0.0018381613,0.00068056246,0.0018683777,0.0023909472],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003337502,0.00008260618,0.00014211782,0.011598556,0.000045338194,0.00025838113,0.00009309024,0.0004148278,0.00958929,0.0096027665,0.020844584,0.9472951],"study_design_scores_gemma":[0.0000064918163,0.000058849902,0.00022949811,0.00087566214,0.000022412076,0.0009181128,0.000042326392,0.00008244883,0.0021145523,0.0016703312,0.99396354,0.000015710744],"about_ca_topic_score_codex":0.0006234929,"about_ca_topic_score_gemma":0.0012332592,"teacher_disagreement_score":0.0022332454,"about_ca_system_score_codex":0.00055425323,"about_ca_system_score_gemma":0.0005814864,"threshold_uncertainty_score":0.007470906},"labels":[],"label_agreement":null},{"id":"W2001539539","doi":"10.3390/s130809878","title":"Single-Chip Fully Integrated Direct-Modulation CMOS RF Transmitters for Short-Range Wireless Applications","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"King Abdulaziz City for Science and Technology","keywords":"CMOS; Wireless; Chip; Modulation (music); Transmitter; Electronic engineering; Electrical engineering; Radio frequency; Engineering; Computer science; Telecommunications; Physics; Channel (broadcasting)","score_opus":0.019567920640084127,"score_gpt":0.211734441202139,"score_spread":0.19216652056205485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001539539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7245186,0.00407687,0.25972065,0.00027924858,0.0002956104,0.00014001304,0.00032523813,0.0011289988,0.009514688],"genre_scores_gemma":[0.8903924,0.0008768618,0.101411976,0.00010827885,0.000059810267,0.00007787632,0.00022001176,0.00006453724,0.006788249],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976534,0.00002368042,0.000012046106,0.00004786899,0.00012876105,0.00002234785],"domain_scores_gemma":[0.99976224,0.00004540788,0.000059045957,0.000029445218,0.00008869759,0.000015232466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021761103,0.0003361723,0.0002967219,0.00015845525,0.00013107754,0.00046022757,0.0006095605,0.00036416747,0.0015101344],"category_scores_gemma":[0.0004895742,0.00028282087,0.0002126127,0.00019396462,0.000106562715,0.0006052973,0.00023282175,0.00027692068,0.0005492548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000872367,0.000046263776,0.0009444463,0.0001246095,0.000026108015,0.00006912583,0.00004484894,0.0016408708,0.9644353,0.00054060994,0.00042504788,0.031615533],"study_design_scores_gemma":[0.00007097308,0.0017437654,0.004915565,0.000028103037,0.00011587027,0.0007656151,0.000039311406,0.030368755,0.9483547,0.00030243074,0.013265704,0.000029192004],"about_ca_topic_score_codex":0.00027176566,"about_ca_topic_score_gemma":0.0012838807,"teacher_disagreement_score":0.0015101344,"about_ca_system_score_codex":0.00029410011,"about_ca_system_score_gemma":0.00035925824,"threshold_uncertainty_score":0.005051911},"labels":[],"label_agreement":null},{"id":"W2004125450","doi":"10.3390/s141223283","title":"Droplet Microfluidics for Chip-Based Diagnostics","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microfluidics; Fluidics; Nanotechnology; Fabrication; Lab-on-a-chip; Microfluidic chip; Materials science; Chip; Biochip; Detector; Computer science; Engineering; Electrical engineering","score_opus":0.005124349446777354,"score_gpt":0.18949927277659015,"score_spread":0.18437492332981278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004125450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02363232,0.2793604,0.6304685,0.0044356836,0.006913427,0.0012511677,0.0021283596,0.0049483865,0.046861663],"genre_scores_gemma":[0.2153152,0.13028722,0.6190861,0.0030744313,0.0017809096,0.0011795433,0.0012894574,0.00032020395,0.027666941],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992698,0.00008984635,0.00004272329,0.0001716689,0.00037487713,0.000051038667],"domain_scores_gemma":[0.99979275,0.0000763595,0.00003115271,0.000030142572,0.000045425117,0.000024205898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080616004,0.00070681906,0.0007267545,0.00086867926,0.00039280232,0.001100178,0.0010689094,0.0010101185,0.004906912],"category_scores_gemma":[0.0008400585,0.0004778206,0.00044515496,0.00065478456,0.00058953994,0.0011223791,0.0010844248,0.0011195361,0.0023206472],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110199944,0.00010632031,0.00036552158,0.0020210736,0.000064272375,0.00033387681,0.00016290706,0.0020290464,0.6681028,0.07975336,0.023420488,0.22353011],"study_design_scores_gemma":[0.00008560231,0.00038879114,0.00050154625,0.0002674372,0.000055402994,0.0008109415,0.000034216428,0.015270585,0.51474893,0.013465498,0.4542706,0.00010041232],"about_ca_topic_score_codex":0.0003168792,"about_ca_topic_score_gemma":0.0005483734,"teacher_disagreement_score":0.004906912,"about_ca_system_score_codex":0.001070022,"about_ca_system_score_gemma":0.0005701021,"threshold_uncertainty_score":0.016415298},"labels":[],"label_agreement":null},{"id":"W2004433018","doi":"10.3390/s120404534","title":"The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Photovoltaic system; Software deployment; Lidar; Computer science; Geographic information system; Remote sensing; Environmental science; Software; Systems engineering; Engineering; Geography","score_opus":0.014951307341340392,"score_gpt":0.29173179139276373,"score_spread":0.27678048405142336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004433018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95673573,0.000112644164,0.036490425,0.00014365562,0.000012955814,0.00015565842,0.00070925686,0.00026757515,0.005372013],"genre_scores_gemma":[0.9728391,0.00006738877,0.026067812,0.000015115399,0.000004025389,0.000050209823,0.00016006136,0.000007916228,0.0007882506],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997359,0.000065946544,0.000016498101,0.000041210867,0.00010668185,0.000033827382],"domain_scores_gemma":[0.9996655,0.000111085305,0.000046790585,0.000029475857,0.000119027885,0.000028069306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030906443,0.00022557717,0.00019482891,0.0013639851,0.00057617825,0.0005347507,0.00048455317,0.0005745931,0.0012661428],"category_scores_gemma":[0.0011300375,0.00020758125,0.00023076795,0.0016781028,0.00016099682,0.00026993096,0.000625671,0.00026204137,0.00028407126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002637186,0.0005645383,0.40830997,0.00055614725,0.00014084058,0.003729883,0.003072163,0.13885996,0.09918653,0.0027388327,0.0029351122,0.3396423],"study_design_scores_gemma":[0.00004455813,0.00073433155,0.35864168,0.000103338476,0.0000932417,0.0014846416,0.007763187,0.57517797,0.047143526,0.0026015474,0.006104802,0.00010725036],"about_ca_topic_score_codex":0.010392855,"about_ca_topic_score_gemma":0.02913393,"teacher_disagreement_score":0.010392855,"about_ca_system_score_codex":0.00047528066,"about_ca_system_score_gemma":0.00050549995,"threshold_uncertainty_score":0.020664692},"labels":[],"label_agreement":null},{"id":"W2004450415","doi":"10.3390/s150101785","title":"Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Association Nationale de la Recherche et de la Technologie","keywords":"Kinematics; Software; Set (abstract data type); Computer science; Elbow; Shoulder joint; Simulation; Human–computer interaction; Joint (building); Artificial intelligence; Computer vision; Engineering","score_opus":0.16879873468350196,"score_gpt":0.42306807546129815,"score_spread":0.25426934077779617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004450415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74252594,0.0015176524,0.24950314,0.00019486439,0.00027911956,0.0004099911,0.0014765178,0.001521307,0.0025714533],"genre_scores_gemma":[0.9144782,0.00038967468,0.08244797,0.000071325376,0.00003565577,0.00029583517,0.0011148168,0.00015481825,0.0010116916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974469,0.0009009363,0.00020957616,0.0004509676,0.0008579431,0.0001336828],"domain_scores_gemma":[0.99753714,0.0011563639,0.00023118962,0.00033528375,0.00055002945,0.00019000751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028121758,0.0017851723,0.00094948127,0.0016629582,0.00029500184,0.0008309852,0.0008880926,0.0009671246,0.0017963133],"category_scores_gemma":[0.008127861,0.00036059812,0.00062201143,0.000604914,0.0004833438,0.00081859087,0.0013255368,0.00034611532,0.0007097722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055290814,0.0011879498,0.08894785,0.0023148218,0.0006735109,0.0007780578,0.001452997,0.08125152,0.3292291,0.00119032,0.0026353006,0.48480955],"study_design_scores_gemma":[0.00027660845,0.005579918,0.37232447,0.000435662,0.0004207025,0.0020478556,0.001048661,0.48267403,0.12819248,0.001338032,0.005300752,0.00036071832],"about_ca_topic_score_codex":0.0021401218,"about_ca_topic_score_gemma":0.002865356,"teacher_disagreement_score":0.0028121758,"about_ca_system_score_codex":0.00018737388,"about_ca_system_score_gemma":0.0004436644,"threshold_uncertainty_score":0.014872372},"labels":[],"label_agreement":null},{"id":"W2004985708","doi":"10.3390/s150203236","title":"Wireless Integrated Biosensors for Point-of-Care Diagnostic Applications","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless; Wearable computer; Point of care; Computer science; Wireless sensor network; Risk analysis (engineering); Engineering; Systems engineering; Telecommunications; Medicine; Embedded system; Pathology","score_opus":0.03518805910728074,"score_gpt":0.3162078993909465,"score_spread":0.2810198402836658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004985708","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061952724,0.9898056,0.0037254042,0.00043293924,0.0006434392,0.000028085915,0.000046104204,0.000047244554,0.0046516554],"genre_scores_gemma":[0.004373356,0.9873559,0.0031811453,0.00034982438,0.0002938858,0.00003211141,0.000068261674,0.000006511261,0.004339047],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961275,0.000043688335,0.000030414207,0.0000699738,0.00020556051,0.00003769605],"domain_scores_gemma":[0.9997404,0.00009182911,0.0000411494,0.000010609928,0.00009838709,0.000017610486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067174115,0.0011281585,0.0009960078,0.0026233068,0.00029951066,0.0009304335,0.0010668027,0.0015659566,0.004319238],"category_scores_gemma":[0.0006428267,0.000458989,0.0007162634,0.0023785378,0.00040228214,0.0014417678,0.00070017844,0.001776296,0.0039636875],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044569646,0.00014096382,0.00026826697,0.014677958,0.00008189002,0.00029493697,0.000080355465,0.00076871173,0.0312538,0.010231545,0.023276685,0.9188803],"study_design_scores_gemma":[0.000007876969,0.00013248422,0.0005288711,0.0012889205,0.000079771926,0.001215025,0.000053179512,0.00050540705,0.010237772,0.002442891,0.98347604,0.000031738313],"about_ca_topic_score_codex":0.00055779156,"about_ca_topic_score_gemma":0.0009370534,"teacher_disagreement_score":0.004319238,"about_ca_system_score_codex":0.0005192752,"about_ca_system_score_gemma":0.00076005835,"threshold_uncertainty_score":0.014449358},"labels":[],"label_agreement":null},{"id":"W2005451395","doi":"10.3390/s131215985","title":"Embedded NMR Sensor to Monitor Compressive Strength Development and Pore Size Distribution in Hydrating Concrete","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México","keywords":"Materials science; Cement; Compressive strength; Composite material; Porosity; Magnet; Neodymium magnet; Electromagnetic coil; Electrical engineering","score_opus":0.0063615186150251775,"score_gpt":0.2774818541255486,"score_spread":0.27112033551052345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005451395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80570924,0.0022991283,0.18894279,0.00006908737,0.00007685491,0.000096887416,0.0003718437,0.0009069831,0.001527289],"genre_scores_gemma":[0.8865916,0.00079550775,0.1095099,0.00006333501,0.00002423762,0.000059912814,0.00021656182,0.00004733166,0.0026916238],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981743,0.000030369361,0.000007649108,0.00005778513,0.00007361665,0.000013172985],"domain_scores_gemma":[0.99971575,0.000090502144,0.00008174067,0.0000213577,0.00006968558,0.000020949601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034689807,0.00045405875,0.00033837275,0.00022749322,0.00008082262,0.00018497178,0.00027704108,0.00043934197,0.00076363096],"category_scores_gemma":[0.00043635935,0.0001940227,0.00015259116,0.00019831842,0.00018617215,0.00035289285,0.00022717525,0.00032998627,0.0002973435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018874489,0.0000074304467,0.00016698851,0.000027309745,0.0000034466582,0.000013492798,0.0000055621654,0.00016309938,0.99733955,0.00002381426,0.000010468841,0.0022199957],"study_design_scores_gemma":[0.000005958552,0.00022996114,0.003327603,0.0000044395547,0.000023754625,0.0002389902,0.000014073115,0.008877518,0.986273,0.000028288816,0.0009613772,0.000015051899],"about_ca_topic_score_codex":0.00033057478,"about_ca_topic_score_gemma":0.00067376916,"teacher_disagreement_score":0.00076363096,"about_ca_system_score_codex":0.00017955006,"about_ca_system_score_gemma":0.00013995907,"threshold_uncertainty_score":0.0025545955},"labels":[],"label_agreement":null},{"id":"W2005670532","doi":"10.3390/s120506102","title":"Inertial Sensor-Based Methods in Walking Speed Estimation: A Systematic Review","year":2012,"lang":"en","type":"review","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":200,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gyroscope; Accelerometer; Inertial measurement unit; Inertial frame of reference; Computer science; Gait; Preferred walking speed; Simulation; Engineering; Artificial intelligence; Physical medicine and rehabilitation; Medicine; Aerospace engineering","score_opus":0.12074333091184604,"score_gpt":0.4960925995993387,"score_spread":0.3753492686874927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005670532","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021077895,0.99921656,0.0001474472,0.00008640415,0.00004861249,0.00010187152,0.0000843158,0.0000032990165,0.00010069928],"genre_scores_gemma":[0.002486534,0.99645364,0.00059112307,0.00010418799,0.00004147641,0.00020496499,0.00006378944,0.0000020534142,0.000052294316],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9941864,0.0017250766,0.00209778,0.00044887725,0.0014222553,0.00011959499],"domain_scores_gemma":[0.97030914,0.0224226,0.0035756335,0.00036662683,0.0031264806,0.00019959216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008139528,0.0018980083,0.007373587,0.010708978,0.000541528,0.0021685958,0.0020577593,0.0018075075,0.0051985187],"category_scores_gemma":[0.03431595,0.0009778697,0.005610938,0.011254038,0.0007318541,0.0023599435,0.0012031359,0.0010220318,0.0005128273],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015394315,0.000041672552,0.00065750757,0.83635867,0.0032949944,0.000077802695,0.00012975262,0.000112190384,0.00012794424,0.0002157609,0.0019124446,0.1569174],"study_design_scores_gemma":[0.0002225614,0.0003236102,0.0057417187,0.911303,0.043613084,0.0005315869,0.00028670236,0.00020320877,0.00034869718,0.00043425933,0.036934596,0.000056945155],"about_ca_topic_score_codex":0.006750029,"about_ca_topic_score_gemma":0.018136872,"teacher_disagreement_score":0.010708978,"about_ca_system_score_codex":0.0020438868,"about_ca_system_score_gemma":0.008865634,"threshold_uncertainty_score":0.043046474},"labels":[],"label_agreement":null},{"id":"W2007022279","doi":"10.3390/s140815262","title":"Particle Swarm Inspired Underwater Sensor Self-Deployment","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Program for New Century Excellent Talents in University","keywords":"Software deployment; Underwater; Wireless sensor network; Computer science; Real-time computing; Particle swarm optimization; Cover (algebra); Convergence (economics); Distributed computing; Engineering; Computer network; Algorithm","score_opus":0.014386913723939417,"score_gpt":0.20763427003240906,"score_spread":0.19324735630846965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007022279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06251976,0.0003983626,0.9330115,0.00018321768,0.000096088195,0.000043304724,0.00002482201,0.00029464148,0.0034283658],"genre_scores_gemma":[0.8448024,0.00047019674,0.15069133,0.00008575529,0.000029025237,0.00009610495,0.000074895164,0.00003778463,0.0037124995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999907,0.000027697804,0.0000046315495,0.00001586727,0.000035431738,0.000009347104],"domain_scores_gemma":[0.9998764,0.00005094211,0.000021105443,0.000016009388,0.000025725956,0.00000983944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021197609,0.00030430386,0.00035879304,0.00018455954,0.0002509895,0.00025017172,0.00036644036,0.00034912574,0.000378981],"category_scores_gemma":[0.0004406344,0.00014986869,0.00021081128,0.00022163415,0.00024863397,0.0003581931,0.00037324565,0.00024371268,0.000076376054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036407928,0.000035785684,0.00094575,0.00004599024,0.00002963887,0.00007090413,0.000061719475,0.94073117,0.005521253,0.009495177,0.0013206007,0.04170563],"study_design_scores_gemma":[0.0000039718057,0.000013443059,0.00008782761,0.0000012452417,0.0000025831207,0.000009232902,0.000004122638,0.9982364,0.0004927637,0.0006341866,0.00051272236,0.0000015086132],"about_ca_topic_score_codex":0.0018753588,"about_ca_topic_score_gemma":0.0013320467,"teacher_disagreement_score":0.0018753588,"about_ca_system_score_codex":0.0002749696,"about_ca_system_score_gemma":0.0002979471,"threshold_uncertainty_score":0.0037288666},"labels":[],"label_agreement":null},{"id":"W2007650467","doi":"10.3390/s100301743","title":"Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Ministerio de Ciencia e Innovación","keywords":"Deconvolution; Stars; Artificial intelligence; Computer vision; Space debris; Aperture (computer memory); Image (mathematics); Physics; Computer science; Image restoration; Telescope; Image sensor; SIGNAL (programming language); Remote sensing; Noise (video); Image processing; Optics; Astronomy; Geology; Acoustics","score_opus":0.005295201805301757,"score_gpt":0.2201935460773217,"score_spread":0.21489834427201993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007650467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09571185,0.0018250772,0.89663905,0.00026021004,0.000074702664,0.000047360118,0.000051436535,0.0011500128,0.0042403205],"genre_scores_gemma":[0.4013343,0.0020818054,0.5915555,0.00031500222,0.00006499431,0.00005243915,0.00013127696,0.00017585221,0.004288909],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965894,0.00003414792,0.000018272383,0.00010518992,0.00014919808,0.000034333494],"domain_scores_gemma":[0.9993278,0.00033386514,0.00009344439,0.000104481034,0.00011468465,0.000025836807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060122675,0.00062959205,0.00057404133,0.0005195182,0.00026923596,0.0007397425,0.0006073837,0.00086907047,0.0017243762],"category_scores_gemma":[0.0016209749,0.00028013886,0.0003456634,0.0003459047,0.00085091754,0.0017106639,0.00080574915,0.0007107567,0.0010920746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017210282,0.00006910631,0.0016435626,0.00035970792,0.000049748218,0.000113606235,0.00017366452,0.0076326383,0.84390306,0.006408553,0.0005241239,0.13895003],"study_design_scores_gemma":[0.000018082379,0.00016966449,0.0024695985,0.00003288967,0.00006958111,0.00067437266,0.00006114518,0.06838361,0.91497034,0.0032370044,0.009847299,0.00006639938],"about_ca_topic_score_codex":0.00041257346,"about_ca_topic_score_gemma":0.00038567238,"teacher_disagreement_score":0.0017243762,"about_ca_system_score_codex":0.00027557297,"about_ca_system_score_gemma":0.00026815155,"threshold_uncertainty_score":0.0057685375},"labels":[],"label_agreement":null},{"id":"W2007770823","doi":"10.3390/s141019354","title":"Detection of Surface and Subsurface Cracks in Metallic and Non-Metallic Materials Using a Complementary Split-Ring Resonator","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University; CMC Microsystems","keywords":"Ground plane; Split-ring resonator; Resonator; Microwave; Microstrip; Materials science; Printed circuit board; Acoustics; Optoelectronics; Millimeter; Electronic engineering; Optics; Computer science; Electrical engineering; Engineering; Telecommunications; Physics","score_opus":0.012153482494826633,"score_gpt":0.22008443270515185,"score_spread":0.2079309502103252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007770823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79955006,0.0012196512,0.19483463,0.00018150207,0.00013322229,0.00010145171,0.00012667986,0.0005675917,0.0032851396],"genre_scores_gemma":[0.82814425,0.0003299105,0.17025924,0.0000499913,0.000023678916,0.00003128095,0.000039521263,0.000020274732,0.0011018459],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958414,0.00005535652,0.000013774664,0.00012106691,0.00020256378,0.000023074183],"domain_scores_gemma":[0.9994209,0.00027656884,0.00011469599,0.00007092336,0.00008615644,0.00003083255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003554627,0.0003091552,0.0004725978,0.00031723527,0.00011739067,0.00027669818,0.0004970287,0.0005489559,0.0005738365],"category_scores_gemma":[0.00051575166,0.0002734457,0.00032138446,0.00013726088,0.00039542248,0.00048332612,0.00032139118,0.0002453419,0.0002689299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061188395,0.000020622794,0.0003066489,0.00007095941,0.0000091571665,0.000067496796,0.000029852372,0.0005345645,0.9918731,0.0003120717,0.000052469495,0.0066619655],"study_design_scores_gemma":[0.000037293346,0.00079434615,0.0029189761,0.000012123044,0.0000391561,0.0009610314,0.000064194784,0.067869745,0.9249827,0.000254155,0.0020225935,0.000043749205],"about_ca_topic_score_codex":0.00014865948,"about_ca_topic_score_gemma":0.0004080618,"teacher_disagreement_score":0.0005738365,"about_ca_system_score_codex":0.00014511497,"about_ca_system_score_gemma":0.00014548989,"threshold_uncertainty_score":0.0019196868},"labels":[],"label_agreement":null},{"id":"W2008242799","doi":"10.3390/s130303066","title":"Link-Quality Measurement and Reporting in Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Testbed; Wireless sensor network; Software deployment; Wireless; Latency (audio); Computer science; Computer network; Wireless network; Throughput; Embedded system; Key distribution in wireless sensor networks; Signal strength; Real-time computing; Telecommunications; Operating system","score_opus":0.050201006991654494,"score_gpt":0.2702535112277431,"score_spread":0.2200525042360886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008242799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024514979,0.0049284045,0.96654207,0.00033696173,0.00014151924,0.00015129751,0.000107291526,0.0011328867,0.0021446717],"genre_scores_gemma":[0.7269204,0.006782559,0.26219374,0.00023476778,0.00042959978,0.0003759272,0.00044755932,0.00014134895,0.0024740568],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9954293,0.0020866392,0.0003296196,0.000420288,0.0016132612,0.000120839024],"domain_scores_gemma":[0.99464697,0.0029957762,0.00074859173,0.0008076876,0.0006885875,0.00011242311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042827674,0.00066738727,0.0008147489,0.0013616566,0.00044305244,0.001499046,0.001366494,0.0009173462,0.0005308487],"category_scores_gemma":[0.012723594,0.0004228659,0.0003047016,0.002232214,0.0009716104,0.0027686027,0.00076695136,0.0006688336,0.0003609713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005329907,0.00042190368,0.01053974,0.0010193215,0.00022853697,0.00064085366,0.0005291,0.3978183,0.04441086,0.041524775,0.0049607954,0.49737287],"study_design_scores_gemma":[0.00006979501,0.00047290654,0.0035523507,0.00011173608,0.00009136451,0.0006512745,0.00016724903,0.92409956,0.02854705,0.03088023,0.011256161,0.000100274745],"about_ca_topic_score_codex":0.0012729008,"about_ca_topic_score_gemma":0.000677079,"teacher_disagreement_score":0.0042827674,"about_ca_system_score_codex":0.00058339414,"about_ca_system_score_gemma":0.00041774486,"threshold_uncertainty_score":0.022649765},"labels":[],"label_agreement":null},{"id":"W2008510355","doi":"10.3390/s131216882","title":"Design and Evaluation of a Low-Cost Smartphone Pulse Oximeter","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; BC Children's Hospital; Child and Family Research Institute","keywords":"Pulse oximetry; Headset; Smartphone application; Medicine; Computer science; Telecommunications; Multimedia","score_opus":0.02666010525215646,"score_gpt":0.23738700164566762,"score_spread":0.21072689639351116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008510355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4800188,0.0014060745,0.5028228,0.0011484481,0.0007309894,0.003696641,0.0005129266,0.0031087496,0.0065545933],"genre_scores_gemma":[0.79251164,0.00051090494,0.19794205,0.00033963946,0.00007208443,0.0010009999,0.0003402796,0.00012176882,0.007160636],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984572,0.00028225165,0.00012765857,0.00027384385,0.00075011,0.00010894252],"domain_scores_gemma":[0.99796295,0.00033067926,0.00017947181,0.0001949267,0.0011881163,0.00014378231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014389744,0.00073629414,0.00075657194,0.0006140873,0.00037551916,0.0008427535,0.0021199104,0.0012011445,0.0023281577],"category_scores_gemma":[0.002949985,0.0003943859,0.0004178759,0.0001826163,0.00033452312,0.0010078038,0.0007116958,0.0004572534,0.0010411871],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012034691,0.0011632863,0.015163303,0.0016465512,0.0001578647,0.0011878182,0.00066072703,0.0060092025,0.7622724,0.0017242564,0.0038882764,0.20492283],"study_design_scores_gemma":[0.0005796021,0.022125874,0.028203286,0.00017178914,0.0005640297,0.00337728,0.0005729919,0.13876167,0.7535782,0.0003999708,0.05144265,0.00022264883],"about_ca_topic_score_codex":0.0008598882,"about_ca_topic_score_gemma":0.00072602724,"teacher_disagreement_score":0.0023281577,"about_ca_system_score_codex":0.00049342244,"about_ca_system_score_gemma":0.0010123097,"threshold_uncertainty_score":0.0077884793},"labels":[],"label_agreement":null},{"id":"W2009218399","doi":"10.3390/s90301518","title":"A Novel Energy-Efficient MAC Aware Data Aggregation Routing in Wireless Sensor Networks #","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intelligent Mechatronic Systems (Canada)","funders":"National Science Council","keywords":"Data aggregator; Computer science; Retransmission; Wireless sensor network; Energy consumption; Computer network; Data transmission; Node (physics); Distributed computing; Network packet; Engineering","score_opus":0.021478907755013675,"score_gpt":0.24418349554672364,"score_spread":0.22270458779170996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009218399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010924747,0.000658922,0.9851295,0.00036617738,0.000100729514,0.000057177665,0.000047824462,0.00026567004,0.0024492943],"genre_scores_gemma":[0.27535728,0.0008807773,0.7150397,0.00026374153,0.00010774526,0.00017039105,0.00020377702,0.00008304038,0.00789352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.00008413518,0.000018181632,0.000059395887,0.00011698974,0.000026764113],"domain_scores_gemma":[0.9997526,0.00008983628,0.000039500697,0.000035863384,0.00006925987,0.000013027899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055062183,0.00037169625,0.0003825429,0.00030630908,0.0003453755,0.0006185486,0.00095914357,0.00038776157,0.0008229904],"category_scores_gemma":[0.00074829214,0.00024688072,0.00036998463,0.0005646626,0.00032136793,0.0011017245,0.00058518397,0.0004324031,0.00023273852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013252579,0.00018363535,0.0007602906,0.00038446003,0.00008076114,0.00017604162,0.00018709622,0.49735492,0.051271006,0.08672561,0.0107440995,0.35199952],"study_design_scores_gemma":[0.000007984329,0.00006863629,0.00011558347,0.000007895396,0.000010181025,0.00007226672,0.000014227621,0.98561513,0.0036612425,0.0046179253,0.0058002877,0.000008528318],"about_ca_topic_score_codex":0.00096920284,"about_ca_topic_score_gemma":0.0020591402,"teacher_disagreement_score":0.00096920284,"about_ca_system_score_codex":0.00045506054,"about_ca_system_score_gemma":0.0009190763,"threshold_uncertainty_score":0.0033017397},"labels":[],"label_agreement":null},{"id":"W2009379379","doi":"10.3390/s140610977","title":"Frequency-Shifted Interferometry — A Versatile Fiber-Optic Sensing Technique","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fiber optic sensor; Optical fiber; Interferometry; Fiber optic splitter; Multiplexing; Optics; Sensitivity (control systems); Multi-mode optical fiber; Detector; Computer science; Electronic engineering; Physics; Engineering; Telecommunications","score_opus":0.00724198507029718,"score_gpt":0.20952019125990254,"score_spread":0.20227820618960535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009379379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13134083,0.027049445,0.81000644,0.0016486987,0.00072032766,0.00018525904,0.00034498406,0.00142032,0.027283741],"genre_scores_gemma":[0.6468672,0.008816563,0.3391991,0.00035568397,0.00036886404,0.0000746996,0.00011562674,0.00007891423,0.0041233473],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957186,0.000056430523,0.0000140386865,0.00009575968,0.0002235995,0.000038298007],"domain_scores_gemma":[0.9998342,0.00005655957,0.00003578705,0.000028106562,0.000034497723,0.000010936418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005148447,0.00043946825,0.00034743574,0.0007351057,0.0003521184,0.00047124014,0.0006326552,0.0004657817,0.00069827423],"category_scores_gemma":[0.00033456204,0.00027831874,0.00026257633,0.0005939218,0.000900635,0.0010875697,0.00048384396,0.0007083167,0.00030105546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006743801,0.00002148813,0.0006609194,0.00021748923,0.000018176177,0.000120999044,0.00011624674,0.0010105434,0.88850695,0.015283881,0.0013597142,0.09261608],"study_design_scores_gemma":[0.000019390205,0.00032133597,0.0014614263,0.000039935134,0.0000368831,0.0017428965,0.00008825312,0.020568868,0.9122208,0.0057608476,0.05765606,0.000083351275],"about_ca_topic_score_codex":0.00025295033,"about_ca_topic_score_gemma":0.000429478,"teacher_disagreement_score":0.0007351057,"about_ca_system_score_codex":0.0004194669,"about_ca_system_score_gemma":0.00029865667,"threshold_uncertainty_score":0.0030434728},"labels":[],"label_agreement":null},{"id":"W2010444791","doi":"10.3390/s150407228","title":"Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Government of Ontario","keywords":"Extended Kalman filter; Global Positioning System; GPS/INS; Pseudorange; Inertial navigation system; Kalman filter; Inertial measurement unit; Control theory (sociology); Particle filter; Precise Point Positioning; Computer science; Engineering; GNSS applications; Assisted GPS; Inertial frame of reference; Aerospace engineering; Telecommunications; Artificial intelligence; Physics","score_opus":0.03876903828067448,"score_gpt":0.2511733930719401,"score_spread":0.2124043547912656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010444791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027996937,0.00037062494,0.96852565,0.000083856066,0.00013564741,0.00004178196,0.000046004625,0.00088588544,0.0019136419],"genre_scores_gemma":[0.65772444,0.000769426,0.33659804,0.0001260693,0.0000954221,0.000119404634,0.00034612784,0.00005547808,0.0041656327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993864,0.00006571936,0.000035278288,0.000110492394,0.00035701244,0.000045025037],"domain_scores_gemma":[0.99975985,0.000049875245,0.000033014225,0.000032271728,0.00011607473,0.00000893485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043899656,0.0006267829,0.0006381135,0.00047316778,0.0003024,0.00047166483,0.0005859251,0.00063160126,0.000544765],"category_scores_gemma":[0.00089738256,0.00028947616,0.0006509117,0.00060158444,0.00017583843,0.000838306,0.00058172864,0.0005367581,0.00027812668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029336702,0.00014993027,0.009052786,0.0002809681,0.00028796648,0.00045091542,0.00024001393,0.384237,0.066268176,0.0063077896,0.0031883891,0.52924275],"study_design_scores_gemma":[0.000013195958,0.000107258085,0.002457637,0.00000986853,0.00003190629,0.0000749681,0.000017124654,0.9850483,0.009037457,0.00045411882,0.0027322827,0.000016017224],"about_ca_topic_score_codex":0.008207697,"about_ca_topic_score_gemma":0.007244335,"teacher_disagreement_score":0.008207697,"about_ca_system_score_codex":0.0003712012,"about_ca_system_score_gemma":0.00089826796,"threshold_uncertainty_score":0.016319871},"labels":[],"label_agreement":null},{"id":"W2012281465","doi":"10.3390/s140405742","title":"Context-Aware Personal Navigation Using Embedded Sensor Fusion in Smartphones","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Context (archaeology); Sensor fusion; Computer science; Embedded system; Human–computer interaction; Real-time computing; Artificial intelligence; Geography","score_opus":0.02909765489912279,"score_gpt":0.2634323839016649,"score_spread":0.2343347290025421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012281465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4667883,0.0018945459,0.52444637,0.00026887382,0.00019447724,0.0000689976,0.00017470741,0.002241179,0.003922493],"genre_scores_gemma":[0.9542051,0.00027479933,0.04453098,0.000057053647,0.000020607866,0.000017331748,0.00006176817,0.00001278119,0.0008195747],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998079,0.000033667922,0.000011697985,0.000056684436,0.00006810324,0.000021994574],"domain_scores_gemma":[0.9998692,0.000025231993,0.000020940312,0.000019435434,0.000054645792,0.000010492852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017608893,0.00034702872,0.0004873154,0.00023585229,0.0001868164,0.00031669647,0.00027820413,0.00038452147,0.00053045165],"category_scores_gemma":[0.00039086535,0.00017382766,0.00025095482,0.0002784947,0.00014763504,0.00057701854,0.0004687588,0.00023666752,0.00020406632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070870214,0.00017781484,0.009772387,0.00039264665,0.00013154103,0.0009292368,0.00056165166,0.060984775,0.475986,0.0019830468,0.002713646,0.44565848],"study_design_scores_gemma":[0.000046770478,0.0006140232,0.015910761,0.000056438515,0.00013344639,0.0012667914,0.000285452,0.82347745,0.14953895,0.0020800387,0.0065041296,0.000085755324],"about_ca_topic_score_codex":0.0027307617,"about_ca_topic_score_gemma":0.0034665207,"teacher_disagreement_score":0.0027307617,"about_ca_system_score_codex":0.0001932806,"about_ca_system_score_gemma":0.00021647614,"threshold_uncertainty_score":0.0054298043},"labels":[],"label_agreement":null},{"id":"W2012492762","doi":"10.3390/s110201819","title":"High-Performance Piezoresistive MEMS Strain Sensor with Low Thermal Sensitivity","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Syncrude","keywords":"Piezoresistive effect; Microelectromechanical systems; Finite element method; Materials science; Sensitivity (control systems); Stress (linguistics); Optoelectronics; Electronic engineering; Adhesive; SIGNAL (programming language); Acoustics; Composite material; Structural engineering; Layer (electronics); Computer science; Engineering","score_opus":0.009110727012273433,"score_gpt":0.17976642986713626,"score_spread":0.1706557028548628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012492762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.943315,0.0011929685,0.05122003,0.00021747522,0.00015979835,0.00011825818,0.00020874017,0.0005184952,0.0030492889],"genre_scores_gemma":[0.92374796,0.00038669055,0.073310584,0.00007257905,0.000051782183,0.00006463107,0.000110402616,0.000036321988,0.0022191016],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988557,0.00010128267,0.000045781035,0.00017017146,0.0007723712,0.000054688935],"domain_scores_gemma":[0.9995296,0.00011691122,0.00009141676,0.00006274001,0.0001643557,0.000035048724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006014483,0.0004226507,0.0006154507,0.00028462784,0.00021262567,0.00033538946,0.0007043022,0.0008519949,0.0007872941],"category_scores_gemma":[0.00067559554,0.0002903266,0.00019227699,0.00022718521,0.0003508961,0.0009019828,0.0002993525,0.00044009686,0.0003783375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036311038,0.000022891127,0.00021502041,0.00004563833,0.0000047886547,0.000035984816,0.000013568185,0.00015551351,0.99693954,0.00006202807,0.000044645112,0.0024241088],"study_design_scores_gemma":[0.000019234487,0.0005775257,0.0042263833,0.0000047547437,0.000017914941,0.00038581877,0.000019346837,0.0067431214,0.98661405,0.00003292693,0.0013417877,0.00001717014],"about_ca_topic_score_codex":0.00028018,"about_ca_topic_score_gemma":0.0011574753,"teacher_disagreement_score":0.0008519949,"about_ca_system_score_codex":0.00023344126,"about_ca_system_score_gemma":0.0002257495,"threshold_uncertainty_score":0.0031808615},"labels":[],"label_agreement":null},{"id":"W2012782082","doi":"10.3390/s150407844","title":"Dielectric Sensors Based on Electromagnetic Energy Tunneling","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic Crystals and Applications","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantum tunnelling; Waveguide; Maxima and minima; Electromagnetic field; Electromagnetic radiation; Electrical impedance; Resonance (particle physics); Excited state; Dielectric; Energy (signal processing); Physics; Materials science; Optoelectronics; Optics; Atomic physics; Quantum mechanics","score_opus":0.012010904087144664,"score_gpt":0.23145028752064187,"score_spread":0.2194393834334972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012782082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.913327,0.00091972016,0.07901856,0.00014551531,0.000076375814,0.000052236097,0.00012511002,0.00040219986,0.0059332093],"genre_scores_gemma":[0.9666613,0.0007304006,0.031103497,0.000045109893,0.0000069817975,0.00003205,0.00006110747,0.0000267765,0.0013328167],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989593,0.000014098712,0.000003979038,0.000022060944,0.00004935301,0.000014613161],"domain_scores_gemma":[0.99988043,0.00004683967,0.000031690164,0.000017072198,0.000016112366,0.000007884304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013329247,0.00031736505,0.00027365435,0.00017499477,0.00012738013,0.0002754403,0.00037385066,0.0003257118,0.0004407949],"category_scores_gemma":[0.00030593085,0.00021428602,0.00015979442,0.00016340268,0.00041976775,0.00050986995,0.00042122722,0.00034340017,0.0002205502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000130732,0.000037534624,0.00042593293,0.00013499682,0.000014853338,0.00020564032,0.0000427592,0.004709951,0.9814151,0.0055075786,0.0001218582,0.007253082],"study_design_scores_gemma":[0.000013493251,0.00016854818,0.0007764664,0.000008751043,0.000013712708,0.00022866963,0.00002049491,0.05191592,0.9438798,0.0014001856,0.0015565411,0.000017435588],"about_ca_topic_score_codex":0.00012559642,"about_ca_topic_score_gemma":0.00017366317,"teacher_disagreement_score":0.0004407949,"about_ca_system_score_codex":0.00019341112,"about_ca_system_score_gemma":0.00010895155,"threshold_uncertainty_score":0.0014746189},"labels":[],"label_agreement":null},{"id":"W2017465745","doi":"10.3390/s120303512","title":"Inertial Sensing to Determine Movement Disorder Motion Present before and after Treatment","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Inertial frame of reference; Motion (physics); Movement (music); Inertial measurement unit; Motion sensors; Computer science; Artificial intelligence; Physical medicine and rehabilitation; Medicine; Acoustics; Physics; Classical mechanics","score_opus":0.0072990816805304625,"score_gpt":0.21607017139733226,"score_spread":0.2087710897168018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017465745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90146035,0.014853732,0.04554283,0.00090793404,0.0005482464,0.0005000998,0.0038239304,0.0006164823,0.03174631],"genre_scores_gemma":[0.9789874,0.0033288929,0.011453376,0.0003518215,0.00023739587,0.00021707852,0.0012164587,0.00004756206,0.0041601183],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999522,0.00009224682,0.000053373344,0.000077642275,0.00020815816,0.00004655244],"domain_scores_gemma":[0.99937016,0.00017584642,0.0001911943,0.000041203417,0.00018264761,0.000039000643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046073392,0.00033454638,0.00048068952,0.0012049748,0.00016861335,0.0005120929,0.0001756114,0.0004600401,0.0032579605],"category_scores_gemma":[0.0019197783,0.00012247308,0.00030770202,0.0008562886,0.0002149074,0.0003713263,0.00031002736,0.00037462986,0.00071432005],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004418886,0.00045789193,0.25448766,0.0012330855,0.00045615982,0.0008027353,0.0014870048,0.003200092,0.23903848,0.0017993033,0.0065327017,0.4860859],"study_design_scores_gemma":[0.000119506774,0.003108819,0.93456054,0.00019590206,0.00032472768,0.0012168308,0.0012912021,0.009427278,0.033340912,0.00090333243,0.015403696,0.000107309854],"about_ca_topic_score_codex":0.0019097532,"about_ca_topic_score_gemma":0.0026013325,"teacher_disagreement_score":0.0032579605,"about_ca_system_score_codex":0.00022667716,"about_ca_system_score_gemma":0.00020355328,"threshold_uncertainty_score":0.010898948},"labels":[],"label_agreement":null},{"id":"W2018131258","doi":"10.3390/s110403831","title":"Estimation of the Distribution of Tabebuia guayacan (Bignoniaceae) Using High-Resolution Remote Sensing Imagery","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Smithsonian Tropical Research Institute; National Science Foundation","keywords":"Remote sensing; Liana; Satellite imagery; Bignoniaceae; Phenology; High resolution; Geography; Tropical forest; Cartography; Ecology; Biology","score_opus":0.0153655722373691,"score_gpt":0.21031802659958374,"score_spread":0.19495245436221464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018131258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99832624,0.00017518127,0.00058022275,0.000015270733,0.0000013894145,0.0000065112813,0.0002840031,0.000024964103,0.00058610115],"genre_scores_gemma":[0.996621,0.000104749204,0.0025881964,0.0000070331153,0.0000023328334,0.0000075628004,0.000480579,0.0000034464617,0.00018500561],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999651,0.000007485788,0.0000016439421,0.000011939734,0.00000740322,0.0000064168653],"domain_scores_gemma":[0.999892,0.000029754547,0.00003375293,0.0000061869277,0.000019180938,0.000019183866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107390624,0.00013712588,0.00010641841,0.0009000663,0.00017611144,0.00021238596,0.00012822344,0.000112316746,0.000564265],"category_scores_gemma":[0.00028835313,0.0000655828,0.00007780697,0.0004827954,0.00009096984,0.00018534536,0.000119211116,0.0000928903,0.000090864414],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073439354,0.000049112532,0.9272758,0.000052109684,0.000032024094,0.000078177574,0.00043854542,0.0019926603,0.0216211,0.000067744724,0.00026146544,0.048057858],"study_design_scores_gemma":[0.0000035175542,0.00002157714,0.99454415,0.000005459303,0.0000072632565,0.000042413438,0.00024193303,0.004207808,0.000448476,0.000027554033,0.00044499757,0.000004814612],"about_ca_topic_score_codex":0.04227546,"about_ca_topic_score_gemma":0.09825699,"teacher_disagreement_score":0.04227546,"about_ca_system_score_codex":0.00028208192,"about_ca_system_score_gemma":0.000118012336,"threshold_uncertainty_score":0.08405876},"labels":[],"label_agreement":null},{"id":"W2019170204","doi":"10.3390/s131216657","title":"Heater-Integrated Cantilevers for Nano-Samples Thermogravimetric Analysis","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"thermodynamics and calorimetric analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Associazione Italiana per la Ricerca sul Cancro; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Thermogravimetric analysis; Cantilever; Thermometer; Materials science; Resistor; Finite element method; Nano-; Thermal analysis; Composite material; Thermal; Engineering; Structural engineering; Electrical engineering; Chemical engineering; Physics","score_opus":0.015670178927905867,"score_gpt":0.2348909289885431,"score_spread":0.21922075006063724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019170204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37697545,0.005586359,0.6041528,0.0006633889,0.00090319244,0.0006730545,0.002194966,0.003009921,0.0058408915],"genre_scores_gemma":[0.43034413,0.0014246429,0.55596894,0.00035141327,0.00010799497,0.00095740595,0.0009990298,0.00031037707,0.009536165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991955,0.00004802153,0.00003617562,0.00023470557,0.00042577783,0.000059669343],"domain_scores_gemma":[0.999255,0.00029685034,0.00006905271,0.00010396672,0.00023235475,0.00004268316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000820188,0.0006382394,0.00068288937,0.00035878055,0.000313185,0.00030968647,0.0012412813,0.0009655594,0.0056014988],"category_scores_gemma":[0.0009893327,0.0006087254,0.00026938232,0.00031378723,0.00029988526,0.00070752366,0.0004554535,0.0010492277,0.0013288074],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019729288,0.00001240124,0.000075232565,0.000070412156,0.0000058493847,0.000013645854,0.000016075832,0.000114707,0.9962907,0.00021460767,0.00023388446,0.0029328482],"study_design_scores_gemma":[0.000021651957,0.00021145782,0.0023536365,0.000015846545,0.000021384261,0.00020087897,0.000025196914,0.012899209,0.97410744,0.00022250018,0.009887464,0.00003343411],"about_ca_topic_score_codex":0.00054221984,"about_ca_topic_score_gemma":0.0029569287,"teacher_disagreement_score":0.0056014988,"about_ca_system_score_codex":0.0005644611,"about_ca_system_score_gemma":0.00030444114,"threshold_uncertainty_score":0.018738866},"labels":[],"label_agreement":null},{"id":"W2020122740","doi":"10.3390/s140915729","title":"A Low-Rank Matrix Recovery Approach for Energy Efficient EEG Acquisition for a Wireless Body Area Network","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Energy (signal processing); Computer science; Compressed sensing; Transmission (telecommunications); Sampling (signal processing); Wireless sensor network; Matrix (chemical analysis); SIGNAL (programming language); Wireless; Body area network; Real-time computing; Signal processing; Power (physics); Algorithm; Computer hardware; Telecommunications; Digital signal processing; Computer network; Mathematics; Statistics; Detector","score_opus":0.008327967309131993,"score_gpt":0.21130374504909824,"score_spread":0.20297577773996625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020122740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029620011,0.000083700914,0.99616724,0.00011018385,0.000013232274,0.000019647106,0.000018361488,0.000090744004,0.000534897],"genre_scores_gemma":[0.14730111,0.00058488484,0.8475365,0.000123496,0.00012361558,0.00012644571,0.00013215806,0.000075381155,0.003996362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978524,0.000049636074,0.000010568342,0.000038045255,0.00009908531,0.000017392193],"domain_scores_gemma":[0.99971575,0.0001340403,0.00003555449,0.000036633715,0.00006249172,0.000015602829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035506574,0.0005563304,0.0004427867,0.00035304527,0.00025058855,0.00041898497,0.00051287276,0.00052946457,0.0031166864],"category_scores_gemma":[0.0012644582,0.00020643823,0.00039453234,0.00053804304,0.0004580827,0.0007135182,0.0006001685,0.0008724237,0.00063745363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020026269,0.00016638832,0.0004382977,0.0003351267,0.000064263375,0.00024671087,0.00020590045,0.44903567,0.07636707,0.04323637,0.0052126385,0.4244913],"study_design_scores_gemma":[0.000009889507,0.00006256462,0.00008895576,0.000005466669,0.000007126896,0.00007608572,0.000014230519,0.9890694,0.0059342515,0.003116046,0.0016082136,0.000007862145],"about_ca_topic_score_codex":0.0015355041,"about_ca_topic_score_gemma":0.0026760497,"teacher_disagreement_score":0.0031166864,"about_ca_system_score_codex":0.00025853928,"about_ca_system_score_gemma":0.0006845868,"threshold_uncertainty_score":0.010426402},"labels":[],"label_agreement":null},{"id":"W2022203402","doi":"10.3390/s150101292","title":"Received Signal Strength Recovery in Green WLAN Indoor Positioning System Using Singular Value Thresholding","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Heilongjiang Province; University of Toronto; National Natural Science Foundation of China","keywords":"RSS; Computer science; Thresholding; Real-time computing; Indoor positioning system; Compressed sensing; Computer network; Algorithm; Artificial intelligence","score_opus":0.021279958852903867,"score_gpt":0.22441092850389144,"score_spread":0.20313096965098756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022203402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070235334,0.0001239365,0.9274382,0.0001130305,0.000033951055,0.000018816869,0.00002716776,0.0007340085,0.0012754505],"genre_scores_gemma":[0.8004556,0.0001379801,0.19726248,0.00009514004,0.00003074251,0.00003635886,0.00010991011,0.00003573626,0.0018359699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960667,0.00007896174,0.000018960505,0.00008837276,0.00016663388,0.00004050285],"domain_scores_gemma":[0.9997533,0.000055699693,0.000046595902,0.000042646243,0.00008279609,0.00001890839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035781373,0.00033870697,0.00053824566,0.00038957284,0.00022970176,0.00043140116,0.00048364408,0.00053326547,0.00049223535],"category_scores_gemma":[0.00093593594,0.0001611707,0.0002847394,0.00057395047,0.00032377525,0.0004985185,0.0004752163,0.00033387917,0.00031319217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005925976,0.00011996942,0.003933639,0.00015518063,0.00008209607,0.0005688069,0.00030175442,0.18766905,0.25085148,0.0060755997,0.0019493335,0.5477004],"study_design_scores_gemma":[0.000023331208,0.00013863134,0.0016621932,0.0000086072205,0.000022433467,0.0002479733,0.000039809063,0.9586756,0.036536332,0.0015575545,0.0010628965,0.000024688796],"about_ca_topic_score_codex":0.0011781697,"about_ca_topic_score_gemma":0.00091569626,"teacher_disagreement_score":0.0011781697,"about_ca_system_score_codex":0.00024195031,"about_ca_system_score_gemma":0.0003493923,"threshold_uncertainty_score":0.0023425817},"labels":[],"label_agreement":null},{"id":"W2022778022","doi":"10.3390/s130100574","title":"Design of an Oximeter Based on LED-LED Configuration and FPGA Technology","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Light-emitting diode; Field-programmable gate array; Optoelectronics; Materials science; Photoplethysmogram; Optical power; Electronic engineering; Computer science; Optics; Computer hardware; Wireless; Engineering; Physics; Telecommunications","score_opus":0.010323409086040935,"score_gpt":0.2066225488646254,"score_spread":0.19629913977858446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022778022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07039291,0.003088747,0.89637923,0.00030882948,0.0006821295,0.0008753431,0.00041606044,0.007196552,0.020660138],"genre_scores_gemma":[0.4993739,0.0013518494,0.48555127,0.00040622082,0.00014831912,0.00054687,0.00041103724,0.000116215124,0.012094288],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969506,0.00004549147,0.00002061799,0.00008301668,0.00011661008,0.000039260853],"domain_scores_gemma":[0.99980825,0.00003865876,0.000021294329,0.000016649394,0.0000982428,0.000016977352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024247247,0.0006220079,0.0006138035,0.00063396984,0.00026992708,0.0006086949,0.0013051198,0.0004761329,0.0031166216],"category_scores_gemma":[0.00033204825,0.000324732,0.00023256084,0.00039378932,0.00012777367,0.00046237413,0.00026503488,0.00032148315,0.0010300203],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005894488,0.00023085445,0.0042614667,0.0014066065,0.00019906156,0.00092573237,0.00015404893,0.0104064625,0.46145213,0.008097914,0.0085552875,0.50372094],"study_design_scores_gemma":[0.00045495818,0.0035807919,0.013474964,0.0003338359,0.0005196593,0.009702096,0.00012962808,0.1958011,0.61901087,0.0025939033,0.15419802,0.00020030851],"about_ca_topic_score_codex":0.000545443,"about_ca_topic_score_gemma":0.0006995241,"teacher_disagreement_score":0.0031166216,"about_ca_system_score_codex":0.00035346166,"about_ca_system_score_gemma":0.0004062457,"threshold_uncertainty_score":0.010426104},"labels":[],"label_agreement":null},{"id":"W2023049557","doi":"10.3390/s101009286","title":"Optical Oxygen Sensors for Applications in Microfluidic Cell Culture","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Microfluidics; 3D cell culture; Nanotechnology; Cell culture; Oxygen; Oxygen sensor; Viability assay; Cell; Biochemical engineering; Materials science; Computer science; Chemistry; Engineering; Biology; Biochemistry","score_opus":0.0212947995795603,"score_gpt":0.2881809346385302,"score_spread":0.26688613505896985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023049557","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024786247,0.9596769,0.017642213,0.0006302768,0.0010359029,0.00008853325,0.00008244938,0.00015492555,0.018210033],"genre_scores_gemma":[0.012311493,0.9426988,0.022681627,0.0007365897,0.00043481615,0.00014972444,0.000147896,0.000028226446,0.02081082],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964297,0.000030515366,0.000018466611,0.0000786371,0.00020627427,0.000023110668],"domain_scores_gemma":[0.9997929,0.00008730415,0.000029235865,0.000010183549,0.00006744252,0.000013044325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074400165,0.00092032016,0.0007632985,0.0018261898,0.00031161212,0.0007094449,0.0010441452,0.001355081,0.002744761],"category_scores_gemma":[0.00056677574,0.0004943836,0.00045674783,0.0014351481,0.00047752284,0.0013316403,0.00043992326,0.0012861657,0.0034467198],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006239127,0.00012799026,0.00026037404,0.0060253036,0.00004063984,0.00033094198,0.0001142735,0.0008484343,0.15390837,0.0129870325,0.017958717,0.8073355],"study_design_scores_gemma":[0.0000141323,0.000144898,0.0004901406,0.0005178837,0.000035007077,0.0013681046,0.00003761199,0.0006256726,0.06417023,0.0019247754,0.9306369,0.000034653458],"about_ca_topic_score_codex":0.0008285157,"about_ca_topic_score_gemma":0.0014375356,"teacher_disagreement_score":0.002744761,"about_ca_system_score_codex":0.00084218796,"about_ca_system_score_gemma":0.0005059169,"threshold_uncertainty_score":0.009182155},"labels":[],"label_agreement":null},{"id":"W2023950339","doi":"10.3390/s130606981","title":"Magnetic Resonance Imaging of Ischemia Viability Thresholds and the Neurovascular Unit","year":2013,"lang":"en","type":"review","venue":"Sensors","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Penumbra; Medicine; Neuroimaging; Magnetic resonance imaging; Stroke (engine); Ischemia; Neurovascular bundle; Cerebral blood flow; Neuroscience; Radiology; Pathology; Cardiology; Psychology","score_opus":0.021345808822134736,"score_gpt":0.27984937039636476,"score_spread":0.25850356157423005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023950339","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061829067,0.9656253,0.014884082,0.0031040634,0.00048152593,0.000030435898,0.00012827161,0.00009259689,0.0094708735],"genre_scores_gemma":[0.07238267,0.90669376,0.013862023,0.0016384557,0.0021774075,0.00012105243,0.00022210424,0.00004765916,0.00285484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994685,0.00017888226,0.00006453372,0.000095549374,0.00013304687,0.000059468774],"domain_scores_gemma":[0.99911314,0.00047101657,0.00016445103,0.000030326502,0.00018214341,0.00003891349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012659632,0.00064363255,0.0010124954,0.002271212,0.00019804102,0.0017487686,0.0008812307,0.0014170227,0.001826261],"category_scores_gemma":[0.002528077,0.00030290004,0.00042232242,0.001455772,0.0014216291,0.0020576378,0.0008022365,0.0016094529,0.0010511356],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049070077,0.000093542,0.0056590275,0.010150331,0.0002501772,0.0029915161,0.0007822535,0.0020418062,0.04743326,0.043324333,0.023111649,0.8636714],"study_design_scores_gemma":[0.00005727374,0.0012035035,0.058709983,0.012134129,0.0007518751,0.044155005,0.001731776,0.0058629187,0.04694814,0.1213178,0.7066944,0.00043305755],"about_ca_topic_score_codex":0.0010750265,"about_ca_topic_score_gemma":0.00080937747,"teacher_disagreement_score":0.002271212,"about_ca_system_score_codex":0.00091886835,"about_ca_system_score_gemma":0.00067622395,"threshold_uncertainty_score":0.0066950917},"labels":[],"label_agreement":null},{"id":"W2024801412","doi":"10.3390/s111110534","title":"Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"British Columbia Knowledge Development Fund; Ministero delle Politiche Agricole Alimentari e Forestali; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"RGB color model; Artificial intelligence; Benthic zone; Computer science; Remote sensing; Computer vision; Geology; Oceanography","score_opus":0.02388344003385349,"score_gpt":0.22484108708398423,"score_spread":0.20095764705013075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024801412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3826673,0.00017593939,0.598565,0.00008819638,0.000044256452,0.00093986513,0.003453797,0.008995307,0.0050703855],"genre_scores_gemma":[0.27122155,0.00013242813,0.7222028,0.000042601543,0.000014868428,0.0007564639,0.002726954,0.00036314141,0.0025391285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928254,0.0000985388,0.00005214266,0.00018700147,0.00030969805,0.00007003151],"domain_scores_gemma":[0.99904245,0.00020740453,0.00013789111,0.00013349601,0.0004254135,0.000053311935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011061397,0.00043539164,0.00033628015,0.0026145156,0.0002962962,0.0004326882,0.0006338807,0.00024312342,0.0033972177],"category_scores_gemma":[0.0014587985,0.00026598977,0.0003815568,0.0012652038,0.00023931214,0.00048477447,0.00063857436,0.00026582193,0.00085494295],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067380525,0.00021795768,0.034269255,0.00033381427,0.00013989763,0.00023517315,0.0006487719,0.0094323,0.4416227,0.0015986742,0.0053758877,0.50545186],"study_design_scores_gemma":[0.000075963115,0.00042632726,0.3307643,0.00009871392,0.000113436814,0.0009331338,0.0005207634,0.39300048,0.2535045,0.0013058896,0.019107591,0.00014894138],"about_ca_topic_score_codex":0.009572022,"about_ca_topic_score_gemma":0.02172941,"teacher_disagreement_score":0.009572022,"about_ca_system_score_codex":0.0007564898,"about_ca_system_score_gemma":0.0009526577,"threshold_uncertainty_score":0.019032657},"labels":[],"label_agreement":null},{"id":"W2025089751","doi":"10.3390/s130607224","title":"Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Killam Trusts","keywords":"Calibration; Collimated light; Remote sensing; Computer science; Lidar; Point (geometry); Identification (biology); Plane (geometry); Feature (linguistics); Residual; Laser; Computer vision; Optics; Algorithm; Geology; Mathematics; Physics; Geometry; Statistics","score_opus":0.033554851431694864,"score_gpt":0.3069314959784619,"score_spread":0.2733766445467671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025089751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20395194,0.0049646716,0.76054984,0.00048005505,0.0007829388,0.00028042277,0.0009352821,0.0061774724,0.02187735],"genre_scores_gemma":[0.5709454,0.0021175554,0.41605675,0.00018790152,0.00021313342,0.00013825002,0.0022403747,0.0007096898,0.0073909136],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99499327,0.0008611296,0.00023055717,0.0007196119,0.0030080036,0.00018741931],"domain_scores_gemma":[0.9930413,0.0011529671,0.0003110152,0.0018047426,0.0035710905,0.000119030556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043355427,0.0007562231,0.0006907824,0.0028006048,0.0007493207,0.0013852689,0.002074317,0.00086684077,0.0035381822],"category_scores_gemma":[0.0087045645,0.00040366102,0.0009446308,0.0028497495,0.00045125902,0.0021771716,0.0018866468,0.00082747184,0.00152462],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003774418,0.0002334279,0.014709364,0.00058330514,0.00025890997,0.00011320371,0.0003675435,0.04200992,0.03408396,0.0056563057,0.0037448704,0.8978617],"study_design_scores_gemma":[0.00009958242,0.00089746615,0.07325657,0.0003396994,0.0004049321,0.0017504917,0.00083423953,0.7065386,0.13026802,0.0061526494,0.07914153,0.00031618567],"about_ca_topic_score_codex":0.0028692668,"about_ca_topic_score_gemma":0.0030180814,"teacher_disagreement_score":0.0043355427,"about_ca_system_score_codex":0.00063812686,"about_ca_system_score_gemma":0.0010173992,"threshold_uncertainty_score":0.022928774},"labels":[],"label_agreement":null},{"id":"W2025797281","doi":"10.3390/s130201539","title":"Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":224,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Ministry of Advanced Education, Government of Alberta","keywords":"Computer science; Accelerometer; Algorithm; Mobile phone; Microelectromechanical systems; Inertial measurement unit; Step detection; Global Positioning System; Real-time computing; Mobile device; Artificial intelligence; Computer vision; Telecommunications","score_opus":0.015808631122497162,"score_gpt":0.21772004089192365,"score_spread":0.20191140976942648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025797281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36702496,0.00053354295,0.62764263,0.00010361485,0.00005396836,0.00015625962,0.00041280268,0.0024472014,0.001625033],"genre_scores_gemma":[0.73585254,0.00030302734,0.258973,0.00006638965,0.000037531725,0.0001914033,0.0008788147,0.00004940807,0.0036478473],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999068,0.000014127749,0.000010312431,0.000027577003,0.000025349778,0.00001577332],"domain_scores_gemma":[0.99975735,0.00009606231,0.000028444043,0.000022170841,0.00007650286,0.00001947361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001587441,0.0003298809,0.0003167034,0.00083520403,0.00014372343,0.0002780694,0.0003377575,0.00039210217,0.0015229174],"category_scores_gemma":[0.0009084153,0.00010745854,0.0002721956,0.00032999663,0.00008483159,0.00024601366,0.00017487335,0.00025805342,0.0009133588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006085391,0.00020742851,0.016407011,0.00009431927,0.000045022385,0.00027814475,0.00007466807,0.020215724,0.07126705,0.0009563553,0.0028442855,0.88700145],"study_design_scores_gemma":[0.000027086795,0.00029517082,0.025509004,0.000017682234,0.000040149218,0.00061485794,0.000084722844,0.92644846,0.043646377,0.0010709976,0.0022150292,0.000030564835],"about_ca_topic_score_codex":0.0013298444,"about_ca_topic_score_gemma":0.0014625096,"teacher_disagreement_score":0.0015229174,"about_ca_system_score_codex":0.00013436799,"about_ca_system_score_gemma":0.00024595505,"threshold_uncertainty_score":0.005094707},"labels":[],"label_agreement":null},{"id":"W2026094633","doi":"10.3390/s141018328","title":"Two-Photon Luminescence and Second Harmonic Generation from Gold Micro-Plates","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education of Guangdong Province; Chinese Academy of Sciences; Institute of Genetics; National Natural Science Foundation of China","keywords":"Second-harmonic generation; Luminescence; Materials science; Excitation wavelength; Excitation; Wavelength; Polarization (electrochemistry); Optics; Optoelectronics; Intensity (physics); Two-photon excitation microscopy; Laser; Chloroauric acid; Colloidal gold; Chemistry; Nanotechnology; Physics; Fluorescence; Nanoparticle","score_opus":0.015556341239511254,"score_gpt":0.22169427858920213,"score_spread":0.20613793734969088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026094633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9817092,0.0015835913,0.013721229,0.00010916062,0.000029951456,0.000042770913,0.00023995935,0.00016065815,0.0024033736],"genre_scores_gemma":[0.9808879,0.0006869701,0.015054521,0.000049639522,0.000008123795,0.000055799504,0.0002019823,0.00002081356,0.0030343828],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997613,0.00003287424,0.0000108387285,0.00006461896,0.00008733837,0.000043056883],"domain_scores_gemma":[0.99986684,0.000042593696,0.000031252446,0.000013753471,0.000029853478,0.000015811996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022762688,0.0005773321,0.0002746625,0.00047952918,0.00018088247,0.00023535787,0.00046138128,0.00040554884,0.0008668941],"category_scores_gemma":[0.00019187004,0.00029102218,0.00025363473,0.00031028816,0.00037397028,0.00025826483,0.00028066148,0.00045717863,0.0002750501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026781492,0.0000039445335,0.00008389494,0.000037163274,0.0000024998128,0.00003861923,0.000015882708,0.00005326806,0.99908507,0.000065405584,0.000014179554,0.00057324977],"study_design_scores_gemma":[0.0000034467835,0.000043428616,0.0005925784,0.0000020257087,0.0000030034264,0.00005138806,0.000014797996,0.0003726087,0.9986588,0.00002337531,0.00022954201,0.000004978785],"about_ca_topic_score_codex":0.00094681635,"about_ca_topic_score_gemma":0.0011137956,"teacher_disagreement_score":0.00094681635,"about_ca_system_score_codex":0.00043794143,"about_ca_system_score_gemma":0.00016907108,"threshold_uncertainty_score":0.0031775236},"labels":[],"label_agreement":null},{"id":"W2027837438","doi":"10.3390/s91210411","title":"Novel Absolute Displacement Sensor with Wide Range Based on Malus Law","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de l'Outaouais","funders":"","keywords":"Linearity; Displacement (psychology); Range (aeronautics); Dynamic range; Optics; Pulley; Acoustics; Physics; Engineering; Computer science; Electronic engineering; Structural engineering","score_opus":0.00952041948962491,"score_gpt":0.19695560456937883,"score_spread":0.18743518507975393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027837438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2510465,0.0052297167,0.7277641,0.00071318453,0.0006921011,0.00022119172,0.0004691857,0.0039788145,0.009885224],"genre_scores_gemma":[0.7180251,0.0013257532,0.27096918,0.0004425362,0.00018971367,0.00014138284,0.00030716605,0.000102743106,0.008496475],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984674,0.00012798315,0.000052450345,0.00036163774,0.0009290408,0.0000614671],"domain_scores_gemma":[0.99932563,0.00018156778,0.000105754545,0.00006846897,0.000277744,0.000040926858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005317549,0.000734534,0.0007823593,0.0007943825,0.00032480012,0.0009082966,0.0014612873,0.0010421894,0.0019588945],"category_scores_gemma":[0.00095299305,0.00046179685,0.00023388948,0.0007222459,0.0006171856,0.002256697,0.00083304656,0.0006720853,0.00077714043],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022109986,0.000067889785,0.0032550183,0.00043502593,0.000031464973,0.00025354084,0.00020497735,0.0012499352,0.86999315,0.0037844817,0.0017386557,0.11876484],"study_design_scores_gemma":[0.00004175737,0.0006124455,0.0061452426,0.000036304904,0.00005809015,0.002070424,0.0001225979,0.061485007,0.9091506,0.0011656674,0.018960478,0.00015148685],"about_ca_topic_score_codex":0.00027589948,"about_ca_topic_score_gemma":0.0005483253,"teacher_disagreement_score":0.0019588945,"about_ca_system_score_codex":0.00037183214,"about_ca_system_score_gemma":0.00028057242,"threshold_uncertainty_score":0.006553173},"labels":[],"label_agreement":null},{"id":"W2028005590","doi":"10.3390/s140916829","title":"The Intersection of CMOS Microsystems and Upconversion Nanoparticles for Luminescence Bioimaging and Bioassays","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Luminescence Properties of Advanced Materials","field":"Materials Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College of Family Physicians of Canada; University of Toronto","funders":"","keywords":"Optoelectronics; Materials science; Photon upconversion; Luminescence; Nanotechnology; CMOS","score_opus":0.0238925957035626,"score_gpt":0.29336672985144696,"score_spread":0.26947413414788435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028005590","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025184636,0.99257046,0.002312919,0.00044797224,0.00027726177,0.000013365099,0.0000072748544,0.000022033748,0.0040969304],"genre_scores_gemma":[0.0038118109,0.98853755,0.0038666609,0.00041981917,0.00037847736,0.000037758924,0.000015093641,0.0000053355543,0.002927585],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995714,0.000087194036,0.000037784826,0.00007689045,0.00019580622,0.00003103288],"domain_scores_gemma":[0.9997056,0.00014523393,0.000031796408,0.000016342745,0.00008336999,0.000017576585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006994078,0.0010492161,0.00080796,0.0021196615,0.00037026816,0.0009673771,0.0009327164,0.0014968999,0.0022439437],"category_scores_gemma":[0.00056492665,0.0004493865,0.0005479178,0.0022642931,0.0009005286,0.0018629584,0.00074856385,0.002499904,0.0016896181],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031028067,0.00009399182,0.00017963107,0.010388926,0.00006299351,0.0004550137,0.00016117189,0.0007841561,0.01901358,0.06583616,0.013913445,0.8890799],"study_design_scores_gemma":[0.000006071968,0.00007697294,0.00040683668,0.0012837054,0.000025989653,0.0014070261,0.00005283128,0.00024672126,0.0055930153,0.008172235,0.9827064,0.000022200318],"about_ca_topic_score_codex":0.0009280205,"about_ca_topic_score_gemma":0.0011645694,"teacher_disagreement_score":0.0022439437,"about_ca_system_score_codex":0.0012217509,"about_ca_system_score_gemma":0.0009809854,"threshold_uncertainty_score":0.008864403},"labels":[],"label_agreement":null},{"id":"W2028350101","doi":"10.3390/s120100115","title":"FPGA-Based Real-Time Embedded System for RISS/GPS Integrated Navigation","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University; Trusted Positioning (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Innovation Trust","keywords":"Global Positioning System; Navigation system; Inertial navigation system; Real-time computing; GPS/INS; Inertial measurement unit; Computer science; Kalman filter; Field-programmable gate array; Gyroscope; Precision Lightweight GPS Receiver; Assisted GPS; GPS signals; Embedded system; Odometer; Engineering; Artificial intelligence; Telecommunications; Inertial frame of reference","score_opus":0.01391415614383874,"score_gpt":0.21399821671309335,"score_spread":0.20008406056925462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028350101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09167611,0.0009295347,0.8363708,0.00024782922,0.00070704555,0.0005427852,0.0005406463,0.030763598,0.038221534],"genre_scores_gemma":[0.7506468,0.00035032793,0.22050513,0.0003196798,0.00009833481,0.00032648037,0.0006638572,0.0002767139,0.02681261],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975795,0.00003583363,0.000018992805,0.000047683337,0.00010477391,0.000034817072],"domain_scores_gemma":[0.9997931,0.00003200337,0.000024990411,0.000037291327,0.00009487965,0.000017691405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024522236,0.00044739092,0.00035132552,0.0003923317,0.00016645902,0.00042830256,0.00075225567,0.00031894958,0.011585174],"category_scores_gemma":[0.00044788801,0.00016014892,0.00015538176,0.00021770732,0.0001332931,0.0003082396,0.00022563997,0.00039343382,0.0034648175],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015633147,0.00039616623,0.0055982927,0.0011583572,0.00020553991,0.0010373545,0.00041268894,0.03748943,0.2659543,0.021920104,0.033719905,0.6305446],"study_design_scores_gemma":[0.0008216423,0.0033487969,0.012964732,0.0003058805,0.00041144437,0.0036581587,0.00012311236,0.41640142,0.3434,0.0036503975,0.21470606,0.0002083101],"about_ca_topic_score_codex":0.0012535929,"about_ca_topic_score_gemma":0.0016664783,"teacher_disagreement_score":0.011585174,"about_ca_system_score_codex":0.0002993037,"about_ca_system_score_gemma":0.0005176895,"threshold_uncertainty_score":0.03875625},"labels":[],"label_agreement":null},{"id":"W2028956118","doi":"10.3390/s101109647","title":"Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":306,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Jet Propulsion Laboratory","keywords":"Remote sensing; Hyperspectral imaging; Sensor fusion; Lidar; Context (archaeology); Biodiversity; Field (mathematics); Geography; Computer science; Remote sensing application; Environmental science; Data science; Environmental resource management; Ecology; Artificial intelligence","score_opus":0.028564842285157554,"score_gpt":0.272354566296642,"score_spread":0.24378972401148447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028956118","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011111509,0.998348,0.00017182603,0.00022628342,0.00013622626,0.000004723826,0.000023253435,0.0000059141207,0.0009725805],"genre_scores_gemma":[0.00043088503,0.99872714,0.0002989173,0.00007498232,0.00010561362,0.000004893417,0.000031663094,0.0000010764368,0.0003247577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997507,0.000040495976,0.00003822796,0.000054484248,0.00009854793,0.000017507264],"domain_scores_gemma":[0.99937147,0.00031436075,0.00007314346,0.00001542477,0.00018129061,0.000044260214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071120186,0.0011324239,0.001707643,0.0032323522,0.00030069862,0.001111674,0.0011033535,0.0010746487,0.0045478297],"category_scores_gemma":[0.00078590255,0.00036774884,0.0005066726,0.0053900634,0.00055147085,0.002124945,0.0006500333,0.0013059145,0.0024737136],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004826394,0.000080203856,0.0002581207,0.021548614,0.00007311675,0.00019557153,0.00010134309,0.00043770342,0.0013847739,0.0030020038,0.026409129,0.94646126],"study_design_scores_gemma":[0.000010314644,0.00008152271,0.0015183939,0.0058142543,0.00011729658,0.0009895465,0.00013082621,0.000113405986,0.0003520119,0.0018469294,0.98900044,0.000024987676],"about_ca_topic_score_codex":0.0023652336,"about_ca_topic_score_gemma":0.00325074,"teacher_disagreement_score":0.0045478297,"about_ca_system_score_codex":0.00060963404,"about_ca_system_score_gemma":0.0014597268,"threshold_uncertainty_score":0.015214026},"labels":[],"label_agreement":null},{"id":"W2029410598","doi":"10.3390/s140712784","title":"Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Shanghai Municipal Education Commission","keywords":"Support vector machine; Artificial intelligence; Computer science; Radial basis function kernel; Brain–computer interface; Pattern recognition (psychology); Kernel (algebra); Polynomial kernel; Multiple kernel learning; Electroencephalography; Machine learning; Kernel method; Mathematics","score_opus":0.04985856570991843,"score_gpt":0.2920877395065981,"score_spread":0.2422291737966797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029410598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040323388,0.00043733185,0.9581133,0.00009207996,0.00006835523,0.000047925598,0.00004853254,0.0005025236,0.00036652054],"genre_scores_gemma":[0.6964996,0.0006132496,0.3006368,0.0000624285,0.000092543225,0.00013576094,0.00027240659,0.000051456464,0.0016356698],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990615,0.00022007046,0.000114455805,0.00020301585,0.00032175594,0.00007914282],"domain_scores_gemma":[0.99893767,0.00037322743,0.00013539335,0.00009572448,0.0004291743,0.000028867458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009282111,0.0007560728,0.0009538907,0.0010585639,0.00024213307,0.00074318814,0.00078994123,0.00082648016,0.00073685957],"category_scores_gemma":[0.0034172272,0.00020931252,0.0007507932,0.0009834765,0.00032363238,0.0011366528,0.00047945764,0.0007171189,0.0005096977],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004192602,0.00024839348,0.0033599145,0.00030715295,0.0001608778,0.00018679441,0.00011808635,0.1168135,0.04559465,0.0029278249,0.0013578442,0.8285057],"study_design_scores_gemma":[0.000010444036,0.00012333957,0.0014562762,0.000009293504,0.000019491014,0.000085382126,0.000018702505,0.98921263,0.007708125,0.00062780257,0.0007100986,0.000018427252],"about_ca_topic_score_codex":0.0010473612,"about_ca_topic_score_gemma":0.0006129298,"teacher_disagreement_score":0.0010585639,"about_ca_system_score_codex":0.00028178384,"about_ca_system_score_gemma":0.00043096277,"threshold_uncertainty_score":0.0049089193},"labels":[],"label_agreement":null},{"id":"W2030530735","doi":"10.3390/s90200895","title":"A Real-Time De-Noising Algorithm for E-Noses in a Wireless Sensor Network","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Kalman filter; Noise (video); Wireless sensor network; Algorithm; Computer science; Filter (signal processing); Real-time computing; Artificial intelligence; Computer vision","score_opus":0.006465471777255378,"score_gpt":0.22824074214413545,"score_spread":0.22177527036688008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030530735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009438668,0.0001493937,0.9897855,0.000046129924,0.000023617506,0.000016235866,0.0000056962936,0.00020381584,0.00033103026],"genre_scores_gemma":[0.45084742,0.000405826,0.54521793,0.000080211335,0.000050072667,0.00014384235,0.000059074395,0.00003623485,0.003159407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976355,0.0000451862,0.000020225756,0.00006176091,0.000086202905,0.000023126884],"domain_scores_gemma":[0.9996804,0.00014155573,0.00004264468,0.000021501924,0.00010182224,0.000012079433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005430994,0.0004066853,0.0004993756,0.0002455682,0.0003262152,0.00045282574,0.0006815474,0.00061704445,0.0006649893],"category_scores_gemma":[0.0012788129,0.00021204643,0.00022246197,0.00029049328,0.00033330926,0.0008087187,0.00031950776,0.0005347367,0.00021632634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025841262,0.00009557766,0.0014279947,0.00013375135,0.000050047292,0.000106205654,0.0001222077,0.5084678,0.05030216,0.008252686,0.0014598448,0.42932338],"study_design_scores_gemma":[0.000012349268,0.000044995206,0.00022515311,0.0000034983016,0.0000059028976,0.000030104244,0.0000070277356,0.9939857,0.004492738,0.00046108122,0.0007253276,0.0000060822535],"about_ca_topic_score_codex":0.0023963512,"about_ca_topic_score_gemma":0.00299221,"teacher_disagreement_score":0.0023963512,"about_ca_system_score_codex":0.0004532777,"about_ca_system_score_gemma":0.00051122135,"threshold_uncertainty_score":0.0047647953},"labels":[],"label_agreement":null},{"id":"W2032611465","doi":"10.3390/s101211512","title":"Characterization of Buoyant Fluorescent Particles for Field Observations of Water Flows","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; University of Waterloo; York University; National Science Foundation","keywords":"Environmental science; Turbidity; Particle (ecology); Fluorescence; Materials science; Drainage; Geology; Optics","score_opus":0.018652350994445146,"score_gpt":0.21148901225751449,"score_spread":0.19283666126306934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032611465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9214328,0.0004998868,0.07690898,0.00007180588,0.000018436667,0.000087143526,0.00013882534,0.000117623065,0.00072442705],"genre_scores_gemma":[0.93934625,0.00040038212,0.05886552,0.00003650278,0.000010010407,0.00008663636,0.00019541167,0.000025239275,0.0010339712],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998301,0.000026023683,0.000008921411,0.00003868217,0.00007737685,0.000018865476],"domain_scores_gemma":[0.99972075,0.000099221215,0.000066491666,0.000015761048,0.00007599245,0.00002172541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003360302,0.0002786134,0.00019558815,0.00023880751,0.00016929756,0.00023941735,0.0001941539,0.00033354864,0.00044917283],"category_scores_gemma":[0.00060162746,0.00011197829,0.000114097566,0.00010167471,0.00020721462,0.00025322058,0.00015780509,0.00024600147,0.00012938384],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016475688,0.000006606469,0.00034615555,0.000014574755,9.443059e-7,0.0000111494855,0.000015069335,0.00009503944,0.9969914,0.00004255529,0.000008467033,0.0024514815],"study_design_scores_gemma":[0.0000051980087,0.00017157849,0.004270525,0.0000029135726,0.0000068487675,0.00008396341,0.000019603522,0.0034262,0.9913653,0.000028641554,0.0006137135,0.000005526475],"about_ca_topic_score_codex":0.0013384307,"about_ca_topic_score_gemma":0.0020784135,"teacher_disagreement_score":0.0013384307,"about_ca_system_score_codex":0.00020983307,"about_ca_system_score_gemma":0.00023597782,"threshold_uncertainty_score":0.0026612878},"labels":[],"label_agreement":null},{"id":"W2032762924","doi":"10.3390/s120607350","title":"A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Universiti Malaya; University of Washington","keywords":"Cluster analysis; Computer science; Routing protocol; Scalability; Interior gateway protocol; Routing (electronic design automation); Distributed computing; Wireless sensor network; Link-state routing protocol; Homogeneous; Computer network; Data mining; Artificial intelligence","score_opus":0.05479923532822694,"score_gpt":0.2683254162327451,"score_spread":0.21352618090451814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032762924","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044961474,0.37817854,0.5678106,0.0039841984,0.0027653906,0.0011064137,0.00059962616,0.0013169715,0.03974206],"genre_scores_gemma":[0.03517369,0.5267344,0.41871187,0.0022324594,0.0022196858,0.0015789345,0.0017838618,0.00032481796,0.011240343],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99694604,0.00065390626,0.0005195847,0.0003533145,0.0013813945,0.00014581732],"domain_scores_gemma":[0.9974458,0.0011468919,0.00022544702,0.0002741732,0.0008196799,0.00008801436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028167325,0.0014641344,0.0016772194,0.005194715,0.0014654931,0.0030022012,0.003222265,0.002444733,0.0022048024],"category_scores_gemma":[0.0054917275,0.00089838944,0.0009639431,0.013291093,0.0011528466,0.0060777585,0.0016190283,0.002425002,0.0021127348],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088703004,0.00018431366,0.0013043156,0.007682011,0.00011009483,0.00037355657,0.0007242708,0.015458381,0.0037634566,0.20219603,0.043056317,0.7250585],"study_design_scores_gemma":[0.000023617147,0.00030317166,0.0009862938,0.0025214143,0.00010313794,0.0018104719,0.00039098115,0.03544516,0.0022864288,0.11252618,0.84347993,0.00012321731],"about_ca_topic_score_codex":0.0016860387,"about_ca_topic_score_gemma":0.0012974816,"teacher_disagreement_score":0.005194715,"about_ca_system_score_codex":0.0014239853,"about_ca_system_score_gemma":0.002028663,"threshold_uncertainty_score":0.014896512},"labels":[],"label_agreement":null},{"id":"W2033611811","doi":"10.3390/s130404884","title":"Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Toronto Metropolitan University","funders":"","keywords":"Relay; Computer science; Throughput; Heuristic; Resource allocation; Mathematical optimization; Cognitive radio; Optimization problem; Computer network; Wireless; Power (physics); Algorithm; Telecommunications","score_opus":0.021871340259181044,"score_gpt":0.2676694551101651,"score_spread":0.24579811485098405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033611811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075427853,0.00016647583,0.9910211,0.00010173193,0.000012964452,0.000033587614,0.00001269746,0.00013142049,0.0009773175],"genre_scores_gemma":[0.69269884,0.00039841345,0.3039369,0.00019710486,0.000039567898,0.00037513705,0.00011035459,0.00008416402,0.0021594977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920243,0.00034947033,0.000028501332,0.00012253708,0.0001887021,0.00010846857],"domain_scores_gemma":[0.9981159,0.0013519813,0.0001671502,0.00006767908,0.00024407274,0.00005329008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019335763,0.00097378914,0.000998735,0.0008121012,0.00061764405,0.0009010736,0.001268191,0.00084103446,0.0011675813],"category_scores_gemma":[0.0050496073,0.00043894467,0.0004396677,0.0010073177,0.0008712947,0.0011884918,0.0011799684,0.0009232298,0.00022455076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049387814,0.000028996215,0.00026967083,0.000025471085,0.000015920012,0.000018739513,0.00004245721,0.96484894,0.00054044736,0.0062196837,0.00048809798,0.027452262],"study_design_scores_gemma":[0.0000055535584,0.0000068001386,0.000021073734,0.0000018034406,0.0000015873018,0.0000037225632,0.0000046807095,0.9982284,0.00012439629,0.0015035648,0.00009675943,0.0000017004512],"about_ca_topic_score_codex":0.0066879713,"about_ca_topic_score_gemma":0.0061311205,"teacher_disagreement_score":0.0066879713,"about_ca_system_score_codex":0.0016866488,"about_ca_system_score_gemma":0.0019147114,"threshold_uncertainty_score":0.013298094},"labels":[],"label_agreement":null},{"id":"W2034394311","doi":"10.3390/s150100769","title":"Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":285,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Arable land; Production (economics); Environmental science; Food security; Staple food; Population; Computer science; Agriculture; Geography","score_opus":0.0700112420469019,"score_gpt":0.287170028341803,"score_spread":0.2171587862949011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034394311","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012406545,0.992867,0.0032223125,0.00029208092,0.00030911062,0.00002176451,0.000087431705,0.000032283995,0.0019273235],"genre_scores_gemma":[0.007177326,0.988447,0.0032932914,0.00015092295,0.00027316838,0.000018575818,0.00011903834,0.0000085247275,0.00051212724],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952483,0.000080067046,0.00008055285,0.000120486606,0.0001619986,0.000032015105],"domain_scores_gemma":[0.998459,0.00087368506,0.00015673783,0.000037874528,0.0004421371,0.000030471898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012462117,0.0010642066,0.001017918,0.0030482973,0.00024504008,0.0011544197,0.0010925927,0.0011198396,0.0016568525],"category_scores_gemma":[0.0019177262,0.0004596756,0.0012350713,0.0042862967,0.00040504386,0.0019110561,0.00042643546,0.00074812456,0.00087573763],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007111423,0.00008142004,0.0023285882,0.028728103,0.00025518454,0.00031624577,0.00019178295,0.0026337225,0.006104055,0.0028740643,0.013214549,0.9432011],"study_design_scores_gemma":[0.000017816052,0.00036942167,0.0117535675,0.011060546,0.0013069056,0.0023613265,0.000616104,0.006087731,0.008355901,0.0031610487,0.9547017,0.00020803516],"about_ca_topic_score_codex":0.003437743,"about_ca_topic_score_gemma":0.0025811559,"teacher_disagreement_score":0.003437743,"about_ca_system_score_codex":0.0003942158,"about_ca_system_score_gemma":0.00097195565,"threshold_uncertainty_score":0.0068355203},"labels":[],"label_agreement":null},{"id":"W2035349853","doi":"10.3390/s141018370","title":"A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Electroencephalography; Artifact (error); Computer science; Channel (broadcasting); Hilbert–Huang transform; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Psychology; Computer vision; Neuroscience; Telecommunications","score_opus":0.02171559706886661,"score_gpt":0.25312034507677195,"score_spread":0.23140474800790534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035349853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39343163,0.0027163064,0.59715253,0.00044298355,0.00019645038,0.00020700574,0.00012714758,0.00025158253,0.0054743807],"genre_scores_gemma":[0.83927745,0.0019775263,0.15517817,0.000089163725,0.00010416266,0.00010216317,0.00014691867,0.00002947565,0.0030950243],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974936,0.000086477274,0.00001793231,0.0000398911,0.00008920602,0.000017131639],"domain_scores_gemma":[0.99863064,0.00082758866,0.000061011528,0.00009563338,0.00034556093,0.0000394901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004958986,0.00031151055,0.0003890399,0.00026212583,0.00025825473,0.00033138634,0.00024404869,0.0004996443,0.001237526],"category_scores_gemma":[0.0032398722,0.000099219025,0.0004043051,0.00034978657,0.00027830093,0.0005002319,0.00021320324,0.0002611943,0.00023305252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012319955,0.00060048647,0.009606966,0.0024326001,0.00027092855,0.0027837537,0.0009736453,0.1875572,0.37910303,0.011264001,0.0033584163,0.400817],"study_design_scores_gemma":[0.0000802109,0.0025672936,0.015367519,0.00010580037,0.00010762812,0.0021446142,0.00038160704,0.8482544,0.11887645,0.003785182,0.008256744,0.000072623145],"about_ca_topic_score_codex":0.0012103538,"about_ca_topic_score_gemma":0.001156105,"teacher_disagreement_score":0.001237526,"about_ca_system_score_codex":0.00010132163,"about_ca_system_score_gemma":0.0002877742,"threshold_uncertainty_score":0.0041399},"labels":[],"label_agreement":null},{"id":"W2035586800","doi":"10.3390/s130201730","title":"Hybrid Modeling Method for a DEP Based Particle Manipulation","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dielectrophoresis; MATLAB; Finite element method; Displacement (psychology); Interface (matter); Particle (ecology); Computer science; Field (mathematics); Modeling and simulation; Simulation; Engineering; Electric field; Physics; Structural engineering","score_opus":0.028300705179938736,"score_gpt":0.24130724158134034,"score_spread":0.2130065364014016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035586800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023601444,0.00014481496,0.9939073,0.000061218234,0.000040164523,0.000022205148,0.00003937693,0.00019505594,0.0032297506],"genre_scores_gemma":[0.28411472,0.001288475,0.6813499,0.0002525109,0.00009644062,0.00062160695,0.0004102121,0.00038734806,0.031478897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998337,0.000029855308,0.000007929329,0.000028185574,0.000090322,0.0000098417895],"domain_scores_gemma":[0.9998797,0.000050968425,0.000012645973,0.000015220591,0.000035035795,0.000006398462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027692804,0.0004989433,0.00036442015,0.0003637149,0.00026830935,0.00057193596,0.0008110749,0.0008881066,0.0029719565],"category_scores_gemma":[0.0003245602,0.00029293404,0.0007173599,0.00022428931,0.00022391025,0.0006098104,0.00053020497,0.0005822914,0.0011851556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006362615,0.000091639915,0.00052490726,0.00026607653,0.00007707639,0.00029348527,0.00020427289,0.7683861,0.07341379,0.07700659,0.0024000648,0.07727241],"study_design_scores_gemma":[0.0000035487215,0.000015729443,0.000055543704,0.0000074274276,0.0000057665693,0.000045061723,0.000006390136,0.98916954,0.0027847597,0.0020759553,0.005822796,0.0000073768906],"about_ca_topic_score_codex":0.0016909619,"about_ca_topic_score_gemma":0.0012463637,"teacher_disagreement_score":0.0029719565,"about_ca_system_score_codex":0.00033836838,"about_ca_system_score_gemma":0.00042429997,"threshold_uncertainty_score":0.009942174},"labels":[],"label_agreement":null},{"id":"W2036057345","doi":"10.3390/s100201041","title":"A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Background subtraction; Artificial intelligence; Computer vision; Histogram; Computer science; Subtraction; Noise (video); Algorithm; Mixture model; Division (mathematics); Gaussian; Pattern recognition (psychology); Motion (physics); Motion detection; Image (mathematics); Pixel; Mathematics","score_opus":0.02169145873165617,"score_gpt":0.27228805612461715,"score_spread":0.250596597392961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036057345","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043679983,0.00024742217,0.9934928,0.000030373183,0.00003429959,0.00002628976,0.000041342755,0.0011326072,0.00062682544],"genre_scores_gemma":[0.049588643,0.00027297204,0.94734764,0.000064398155,0.00005119703,0.000054972454,0.00020812932,0.00017094328,0.0022410888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994624,0.000048504633,0.00002338433,0.00013648371,0.00029025783,0.00003896483],"domain_scores_gemma":[0.99973506,0.000058379785,0.00002931581,0.000038986913,0.00011416655,0.000024038754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000495446,0.0007025606,0.00092723925,0.0013130729,0.00030149118,0.0005261164,0.0012696692,0.0006169898,0.0025059232],"category_scores_gemma":[0.0009208009,0.00050560065,0.0009173388,0.0008001705,0.0002422901,0.00083998486,0.00071687123,0.00065170345,0.0015693511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015636368,0.00008166905,0.0007241672,0.00015210235,0.00012540842,0.0001643808,0.000081061786,0.025959477,0.22562784,0.0057838317,0.0037142395,0.7374295],"study_design_scores_gemma":[0.000038582773,0.00015516905,0.0032071695,0.000024246565,0.00012626746,0.0008171834,0.00002484827,0.8710068,0.09880926,0.002496923,0.023218311,0.0000753156],"about_ca_topic_score_codex":0.0019210341,"about_ca_topic_score_gemma":0.0022402138,"teacher_disagreement_score":0.0025059232,"about_ca_system_score_codex":0.0003771754,"about_ca_system_score_gemma":0.0005111909,"threshold_uncertainty_score":0.008383155},"labels":[],"label_agreement":null},{"id":"W2036433385","doi":"10.3390/s111009732","title":"Biosensing with Quantum Dots: A Microfluidic Approach","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biosensor; Microfluidics; Nanotechnology; Quantum dot; Förster resonance energy transfer; Multiplexing; Computer science; Materials science; Fluorescence; Physics; Telecommunications","score_opus":0.03255809120285185,"score_gpt":0.295138056698963,"score_spread":0.2625799654961111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036433385","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044523943,0.4695317,0.4567196,0.003628072,0.0028462783,0.00056383567,0.0005513893,0.0016552988,0.019979954],"genre_scores_gemma":[0.25071788,0.3319699,0.39932176,0.0025157887,0.0010201243,0.00076968817,0.00048293005,0.000097913304,0.013104017],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996289,0.000053454343,0.000033973112,0.00011205094,0.00013363121,0.000037993694],"domain_scores_gemma":[0.99993205,0.000024579513,0.000009946604,0.0000058784835,0.00001804264,0.000009589778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043810363,0.00062558096,0.00074138225,0.0008100067,0.00037345279,0.0012220581,0.000895053,0.0011764516,0.0006427887],"category_scores_gemma":[0.00026033804,0.00060247444,0.0005137182,0.0005541146,0.00057591667,0.001033907,0.000841987,0.0010616517,0.0004998599],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086824824,0.00011406293,0.00030101615,0.0028661473,0.000071324794,0.000347861,0.00019869454,0.0019831157,0.8156206,0.037069425,0.003119137,0.13822177],"study_design_scores_gemma":[0.000041070703,0.0004284456,0.00076374935,0.00037275066,0.00009114125,0.0013954003,0.0000675672,0.014288509,0.66483533,0.010693229,0.306877,0.00014579283],"about_ca_topic_score_codex":0.0004471737,"about_ca_topic_score_gemma":0.0006147593,"teacher_disagreement_score":0.0012220581,"about_ca_system_score_codex":0.0010798286,"about_ca_system_score_gemma":0.00070644333,"threshold_uncertainty_score":0.0078347325},"labels":[],"label_agreement":null},{"id":"W2036872036","doi":"10.3390/s130708750","title":"Multi-View Human Activity Recognition in Distributed Camera Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Activity recognition; Computer science; Smart camera; Artificial intelligence; Wireless sensor network; Computer vision; Distributed computing; Real-time computing; Computer network","score_opus":0.04845155648201575,"score_gpt":0.3046635040526914,"score_spread":0.2562119475706756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036872036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07755543,0.0005178915,0.9201734,0.00018766163,0.000048895436,0.000054605356,0.00008272162,0.00057709776,0.00080228894],"genre_scores_gemma":[0.87891793,0.0003469692,0.11870778,0.00007965601,0.000089754096,0.00009264206,0.00033930782,0.00002989198,0.0013960474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920124,0.00024281477,0.000032456202,0.0002740244,0.00017573171,0.000073856],"domain_scores_gemma":[0.99910295,0.0003868609,0.00015904767,0.00012301872,0.00015496912,0.00007319064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010529817,0.00068112795,0.001091739,0.0007559609,0.00029276987,0.00053196255,0.0011863292,0.0006512959,0.00042467564],"category_scores_gemma":[0.0023745154,0.00037470413,0.00045925996,0.0008111132,0.0005153553,0.0010503663,0.0006381597,0.0007342572,0.00018809653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040419228,0.00022975054,0.0027261989,0.00007461476,0.000084732594,0.00016856602,0.00012042814,0.7533903,0.008962195,0.0026591993,0.0016894357,0.22949034],"study_design_scores_gemma":[0.0000063504995,0.000016930484,0.0004831521,0.0000014971515,0.000002561638,0.000021134623,0.000014164537,0.99756265,0.00077239046,0.0009961725,0.00012021843,0.0000028233997],"about_ca_topic_score_codex":0.005984155,"about_ca_topic_score_gemma":0.004900364,"teacher_disagreement_score":0.005984155,"about_ca_system_score_codex":0.00056482194,"about_ca_system_score_gemma":0.00041460473,"threshold_uncertainty_score":0.011898637},"labels":[],"label_agreement":null},{"id":"W2039122738","doi":"10.3390/s90100281","title":"A Localized Coverage Preserving Protocol for Wireless Sensor Networks","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Beihang University; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Redundancy (engineering); Wireless sensor network; Protocol (science); Node (physics); Network topology; Medicine; Engineering","score_opus":0.01729389828407058,"score_gpt":0.27785839686496216,"score_spread":0.2605644985808916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039122738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029517947,0.00064339215,0.965505,0.0002474515,0.0000552425,0.00027228807,0.00008521346,0.0008887754,0.0027846012],"genre_scores_gemma":[0.7408587,0.00086142175,0.2523653,0.00023580164,0.00007113652,0.00066806097,0.00032329943,0.00007869149,0.0045376318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995105,0.00011807384,0.000038826416,0.000106931875,0.00018495845,0.000040686242],"domain_scores_gemma":[0.9992939,0.00028458599,0.00012065583,0.00015028726,0.0001056261,0.00004483803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000506805,0.00047338323,0.00036557484,0.0005278717,0.00066650123,0.0004887051,0.0009507314,0.00047794974,0.0007094148],"category_scores_gemma":[0.0017743363,0.00017571641,0.00035470625,0.00061162707,0.00082376914,0.0011764707,0.0012183082,0.00066756574,0.00023259033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037192443,0.00018813914,0.001705271,0.0006296101,0.0001074574,0.0007091573,0.0010682208,0.2257612,0.12846632,0.06949324,0.00844106,0.5630584],"study_design_scores_gemma":[0.00011219443,0.0010913867,0.0014653505,0.00006958662,0.0001392673,0.0011494989,0.000297053,0.863669,0.060077604,0.037371255,0.03445626,0.00010154005],"about_ca_topic_score_codex":0.0013585945,"about_ca_topic_score_gemma":0.0018639996,"teacher_disagreement_score":0.0013585945,"about_ca_system_score_codex":0.00046182924,"about_ca_system_score_gemma":0.00089020166,"threshold_uncertainty_score":0.003350854},"labels":[],"label_agreement":null},{"id":"W2039141855","doi":"10.3390/s100301823","title":"Optical Fiber Sensing Based on Reflection Laser Spectroscopy","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council; Ministero dell’Istruzione, dell’Università e della Ricerca; Queen's University","keywords":"Materials science; Optics; Fiber Bragg grating; Optical fiber; Spectroscopy; Fiber optic sensor; Laser; Reflection (computer programming); Fiber laser; Heterodyne (poetry); Resonator; Fiber; Optoelectronics; Acoustics; Physics; Computer science","score_opus":0.024648287506775286,"score_gpt":0.3052439640656388,"score_spread":0.2805956765588635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039141855","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042396127,0.9611596,0.015388078,0.0002676033,0.0004557585,0.000051067775,0.000055123513,0.00013232854,0.018250855],"genre_scores_gemma":[0.02666984,0.9428078,0.015182444,0.00040084514,0.0005338609,0.0000917216,0.00012114216,0.000016147636,0.014176217],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996001,0.00004452753,0.000021080043,0.00011195959,0.0001908608,0.00003138785],"domain_scores_gemma":[0.99988246,0.000032729877,0.000019372403,0.0000094079915,0.000048760445,0.00000715811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034842733,0.0010021491,0.0010341406,0.0012661562,0.00021602109,0.00058188336,0.00082125404,0.00084851886,0.0013962727],"category_scores_gemma":[0.00025044428,0.00038772868,0.00042816944,0.0011287959,0.00042501788,0.001068719,0.0004115916,0.00080017623,0.0026602356],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054577657,0.00012565419,0.00028193922,0.008031824,0.00006466218,0.00029275217,0.0000913285,0.0009408647,0.1721759,0.012439684,0.008847241,0.7966536],"study_design_scores_gemma":[0.00001556566,0.00033764445,0.001464907,0.0011459297,0.000082141625,0.0038801164,0.00008327491,0.0015930162,0.13380866,0.005450233,0.85206985,0.00006865808],"about_ca_topic_score_codex":0.0004651727,"about_ca_topic_score_gemma":0.00042602103,"teacher_disagreement_score":0.0013962727,"about_ca_system_score_codex":0.00038714972,"about_ca_system_score_gemma":0.00035038195,"threshold_uncertainty_score":0.0046709776},"labels":[],"label_agreement":null},{"id":"W2040332509","doi":"10.3390/s130708188","title":"Rapid Detection of Viable Microorganisms Based on a Plate Count Technique Using Arrayed Microelectrodes","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; PBR Laboratories; Alberta Health Services","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microsystem; Biochip; Biosensor; Microelectrode; Food microbiology; Miniaturization; Agar plate; Agar; Materials science; Nanotechnology; Bacteria; Biology; Chemistry; Electrode","score_opus":0.006512239221299841,"score_gpt":0.18169120326076102,"score_spread":0.17517896403946118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040332509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13112025,0.005438182,0.85642016,0.0003661686,0.0005411887,0.0005859666,0.0007696883,0.0022511012,0.0025073641],"genre_scores_gemma":[0.24298579,0.006840527,0.7429063,0.0002605201,0.0001254821,0.00078318646,0.00069542986,0.00010940976,0.0052933446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884593,0.00015535715,0.00007276778,0.00021430437,0.0006537606,0.000057926743],"domain_scores_gemma":[0.999302,0.00032956604,0.000076436816,0.000069335234,0.00017738904,0.000045199293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065208715,0.00095641194,0.00078801427,0.0011525677,0.00024350572,0.00078283204,0.0012294609,0.00088009465,0.00079477736],"category_scores_gemma":[0.0011530146,0.00060124684,0.0004815742,0.000656123,0.0005502347,0.0008611078,0.00063535926,0.0012356838,0.00096801325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003232145,0.000020930265,0.00013023098,0.000106252315,0.0000087119115,0.00003545764,0.000016327811,0.00014765213,0.990666,0.00018051527,0.00008729739,0.008568292],"study_design_scores_gemma":[0.0000070669084,0.00025104798,0.00074495433,0.000014167481,0.000017245382,0.00025760135,0.00002254894,0.0047166897,0.9912145,0.00016236631,0.0025686675,0.000023053006],"about_ca_topic_score_codex":0.00032627056,"about_ca_topic_score_gemma":0.0006691693,"teacher_disagreement_score":0.0012294609,"about_ca_system_score_codex":0.0002960254,"about_ca_system_score_gemma":0.00033269468,"threshold_uncertainty_score":0.0034486055},"labels":[],"label_agreement":null},{"id":"W2040356915","doi":"10.3390/s140917807","title":"Implementation of a Rotational Ultrasound Biomicroscopy System Equipped with a High-Frequency Angled Needle Transducer — Ex Vivo Ultrasound Imaging of Porcine Ocular Posterior Tissues","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Foundation of Korea","keywords":"Transducer; Sclera; Ultrasound; Ultrasound biomicroscopy; Ultrasonic sensor; Materials science; Optics; Focal length; Biomedical engineering; Acoustics; Lens (geology); Medicine; Physics; Surgery","score_opus":0.004686457273921546,"score_gpt":0.2299944554756792,"score_spread":0.22530799820175765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040356915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.695744,0.0013583248,0.29924187,0.0002627318,0.00013174962,0.00038228402,0.00020688673,0.00076821685,0.0019038465],"genre_scores_gemma":[0.6574247,0.0007701487,0.33983007,0.00013801207,0.000028574696,0.00024701018,0.00022254985,0.000051115283,0.0012877906],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996308,0.0000770552,0.000038714505,0.00010171568,0.000106684274,0.000045072182],"domain_scores_gemma":[0.99953973,0.00008023471,0.00013104986,0.00010942486,0.000094243806,0.00004525931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005483488,0.00037037706,0.00026097675,0.00023479667,0.00015598534,0.00031420545,0.00038694678,0.0005085548,0.0007861089],"category_scores_gemma":[0.00053252815,0.00026631507,0.00024619247,0.00012735621,0.00027966304,0.00037632583,0.00030123405,0.00029447328,0.00029794965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023770084,0.00000993897,0.00023672165,0.000028597753,0.0000020930117,0.00004147237,0.000014103107,0.000065088025,0.9962417,0.00006155662,0.000031071086,0.0032438305],"study_design_scores_gemma":[0.00002560848,0.0014555089,0.008355149,0.000014676514,0.00003669096,0.0012777715,0.000047760197,0.004884438,0.9792901,0.000058044472,0.0045186873,0.000035549005],"about_ca_topic_score_codex":0.0006968838,"about_ca_topic_score_gemma":0.0008007502,"teacher_disagreement_score":0.0007861089,"about_ca_system_score_codex":0.00020968147,"about_ca_system_score_gemma":0.00043228889,"threshold_uncertainty_score":0.0029000044},"labels":[],"label_agreement":null},{"id":"W2041761647","doi":"10.3390/s120608437","title":"Correction: Renaudin, V. et al. Use of Earth’s Magnetic Field for Mitigating Gyroscope Errors Regardless of Magnetic Perturbation. Sensors 2011, 11, 11390-11414","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Gyroscope; Skew; Perturbation (astronomy); Magnetic field; Physics; Classical mechanics; Computer science; Geodesy; Computational physics; Algorithm; Quantum mechanics; Geology","score_opus":0.016724312435459285,"score_gpt":0.2391620485862587,"score_spread":0.22243773615079943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041761647","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003325068,0.0024651426,0.0028264534,0.020083705,0.9646207,0.000052659158,0.005164168,0.0012162165,0.0032384517],"genre_scores_gemma":[0.054285668,0.019837622,0.031139268,0.06676513,0.33771315,0.0007507825,0.030342994,0.007902504,0.4512629],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99463403,0.0006344883,0.00082201336,0.00084277656,0.002656231,0.00041051584],"domain_scores_gemma":[0.9618389,0.0040090913,0.0019201017,0.0034534943,0.027668983,0.0011094355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043420563,0.0033556072,0.0022610635,0.005055226,0.0020789,0.0029821922,0.004087045,0.0047427746,0.07267213],"category_scores_gemma":[0.068606384,0.0012722707,0.0021704433,0.0039420687,0.0017870535,0.0026813403,0.0026693463,0.006239916,0.053525914],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000392662,0.000005511398,0.0001313133,0.0002795708,0.000021541151,0.000094599105,0.000043962307,0.00006949743,0.000104206294,0.0005810332,0.9881819,0.010447606],"study_design_scores_gemma":[0.000038573697,0.000025536221,0.0018671994,0.000362057,0.0000633622,0.00056022074,0.000082547725,0.00037862116,0.00074130326,0.00082420494,0.99500966,0.00004663627],"about_ca_topic_score_codex":0.020629069,"about_ca_topic_score_gemma":0.019085456,"teacher_disagreement_score":0.07267213,"about_ca_system_score_codex":0.0026817496,"about_ca_system_score_gemma":0.0046496117,"threshold_uncertainty_score":0.2431125},"labels":[],"label_agreement":null},{"id":"W2041867049","doi":"10.3390/s131216216","title":"Assessing the Potential of Low-Cost 3D Cameras for the Rapid Measurement of Plant Woody Structure","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; Université du Québec à Montréal","funders":"","keywords":"Software; Computer science; Ranging; Artificial intelligence; Computer vision; Computer graphics (images)","score_opus":0.02120986317856973,"score_gpt":0.2208279540699104,"score_spread":0.19961809089134067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041867049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8263165,0.0019789462,0.16520639,0.00024224378,0.00007069489,0.00025513113,0.00078953936,0.00048195178,0.0046585645],"genre_scores_gemma":[0.80424124,0.0010428145,0.19262959,0.00013715794,0.000023360932,0.00024516942,0.00048625324,0.00009521343,0.0010992049],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985703,0.00033161498,0.00007585037,0.00023610436,0.0006938117,0.00009233819],"domain_scores_gemma":[0.994885,0.0032412119,0.0004134808,0.0003889252,0.0009345352,0.00013685472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019075386,0.00048010403,0.00024872593,0.0007899612,0.0003691259,0.0007898321,0.0006529637,0.000930919,0.0020174158],"category_scores_gemma":[0.0049294103,0.00037567795,0.00037527524,0.00058401085,0.0003881044,0.0013225967,0.00065849285,0.0005041288,0.0003505527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039582766,0.00023630381,0.029329658,0.00050497893,0.00006750387,0.00010991684,0.00022638722,0.009986853,0.8905058,0.0009305419,0.00045470588,0.067251466],"study_design_scores_gemma":[0.00008872854,0.0028189996,0.18446575,0.000115094605,0.00025332204,0.0007629579,0.00047816796,0.091431625,0.7076547,0.001875621,0.0098646805,0.00019039663],"about_ca_topic_score_codex":0.0016469094,"about_ca_topic_score_gemma":0.004670395,"teacher_disagreement_score":0.0020174158,"about_ca_system_score_codex":0.00051179325,"about_ca_system_score_gemma":0.00037108694,"threshold_uncertainty_score":0.010088146},"labels":[],"label_agreement":null},{"id":"W2043610723","doi":"10.3390/s140202052","title":"Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Muscular fatigue; Muscle fatigue; Accelerometer; Physical medicine and rehabilitation; Electromyography; Simulation; Computer science; Structural engineering; Medicine; Engineering","score_opus":0.02701827007738874,"score_gpt":0.27646175074763435,"score_spread":0.2494434806702456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043610723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068228155,0.0005302883,0.9281143,0.000061266896,0.00006334453,0.00016652464,0.00023128148,0.0013512619,0.0012536146],"genre_scores_gemma":[0.51046246,0.00068301905,0.4846259,0.00012048796,0.00012466207,0.00049863255,0.00036254118,0.00006689206,0.0030553585],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996842,0.00007370083,0.000024220933,0.000089815236,0.00011436736,0.000013688991],"domain_scores_gemma":[0.9997414,0.00006059765,0.000041938874,0.000026617707,0.00010721352,0.0000222254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005353599,0.00056171295,0.00060094974,0.0007813523,0.00014595746,0.00039023205,0.00039049867,0.00082019693,0.0012322606],"category_scores_gemma":[0.00072554586,0.00021867032,0.0002680452,0.0004993718,0.00013903924,0.000493994,0.00035493274,0.00030498003,0.0007806836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005582471,0.00022884809,0.010695357,0.0005585541,0.0001221408,0.00013851842,0.00023387672,0.0070354613,0.47521207,0.0006502108,0.001508829,0.50305796],"study_design_scores_gemma":[0.00029358338,0.0036497547,0.15296614,0.00025993306,0.0004997642,0.0025126475,0.00032558572,0.61262804,0.20997268,0.002316409,0.014329066,0.00024632603],"about_ca_topic_score_codex":0.00038877854,"about_ca_topic_score_gemma":0.0009350646,"teacher_disagreement_score":0.0012322606,"about_ca_system_score_codex":0.00010262795,"about_ca_system_score_gemma":0.00021307722,"threshold_uncertainty_score":0.004122317},"labels":[],"label_agreement":null},{"id":"W2045811549","doi":"10.3390/s110101212","title":"A New Tissue Resonator Indenter Device and Reliability Study","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Reliability (semiconductor); Resonator; Viscoelasticity; Biological tissue; Reliability engineering; Biomedical engineering; Biomechanics; Mechanical engineering; Materials science; Engineering; Computer science; Electrical engineering; Composite material; Medicine; Physics; Anatomy","score_opus":0.019445089807565458,"score_gpt":0.2729001709244211,"score_spread":0.25345508111685566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045811549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2230386,0.0055654584,0.76429677,0.000678689,0.00071049115,0.0006404991,0.0005580686,0.0021871524,0.0023243425],"genre_scores_gemma":[0.5496507,0.0015862323,0.44382998,0.00033133256,0.00024449182,0.00039345553,0.00039323495,0.00015864069,0.0034119098],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99577266,0.00076204387,0.00028076195,0.0010221605,0.0020311645,0.00013122403],"domain_scores_gemma":[0.99289286,0.0031498803,0.00070761685,0.0014150345,0.0015811947,0.00025348988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039535086,0.0008860662,0.0011206772,0.0013213251,0.0003459827,0.000572366,0.0015457291,0.001304982,0.00153815],"category_scores_gemma":[0.006377401,0.00043041943,0.00056016055,0.00068590534,0.0006957087,0.0011361998,0.0008077918,0.0008153174,0.00055571453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055509305,0.00011169539,0.0051636347,0.00050103065,0.00007505013,0.0004948559,0.00034297578,0.0017830271,0.92268896,0.0011736434,0.00088280684,0.066227265],"study_design_scores_gemma":[0.00012060321,0.0062544765,0.02000988,0.000069177186,0.00025199252,0.0055939066,0.00015778391,0.04114356,0.90516925,0.000585603,0.020380726,0.00026304755],"about_ca_topic_score_codex":0.00032024606,"about_ca_topic_score_gemma":0.00033260588,"teacher_disagreement_score":0.0039535086,"about_ca_system_score_codex":0.00043016163,"about_ca_system_score_gemma":0.0003904821,"threshold_uncertainty_score":0.020908356},"labels":[],"label_agreement":null},{"id":"W2046916345","doi":"10.3390/s141120779","title":"Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Gastrointestinal Bleeding Diagnosis and Treatment","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada; Canada Foundation for Innovation","keywords":"Lossless compression; Image compression; Computer science; Lossy compression; Data compression; Computer vision; Artificial intelligence; Field-programmable gate array; Compression ratio; Capsule endoscopy; Compression (physics); Computer hardware; Algorithm; Biomedical engineering; Image processing; Image (mathematics); Materials science; Engineering; Medicine; Radiology","score_opus":0.05021997783637609,"score_gpt":0.3070218431156082,"score_spread":0.2568018652792321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046916345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24183702,0.0023284273,0.74891317,0.00057062745,0.00021083579,0.0010960107,0.00019697388,0.0017189423,0.003127957],"genre_scores_gemma":[0.6103883,0.0014680631,0.38127297,0.00040707045,0.00009972759,0.0005415308,0.00035618516,0.00012938022,0.005336759],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997009,0.000050677634,0.000016647098,0.00005786188,0.000150337,0.000023596727],"domain_scores_gemma":[0.99956805,0.00010308049,0.00006966246,0.000049203005,0.00017844205,0.000031432646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062306994,0.00044285884,0.0003451038,0.0004937443,0.00020233383,0.0004956262,0.00069406437,0.00064215617,0.0014634953],"category_scores_gemma":[0.0010251475,0.00018203288,0.00018324092,0.00024097142,0.0002637513,0.0007205595,0.00024018282,0.00029674,0.00044922295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011591939,0.00031760748,0.0027109561,0.0005253852,0.00007537413,0.00041340178,0.00013292715,0.0044742385,0.78777176,0.0013234068,0.0018519098,0.19924377],"study_design_scores_gemma":[0.00025251877,0.004761466,0.0102570215,0.000055413064,0.00020992018,0.0030813469,0.0000676432,0.09809449,0.8668406,0.0002619547,0.01602247,0.000095162315],"about_ca_topic_score_codex":0.00039958244,"about_ca_topic_score_gemma":0.00030607378,"teacher_disagreement_score":0.0014634953,"about_ca_system_score_codex":0.0003689268,"about_ca_system_score_gemma":0.00037618648,"threshold_uncertainty_score":0.0048959255},"labels":[],"label_agreement":null},{"id":"W2047281995","doi":"10.3390/s140101511","title":"A Strapdown Interial Navigation System/Beidou/Doppler Velocity Log Integrated Navigation Algorithm Based on a Cubature Kalman Filter","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Inertial navigation system; Navigation system; Kalman filter; Computer science; Algorithm; Sampling (signal processing); Nonlinear system; Extended Kalman filter; Filter (signal processing); Real-time computing; Computer vision; Artificial intelligence; Mathematics; Orientation (vector space)","score_opus":0.009812757209943538,"score_gpt":0.22793338613526856,"score_spread":0.21812062892532502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047281995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044999477,0.00022691717,0.99369425,0.000055812303,0.000061342624,0.000034094515,0.000026733156,0.0004716631,0.00092920184],"genre_scores_gemma":[0.2891815,0.00075531006,0.70266116,0.00015249063,0.0000822761,0.00034738908,0.00037450567,0.00009256868,0.0063527524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962115,0.000048869515,0.000026988491,0.000118341355,0.00015344992,0.00003118086],"domain_scores_gemma":[0.9997147,0.00005296542,0.00004409746,0.0000298167,0.00014237469,0.000016032694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005323582,0.0006143844,0.0009954856,0.0006538701,0.00053586636,0.0008864247,0.00088919164,0.0006527064,0.0012473939],"category_scores_gemma":[0.0009084659,0.0004232823,0.00052759884,0.0007834156,0.0003795575,0.0011110769,0.0007169935,0.0010682844,0.0004858498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021745835,0.00010556292,0.0030639954,0.0002480406,0.00013977535,0.00009591056,0.00018590355,0.24712148,0.022591066,0.015288099,0.004365906,0.70657676],"study_design_scores_gemma":[0.000034975877,0.000074915515,0.0007097284,0.000015669728,0.0000310871,0.000099377925,0.0000151795975,0.98945487,0.0037740928,0.0012439518,0.004518941,0.000027268843],"about_ca_topic_score_codex":0.016707398,"about_ca_topic_score_gemma":0.011379844,"teacher_disagreement_score":0.016707398,"about_ca_system_score_codex":0.0008387847,"about_ca_system_score_gemma":0.0021194848,"threshold_uncertainty_score":0.03322029},"labels":[],"label_agreement":null},{"id":"W2048435655","doi":"10.3390/s150304734","title":"Development of an NDIR CO2 Sensor-Based System for Assessing Soil Toxicity Using Substrate-Induced Respiration","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Contamination; Environmental science; Environmental remediation; Soil contamination; Soil water; Soil respiration; Soil quality; Substrate (aquarium); Environmental chemistry; Diesel fuel; Environmental engineering; Soil science; Chemistry; Waste management; Engineering; Ecology","score_opus":0.08255857992564791,"score_gpt":0.29950433692739675,"score_spread":0.21694575700174884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048435655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52029777,0.0015446948,0.4693469,0.00033391776,0.00029100617,0.0007946851,0.00080875,0.0020246273,0.0045576952],"genre_scores_gemma":[0.56073713,0.0009077476,0.43069133,0.00027906115,0.00003669874,0.00049931783,0.000561401,0.0000623338,0.006225031],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956304,0.000042320116,0.000026314423,0.00014472491,0.00019784401,0.000025793055],"domain_scores_gemma":[0.99977595,0.000040876206,0.000035468507,0.000025331889,0.00009485187,0.00002749475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048389335,0.00056605804,0.00045760899,0.00043895264,0.00020954917,0.00039135787,0.0010011037,0.00067802443,0.00072369934],"category_scores_gemma":[0.00044895065,0.00026373824,0.00023303785,0.0003516249,0.00023658585,0.0005831303,0.00042562647,0.00042678008,0.00033474484],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043098775,0.000029967636,0.00041606836,0.00005388241,0.000005727851,0.000023454755,0.000013507729,0.0001649689,0.99240476,0.00008611625,0.0000624488,0.006696081],"study_design_scores_gemma":[0.000008917402,0.00017288211,0.0019210608,0.0000041261164,0.000014249057,0.00012704298,0.000016569647,0.009137688,0.98652506,0.00004165354,0.002012666,0.000018156557],"about_ca_topic_score_codex":0.0012719678,"about_ca_topic_score_gemma":0.003046876,"teacher_disagreement_score":0.0012719678,"about_ca_system_score_codex":0.00048643735,"about_ca_system_score_gemma":0.00054295757,"threshold_uncertainty_score":0.0035293102},"labels":[],"label_agreement":null},{"id":"W2048842194","doi":"10.3390/s130708523","title":"Major Odorants Released as Urinary Volatiles by Urinary Incontinent Patients","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Nasal Surgery and Airway Studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"National Research Foundation of Korea; National Research Foundation; Ministry of Education, Science and Technology; Kimberly-Clark","keywords":"Odor; Methanethiol; Trimethylamine; Urine; Chemistry; Chromatography; Urinary system; Hydrogen sulfide; Urine sample; Hexanal; Acetaldehyde; Dimethyl sulfide; Urinary incontinence; Sulfur; Organic chemistry; Medicine; Biochemistry; Urology; Internal medicine","score_opus":0.007669343185286205,"score_gpt":0.2288854751917593,"score_spread":0.2212161320064731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048842194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992106,0.00041472263,0.000090222165,0.000010422204,0.0000053407907,0.000005685318,0.00005107796,0.000003299239,0.00020855185],"genre_scores_gemma":[0.99914193,0.00027281654,0.0002742481,0.0000422795,0.00000641143,0.0000063779376,0.00009045693,0.0000017165872,0.00016375573],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99978966,0.000042492713,0.000023911976,0.000034727513,0.00006095211,0.000048222697],"domain_scores_gemma":[0.9998273,0.000029656023,0.000060040882,0.000010135474,0.000029102623,0.00004374153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018797693,0.00025798747,0.0004068666,0.00044448607,0.00032476668,0.0005346865,0.000113863454,0.00025560378,0.0007012536],"category_scores_gemma":[0.0006393428,0.00011433609,0.00025703412,0.00036630014,0.00018342586,0.00018992828,0.00029008175,0.00022202058,0.00009154994],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017503053,0.0002308009,0.8963068,0.00017919081,0.000107146465,0.001941609,0.001102973,0.00009672033,0.07164665,0.0000655433,0.00020177806,0.026370488],"study_design_scores_gemma":[0.000010322207,0.0012913003,0.98043734,0.000028460765,0.00007488615,0.003450951,0.0015352605,0.00021267406,0.012019523,0.00004112986,0.00088169443,0.00001638878],"about_ca_topic_score_codex":0.00040364466,"about_ca_topic_score_gemma":0.0005920265,"teacher_disagreement_score":0.0007012536,"about_ca_system_score_codex":0.000099611505,"about_ca_system_score_gemma":0.00012915641,"threshold_uncertainty_score":0.0023459196},"labels":[],"label_agreement":null},{"id":"W2050507001","doi":"10.3390/s140609669","title":"Precise Calibration of a GNSS Antenna Array for Adaptive Beamforming Applications","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Calibration; Beamforming; Computer science; Antenna (radio); Antenna array; Electronic engineering; Interference (communication); Global Positioning System; GPS signals; Noise (video); Engineering; Telecommunications; Assisted GPS; Channel (broadcasting); Artificial intelligence; Physics","score_opus":0.01696874679609427,"score_gpt":0.261891165710614,"score_spread":0.2449224189145197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050507001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072115967,0.00012113723,0.9911424,0.000065126966,0.000031379896,0.000022229875,0.00003545771,0.00040017493,0.0009704251],"genre_scores_gemma":[0.28702852,0.0004455755,0.70981514,0.00018095502,0.00007100451,0.0001440318,0.00033063197,0.0001813496,0.0018027652],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990115,0.00025391494,0.00004826482,0.00016993332,0.0004724399,0.000043921693],"domain_scores_gemma":[0.99916434,0.00019058482,0.00013220223,0.00019966452,0.00029365218,0.000019550645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064495736,0.0010015301,0.0005796797,0.00063132285,0.00034029435,0.00059778156,0.0006404857,0.00084973173,0.0013326044],"category_scores_gemma":[0.0023870594,0.0003652871,0.00039295678,0.00081953726,0.0003838055,0.0009412698,0.00069961784,0.0009998484,0.0014340278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017384587,0.00005605646,0.0036020777,0.00023718229,0.00008617612,0.00009187259,0.00024085719,0.14104155,0.3689482,0.007961025,0.0022378608,0.47532335],"study_design_scores_gemma":[0.0000598583,0.00033753098,0.008766434,0.00009911522,0.00007583956,0.0005620221,0.00015751141,0.6762412,0.28265128,0.0070612016,0.023845933,0.00014206431],"about_ca_topic_score_codex":0.0011441747,"about_ca_topic_score_gemma":0.0019723368,"teacher_disagreement_score":0.0013326044,"about_ca_system_score_codex":0.00041897438,"about_ca_system_score_gemma":0.00065651827,"threshold_uncertainty_score":0.00445801},"labels":[],"label_agreement":null},{"id":"W2050712071","doi":"10.3390/s150407172","title":"Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National University of Sciences and Technology; Umm Al-Qura University","keywords":"Cyber-physical system; Cloud computing; Computer science; Wireless sensor network; Provisioning; Condition monitoring; Network architecture; Systems engineering; Engineering; Computer network","score_opus":0.07446574327178995,"score_gpt":0.32361078015616696,"score_spread":0.249145036884377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050712071","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012643061,0.9907569,0.0022412618,0.0007761106,0.00027942233,0.000014210536,0.000012057128,0.000012250306,0.004643555],"genre_scores_gemma":[0.010166871,0.9855613,0.0014296741,0.0003897427,0.00029437203,0.000016935868,0.000021146147,0.000004396608,0.0021155516],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974257,0.00006086931,0.000028049311,0.00003641928,0.00011026918,0.000021890664],"domain_scores_gemma":[0.9995289,0.00027929153,0.000050597642,0.000013822814,0.000109957175,0.00001741759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045687388,0.00059508707,0.00057062914,0.0013136838,0.00034393126,0.0008264289,0.0005745775,0.0013929133,0.0014446995],"category_scores_gemma":[0.0008213819,0.00025343482,0.00040452118,0.0015622206,0.00042571226,0.0014585041,0.00047724272,0.0010117143,0.0006656034],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040648534,0.000076092394,0.00042922542,0.014455612,0.000059230646,0.0010946239,0.0005193093,0.0033660952,0.0037043998,0.03977288,0.025884382,0.9105975],"study_design_scores_gemma":[0.0000049712794,0.00014318815,0.0011290215,0.0038531222,0.00007563331,0.003369898,0.0004465654,0.0014331985,0.0018482528,0.00837569,0.9792796,0.00004086513],"about_ca_topic_score_codex":0.0010981587,"about_ca_topic_score_gemma":0.0018020476,"teacher_disagreement_score":0.0014446995,"about_ca_system_score_codex":0.0004565613,"about_ca_system_score_gemma":0.00066970015,"threshold_uncertainty_score":0.004832983},"labels":[],"label_agreement":null},{"id":"W2051663131","doi":"10.3390/s150101047","title":"Accelerated Detection of Viral Particles by Combining AC Electric Field Effects and Micro-Raman Spectroscopy","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Raman spectroscopy; Electric field; Surface-enhanced Raman spectroscopy; Materials science; Microelectrode; Polystyrene; Analytical Chemistry (journal); Spectroscopy; Nanotechnology; Chemistry; Electrode; Raman scattering; Optics; Chromatography; Physics","score_opus":0.010140996740909718,"score_gpt":0.21200413891882058,"score_spread":0.20186314217791088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051663131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70663446,0.0033825722,0.2851346,0.00021825044,0.00018269416,0.00036274423,0.00014788353,0.0010933039,0.0028435364],"genre_scores_gemma":[0.77062297,0.0019037271,0.2242354,0.00010013971,0.0000739149,0.00018313648,0.000119988894,0.00006935595,0.0026912813],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994116,0.00008430025,0.000027215554,0.0001309506,0.00027893615,0.00006715236],"domain_scores_gemma":[0.99966717,0.00015010429,0.00004581499,0.00003739403,0.000070680166,0.000028874932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005567855,0.00068669545,0.00046948437,0.00051882095,0.00019846443,0.0005732114,0.0006821105,0.0005689405,0.0006341844],"category_scores_gemma":[0.0005722157,0.00034438452,0.00034444462,0.00028900514,0.00044462676,0.0005135118,0.0006268528,0.0007285175,0.0003522149],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019616777,0.000013266441,0.000066187946,0.00003115826,0.000003996633,0.000015118967,0.000008972979,0.00007263204,0.9962053,0.00010849589,0.000012519585,0.0034428486],"study_design_scores_gemma":[0.000005196633,0.00009352767,0.00046425586,0.0000020074012,0.000006852167,0.00011253308,0.000008585606,0.0023224195,0.9963636,0.000059317113,0.0005535869,0.000008066507],"about_ca_topic_score_codex":0.00043939013,"about_ca_topic_score_gemma":0.0007296859,"teacher_disagreement_score":0.00068669545,"about_ca_system_score_codex":0.00025854225,"about_ca_system_score_gemma":0.00031079384,"threshold_uncertainty_score":0.0029445887},"labels":[],"label_agreement":null},{"id":"W2052362249","doi":"10.3390/s120912455","title":"Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"Else Kröner-Fresenius-Stiftung; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Accelerometer; Gyroscope; Heading (navigation); Magnetometer; Calibration; Inertial navigation system; Inertial measurement unit; Global Positioning System; Particle swarm optimization; Computer science; Orientation (vector space); Real-time computing; Step detection; Magnetic field; Engineering; Computer vision; Algorithm; Physics; Aerospace engineering; Mathematics; Telecommunications","score_opus":0.012484124373235013,"score_gpt":0.22348250345857304,"score_spread":0.21099837908533803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052362249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019513689,0.0001931971,0.9779529,0.00008915135,0.000038611648,0.00003486552,0.000022825801,0.00042791056,0.0017269375],"genre_scores_gemma":[0.7088315,0.0002596806,0.28775498,0.00006699863,0.000038962884,0.0001715111,0.00012714362,0.00007231475,0.0026769098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979275,0.00007012268,0.0000140881175,0.000048549366,0.000062107494,0.00001247455],"domain_scores_gemma":[0.99963474,0.0001478256,0.00007897555,0.00003799924,0.00008924949,0.000011128743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004091747,0.0005681496,0.0005229678,0.00028047292,0.00022081974,0.00039031406,0.0004009622,0.0004540213,0.00075423456],"category_scores_gemma":[0.0017120106,0.00026746982,0.00033296755,0.00030552072,0.00021546589,0.00038334256,0.00037538813,0.0004184986,0.0002743142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075193566,0.00003278895,0.0013837015,0.000087828485,0.00004280708,0.000057760662,0.00006591727,0.86448956,0.007415979,0.0021113388,0.0012902622,0.12294693],"study_design_scores_gemma":[0.0000074941186,0.000019073514,0.00041998402,0.0000048273528,0.0000038759954,0.000011540022,0.0000054434736,0.99776196,0.00089223,0.00038340184,0.00048600044,0.0000040812056],"about_ca_topic_score_codex":0.0034161329,"about_ca_topic_score_gemma":0.0018204818,"teacher_disagreement_score":0.0034161329,"about_ca_system_score_codex":0.00036323047,"about_ca_system_score_gemma":0.00043260283,"threshold_uncertainty_score":0.0067924857},"labels":[],"label_agreement":null},{"id":"W2052510160","doi":"10.3390/s100301716","title":"Chemical Sensing Using Fiber Cavity Ring-Down Spectroscopy","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Ontario Centres of Excellence; Eli Lilly and Company; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Fiber; Cavity ring-down spectroscopy; Analyte; Refractive index; Optical fiber; Attenuation; Spectroscopy; Chemical sensor; Materials science; Absorption (acoustics); Fiber optic sensor; Absorption spectroscopy; Ring (chemistry); Optics; Analytical Chemistry (journal); Optoelectronics; Chemistry; Composite material; Chromatography","score_opus":0.026836522503181613,"score_gpt":0.29821744021733904,"score_spread":0.2713809177141574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052510160","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047241324,0.9729499,0.010350287,0.000302874,0.00031355934,0.000053266995,0.00005270017,0.00007569886,0.011177516],"genre_scores_gemma":[0.027522724,0.95107317,0.009946663,0.0003330622,0.00023989181,0.00008491786,0.000094264265,0.000008311286,0.010697046],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997087,0.000032185526,0.000013583274,0.00007840025,0.00014226919,0.000024844969],"domain_scores_gemma":[0.99989235,0.000027667724,0.000017684153,0.0000075579073,0.000048126756,0.000006569931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043618467,0.0007469797,0.00075239077,0.0012288527,0.00019362239,0.00054186,0.00072885706,0.0007475723,0.0012703678],"category_scores_gemma":[0.0002839023,0.00034916704,0.00037280878,0.0012645826,0.0003520853,0.00089707173,0.00039555613,0.0007778369,0.002005952],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059732825,0.00012511495,0.00035198685,0.0071559357,0.00006776018,0.0003039734,0.000080823826,0.0010468337,0.17746714,0.0076014115,0.007076334,0.79866296],"study_design_scores_gemma":[0.000020266702,0.00034335587,0.0018774838,0.0007801218,0.000070750495,0.0024826562,0.000075109514,0.0015111781,0.1258505,0.0035520461,0.8633684,0.00006817089],"about_ca_topic_score_codex":0.0007178451,"about_ca_topic_score_gemma":0.00089569925,"teacher_disagreement_score":0.0012703678,"about_ca_system_score_codex":0.0005183672,"about_ca_system_score_gemma":0.00036830863,"threshold_uncertainty_score":0.0042497516},"labels":[],"label_agreement":null},{"id":"W2052891292","doi":"10.3390/s150100760","title":"Creating Diversified Response Profiles from a Single Quenchometric Sensor Element by Using Phase-Resolved Luminescence","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Analyte; Luminescence; Amplifier; Detector; Materials science; Phase response; Lock-in amplifier; Response time; Phase (matter); Excitation; Sensitivity (control systems); Frequency response; Analytical Chemistry (journal); Optoelectronics; Chemistry; Optics; Electronic engineering; Physics; Computer science; Chromatography; Engineering; Electrical engineering","score_opus":0.05283619317086399,"score_gpt":0.2783558428402895,"score_spread":0.22551964966942553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052891292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5362158,0.00074205216,0.45816398,0.0003029883,0.00006838266,0.00017283572,0.0002780234,0.0019509718,0.0021049294],"genre_scores_gemma":[0.5807642,0.00067038235,0.41414076,0.00043274264,0.00004163283,0.0002766591,0.0003769711,0.00038577747,0.0029109446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999298,0.0001062661,0.000048798633,0.00019743461,0.00028670896,0.00006272421],"domain_scores_gemma":[0.99900573,0.00035459848,0.0002247831,0.00012391938,0.00020256436,0.000088451714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097613916,0.00055502105,0.0005809795,0.0005973157,0.00020300418,0.00076934724,0.001059896,0.00063645927,0.0008054529],"category_scores_gemma":[0.0013839027,0.0004995753,0.00043024632,0.000335899,0.0004914733,0.0010221662,0.00059162435,0.001223433,0.0007490188],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037241905,0.000037343445,0.000091024405,0.000029200026,0.0000045378065,0.000023585437,0.000024527078,0.0002558131,0.9928308,0.00023324635,0.00004141758,0.0063913623],"study_design_scores_gemma":[0.000007850121,0.000117064264,0.00012248753,0.0000029191126,0.0000057843095,0.00008460998,0.000007534225,0.0037571818,0.99486756,0.0001442913,0.00086795475,0.000014751409],"about_ca_topic_score_codex":0.00010931379,"about_ca_topic_score_gemma":0.00024659277,"teacher_disagreement_score":0.001059896,"about_ca_system_score_codex":0.00026535348,"about_ca_system_score_gemma":0.00031089282,"threshold_uncertainty_score":0.005162418},"labels":[],"label_agreement":null},{"id":"W2054684978","doi":"10.3390/s90200859","title":"Performance of a Diaphragmed Microlens for a Packaged Microspectrometer","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced optical system design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute","keywords":"Microlens; Fabrication; Materials science; Optics; Microelectromechanical systems; Lens (geology); Calibration; Focal length; Molding (decorative); Measure (data warehouse); Lithography; Interferometry; Optoelectronics; Computer science; Physics","score_opus":0.00619124377316498,"score_gpt":0.20066329431352142,"score_spread":0.19447205054035643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054684978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93624383,0.0012032152,0.059134837,0.00020713118,0.00016364826,0.000110428875,0.00034884302,0.0012230236,0.0013651364],"genre_scores_gemma":[0.88989276,0.00028146463,0.10688801,0.00012465024,0.000045274282,0.000060101214,0.00028930133,0.00012591503,0.0022924328],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992932,0.000055390043,0.000038838785,0.00019272901,0.00035459417,0.00006513484],"domain_scores_gemma":[0.9988519,0.00031258428,0.0002474951,0.00016770873,0.00028545252,0.00013477857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005737443,0.0005939334,0.0003516439,0.0002894248,0.00023745703,0.0004169855,0.0008837504,0.00086810713,0.0009778435],"category_scores_gemma":[0.00094368844,0.0003926056,0.00030489426,0.00014194756,0.0002767974,0.00060091313,0.000337861,0.00041089204,0.0006365512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046936188,0.000019384108,0.0003529704,0.00003990925,0.0000070954334,0.000049321774,0.000026190552,0.00016355583,0.99592376,0.00006818671,0.00006294482,0.0032397662],"study_design_scores_gemma":[0.000009336759,0.00046714974,0.00288543,0.0000044396506,0.000018511675,0.00021083828,0.000012213367,0.0041848007,0.9908008,0.000015452577,0.0013777475,0.000013234697],"about_ca_topic_score_codex":0.00050785317,"about_ca_topic_score_gemma":0.0008035701,"teacher_disagreement_score":0.0009778435,"about_ca_system_score_codex":0.0004734809,"about_ca_system_score_gemma":0.00034058857,"threshold_uncertainty_score":0.0034353733},"labels":[],"label_agreement":null},{"id":"W2055567224","doi":"10.3390/s140712990","title":"A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Search engine indexing; Data mining; Tree (set theory); Hash table; Trajectory; Database; Access method; NoSQL; Hash function; Temporal database; Data structure; R-tree; Database index; Field (mathematics); Table (database); Spatial database; Big data; Information retrieval; Spatial analysis; Mathematics","score_opus":0.06935513009240445,"score_gpt":0.32216713151688803,"score_spread":0.25281200142448357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055567224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008090142,0.0008374264,0.98489016,0.00016090518,0.000089108675,0.00016483487,0.0011339025,0.0031281223,0.0015054366],"genre_scores_gemma":[0.13104816,0.0009406614,0.85944855,0.00013510039,0.00012050735,0.0003026026,0.0047922265,0.00028155593,0.0029306153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983505,0.00017551899,0.0002471478,0.00035726122,0.0007706348,0.000098960474],"domain_scores_gemma":[0.9982887,0.00026335812,0.0001422755,0.00059102575,0.0006002008,0.00011440676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010028105,0.0006083095,0.0010164285,0.0034725564,0.0011440581,0.001986074,0.0021895014,0.00067962555,0.0026184355],"category_scores_gemma":[0.0029343057,0.0003935019,0.0009722724,0.007849837,0.00041606554,0.0048457063,0.0019490496,0.0007173188,0.001459929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005438757,0.00029067558,0.004222182,0.0005057272,0.00017513194,0.0002884713,0.00048691765,0.01975402,0.033681776,0.0402722,0.020488549,0.87929046],"study_design_scores_gemma":[0.00019440668,0.00041911306,0.0029910742,0.000099705794,0.000169326,0.0021134506,0.00065063545,0.8242602,0.046885714,0.036131408,0.08587642,0.00020855141],"about_ca_topic_score_codex":0.0064986977,"about_ca_topic_score_gemma":0.0047499156,"teacher_disagreement_score":0.0064986977,"about_ca_system_score_codex":0.00090189907,"about_ca_system_score_gemma":0.0017717219,"threshold_uncertainty_score":0.012921751},"labels":[],"label_agreement":null},{"id":"W2057009690","doi":"10.3390/s141222785","title":"GaAs Coupled Micro Resonators with Enhanced Sensitive Mass Detection","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Resonator; Sensitivity (control systems); Piezoelectricity; Finite element method; Optoelectronics; Materials science; Amplitude; Added mass; Acoustics; Electronic engineering; Nanotechnology; Vibration; Physics; Optics; Engineering; Structural engineering","score_opus":0.0042463746485806395,"score_gpt":0.20247321808150226,"score_spread":0.19822684343292163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057009690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83669066,0.0039791474,0.14686075,0.00063952914,0.0008011646,0.00020104936,0.00022666629,0.0010954846,0.009505498],"genre_scores_gemma":[0.8814513,0.0006438667,0.113019355,0.00011352443,0.00009317733,0.000052291627,0.000052931042,0.000047681955,0.004525932],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999332,0.00010930951,0.000025450185,0.00014785107,0.000334414,0.000050945753],"domain_scores_gemma":[0.9996692,0.00014400615,0.00005472857,0.0000414306,0.000071045244,0.000019676165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002962572,0.0005782339,0.0003752019,0.00017881386,0.00013509643,0.0004806203,0.00075587473,0.0009827741,0.00092107285],"category_scores_gemma":[0.00049617025,0.00033766992,0.00038469234,0.00018991124,0.00038438037,0.00041647165,0.00039100807,0.00042229367,0.000580843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025638607,0.000011768787,0.000073714255,0.00004398591,0.000006635637,0.0000751799,0.000027402733,0.0003988419,0.99619627,0.0005715187,0.000067357345,0.002501633],"study_design_scores_gemma":[0.000032396503,0.00030545658,0.0008175698,0.0000065441823,0.000024206493,0.00031141614,0.000021083219,0.017758295,0.9764322,0.00028218798,0.0039796615,0.00002900909],"about_ca_topic_score_codex":0.00044233695,"about_ca_topic_score_gemma":0.001035986,"teacher_disagreement_score":0.0009827741,"about_ca_system_score_codex":0.0003419091,"about_ca_system_score_gemma":0.00016022222,"threshold_uncertainty_score":0.003081262},"labels":[],"label_agreement":null},{"id":"W2057355568","doi":"10.3390/s150203070","title":"Some Insights on Grassland Health Assessment Based on Remote Sensing","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"China Scholarship Council","keywords":"Grassland; Grassland ecosystem; Environmental science; Ecosystem; Ecosystem health; Vegetation (pathology); Rangeland; Health assessment; Ecosystem services; Environmental resource management; Grazing; Wildlife; Remote sensing; Agroforestry; Ecology; Geography; Medicine; Biology","score_opus":0.025206302225802713,"score_gpt":0.27212214103859433,"score_spread":0.2469158388127916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057355568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28577062,0.14571096,0.50282645,0.006209441,0.00073405093,0.00023473047,0.0016589173,0.00038954846,0.05646516],"genre_scores_gemma":[0.8867766,0.052534636,0.0545535,0.00071043504,0.0005442644,0.00009567806,0.00083150365,0.000016358987,0.0039369934],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99976975,0.0000548094,0.000022445001,0.00005963512,0.00006229832,0.000031098563],"domain_scores_gemma":[0.9997199,0.0001339757,0.000039732175,0.000012253652,0.00008312284,0.000011044291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006136192,0.00053358806,0.00031481532,0.0016184573,0.0001921335,0.0010324793,0.00040193042,0.0007155171,0.0012012372],"category_scores_gemma":[0.00096140336,0.00018516429,0.00053630804,0.0011104399,0.0003364875,0.0012087218,0.00037730954,0.00038757548,0.00016470665],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021512386,0.00020584327,0.12194001,0.0034609314,0.0004480604,0.0031745043,0.0023803834,0.06646216,0.02848815,0.08439472,0.011713876,0.6771163],"study_design_scores_gemma":[0.000037773723,0.00078779657,0.29735902,0.0012101335,0.0007146791,0.004456579,0.004797856,0.48705825,0.0127836205,0.12549426,0.06490225,0.00039776287],"about_ca_topic_score_codex":0.0032563999,"about_ca_topic_score_gemma":0.0030676662,"teacher_disagreement_score":0.0032563999,"about_ca_system_score_codex":0.00038977366,"about_ca_system_score_gemma":0.00025569467,"threshold_uncertainty_score":0.006474912},"labels":[],"label_agreement":null},{"id":"W2057357864","doi":"10.3390/s140610497","title":"Simulation and Implementation of a Morphology-Tuned Gold Nano-Islands Integrated Plasmonic Sensor","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Plasmonic and Surface Plasmon Research","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Materials science; Nano-; Surface plasmon resonance; Refractive index; Plasmon; Nanotechnology; Adsorption; Morphology (biology); Colloidal gold; Finite-difference time-domain method; Absorbance; Layer (electronics); Optoelectronics; Nanoparticle; Optics; Chemistry; Composite material","score_opus":0.013340788288706997,"score_gpt":0.2727208820604174,"score_spread":0.2593800937717104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057357864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.744664,0.00020928838,0.23609893,0.0003420505,0.00009476377,0.00015612895,0.0004894706,0.0011295334,0.0168158],"genre_scores_gemma":[0.9513427,0.000086901375,0.04592587,0.000044141947,0.0000064159826,0.00012261781,0.00018214798,0.000051078987,0.002238071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992144,0.000013874874,0.000003131363,0.00001398447,0.000028810065,0.000018729928],"domain_scores_gemma":[0.99981004,0.00009016556,0.00001817815,0.000018185954,0.000046515077,0.000016891174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016718851,0.00034603238,0.0003569579,0.0001973001,0.00032510312,0.00041431334,0.0007477108,0.0010288855,0.0016587608],"category_scores_gemma":[0.000552932,0.00029250677,0.00042443973,0.00015335578,0.00032056557,0.00032285452,0.00028854125,0.00039584716,0.00014772361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041914227,0.00003647807,0.0008501914,0.00003437422,0.000015061632,0.00007655124,0.00003234349,0.9863331,0.00939231,0.0008795592,0.00009990297,0.002208179],"study_design_scores_gemma":[0.000008302063,0.000014495261,0.00011742535,0.0000013207283,0.0000028616591,0.0000061884207,0.000005705435,0.99768686,0.0019101904,0.00009465277,0.00014975108,0.0000021878873],"about_ca_topic_score_codex":0.0106206015,"about_ca_topic_score_gemma":0.005833677,"teacher_disagreement_score":0.0106206015,"about_ca_system_score_codex":0.0006011047,"about_ca_system_score_gemma":0.00093583774,"threshold_uncertainty_score":0.021117568},"labels":[],"label_agreement":null},{"id":"W2059308167","doi":"10.3390/s140609429","title":"Arterial Mechanical Motion Estimation Based on a Semi-Rigid Body Deformation Approach","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Optical flow; Classification of discontinuities; Elasticity (physics); Artificial intelligence; Computer vision; Mathematics; Physics; Image (mathematics)","score_opus":0.010770975533375072,"score_gpt":0.25677993427615964,"score_spread":0.24600895874278456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059308167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021728126,0.00020364777,0.97701126,0.00006145311,0.000021095602,0.00003789947,0.00003218125,0.0004337249,0.0004705559],"genre_scores_gemma":[0.57921326,0.00068670727,0.41632113,0.00013853505,0.00006105348,0.00016359307,0.00031052742,0.00015050691,0.0029547152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971396,0.00007414632,0.000021912512,0.00007040012,0.00009514142,0.000024450163],"domain_scores_gemma":[0.9995721,0.00017517271,0.0000834543,0.000069410744,0.0000787827,0.00002116216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046381875,0.0005504605,0.0005513287,0.00091845565,0.0002234624,0.00049916306,0.00059034966,0.00085886585,0.0011736506],"category_scores_gemma":[0.001290579,0.0003579043,0.0007498165,0.0005256267,0.00032519762,0.00046172226,0.00052134553,0.00048998836,0.0005117991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018265727,0.00012435936,0.0019180253,0.00014849356,0.00011785363,0.00014812978,0.000118161435,0.5274543,0.081983835,0.002792883,0.0006947078,0.3843166],"study_design_scores_gemma":[0.0000036867218,0.000047860318,0.0010104465,0.000008251501,0.000011586406,0.00009279468,0.0000062296463,0.99357283,0.0043552057,0.0003309099,0.00055005914,0.000010272352],"about_ca_topic_score_codex":0.0026363228,"about_ca_topic_score_gemma":0.0026092427,"teacher_disagreement_score":0.0026363228,"about_ca_system_score_codex":0.0002204741,"about_ca_system_score_gemma":0.0005904839,"threshold_uncertainty_score":0.0052419305},"labels":[],"label_agreement":null},{"id":"W2059479352","doi":"10.3390/s130303902","title":"Rank Awareness in Group-Sparse Recovery of Multi-Echo MR Images","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar National Research Fund; Fonds National de la Recherche Luxembourg","keywords":"Concatenation (mathematics); Pattern recognition (psychology); Compressed sensing; Redundancy (engineering); Artificial intelligence; Computer science; Algorithm; Iterative reconstruction; Matrix (chemical analysis); Mathematics; Combinatorics","score_opus":0.027472956770341773,"score_gpt":0.31902427745699397,"score_spread":0.2915513206866522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059479352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025022535,0.00037216803,0.9736072,0.00014950118,0.000016378743,0.00001722637,0.000028573562,0.00011637369,0.00067009864],"genre_scores_gemma":[0.45809096,0.0011619516,0.5377365,0.00011200411,0.00009369063,0.000060563718,0.00016727427,0.00008012699,0.002497009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941576,0.0002266955,0.000025899772,0.000082100385,0.000207272,0.000042359825],"domain_scores_gemma":[0.99906033,0.0005071316,0.00016680096,0.00013962481,0.00009269804,0.00003345845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094712526,0.00048486478,0.0005676359,0.00043031704,0.00022595601,0.00063609116,0.00043755627,0.0007806914,0.00075446744],"category_scores_gemma":[0.0027979144,0.0002969098,0.0003790368,0.00056406716,0.0007246603,0.0011259163,0.000851252,0.0006409452,0.0003042853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050940085,0.00010441491,0.0010049652,0.00061921787,0.00012156293,0.00047334193,0.00042983118,0.4634211,0.12957339,0.040618073,0.0019390617,0.36118567],"study_design_scores_gemma":[0.000016770693,0.000119133896,0.00047731088,0.000015081689,0.000021034137,0.0002774583,0.000047192683,0.9541936,0.02850016,0.0143361455,0.001969157,0.000026836395],"about_ca_topic_score_codex":0.0007430837,"about_ca_topic_score_gemma":0.0008660508,"teacher_disagreement_score":0.00094712526,"about_ca_system_score_codex":0.00021303477,"about_ca_system_score_gemma":0.00046964549,"threshold_uncertainty_score":0.0050089955},"labels":[],"label_agreement":null},{"id":"W2059797407","doi":"10.3390/s7030319","title":"Studying the Effect of Deposition Conditions on the Performance and Reliability of MEMS Gas Sensors","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multiphysics; Microelectromechanical systems; Microfabrication; Microheater; Reliability (semiconductor); Finite element method; Nonlinear system; Coupling (piping); Parametric statistics; Field (mathematics); Mechanical engineering; Electronic engineering; Thermal; Process (computing); Computer science; Engineering; Materials science; Nanotechnology; Structural engineering; Fabrication; Physics","score_opus":0.0069873963912997,"score_gpt":0.22937066831537437,"score_spread":0.22238327192407467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059797407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98371685,0.0015832904,0.013676726,0.00011656595,0.000039234917,0.00002509134,0.00009787663,0.000105752595,0.00063862914],"genre_scores_gemma":[0.9933194,0.00039918686,0.005839395,0.000015562626,0.000015720609,0.000014801195,0.00005936099,0.00004063249,0.00029594518],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993111,0.00012013536,0.00005411621,0.00010870438,0.00033310364,0.00007294073],"domain_scores_gemma":[0.9970962,0.0018016521,0.0004002677,0.00020309316,0.00045989009,0.000039021153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084578077,0.00050184736,0.00058889214,0.00024872308,0.0003006327,0.00040515547,0.00040410418,0.00056129496,0.0006976657],"category_scores_gemma":[0.0039623682,0.00040206112,0.00023539602,0.00017395236,0.0003380534,0.00070058124,0.00028368423,0.0003654238,0.00022724173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012078538,0.000021078093,0.0015261634,0.00014888868,0.000025735386,0.00015400861,0.00006862926,0.00925834,0.983431,0.00007571607,0.00005206252,0.005117605],"study_design_scores_gemma":[0.000004888386,0.00028464064,0.0036114461,0.0000055612277,0.000020822183,0.00011527358,0.000029364404,0.019357244,0.97616065,0.000029610634,0.00036893017,0.0000115899975],"about_ca_topic_score_codex":0.0005862843,"about_ca_topic_score_gemma":0.0009466318,"teacher_disagreement_score":0.00084578077,"about_ca_system_score_codex":0.00029263954,"about_ca_system_score_gemma":0.00017143664,"threshold_uncertainty_score":0.0044729114},"labels":[],"label_agreement":null},{"id":"W2060472004","doi":"10.3390/s140916994","title":"Evaluation of Two Approaches for Aligning Data Obtained from a Motion Capture System and an In-Shoe Pressure Measurement System","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute for Occupational Safety and Health; Canadian Dairy Commission","keywords":"Motion capture; System of measurement; Nonlinear system; Coordinate system; Transformation (genetics); Motion (physics); Center of pressure (fluid mechanics); Measure (data warehouse); Computer science; Rigid body; Deformation (meteorology); Simulation; Engineering; Artificial intelligence; Data mining; Materials science; Physics","score_opus":0.19570758287076598,"score_gpt":0.383599500468604,"score_spread":0.18789191759783805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060472004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6440931,0.0005458639,0.3493022,0.00022330634,0.00025498786,0.00058307045,0.00031413703,0.00076666527,0.0039167125],"genre_scores_gemma":[0.84481853,0.0002034189,0.15302606,0.000057167414,0.000026862856,0.00024330546,0.00037220394,0.00004784193,0.0012046201],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975069,0.0008220849,0.00022483106,0.00051371194,0.000768438,0.0001640228],"domain_scores_gemma":[0.9950113,0.0019461476,0.00040814478,0.00025007158,0.0022332566,0.0001510545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048319567,0.00093893916,0.00044041206,0.0015146162,0.00033928012,0.0011441049,0.000592403,0.0010975196,0.0014542444],"category_scores_gemma":[0.017568279,0.00044070435,0.00042231218,0.00091552874,0.0003327216,0.0012184562,0.0010947243,0.00042497757,0.0004182172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005538541,0.0007622193,0.070357144,0.0011147214,0.0005696167,0.00025415258,0.0015886308,0.04193108,0.12933826,0.0012613669,0.0011050871,0.7461791],"study_design_scores_gemma":[0.00031681577,0.0052902,0.2417596,0.0002462298,0.00048901484,0.00059215573,0.001701486,0.6576253,0.08733104,0.0006706455,0.0038005733,0.00017694333],"about_ca_topic_score_codex":0.006079401,"about_ca_topic_score_gemma":0.007757956,"teacher_disagreement_score":0.006079401,"about_ca_system_score_codex":0.0005892619,"about_ca_system_score_gemma":0.0007990611,"threshold_uncertainty_score":0.02555412},"labels":[],"label_agreement":null},{"id":"W2060537416","doi":"10.3390/s130201836","title":"Distributed Temperature and Strain Discrimination with Stimulated Brillouin Scattering and Rayleigh Backscatter in an Optical Fiber","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Distributed acoustic sensing; Reflectometry; Backscatter (email); Rayleigh scattering; Brillouin scattering; Brillouin zone; Materials science; Optics; Optical time-domain reflectometer; Optical fiber; Fiber optic sensor; Temperature measurement; Dispersion (optics); Image resolution; Strain (injury); Time domain; Polarization-maintaining optical fiber; Physics; Telecommunications","score_opus":0.006398597354748219,"score_gpt":0.20926252419517574,"score_spread":0.20286392684042753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060537416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89063925,0.00052652723,0.10743033,0.00010751419,0.000040537583,0.00004199729,0.000083095736,0.00036265666,0.0007680722],"genre_scores_gemma":[0.9250576,0.00018834043,0.073949,0.000034566212,0.000020605223,0.000028391987,0.00004429332,0.000012523541,0.00066468946],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995153,0.00005335243,0.000014645915,0.00013913705,0.00025051096,0.000027100321],"domain_scores_gemma":[0.99956614,0.00011236643,0.000115208866,0.000043056363,0.00012190887,0.000041323186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037433376,0.0005214151,0.0004441593,0.00042737776,0.00020798639,0.00041843535,0.0009192112,0.00048518192,0.00016266116],"category_scores_gemma":[0.00044214263,0.0004055668,0.00019605157,0.0002770926,0.00047360096,0.00064091105,0.00045156322,0.0003538973,0.00013272464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008468444,0.00003140163,0.00084502803,0.000026686204,0.000004905754,0.000026464873,0.00002776946,0.0005780512,0.99174845,0.00014630247,0.000022432616,0.00645797],"study_design_scores_gemma":[0.00004190994,0.0003958563,0.004104244,0.000007738436,0.000025978956,0.00034969227,0.000030206153,0.049732316,0.944512,0.0002049207,0.0005558716,0.000039233826],"about_ca_topic_score_codex":0.0009052257,"about_ca_topic_score_gemma":0.0019869541,"teacher_disagreement_score":0.0009192112,"about_ca_system_score_codex":0.00048213755,"about_ca_system_score_gemma":0.0003952557,"threshold_uncertainty_score":0.0034981966},"labels":[],"label_agreement":null},{"id":"W2062521206","doi":"10.3390/s120302519","title":"Quorum Sensing and Bacterial Social Interactions in Biofilms","year":2012,"lang":"en","type":"review","venue":"Sensors","topic":"Bacterial biofilms and quorum sensing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":681,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Quorum sensing; Biofilm; Bacteria; Virulence; Organism; Biology; Adaptation (eye); Microbiology; Autoinducer; Genetics; Neuroscience; Gene","score_opus":0.042353598137811456,"score_gpt":0.3142610052790427,"score_spread":0.27190740714123124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062521206","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011042693,0.99830973,0.00019431092,0.00021631518,0.00018533005,0.0000034322964,0.000007825377,0.0000059646095,0.00096666464],"genre_scores_gemma":[0.00095307844,0.9977315,0.00019585686,0.00010309097,0.00019156047,0.0000062592717,0.000017397002,0.0000011218467,0.0008001373],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978,0.00003509229,0.000021551472,0.00004061621,0.00010055976,0.000022135006],"domain_scores_gemma":[0.9997811,0.00007489241,0.00003651371,0.000007900034,0.000063758765,0.000035942892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046351727,0.0010790398,0.0014034556,0.0020361498,0.00050704536,0.0010536601,0.000986973,0.0018451377,0.0029020524],"category_scores_gemma":[0.00048191473,0.00031366787,0.00038041218,0.0023138786,0.00094147923,0.0018799145,0.0010308694,0.0016003287,0.002368321],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000530186,0.00006262711,0.0001985027,0.013102529,0.00004436207,0.00026675843,0.00013099649,0.00046641933,0.005259964,0.0120686935,0.025205905,0.9431402],"study_design_scores_gemma":[0.000010539731,0.00006854662,0.0009951761,0.0018416477,0.00004370164,0.0013323883,0.000082865205,0.00013072645,0.00087007286,0.006222396,0.9883753,0.000026677717],"about_ca_topic_score_codex":0.0010595991,"about_ca_topic_score_gemma":0.0012611703,"teacher_disagreement_score":0.0029020524,"about_ca_system_score_codex":0.0007716575,"about_ca_system_score_gemma":0.0009877327,"threshold_uncertainty_score":0.009708345},"labels":[],"label_agreement":null},{"id":"W2063826961","doi":"10.3390/s131012744","title":"Tip-Enhanced Raman Imaging and Nano Spectroscopy of Etched Silicon Nanowires","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Nanowire Synthesis and Applications","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanowire; Materials science; Silicon; Raman spectroscopy; Wafer; Optoelectronics; Diamond; Nanocrystalline material; Etching (microfabrication); Phonon; Nanocrystalline silicon; Spectroscopy; Optics; Nanotechnology; Crystalline silicon; Layer (electronics); Composite material","score_opus":0.004241946146561386,"score_gpt":0.200696654900794,"score_spread":0.1964547087542326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063826961","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9746878,0.00053254195,0.022264395,0.000050460294,0.00004250808,0.000026007476,0.00026150496,0.00017203677,0.0019627055],"genre_scores_gemma":[0.9375421,0.00058624544,0.059146643,0.000047754253,0.00001910375,0.00004940668,0.00032352214,0.00006308625,0.0022222367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963844,0.000030157815,0.00001431549,0.00009920312,0.00017233746,0.00004554776],"domain_scores_gemma":[0.99978584,0.00006490435,0.000038768834,0.000034301815,0.00005476305,0.000021516336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016798334,0.00035062523,0.00040067124,0.0002790885,0.00019157045,0.00024514244,0.00054865045,0.00048545812,0.0011094973],"category_scores_gemma":[0.0003166741,0.00032343125,0.00025352353,0.0002748294,0.0002441746,0.00033489263,0.0003990453,0.0004561004,0.0003980417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013800045,0.000005927915,0.00006403981,0.00001434604,0.000001875989,0.000030349955,0.000012258245,0.00012582274,0.9988336,0.000051014544,0.000014925136,0.00083201873],"study_design_scores_gemma":[0.000005888912,0.00004014597,0.0011468059,0.0000029499129,0.0000035550788,0.00011053505,0.00002709473,0.0041772467,0.9941419,0.000055189183,0.00028382757,0.0000049153095],"about_ca_topic_score_codex":0.0004183145,"about_ca_topic_score_gemma":0.00087891216,"teacher_disagreement_score":0.0011094973,"about_ca_system_score_codex":0.00021352568,"about_ca_system_score_gemma":0.000164059,"threshold_uncertainty_score":0.0037117004},"labels":[],"label_agreement":null},{"id":"W2066633707","doi":"10.3390/s140713243","title":"Vertical Soil Profiling Using a Galvanic Contact Resistivity Scanning Approach","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Depth sounding; Electrical resistivity and conductivity; Capacitive sensing; Soil science; Soil horizon; Geology; Capacitive coupling; Acoustics; Materials science; Electrical engineering; Engineering; Soil water; Physics; Voltage","score_opus":0.025831237456330536,"score_gpt":0.26402836582875255,"score_spread":0.23819712837242202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066633707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69422245,0.00029983584,0.29785743,0.00007126603,0.00002123841,0.00013839769,0.0006233687,0.0015500109,0.005215981],"genre_scores_gemma":[0.8983016,0.00017263833,0.09998748,0.000023598765,0.0000074852633,0.000049891594,0.00018330872,0.000023324787,0.0012506656],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978477,0.00003314733,0.0000073235665,0.000056737208,0.00009376016,0.000024301717],"domain_scores_gemma":[0.9997694,0.000055094155,0.000039880146,0.000047309957,0.00007465728,0.000013678621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013295443,0.00032176153,0.0001964224,0.0007703225,0.00012738998,0.00032294504,0.00038473727,0.00030911952,0.001159865],"category_scores_gemma":[0.00032481313,0.00021166769,0.00012892473,0.00069005333,0.0001446309,0.00032439316,0.0003515604,0.00016523096,0.00034355652],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012131809,0.000057762787,0.0075688963,0.00008596589,0.000012937683,0.00008985124,0.00014033153,0.005547108,0.86564666,0.0003665635,0.00028081203,0.12008181],"study_design_scores_gemma":[0.00006363455,0.00137092,0.09856656,0.00005129737,0.000070640854,0.0010587826,0.00041427737,0.23861456,0.648498,0.0012335344,0.00995027,0.00010757729],"about_ca_topic_score_codex":0.0016285162,"about_ca_topic_score_gemma":0.0034284682,"teacher_disagreement_score":0.0016285162,"about_ca_system_score_codex":0.00017316034,"about_ca_system_score_gemma":0.00022601699,"threshold_uncertainty_score":0.0038802028},"labels":[],"label_agreement":null},{"id":"W2066866441","doi":"10.3390/s91108579","title":"Optimal Sensor Location Design for Reliable Fault Detection in Presence of False Alarms","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Suncor Energy Incorporated","keywords":"False alarm; Computer science; Identifiability; Reliability (semiconductor); Fault detection and isolation; ALARM; Minification; Heuristic; Wireless sensor network; Reliability engineering; Real-time computing; Artificial intelligence; Engineering; Machine learning; Power (physics)","score_opus":0.011583229297993203,"score_gpt":0.2277436639695498,"score_spread":0.21616043467155657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066866441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013266367,0.00013386019,0.9856726,0.00007133718,0.000015472759,0.000027199134,0.000014768858,0.0001872389,0.00061112083],"genre_scores_gemma":[0.86236763,0.00018209509,0.13616377,0.00005769587,0.000026219604,0.00015524805,0.000048023798,0.000049497423,0.0009497642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998835,0.00042102838,0.000056910267,0.00031105112,0.00025864158,0.00011734486],"domain_scores_gemma":[0.9983328,0.0006922489,0.00037184817,0.00009894456,0.00045264434,0.000051597286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012134417,0.0012301017,0.001060651,0.00073114375,0.00035632073,0.00087778986,0.0010475268,0.0010488953,0.0008785961],"category_scores_gemma":[0.004458921,0.0006933391,0.00036443007,0.00043138716,0.00081511197,0.0011473087,0.00053490314,0.00053315336,0.00034109256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023928755,0.000048548285,0.0005246686,0.00015974091,0.00003875793,0.00013629421,0.00013564108,0.91591376,0.023794279,0.009365785,0.00060684344,0.049036417],"study_design_scores_gemma":[0.00003319788,0.00013257767,0.00028296214,0.000012687057,0.000021840055,0.000047140384,0.00002677242,0.9886485,0.006042431,0.0042234976,0.0005147104,0.000013662518],"about_ca_topic_score_codex":0.0015077264,"about_ca_topic_score_gemma":0.0014051012,"teacher_disagreement_score":0.0015077264,"about_ca_system_score_codex":0.00077655975,"about_ca_system_score_gemma":0.0011033014,"threshold_uncertainty_score":0.0064173937},"labels":[],"label_agreement":null},{"id":"W2067045900","doi":"10.3390/s140406797","title":"Degradation of Phosphate Ester Hydraulic Fluid in Power Station Turbines Investigated by a Three-Magnet Unilateral Magnet Array","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Magnet; Turbine; Transverse plane; Nuclear magnetic resonance; Materials science; Mechanics; Power (physics); Relaxation (psychology); Analytical Chemistry (journal); Environmental science; Chemistry; Physics; Mechanical engineering; Thermodynamics; Engineering; Chromatography; Structural engineering","score_opus":0.005828389095000847,"score_gpt":0.25214942618815805,"score_spread":0.2463210370931572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067045900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935023,0.00016297963,0.0060600066,0.000025734302,0.0000057921593,0.000005894751,0.000033151668,0.00003378764,0.00017047556],"genre_scores_gemma":[0.99229264,0.00019621551,0.006887478,0.000023685008,0.000004881147,0.000011121411,0.00004595633,0.000008100503,0.00052996],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988174,0.000022250199,0.00000549397,0.000030452553,0.000044102348,0.000015915593],"domain_scores_gemma":[0.99981564,0.000047783004,0.00005202272,0.0000103241455,0.00005573452,0.000018561517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034980522,0.00022907121,0.00024456883,0.00016170023,0.00012611762,0.00015846961,0.00024097902,0.00047139605,0.00039727028],"category_scores_gemma":[0.0003701416,0.00017102095,0.000110255874,0.00021766186,0.00026556457,0.00026871587,0.000134012,0.0002064583,0.00012210495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050857234,0.0000039027727,0.00042839485,0.000016197597,0.0000016933941,0.000017174445,0.000019502662,0.00010264209,0.9981438,0.000013093094,0.000011914612,0.0011909558],"study_design_scores_gemma":[0.000006068062,0.0002563011,0.0065517863,0.0000021968222,0.0000131554525,0.000108061424,0.00004576556,0.004631826,0.98795855,0.000019504248,0.0003980113,0.000008727566],"about_ca_topic_score_codex":0.0011162428,"about_ca_topic_score_gemma":0.0013679373,"teacher_disagreement_score":0.0011162428,"about_ca_system_score_codex":0.00016141955,"about_ca_system_score_gemma":0.00022814942,"threshold_uncertainty_score":0.0022194386},"labels":[],"label_agreement":null},{"id":"W2067735994","doi":"10.3390/s8127636","title":"Direct-Dispense Polymeric Waveguides Platform for Optical Chemical Sensors","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Microfabrication; Materials science; Oxygen sensor; Luminophore; Photodiode; Microfluidics; Optoelectronics; Nanotechnology; Waveguide; Luminescence; Fabrication; Chemistry; Oxygen","score_opus":0.023811436744777037,"score_gpt":0.2410028987473988,"score_spread":0.21719146200262177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067735994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4570513,0.0050028726,0.51018816,0.0006973474,0.00049649004,0.0007477833,0.0006274905,0.0034438516,0.021744672],"genre_scores_gemma":[0.5045786,0.0025109067,0.47650504,0.00036832038,0.000120146644,0.000445128,0.00040445177,0.00012953997,0.014937854],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974555,0.000014586556,0.0000109632765,0.000057874673,0.00014906935,0.00002196325],"domain_scores_gemma":[0.9998404,0.000046767633,0.000050965544,0.000022133598,0.000023946051,0.000015742771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002117012,0.00065151433,0.00021895989,0.00034520993,0.00024738378,0.0004311376,0.00068130746,0.00039485123,0.0017982108],"category_scores_gemma":[0.00023289035,0.00031991376,0.00026908098,0.00017308374,0.00028231277,0.00044198436,0.0004062285,0.00080128456,0.0010032414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015806861,0.000040359846,0.00006958921,0.00006420054,0.0000035218316,0.000043975797,0.000011611969,0.00041770312,0.9870919,0.0006075316,0.00017235671,0.011461423],"study_design_scores_gemma":[0.000008071643,0.00010340683,0.00013789123,0.0000027374106,0.0000033276356,0.0001179868,0.000004583985,0.0019565846,0.9930021,0.000094706105,0.0045630173,0.000005504603],"about_ca_topic_score_codex":0.0002281827,"about_ca_topic_score_gemma":0.0007168398,"teacher_disagreement_score":0.0017982108,"about_ca_system_score_codex":0.0002843528,"about_ca_system_score_gemma":0.00028898584,"threshold_uncertainty_score":0.006015599},"labels":[],"label_agreement":null},{"id":"W2069104859","doi":"10.3390/s140508794","title":"Intelligent Prediction of Fan Rotation Stall in Power Plants Based on Pressure Sensor Data Measured In-Situ","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Stall (fluid mechanics); Engineering; Mechanical fan; Automotive engineering; Simulation; Computer science; Mechanical engineering; Aerospace engineering","score_opus":0.030861951953489192,"score_gpt":0.22292366152628967,"score_spread":0.19206170957280047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069104859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8929964,0.00012116127,0.10464139,0.00011800282,0.000027874132,0.000019944982,0.00015235903,0.0012533204,0.00066957506],"genre_scores_gemma":[0.9954572,0.000033164422,0.004293678,0.000005557611,0.000003505278,0.000006019512,0.00006474066,0.000007765468,0.0001284456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999248,0.000012045937,0.0000044477447,0.000022488575,0.00002505604,0.000011220458],"domain_scores_gemma":[0.99972314,0.00014433457,0.00004730804,0.000020074436,0.000052200085,0.0000129385535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002498128,0.00047290922,0.00030213056,0.00030207957,0.00014979312,0.00020287512,0.00025703854,0.00030077144,0.0003487176],"category_scores_gemma":[0.00092638884,0.00018721724,0.0001730745,0.00018855926,0.00017923173,0.00032785736,0.00012630211,0.00030980044,0.00009264314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006426105,0.00032940286,0.0763687,0.00023675323,0.00006089497,0.00040615586,0.0002611797,0.632586,0.14983596,0.00044396037,0.0014385427,0.1373898],"study_design_scores_gemma":[0.000003386955,0.000043118453,0.006621608,0.000002179321,0.0000032517714,0.000010838338,0.000013854833,0.9857253,0.0074629583,0.00005096192,0.000058021116,0.0000045498814],"about_ca_topic_score_codex":0.004798258,"about_ca_topic_score_gemma":0.0045925947,"teacher_disagreement_score":0.004798258,"about_ca_system_score_codex":0.00019820688,"about_ca_system_score_gemma":0.00028354226,"threshold_uncertainty_score":0.009540677},"labels":[],"label_agreement":null},{"id":"W2069119884","doi":"10.3390/s140917530","title":"Combined GPS/GLONASS Precise Point Positioning with Fixed GPS Ambiguities","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Key Research and Development Program of China; Hong Kong Polytechnic University; China Postdoctoral Science Foundation; Centre National d’Etudes Spatiales; National Natural Science Foundation of China","keywords":"Global Positioning System; Precise Point Positioning; GLONASS; Float (project management); Ambiguity resolution; Computer science; Ambiguity; Geodesy; Real-time computing; Algorithm; Remote sensing; Geography; Engineering; GNSS applications; Telecommunications","score_opus":0.005873814666988294,"score_gpt":0.1813285750380435,"score_spread":0.1754547603710552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069119884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49703324,0.002586369,0.46822232,0.0005121372,0.00055400585,0.0003911402,0.007332393,0.0072157998,0.016152581],"genre_scores_gemma":[0.72243255,0.0005421975,0.2661192,0.00015558935,0.00012997398,0.00015560612,0.008051496,0.00020487793,0.0022085537],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983809,0.00030941892,0.00008091412,0.0004126864,0.00066586485,0.00015020021],"domain_scores_gemma":[0.9992613,0.000095637304,0.000074172145,0.00027443294,0.00026111412,0.000033294906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011844401,0.0012142088,0.0013018351,0.001645123,0.00039361796,0.001140983,0.001168113,0.00080450583,0.0011974997],"category_scores_gemma":[0.0024487297,0.000388861,0.0012725834,0.0030157147,0.00038958565,0.0012948477,0.0014339965,0.0009368084,0.0009002918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007463711,0.0002742663,0.045618065,0.0007910546,0.0010102547,0.0007575874,0.00034297054,0.31925392,0.03935747,0.0039878082,0.011570509,0.5762897],"study_design_scores_gemma":[0.00034008484,0.0004935009,0.08150048,0.00010624738,0.00038720007,0.0007719646,0.00029319213,0.86065483,0.024664208,0.0035742617,0.027034888,0.00017911977],"about_ca_topic_score_codex":0.012128845,"about_ca_topic_score_gemma":0.013436312,"teacher_disagreement_score":0.012128845,"about_ca_system_score_codex":0.0003784488,"about_ca_system_score_gemma":0.00091442326,"threshold_uncertainty_score":0.024116457},"labels":[],"label_agreement":null},{"id":"W2069292312","doi":"10.3390/s130303169","title":"Opening up the Window into “Chemobrain”: A Neuroimaging Review","year":2013,"lang":"en","type":"review","venue":"Sensors","topic":"Cancer-related cognitive impairment studies","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Neuroimaging; Generalizability theory; Medicine; Cognition; Clinical trial; Intensive care medicine; Psychology; Psychiatry; Developmental psychology; Pathology","score_opus":0.07137889046413241,"score_gpt":0.38620408061973277,"score_spread":0.31482519015560034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069292312","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003566182,0.99961805,0.000027355802,0.00010594178,0.00004193806,0.000002429999,0.000007737906,0.0000015199166,0.00015935065],"genre_scores_gemma":[0.00031190758,0.9994318,0.000081304366,0.00006903424,0.00004535371,0.0000038047924,0.000008596462,4.476248e-7,0.00004774517],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99971694,0.000069498565,0.00007937788,0.000042695887,0.00007538978,0.000016003552],"domain_scores_gemma":[0.9990128,0.00062068866,0.00015201168,0.000016476532,0.00016591918,0.000032187698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084201025,0.00090756133,0.0021418943,0.00528583,0.00030531108,0.0010929503,0.0009643395,0.0011738793,0.0029741726],"category_scores_gemma":[0.0021690796,0.00040327854,0.0008444802,0.0053063403,0.0005320971,0.0018187887,0.00067153375,0.00096446025,0.0008353637],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008128091,0.000041927142,0.00035393963,0.11334343,0.00035001134,0.00034432902,0.00013714383,0.00020036151,0.00063599175,0.0017783901,0.023047542,0.85968566],"study_design_scores_gemma":[0.000040424304,0.00018392269,0.0046003703,0.106209114,0.0014507341,0.004712317,0.00042092934,0.00014721027,0.00057883974,0.00346451,0.8781314,0.00006039205],"about_ca_topic_score_codex":0.002895591,"about_ca_topic_score_gemma":0.0054063257,"teacher_disagreement_score":0.00528583,"about_ca_system_score_codex":0.0006524452,"about_ca_system_score_gemma":0.0019459012,"threshold_uncertainty_score":0.009949625},"labels":[],"label_agreement":null},{"id":"W2069337138","doi":"10.3390/s150204253","title":"Differential Wide Temperature Range CMOS Interface Circuit for Capacitive MEMS Pressure Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CMOS; Capacitive sensing; Linearity; Materials science; Capacitance; Microelectromechanical systems; Electrical engineering; Interface (matter); Pressure sensor; Optoelectronics; Electronic engineering; Biasing; Voltage; Engineering; Chemistry; Mechanical engineering","score_opus":0.02118914740771866,"score_gpt":0.23688788736746394,"score_spread":0.21569873995974528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069337138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16088298,0.0044236477,0.80745614,0.0009255176,0.00087596255,0.00039365757,0.00095697795,0.0077888723,0.0162962],"genre_scores_gemma":[0.7530988,0.0010187422,0.23344825,0.0013367549,0.0002559572,0.00027793244,0.00045685214,0.00023869374,0.009867935],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955636,0.0000299539,0.000020922564,0.00011992432,0.00022739738,0.00004538342],"domain_scores_gemma":[0.9997423,0.000058784586,0.000040183677,0.000030191282,0.000108493245,0.000020079398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019818646,0.00048426478,0.00029290194,0.0003121816,0.00031160648,0.000437206,0.0014926426,0.00045891115,0.0044808313],"category_scores_gemma":[0.0006293347,0.00022852694,0.00020992752,0.00034081918,0.00022358898,0.0007351047,0.0004262359,0.00051986275,0.0010550794],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001567804,0.000066995046,0.00068871264,0.0003354519,0.000020435376,0.0003037485,0.000113864415,0.001416459,0.91418,0.0031329955,0.0035398817,0.07604459],"study_design_scores_gemma":[0.00006269478,0.0006691315,0.0021203153,0.000042470467,0.000065155575,0.0022252733,0.000039908464,0.033281423,0.9194039,0.000803993,0.041224282,0.00006145577],"about_ca_topic_score_codex":0.00039917673,"about_ca_topic_score_gemma":0.00077111024,"teacher_disagreement_score":0.0044808313,"about_ca_system_score_codex":0.0004880041,"about_ca_system_score_gemma":0.00028007422,"threshold_uncertainty_score":0.0149899125},"labels":[],"label_agreement":null},{"id":"W2069601597","doi":"10.3390/s90402389","title":"Thermal Actuation Based 3-DoF Non-Resonant Microgyroscope Using MetalMUMPs","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Higher Education Commision, Pakistan; Pakistan Institute of Engineering and Applied Sciences","keywords":"Gyroscope; Actuator; Voltage; Sensitivity (control systems); Displacement (psychology); Oscillation (cell signaling); Thermal; Bandwidth (computing); Parametric statistics; Control theory (sociology); Proof mass; Amplifier; Acoustics; Physics; Microelectromechanical systems; Materials science; Engineering; Electronic engineering; Electrical engineering; Optoelectronics; CMOS; Aerospace engineering; Computer science","score_opus":0.013460608702975026,"score_gpt":0.2413326373782157,"score_spread":0.22787202867524067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069601597","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81149745,0.0010291344,0.18007705,0.00012778715,0.000080499936,0.00005719684,0.00023298895,0.0018869173,0.0050110393],"genre_scores_gemma":[0.93036497,0.00017792471,0.067480184,0.000016408168,0.000009665147,0.000029827983,0.00006186181,0.000019238973,0.0018398453],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991214,0.000007093721,0.0000032124528,0.000022155018,0.000044764824,0.000010555037],"domain_scores_gemma":[0.9999434,0.0000150883925,0.000016194415,0.000011935605,0.000008327343,0.0000050490353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007873172,0.0002482294,0.00021533671,0.00011442204,0.00011792032,0.0001333423,0.00042634562,0.00022315643,0.00072290807],"category_scores_gemma":[0.00015076107,0.00016475237,0.00019441955,0.00007754365,0.00017457962,0.00024096819,0.00019460058,0.00012248919,0.00013073551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008236231,0.000012098412,0.00086166465,0.0001794789,0.000010178726,0.00021538042,0.00011292216,0.010368983,0.9691779,0.0016163965,0.00039867047,0.016963948],"study_design_scores_gemma":[0.000026138812,0.0006636756,0.0072768508,0.000028249326,0.000028005974,0.0009160025,0.0000891495,0.2865527,0.68765813,0.0005614795,0.016131302,0.00006831322],"about_ca_topic_score_codex":0.0007262134,"about_ca_topic_score_gemma":0.0013089254,"teacher_disagreement_score":0.0007262134,"about_ca_system_score_codex":0.00018664174,"about_ca_system_score_gemma":0.00015394182,"threshold_uncertainty_score":0.0024183989},"labels":[],"label_agreement":null},{"id":"W2070214458","doi":"10.3390/s140508984","title":"Poly (N-isopropylacrylamide) Microgel-Based Optical Devices for Sensing and Biosensing","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada; University of Alberta","keywords":"Poly(N-isopropylacrylamide); Biosensor; Nanotechnology; Materials science; Optical sensing; Polymer; Optoelectronics; Copolymer; Composite material","score_opus":0.02637754171176809,"score_gpt":0.2817178419738337,"score_spread":0.2553403002620656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070214458","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008382352,0.995413,0.00082028494,0.00015099432,0.00023764576,0.000015688898,0.000021016202,0.000019423318,0.0024837665],"genre_scores_gemma":[0.0026463051,0.9938653,0.0010512194,0.00012343857,0.000100250014,0.00001715216,0.00003336787,0.0000018223561,0.0021611503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998876,0.000009825317,0.000012729635,0.000022681517,0.000052724896,0.000014443783],"domain_scores_gemma":[0.9998926,0.000034137793,0.00002137381,0.000004229345,0.000036068664,0.000011607325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002866239,0.00076681946,0.00068685773,0.001879319,0.00022521826,0.0004766374,0.0005058764,0.0006225404,0.0017903245],"category_scores_gemma":[0.0002432922,0.0002882531,0.00031557528,0.0017891625,0.00023343368,0.00080651237,0.0003970189,0.0009513095,0.0014624719],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026972859,0.00013142658,0.00017518605,0.015616293,0.000050694107,0.00034082486,0.0000620952,0.0004382689,0.034203153,0.004120589,0.014046557,0.9307879],"study_design_scores_gemma":[0.000009341698,0.00015480604,0.0009796517,0.0015057045,0.00007917479,0.0021553857,0.0000669584,0.00027936767,0.012979892,0.0011120748,0.980645,0.000032645185],"about_ca_topic_score_codex":0.00050618825,"about_ca_topic_score_gemma":0.001367641,"teacher_disagreement_score":0.001879319,"about_ca_system_score_codex":0.00032936299,"about_ca_system_score_gemma":0.0005669983,"threshold_uncertainty_score":0.005989194},"labels":[],"label_agreement":null},{"id":"W2071136932","doi":"10.3390/s8106396","title":"Implantable Biosensors for Real-time Strain and Pressure Monitoring","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BioPhage Pharma (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Magnet; Materials science; Magnetic field; Linearity; Pressure sensor; Harmonic; Acoustics; Stress (linguistics); Electrical engineering; Mechanical engineering; Physics; Engineering","score_opus":0.016673263987206977,"score_gpt":0.22627893194819046,"score_spread":0.20960566796098348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071136932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22470175,0.033928774,0.7279633,0.0012390129,0.0025124433,0.00047458368,0.00078796496,0.0022967628,0.0060953624],"genre_scores_gemma":[0.6834161,0.015651213,0.2841354,0.0011051961,0.00044857102,0.00061637093,0.00054858247,0.000107786436,0.013970615],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993136,0.000099794765,0.000042778087,0.00013824906,0.0003608078,0.000044787037],"domain_scores_gemma":[0.99956053,0.00012774332,0.00012345913,0.00003451464,0.00011125853,0.000042488642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062333187,0.00093287416,0.000661219,0.0006055866,0.00017964748,0.0006462067,0.0011287228,0.001282201,0.0010708612],"category_scores_gemma":[0.0009149244,0.00041814146,0.0002930103,0.0005823533,0.00052056194,0.00095879275,0.0004232006,0.0010073637,0.00060535496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004445518,0.000043463457,0.00014628432,0.00019720897,0.000010390095,0.00009355368,0.000027927372,0.00027697688,0.97849613,0.001092753,0.0004119712,0.019158978],"study_design_scores_gemma":[0.000028138082,0.0005739865,0.0011034912,0.000031123753,0.000038990598,0.0005899185,0.00003378503,0.008641629,0.9709664,0.00081135076,0.017133499,0.000047674705],"about_ca_topic_score_codex":0.00016798286,"about_ca_topic_score_gemma":0.00028495528,"teacher_disagreement_score":0.001282201,"about_ca_system_score_codex":0.0004834844,"about_ca_system_score_gemma":0.00029413938,"threshold_uncertainty_score":0.0035823584},"labels":[],"label_agreement":null},{"id":"W2072161944","doi":"10.3390/s130607633","title":"Development of a Control System for the Teat-End Vacuum in Individual Quarter Milking Systems","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Milking; Quarter (Canadian coin); Udder; Volumetric flow rate; Vacuum level; Level sensor; Animal science; Electrical engineering; Operations management; Engineering; Mechanical engineering; Mastitis; Physics; Biology; Mechanics","score_opus":0.03492184454535237,"score_gpt":0.22804376463290557,"score_spread":0.1931219200875532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072161944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15601574,0.0005848855,0.83134675,0.00017742414,0.00022194942,0.00075308915,0.00024980018,0.007606809,0.0030434881],"genre_scores_gemma":[0.6649746,0.00034203092,0.32624066,0.00019761328,0.00009235347,0.0006590622,0.00034423603,0.00023900038,0.006910491],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991934,0.000063980144,0.000061183,0.0002343593,0.00037126476,0.0000758367],"domain_scores_gemma":[0.999191,0.00014611098,0.0001240165,0.00010148019,0.00037506712,0.00006229836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076405023,0.00040686078,0.0005747815,0.0004975209,0.00041072012,0.00058223616,0.0014150626,0.00054271985,0.002154848],"category_scores_gemma":[0.001036889,0.00033608,0.0004071537,0.00020507435,0.00025178815,0.0005768231,0.00055101665,0.00050836604,0.00066396623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040979547,0.00021538019,0.0042708083,0.00033594508,0.000044657718,0.00018680692,0.00033946682,0.0035745006,0.83475715,0.001147885,0.0016107865,0.15310685],"study_design_scores_gemma":[0.00011010842,0.0018103231,0.013238078,0.00004832618,0.00014118206,0.0008733478,0.00008488321,0.078261524,0.8682726,0.0003336854,0.03670571,0.000120260964],"about_ca_topic_score_codex":0.0010249693,"about_ca_topic_score_gemma":0.0006436878,"teacher_disagreement_score":0.002154848,"about_ca_system_score_codex":0.0003713936,"about_ca_system_score_gemma":0.0005811,"threshold_uncertainty_score":0.007208705},"labels":[],"label_agreement":null},{"id":"W2072193727","doi":"10.3390/s110302652","title":"New Generation Sensor Web Enablement","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":435,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"European Regional Development Fund","keywords":"Sensor web; Interoperability; Web service; Wireless sensor network; Computer science; World Wide Web; Geospatial analysis; Web modeling; Data science; Key distribution in wireless sensor networks; Telecommunications; Computer network; Remote sensing; Wireless","score_opus":0.06469079370003898,"score_gpt":0.28260633949409786,"score_spread":0.21791554579405886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072193727","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005586613,0.7300476,0.13034694,0.0059365854,0.0026410327,0.00028177246,0.00032833047,0.0012095259,0.12362168],"genre_scores_gemma":[0.039012745,0.8356766,0.06433591,0.0040521896,0.0014563361,0.00048713465,0.0011486833,0.0002307905,0.05359961],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987213,0.00017698729,0.0001099113,0.00013224472,0.0007563353,0.00010316982],"domain_scores_gemma":[0.99877113,0.00045221683,0.00014625909,0.00009217585,0.00046265707,0.00007556235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017624157,0.0010784982,0.00084625196,0.0028706875,0.00033984307,0.0017294639,0.0015767367,0.0018629486,0.0037110858],"category_scores_gemma":[0.002108316,0.0005890945,0.00078100205,0.003085,0.0007395374,0.006730515,0.0019342271,0.0018757177,0.0028174194],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042060885,0.00007871576,0.000405435,0.004119546,0.000040680432,0.0004664066,0.0002033314,0.0018211618,0.005641115,0.15895745,0.03153146,0.79669255],"study_design_scores_gemma":[0.0000057459442,0.00005129203,0.00026977406,0.0007439887,0.000021224594,0.0011082445,0.000067520166,0.0010845163,0.0028463877,0.010908454,0.9828692,0.000023713495],"about_ca_topic_score_codex":0.00092914497,"about_ca_topic_score_gemma":0.0009332921,"teacher_disagreement_score":0.0037110858,"about_ca_system_score_codex":0.0010489513,"about_ca_system_score_gemma":0.0015859628,"threshold_uncertainty_score":0.012414753},"labels":[],"label_agreement":null},{"id":"W2072421451","doi":"10.3390/s140203293","title":"Vertical Dynamic Deflection Measurement in Concrete Beams with the Microsoft Kinect","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deflection (physics); Frame rate; Computer science; RGB color model; Displacement (psychology); Computer vision; Segmentation; Artificial intelligence; Vertical displacement; Acoustics; Computer graphics (images); Structural engineering; Geology; Engineering; Optics; Physics","score_opus":0.013291916840289502,"score_gpt":0.22255376206999078,"score_spread":0.2092618452297013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072421451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5478394,0.00088663463,0.43983874,0.00013821469,0.00009602407,0.00021426348,0.0021705292,0.0014719551,0.007344259],"genre_scores_gemma":[0.73710144,0.0005836868,0.25719735,0.0000681834,0.000010920587,0.00017181748,0.0006267461,0.00008723526,0.0041525937],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950755,0.00002979921,0.000017543563,0.00007319131,0.00034660133,0.000025277455],"domain_scores_gemma":[0.9997831,0.000045421297,0.00003761391,0.000018085497,0.000095062285,0.000020593588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042405145,0.0003318166,0.0003369417,0.00066661875,0.00020591296,0.00034603532,0.00040998272,0.00035342344,0.0018933645],"category_scores_gemma":[0.00047765873,0.00021866217,0.00017026956,0.00063656457,0.00021108745,0.00042268954,0.00054322835,0.0003334469,0.0004362508],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035072005,0.00008387816,0.006179794,0.0002626967,0.000027828783,0.00013038593,0.0002611707,0.008103049,0.88021857,0.0007425147,0.00094607944,0.10269338],"study_design_scores_gemma":[0.00006732537,0.0005693658,0.09656002,0.00013703092,0.000042852516,0.0008051389,0.00041641088,0.13927075,0.75382805,0.00071514514,0.0074360915,0.00015184411],"about_ca_topic_score_codex":0.002310506,"about_ca_topic_score_gemma":0.0055195587,"teacher_disagreement_score":0.002310506,"about_ca_system_score_codex":0.00025508302,"about_ca_system_score_gemma":0.0004885382,"threshold_uncertainty_score":0.006333947},"labels":[],"label_agreement":null},{"id":"W2072766448","doi":"10.3390/s91109398","title":"Field Performance of Nine Soil Water Content Sensors on a Sandy Loam Soil in New Brunswick, Maritime Region, Canada","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick; Agriculture and Agri-Food Canada","funders":"","keywords":"Loam; Environmental science; Water content; Field (mathematics); Soil water; Field capacity; Hydrology (agriculture); Soil science; Geotechnical engineering; Geology; Mathematics","score_opus":0.01053856983905012,"score_gpt":0.18699460130001225,"score_spread":0.17645603146096211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072766448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982938,0.000048465805,0.0007968186,0.000014513087,0.000003949593,0.000060016522,0.0002812509,0.00004729143,0.00045394423],"genre_scores_gemma":[0.9924178,0.00013587164,0.005621212,0.000032709453,0.0000025099653,0.000048455215,0.0006530889,0.000013025881,0.0010752223],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99948776,0.000029353929,0.000026578262,0.00015221859,0.00022902575,0.00007512362],"domain_scores_gemma":[0.9991374,0.00011473094,0.000059076905,0.000043049142,0.0005672592,0.00007838362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006701589,0.0006812097,0.00057801814,0.000720917,0.0007395543,0.00039884375,0.0011848396,0.0004554874,0.0004959924],"category_scores_gemma":[0.0007328619,0.00038188248,0.00029682915,0.0011316389,0.0005318662,0.00031773106,0.00034993232,0.00024097256,0.00017601726],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024641412,0.0007049972,0.23616873,0.0003291918,0.0002330837,0.0004193117,0.0011230607,0.01178785,0.69479424,0.00012768961,0.00067403243,0.05117363],"study_design_scores_gemma":[0.00029880265,0.002278914,0.5657156,0.000034202472,0.00027221316,0.00021973434,0.0012602378,0.04326533,0.38392743,0.00004924743,0.0026006873,0.00007757971],"about_ca_topic_score_codex":0.5554671,"about_ca_topic_score_gemma":0.81347924,"teacher_disagreement_score":0.44453287,"about_ca_system_score_codex":0.003062051,"about_ca_system_score_gemma":0.0020478931,"threshold_uncertainty_score":0.89430165},"labels":[],"label_agreement":null},{"id":"W2073034856","doi":"10.3390/s130911522","title":"Microseismic Monitoring of CO2 Injection at the Penn West Enhanced Oil Recovery Pilot Project, Canada: Implications for Detection of Wellbore Leakage","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Helmholtz-Alberta Initiative","keywords":"Geophone; Microseism; Induced seismicity; Borehole; Petroleum engineering; Wellbore; Leakage (economics); Seismology; Geology; Vertical seismic profile; Hydraulic fracturing; Environmental science; Geotechnical engineering","score_opus":0.016230638746855403,"score_gpt":0.21659021812112192,"score_spread":0.20035957937426652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073034856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998874,0.00001886774,0.00036565363,0.00003109489,9.174515e-7,0.000015197858,0.00018094169,0.000018466597,0.0004948551],"genre_scores_gemma":[0.9989698,0.00003335103,0.00055227295,0.000006540063,9.896077e-7,0.000007700376,0.0001160859,0.000002700659,0.00031069876],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998356,0.000009500876,0.0000044739095,0.000022970942,0.00007931451,0.000048117872],"domain_scores_gemma":[0.99963987,0.000040505278,0.000068217494,0.000009158668,0.00018345324,0.0000589164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002031558,0.0002336813,0.00015445039,0.0005390106,0.00046682006,0.000329385,0.00038774198,0.00027377935,0.00033169988],"category_scores_gemma":[0.0004944399,0.00012509823,0.00007116856,0.0007082296,0.00031362812,0.00020577459,0.0002809651,0.00021558283,0.00005952666],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009340619,0.00028267596,0.6476356,0.00012575458,0.00005465852,0.0011922698,0.0022227827,0.005419982,0.29541478,0.0001808355,0.00068062474,0.045855973],"study_design_scores_gemma":[0.000011564678,0.00012485399,0.9762275,0.000007696137,0.000015549589,0.00007356178,0.0010346649,0.0066142567,0.01540367,0.00002085511,0.0004523726,0.000013385447],"about_ca_topic_score_codex":0.57006687,"about_ca_topic_score_gemma":0.7820514,"teacher_disagreement_score":0.42993313,"about_ca_system_score_codex":0.001664914,"about_ca_system_score_gemma":0.0027813232,"threshold_uncertainty_score":0.86493015},"labels":[],"label_agreement":null},{"id":"W2073636983","doi":"10.3390/s150203625","title":"Chain-Based Communication in Cylindrical Underwater Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"King Saud University","keywords":"Computer science; Routing (electronic design automation); Network packet; Wireless sensor network; Computer network; Routing protocol; Transmission (telecommunications); Path (computing); Scheme (mathematics); Topology (electrical circuits); Engineering; Telecommunications; Electrical engineering; Mathematics","score_opus":0.028123449586174687,"score_gpt":0.2299615815987245,"score_spread":0.20183813201254983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073636983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039542846,0.00050649286,0.95571405,0.00020061653,0.000047544658,0.000058382626,0.00006781953,0.0002354919,0.0036267422],"genre_scores_gemma":[0.8461192,0.001388293,0.1470023,0.000098222125,0.00004250255,0.00018267894,0.0001741292,0.000038499275,0.004954213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996443,0.000112904585,0.000022404078,0.00008227209,0.00009925952,0.000038881677],"domain_scores_gemma":[0.9995066,0.00021688228,0.00010941904,0.000055215147,0.000083066756,0.000028890181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039206736,0.00041458162,0.00042509224,0.00044714278,0.0006610567,0.00049601845,0.00078759127,0.000585665,0.0014343297],"category_scores_gemma":[0.0010347797,0.00025841704,0.00031056255,0.0008107295,0.00070608826,0.0014420176,0.0011555281,0.00042858865,0.00025423686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011028988,0.000024095534,0.00065799843,0.00011890791,0.000022940101,0.00028783228,0.00015314673,0.9045169,0.01576057,0.043221384,0.0010398712,0.034086],"study_design_scores_gemma":[0.000008852301,0.00008058849,0.0001478381,0.000008055778,0.000007575748,0.00006991693,0.000039013295,0.98259556,0.0021559428,0.0123796845,0.0024933016,0.0000136644785],"about_ca_topic_score_codex":0.0026965125,"about_ca_topic_score_gemma":0.0027179322,"teacher_disagreement_score":0.0026965125,"about_ca_system_score_codex":0.000637535,"about_ca_system_score_gemma":0.0005448579,"threshold_uncertainty_score":0.0053616166},"labels":[],"label_agreement":null},{"id":"W2074072700","doi":"10.3390/s150101199","title":"Average Dielectric Property Analysis of Complex Breast Tissue with Microwave Transmission Measurements","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures; Keysight Technologies","keywords":"Multipath propagation; Microwave; Computer science; Transmission (telecommunications); Electronic engineering; Antenna (radio); Microwave imaging; Algorithm; Acoustics; Telecommunications; Physics; Engineering","score_opus":0.033442788368186954,"score_gpt":0.22971251456401293,"score_spread":0.19626972619582597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074072700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16571648,0.00017300558,0.83258265,0.000021479793,0.000013324101,0.000020974712,0.00006508705,0.0004200099,0.0009869498],"genre_scores_gemma":[0.5850605,0.0003977464,0.41297033,0.000027790697,0.000020464564,0.00006000438,0.00015458847,0.00011894869,0.0011896082],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997009,0.000055045948,0.000014937824,0.000065565095,0.00014120282,0.00002241236],"domain_scores_gemma":[0.9993285,0.00029833693,0.000118378826,0.00011857554,0.00011583857,0.000020384012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005374103,0.00045247693,0.00030425747,0.0008182439,0.00015224077,0.000533946,0.0005243754,0.0003065381,0.0010389304],"category_scores_gemma":[0.002074849,0.00028491943,0.00042562748,0.00091271935,0.0003727617,0.00086967065,0.00042884875,0.0003755127,0.0003303725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040747583,0.00011310782,0.004373884,0.00023942643,0.00009450413,0.0003727132,0.00047841438,0.12898526,0.6630473,0.0055229566,0.00043455258,0.19593042],"study_design_scores_gemma":[0.000013670593,0.00018213957,0.00655479,0.00001669558,0.000056320558,0.0010510521,0.00011973541,0.8077742,0.17907895,0.0026767524,0.0024258299,0.000049929135],"about_ca_topic_score_codex":0.0006387715,"about_ca_topic_score_gemma":0.0006944269,"teacher_disagreement_score":0.0010389304,"about_ca_system_score_codex":0.00028110578,"about_ca_system_score_gemma":0.00031016365,"threshold_uncertainty_score":0.0034756064},"labels":[],"label_agreement":null},{"id":"W2075874225","doi":"10.3390/s150306560","title":"New Calibration Method Using Low Cost MEM IMUs to Verify the Performance of UAV-Borne MMS Payloads","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Calibration; Inertial measurement unit; Computer science; Photogrammetry; Inertial navigation system; Flexibility (engineering); Orientation (vector space); Mobile mapping; Remote sensing; Simulation; Artificial intelligence; Point cloud","score_opus":0.026998185918737632,"score_gpt":0.2632046024654577,"score_spread":0.23620641654672006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075874225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31885675,0.000710515,0.6676614,0.00014234152,0.0003407634,0.00019950693,0.00036078595,0.0025666682,0.009161177],"genre_scores_gemma":[0.8227302,0.00024748736,0.17397848,0.00005683123,0.00003518543,0.00012264278,0.00021927251,0.0000815103,0.0025284633],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993531,0.00007847557,0.000031063526,0.0001201041,0.0003783046,0.00003880964],"domain_scores_gemma":[0.999488,0.0000563998,0.000085411346,0.00010564067,0.00024944113,0.000014989107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047442853,0.0007902473,0.00027687816,0.0008153446,0.00028442167,0.00036055033,0.0006078766,0.00042844404,0.0017684377],"category_scores_gemma":[0.0012830354,0.00020581146,0.00019629771,0.00061852764,0.00021468212,0.00073726766,0.0005049002,0.00033118326,0.00065161247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027168414,0.00013711624,0.018126369,0.00045966284,0.00010429134,0.0002479979,0.0005532407,0.018005341,0.5822941,0.003711425,0.0020362597,0.37405246],"study_design_scores_gemma":[0.000097394455,0.0010458196,0.04462517,0.00007274569,0.00012428188,0.0011019742,0.0004319428,0.18792425,0.73810506,0.000964504,0.02538266,0.0001242967],"about_ca_topic_score_codex":0.0009051373,"about_ca_topic_score_gemma":0.0010605908,"teacher_disagreement_score":0.0017684377,"about_ca_system_score_codex":0.0003709958,"about_ca_system_score_gemma":0.00030175285,"threshold_uncertainty_score":0.0059159994},"labels":[],"label_agreement":null},{"id":"W2079047681","doi":"10.3390/s140712399","title":"Wireless Displacement Sensing of Micromachined Spiral-Coil Actuator Using Resonant Frequency Tracking","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Universiti Teknologi Malaysia; British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems; Ministry of Education, India; Ministério da Ciência, Tecnologia e Inovação; Kementerian Sains, Teknologi dan Inovasi; Canada Research Chairs","keywords":"Electromagnetic coil; Miniaturization; Actuator; Microactuator; Acoustics; Displacement (psychology); Spiral (railway); Antenna (radio); Electrical engineering; Materials science; Capacitor; Engineering; Physics; Mechanical engineering","score_opus":0.02236218979105078,"score_gpt":0.26580016301126796,"score_spread":0.24343797322021718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079047681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8097645,0.0025196364,0.18267626,0.00019883938,0.00019560066,0.000083115694,0.00017249092,0.00072155305,0.0036680775],"genre_scores_gemma":[0.89575934,0.0006566762,0.10126502,0.00007631323,0.000033548135,0.000038188777,0.00007414032,0.000022891196,0.002073925],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998029,0.000020358184,0.00001223049,0.000054538406,0.0000945315,0.00001545931],"domain_scores_gemma":[0.9998209,0.000063828345,0.00005470092,0.000019485902,0.00003134435,0.000009829573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017489352,0.00023729022,0.00022826584,0.00021253566,0.00010897493,0.00018127078,0.00039212665,0.00027149712,0.00044406022],"category_scores_gemma":[0.00030504222,0.00015666817,0.00012416152,0.00016065837,0.00020986328,0.00031265785,0.00021105523,0.00017193281,0.00016545404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002920536,0.000007437071,0.00028950113,0.00004506183,0.0000039309307,0.000025712974,0.000024773622,0.00028139752,0.98909175,0.0001566222,0.00007113826,0.009973369],"study_design_scores_gemma":[0.000009870201,0.00011208636,0.0013221727,0.0000044474527,0.000012251444,0.00020330875,0.00001688862,0.007896157,0.98796064,0.000050403327,0.0023983377,0.000013354419],"about_ca_topic_score_codex":0.000273069,"about_ca_topic_score_gemma":0.0008635613,"teacher_disagreement_score":0.00044406022,"about_ca_system_score_codex":0.00023740834,"about_ca_system_score_gemma":0.00015040726,"threshold_uncertainty_score":0.0017225146},"labels":[],"label_agreement":null},{"id":"W2079109852","doi":"10.3390/s100301679","title":"Microfabricated Reference Electrodes and their Biosensing Applications","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":328,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Miniaturization; Repeatability; Electrode; Reliability (semiconductor); Computer science; Automation; Nanotechnology; Reference electrode; Chip; Materials science; Electrochemistry; Engineering; Chemistry; Mechanical engineering; Telecommunications","score_opus":0.03201136540700799,"score_gpt":0.2852384999277265,"score_spread":0.2532271345207185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079109852","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010566432,0.9802868,0.011623504,0.00040381541,0.0006285197,0.000048785885,0.000059666418,0.000116130155,0.005776247],"genre_scores_gemma":[0.010608698,0.9577729,0.018480446,0.0007987021,0.0005141443,0.000120776516,0.00018934713,0.000022482902,0.011492545],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909675,0.000101241916,0.00006108589,0.00019791453,0.00049062585,0.000052410334],"domain_scores_gemma":[0.9994671,0.00021467559,0.000066465036,0.000043473923,0.00018937683,0.000018932149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011364401,0.0011392989,0.0013904697,0.0020122775,0.00027674012,0.0009752041,0.0024169844,0.0023119717,0.0027824554],"category_scores_gemma":[0.001415097,0.00067043386,0.0006632595,0.0022173158,0.00072320434,0.0015350019,0.0005783083,0.0014233731,0.004334549],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061611594,0.00008603517,0.00021786807,0.00955113,0.0000504046,0.0005365624,0.00010314507,0.0009953844,0.07329647,0.012365151,0.0141896345,0.88854665],"study_design_scores_gemma":[0.000015241335,0.00016904467,0.0008257627,0.0007516181,0.000063440675,0.0033731977,0.00004665566,0.00070091663,0.06403719,0.0033574924,0.9266119,0.000047595735],"about_ca_topic_score_codex":0.00077246095,"about_ca_topic_score_gemma":0.0008943677,"teacher_disagreement_score":0.0027824554,"about_ca_system_score_codex":0.0007124161,"about_ca_system_score_gemma":0.00049557304,"threshold_uncertainty_score":0.009308279},"labels":[],"label_agreement":null},{"id":"W2080434013","doi":"10.3390/s131216641","title":"A Novel Comb Architecture for Enhancing the Sensitivity of Bulk Mode Gyroscopes","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"CMC Microsystems","keywords":"Sensitivity (control systems); Gyroscope; Materials science; Transducer; Capacitance; Vibration; Microelectromechanical systems; Optoelectronics; Electronic engineering; Electrical engineering; Electrode; Acoustics; Engineering; Physics","score_opus":0.008907234408607023,"score_gpt":0.22642836052692297,"score_spread":0.21752112611831595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080434013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78791964,0.0019995356,0.19739662,0.00031511023,0.00031135118,0.00013833772,0.0002008733,0.0012612959,0.010457204],"genre_scores_gemma":[0.8691417,0.00034663526,0.12735137,0.00010387422,0.00015289504,0.00007674742,0.00008562823,0.000048329315,0.0026928692],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997887,0.000018503084,0.000009812192,0.000052948235,0.000110965266,0.00001908541],"domain_scores_gemma":[0.99962974,0.00009922912,0.000071276496,0.000039353123,0.00013396693,0.000026468808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018322625,0.00032096438,0.00027653255,0.00052748946,0.0002487122,0.00034669737,0.0005881408,0.00041288845,0.0009779438],"category_scores_gemma":[0.00046048604,0.00019356223,0.0001210621,0.0002319826,0.00023563245,0.0007408437,0.00039435524,0.0002930078,0.00041198966],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049279064,0.000020437403,0.00030759705,0.00006267584,0.000005490244,0.00010829558,0.00003730501,0.00032204704,0.9808374,0.00084166083,0.00019714597,0.017210748],"study_design_scores_gemma":[0.00004779227,0.0007312353,0.0025043532,0.00001589874,0.000029640842,0.00095603557,0.000042978496,0.04027019,0.9423238,0.0008521819,0.012186987,0.000038833205],"about_ca_topic_score_codex":0.00016804469,"about_ca_topic_score_gemma":0.00045295054,"teacher_disagreement_score":0.0009779438,"about_ca_system_score_codex":0.0001662422,"about_ca_system_score_gemma":0.00014613362,"threshold_uncertainty_score":0.0032715201},"labels":[],"label_agreement":null},{"id":"W2080560959","doi":"10.3390/s150204212","title":"Sol-Gel Deposition of Iridium Oxide for Biomedical Micro-Devices","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"University of Texas at Arlington","keywords":"Electrode; Reference electrode; Materials science; Iridium; Electrochemistry; Fabrication; Working electrode; Oxide; Analytical Chemistry (journal); Chemistry; Chromatography; Metallurgy; Catalysis","score_opus":0.012439988200364795,"score_gpt":0.22433963639938964,"score_spread":0.21189964819902485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080560959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71879077,0.028705962,0.21841942,0.0011471764,0.0013658768,0.000698116,0.0013016169,0.0022961136,0.027275093],"genre_scores_gemma":[0.75142,0.008253291,0.22478592,0.0005023725,0.00012640108,0.00045701096,0.0008433854,0.00017413282,0.013437513],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998447,0.00001197988,0.000012724804,0.00004408902,0.00006850499,0.000017998702],"domain_scores_gemma":[0.9999031,0.000026921645,0.00002341138,0.000014308359,0.000024005863,0.0000083596215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002628461,0.00062491535,0.00022145547,0.00030509982,0.00015166149,0.00026620083,0.0005152681,0.00046064804,0.0014684609],"category_scores_gemma":[0.00033304215,0.00035338703,0.00024111253,0.00018840346,0.00015215325,0.00031619897,0.00025956726,0.0004570124,0.0006424044],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076219717,0.0000066570547,0.000018532108,0.00010818454,0.0000028108957,0.00003645068,0.000012247515,0.00006189848,0.9962447,0.00013092466,0.00009523054,0.0032746869],"study_design_scores_gemma":[0.000006641683,0.00007772275,0.00037044403,0.000012463314,0.000008798222,0.00016346238,0.000009337717,0.0010178541,0.99207014,0.00007572417,0.0061804745,0.0000070430087],"about_ca_topic_score_codex":0.0002477976,"about_ca_topic_score_gemma":0.0008105889,"teacher_disagreement_score":0.0014684609,"about_ca_system_score_codex":0.00027535943,"about_ca_system_score_gemma":0.00020333623,"threshold_uncertainty_score":0.004912436},"labels":[],"label_agreement":null},{"id":"W2080694695","doi":"10.3390/s91209945","title":"A Reduced Three Dimensional Model for SAW Sensors Using Finite Element Analysis","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Finite element method; Surface acoustic wave; Microelectromechanical systems; Frequency response; Acoustics; Plane (geometry); Computer science; Electronic engineering; Materials science; Engineering; Structural engineering; Electrical engineering; Physics; Mathematics; Optoelectronics; Geometry","score_opus":0.028045574313178185,"score_gpt":0.2576416213460862,"score_spread":0.229596047032908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080694695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005867379,0.00013942971,0.98898226,0.00010837858,0.00003248007,0.0000427195,0.00013201886,0.00030041323,0.0043949415],"genre_scores_gemma":[0.26334426,0.0010720035,0.7167188,0.00023088745,0.000044832203,0.00090228365,0.00075823226,0.00020950493,0.016719267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997782,0.000041899882,0.000011235591,0.000031261097,0.0001265111,0.00001098971],"domain_scores_gemma":[0.999821,0.00007948083,0.000017407003,0.00002847077,0.000046682955,0.0000069321804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031595095,0.00060617883,0.0005727417,0.00050388667,0.00029352168,0.00088039227,0.001069325,0.0015537738,0.0033850926],"category_scores_gemma":[0.0006244524,0.00060851366,0.0011005239,0.00033900482,0.00034981675,0.0007703842,0.0004162409,0.00084464165,0.0017259866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022036844,0.000048756683,0.00032037759,0.00010321121,0.0000206265,0.000120306584,0.000080925,0.9397418,0.025790874,0.014811151,0.0008255365,0.018114382],"study_design_scores_gemma":[0.0000024879096,0.000009798502,0.000042831864,0.0000049398122,0.0000026994749,0.000025401301,0.0000076220335,0.99564016,0.0009787491,0.0011919359,0.0020888157,0.0000045416464],"about_ca_topic_score_codex":0.0021558478,"about_ca_topic_score_gemma":0.0021663557,"teacher_disagreement_score":0.0033850926,"about_ca_system_score_codex":0.0003506685,"about_ca_system_score_gemma":0.0007119529,"threshold_uncertainty_score":0.011324286},"labels":[],"label_agreement":null},{"id":"W2082148336","doi":"10.3390/s150202763","title":"Simultaneous Characterization of Instantaneous Young’s Modulus and Specific Membrane Capacitance of Single Cells Using a Microfluidic System","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Constriction; Microfluidics; Membrane; Materials science; Capacitance; Electrical impedance; Biomedical engineering; Chemistry; Nanotechnology; Electrode; Electrical engineering; Engineering; Biology","score_opus":0.01893283909884883,"score_gpt":0.18463213334214537,"score_spread":0.16569929424329655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082148336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89219874,0.0008633703,0.10523144,0.000111574824,0.000046861438,0.000049815422,0.0004381181,0.00018246035,0.000877501],"genre_scores_gemma":[0.9378417,0.0006784061,0.060528204,0.000046369794,0.00002316482,0.00008443959,0.00015784784,0.000011246313,0.0006285796],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998524,0.000009174237,0.00001088567,0.00004922597,0.000064498396,0.00001387426],"domain_scores_gemma":[0.99981314,0.00007641875,0.00003553724,0.000017857015,0.000031285363,0.000025907797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002557276,0.00020780423,0.000376663,0.0003690923,0.0001709189,0.0002720101,0.00032165786,0.0003251344,0.0002933474],"category_scores_gemma":[0.00036956478,0.00012657774,0.00014782746,0.00028290178,0.00022872488,0.00048616316,0.000263558,0.00028115616,0.00008834478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018142799,0.0000071999334,0.00042301492,0.000019547515,0.0000024944889,0.000021075963,0.000019258267,0.0002280391,0.9957847,0.000106287276,0.000014866782,0.0033553496],"study_design_scores_gemma":[0.000006426554,0.00010809106,0.004584528,0.0000037599395,0.000009236655,0.00013311477,0.000029115405,0.0133811375,0.9804772,0.0001509224,0.0010958131,0.000020709036],"about_ca_topic_score_codex":0.00027944127,"about_ca_topic_score_gemma":0.0005363725,"teacher_disagreement_score":0.000376663,"about_ca_system_score_codex":0.00024233032,"about_ca_system_score_gemma":0.00024617274,"threshold_uncertainty_score":0.0017582774},"labels":[],"label_agreement":null},{"id":"W2082982553","doi":"10.3390/s140917174","title":"A Vibration-Based Strategy for Health Monitoring of Offshore Pipelines’ Girth-Welds","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vibration; Structural engineering; Acoustics; Transducer; Structural health monitoring; Engineering; Piezoelectricity; Hilbert–Huang transform; Pipeline transport; Actuator; Marine engineering; Mechanical engineering; Physics; Telecommunications; Electrical engineering","score_opus":0.029488901200662856,"score_gpt":0.3177193154530391,"score_spread":0.2882304142523763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082982553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44442487,0.00020615723,0.55348855,0.00010571761,0.000016159298,0.0000495001,0.000024054218,0.0003041592,0.001380732],"genre_scores_gemma":[0.9705372,0.00004233271,0.029103205,0.000009909886,0.000002398597,0.000013999203,0.0000071740674,0.0000033322224,0.00028042684],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99993145,0.000014239774,0.0000029862313,0.000016126794,0.000029351337,0.00000584203],"domain_scores_gemma":[0.9998958,0.00004675301,0.0000214828,0.000009704189,0.000019236573,0.0000070608703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019956009,0.00024488522,0.00020575419,0.00020587015,0.00011221505,0.00015043093,0.00030181013,0.00030498902,0.00053672533],"category_scores_gemma":[0.00040565227,0.00010809377,0.00013758823,0.00008310934,0.00027857916,0.0002599194,0.00023393655,0.00014253648,0.00006633569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002602343,0.0001414683,0.0051151994,0.00015674355,0.000031136748,0.00020599423,0.00020996579,0.32756576,0.52632517,0.0020661391,0.0001905906,0.13773161],"study_design_scores_gemma":[0.000012534056,0.000309356,0.0030850365,0.0000065021045,0.000013205136,0.00006457546,0.000034922836,0.9686701,0.027150063,0.0003684593,0.00027457337,0.000010580856],"about_ca_topic_score_codex":0.0006502201,"about_ca_topic_score_gemma":0.0008919396,"teacher_disagreement_score":0.0006502201,"about_ca_system_score_codex":0.00017411963,"about_ca_system_score_gemma":0.0001750944,"threshold_uncertainty_score":0.0017955899},"labels":[],"label_agreement":null},{"id":"W2083891787","doi":"10.3390/s120201898","title":"Fiber Bragg Grating Sensors for Harsh Environments","year":2012,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":810,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Fiber Bragg grating; Optical fiber; Materials science; Optics; Electromagnetic interference; Fiber optic sensor; Laser; Instrumentation (computer programming); Optoelectronics; Computer science; Electronic engineering; Physics; Engineering","score_opus":0.04196882762611736,"score_gpt":0.2822954976509426,"score_spread":0.24032667002482522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083891787","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062147423,0.99008894,0.0026664734,0.00041912863,0.0005737683,0.000020648045,0.000033678876,0.000044287277,0.0055315513],"genre_scores_gemma":[0.0046520066,0.9806855,0.0043624956,0.00039019663,0.00039398108,0.000024829966,0.000084163716,0.000008616465,0.0093983095],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996606,0.00003153326,0.000021002597,0.000056125085,0.00020765522,0.000023103803],"domain_scores_gemma":[0.99977916,0.000049026818,0.000040388713,0.00000963146,0.00010304319,0.00001869593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005643156,0.0010722261,0.0007967417,0.0019101041,0.00032826647,0.0007446613,0.00087504275,0.0012945455,0.0036053157],"category_scores_gemma":[0.0004493418,0.0003243038,0.00045435078,0.0019317755,0.00038110855,0.0014781837,0.0006088873,0.001584765,0.00397725],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041115833,0.00011200257,0.0002592647,0.006964772,0.000040736766,0.00022417108,0.00006766595,0.00054879405,0.0286456,0.0055740234,0.024232768,0.93328905],"study_design_scores_gemma":[0.0000059628505,0.000094462324,0.0006435407,0.00084709004,0.00003544128,0.0013690154,0.000048168848,0.00035706803,0.0077449423,0.0020397326,0.9867879,0.000026726117],"about_ca_topic_score_codex":0.00081811205,"about_ca_topic_score_gemma":0.0014301314,"teacher_disagreement_score":0.0036053157,"about_ca_system_score_codex":0.00043899158,"about_ca_system_score_gemma":0.00072965387,"threshold_uncertainty_score":0.01206094},"labels":[],"label_agreement":null},{"id":"W2084382953","doi":"10.3390/s91108473","title":"A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Kalman filter; Inertial measurement unit; Autoregressive model; Stochastic modelling; Inertial navigation system; Inertial frame of reference; Stochastic process; Kinematics; Control theory (sociology); Computer science; Mathematics; Engineering; Physics; Statistics; Artificial intelligence; Classical mechanics","score_opus":0.01660370839554134,"score_gpt":0.22746406595379798,"score_spread":0.21086035755825663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084382953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21792369,0.00010199334,0.7805216,0.00014594427,0.000030233998,0.000053169202,0.00011479477,0.00031455627,0.00079406024],"genre_scores_gemma":[0.9629699,0.000092039896,0.036094498,0.00003420054,0.000013130909,0.00007509765,0.00020080426,0.000036438556,0.00048393643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684936,0.0006712955,0.0001728664,0.00033522214,0.0017543015,0.00021685881],"domain_scores_gemma":[0.994118,0.003070958,0.0011714604,0.0006492318,0.00091453036,0.000075844815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032282714,0.00075670175,0.00088693446,0.00037101813,0.00029294466,0.0005901027,0.0008838936,0.0009445542,0.00053524354],"category_scores_gemma":[0.015139798,0.0004547516,0.0011266024,0.00032810628,0.0009449782,0.0013809418,0.00072245416,0.0008986583,0.00014285036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096009615,0.00007955128,0.0034329256,0.000076013406,0.000071084156,0.00010650215,0.00007866273,0.9544317,0.02427233,0.007691834,0.00014071671,0.009522691],"study_design_scores_gemma":[0.000008185012,0.00007742186,0.0015323235,0.0000039404317,0.000008315232,0.00002593698,0.0000071184677,0.99099755,0.0065097706,0.0007189017,0.00009500256,0.000015579331],"about_ca_topic_score_codex":0.006429077,"about_ca_topic_score_gemma":0.0038903004,"teacher_disagreement_score":0.006429077,"about_ca_system_score_codex":0.00056910043,"about_ca_system_score_gemma":0.0017349173,"threshold_uncertainty_score":0.017072976},"labels":[],"label_agreement":null},{"id":"W2088867600","doi":"10.3390/s140916932","title":"Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":400,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Guangdong Ocean University","keywords":"Wireless sensor network; Software deployment; Environmental monitoring; Wireless; Computer science; Systems engineering; Architecture; Engineering; Telecommunications; Computer network; Geography","score_opus":0.025075004782479714,"score_gpt":0.27323938271764603,"score_spread":0.24816437793516632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088867600","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049338676,0.9917395,0.0026881096,0.000518204,0.00037700468,0.000019839856,0.000028443248,0.00002709933,0.004108363],"genre_scores_gemma":[0.0015916352,0.99567956,0.0013448558,0.00015247826,0.00027692257,0.000010950483,0.000037073456,0.000004082242,0.0009024983],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995172,0.00007330707,0.00006797632,0.00008328022,0.00022539246,0.00003275457],"domain_scores_gemma":[0.9989943,0.00049163745,0.00009520403,0.00003157626,0.00033709206,0.000050235834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007990558,0.0009792555,0.0010218843,0.0030581858,0.00036228704,0.00087243074,0.001168807,0.0012639685,0.0031201758],"category_scores_gemma":[0.0011108033,0.00042920912,0.0005596773,0.005980588,0.00048370936,0.0023027787,0.0007573503,0.0015246208,0.0021890001],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003465989,0.000091717244,0.00057295954,0.010696212,0.000053400792,0.00020605822,0.00009023608,0.0014232275,0.0024597498,0.0058036675,0.01691538,0.9616527],"study_design_scores_gemma":[0.000008445218,0.00020723091,0.0015934508,0.0047024,0.000114866176,0.001964335,0.00017876981,0.0013346908,0.0017569648,0.0047043576,0.98338103,0.000053480275],"about_ca_topic_score_codex":0.0009863417,"about_ca_topic_score_gemma":0.0012760554,"teacher_disagreement_score":0.0031201758,"about_ca_system_score_codex":0.00043511076,"about_ca_system_score_gemma":0.0009372722,"threshold_uncertainty_score":0.010438085},"labels":[],"label_agreement":null},{"id":"W2088922279","doi":"10.3390/s140304271","title":"PypeTree: A Tool for Reconstructing Tree Perennial Tissues from Point Clouds","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Perennial plant; Tree (set theory); Point (geometry); Computer science; Computer graphics (images); Artificial intelligence; Mathematics; Biology; Geometry; Botany; Combinatorics","score_opus":0.011048563393474625,"score_gpt":0.23421883672614918,"score_spread":0.22317027333267456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088922279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035588075,0.000083733496,0.96608114,0.000049702845,0.000036156896,0.00009433472,0.0017480582,0.027768493,0.0005795408],"genre_scores_gemma":[0.05239773,0.00037255735,0.93055636,0.000074145,0.000024919638,0.0004339661,0.0068780696,0.007652561,0.0016096972],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946195,0.00006934249,0.00004611767,0.00012539538,0.00025111253,0.000046088124],"domain_scores_gemma":[0.9989874,0.00052129925,0.00011395264,0.00017781617,0.00014423963,0.000055314453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013423152,0.0014880246,0.0009524217,0.0021893482,0.0004942648,0.0021422685,0.0029184462,0.0014001029,0.009448519],"category_scores_gemma":[0.0037730413,0.0017098085,0.0020634797,0.0016462432,0.0005454271,0.0019658136,0.0022293474,0.0022029255,0.0040768757],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005407355,0.00024730456,0.0061857696,0.0024367988,0.0006590585,0.001123527,0.001370125,0.1747153,0.07567794,0.01951272,0.05731545,0.6602152],"study_design_scores_gemma":[0.00009992102,0.00008320751,0.002709512,0.00017379998,0.00006075435,0.000880042,0.00018888763,0.8981105,0.032057445,0.01305653,0.052428085,0.00015137736],"about_ca_topic_score_codex":0.0029377467,"about_ca_topic_score_gemma":0.0055912533,"teacher_disagreement_score":0.009448519,"about_ca_system_score_codex":0.00039508336,"about_ca_system_score_gemma":0.0012322929,"threshold_uncertainty_score":0.031608462},"labels":[],"label_agreement":null},{"id":"W2089331067","doi":"10.3390/s110100905","title":"Detection of Single Molecules Illuminated by a Light-Emitting Diode","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation Singapore; National Research Foundation","keywords":"Microscope; Optics; Common emitter; Optoelectronics; Materials science; Diode; Laser; Light-emitting diode; SIGNAL (programming language); Spectroscopy; Laser diode; Fluorescence; Microscopy; Computer science; Physics","score_opus":0.00942241043586649,"score_gpt":0.2279991684368189,"score_spread":0.2185767580009524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089331067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94044536,0.0013352247,0.05517492,0.00020086588,0.0000879237,0.000050673156,0.00014728718,0.00038017775,0.0021776373],"genre_scores_gemma":[0.94893485,0.0008436986,0.048091494,0.00010800732,0.000032037005,0.00006213214,0.000099446166,0.000025547612,0.0018028168],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966013,0.00003739434,0.000011729678,0.00011724897,0.00014637293,0.000027201537],"domain_scores_gemma":[0.99963224,0.00020718091,0.00004921063,0.000027403894,0.00004488385,0.000039057773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029959122,0.00024438842,0.0005049728,0.00033934807,0.00024940106,0.00041923227,0.0005266575,0.00062756473,0.0007859733],"category_scores_gemma":[0.0004873087,0.0002096507,0.0001836917,0.00024317559,0.00041427408,0.00043788462,0.00027819935,0.0004708289,0.0003380505],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021990449,0.000009690513,0.000180042,0.000028552113,0.0000038379553,0.00002916294,0.0000113752185,0.00012070699,0.9975501,0.00022075308,0.000035172252,0.0017887525],"study_design_scores_gemma":[0.0000069591656,0.000083118946,0.0007034319,0.0000037311806,0.000007048911,0.00015112241,0.000012149357,0.0035461448,0.9947507,0.00010954629,0.0006205255,0.0000055465],"about_ca_topic_score_codex":0.00025241237,"about_ca_topic_score_gemma":0.00031099777,"teacher_disagreement_score":0.0007859733,"about_ca_system_score_codex":0.00039328582,"about_ca_system_score_gemma":0.00030632428,"threshold_uncertainty_score":0.0028535128},"labels":[],"label_agreement":null},{"id":"W2090247088","doi":"10.3390/s8117050","title":"Recent Progress in Nucleic Acid Aptamer-Based Biosensors and Bioassays","year":2008,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Aptamer; Nucleic acid; Oligonucleotide; Computational biology; Biomolecule; Systematic evolution of ligands by exponential enrichment; Small molecule; DNA; RNA; Biology; Nanotechnology; Chemistry; Combinatorial chemistry; Biochemistry; Gene; Genetics; Materials science","score_opus":0.024478251001895948,"score_gpt":0.31630742965626235,"score_spread":0.2918291786543664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090247088","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040906717,0.9909932,0.0039014483,0.00032422956,0.00041214837,0.000016087726,0.000020125028,0.000061522995,0.003862213],"genre_scores_gemma":[0.0020513565,0.9886749,0.0053093764,0.00047728984,0.00036611312,0.00002957446,0.000058099242,0.000010353452,0.0030228093],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99912053,0.0001293683,0.000077024095,0.00019030929,0.00042848696,0.000054204174],"domain_scores_gemma":[0.99918574,0.0004259678,0.00007596613,0.000036552014,0.00021901153,0.000056697158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013970288,0.0015281084,0.0019388472,0.0025046568,0.0003480547,0.0013667542,0.0020272145,0.0018044808,0.0032092868],"category_scores_gemma":[0.001303261,0.0005965178,0.0005106065,0.0034808842,0.00089979876,0.0024996889,0.000879758,0.0020199798,0.0049102544],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007342831,0.00012317291,0.0002289403,0.011066508,0.00006403541,0.0003338026,0.000119891345,0.0008589324,0.020954847,0.008970837,0.01341558,0.94379014],"study_design_scores_gemma":[0.000011950304,0.000105273706,0.00036985596,0.0009971323,0.00005541676,0.0012513676,0.00005860046,0.0003791182,0.007877248,0.0027171327,0.98613733,0.000039647348],"about_ca_topic_score_codex":0.00067008095,"about_ca_topic_score_gemma":0.0006452433,"teacher_disagreement_score":0.0032092868,"about_ca_system_score_codex":0.0008666522,"about_ca_system_score_gemma":0.0008412989,"threshold_uncertainty_score":0.010736167},"labels":[],"label_agreement":null},{"id":"W2092159649","doi":"10.3390/s110100032","title":"Femtosecond Laser Filamentation for Atmospheric Sensing","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Laser-Matter Interactions and Applications","field":"Physics and Astronomy","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Program for New Century Excellent Talents in University; Fonds Québécois de la Recherche sur la Nature et les Technologies; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Filamentation; Femtosecond; Supercontinuum; Laser; Optics; Materials science; Absorption (acoustics); Emission spectrum; Optoelectronics; Wavelength; Spectral line; Physics; Astronomy","score_opus":0.0077648217653250255,"score_gpt":0.26711838274693694,"score_spread":0.25935356098161194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092159649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2026119,0.35534427,0.35670865,0.0034975533,0.0016707788,0.00021595489,0.0005398579,0.0021722678,0.07723876],"genre_scores_gemma":[0.7377979,0.07185816,0.17140526,0.00065037975,0.0004834659,0.0001452934,0.0003196813,0.00010083472,0.017239029],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998559,0.000017337281,0.000006431793,0.000027575259,0.00007931748,0.0000134987085],"domain_scores_gemma":[0.9999223,0.00003270859,0.000013386709,0.000007670884,0.000016924596,0.0000070060137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017192234,0.0002431306,0.00029686594,0.00051354297,0.00025862508,0.00037831662,0.00028892138,0.0006359957,0.0024552564],"category_scores_gemma":[0.00019819876,0.00015006488,0.00019616133,0.0007451235,0.00027134264,0.0005861659,0.00030408867,0.00061847316,0.0005680023],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012450201,0.000055315653,0.0007997679,0.0005605934,0.000022453309,0.00027445078,0.00013234049,0.0011694817,0.787644,0.012702476,0.0037175056,0.19279711],"study_design_scores_gemma":[0.00003277977,0.00037095405,0.002808637,0.00015635222,0.000049622497,0.0015986344,0.00009896924,0.021535235,0.7822546,0.011765747,0.17926757,0.000060910064],"about_ca_topic_score_codex":0.00042816842,"about_ca_topic_score_gemma":0.0005381972,"teacher_disagreement_score":0.0024552564,"about_ca_system_score_codex":0.00044146937,"about_ca_system_score_gemma":0.00017267097,"threshold_uncertainty_score":0.008213699},"labels":[],"label_agreement":null},{"id":"W2092668986","doi":"10.3390/s120403798","title":"Submersible UV-Vis Spectroscopy for Quantifying Streamwater Organic Carbon Dynamics: Implementation and Challenges before and after Forest Harvest in a Headwater Stream","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Dissolved organic carbon; Biogeochemical cycle; Environmental science; Aquatic ecosystem; Carbon cycle; Ecosystem; Total organic carbon; Hydrology (agriculture); Ecology; Environmental chemistry; Chemistry; Geology","score_opus":0.028774147997254976,"score_gpt":0.28189040819722805,"score_spread":0.2531162601999731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092668986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987661,0.00029360843,0.009004967,0.00033314314,0.000019429563,0.00019120848,0.00024288359,0.00018990676,0.0020638579],"genre_scores_gemma":[0.95038915,0.0005168769,0.047314648,0.0001410478,0.000018785076,0.0001510772,0.00030583172,0.00003157763,0.0011310105],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994286,0.00008517608,0.00002179602,0.00013109528,0.0002600692,0.00007320779],"domain_scores_gemma":[0.9993843,0.00011230464,0.000087193934,0.000045540826,0.00029493615,0.00007570831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016183555,0.0003104234,0.00029336306,0.00033946324,0.00092303683,0.0009612339,0.0005997956,0.000656295,0.00038802763],"category_scores_gemma":[0.0010113202,0.00020174416,0.00017962763,0.0005481918,0.0003123894,0.00058759394,0.00029977836,0.00059109536,0.00016813574],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047962138,0.0013485493,0.2950064,0.000356225,0.00006476939,0.0005067802,0.0014972247,0.005925827,0.43508968,0.00029151968,0.0012781891,0.25815523],"study_design_scores_gemma":[0.00007612291,0.0035317505,0.72999084,0.00015201376,0.00017425853,0.0005411739,0.004699091,0.0681604,0.18039979,0.00055396545,0.01161064,0.00011003205],"about_ca_topic_score_codex":0.034515906,"about_ca_topic_score_gemma":0.10554586,"teacher_disagreement_score":0.034515906,"about_ca_system_score_codex":0.0011777921,"about_ca_system_score_gemma":0.001735507,"threshold_uncertainty_score":0.06863004},"labels":[],"label_agreement":null},{"id":"W2094346949","doi":"10.3390/s140406207","title":"Enhancement of the Wear Particle Monitoring Capability of Oil Debris Sensors Using a Maximal Overlap Discrete Wavelet Transform with Optimal Decomposition Depth","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Lubricants and Their Additives","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Discrete wavelet transform; Particle (ecology); Debris; Computer science; Wavelet; Acoustics; Materials science; Wavelet transform; Remote sensing; Biological system; Artificial intelligence; Geology; Physics","score_opus":0.008999689785080721,"score_gpt":0.22675520948035777,"score_spread":0.21775551969527704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094346949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28867042,0.0005483919,0.7095261,0.000074622236,0.00002753064,0.000023393108,0.000024569532,0.00020834309,0.00089664565],"genre_scores_gemma":[0.71617335,0.0003573044,0.28264257,0.000024307217,0.000022853057,0.00002295338,0.000039457373,0.000024473859,0.0006926979],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998398,0.0000249579,0.000009848902,0.000025816886,0.00008440758,0.0000151895465],"domain_scores_gemma":[0.9997837,0.00007044895,0.00004489793,0.000024409419,0.00006409656,0.000012439605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003220795,0.00034785346,0.00029492,0.00032831467,0.000089590954,0.00027531324,0.00021328496,0.0002603944,0.00024234512],"category_scores_gemma":[0.0005983191,0.00013168965,0.00022052623,0.00027946447,0.00025051113,0.00044073816,0.00028864687,0.00022144784,0.000090502086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003315623,0.00008397795,0.0013018146,0.00015652682,0.000020827294,0.00008852668,0.000080023725,0.01679691,0.78733367,0.0012512374,0.00018069621,0.19237417],"study_design_scores_gemma":[0.0000290624,0.00038687757,0.003816097,0.000016120623,0.000041391213,0.00035071213,0.00005555346,0.45387733,0.5383508,0.0006547608,0.0023869574,0.000034367804],"about_ca_topic_score_codex":0.00023476372,"about_ca_topic_score_gemma":0.00032928423,"teacher_disagreement_score":0.00034785346,"about_ca_system_score_codex":0.00012878256,"about_ca_system_score_gemma":0.00017869727,"threshold_uncertainty_score":0.0017033219},"labels":[],"label_agreement":null},{"id":"W2095182469","doi":"10.3390/s100201232","title":"A Finite Element Model of a MEMS-based Surface Acoustic Wave Hydrogen Sensor","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Glenrose Rehabilitation Hospital; Government of Alberta","keywords":"Hydrogen sensor; Materials science; Surface acoustic wave; Hydrogen; Microelectromechanical systems; Finite element method; Young's modulus; Surface acoustic wave sensor; Palladium; Lithium niobate; Explosive material; Fabrication; Attenuation; Acoustics; Composite material; Optoelectronics; Optics; Catalysis; Chemistry; Structural engineering","score_opus":0.015639576630182308,"score_gpt":0.2162840770696361,"score_spread":0.20064450043945378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095182469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0377932,0.00031227575,0.9398289,0.00041314954,0.000120745244,0.00015414915,0.0006460448,0.0007380533,0.019993588],"genre_scores_gemma":[0.74645364,0.00092452066,0.20088764,0.00030500614,0.000047930553,0.00167533,0.0011458016,0.00019468587,0.048365436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999721,0.00005234225,0.000019736932,0.000058100482,0.00012580931,0.000022936436],"domain_scores_gemma":[0.99978954,0.00008606312,0.000027638718,0.000019197143,0.00006795744,0.000009631009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003574024,0.00062314654,0.0007223533,0.0005145138,0.00048116056,0.00083566544,0.0012435977,0.002506667,0.0075466414],"category_scores_gemma":[0.000627235,0.00067807094,0.0007529495,0.0003985862,0.0006549212,0.00072294456,0.0005624421,0.00071846956,0.001441288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045888944,0.000035990248,0.00038359227,0.00010364308,0.000021929618,0.0001603196,0.00009462565,0.971271,0.013714602,0.0048607956,0.00038076466,0.008926838],"study_design_scores_gemma":[0.000009931934,0.00003890798,0.00019825507,0.000013764556,0.000009123465,0.00004752176,0.00002890315,0.9949214,0.0017191024,0.0006999164,0.0023036425,0.000009549663],"about_ca_topic_score_codex":0.004939787,"about_ca_topic_score_gemma":0.0026081547,"teacher_disagreement_score":0.0075466414,"about_ca_system_score_codex":0.0004417125,"about_ca_system_score_gemma":0.0010895432,"threshold_uncertainty_score":0.025246024},"labels":[],"label_agreement":null},{"id":"W2095639114","doi":"10.3390/s151026726","title":"Comparison of Different Classification Methods for Analyzing Electronic Nose Data to Characterize Sesame Oils and Blends","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Nanjing University; Nanjing University of Finance and Economics; National Natural Science Foundation of China","keywords":"Sesame oil; Electronic nose; Support vector machine; Soybean oil; Pattern recognition (psychology); Mathematics; Artificial intelligence; Computer science; Food science; Chemistry; Sesamum; Biology","score_opus":0.151640665378989,"score_gpt":0.4059848754535583,"score_spread":0.2543442100745693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095639114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5162585,0.0032531084,0.4714602,0.00038211013,0.00022769981,0.00040753806,0.0010004891,0.0025460187,0.004464249],"genre_scores_gemma":[0.7448194,0.0013035628,0.25044206,0.00017686209,0.000055534638,0.00035220187,0.0008830464,0.00014457767,0.0018228785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987727,0.00029620208,0.000106903186,0.00019442067,0.0005454348,0.0000843631],"domain_scores_gemma":[0.9974825,0.0013774941,0.00016564479,0.00010968686,0.0008229653,0.00004172075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027838978,0.001070674,0.0007809269,0.0021410293,0.00023755645,0.00069628045,0.0006442429,0.00064653164,0.0006804632],"category_scores_gemma":[0.0045853676,0.00023370514,0.0007150433,0.0010072398,0.00023384893,0.0009890585,0.0003602794,0.00047500705,0.0004189085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025958533,0.00088631234,0.039501544,0.0009671162,0.00063155626,0.0001851199,0.0002595721,0.040486436,0.15759306,0.00093656365,0.0019947523,0.75396216],"study_design_scores_gemma":[0.000082793435,0.00051884557,0.031403158,0.00006854488,0.00019775738,0.0002903997,0.00017967576,0.86766464,0.09693476,0.00050954684,0.002030735,0.0001190587],"about_ca_topic_score_codex":0.0025001098,"about_ca_topic_score_gemma":0.0029386084,"teacher_disagreement_score":0.0027838978,"about_ca_system_score_codex":0.00031957446,"about_ca_system_score_gemma":0.00036913366,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2096301843","doi":"10.3390/s101211072","title":"Scaling up Semi-Arid Grassland Biochemical Content from the Leaf to the Canopy Level: Challenges and Opportunities","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto Mississauga; University of Toronto","keywords":"Canopy; Environmental science; Remote sensing; Grassland; Vegetation (pathology); Arid; Tree canopy; Agroforestry; Ecology; Geography; Biology","score_opus":0.1867103536468973,"score_gpt":0.28319806190520674,"score_spread":0.09648770825830943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096301843","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028261512,0.9911742,0.002942454,0.00035562445,0.00007929929,0.000012286372,0.000029692856,0.00002720304,0.0025530434],"genre_scores_gemma":[0.009901701,0.9834471,0.0049563255,0.00015886179,0.00007854582,0.000016994276,0.00005863906,0.0000045731626,0.0013772909],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998839,0.000015803567,0.000011079018,0.000028009223,0.00005150289,0.000009693436],"domain_scores_gemma":[0.9997546,0.00008599382,0.000037063834,0.000011379567,0.00009799033,0.000012999194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006827716,0.00047791257,0.0008130078,0.0010134276,0.00011717505,0.00058148033,0.00075733295,0.00070009165,0.0010187402],"category_scores_gemma":[0.0005270638,0.0002241698,0.00035264716,0.0013468395,0.00026637374,0.0011206656,0.00031384945,0.0005242065,0.00080418633],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002450162,0.000048464703,0.0005323431,0.003985385,0.000037029946,0.00006335551,0.00003488422,0.00091249344,0.0072271656,0.0015528855,0.0018119896,0.98376954],"study_design_scores_gemma":[0.000039114642,0.0006015049,0.012769473,0.003900256,0.00026193683,0.0015662719,0.0003720653,0.0035419594,0.015478156,0.007542209,0.9538499,0.000077158234],"about_ca_topic_score_codex":0.001601302,"about_ca_topic_score_gemma":0.002753441,"teacher_disagreement_score":0.001601302,"about_ca_system_score_codex":0.00035555722,"about_ca_system_score_gemma":0.00055267353,"threshold_uncertainty_score":0.003610909},"labels":[],"label_agreement":null},{"id":"W2097555825","doi":"10.3390/s8095927","title":"Influence of Fluid Cell Design on the Frequency Response of AFM Microcantilevers in Liquid Media","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cantilever; Rheology; Frequency response; Atomic force microscopy; Materials science; Acoustics; Broad spectrum; Computer science; Fluid dynamics; Biological system; Nanotechnology; Mechanics; Composite material; Engineering; Chemistry; Physics; Electrical engineering","score_opus":0.014843158668608988,"score_gpt":0.23856750310229985,"score_spread":0.22372434443369085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097555825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9768191,0.0017440553,0.020014701,0.00013148972,0.000072750554,0.00005226934,0.00007098956,0.00013531014,0.00095944083],"genre_scores_gemma":[0.98555225,0.0008115157,0.012878213,0.00006095958,0.00002327193,0.000053260937,0.00007679528,0.000053683532,0.00048999756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999014,0.00027119854,0.00007559139,0.00015890798,0.00035956287,0.00012072101],"domain_scores_gemma":[0.9950558,0.0038270142,0.00030215926,0.00020222926,0.0005136912,0.00009911498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011554544,0.00047476985,0.00056278636,0.00029836097,0.00032693907,0.0007445822,0.00043109263,0.0007506505,0.00067725003],"category_scores_gemma":[0.0045289174,0.00028078488,0.00018838853,0.0002506497,0.00051147747,0.00059607025,0.00039973232,0.0003513475,0.00034997636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021741433,0.000026034304,0.0003284324,0.000059467424,0.000005954633,0.00010764904,0.00007015199,0.0006906636,0.99533856,0.00007832299,0.000026534673,0.0030508984],"study_design_scores_gemma":[0.000014906528,0.00036501518,0.0012144538,0.00000863868,0.000018903338,0.00014407774,0.00006294368,0.0055312556,0.9917762,0.00003812757,0.0008114949,0.000013976356],"about_ca_topic_score_codex":0.00057535013,"about_ca_topic_score_gemma":0.0005623472,"teacher_disagreement_score":0.0011554544,"about_ca_system_score_codex":0.00030374737,"about_ca_system_score_gemma":0.00017536961,"threshold_uncertainty_score":0.006110668},"labels":[],"label_agreement":null},{"id":"W2098123855","doi":"10.3390/s140814654","title":"Development of a PET Scanner for Simultaneously Imaging Small Animals with MRI and PET","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; TRIUMF; Lawson Health Research Institute; Western University; University of Manitoba; McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Silicon photomultiplier; Positron emission tomography; Scanner; Lyso-; Magnetic resonance imaging; Medical physics; Avalanche photodiode; Preclinical imaging; Physics; Detector; Nuclear medicine; Computer science; Medicine; Optics; Radiology; Scintillator","score_opus":0.015361477570210993,"score_gpt":0.2776777855393546,"score_spread":0.2623163079691436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098123855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17444575,0.005491528,0.80010545,0.0015045954,0.00032880832,0.001140741,0.00061092555,0.0029458269,0.013426387],"genre_scores_gemma":[0.15924947,0.0018475296,0.830221,0.00022489065,0.00004524063,0.00031447638,0.0003441086,0.00011785186,0.0076353843],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996388,0.000060642247,0.000013762849,0.00006402783,0.00018736809,0.000035545043],"domain_scores_gemma":[0.99965596,0.00010251768,0.00002698009,0.000046618396,0.00011745351,0.0000504329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012872086,0.0002942914,0.00046402126,0.00042781862,0.0002426149,0.00046682893,0.0009142182,0.00086371356,0.0015369969],"category_scores_gemma":[0.00065667374,0.00047156576,0.00030339367,0.0003554115,0.00048926985,0.00063953665,0.00041736133,0.0008578782,0.00061506993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002036219,0.000053973625,0.0007795649,0.00010358514,0.000020231793,0.00015345523,0.000047718175,0.00094969035,0.96398157,0.0023282531,0.00066548056,0.03071284],"study_design_scores_gemma":[0.00007202027,0.0011867412,0.0043553454,0.000036372454,0.00008259835,0.0024264033,0.00006328381,0.014471572,0.9169745,0.00059151853,0.05967683,0.00006280271],"about_ca_topic_score_codex":0.0013223568,"about_ca_topic_score_gemma":0.0015844429,"teacher_disagreement_score":0.0015369969,"about_ca_system_score_codex":0.0004727841,"about_ca_system_score_gemma":0.0011063921,"threshold_uncertainty_score":0.006807506},"labels":[],"label_agreement":null},{"id":"W2098841353","doi":"10.3390/s8010051","title":"Changes in Spectral Properties, Chlorophyll Content and Internal Mesophyll Structure of Senescing Populus balsamifera and Populus tremuloides Leaves","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Chlorophyll; Chlorophyll a; Botany; Spongy tissue; Chlorophyll b; Chemistry; Reflectivity; Horticulture; Biology; Optics; Physics","score_opus":0.020889168075791203,"score_gpt":0.19430230825353342,"score_spread":0.17341314017774223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098841353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9998399,0.000021852997,0.00006569746,0.0000011687113,1.7577987e-7,6.034271e-7,0.000027319424,0.000003324247,0.00004010039],"genre_scores_gemma":[0.9993437,0.000030829822,0.00025814376,0.000005206155,5.727019e-7,0.0000023533887,0.00020155821,0.000002919898,0.00015466654],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99996793,0.0000042820857,0.0000018768591,0.000012597349,0.000006899816,0.0000063529783],"domain_scores_gemma":[0.99987745,0.000022445365,0.000052254873,0.00000970777,0.000016226228,0.0000219526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010727609,0.00021539134,0.000110418216,0.0003048899,0.00014306711,0.00017241498,0.0001021549,0.0001473001,0.00033812475],"category_scores_gemma":[0.00018620658,0.00010202036,0.00011683888,0.00015558307,0.00013091341,0.00021091684,0.00009983079,0.0001093408,0.00009920001],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029967105,0.000059350918,0.20316091,0.00005220246,0.000055335015,0.00011008513,0.00031904504,0.0003352799,0.7873676,0.00003403695,0.000031334966,0.008175038],"study_design_scores_gemma":[0.0000027789074,0.00010229388,0.9859599,0.0000010105574,0.000010586659,0.00011570982,0.000079250225,0.00047494125,0.013137914,0.000021008995,0.00009144666,0.0000031423535],"about_ca_topic_score_codex":0.0013015858,"about_ca_topic_score_gemma":0.0028746363,"teacher_disagreement_score":0.0013015858,"about_ca_system_score_codex":0.00012396846,"about_ca_system_score_gemma":0.000043835124,"threshold_uncertainty_score":0.0025880337},"labels":[],"label_agreement":null},{"id":"W2099042452","doi":"10.3390/s7040459","title":"Step Prediction During Perturbed Standing Using Center Of Pressure Measurements","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Toronto Rehabilitation Institute; Canadian Institutes of Health Research; Ontario Innovation Trust","keywords":"Center of pressure (fluid mechanics); Control theory (sociology); Perturbation (astronomy); Force platform; Pressure sensor; QUIET; Instability; Simulation; Displacement (psychology); Computer science; Mathematics; Mechanics; Physics; Engineering; Artificial intelligence; Classical mechanics; Control (management); Mechanical engineering","score_opus":0.06673386922907731,"score_gpt":0.3648293619097587,"score_spread":0.29809549268068136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099042452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91948533,0.0002853518,0.07820725,0.000085517895,0.000044491815,0.000092670554,0.00035605248,0.0005846173,0.000858705],"genre_scores_gemma":[0.9880683,0.00007034437,0.01139297,0.000011490985,0.000009161411,0.000027030936,0.00013242279,0.000009303393,0.00027887407],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998592,0.000029953604,0.000010656957,0.000029285078,0.000056560068,0.0000142668005],"domain_scores_gemma":[0.99946696,0.00022551056,0.00007808129,0.000022990605,0.00015754499,0.000048955266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018497319,0.00045101647,0.00030974613,0.0004554014,0.00017231554,0.00027511513,0.00019422667,0.00040210402,0.00077318266],"category_scores_gemma":[0.0020240336,0.00017229731,0.00010389958,0.0002094389,0.00009058126,0.00021194718,0.00016804837,0.0002778746,0.00027591208],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0067678113,0.0006294591,0.20151311,0.00043459592,0.00020505796,0.0012288997,0.00066931423,0.050489113,0.3177819,0.0003427665,0.001863719,0.41807443],"study_design_scores_gemma":[0.00013215686,0.0016878771,0.3524816,0.00003955158,0.00007823309,0.0007114473,0.00021491494,0.5811341,0.062239558,0.0004484826,0.0007614994,0.0000705164],"about_ca_topic_score_codex":0.002744004,"about_ca_topic_score_gemma":0.0037817643,"teacher_disagreement_score":0.002744004,"about_ca_system_score_codex":0.00010501617,"about_ca_system_score_gemma":0.00023512596,"threshold_uncertainty_score":0.00545609},"labels":[],"label_agreement":null},{"id":"W2099541227","doi":"10.3390/s150924269","title":"An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle’s Motion Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Accelerometer; Extended Kalman filter; GPS/INS; Inertial measurement unit; Tilt (camera); Kalman filter; Computer science; Inertial navigation system; Control theory (sociology); Linearization; Simulation; Assisted GPS; Orientation (vector space); Engineering; Computer vision; Artificial intelligence; Mathematics","score_opus":0.024727683982060104,"score_gpt":0.2588397401887893,"score_spread":0.2341120562067292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099541227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008964791,0.00012732712,0.9895691,0.00003571541,0.000053252203,0.000023252742,0.000049860875,0.00037128554,0.00080528995],"genre_scores_gemma":[0.8514322,0.00048004836,0.13991244,0.00011996014,0.00006821994,0.00019786497,0.0005338634,0.000117040545,0.0071384064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939513,0.000067953566,0.000051046336,0.00016404751,0.00026536098,0.00005658446],"domain_scores_gemma":[0.9996573,0.000057949477,0.000051567684,0.00004693541,0.00017678169,0.000009512989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004951424,0.0008347923,0.0007997657,0.00035281258,0.00027400628,0.0006474692,0.001025482,0.00083057437,0.0009922212],"category_scores_gemma":[0.0010644535,0.0003825865,0.0006763538,0.0003593524,0.0003728104,0.0011523109,0.0007197154,0.0010044919,0.0006388181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013274538,0.0000531051,0.0019182208,0.00019102266,0.000082353465,0.00020390301,0.0001582299,0.8614569,0.029125713,0.0062876935,0.001030924,0.099359185],"study_design_scores_gemma":[0.00000720028,0.00004833825,0.00050219864,0.000007815293,0.000016675123,0.000036860245,0.0000068920144,0.9950323,0.0027279276,0.0004724548,0.0011274895,0.000013924363],"about_ca_topic_score_codex":0.012479311,"about_ca_topic_score_gemma":0.007607734,"teacher_disagreement_score":0.012479311,"about_ca_system_score_codex":0.00044594792,"about_ca_system_score_gemma":0.00086862297,"threshold_uncertainty_score":0.024813354},"labels":[],"label_agreement":null},{"id":"W2100851166","doi":"10.3390/s150924409","title":"Toward Epileptic Brain Region Detection Based on Magnetic Nanoparticle Patterning","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Epilepsy; Magnetic resonance imaging; Neuroscience; Superparamagnetism; Computer science; Electroencephalography; Epileptic seizure; Medicine; Radiology; Magnetic field; Psychology; Physics; Magnetization","score_opus":0.025969222349933718,"score_gpt":0.21193461674877112,"score_spread":0.1859653943988374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100851166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47898012,0.0009975837,0.49793267,0.0007500281,0.00012422526,0.00012956987,0.00011638907,0.0007578322,0.02021147],"genre_scores_gemma":[0.9035654,0.0005105672,0.09212987,0.000086196844,0.000012921643,0.00007664423,0.00003859843,0.000031337953,0.0035484848],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999941,0.000010425278,0.0000024451224,0.00001568462,0.00002412918,0.0000063466427],"domain_scores_gemma":[0.9999312,0.000030199088,0.000016254993,0.0000072259922,0.000011170232,0.0000039317097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081891834,0.00017664321,0.0001619051,0.00013059808,0.00012897489,0.00024157182,0.00023394592,0.00040261878,0.0005246261],"category_scores_gemma":[0.00030985067,0.00018032554,0.00018732439,0.00009455679,0.00026361254,0.00035371887,0.00025488294,0.00014422354,0.00019966516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044978442,0.000037450987,0.00057186297,0.000122064404,0.000010487719,0.00030145995,0.00008579077,0.07802558,0.8971877,0.010164586,0.00054197764,0.012906097],"study_design_scores_gemma":[0.00001822842,0.000088738365,0.00061042455,0.000010592667,0.000010126949,0.00021605816,0.00002468682,0.7579505,0.23565838,0.0023702884,0.003024293,0.000017760744],"about_ca_topic_score_codex":0.0006272942,"about_ca_topic_score_gemma":0.000641509,"teacher_disagreement_score":0.0006272942,"about_ca_system_score_codex":0.00025835866,"about_ca_system_score_gemma":0.0001525983,"threshold_uncertainty_score":0.0018745065},"labels":[],"label_agreement":null},{"id":"W2100916320","doi":"10.3390/s150717534","title":"Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National High-tech Research and Development Program; China Scholarship Council; National Natural Science Foundation of China","keywords":"Fingerprint (computing); Computer science; Real-time computing; Fingerprint recognition; Term (time); Global Positioning System; Database; Artificial intelligence; Telecommunications","score_opus":0.021217863686430985,"score_gpt":0.24606836311502098,"score_spread":0.22485049942858998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100916320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28812376,0.0012033975,0.7027344,0.00011280152,0.00021309158,0.00011952328,0.0004983315,0.0037715125,0.003223201],"genre_scores_gemma":[0.85265356,0.00033439894,0.14465825,0.00007088835,0.00006176892,0.00006202197,0.0003340177,0.000049948918,0.0017750844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923015,0.0000933251,0.000044404795,0.00019413188,0.00035680583,0.000081226746],"domain_scores_gemma":[0.9991935,0.00014334818,0.0001278946,0.00025873462,0.00023441274,0.000042051623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000370981,0.0007027612,0.0010056588,0.001037056,0.0003508195,0.0006130239,0.0009557401,0.00065188366,0.0011168448],"category_scores_gemma":[0.0018010194,0.0003262771,0.00043686878,0.0010732823,0.00013309433,0.0010936304,0.0007585417,0.00027514476,0.000859228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070844084,0.00023248614,0.019200185,0.00033639607,0.00020742626,0.00051423116,0.00022401725,0.02587284,0.14983279,0.00084497285,0.002360105,0.7996661],"study_design_scores_gemma":[0.00009350909,0.0010745255,0.049209554,0.00010246942,0.00035936656,0.004628956,0.00033385202,0.6809312,0.24803863,0.0017087494,0.0133207645,0.00019833515],"about_ca_topic_score_codex":0.0027223418,"about_ca_topic_score_gemma":0.0046256217,"teacher_disagreement_score":0.0027223418,"about_ca_system_score_codex":0.00022732126,"about_ca_system_score_gemma":0.00031024293,"threshold_uncertainty_score":0.005412936},"labels":[],"label_agreement":null},{"id":"W2101300165","doi":"10.3390/s100807263","title":"A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Field-programmable gate array; Wireless sensor network; Embedded system; Gate array; Field (mathematics); Wireless; Node (physics); Computer science; Engineering; Computer hardware; Computer network; Telecommunications","score_opus":0.011723115862835354,"score_gpt":0.23992126219335544,"score_spread":0.22819814633052007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101300165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3019918,0.0010266762,0.6826748,0.00043071053,0.00020714907,0.00017407739,0.00018029343,0.0015743577,0.011740082],"genre_scores_gemma":[0.9233162,0.00020405382,0.07346607,0.00010722948,0.000023952638,0.000054739732,0.000070366616,0.000012300071,0.00274514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986136,0.00003459754,0.00000603723,0.000034057808,0.0000529067,0.00001093805],"domain_scores_gemma":[0.9998635,0.00004151357,0.000032015934,0.000019076666,0.00003277683,0.000011060424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019762112,0.00021112668,0.00016504766,0.00017934559,0.00012823078,0.00021866467,0.00057649193,0.00020148637,0.0007674013],"category_scores_gemma":[0.00024403846,0.00008041294,0.00007156641,0.00014573564,0.00018024494,0.00046055389,0.0001891589,0.00018986562,0.0001531902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008213172,0.00023789423,0.005540445,0.0003172124,0.00006213149,0.00045007817,0.00012765173,0.08045119,0.5725389,0.014101526,0.005360781,0.31999072],"study_design_scores_gemma":[0.00011688848,0.0015235278,0.0061852,0.000028678622,0.00008523962,0.00086612965,0.00005227422,0.67150825,0.29007128,0.0033583846,0.026149465,0.000054689935],"about_ca_topic_score_codex":0.0003101696,"about_ca_topic_score_gemma":0.00081394153,"teacher_disagreement_score":0.0007674013,"about_ca_system_score_codex":0.00022908126,"about_ca_system_score_gemma":0.00022147699,"threshold_uncertainty_score":0.0025672317},"labels":[],"label_agreement":null},{"id":"W2101399910","doi":"10.3390/s30800314","title":"Real Time Microelectrode Measurement of Nitric Oxide in Kidney Tubular Fluid in vivo","year":2003,"lang":"en","type":"article","venue":"Sensors","topic":"Nitric Oxide and Endothelin Effects","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canadian Institutes of Health Research","keywords":"Amperometry; Microelectrode; Pipette; Electrode; Biomedical engineering; In vivo; Materials science; Chemistry; Nanotechnology; Medicine; Electrochemistry","score_opus":0.010327994348327206,"score_gpt":0.2305081663542325,"score_spread":0.22018017200590528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101399910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2727215,0.24787937,0.46927387,0.00090714876,0.0009919421,0.00015302224,0.00051877555,0.0008628455,0.006691671],"genre_scores_gemma":[0.45936373,0.14887023,0.37784612,0.00092890963,0.0005207251,0.00022201949,0.0006046089,0.00017277955,0.01147093],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9993794,0.00018615034,0.00004785188,0.00015213041,0.00020632728,0.000028055065],"domain_scores_gemma":[0.9995602,0.00022557375,0.000054263666,0.000030154362,0.00010417695,0.000025662574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012886745,0.00059688493,0.000712572,0.00042272604,0.00015139679,0.00043672931,0.00063875446,0.00089609576,0.0006801922],"category_scores_gemma":[0.0009172047,0.00016201938,0.00023351025,0.00029230243,0.00030404606,0.00070670893,0.00026859704,0.00041537994,0.000536654],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119761244,0.000027496397,0.0009473207,0.00059162785,0.000040430314,0.00019177528,0.00010103188,0.00025218583,0.9615685,0.00017660025,0.000198897,0.035784367],"study_design_scores_gemma":[0.000020370095,0.0011300349,0.00795182,0.000121569814,0.00021886762,0.004200155,0.00026021554,0.0028709664,0.95723313,0.0007125348,0.025210582,0.00006977653],"about_ca_topic_score_codex":0.00027000738,"about_ca_topic_score_gemma":0.00049769547,"teacher_disagreement_score":0.0012886745,"about_ca_system_score_codex":0.00022549697,"about_ca_system_score_gemma":0.00014398502,"threshold_uncertainty_score":0.006815195},"labels":[],"label_agreement":null},{"id":"W2102110903","doi":"10.3390/s141224305","title":"Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Compressed sensing; Computer science; Wireless; Wireless sensor network; Block (permutation group theory); Mean squared error; Sampling (signal processing); Noise (video); Nyquist rate; Artificial intelligence; Real-time computing; Electronic engineering; Engineering; Embedded system; Computer vision; Telecommunications; Mathematics","score_opus":0.006420607085791088,"score_gpt":0.19447193452305436,"score_spread":0.18805132743726327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102110903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02065363,0.00043450212,0.97547174,0.00026859605,0.00006740713,0.00010239978,0.00003698724,0.00037433003,0.002590443],"genre_scores_gemma":[0.4502417,0.0008413262,0.5435351,0.00038786005,0.000104564286,0.00026492472,0.00016546328,0.000052446263,0.004406557],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996563,0.000062283165,0.000024720204,0.00006931829,0.00017092847,0.000016554926],"domain_scores_gemma":[0.99982363,0.00003495487,0.000035607125,0.000017513768,0.000077196215,0.000011052222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022176627,0.00045663596,0.00040267463,0.00028129717,0.00018017346,0.00039877172,0.0008274797,0.0005844705,0.0012113918],"category_scores_gemma":[0.00039557915,0.00020218262,0.00026777395,0.00029567,0.00023066699,0.0007919732,0.00034124218,0.00030770202,0.00042822518],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027006955,0.00017354732,0.0013991203,0.000574903,0.00008568411,0.00032999236,0.0001683187,0.04517986,0.59988946,0.01180014,0.0033541361,0.3367748],"study_design_scores_gemma":[0.00007607092,0.0012148705,0.001741823,0.00005607781,0.00008556735,0.0011307634,0.000056151443,0.7492014,0.22472562,0.0023666956,0.019291434,0.000053537795],"about_ca_topic_score_codex":0.0003311839,"about_ca_topic_score_gemma":0.00045701084,"teacher_disagreement_score":0.0012113918,"about_ca_system_score_codex":0.0002455787,"about_ca_system_score_gemma":0.00034590325,"threshold_uncertainty_score":0.0040525794},"labels":[],"label_agreement":null},{"id":"W2102483831","doi":"10.3390/s141019260","title":"Novel Wireless-Communicating Textiles Made from Multi-Material and Minimally-Invasive Fibers","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Textile; Wireless; Fiber; Realization (probability); Electrically conductive; Clothing; Computer science; Electromagnetic shielding; Materials science; Engineering; Electrical engineering; Telecommunications; Composite material","score_opus":0.016098511279182312,"score_gpt":0.22194599819520291,"score_spread":0.20584748691602062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102483831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8775273,0.0032207756,0.108607255,0.00026378955,0.00032894732,0.00007244285,0.00013762151,0.0005106525,0.00933118],"genre_scores_gemma":[0.9241995,0.0009350905,0.071365856,0.00008069378,0.000046118457,0.000037591068,0.00008019466,0.000035162084,0.0032197472],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986696,0.000019119807,0.000009485675,0.000039090348,0.000046118013,0.000019202438],"domain_scores_gemma":[0.9998271,0.000029579189,0.00006678366,0.000026074855,0.00002816577,0.00002239807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015337435,0.00036261082,0.00012905363,0.00031599886,0.0001717244,0.00027250772,0.0003342821,0.0003448154,0.000521473],"category_scores_gemma":[0.00022255213,0.00015877605,0.000168637,0.00020231628,0.00023755374,0.00053398847,0.00026042692,0.00021227889,0.00021969683],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037316895,0.00001874528,0.0003676529,0.00008651427,0.0000056374915,0.00029481368,0.000047580834,0.00052932417,0.9877586,0.00085230125,0.00013019623,0.00987134],"study_design_scores_gemma":[0.000016310725,0.0005093623,0.004770281,0.000030891188,0.000026710217,0.0018676638,0.00006107269,0.010168581,0.966653,0.0005466323,0.015318807,0.00003064547],"about_ca_topic_score_codex":0.000059654005,"about_ca_topic_score_gemma":0.00014229746,"teacher_disagreement_score":0.000521473,"about_ca_system_score_codex":0.00012068761,"about_ca_system_score_gemma":0.00006268096,"threshold_uncertainty_score":0.0017445087},"labels":[],"label_agreement":null},{"id":"W2102646261","doi":"10.3390/s8042762","title":"Deployment of a Prototype Plant GFP Imager at the Arthur Clarke Mars Greenhouse of the Haughton Mars Project","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Light effects on plants","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Simon Fraser University; Canadian Space Agency","funders":"Canadian Space Agency; Secretário de Ciência, Tecnologia e Ensino Superior, Governo do Estado de Parana; National Aeronautics and Space Administration; Simon Fraser University; Ontario Centres of Excellence; University of Florida","keywords":"Mars Exploration Program; Software deployment; Systems engineering; Exploration of Mars; Green fluorescent protein; Computer science; Engineering; Astrobiology; Remote sensing; Biology; Software engineering; Geography; Gene","score_opus":0.022929870498845064,"score_gpt":0.20741585459504408,"score_spread":0.184485984096199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102646261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8860526,0.00037827063,0.09361998,0.00089507713,0.00017926971,0.000941524,0.0019698911,0.009144553,0.0068188882],"genre_scores_gemma":[0.7596153,0.00036552464,0.22690989,0.00033580774,0.000024980383,0.00060300063,0.0017112952,0.00039280797,0.010041318],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996239,0.000033532473,0.000007969006,0.00012445217,0.00014762368,0.00006245072],"domain_scores_gemma":[0.9996307,0.000048438185,0.000036130954,0.000048319336,0.00012518986,0.000111178626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007240222,0.0005110228,0.0003310513,0.0003556932,0.00092543074,0.00059709174,0.0009139499,0.000620687,0.0017761422],"category_scores_gemma":[0.0003549236,0.00023203349,0.00035825506,0.0002159066,0.00039119393,0.0004342352,0.00056475354,0.00071466825,0.00069633714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018741513,0.00008173955,0.0027878145,0.000058206806,0.000010841724,0.00039382253,0.00037222452,0.0009851622,0.97464037,0.00034908665,0.0018298659,0.018303555],"study_design_scores_gemma":[0.00010884245,0.0025560593,0.046824526,0.000037885602,0.00008853673,0.001419865,0.0010939857,0.020825988,0.8596594,0.00023061644,0.066997275,0.00015707519],"about_ca_topic_score_codex":0.018116295,"about_ca_topic_score_gemma":0.028169744,"teacher_disagreement_score":0.018116295,"about_ca_system_score_codex":0.00089878973,"about_ca_system_score_gemma":0.0013134123,"threshold_uncertainty_score":0.03602171},"labels":[],"label_agreement":null},{"id":"W2103217435","doi":"10.3390/s111211390","title":"Use of Earth’s Magnetic Field for Mitigating Gyroscope Errors Regardless of Magnetic Perturbation","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Else Kröner-Fresenius-Stiftung; Ministry of Advanced Education, Government of Alberta","keywords":"Gyroscope; Dead reckoning; Computer science; Extended Kalman filter; Global Positioning System; Kalman filter; Control theory (sociology); Sensor fusion; Angular velocity; Navigation system; Real-time computing; Simulation; Computer vision; Engineering; Artificial intelligence; Physics; Aerospace engineering; Telecommunications","score_opus":0.030207736355158023,"score_gpt":0.21936173267376527,"score_spread":0.18915399631860724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103217435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1753907,0.0013215286,0.8178594,0.0001582371,0.00015294594,0.000048981292,0.000073742835,0.0011338701,0.003860664],"genre_scores_gemma":[0.93223226,0.0004941318,0.0659876,0.000041219002,0.000034721437,0.000017834736,0.00006654881,0.000019327177,0.0011064155],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981505,0.000027887516,0.000008315632,0.000044113665,0.00008694242,0.000017718912],"domain_scores_gemma":[0.99982053,0.00002527134,0.000051202664,0.000022088077,0.00007161275,0.000009299078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015320422,0.0004649134,0.00025981336,0.00032714114,0.00018581728,0.00023177192,0.0002227003,0.00024132778,0.00039171244],"category_scores_gemma":[0.00052352884,0.00012675287,0.00014770348,0.00027297996,0.0001755831,0.00040177716,0.000361349,0.0002194362,0.0001819154],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003769352,0.00006312937,0.009741136,0.00037204957,0.00006402813,0.0002521825,0.00030529627,0.058506723,0.40448508,0.0027700397,0.0019222789,0.5211412],"study_design_scores_gemma":[0.00005398825,0.00091008446,0.03334818,0.000086973596,0.00015362348,0.00097566645,0.00024457966,0.59859204,0.3443946,0.0017127881,0.019440962,0.00008646465],"about_ca_topic_score_codex":0.0018701196,"about_ca_topic_score_gemma":0.0033351423,"teacher_disagreement_score":0.0018701196,"about_ca_system_score_codex":0.00014308402,"about_ca_system_score_gemma":0.00027281605,"threshold_uncertainty_score":0.0037184358},"labels":[],"label_agreement":null},{"id":"W2103461728","doi":"10.3390/s8010529","title":"Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke; Natural Resources Canada; Canadian Forest Service","funders":"Natural Resources Canada; Government of Canada","keywords":"Forest inventory; Biomass (ecology); Remote sensing; Satellite imagery; Estimation; Environmental science; Geographic information system; Computer science; Geography; Forest management; Geology; Engineering; Agroforestry","score_opus":0.06204734847667625,"score_gpt":0.2371632486128926,"score_spread":0.17511590013621633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103461728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23766583,0.0011087888,0.7439469,0.00044749136,0.000070395974,0.00046980695,0.0065690684,0.0045950348,0.005126518],"genre_scores_gemma":[0.40985733,0.0003478334,0.5821985,0.000080024576,0.000033778513,0.00040502014,0.0049553243,0.00011582439,0.002006422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990878,0.00021422729,0.000096662625,0.00029279196,0.00023903113,0.00006946557],"domain_scores_gemma":[0.99871945,0.00037431743,0.0001983529,0.00034008097,0.00033080592,0.00003699955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014791694,0.0009880004,0.0005451059,0.0036526464,0.00037388277,0.0016183662,0.0014596483,0.0006343691,0.0023987247],"category_scores_gemma":[0.0032129104,0.000843426,0.0011231108,0.0036025965,0.0003145453,0.0019114619,0.0018656303,0.000394428,0.0007306468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002132092,0.00046967418,0.13439587,0.0005211209,0.0013225424,0.00030362382,0.000770723,0.1561703,0.012237471,0.0040374654,0.0034717366,0.68608624],"study_design_scores_gemma":[0.000115855364,0.00014344453,0.17467932,0.00016235618,0.00040802915,0.00040563312,0.0010527209,0.78363425,0.014388503,0.009160703,0.015574434,0.00027478056],"about_ca_topic_score_codex":0.02008071,"about_ca_topic_score_gemma":0.048662774,"teacher_disagreement_score":0.02008071,"about_ca_system_score_codex":0.00080016744,"about_ca_system_score_gemma":0.00086333917,"threshold_uncertainty_score":0.03992766},"labels":[],"label_agreement":null},{"id":"W2103517951","doi":"10.3390/s141120825","title":"Dual-Frequency Piezoelectric Transducers for Contrast Enhanced Ultrasound Imaging","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Institutes of Health; Terry Fox Foundation; U.S. Department of Defense","keywords":"Second-harmonic imaging microscopy; Microbubbles; Transducer; Ultrasound; Acoustics; Biomedical engineering; Harmonic; Computer science; Ultrasonic sensor; Contrast (vision); SIGNAL (programming language); Medicine; Artificial intelligence; Physics; Optics; Second-harmonic generation","score_opus":0.004899293484253255,"score_gpt":0.20054984121572042,"score_spread":0.19565054773146717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103517951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026291687,0.15604249,0.7636837,0.0024976064,0.0029199685,0.00042387669,0.00043181982,0.0011864777,0.046522386],"genre_scores_gemma":[0.24045138,0.07202387,0.64450085,0.0017205602,0.0009827083,0.0005646058,0.00044311094,0.00015660509,0.03915626],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992423,0.000061397644,0.000030710842,0.00015563045,0.0004694338,0.00004047365],"domain_scores_gemma":[0.99977535,0.000083672036,0.000034895365,0.000017910457,0.00006831223,0.000019920775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005095733,0.00055392925,0.00044567848,0.00072175567,0.00020386794,0.00060646,0.00097118696,0.0015111681,0.004542018],"category_scores_gemma":[0.00073740183,0.00045546956,0.00030211502,0.0007372997,0.00041853788,0.0009468804,0.0007201353,0.0012444675,0.0029964638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090825895,0.00005584239,0.00034586492,0.0010039373,0.00002040203,0.00035811972,0.00013949213,0.0006894053,0.75488365,0.015676524,0.006627558,0.22010836],"study_design_scores_gemma":[0.000058287736,0.0005439879,0.0018063434,0.000320722,0.000086296546,0.0052785487,0.00010273205,0.020443933,0.509175,0.007367702,0.45468643,0.00012998741],"about_ca_topic_score_codex":0.00019379804,"about_ca_topic_score_gemma":0.00039670867,"teacher_disagreement_score":0.004542018,"about_ca_system_score_codex":0.00041983795,"about_ca_system_score_gemma":0.00033881902,"threshold_uncertainty_score":0.015194595},"labels":[],"label_agreement":null},{"id":"W2104056286","doi":"10.3390/s121114416","title":"Towards Real-Time and Rotation-Invariant American Sign Language Alphabet Recognition Using a Range Camera","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Invariant (physics); Gesture; Gesture recognition; Segmentation; Pattern recognition (psychology); Rotation (mathematics); Sign language; Biometrics; Speech recognition; Mathematics","score_opus":0.024726690478161374,"score_gpt":0.26882625950924444,"score_spread":0.24409956903108307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104056286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14495568,0.0003281293,0.8492296,0.000071911956,0.00005772543,0.00011008055,0.000103760205,0.0023901907,0.0027529509],"genre_scores_gemma":[0.44843635,0.00032537943,0.5482255,0.000071812945,0.000025246725,0.00008161518,0.00018556311,0.00007346744,0.002574961],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994814,0.00012362537,0.000023739623,0.000101917954,0.00022330096,0.00004615253],"domain_scores_gemma":[0.9996383,0.000059130653,0.000040957435,0.000063938656,0.00016281096,0.0000347765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004910319,0.0003212509,0.0004807922,0.0005223759,0.00014568062,0.0006041981,0.00038394146,0.0003973504,0.0009904853],"category_scores_gemma":[0.0009487764,0.00020679343,0.0002802588,0.0003553126,0.00025800863,0.00047223753,0.00032389935,0.00035065247,0.001049882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031461133,0.00009121583,0.0025518942,0.00011348691,0.00002715363,0.0001114479,0.000114949675,0.005114422,0.6190211,0.0010915096,0.0010940003,0.37035412],"study_design_scores_gemma":[0.00006523998,0.00070549146,0.020072322,0.000042884734,0.00007392715,0.0013870498,0.00017860989,0.41557166,0.5536287,0.0007658264,0.007414932,0.000093458555],"about_ca_topic_score_codex":0.0014396742,"about_ca_topic_score_gemma":0.0020066288,"teacher_disagreement_score":0.0014396742,"about_ca_system_score_codex":0.00022805786,"about_ca_system_score_gemma":0.0006060636,"threshold_uncertainty_score":0.0033134818},"labels":[],"label_agreement":null},{"id":"W2104191382","doi":"10.3390/s130201425","title":"Fiber Optic pH Sensor with Self-Assembled Polymer Multilayer Nanocoatings","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Hong Kong Polytechnic University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Materials science; Refractive index; Fiber Bragg grating; Acrylic acid; Layer (electronics); Fiber optic sensor; Fiber; Wavelength; Optical fiber; Optoelectronics; Optics; Polymer; Nanotechnology; Composite material","score_opus":0.005453505519747472,"score_gpt":0.19645139446355356,"score_spread":0.1909978889438061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104191382","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9508619,0.0030241082,0.043579858,0.00019793482,0.0001581589,0.00005538937,0.00028441817,0.00093119155,0.0009072009],"genre_scores_gemma":[0.9159951,0.0011068166,0.08060577,0.00011919149,0.00006278374,0.00004968072,0.0002399139,0.000039936407,0.0017809098],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996265,0.000034082004,0.000028287759,0.00010446184,0.0001650973,0.000041578132],"domain_scores_gemma":[0.9997085,0.000043553897,0.00009268301,0.000025067731,0.00008801939,0.00004209898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026200863,0.00054561166,0.0004571793,0.00031542458,0.00015275028,0.00028859213,0.0004935137,0.0004896692,0.00029812413],"category_scores_gemma":[0.00042309027,0.00034921215,0.00033216126,0.0002469464,0.00015702908,0.00058384944,0.000298567,0.0003508379,0.00023726081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018346778,0.0000061712194,0.00008636163,0.000018223927,0.0000038389367,0.000015394076,0.0000043323494,0.00009650981,0.9988286,0.000009297934,0.000017274351,0.000895675],"study_design_scores_gemma":[0.000004775062,0.00012666322,0.0009045106,0.0000023270552,0.000015653557,0.00011968867,0.000004884301,0.0040499736,0.99417514,0.000008761652,0.00057954906,0.000008146423],"about_ca_topic_score_codex":0.0015743619,"about_ca_topic_score_gemma":0.0019810137,"teacher_disagreement_score":0.0015743619,"about_ca_system_score_codex":0.000564268,"about_ca_system_score_gemma":0.0001924039,"threshold_uncertainty_score":0.0040940046},"labels":[],"label_agreement":null},{"id":"W2105068986","doi":"10.3390/s6080823","title":"Single-crystal Sapphire Based Optical Polarimetric Sensor for High Temperature Measurement","year":2006,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Energy Technology Laboratory; U.S. Department of Energy","keywords":"Birefringence; Materials science; Interferometry; Polarimetry; Sapphire; Temperature measurement; Optoelectronics; Interference (communication); Optics; Electromagnetic interference; Electronic engineering; Laser; Computer science; Physics; Engineering; Telecommunications; Channel (broadcasting)","score_opus":0.014012747963055608,"score_gpt":0.1956417541953969,"score_spread":0.1816290062323413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105068986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7809666,0.005981751,0.19805774,0.00040020118,0.00043497747,0.0002010342,0.00059770944,0.0012022053,0.012157799],"genre_scores_gemma":[0.88749677,0.0018614174,0.10445924,0.00011166225,0.00007666987,0.00007283116,0.00030453719,0.000042740245,0.005574076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997335,0.00002386712,0.0000071546274,0.000050450613,0.00016183459,0.000023171156],"domain_scores_gemma":[0.9997993,0.00005000376,0.000038687216,0.00002127319,0.00007196711,0.000018748871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021821087,0.0003795365,0.0004634658,0.00031255203,0.00020772875,0.00026677936,0.00040972367,0.00039976282,0.0012890127],"category_scores_gemma":[0.00025293007,0.00018826034,0.00016352597,0.00032164407,0.000307378,0.00048287804,0.00018550518,0.00053144206,0.0005450451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002765176,0.000013513315,0.00014637437,0.000048963993,0.00000292225,0.000021973363,0.0000074561526,0.00014334351,0.99505687,0.00015930527,0.000119595294,0.0042520645],"study_design_scores_gemma":[0.00000564701,0.00012482316,0.0014071562,0.0000032210507,0.000007672393,0.00019585855,0.000010720976,0.005272774,0.99177295,0.000050128718,0.001140326,0.000008651909],"about_ca_topic_score_codex":0.00041148116,"about_ca_topic_score_gemma":0.0013477851,"teacher_disagreement_score":0.0012890127,"about_ca_system_score_codex":0.00025497068,"about_ca_system_score_gemma":0.00034176666,"threshold_uncertainty_score":0.0043121576},"labels":[],"label_agreement":null},{"id":"W2105482660","doi":"10.3390/s150923953","title":"An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council","keywords":"GNSS applications; Kalman filter; Inertial measurement unit; Computer science; Observability; Filter (signal processing); Satellite system; Inertial navigation system; Heading (navigation); Control theory (sociology); Adaptive filter; Noise (video); SIGNAL (programming language); Allan variance; Real-time computing; Engineering; Global Positioning System; Artificial intelligence; Algorithm; Computer vision; Inertial frame of reference; Mathematics; Standard deviation; Telecommunications","score_opus":0.032930838898187674,"score_gpt":0.2067426878573327,"score_spread":0.17381184895914503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105482660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0931675,0.0003482813,0.8996757,0.00017041672,0.00016306284,0.00014824289,0.0000715146,0.0019869837,0.0042682816],"genre_scores_gemma":[0.7201763,0.00014909977,0.2738771,0.00026433845,0.0000851853,0.00016910823,0.00018788932,0.000039729744,0.00505136],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994814,0.00006653714,0.000028161565,0.00012829676,0.000257161,0.000038576047],"domain_scores_gemma":[0.99976605,0.000021985643,0.000037763075,0.00004029673,0.000107858876,0.000026018864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033253038,0.00060604885,0.00047775774,0.0002865698,0.00044007757,0.00040402563,0.0010586336,0.0006882935,0.00088240206],"category_scores_gemma":[0.0004330319,0.00026688748,0.00028781398,0.0003773842,0.00024421906,0.00070145476,0.0010042341,0.0005639123,0.00052322977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004404617,0.00023654522,0.008930066,0.0003177141,0.00015749752,0.0006404122,0.0004123605,0.050424892,0.53870684,0.0064818556,0.0043247314,0.3889266],"study_design_scores_gemma":[0.00017439478,0.0016678496,0.011320576,0.00005427524,0.00025695981,0.0009853573,0.000104897896,0.7967665,0.16494201,0.0015434845,0.022024995,0.00015867608],"about_ca_topic_score_codex":0.0023130544,"about_ca_topic_score_gemma":0.0024577663,"teacher_disagreement_score":0.0023130544,"about_ca_system_score_codex":0.0003270797,"about_ca_system_score_gemma":0.00070803694,"threshold_uncertainty_score":0.0045992136},"labels":[],"label_agreement":null},{"id":"W2106842089","doi":"10.3390/s8010412","title":"Validating Evapotranspiraiton Equations Using Bowen Ratio in New Brunswick, Maritime, Canada","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick; Agriculture and Agri-Food Canada","funders":"","keywords":"Evapotranspiration; Environmental science; Weather station; Meteorology; Automatic weather station; Bowen ratio; Daytime; Crop coefficient; Offset (computer science); Atmospheric sciences; Hydrology (agriculture); Geography; Ecology; Computer science; Geology","score_opus":0.019413552054824403,"score_gpt":0.21074723038637097,"score_spread":0.19133367833154658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106842089","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96707815,0.0010317583,0.011178956,0.0003227549,0.000058703587,0.0002780162,0.008398714,0.0014437734,0.010209305],"genre_scores_gemma":[0.96127254,0.0005857796,0.022107862,0.000112123016,0.000005229356,0.00012178117,0.011999839,0.00021558837,0.0035792473],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993242,0.00004679469,0.000047853893,0.00016886099,0.0002973378,0.00011488741],"domain_scores_gemma":[0.9980889,0.00020743474,0.000091501584,0.00008162851,0.0014487501,0.000081803075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001329158,0.00092907983,0.0005185627,0.0011692017,0.0014611683,0.001211243,0.0021590968,0.00043425674,0.0015824971],"category_scores_gemma":[0.0028349685,0.00047702467,0.00065750984,0.0023292701,0.0005082728,0.0007314156,0.000584253,0.000683122,0.00043057516],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060715235,0.0006397064,0.5093836,0.00061423663,0.0005304057,0.00081148086,0.0008765691,0.3043611,0.018309403,0.0021889443,0.016927347,0.14475007],"study_design_scores_gemma":[0.00018873341,0.00009945968,0.2962143,0.00014753525,0.00013533974,0.000094988405,0.0012213729,0.6738521,0.014110014,0.00023195593,0.013562642,0.0001415713],"about_ca_topic_score_codex":0.99334407,"about_ca_topic_score_gemma":0.99540484,"teacher_disagreement_score":0.02205491,"about_ca_system_score_codex":0.02205491,"about_ca_system_score_gemma":0.029648291,"threshold_uncertainty_score":0.16002035},"labels":[],"label_agreement":null},{"id":"W2106962600","doi":"10.3390/s7010052","title":"A Non-invasive and Real-time Monitoring of the Regulation of Photosynthetic Metabolism Biosensor Based on Measurement of Delayed Fluorescence in Vivo","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Photosynthetic Processes and Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Chinese Academy of Sciences; Institute of Genetics; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Biosensor; Photosynthesis; Fluorescence; Biophysics; In vivo; Metabolism; Chemistry; Chlorophyll fluorescence; Biological system; Biochemistry; Biology; Biotechnology; Optics","score_opus":0.008513707892254778,"score_gpt":0.22203786064798497,"score_spread":0.2135241527557302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106962600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39836615,0.006835863,0.5896194,0.0007323892,0.0005157601,0.0002004953,0.00041814754,0.0012481103,0.0020637016],"genre_scores_gemma":[0.6202214,0.003242497,0.37043738,0.00042064054,0.00010018263,0.0002880922,0.00045932698,0.000052145,0.0047783344],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996081,0.000050925526,0.000019801912,0.00012767146,0.00016264665,0.000030840383],"domain_scores_gemma":[0.999688,0.0000950769,0.00006372475,0.000037584017,0.00007425981,0.00004136569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057523284,0.00054464216,0.00055142806,0.00030137852,0.00016861512,0.0003726379,0.00086856424,0.0010911984,0.0006917614],"category_scores_gemma":[0.0003831418,0.00027900923,0.00028176073,0.00018713587,0.0003714642,0.000770852,0.0003303117,0.0010487675,0.00035495678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014004781,0.000010263828,0.00007480526,0.00003283194,0.0000019134154,0.0000082994975,0.0000050846716,0.000027449412,0.997544,0.000078519144,0.000024309626,0.0021785314],"study_design_scores_gemma":[0.000008135555,0.00015182477,0.0008096194,0.000003634332,0.000009027758,0.00021399987,0.000009842811,0.0024983296,0.9944786,0.00006520841,0.0017385917,0.000013066506],"about_ca_topic_score_codex":0.00031793618,"about_ca_topic_score_gemma":0.0005471248,"teacher_disagreement_score":0.0010911984,"about_ca_system_score_codex":0.00039839608,"about_ca_system_score_gemma":0.00027086394,"threshold_uncertainty_score":0.0030421615},"labels":[],"label_agreement":null},{"id":"W2107664639","doi":"10.3390/s150510791","title":"Toward Realization of 2.4 GHz Balunless Narrowband Receiver Front-End for Short Range Wireless Applications","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; King Abdulaziz City for Science and Technology","keywords":"RF front end; NMOS logic; Balun; CMOS; Electrical engineering; Radio receiver design; Superheterodyne receiver; Noise figure; Transmitter; PMOS logic; Transceiver; Low-noise amplifier; Transistor; Radio frequency; Electronic engineering; Analog front-end; Engineering; Amplifier; Channel (broadcasting); Voltage; Antenna (radio)","score_opus":0.04814715741560979,"score_gpt":0.2520411374598025,"score_spread":0.2038939800441927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107664639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23388214,0.0029036445,0.7447238,0.00059688365,0.00031641262,0.00015196374,0.0002037423,0.0022712292,0.0149502605],"genre_scores_gemma":[0.6202378,0.0014282796,0.36082748,0.0006886793,0.000263942,0.00012641257,0.0004513373,0.00015012782,0.015825888],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997054,0.000029472762,0.000016438013,0.00008098392,0.00012069895,0.00004696901],"domain_scores_gemma":[0.999765,0.000028415629,0.00005489304,0.000023954273,0.0001076325,0.000020095447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027349681,0.0005436821,0.00041907825,0.00035328936,0.00019002736,0.0007563813,0.0011710426,0.0009776647,0.0014929319],"category_scores_gemma":[0.00029756952,0.00036814087,0.0004261486,0.00023758365,0.00018912158,0.0008859855,0.0003760212,0.0005896243,0.0018128866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070726404,0.000047061632,0.00059857295,0.0001198308,0.000028030829,0.00015993409,0.000072270224,0.0014812887,0.97501343,0.0027569423,0.00050981814,0.01914204],"study_design_scores_gemma":[0.000041718744,0.0010359428,0.0018953871,0.000057257574,0.000097525844,0.0011183994,0.000057812966,0.040493023,0.91972756,0.0009202468,0.034509704,0.000045353776],"about_ca_topic_score_codex":0.00030048168,"about_ca_topic_score_gemma":0.00064300315,"teacher_disagreement_score":0.0014929319,"about_ca_system_score_codex":0.00036541198,"about_ca_system_score_gemma":0.00043759486,"threshold_uncertainty_score":0.004994333},"labels":[],"label_agreement":null},{"id":"W2109750803","doi":"10.3390/s131114714","title":"A Microfluidic Bioreactor with in Situ SERS Imaging for the Study of Controlled Flow Patterns of Biofilm Precursor Materials","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Biofilm; Microfluidics; Bioreactor; In situ; Materials science; Nanotechnology; Microchannel; Raman spectroscopy; Surface-enhanced Raman spectroscopy; Chemistry; Raman scattering; Optics","score_opus":0.008643121188046377,"score_gpt":0.2216936612270695,"score_spread":0.2130505400390231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109750803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4898629,0.011913877,0.4871409,0.0012830538,0.00095529953,0.00062360376,0.0014455951,0.0037586137,0.0030162826],"genre_scores_gemma":[0.48962867,0.0032199763,0.50195634,0.00043435264,0.00021476747,0.0006505821,0.00070688815,0.000105643114,0.0030828044],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959,0.000048404767,0.0000284472,0.00012717712,0.00015505517,0.00005091018],"domain_scores_gemma":[0.99962234,0.0001002577,0.00010557399,0.000033460416,0.00006882894,0.00006949167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060824986,0.0009780588,0.0007491983,0.00041171737,0.00040049021,0.0004994139,0.0011375173,0.00090327725,0.0005658436],"category_scores_gemma":[0.0005028532,0.00046569845,0.00043161094,0.00026061668,0.00036682675,0.00044485764,0.00059527514,0.0005743716,0.00042515647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021747284,0.000014527032,0.00009688767,0.000044625212,0.0000029113144,0.000025537836,0.0000075595026,0.00008103873,0.99696475,0.00011307414,0.00010535378,0.002522075],"study_design_scores_gemma":[0.000026422702,0.0003043795,0.0018256676,0.000009657113,0.000026597372,0.000499298,0.000010424506,0.0056063994,0.9848622,0.00010398298,0.006695331,0.000029592726],"about_ca_topic_score_codex":0.00070578384,"about_ca_topic_score_gemma":0.0011238047,"teacher_disagreement_score":0.0011375173,"about_ca_system_score_codex":0.00079067744,"about_ca_system_score_gemma":0.00090908847,"threshold_uncertainty_score":0.0057367682},"labels":[],"label_agreement":null},{"id":"W2110410634","doi":"10.3390/s7091901","title":"Hybrid Integrated Silicon Microfluidic Platform for Fluorescence Based Biodetection","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Concordia University","funders":"","keywords":"Microfluidics; Microfabrication; Nanotechnology; Lab-on-a-chip; Biosensor; Materials science; Silicon; Chip; Microfluidic chip; Microelectromechanical systems; Biochip; Digital microfluidics; Biomolecule; Fabrication; Optoelectronics; Engineering; Electrowetting; Electrical engineering","score_opus":0.008984346028726392,"score_gpt":0.21133279190775092,"score_spread":0.20234844587902454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110410634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61994237,0.009483013,0.35400382,0.00069272664,0.0007667042,0.00027011678,0.001049783,0.002150535,0.011641025],"genre_scores_gemma":[0.7346307,0.0032362072,0.2513137,0.0003243822,0.00011915432,0.00023331775,0.00086846034,0.000055173772,0.009218823],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998425,0.000016513368,0.000008164316,0.000042242624,0.000069650356,0.000020948202],"domain_scores_gemma":[0.99992,0.00002697435,0.000011736644,0.000007934264,0.000024185647,0.000009165237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003231782,0.00029080917,0.00024652603,0.00029595796,0.00014102501,0.0004506604,0.0006206604,0.00035465878,0.0013418506],"category_scores_gemma":[0.00015217517,0.00020804608,0.00020921338,0.00017640655,0.00020422417,0.0003316512,0.00028937653,0.0002400132,0.00042918455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056639965,0.0000351079,0.00011784693,0.000077351455,0.000010013235,0.000045292745,0.000014942317,0.0006839696,0.98583394,0.0013512145,0.00035625743,0.011417456],"study_design_scores_gemma":[0.000038614275,0.00035380104,0.0005912896,0.000010921601,0.000024886232,0.00016189094,0.000012197576,0.017191542,0.96978855,0.00034252397,0.011459803,0.0000239936],"about_ca_topic_score_codex":0.00026123316,"about_ca_topic_score_gemma":0.0006238055,"teacher_disagreement_score":0.0013418506,"about_ca_system_score_codex":0.00034744965,"about_ca_system_score_gemma":0.00042317677,"threshold_uncertainty_score":0.004488945},"labels":[],"label_agreement":null},{"id":"W2110782990","doi":"10.3390/s5010085","title":"Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose","year":2005,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Histogram; Noise (video); Electronic nose; Electronic engineering; Acoustics; Biological system; Pattern recognition (psychology); Mathematics; Computer science; Engineering; Artificial intelligence; Physics; Biology","score_opus":0.011692222021316465,"score_gpt":0.23078452822484344,"score_spread":0.219092306203527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110782990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3594229,0.0006606077,0.6370929,0.000099569486,0.00003713033,0.000042309966,0.00010505687,0.0010212093,0.0015182628],"genre_scores_gemma":[0.9618965,0.0001655154,0.03688006,0.000041999778,0.0000144515725,0.000018649736,0.00008023213,0.000042402884,0.0008602572],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998499,0.000029206602,0.000004658594,0.00003064149,0.0000761616,0.000009413716],"domain_scores_gemma":[0.9996197,0.00022414395,0.000026154414,0.000029425973,0.000091115224,0.000009481602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019292784,0.00022781924,0.00020162783,0.00041075732,0.00013535612,0.00018348952,0.00043448282,0.00033453043,0.0010147967],"category_scores_gemma":[0.0009046562,0.000108980435,0.00016924509,0.00021853192,0.0002194452,0.00045838914,0.00009173827,0.0001688373,0.00015309907],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056629337,0.00013704221,0.0052785054,0.00031688585,0.00010771112,0.0003349782,0.00010678091,0.12758367,0.7285078,0.004770424,0.00044845027,0.13184148],"study_design_scores_gemma":[0.000018426252,0.00031173596,0.0072645936,0.000007775514,0.000041117837,0.00031156,0.000021383716,0.7832365,0.20583816,0.0017623971,0.0011651877,0.000021164076],"about_ca_topic_score_codex":0.0007003002,"about_ca_topic_score_gemma":0.0007360149,"teacher_disagreement_score":0.0010147967,"about_ca_system_score_codex":0.0003038212,"about_ca_system_score_gemma":0.00013668725,"threshold_uncertainty_score":0.0033948421},"labels":[],"label_agreement":null},{"id":"W2110891573","doi":"10.3390/s7123071","title":"Three Cavity Tunable MEMS Fabry Perot Interferometer","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Fabry–Pérot interferometer; Interferometry; Optics; Microelectromechanical systems; Ranging; Range (aeronautics); Physics; Materials science; Optoelectronics; Computer science; Telecommunications; Wavelength","score_opus":0.01327760607952782,"score_gpt":0.22713220126777522,"score_spread":0.2138545951882474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110891573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43167746,0.0020010278,0.5484331,0.0006851544,0.0005408634,0.00019100569,0.00032675837,0.0020555025,0.014089103],"genre_scores_gemma":[0.8068702,0.0003255062,0.18811908,0.00011231946,0.00006090935,0.000094063704,0.00008494253,0.0000235356,0.004309488],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924845,0.00007415471,0.000021480811,0.0001622757,0.00041817696,0.00007544218],"domain_scores_gemma":[0.9996953,0.00007279147,0.000063899926,0.00006884007,0.00006749262,0.000031723117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040105815,0.00052483217,0.0006401449,0.00042784942,0.0004168657,0.00047647863,0.0013924589,0.0008346913,0.001240237],"category_scores_gemma":[0.00045801242,0.00027666916,0.0005397327,0.00022286065,0.0005753869,0.00070248265,0.0007253516,0.0005828378,0.00045203054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030893594,0.000068220594,0.0011886887,0.00013102933,0.000048380076,0.00027906548,0.00010351946,0.004717333,0.9324456,0.021771358,0.0008291526,0.038108755],"study_design_scores_gemma":[0.000074969656,0.0004066709,0.002348934,0.000019960082,0.00006173502,0.0011053465,0.000042071733,0.12681635,0.84972996,0.0041510547,0.015101792,0.00014129469],"about_ca_topic_score_codex":0.00054291904,"about_ca_topic_score_gemma":0.00063448155,"teacher_disagreement_score":0.0013924589,"about_ca_system_score_codex":0.00095144816,"about_ca_system_score_gemma":0.00042919643,"threshold_uncertainty_score":0.0069032907},"labels":[],"label_agreement":null},{"id":"W2113099614","doi":"10.3390/s150820030","title":"Designing a Microfluidic Device with Integrated Ratiometric Oxygen Sensors for the Long-Term Control and Monitoring of Chronic and Cyclic Hypoxia","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Oxygen; Hypoxia (environmental); Microfluidics; Oxygen sensor; Limiting oxygen concentration; Volumetric flow rate; Biomedical engineering; Biological system; Materials science; Oxygenation; Biophysics; Chemistry; Nanotechnology; Biology; Mechanics; Engineering","score_opus":0.01917579604990659,"score_gpt":0.25863626556882696,"score_spread":0.23946046951892036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113099614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71419007,0.003095567,0.2786425,0.00054792094,0.0003056802,0.00045868478,0.0006225943,0.00086884084,0.0012681262],"genre_scores_gemma":[0.65157735,0.0012234289,0.34452662,0.00028810443,0.00006285045,0.00069261616,0.00025443366,0.000059224916,0.0013155263],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975485,0.000023394294,0.000028004004,0.000094629504,0.000058963986,0.000040181425],"domain_scores_gemma":[0.999676,0.00012650699,0.00009870441,0.000027789023,0.000048015125,0.000023074213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061664113,0.00047765483,0.0004018279,0.00026226434,0.00023144245,0.00036570907,0.00084638625,0.0006116958,0.00031738105],"category_scores_gemma":[0.0007009836,0.00032823265,0.00031199236,0.00019627769,0.00035082185,0.0005373844,0.00035952442,0.00034293364,0.00014149242],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033153818,0.000021606484,0.00024136678,0.000054190736,0.00000472388,0.000019404644,0.000017398073,0.0004152791,0.99625754,0.00019026539,0.000064606436,0.0026804763],"study_design_scores_gemma":[0.000018310182,0.00021134962,0.0012480181,0.000008601597,0.000020347876,0.00009625403,0.000010538355,0.00883676,0.987082,0.00008850254,0.0023574873,0.00002166516],"about_ca_topic_score_codex":0.00046481792,"about_ca_topic_score_gemma":0.00077131676,"teacher_disagreement_score":0.00084638625,"about_ca_system_score_codex":0.00052346796,"about_ca_system_score_gemma":0.0004030894,"threshold_uncertainty_score":0.003798008},"labels":[],"label_agreement":null},{"id":"W2114231583","doi":"10.3390/s131216372","title":"Capability for Fine Tuning of the Refractive Index Sensing Properties of Long-Period Gratings by Atomic Layer Deposited Al2O3 Overlays","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Fundacja na rzecz Nauki Polskiej; Narodowe Centrum Badań i Rozwoju; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; European Commission","keywords":"Overlay; Refractive index; Materials science; Period (music); Layer (electronics); Atomic layer deposition; Optics; Optoelectronics; Index (typography); Computer science; Nanotechnology; Physics; Acoustics","score_opus":0.009457004303814505,"score_gpt":0.20036116212531388,"score_spread":0.1909041578214994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114231583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9814881,0.000418881,0.016814573,0.000036527523,0.000027387165,0.000008441244,0.00006488031,0.0002099129,0.0009312751],"genre_scores_gemma":[0.99035186,0.00019533391,0.009152681,0.000009354877,0.000004342157,0.0000061508367,0.000032688553,0.000015672089,0.00023189175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999471,0.0000039670217,0.0000037080215,0.000010857845,0.000024123507,0.000010353921],"domain_scores_gemma":[0.9998975,0.00003786484,0.00003130585,0.000013598245,0.0000136953595,0.00000599725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010278887,0.00020243187,0.0001357294,0.000116208204,0.00010881128,0.0002132016,0.00021151153,0.00021279299,0.00030649605],"category_scores_gemma":[0.00019805519,0.0001344745,0.0001470189,0.00010068212,0.00015109176,0.00020606752,0.0001709246,0.00023777918,0.0000965178],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009552856,0.00000389222,0.00013478851,0.000019932562,0.000002127302,0.000013095315,0.0000075488565,0.0006102029,0.9983369,0.000053978605,0.0000090996855,0.0007988575],"study_design_scores_gemma":[0.000005679739,0.000047887374,0.0012261247,0.0000019905538,0.00000775708,0.00003618191,0.0000121175735,0.020179464,0.97790366,0.00005093007,0.00052234915,0.000005802194],"about_ca_topic_score_codex":0.00063062704,"about_ca_topic_score_gemma":0.0011469983,"teacher_disagreement_score":0.00063062704,"about_ca_system_score_codex":0.0001951928,"about_ca_system_score_gemma":0.000119391654,"threshold_uncertainty_score":0.0014162064},"labels":[],"label_agreement":null},{"id":"W2114328910","doi":"10.3390/s8116885","title":"A Solid Trap and Thermal Desorption System with Application to a Medical Electronic Nose","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Electronic nose; Trap (plumbing); Condensation; Sorbent; Thermal desorption; Thermal; Detection limit; Materials science; Chemistry; Desorption; Process engineering; Analytical Chemistry (journal); Environmental science; Chromatography; Engineering; Nanotechnology; Thermodynamics; Physics; Adsorption; Organic chemistry","score_opus":0.004154110577213781,"score_gpt":0.2110481294796548,"score_spread":0.206894018902441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114328910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21751747,0.005933185,0.7657864,0.00080364075,0.0005452375,0.0009719052,0.00045156816,0.003991117,0.003999461],"genre_scores_gemma":[0.50184786,0.0014168489,0.4872342,0.0011296816,0.0001704855,0.0003864771,0.0003515452,0.00010489303,0.007357918],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994511,0.000086076994,0.000029981096,0.00012317866,0.0002790906,0.00003056787],"domain_scores_gemma":[0.99972016,0.000078726465,0.00003367531,0.000025921501,0.00010346711,0.000037950012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006174133,0.0003951295,0.0005017639,0.00038569333,0.0002981022,0.0003792625,0.00086990354,0.000757743,0.0016313563],"category_scores_gemma":[0.0005687602,0.00033842484,0.00029629815,0.00019075714,0.0003792703,0.00047422078,0.0005149978,0.00046813456,0.00078403513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002567786,0.000053955755,0.0005192134,0.00023558723,0.000023068482,0.00013287597,0.000044272878,0.0004412127,0.95678973,0.00048139383,0.0004114767,0.04061053],"study_design_scores_gemma":[0.00011518074,0.0013716684,0.0020855435,0.00002582203,0.000074942815,0.0023652483,0.00003350116,0.021824986,0.954476,0.00025216828,0.017284017,0.000090851434],"about_ca_topic_score_codex":0.0003766431,"about_ca_topic_score_gemma":0.00054205704,"teacher_disagreement_score":0.0016313563,"about_ca_system_score_codex":0.0002444336,"about_ca_system_score_gemma":0.00051615865,"threshold_uncertainty_score":0.0054574013},"labels":[],"label_agreement":null},{"id":"W2114929173","doi":"10.3390/s100403681","title":"Quantitative Modeling of Coupled Piezo-Elastodynamic Behavior of Piezoelectric Actuators Bonded to an Elastic Medium for Structural Health Monitoring: A Review","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Office of Experimental Program to Stimulate Competitive Research; National Aeronautics and Space Administration; National Science Foundation","keywords":"Actuator; Piezoelectricity; Structural health monitoring; Acoustics; Piezoelectric sensor; Lamb waves; Materials science; Wave propagation; Mechanical engineering; Engineering; Structural engineering; Physics; Electrical engineering; Optics","score_opus":0.04350126812945728,"score_gpt":0.3496877292268006,"score_spread":0.3061864610973433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114929173","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032167353,0.89888984,0.09163399,0.00027429766,0.00021905632,0.000043345968,0.00015505047,0.00011882102,0.0054488084],"genre_scores_gemma":[0.020380804,0.93424016,0.04121653,0.000088160516,0.00024817832,0.0000884319,0.00023331612,0.000036658563,0.003467713],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997147,0.00004637427,0.000031049734,0.000060543178,0.00013524186,0.000012074779],"domain_scores_gemma":[0.9994673,0.00027356617,0.00005980158,0.000025966701,0.00016210643,0.000011159313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088189763,0.0012499278,0.0017997117,0.0016542438,0.00019992366,0.0010577837,0.0016126513,0.001203349,0.0015586673],"category_scores_gemma":[0.0009922062,0.0005802922,0.0007332996,0.0020173572,0.0003392861,0.0013181795,0.00041688516,0.0005550232,0.0018084913],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004999593,0.00017331462,0.00086161145,0.014130832,0.00017928718,0.00024816423,0.0001177622,0.0572132,0.019788641,0.020671327,0.0077739996,0.87879187],"study_design_scores_gemma":[0.000037435748,0.00046555675,0.0026632412,0.0049486253,0.00055584084,0.0022297879,0.0002722206,0.2108893,0.03587259,0.028692694,0.71312296,0.00024985048],"about_ca_topic_score_codex":0.0011049492,"about_ca_topic_score_gemma":0.0009883483,"teacher_disagreement_score":0.0017997117,"about_ca_system_score_codex":0.0004050124,"about_ca_system_score_gemma":0.0006811812,"threshold_uncertainty_score":0.0052142143},"labels":[],"label_agreement":null},{"id":"W2115549272","doi":"10.3390/s110100539","title":"Driving Circuitry for Focused Ultrasound Noninvasive Surgery and Drug Delivery Applications","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Institutes of Health","keywords":"Phased array; Ultrasound; Microwave; Computer science; High-intensity focused ultrasound; Focused ultrasound; Antenna (radio); Electronic engineering; Therapeutic ultrasound; Systems engineering; Beamforming; Biomedical engineering; Medical physics; Engineering; Medicine; Telecommunications; Radiology","score_opus":0.03192599179504359,"score_gpt":0.23899302981738604,"score_spread":0.20706703802234244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115549272","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025122266,0.96410966,0.018933654,0.00046214802,0.0005121184,0.000055102984,0.00005182245,0.00011292756,0.0132503705],"genre_scores_gemma":[0.011603543,0.9667809,0.010516598,0.0003490785,0.0002968534,0.00007564462,0.000105004925,0.000015039904,0.010257455],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99981886,0.000017925762,0.000013862923,0.00003740898,0.000098964076,0.000013124702],"domain_scores_gemma":[0.9998234,0.00006964664,0.00003253311,0.000009023948,0.000055534714,0.000009848852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003352456,0.00076510594,0.0005581154,0.0013652224,0.00022668712,0.00058957114,0.0007040154,0.001039361,0.003222573],"category_scores_gemma":[0.00037898222,0.00035309303,0.0003908717,0.00095711614,0.0003606435,0.0010089278,0.000386334,0.00091428694,0.0035682349],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028690927,0.000063128915,0.00016260361,0.005563611,0.000023913643,0.00025474557,0.000056415858,0.0006579822,0.062905565,0.006944499,0.006711975,0.9166269],"study_design_scores_gemma":[0.000011897156,0.0002023978,0.00082519057,0.000921314,0.000053922606,0.0025410424,0.000045742494,0.00078553025,0.030527689,0.002328315,0.9617239,0.00003297235],"about_ca_topic_score_codex":0.00045501153,"about_ca_topic_score_gemma":0.0007356258,"teacher_disagreement_score":0.003222573,"about_ca_system_score_codex":0.0003826943,"about_ca_system_score_gemma":0.0004950456,"threshold_uncertainty_score":0.0107806325},"labels":[],"label_agreement":null},{"id":"W2115881480","doi":"10.3390/s150923303","title":"A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Hidden Markov model; Gesture; Gesture recognition; Speech recognition; Computer science; Sign language; Vocabulary; Dynamic time warping; Orientation (vector space); Accelerometer; Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.03901278355635525,"score_gpt":0.2781663472348584,"score_spread":0.23915356367850316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115881480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028046016,0.0004384489,0.9663381,0.00008289967,0.00007831571,0.00015465483,0.000135372,0.0023799143,0.002346255],"genre_scores_gemma":[0.43346626,0.00051373575,0.55702966,0.00018984673,0.00009275078,0.00030359882,0.0006219008,0.00008929662,0.0076929373],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993754,0.000062406434,0.0000533588,0.00019361019,0.00025878844,0.000056483277],"domain_scores_gemma":[0.9997954,0.000021903996,0.000027988584,0.000033820037,0.000101698955,0.000019247558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033917485,0.00080406334,0.00069201883,0.0007344353,0.0002580384,0.0005047113,0.0009895826,0.00043529642,0.0017636294],"category_scores_gemma":[0.0005265915,0.00023504616,0.0005928543,0.0004678961,0.00031395748,0.0007915964,0.0007101778,0.000424687,0.0009972854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031573224,0.00012237673,0.0022817636,0.00020534314,0.00007931889,0.00040403186,0.00014558871,0.02089153,0.25289083,0.0055021215,0.0028060698,0.71435535],"study_design_scores_gemma":[0.00006645858,0.0007651449,0.007282249,0.0000379756,0.00016187962,0.0013726804,0.00012295766,0.85458153,0.11916891,0.0023851506,0.013922043,0.00013306385],"about_ca_topic_score_codex":0.0048850486,"about_ca_topic_score_gemma":0.0053003835,"teacher_disagreement_score":0.0048850486,"about_ca_system_score_codex":0.00025285484,"about_ca_system_score_gemma":0.0009270497,"threshold_uncertainty_score":0.0097132325},"labels":[],"label_agreement":null},{"id":"W2117322335","doi":"10.3390/s110807606","title":"Indoor Pedestrian Navigation Using Foot-Mounted IMU and Portable Ultrasound Range Sensors","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Bentley (Canada); Université de Sherbrooke","funders":"","keywords":"Inertial measurement unit; Computer science; Particle filter; Real-time computing; Tracking (education); Pedestrian; Process (computing); Ranging; Tracking system; Inertial navigation system; Dead reckoning; Artificial intelligence; Position (finance); Computer vision; Simulation; Engineering; Inertial frame of reference; Global Positioning System; Kalman filter; Telecommunications; Transport engineering","score_opus":0.023254401910382392,"score_gpt":0.23020884086404964,"score_spread":0.20695443895366725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117322335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14356433,0.0020661417,0.84178966,0.00010394931,0.00024220068,0.00008449616,0.00025536882,0.0042167897,0.007677021],"genre_scores_gemma":[0.7490821,0.000965446,0.24403468,0.00008520465,0.00009621623,0.00006242945,0.00028622124,0.00004584088,0.0053419955],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997069,0.00007397741,0.000009034643,0.00006973316,0.00010241082,0.000037870836],"domain_scores_gemma":[0.9998652,0.000023501847,0.000032808315,0.000021350528,0.000042535088,0.000014644854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019817457,0.0008656395,0.0005302416,0.0009339574,0.00024658738,0.00040993173,0.0005078977,0.00054184295,0.0010973806],"category_scores_gemma":[0.00040081545,0.0003397956,0.00036876395,0.0008992716,0.0001437137,0.00045847587,0.00045464476,0.00022013675,0.00066906476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066603516,0.00019555174,0.019977149,0.0005738177,0.00023199148,0.0005846081,0.0003435351,0.025556985,0.1927898,0.0022015672,0.0053456905,0.7515332],"study_design_scores_gemma":[0.00018800331,0.0030127675,0.07575481,0.00032117002,0.0008687376,0.0043425225,0.00045389222,0.5742277,0.28227872,0.0025662808,0.05564647,0.00033899193],"about_ca_topic_score_codex":0.0027966218,"about_ca_topic_score_gemma":0.0047489866,"teacher_disagreement_score":0.0027966218,"about_ca_system_score_codex":0.00016897952,"about_ca_system_score_gemma":0.00022816313,"threshold_uncertainty_score":0.0055607557},"labels":[],"label_agreement":null},{"id":"W2117698744","doi":"10.3390/s150511402","title":"Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Higher Education and Scientific Research","keywords":"Metamaterial; Microwave; Rack; Materials science; Acoustics; Automation; Sensitivity (control systems); Nondestructive testing; Millimeter; Computer science; Artificial intelligence; Electronic engineering; Optics; Optoelectronics; Mechanical engineering; Engineering; Telecommunications","score_opus":0.03268774399116464,"score_gpt":0.24861402196530938,"score_spread":0.21592627797414474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117698744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92570406,0.000060298426,0.07287604,0.00005636771,0.000020691365,0.00003486984,0.00014908015,0.0005175417,0.0005809524],"genre_scores_gemma":[0.9393939,0.000041469873,0.05965728,0.000017267674,0.000005307779,0.000021436055,0.00017354918,0.000017699347,0.0006721007],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998517,0.000018999912,0.000008070366,0.00005266446,0.000052796164,0.0000157097],"domain_scores_gemma":[0.99977964,0.000071029244,0.000032978343,0.000041178493,0.00005854017,0.000016604201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020649905,0.00032244698,0.00031391977,0.0003915243,0.00012253008,0.00025457595,0.00037242143,0.00048343092,0.0005047902],"category_scores_gemma":[0.00044292051,0.00013411365,0.00031506692,0.0001992293,0.00021200176,0.00032961808,0.00028324858,0.00022784271,0.00016797276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005054967,0.00019662401,0.00871723,0.00011823205,0.000046662673,0.00023475519,0.00010940393,0.03110699,0.8921205,0.00019066561,0.00026553648,0.06638786],"study_design_scores_gemma":[0.000019507675,0.0006214144,0.023669286,0.000008594899,0.00003918421,0.0002654328,0.00012077439,0.6938892,0.28046474,0.0002322169,0.0006433158,0.000026428745],"about_ca_topic_score_codex":0.0007220426,"about_ca_topic_score_gemma":0.0016148783,"teacher_disagreement_score":0.0007220426,"about_ca_system_score_codex":0.00014592225,"about_ca_system_score_gemma":0.00013233694,"threshold_uncertainty_score":0.0016887188},"labels":[],"label_agreement":null},{"id":"W2118151907","doi":"10.3390/s150818887","title":"Development of a Novel, Low-Cost, Disposable Wooden Pencil Graphite Electrode for Use in the Determination of Antioxidants and Other Biological Compounds","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Electrode; Ferricyanide; Pencil (optics); Glassy carbon; Graphite; Gallic acid; Voltammetry; Square wave; Nanotechnology; Electrochemistry; Materials science; Chemistry; Nuclear chemistry; Cyclic voltammetry; Inorganic chemistry; Voltage; Composite material; Organic chemistry; Electrical engineering; Optics; Antioxidant; Physics","score_opus":0.05899721904387976,"score_gpt":0.25515995112951806,"score_spread":0.1961627320856383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118151907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48396477,0.01950367,0.47343805,0.0028878879,0.0017842072,0.002125457,0.0026787254,0.0029085928,0.010708675],"genre_scores_gemma":[0.42834413,0.006659649,0.5509488,0.0012657043,0.00014516534,0.0004662556,0.0016508705,0.00012979766,0.010389549],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997116,0.000032154814,0.000018637307,0.000089825575,0.00013073183,0.000017122824],"domain_scores_gemma":[0.99973243,0.00007734812,0.00004982847,0.000030732765,0.00007346929,0.0000362477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003409162,0.00076872576,0.00049495697,0.00055614556,0.0001313959,0.00047008132,0.0012788391,0.0014458384,0.0012978838],"category_scores_gemma":[0.00082566263,0.00035919313,0.00035698104,0.00042414767,0.00026515976,0.0008353521,0.00031060516,0.0007705164,0.0008532144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041310086,0.000044798246,0.00018558267,0.00025433992,0.000013935195,0.00031213122,0.000010878273,0.00012815252,0.97461706,0.00017068932,0.00034982516,0.023871195],"study_design_scores_gemma":[0.000031118972,0.00050260226,0.0019254525,0.000020598809,0.000027177157,0.0013838366,0.000019080824,0.0022162953,0.9845775,0.00010384026,0.009170114,0.000022391292],"about_ca_topic_score_codex":0.00026743335,"about_ca_topic_score_gemma":0.00063303247,"teacher_disagreement_score":0.0014458384,"about_ca_system_score_codex":0.00021848523,"about_ca_system_score_gemma":0.00027711134,"threshold_uncertainty_score":0.0043419003},"labels":[],"label_agreement":null},{"id":"W2119363765","doi":"10.3390/s100605378","title":"Integration of a Multi-Camera Vision System and Strapdown Inertial Navigation System (SDINS) with a Modified Kalman Filter","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kalman filter; Inertial navigation system; Computer vision; Position (finance); Artificial intelligence; Control theory (sociology); Computer science; Tracking system; Inertial frame of reference; Tracking (education); Engineering; Physics","score_opus":0.008152829223571264,"score_gpt":0.2198273870928081,"score_spread":0.21167455786923683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119363765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057254853,0.00015548721,0.99270976,0.00002999984,0.000052808962,0.000031061696,0.000014527298,0.000527547,0.000753302],"genre_scores_gemma":[0.34160206,0.00037445084,0.65295595,0.000104607076,0.00007863406,0.00011798677,0.0001409969,0.000082144,0.0045431657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932146,0.000079929916,0.000046274756,0.00015171476,0.00035471952,0.000045960864],"domain_scores_gemma":[0.99934584,0.00016488983,0.00007471844,0.000080384874,0.0003056694,0.000028503413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060047,0.0005065678,0.00047201768,0.0005119843,0.0002556781,0.0005468177,0.00067604164,0.0005251019,0.0010733401],"category_scores_gemma":[0.0012852724,0.00046544417,0.00048453308,0.0002985684,0.0001759191,0.00084928365,0.00043457397,0.00049235055,0.0005303548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003945622,0.00016404517,0.005143012,0.00039793065,0.00026646908,0.00020217772,0.00025366872,0.11006752,0.14090143,0.0069528036,0.0020400793,0.7332162],"study_design_scores_gemma":[0.000064896456,0.00067905686,0.0055609164,0.00005043818,0.00018707433,0.00038151734,0.000040864783,0.87779975,0.08168577,0.0013359194,0.03212585,0.00008803532],"about_ca_topic_score_codex":0.005984096,"about_ca_topic_score_gemma":0.008357398,"teacher_disagreement_score":0.005984096,"about_ca_system_score_codex":0.00049649103,"about_ca_system_score_gemma":0.0007145375,"threshold_uncertainty_score":0.011898518},"labels":[],"label_agreement":null},{"id":"W2120067191","doi":"10.3390/s90100430","title":"CMOS Image Sensors for High Speed Applications","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":195,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McMaster University","funders":"King Abdulaziz City for Science and Technology; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"CMOS; Image sensor; Pixel; Computer science; Rolling shutter; Frame rate; CMOS sensor; Electronic engineering; Chip; Computer hardware; Electrical engineering; Engineering; Artificial intelligence; Telecommunications; Shutter","score_opus":0.0063311035286572,"score_gpt":0.22324519865101727,"score_spread":0.21691409512236007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120067191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019851275,0.14390263,0.66237044,0.0042037903,0.0033373192,0.0005384968,0.001547103,0.006726337,0.15752271],"genre_scores_gemma":[0.21788123,0.08158843,0.57721573,0.0027823518,0.0014751107,0.00048128856,0.0016627266,0.00039060295,0.11652247],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939764,0.00004860461,0.000028074417,0.00006989572,0.00042411833,0.000031654785],"domain_scores_gemma":[0.9997341,0.000037989576,0.000028143768,0.000025166968,0.00015683955,0.00001776316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306456,0.00047459273,0.00038778174,0.00050006935,0.0003405602,0.00091341796,0.0006176485,0.0008163004,0.012652654],"category_scores_gemma":[0.00071153097,0.00026809072,0.00025436367,0.00075342535,0.00022972454,0.0008935461,0.00044093927,0.000772561,0.0063481275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015922236,0.000055415632,0.0005449488,0.0013841285,0.000036808957,0.00016196034,0.00014760882,0.0020252864,0.36803785,0.043417677,0.047019545,0.53700954],"study_design_scores_gemma":[0.000036709134,0.00020698168,0.0008780205,0.00012546172,0.00004356408,0.00078701164,0.000046280365,0.009247534,0.16449395,0.007929079,0.8161656,0.000039802984],"about_ca_topic_score_codex":0.0004116738,"about_ca_topic_score_gemma":0.00049375254,"teacher_disagreement_score":0.012652654,"about_ca_system_score_codex":0.0005176202,"about_ca_system_score_gemma":0.00057228224,"threshold_uncertainty_score":0.042327404},"labels":[],"label_agreement":null},{"id":"W2122906861","doi":"10.3390/s121217295","title":"Towards a Hybrid Energy Efficient Multi-Tree-Based Optimized Routing Protocol for Wireless Networks","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Network packet; Tree (set theory); Routing protocol; Computer network; Geographic routing; Routing (electronic design automation); Node (physics); Path vector protocol; Wireless sensor network; Path (computing); Shortest-path tree; Shortest path problem; Dynamic Source Routing; Distributed computing; Algorithm; Minimum spanning tree; Mathematics; Theoretical computer science; Engineering","score_opus":0.02515446810014572,"score_gpt":0.2757107570138658,"score_spread":0.2505562889137201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122906861","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007242177,0.00040912876,0.9911566,0.00012078988,0.000027542059,0.000032451273,0.000015054315,0.00020133788,0.0007949937],"genre_scores_gemma":[0.18146569,0.00083728164,0.8148269,0.00015460866,0.000047134996,0.00015277711,0.0001229051,0.00009965829,0.0022930985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941206,0.0001869114,0.000031523734,0.000072492396,0.00025668988,0.000040196006],"domain_scores_gemma":[0.99957174,0.00016975334,0.000061784114,0.00006571256,0.000111923,0.000019066973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010812774,0.00044450068,0.00052941137,0.0005920169,0.00038924173,0.00086136966,0.0013350721,0.00060233026,0.0004980251],"category_scores_gemma":[0.0015538365,0.00027237524,0.00036244802,0.00082121795,0.0004785036,0.0015432943,0.0010232282,0.00064695755,0.0002239299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021337415,0.00011846813,0.0008200036,0.00026224717,0.00014563133,0.0001982007,0.00029153723,0.5713654,0.03642571,0.11439486,0.004291622,0.27147287],"study_design_scores_gemma":[0.000021213393,0.00012578754,0.00013736694,0.00001665916,0.000027447693,0.000116895855,0.000035281537,0.9738221,0.0048395204,0.013386082,0.007453678,0.000017948389],"about_ca_topic_score_codex":0.0007826444,"about_ca_topic_score_gemma":0.0015321714,"teacher_disagreement_score":0.0013350721,"about_ca_system_score_codex":0.00040228557,"about_ca_system_score_gemma":0.0006705106,"threshold_uncertainty_score":0.00571841},"labels":[],"label_agreement":null},{"id":"W2123596361","doi":"10.3390/s110201433","title":"MEMS-Based Power Generation Techniques for Implantable Biosensing Applications","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Biosensor; Microelectromechanical systems; Energy harvesting; Battery (electricity); Power (physics); Electrical engineering; Computer science; Electronic engineering; Engineering; Nanotechnology; Materials science","score_opus":0.07290685324870644,"score_gpt":0.30686797068279703,"score_spread":0.2339611174340906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123596361","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12284082,0.10088556,0.684203,0.0039547603,0.0026603267,0.0005436061,0.00056451967,0.0021732096,0.08217425],"genre_scores_gemma":[0.63640815,0.051799808,0.26429793,0.0012706318,0.000831329,0.00045152946,0.00035017903,0.00020328305,0.04438713],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99973863,0.000027389984,0.000012101364,0.000033510685,0.00017106159,0.000017332834],"domain_scores_gemma":[0.99986494,0.00003193974,0.00003889189,0.00001261815,0.000044729935,0.0000069320154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030260766,0.0005988351,0.00024287365,0.00041769707,0.00021147101,0.00043026434,0.0005086166,0.0006598938,0.003641071],"category_scores_gemma":[0.00035253,0.00024021145,0.0002758458,0.00046666258,0.0002347868,0.0006976803,0.00026795708,0.0005961725,0.0012999629],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060283335,0.00004912287,0.00026443342,0.00065259263,0.000028198561,0.00020892387,0.000074202115,0.0013959102,0.84307015,0.010485424,0.0048871716,0.13882352],"study_design_scores_gemma":[0.00003810968,0.00045342743,0.0012180016,0.00010473662,0.00005817647,0.0006633094,0.000054671284,0.0142033575,0.8034482,0.0039479006,0.17577145,0.000038651386],"about_ca_topic_score_codex":0.00016733826,"about_ca_topic_score_gemma":0.00029305974,"teacher_disagreement_score":0.003641071,"about_ca_system_score_codex":0.00030125387,"about_ca_system_score_gemma":0.00017141637,"threshold_uncertainty_score":0.012180567},"labels":[],"label_agreement":null},{"id":"W2123959597","doi":"10.3390/s100605425","title":"A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Routing protocol; Computer science; Wireless sensor network; Cluster analysis; Zone Routing Protocol; Wireless Routing Protocol; Routing (electronic design automation); Computer network; Distributed computing; Dynamic Source Routing; Hierarchical routing; Selection algorithm; Real-time computing; Selection (genetic algorithm); Artificial intelligence","score_opus":0.006197343086395352,"score_gpt":0.20728043839891527,"score_spread":0.20108309531251992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123959597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065904655,0.00048565096,0.99105644,0.00014780532,0.00006374244,0.000051052924,0.00002599694,0.00031822533,0.0012606634],"genre_scores_gemma":[0.20960459,0.0009274589,0.78399956,0.0001977945,0.000049301947,0.00036682133,0.00023010743,0.000060411887,0.004564012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971205,0.00007950121,0.000019143663,0.000047003425,0.00012774418,0.000014491009],"domain_scores_gemma":[0.99982697,0.00006742623,0.000024572057,0.000014579887,0.00005757478,0.000008916161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051644014,0.000454052,0.0005381249,0.00044646196,0.00037169535,0.0004021997,0.00094071927,0.00053107797,0.00046142438],"category_scores_gemma":[0.0010566446,0.00018909923,0.00033916143,0.0006348938,0.0003029093,0.00066247076,0.0005836981,0.00058411044,0.00015799464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009167171,0.00009285045,0.0015882166,0.00015803962,0.000118658485,0.00022461102,0.000185711,0.5003746,0.022316145,0.02267639,0.0042957696,0.4478773],"study_design_scores_gemma":[0.000024969535,0.00007400889,0.00034846735,0.0000079542315,0.00001924147,0.00011860517,0.0000126095765,0.98480785,0.0026450716,0.00399713,0.0079297,0.000014500239],"about_ca_topic_score_codex":0.0014119066,"about_ca_topic_score_gemma":0.0020769523,"teacher_disagreement_score":0.0014119066,"about_ca_system_score_codex":0.00044949044,"about_ca_system_score_gemma":0.00044026767,"threshold_uncertainty_score":0.0032613277},"labels":[],"label_agreement":null},{"id":"W2124157143","doi":"10.3390/s120708732","title":"Method for Vibration Response Simulation and Sensor Placement Optimization of a Machine Tool Spindle System with a Bearing Defect","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; University of British Columbia; National Natural Science Foundation of China","keywords":"Bearing (navigation); Vibration; Structural engineering; Finite element method; Noise (video); Engineering; Nonlinear system; Newmark-beta method; Timoshenko beam theory; Acoustics; Control theory (sociology); Computer science; Physics","score_opus":0.011585658308007427,"score_gpt":0.268087944315238,"score_spread":0.2565022860072306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124157143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018843327,0.00005305215,0.9783718,0.000031726897,0.000013751476,0.000036732803,0.000033156884,0.00034225796,0.002274269],"genre_scores_gemma":[0.68076503,0.00012362802,0.31347638,0.000034560046,0.000009566877,0.00033450377,0.00010612229,0.00010774722,0.0050424626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999089,0.000019268038,0.0000035380162,0.000013144837,0.000045192555,0.0000099327235],"domain_scores_gemma":[0.99985516,0.000079217854,0.00001577956,0.0000088870065,0.000035825014,0.000005069239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026543066,0.00040776783,0.00035435628,0.00023699336,0.00020251815,0.00020670846,0.00049793534,0.0006512049,0.0030427116],"category_scores_gemma":[0.0004405571,0.00025863494,0.00040707184,0.00014487289,0.0001952131,0.00025395656,0.00022942072,0.00036173954,0.000298349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003370186,0.000029912628,0.0003300476,0.00005543212,0.00001546885,0.00005734727,0.000047326506,0.96892357,0.013873576,0.0026902582,0.00025132584,0.013692062],"study_design_scores_gemma":[0.0000032138255,0.000009954131,0.000036460737,0.0000010178214,0.0000011533803,0.0000057739894,0.0000025783852,0.9988619,0.0007702219,0.000116783325,0.00018964829,0.000001267856],"about_ca_topic_score_codex":0.0031746589,"about_ca_topic_score_gemma":0.0027755045,"teacher_disagreement_score":0.0031746589,"about_ca_system_score_codex":0.0002767446,"about_ca_system_score_gemma":0.0005693281,"threshold_uncertainty_score":0.010178864},"labels":[],"label_agreement":null},{"id":"W2124925800","doi":"10.3390/s120405134","title":"Benefits of Combined GPS/GLONASS with Low-Cost MEMS IMUs for Vehicular Urban Navigation","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GLONASS; Global Positioning System; GNSS applications; Inertial navigation system; Real Time Kinematic; Computer science; Microelectromechanical systems; Inertial measurement unit; Satellite system; Satellite navigation; Satellite; Engineering; Aerospace engineering; Telecommunications; Inertial frame of reference; Artificial intelligence; Physics","score_opus":0.0081835339933919,"score_gpt":0.20822121832599652,"score_spread":0.20003768433260463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124925800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7829834,0.0027198112,0.19670396,0.00059069914,0.00016210784,0.00013295263,0.00008827227,0.0012880663,0.015330709],"genre_scores_gemma":[0.9469163,0.00045301073,0.04994543,0.00016308385,0.00007491366,0.000026569418,0.00011844773,0.00006127116,0.0022410671],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989202,0.00020431928,0.000061886094,0.0001454313,0.00049626984,0.00017182418],"domain_scores_gemma":[0.9992341,0.000144391,0.00011751181,0.00013867908,0.0002831164,0.00008208851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006876319,0.0015233329,0.00072505063,0.0005246794,0.00044920004,0.0007708274,0.00089998904,0.0008793079,0.0017693909],"category_scores_gemma":[0.0013198584,0.00043641886,0.00042753769,0.0006540745,0.00040424653,0.0015225201,0.0020312306,0.0007260508,0.0008823398],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023779655,0.00059976376,0.036640976,0.0006147458,0.000422646,0.0008391715,0.00030936292,0.080906756,0.5080854,0.004264964,0.00112062,0.36381757],"study_design_scores_gemma":[0.000414787,0.010477682,0.1211479,0.00022373584,0.0018724278,0.0035977277,0.0014976009,0.40005937,0.4200695,0.0054450054,0.034872197,0.00032198947],"about_ca_topic_score_codex":0.0018034049,"about_ca_topic_score_gemma":0.0035337415,"teacher_disagreement_score":0.0018034049,"about_ca_system_score_codex":0.00026589926,"about_ca_system_score_gemma":0.0005281156,"threshold_uncertainty_score":0.005919218},"labels":[],"label_agreement":null},{"id":"W2125524464","doi":"10.3390/s110404152","title":"Recent Progress in Brillouin Scattering Based Fiber Sensors","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":569,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Brillouin scattering; Optics; Fiber Bragg grating; Optical fiber; Brillouin zone; Distributed acoustic sensing; Materials science; Acoustic wave; Fiber optic sensor; Graded-index fiber; Electrostriction; Acoustics; Physics; Piezoelectricity","score_opus":0.02138784837091841,"score_gpt":0.23021781697046387,"score_spread":0.20882996859954545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125524464","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012181661,0.9461825,0.02837263,0.0013846433,0.00069972844,0.000044779434,0.000050723356,0.00016698267,0.010916393],"genre_scores_gemma":[0.057901382,0.8953019,0.034914434,0.00092986354,0.0018937023,0.000059813847,0.00016257245,0.000058470516,0.008777975],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992999,0.00013159908,0.00005703555,0.00016441966,0.0002894773,0.000057492427],"domain_scores_gemma":[0.9989201,0.00041333475,0.00013367126,0.00003637103,0.0004352812,0.00006128701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012086136,0.0007397337,0.00075195706,0.0012795299,0.00023291323,0.00089788716,0.0007210465,0.0013731126,0.001565935],"category_scores_gemma":[0.0010394824,0.00041333228,0.0004526238,0.0017327863,0.00050156954,0.002491445,0.00047861694,0.0011022005,0.00093241694],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027303942,0.00025596604,0.0020719129,0.01102015,0.00012511927,0.00036528351,0.0004939256,0.0042385166,0.12909752,0.020358846,0.012656251,0.81904346],"study_design_scores_gemma":[0.00002428332,0.0011732033,0.0037297597,0.001361219,0.0002300727,0.0018868515,0.0004244009,0.01836738,0.13717137,0.0071334196,0.8283322,0.00016582313],"about_ca_topic_score_codex":0.00070451474,"about_ca_topic_score_gemma":0.00052350864,"teacher_disagreement_score":0.001565935,"about_ca_system_score_codex":0.0005803882,"about_ca_system_score_gemma":0.0005120277,"threshold_uncertainty_score":0.006391883},"labels":[],"label_agreement":null},{"id":"W2126914085","doi":"10.3390/s131217025","title":"Sensored Field Oriented Control of a Robust Induction Motor Drive Using a Novel Boundary Layer Fuzzy Controller","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Universiti Malaya","keywords":"Control theory (sociology); Induction motor; Vector control; Fuzzy logic; Controller (irrigation); Control engineering; Boundary (topology); Fuzzy control system; Boundary layer; Lyapunov stability; Digital signal processing; Digital signal processor; Engineering; Computer science; Lyapunov function; Electronic engineering; Control (management); Mathematics; Physics; Electrical engineering; Artificial intelligence","score_opus":0.014009038914522461,"score_gpt":0.20789565412592134,"score_spread":0.19388661521139888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126914085","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05379422,0.00049659883,0.9410011,0.00012705033,0.00014096839,0.000070840106,0.000033030035,0.0005852458,0.0037509783],"genre_scores_gemma":[0.8925442,0.0002145253,0.10544063,0.000059791506,0.00003429695,0.00010086474,0.00004249519,0.000013911005,0.0015493052],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998367,0.00001582638,0.000013694579,0.00003871387,0.0000825736,0.000012491206],"domain_scores_gemma":[0.9998753,0.000024903058,0.000025965363,0.000011046007,0.000053436223,0.000009325039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002800219,0.00036666944,0.00032320005,0.00020192488,0.00023438376,0.00039764238,0.00067036395,0.0005453034,0.0006004242],"category_scores_gemma":[0.00036485327,0.00013460446,0.00026193797,0.00012164589,0.00027791728,0.00038975934,0.0002533633,0.00041730542,0.00015194534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006145223,0.00021276822,0.0011327161,0.0006413477,0.0001124515,0.0005086704,0.00033120907,0.21990822,0.44810778,0.011090447,0.001882954,0.31545696],"study_design_scores_gemma":[0.00010700125,0.00041614604,0.0008817381,0.000030957097,0.000032069805,0.00020344889,0.000016503147,0.95280236,0.040725425,0.0008763694,0.0038802987,0.000027648644],"about_ca_topic_score_codex":0.0013680721,"about_ca_topic_score_gemma":0.0014499057,"teacher_disagreement_score":0.0013680721,"about_ca_system_score_codex":0.0002743971,"about_ca_system_score_gemma":0.00035068876,"threshold_uncertainty_score":0.0027201772},"labels":[],"label_agreement":null},{"id":"W2126980271","doi":"10.3390/s101009252","title":"Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council; University of Calgary","keywords":"Global Positioning System; Orientation (vector space); Computer science; Artificial neural network; GPS/INS; Inertial navigation system; Automation; Kalman filter; Mobile mapping; Inertial measurement unit; Real-time computing; Wireless sensor network; Artificial intelligence; Algorithm; Assisted GPS; Engineering; Telecommunications","score_opus":0.008297469134559664,"score_gpt":0.23958987017276503,"score_spread":0.23129240103820536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126980271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010267745,0.00018256682,0.9875021,0.000055293185,0.000031710566,0.00002314463,0.000010146562,0.0004924091,0.0014348794],"genre_scores_gemma":[0.67420524,0.00041055103,0.3216607,0.000103987455,0.000059298312,0.00011448902,0.0000739468,0.000060637994,0.0033111107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997173,0.00006414306,0.000016120419,0.000064363216,0.00011271831,0.00002534328],"domain_scores_gemma":[0.99960655,0.00012486694,0.000073103474,0.000034728208,0.00014863159,0.0000122579895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005651434,0.00070395705,0.00036905674,0.000419324,0.00027239992,0.0005457251,0.00079734926,0.00062284194,0.0008157526],"category_scores_gemma":[0.0013731158,0.00031785315,0.0004279673,0.0005324351,0.00041299648,0.0007026333,0.00056864147,0.00065858033,0.00030900727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104632396,0.000057505826,0.0009169939,0.00007202246,0.000048390135,0.00007003181,0.000097702476,0.72523844,0.009845403,0.006457802,0.0008473455,0.2562437],"study_design_scores_gemma":[0.0000033571594,0.000017428303,0.00014342689,0.0000042586494,0.000007896232,0.000011719404,0.000003973865,0.99739194,0.0014720047,0.0006104698,0.0003300979,0.0000033712183],"about_ca_topic_score_codex":0.00556278,"about_ca_topic_score_gemma":0.005854785,"teacher_disagreement_score":0.00556278,"about_ca_system_score_codex":0.0005226668,"about_ca_system_score_gemma":0.0007046591,"threshold_uncertainty_score":0.011060834},"labels":[],"label_agreement":null},{"id":"W2128615445","doi":"10.3390/s121114344","title":"Inertial Aided Cycle Slip Detection and Identification for Integrated PPP GPS and INS","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Inertial navigation system; Precise Point Positioning; GPS/INS; Real-time computing; Computer science; Identification (biology); Navigation system; Mobile mapping; GPS signals; Automation; Simulation; Engineering; Inertial frame of reference; Assisted GPS; GNSS applications; Telecommunications","score_opus":0.00939192957142769,"score_gpt":0.21572520159661104,"score_spread":0.20633327202518337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128615445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11639981,0.00023993476,0.8793353,0.000042717205,0.00006337564,0.000049368206,0.00013745326,0.0018713415,0.0018607492],"genre_scores_gemma":[0.6523418,0.00015072431,0.3454378,0.00003450441,0.00003943396,0.000057914163,0.00037926613,0.000052219446,0.0015062934],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964976,0.000055595807,0.000021143807,0.000075179814,0.00016165816,0.00003661205],"domain_scores_gemma":[0.9996873,0.000055794106,0.00006570874,0.0000520027,0.00012350777,0.000015752135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037498825,0.0006788196,0.00056564086,0.0011137802,0.00022417791,0.0003901688,0.000422723,0.0003262998,0.00088653865],"category_scores_gemma":[0.0010345401,0.0002822891,0.00017314729,0.0009054871,0.00022305602,0.00045337,0.0005634664,0.00030310545,0.00054745446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004208419,0.000109885084,0.016729167,0.00012473266,0.00006016086,0.00013904428,0.000119921286,0.075173475,0.077490576,0.0012666509,0.0019380894,0.82642746],"study_design_scores_gemma":[0.00004479902,0.00023657741,0.017716058,0.00001820879,0.000039254726,0.0002817591,0.000049886443,0.9472132,0.029911278,0.00081549765,0.0036453803,0.000028035152],"about_ca_topic_score_codex":0.0020887502,"about_ca_topic_score_gemma":0.003293039,"teacher_disagreement_score":0.0020887502,"about_ca_system_score_codex":0.00018187985,"about_ca_system_score_gemma":0.00045599046,"threshold_uncertainty_score":0.0041531324},"labels":[],"label_agreement":null},{"id":"W2128958887","doi":"10.3390/s90705679","title":"Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Land, Transport and Maritime Affairs","keywords":"Photogrammetry; Lidar; Computer science; Process (computing); Object (grammar); Data integration; Planar; Bounded function; Building model; Artificial intelligence; Computer vision; Data mining; Computer graphics (images); Remote sensing; Geology; Mathematics; Simulation; Programming language","score_opus":0.08280628087670538,"score_gpt":0.311042591110168,"score_spread":0.22823631023346264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128958887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006379246,0.000026150352,0.99274737,0.000009199681,0.0000033175686,0.000027989412,0.000030058052,0.00056694617,0.00020971149],"genre_scores_gemma":[0.13604107,0.000058971076,0.86303526,0.000019320896,0.0000049585155,0.000094113544,0.00035340057,0.00011513539,0.00027776448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936706,0.00012213616,0.00003688933,0.000121927376,0.00031057018,0.000041353393],"domain_scores_gemma":[0.9992986,0.00028158523,0.00007146426,0.0001916028,0.00013440977,0.00002243297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000770233,0.0006213715,0.00078667654,0.0011137903,0.00031006284,0.0009205127,0.0011552403,0.0006001539,0.0011718358],"category_scores_gemma":[0.0017510335,0.00073106814,0.0008621468,0.00091841276,0.0005109298,0.0009909685,0.0010346407,0.00057641394,0.0005901758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102973674,0.00013338192,0.002132031,0.00019229615,0.00012379087,0.00024626573,0.00034154614,0.50099105,0.054598182,0.0100153675,0.0013176035,0.42980558],"study_design_scores_gemma":[0.000007956743,0.00002846361,0.00050082814,0.000009076952,0.000012948453,0.000058248454,0.000032863678,0.9853367,0.010321188,0.0023926331,0.0012865921,0.000012394505],"about_ca_topic_score_codex":0.0025301941,"about_ca_topic_score_gemma":0.004593338,"teacher_disagreement_score":0.0025301941,"about_ca_system_score_codex":0.0003446729,"about_ca_system_score_gemma":0.00076334976,"threshold_uncertainty_score":0.0050309896},"labels":[],"label_agreement":null},{"id":"W2129097250","doi":"10.3390/s120911638","title":"Accuracy Enhancement of Inertial Sensors Utilizing High Resolution Spectral Analysis","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial navigation system; GPS/INS; Global Positioning System; Inertial measurement unit; Wavelet; Computer science; Inertial frame of reference; Noise (video); Engineering; Artificial intelligence; Assisted GPS; Telecommunications","score_opus":0.013211508075059704,"score_gpt":0.24480570490591375,"score_spread":0.23159419683085405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129097250","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11798063,0.0003851516,0.87952864,0.00008748354,0.000054659205,0.000029698598,0.000033238208,0.0005113068,0.0013892571],"genre_scores_gemma":[0.54617935,0.0005720826,0.45125085,0.00006687673,0.000054100088,0.000034691286,0.00012839798,0.00008362566,0.0016299277],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957913,0.00006763889,0.000023269817,0.000053875126,0.0002525458,0.000023597726],"domain_scores_gemma":[0.9992471,0.00025826975,0.00014610219,0.000107589665,0.00022922602,0.000011792856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005462231,0.000512067,0.00032253092,0.00056769646,0.00013950857,0.00032484843,0.0003713192,0.00040734105,0.00045259448],"category_scores_gemma":[0.0021905515,0.00018701279,0.00029845798,0.00051566,0.00022526615,0.00080760755,0.00038215853,0.00033513375,0.00027643554],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026534023,0.00012672022,0.003807726,0.00019238367,0.000039849387,0.000102098515,0.00021718415,0.06398797,0.33816257,0.0026562635,0.0006871296,0.58975476],"study_design_scores_gemma":[0.000024573123,0.0003392066,0.010120009,0.000032430926,0.00005279126,0.00033718036,0.000065703665,0.66930085,0.3126473,0.0014544724,0.005572118,0.00005335754],"about_ca_topic_score_codex":0.0007216676,"about_ca_topic_score_gemma":0.0010244943,"teacher_disagreement_score":0.0007216676,"about_ca_system_score_codex":0.0002012884,"about_ca_system_score_gemma":0.0002522365,"threshold_uncertainty_score":0.0028886795},"labels":[],"label_agreement":null},{"id":"W2129712419","doi":"10.3390/s100403771","title":"Optoelectronic Capillary Sensors in Microfluidic and Point-of-Care Instrumentation","year":2010,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Instrumentation (computer programming); Optical fiber; Capillary action; Microfluidics; Point (geometry); Computer science; Point of care; Nanotechnology; Optoelectronics; Engineering; Materials science; Telecommunications","score_opus":0.012495554944683804,"score_gpt":0.27350235092550373,"score_spread":0.26100679598081994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129712419","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011512389,0.9824493,0.009290869,0.00034677537,0.00084645493,0.000037884878,0.000029019877,0.000034540462,0.005814],"genre_scores_gemma":[0.0069994056,0.9701315,0.013896424,0.0005315803,0.0007618807,0.00006916759,0.000060595663,0.000013274476,0.007536191],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993092,0.00009192029,0.00006168729,0.00015626985,0.00033595128,0.000044977438],"domain_scores_gemma":[0.9996019,0.0001947217,0.00004778035,0.000016170447,0.00012292275,0.000016420096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009472648,0.0011596355,0.0012803295,0.00195038,0.00039995875,0.0010800199,0.0014696557,0.0018249793,0.0023323381],"category_scores_gemma":[0.00063542166,0.00062394585,0.0005148536,0.0027696958,0.00097537594,0.0021905839,0.0008852809,0.0013567093,0.002778682],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075719385,0.00020073546,0.0004170119,0.013999122,0.00006118561,0.00062674313,0.00020365036,0.0014716776,0.041108593,0.030926377,0.011959891,0.89894927],"study_design_scores_gemma":[0.0000108170025,0.00017158622,0.00061939895,0.0016971045,0.00005832445,0.0025533962,0.00009659423,0.0007754642,0.025603628,0.006083424,0.9622702,0.000059966653],"about_ca_topic_score_codex":0.0005642341,"about_ca_topic_score_gemma":0.0009065074,"teacher_disagreement_score":0.0023323381,"about_ca_system_score_codex":0.00079812924,"about_ca_system_score_gemma":0.00080133794,"threshold_uncertainty_score":0.007802427},"labels":[],"label_agreement":null},{"id":"W2131167839","doi":"10.3390/s150923572","title":"A Wireless Multi-Sensor Dielectric Impedance Spectroscopy Platform","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Dielectric spectroscopy; Microelectronics; Electronic engineering; Wireless sensor network; Calibration; Electrical impedance; Sensitivity (control systems); Engineering; Electrical engineering; Wireless; Dielectric; Computer science; Materials science; Telecommunications; Computer network; Electrode; Chemistry; Physics","score_opus":0.03389982037884893,"score_gpt":0.26976067820443395,"score_spread":0.23586085782558502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131167839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.156856,0.0012302459,0.8259817,0.00044736298,0.00033490494,0.00063768716,0.0005312752,0.0039302185,0.0100505985],"genre_scores_gemma":[0.47607255,0.0012213752,0.50075024,0.00040962378,0.00014357592,0.0005667215,0.00077841245,0.00016176503,0.019895697],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995152,0.00004800594,0.000024272653,0.000104873914,0.00027028372,0.00003735321],"domain_scores_gemma":[0.99979395,0.000032236258,0.000045423265,0.00003235051,0.00006785354,0.000028275017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032153403,0.00056210253,0.00035775814,0.00049538363,0.00021466616,0.0005236614,0.0012940869,0.0005377967,0.002012813],"category_scores_gemma":[0.00041416212,0.00027716233,0.00021523162,0.0003359317,0.00014969331,0.0010266893,0.0008670498,0.0004997329,0.0010521194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013007663,0.00012743768,0.0007356788,0.00023206475,0.000021038079,0.00028736686,0.00006480024,0.0036169789,0.9101996,0.0023466877,0.0014709914,0.08076729],"study_design_scores_gemma":[0.00006637843,0.0015410682,0.0024582732,0.00003025164,0.000054088792,0.0015475905,0.0000565382,0.06474829,0.8670536,0.000811535,0.061564416,0.00006793493],"about_ca_topic_score_codex":0.0002383137,"about_ca_topic_score_gemma":0.00033122147,"teacher_disagreement_score":0.002012813,"about_ca_system_score_codex":0.00025234596,"about_ca_system_score_gemma":0.0003737479,"threshold_uncertainty_score":0.0067335367},"labels":[],"label_agreement":null},{"id":"W2132281796","doi":"10.3390/s110707243","title":"Direct Sensor Orientation of a Land-Based Mobile Mapping System","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Mobile mapping; Orientation (vector space); Calibration; Global Positioning System; Computer science; Inertial measurement unit; Real-time computing; Process (computing); Sensor fusion; GPS/INS; Computer vision; Assisted GPS; Telecommunications","score_opus":0.014285542157262802,"score_gpt":0.1944750906199764,"score_spread":0.1801895484627136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132281796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16505404,0.00031393932,0.8180255,0.00015150958,0.00020040199,0.00018761466,0.00028956248,0.00504718,0.0107302815],"genre_scores_gemma":[0.74028707,0.00018199728,0.2521467,0.00010244027,0.000044498174,0.00013796419,0.0003611211,0.000063689775,0.0066745416],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995925,0.000061556435,0.000018024648,0.000098813696,0.00019328637,0.0000358625],"domain_scores_gemma":[0.99979717,0.00001700815,0.000026541245,0.000041886055,0.00009718332,0.000020258443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021767242,0.00042802785,0.00048285868,0.00040047473,0.00024597286,0.00062298117,0.0006315377,0.00039672473,0.0021873948],"category_scores_gemma":[0.00047841034,0.0002693055,0.00019206043,0.00035178472,0.00016586504,0.00045901988,0.0007221124,0.00033959563,0.0014961098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004327619,0.00016131805,0.005501069,0.0003189614,0.00006944631,0.0003888753,0.00033849996,0.02213235,0.37571123,0.0036515421,0.0048364047,0.58645767],"study_design_scores_gemma":[0.0001996652,0.0012194987,0.020639027,0.00007163756,0.00016521316,0.0016679788,0.00023190545,0.5382026,0.39648157,0.0015699913,0.03940869,0.00014228838],"about_ca_topic_score_codex":0.0011861506,"about_ca_topic_score_gemma":0.0014836278,"teacher_disagreement_score":0.0021873948,"about_ca_system_score_codex":0.00023188091,"about_ca_system_score_gemma":0.0005441619,"threshold_uncertainty_score":0.0073176026},"labels":[],"label_agreement":null},{"id":"W2132798562","doi":"10.3390/s8042317","title":"A Micromachined Capacitive Pressure Sensor Using a Cavity-Less Structure with Bulk-Metal/Elastomer Layers and Its Wireless Telemetry Application","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"TRIUMF; University of Michigan","keywords":"Materials science; Capacitive sensing; Pressure sensor; Capacitance; Electromagnetic coil; Capacitor; Elastomer; Surface micromachining; Optoelectronics; Acoustics; Electrode; Electrical engineering; Composite material; Voltage; Mechanical engineering; Fabrication; Engineering","score_opus":0.01077642448084951,"score_gpt":0.2064969529416482,"score_spread":0.19572052846079868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132798562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69342726,0.0046476065,0.28926587,0.0009460032,0.0007120332,0.00036028103,0.0006562388,0.00228348,0.0077012093],"genre_scores_gemma":[0.72598976,0.0008272576,0.26617572,0.00032758183,0.00012450179,0.00010524885,0.00026827262,0.000045277284,0.0061362493],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955004,0.000032622782,0.000018026392,0.00016996836,0.00019747291,0.000031906366],"domain_scores_gemma":[0.99973065,0.00006565239,0.00006475047,0.000037828995,0.00006585042,0.00003524329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028522027,0.00047153988,0.000373822,0.00025462377,0.00023311161,0.00040685126,0.0011985839,0.000872181,0.0009831421],"category_scores_gemma":[0.00052499626,0.00035684084,0.00025864135,0.00028113552,0.00037094986,0.00069925754,0.00033124737,0.00056882796,0.0004095751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037511134,0.000019279323,0.00015139031,0.000064436914,0.000005206815,0.00010696255,0.000022871083,0.0002027032,0.99090016,0.00026183508,0.0001413648,0.008086249],"study_design_scores_gemma":[0.000019057226,0.00036442565,0.00114148,0.0000058323035,0.000016825707,0.0008626819,0.000014884562,0.0042002797,0.9885804,0.00009336492,0.0046768934,0.000023805316],"about_ca_topic_score_codex":0.00035704792,"about_ca_topic_score_gemma":0.00065421726,"teacher_disagreement_score":0.0011985839,"about_ca_system_score_codex":0.00033417344,"about_ca_system_score_gemma":0.000426128,"threshold_uncertainty_score":0.0032889843},"labels":[],"label_agreement":null},{"id":"W2133094441","doi":"10.3390/s100201338","title":"Characterization of Thick and Thin Film SiCN for Pressure Sensing at High Temperatures","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor Technologies Research","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Characterization (materials science); Thin film; Materials science; High pressure; Engineering physics; Optoelectronics; Nanotechnology; Composite material; Engineering","score_opus":0.007636641181333644,"score_gpt":0.2334485389268511,"score_spread":0.22581189774551746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133094441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98334056,0.0017451599,0.008782079,0.000070081274,0.00007114423,0.00009479518,0.00082686916,0.00007227004,0.004996944],"genre_scores_gemma":[0.9764107,0.001588344,0.01729381,0.000050405022,0.000016891909,0.00010360366,0.0010165522,0.000046632496,0.003473094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984646,0.000008271971,0.000009577242,0.000025763586,0.000093580304,0.000016345859],"domain_scores_gemma":[0.9997298,0.00005167338,0.00004849032,0.000023480005,0.00011966383,0.000026899428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025347687,0.00023716652,0.00015898966,0.00035616517,0.0001517616,0.0001749272,0.0002572224,0.0002466598,0.0007919155],"category_scores_gemma":[0.0003986839,0.00016872094,0.00018952721,0.0002758984,0.00014844304,0.00019324744,0.000089368,0.00024317186,0.00023883302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000101737005,0.000003833168,0.00013830552,0.00003443905,0.0000014720312,0.000022941002,0.000016425369,0.000082018676,0.99881446,0.000024783125,0.00003856663,0.0008127357],"study_design_scores_gemma":[0.0000027056897,0.00014619282,0.0071022515,0.000015330515,0.000009566795,0.00012359183,0.000046503934,0.0016254814,0.98864996,0.000015677537,0.0022558772,0.000006909708],"about_ca_topic_score_codex":0.0010107837,"about_ca_topic_score_gemma":0.002995572,"teacher_disagreement_score":0.0010107837,"about_ca_system_score_codex":0.000198011,"about_ca_system_score_gemma":0.0001882282,"threshold_uncertainty_score":0.002649188},"labels":[],"label_agreement":null},{"id":"W2133481808","doi":"10.3390/s110100019","title":"Improving the Performance of Catalytic Combustion Type Methane Gas Sensors Using Nanostructure Elements Doped with Rare Earth Cocatalysts","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Cerium; Catalysis; Methane; Catalytic combustion; Materials science; Doping; Thermal stability; Combustion; Nanostructure; Sulfur; Chemical engineering; Nanotechnology; Inorganic chemistry; Analytical Chemistry (journal); Chemistry; Optoelectronics; Organic chemistry; Metallurgy","score_opus":0.007826332767702731,"score_gpt":0.19891974717045122,"score_spread":0.1910934144027485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133481808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9904524,0.00083324494,0.0075415126,0.000048232316,0.00003509143,0.00001427633,0.000050514842,0.00017393823,0.0008508746],"genre_scores_gemma":[0.9906087,0.00040967544,0.0078072813,0.000027664633,0.000008783434,0.000013993693,0.00008262135,0.00003164086,0.0010096283],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969935,0.000036741683,0.000020162772,0.00008379559,0.00011692728,0.000043051834],"domain_scores_gemma":[0.9997814,0.00006726466,0.00003164472,0.000020854237,0.000082989965,0.00001577259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032242792,0.00046576906,0.00046849818,0.00019951841,0.0001116219,0.00032180693,0.00074363337,0.0005896565,0.0004697738],"category_scores_gemma":[0.00075498247,0.00023300895,0.00019745613,0.00018725867,0.00020016839,0.0006064698,0.00026047733,0.0002526558,0.00028543928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006255627,0.0000070680157,0.00014383641,0.000020644908,0.0000038826197,0.000012030623,0.000010739229,0.00006713549,0.99795294,0.000032650147,0.000017443894,0.0016690195],"study_design_scores_gemma":[0.0000027452913,0.000059506856,0.00023923151,0.0000010285471,0.000006429327,0.000023418734,0.0000035644027,0.001494542,0.9979772,0.0000043950954,0.0001860644,0.0000018487066],"about_ca_topic_score_codex":0.0005400687,"about_ca_topic_score_gemma":0.0011013199,"teacher_disagreement_score":0.00074363337,"about_ca_system_score_codex":0.00033923434,"about_ca_system_score_gemma":0.00014384312,"threshold_uncertainty_score":0.0024613738},"labels":[],"label_agreement":null},{"id":"W2133820918","doi":"10.3390/s150923286","title":"INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":163,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Lidar; Global Positioning System; Ranging; Inertial navigation system; Computer science; Robustness (evolution); Navigation system; Remote sensing; GPS/INS; Matching (statistics); Iterative closest point; Assisted GPS; Computer vision; Real-time computing; Artificial intelligence; Geography; Point cloud; Inertial frame of reference; Mathematics","score_opus":0.015479196843464084,"score_gpt":0.21304102988313628,"score_spread":0.1975618330396722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133820918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050076406,0.00013499006,0.9443692,0.00005373642,0.00005195766,0.000056182842,0.00009232197,0.0029808222,0.0021844085],"genre_scores_gemma":[0.45948946,0.00015835269,0.53610176,0.000062836785,0.000028079901,0.00013872648,0.00043004347,0.000102789425,0.0034879711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996822,0.000041392123,0.000015177408,0.00006371332,0.00017111393,0.00002642374],"domain_scores_gemma":[0.99979323,0.00002001488,0.000027693859,0.00003756246,0.00010979489,0.000011807924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023684224,0.00047818877,0.00050529133,0.00074150093,0.0003492566,0.0004729286,0.0008684625,0.0004769301,0.0015713209],"category_scores_gemma":[0.0004440558,0.00024854293,0.00036487886,0.0010325109,0.00017508757,0.00084366434,0.00071496016,0.0002859189,0.00086571375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035350723,0.00013061277,0.008062387,0.0002004662,0.00012474645,0.00026895275,0.00023340159,0.0517146,0.13400865,0.004143175,0.003996116,0.79676336],"study_design_scores_gemma":[0.000087590066,0.00034816988,0.0055893436,0.000025341677,0.00010566013,0.0006803859,0.00015319117,0.9007639,0.07674334,0.0021857123,0.013252795,0.00006455087],"about_ca_topic_score_codex":0.0019349176,"about_ca_topic_score_gemma":0.002324747,"teacher_disagreement_score":0.0019349176,"about_ca_system_score_codex":0.0002099025,"about_ca_system_score_gemma":0.00061293336,"threshold_uncertainty_score":0.005256593},"labels":[],"label_agreement":null},{"id":"W2134436229","doi":"10.3390/s121013349","title":"Ion-Specific Nutrient Management in Closed Systems: The Necessity for Ion-Selective Sensors in Terrestrial and Space-Based Agriculture and Water Management Systems","year":2012,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"COM DEV International; University of Guelph; Canadian Space Agency","funders":"Canadian Space Agency","keywords":"Agriculture; Ion; Environmental science; Nutrient management; Space (punctuation); Computer science; Systems engineering; Remote sensing; Engineering; Chemistry; Ecology; Biology; Geology","score_opus":0.026603950910015394,"score_gpt":0.2499176683119303,"score_spread":0.2233137174019149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134436229","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072158896,0.9945938,0.0019082407,0.00030159415,0.00014895474,0.0000080594245,0.000009488121,0.000017825176,0.0022904885],"genre_scores_gemma":[0.004589595,0.9902395,0.0023028331,0.0003239462,0.00019978771,0.000016767053,0.000023832219,0.000005596071,0.0022981556],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960226,0.00005062129,0.000038830683,0.000073074865,0.0002029561,0.000032338634],"domain_scores_gemma":[0.99958044,0.00018044683,0.0000620863,0.000019316822,0.00013595521,0.000021758955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085852866,0.0006935929,0.0011299923,0.0012140591,0.00028444853,0.0010141844,0.00091686,0.0016325363,0.0010528313],"category_scores_gemma":[0.000620671,0.0004230705,0.0004712923,0.0016732194,0.00068270584,0.0023448083,0.00059453124,0.0014514275,0.0015958141],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005167863,0.0001043967,0.00023267153,0.01196526,0.000049248272,0.00023458901,0.00011713929,0.0011286526,0.021539599,0.007338136,0.0077810334,0.9494575],"study_design_scores_gemma":[0.000011791216,0.00035267643,0.0009492792,0.0015333445,0.000084782514,0.0018877636,0.00016535241,0.00078100356,0.016813343,0.0036970205,0.97367126,0.00005250158],"about_ca_topic_score_codex":0.0009482851,"about_ca_topic_score_gemma":0.001185679,"teacher_disagreement_score":0.0016325363,"about_ca_system_score_codex":0.0006904194,"about_ca_system_score_gemma":0.00087220134,"threshold_uncertainty_score":0.005009353},"labels":[],"label_agreement":null},{"id":"W2135186310","doi":"10.3390/s150923145","title":"Butterfly Encryption Scheme for Resource-Constrained Wireless Networks","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Boeing","keywords":"Computer science; Encryption; Computer network; Wireless; Cryptography; Computer security; Key (lock); Wireless network; Authentication (law); Distributed computing; Telecommunications","score_opus":0.028558386907292354,"score_gpt":0.2499283875143604,"score_spread":0.22137000060706805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135186310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2602754,0.0010838809,0.7255486,0.0008174429,0.00013778369,0.00031386485,0.0002933801,0.0005366113,0.01099297],"genre_scores_gemma":[0.9027026,0.0003431293,0.092192315,0.000117629315,0.000024656327,0.000118689684,0.00015001335,0.00001881843,0.00433209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956757,0.00009544782,0.00003730098,0.000046198613,0.0001912556,0.000062235726],"domain_scores_gemma":[0.99922657,0.0002309636,0.00015111124,0.00023487455,0.00012662933,0.000029849301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045091176,0.00023276605,0.0003262009,0.00040481554,0.0004607708,0.00049143744,0.00043173193,0.0005063219,0.0013836116],"category_scores_gemma":[0.001272261,0.00010349278,0.000313906,0.00035599375,0.00038880206,0.0013480387,0.0006588572,0.00043302405,0.00022043227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010399256,0.0001612106,0.002085848,0.0004468616,0.00013076008,0.001454109,0.00071639783,0.22853373,0.2458007,0.31530502,0.0067337393,0.1975917],"study_design_scores_gemma":[0.00023217333,0.0007472594,0.0017750408,0.00009560361,0.000068020025,0.002255503,0.00014748747,0.80797833,0.093880825,0.069953,0.02275277,0.000113971655],"about_ca_topic_score_codex":0.0006372696,"about_ca_topic_score_gemma":0.00065011816,"teacher_disagreement_score":0.0013836116,"about_ca_system_score_codex":0.00059433095,"about_ca_system_score_gemma":0.00068643107,"threshold_uncertainty_score":0.0046285987},"labels":[],"label_agreement":null},{"id":"W2135292549","doi":"10.3390/s8042240","title":"Error and Performance Analysis of MEMS-based Inertial Sensors with a Low-cost GPS Receiver","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Unavailability; GNSS applications; Inertial navigation system; Precision Lightweight GPS Receiver; Inertial measurement unit; Computer science; Real Time Kinematic; Real-time computing; Assisted GPS; GPS signals; Receiver autonomous integrity monitoring; GPS/INS; Simulation; Engineering; Inertial frame of reference; Telecommunications; Artificial intelligence; Gps receiver; Reliability engineering","score_opus":0.009383118220800565,"score_gpt":0.2054028296074122,"score_spread":0.19601971138661164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135292549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71871644,0.0029068927,0.27369475,0.00026210817,0.00011873836,0.0000653811,0.00024735945,0.0005390745,0.0034493266],"genre_scores_gemma":[0.9900793,0.00034206087,0.008419782,0.000017791543,0.000019806586,0.000025771142,0.00008095708,0.000018788713,0.0009957529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99889,0.00020907787,0.000055706983,0.00015948214,0.00063106074,0.000054635762],"domain_scores_gemma":[0.99742705,0.0014543012,0.0003453469,0.00018836107,0.00056070526,0.000024283816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009525679,0.0005505082,0.00050478126,0.00045988968,0.00022869215,0.00039819087,0.00058956415,0.0006862437,0.0005330289],"category_scores_gemma":[0.003342125,0.00017737216,0.0004316922,0.00037612554,0.00029776877,0.0004054327,0.00028789247,0.00023375796,0.00018202941],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001528795,0.0001669007,0.020452052,0.0008295095,0.00025774317,0.00033448637,0.00038649037,0.6958088,0.15475258,0.0042297198,0.0007846564,0.12046815],"study_design_scores_gemma":[0.000021443768,0.0005796912,0.011075331,0.000023121369,0.000066348875,0.00013423232,0.000031889507,0.9380093,0.049025785,0.00028997363,0.000718212,0.000024581206],"about_ca_topic_score_codex":0.002340394,"about_ca_topic_score_gemma":0.0012930046,"teacher_disagreement_score":0.002340394,"about_ca_system_score_codex":0.00047893554,"about_ca_system_score_gemma":0.00026388865,"threshold_uncertainty_score":0.005037725},"labels":[],"label_agreement":null},{"id":"W2135345420","doi":"10.3390/s91108624","title":"Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics—An Overview","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Chinese Academy of Sciences","keywords":"Eddy covariance; Environmental science; Biogeochemical cycle; Terrestrial ecosystem; Ecosystem; Atmospheric sciences; Remote sensing; Geography; Ecology; Geology","score_opus":0.03243759464496494,"score_gpt":0.23229535500950974,"score_spread":0.19985776036454478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135345420","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047951072,0.24967842,0.6656113,0.0036017054,0.0004333758,0.00011791069,0.001008637,0.0007916991,0.03080572],"genre_scores_gemma":[0.42909315,0.34529597,0.21325484,0.000961922,0.0007827821,0.00025725336,0.0019342604,0.00016581749,0.008254033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99987495,0.000019041132,0.000010122443,0.000045700745,0.000040364008,0.000009729303],"domain_scores_gemma":[0.999887,0.000056180375,0.000012555301,0.000011359277,0.000025163474,0.00000776643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041818194,0.00071605184,0.0010352989,0.00057943823,0.0002952987,0.0014854105,0.0010131487,0.00073841197,0.0021953878],"category_scores_gemma":[0.0004343567,0.00036629505,0.0006389484,0.00070142536,0.00040827307,0.0025634712,0.00068752246,0.0006387843,0.00040886723],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012128954,0.00012880348,0.00599799,0.005571694,0.0007890226,0.0005003163,0.0002586607,0.4472858,0.017934479,0.1280062,0.0079664495,0.3854393],"study_design_scores_gemma":[0.00003049006,0.00014163538,0.0057985364,0.00056432793,0.00034330593,0.00043123396,0.00023118927,0.64700747,0.0039803647,0.21189244,0.12950672,0.000072298615],"about_ca_topic_score_codex":0.0044967807,"about_ca_topic_score_gemma":0.0042495923,"teacher_disagreement_score":0.0044967807,"about_ca_system_score_codex":0.0008743652,"about_ca_system_score_gemma":0.0010662748,"threshold_uncertainty_score":0.008941233},"labels":[],"label_agreement":null},{"id":"W2135465567","doi":"10.3390/s120303720","title":"Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Computer science; Real-time computing; Accelerometer; Flexibility (engineering); Pedestrian; Embedded system; Simulation; Systems engineering; Engineering; Transport engineering; Telecommunications","score_opus":0.04388043409423579,"score_gpt":0.24318745041287865,"score_spread":0.19930701631864287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135465567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5247951,0.00021122511,0.46277627,0.00029459424,0.00029954177,0.0018539409,0.0006349402,0.0034662115,0.0056682215],"genre_scores_gemma":[0.75607383,0.00012736527,0.23595135,0.00013370761,0.000023062044,0.00080301,0.0003777835,0.000106678606,0.006403165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906605,0.00014374068,0.00005344173,0.0001972657,0.00042575088,0.00011376367],"domain_scores_gemma":[0.99898213,0.00016393077,0.000120843855,0.00015514971,0.00044651446,0.00013141368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011360608,0.00077200023,0.00056555565,0.000593724,0.00029920528,0.00048417272,0.0015260398,0.00085545675,0.0025316845],"category_scores_gemma":[0.001401262,0.00043200116,0.0002916972,0.0002635447,0.00039675293,0.00095702487,0.00088716653,0.0004231984,0.0007215869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021026107,0.0008937637,0.0178978,0.0013866548,0.00017415077,0.0017128779,0.0011816801,0.07813849,0.666103,0.00512661,0.0046347515,0.2206476],"study_design_scores_gemma":[0.0003688228,0.01361396,0.017893469,0.000129773,0.00018898021,0.0016882154,0.0004851116,0.28981796,0.64167243,0.001239089,0.032739326,0.00016286917],"about_ca_topic_score_codex":0.0013385696,"about_ca_topic_score_gemma":0.0011465887,"teacher_disagreement_score":0.0025316845,"about_ca_system_score_codex":0.000433983,"about_ca_system_score_gemma":0.0013901037,"threshold_uncertainty_score":0.008469284},"labels":[],"label_agreement":null},{"id":"W2135722987","doi":"10.3390/s150510465","title":"Towards a Dynamic Clamp for Neurochemical Modalities","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Defense Advanced Research Projects Agency; Washington State University","keywords":"Microfluidics; Nanosensor; Neurochemical; Artificial neural network; Neural cell; Computer science; Biological system; Nanotechnology; Lab-on-a-chip; Interface (matter); Microfluidic chip; Artificial intelligence; Materials science; Chemistry; Neuroscience; Cell; Biology","score_opus":0.0788742668681447,"score_gpt":0.30858987859767517,"score_spread":0.22971561172953048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135722987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002761197,0.0013067254,0.9873302,0.00059559185,0.0002818113,0.0000455247,0.000049601065,0.0006931682,0.006936199],"genre_scores_gemma":[0.16080767,0.0029270104,0.8224156,0.0016189071,0.00035312277,0.00047234306,0.00014943091,0.00030513923,0.010950873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926406,0.00010474971,0.00004777623,0.00025700653,0.00027477858,0.00005157651],"domain_scores_gemma":[0.9991566,0.00031836022,0.000055046734,0.00022184616,0.00016202775,0.00008619544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015827895,0.0005029399,0.00080423115,0.00060461974,0.00049513654,0.0028868292,0.003074624,0.001770497,0.0042760577],"category_scores_gemma":[0.002456788,0.0007866207,0.00052786164,0.0002883985,0.002754853,0.004725464,0.0035664535,0.0033506097,0.0020664867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019914907,0.00004735432,0.00024099085,0.00034951454,0.00004140405,0.00011680438,0.00025300332,0.009455391,0.20358546,0.6911462,0.002170979,0.09239371],"study_design_scores_gemma":[0.000072244264,0.00026027788,0.00046650076,0.00033695166,0.00007125491,0.00049081777,0.00015705149,0.19217315,0.19123152,0.4202161,0.1943656,0.0001584225],"about_ca_topic_score_codex":0.00045171994,"about_ca_topic_score_gemma":0.00051676884,"teacher_disagreement_score":0.0042760577,"about_ca_system_score_codex":0.0011132864,"about_ca_system_score_gemma":0.00073544314,"threshold_uncertainty_score":0.014304876},"labels":[],"label_agreement":null},{"id":"W2136253307","doi":"10.3390/s8010236","title":"Advances in Remote Sensing for Oil Spill Disaster Management: State-of-the-Art Sensors Technology for Oil Spill Surveillance","year":2008,"lang":"en","type":"review","venue":"Sensors","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":320,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Oil spill; Environmental science; Remote sensing; Oil pollution; Environmental monitoring; Environmental engineering; Geography","score_opus":0.013427380152952967,"score_gpt":0.2668335864410244,"score_spread":0.2534062062880714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136253307","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017899724,0.7523022,0.1944419,0.004582917,0.0014534217,0.00013397365,0.00031465842,0.0008530705,0.028018136],"genre_scores_gemma":[0.14399329,0.6855287,0.15370081,0.0018110818,0.0021221815,0.00014354577,0.00080033636,0.0001763173,0.01172379],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985207,0.00021292568,0.00005963143,0.00027983374,0.00083697797,0.00009003605],"domain_scores_gemma":[0.9986388,0.00060424494,0.0001261993,0.00011510099,0.0004672192,0.000048361475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017679604,0.0010968669,0.00076436554,0.0017241784,0.0004017174,0.0017270007,0.001226129,0.0015681747,0.0030931572],"category_scores_gemma":[0.0012268177,0.00056043104,0.0008395187,0.0022886938,0.001037061,0.003792241,0.0013150459,0.002207607,0.0016616305],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012523157,0.00018541452,0.0021112836,0.0034705896,0.000093222225,0.00015570555,0.0002190684,0.006987608,0.06834215,0.018515073,0.010881574,0.888913],"study_design_scores_gemma":[0.00003209467,0.0007670717,0.008334922,0.0023319619,0.0002438494,0.0014964441,0.000585862,0.065270975,0.09907385,0.0281938,0.7932904,0.0003788584],"about_ca_topic_score_codex":0.0016315613,"about_ca_topic_score_gemma":0.0014184187,"teacher_disagreement_score":0.0030931572,"about_ca_system_score_codex":0.0008104719,"about_ca_system_score_gemma":0.0006623151,"threshold_uncertainty_score":0.010347605},"labels":[],"label_agreement":null},{"id":"W2139128810","doi":"10.3390/s140814700","title":"A Catheter-Based Acoustic Interrogation Device for Monitoring Motility Dynamics of the Lower Esophageal Sphincter","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Gastroesophageal reflux and treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Alberta","keywords":"Closing (real estate); Microphone; Sound (geography); Acoustics; Sphincter; Simulation; Computer science; Physics; Surgery; Sound pressure; Medicine","score_opus":0.016138511441882417,"score_gpt":0.28026258324157316,"score_spread":0.2641240717996907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139128810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.359512,0.0081400275,0.6255036,0.000932607,0.0005662981,0.00038817048,0.00049559254,0.0014610712,0.003000612],"genre_scores_gemma":[0.67683166,0.0021984675,0.31528878,0.00059723173,0.00024242477,0.0002977491,0.00027328776,0.0000516191,0.0042186812],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995447,0.00008358072,0.00003231066,0.00010853886,0.0002118984,0.00001901728],"domain_scores_gemma":[0.99946064,0.00020899752,0.000115337956,0.00005394493,0.00010142648,0.000059663438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036472996,0.00042662086,0.00044268285,0.0003323691,0.00018104311,0.00034483447,0.0007809994,0.0008186451,0.0009119996],"category_scores_gemma":[0.00078384497,0.00026024747,0.0002702013,0.00019283478,0.00023328196,0.00059130584,0.00043938763,0.0003675594,0.00033600858],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101560654,0.000055052522,0.0015959499,0.00015823939,0.000013783398,0.00017721736,0.000050739694,0.00019323456,0.9640777,0.00018582158,0.00033808095,0.033052564],"study_design_scores_gemma":[0.00010277119,0.0047702817,0.029964238,0.00006581849,0.00018247675,0.010275116,0.00012480283,0.025232702,0.89833766,0.00027207632,0.030515283,0.00015678149],"about_ca_topic_score_codex":0.0001258184,"about_ca_topic_score_gemma":0.00025547342,"teacher_disagreement_score":0.0009119996,"about_ca_system_score_codex":0.00016005915,"about_ca_system_score_gemma":0.00024059447,"threshold_uncertainty_score":0.003050983},"labels":[],"label_agreement":null},{"id":"W2139424762","doi":"10.3390/s120810067","title":"Low-Voltage 96 dB Snapshot CMOS Image Sensor with 4.5 nW Power Dissipation per Pixel","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Image sensor; CMOS; Dynamic range; Computer science; Voltage; Electronic engineering; Low-power electronics; CMOS sensor; Rolling shutter; Low voltage; Pixel; Dissipation; Electrical engineering; Shutter; Power (physics); Engineering; Artificial intelligence; Physics","score_opus":0.0054311534755595815,"score_gpt":0.2066405166605264,"score_spread":0.20120936318496682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139424762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5484167,0.0023821958,0.43545213,0.0008629857,0.00022259877,0.0001486523,0.0009217184,0.0027304017,0.0088626975],"genre_scores_gemma":[0.7946351,0.0007651644,0.1947175,0.00023836945,0.00004863954,0.00007962227,0.0002751831,0.00009545418,0.009144928],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999782,0.000018363065,0.000011504493,0.000052657673,0.00012270863,0.000012653157],"domain_scores_gemma":[0.99972326,0.000092553135,0.000056555073,0.00003533686,0.00007197076,0.00002029736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002723262,0.00027650356,0.000441886,0.00017690801,0.00014469837,0.000374043,0.00073501194,0.0005180118,0.0016083262],"category_scores_gemma":[0.00055717543,0.00022475212,0.00016981101,0.00019721605,0.00027034123,0.00077710825,0.00027893874,0.0003643024,0.00067065307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009451543,0.000020941276,0.00042596078,0.00011523624,0.000011775919,0.00008280927,0.00003463235,0.0007569235,0.98537666,0.000622606,0.00042309155,0.012034833],"study_design_scores_gemma":[0.000022977723,0.00035891382,0.0019251641,0.000008758519,0.00003833865,0.0005818043,0.000032409902,0.024533058,0.9678027,0.00033328406,0.004341715,0.000020955762],"about_ca_topic_score_codex":0.00026934457,"about_ca_topic_score_gemma":0.0007477892,"teacher_disagreement_score":0.0016083262,"about_ca_system_score_codex":0.00031011988,"about_ca_system_score_gemma":0.00021289324,"threshold_uncertainty_score":0.0053803325},"labels":[],"label_agreement":null},{"id":"W2140409241","doi":"10.3390/s120708601","title":"Recent Progress in Distributed Fiber Optic Sensors","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":1260,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Distributed acoustic sensing; Birefringence; Optics; Optical fiber; Ranging; Materials science; Structural health monitoring; Fiber optic sensor; Vibration; Rayleigh scattering; Acoustics; Optical time-domain reflectometer; Image resolution; Polarization-maintaining optical fiber; Optoelectronics; Physics; Computer science; Telecommunications","score_opus":0.012989897489786953,"score_gpt":0.2428877311582608,"score_spread":0.22989783366847383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140409241","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008355444,0.8989258,0.05605957,0.0036780648,0.0018533352,0.000067174544,0.000116250645,0.00029962958,0.030644782],"genre_scores_gemma":[0.08849791,0.8181602,0.065371245,0.002023156,0.0033011367,0.000107616004,0.00047387046,0.00008646413,0.021978498],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989786,0.00015798773,0.000057832647,0.0002516322,0.0004861482,0.000067850146],"domain_scores_gemma":[0.99787843,0.00077258935,0.000133334,0.00012472014,0.0009834289,0.000107463005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018896853,0.000657927,0.00069033814,0.0013708743,0.00030493469,0.0011507794,0.0010460619,0.0014717781,0.003916144],"category_scores_gemma":[0.0019398902,0.0003488019,0.00034448205,0.0022261813,0.00057624176,0.0029148213,0.00070574315,0.0013850749,0.0019484764],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019462964,0.000121911624,0.0010902309,0.004234109,0.000053669795,0.00014437559,0.00015537367,0.0028428806,0.02555333,0.021266693,0.015480423,0.92886233],"study_design_scores_gemma":[0.00002374547,0.00045877852,0.0016165369,0.0009359274,0.00011818037,0.000933472,0.00015122796,0.00947886,0.020951968,0.009602339,0.9556731,0.00005597692],"about_ca_topic_score_codex":0.0007590873,"about_ca_topic_score_gemma":0.0005986671,"teacher_disagreement_score":0.003916144,"about_ca_system_score_codex":0.0007294727,"about_ca_system_score_gemma":0.00079072634,"threshold_uncertainty_score":0.013100803},"labels":[],"label_agreement":null},{"id":"W2140725553","doi":"10.3390/s8031539","title":"Transgenic Plants as Sensors of Environmental Pollution Genotoxicity","year":2008,"lang":"en","type":"review","venue":"Sensors","topic":"Plant Genetic and Mutation Studies","field":"Agricultural and Biological Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pollutant; Pollution; Biomonitoring; Biochemical engineering; Environmental pollution; Organism; Environmental science; Environmental toxicology; Genotoxicity; Environmental planning; Computer science; Risk analysis (engineering); Biotechnology; Environmental protection; Biology; Ecology; Engineering; Business; Toxicity; Chemistry","score_opus":0.041027881336800534,"score_gpt":0.25659271938172395,"score_spread":0.21556483804492343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140725553","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015100816,0.99272776,0.0016877695,0.00023208371,0.00015748516,0.000013674935,0.00003943284,0.00003566713,0.0035960025],"genre_scores_gemma":[0.0041934764,0.99175334,0.0014753493,0.0001746527,0.000040417937,0.000016118118,0.000052254243,0.0000052151113,0.0022891902],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998148,0.000024426945,0.00001361752,0.000034935434,0.000098308024,0.000013938895],"domain_scores_gemma":[0.9998784,0.000053276002,0.000020303029,0.0000071540203,0.00003129552,0.00000952568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004504854,0.000817019,0.0011223475,0.001329175,0.00021841505,0.0006915573,0.00085368805,0.0010692704,0.0014698278],"category_scores_gemma":[0.0003110998,0.0003157399,0.0004052003,0.0016166292,0.00041718828,0.0010842346,0.00034270142,0.0010471386,0.0018191014],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089615605,0.00014102749,0.00034655887,0.01475951,0.00006908071,0.0005584755,0.00011009449,0.00075346505,0.095998295,0.007916551,0.009840903,0.8694164],"study_design_scores_gemma":[0.000021002,0.0001911442,0.0013258321,0.0011859611,0.00010198158,0.0019549062,0.000081041966,0.00024220726,0.025705116,0.0030902906,0.9660683,0.000032011892],"about_ca_topic_score_codex":0.00066083693,"about_ca_topic_score_gemma":0.0011196353,"teacher_disagreement_score":0.0014698278,"about_ca_system_score_codex":0.0005014985,"about_ca_system_score_gemma":0.0003793428,"threshold_uncertainty_score":0.0049170256},"labels":[],"label_agreement":null},{"id":"W2141150244","doi":"10.3390/s151025399","title":"Design and Performance Evaluation of a Dual Antenna Joint Carrier Tracking Loop","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Hubei Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Phase-locked loop; Bandwidth (computing); Carrier recovery; GNSS applications; Electronic engineering; Delay-locked loop; Computer science; Antenna (radio); Control theory (sociology); Engineering; Global Positioning System; Phase noise; Demodulation; Telecommunications","score_opus":0.08436212874720517,"score_gpt":0.2604752597426305,"score_spread":0.17611313099542536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141150244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22140636,0.00045071662,0.77059984,0.00017567535,0.00011374071,0.000208551,0.000069927286,0.0018264811,0.005148701],"genre_scores_gemma":[0.9360358,0.000074930496,0.0622775,0.000054012686,0.000023328827,0.00007252518,0.000051728697,0.000022339616,0.001387756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998995,0.00019672171,0.00006144295,0.00022926547,0.0004010548,0.00011642],"domain_scores_gemma":[0.99886596,0.00021334612,0.00018333063,0.00015050883,0.00051436987,0.0000724304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006919074,0.0004985014,0.0004810301,0.0005559082,0.00039448647,0.0008990056,0.0011010582,0.0009790517,0.0014989853],"category_scores_gemma":[0.0014888537,0.00017677329,0.00024103069,0.00032112413,0.00032911377,0.00067794946,0.0004653077,0.00035171313,0.0004647129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002453103,0.00048382167,0.012133529,0.00052360864,0.00020446735,0.0004992931,0.0004646704,0.19286574,0.43291008,0.007413902,0.0018706243,0.3481772],"study_design_scores_gemma":[0.00019448258,0.0031698989,0.0029247196,0.000027676748,0.00012026085,0.00059977506,0.000048259622,0.82234305,0.163562,0.0005673349,0.0063884826,0.000054054814],"about_ca_topic_score_codex":0.0010105757,"about_ca_topic_score_gemma":0.0005326924,"teacher_disagreement_score":0.0014989853,"about_ca_system_score_codex":0.0005660116,"about_ca_system_score_gemma":0.00064339826,"threshold_uncertainty_score":0.005014658},"labels":[],"label_agreement":null},{"id":"W2142080841","doi":"10.3390/s131013402","title":"GeoCENS: A Geospatial Cyberinfrastructure for the World-Wide Sensor Web","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Jet Propulsion Laboratory; Canarie; Microsoft Research","keywords":"Cyberinfrastructure; Sensor web; Geospatial analysis; Computer science; Architecture; Web Coverage Service; World Wide Web; Web service; Process (computing); Web mapping; Data science; Web modeling; Remote sensing; Geography; Telecommunications; Key distribution in wireless sensor networks","score_opus":0.00877642595381383,"score_gpt":0.21978529625155335,"score_spread":0.21100887029773951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142080841","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020781286,0.0004554428,0.93957,0.0019078299,0.00021167315,0.00032205426,0.00033694654,0.017602941,0.01881186],"genre_scores_gemma":[0.36545783,0.0011024545,0.60607463,0.000855398,0.00019581366,0.00043469665,0.0022415747,0.0013285318,0.022309056],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99937433,0.000121068515,0.000047915157,0.000075643686,0.000334975,0.0000461281],"domain_scores_gemma":[0.9987684,0.00014492022,0.00009924295,0.0004915235,0.00026151273,0.00023437917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012594417,0.00040364772,0.00028617773,0.00078748394,0.00071655057,0.0027470028,0.0014881465,0.000991545,0.0032521896],"category_scores_gemma":[0.002417504,0.00035546455,0.00040274218,0.00079655263,0.001376254,0.0047301897,0.0034121985,0.0015787376,0.0015122052],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003831308,0.0005357963,0.009110397,0.00058458425,0.00013378836,0.001114879,0.0012419908,0.029773869,0.05343563,0.40278062,0.07396952,0.4269357],"study_design_scores_gemma":[0.00010431862,0.00029487387,0.0045222505,0.00016609766,0.00005877174,0.001317378,0.0005249215,0.36712605,0.034364,0.1275064,0.4639126,0.000102291844],"about_ca_topic_score_codex":0.0018374387,"about_ca_topic_score_gemma":0.00263964,"teacher_disagreement_score":0.0032521896,"about_ca_system_score_codex":0.0006009014,"about_ca_system_score_gemma":0.0012263645,"threshold_uncertainty_score":0.010879636},"labels":[],"label_agreement":null},{"id":"W2142898611","doi":"10.3390/s131216714","title":"Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar National Research Fund; Fonds National de la Recherche Luxembourg","keywords":"Imaging phantom; Interpolation (computer graphics); Calibration; Sampling (signal processing); Computer science; Acceleration; Algorithm; Iterative reconstruction; Joint (building); Cartesian coordinate system; Artificial intelligence; Mathematics; Computer vision; Image (mathematics); Statistics; Nuclear medicine","score_opus":0.02669055274408243,"score_gpt":0.2724941272813748,"score_spread":0.24580357453729235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142898611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063601052,0.00007558771,0.99254864,0.00014505956,0.000009219839,0.000018246976,0.00002801163,0.00014704112,0.00066811225],"genre_scores_gemma":[0.39301553,0.0009154317,0.59913975,0.00033774643,0.00010007601,0.0001917146,0.0003610756,0.0002618974,0.0056768237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999373,0.00024317658,0.000024616289,0.000112076435,0.00021182686,0.00003530441],"domain_scores_gemma":[0.9990163,0.00041711208,0.00012863489,0.00022656361,0.0001706845,0.000040694056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014440439,0.00049668533,0.0005129231,0.00038943934,0.00017546394,0.00064872217,0.0011590531,0.00084349787,0.0013160387],"category_scores_gemma":[0.0031024034,0.00045212122,0.0005268395,0.00063135085,0.0009114497,0.0015525749,0.0010661905,0.0012424706,0.00055817264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030437682,0.00014531014,0.0013413754,0.00023923663,0.000096962984,0.00027436044,0.00020914905,0.75000554,0.04538444,0.04276382,0.002630601,0.15660478],"study_design_scores_gemma":[0.000010171116,0.000044489076,0.00021044102,0.000005996456,0.000012287548,0.00013984984,0.00001026617,0.9869855,0.004732808,0.0065652635,0.0012708725,0.00001209745],"about_ca_topic_score_codex":0.0011205641,"about_ca_topic_score_gemma":0.0014939628,"teacher_disagreement_score":0.0014440439,"about_ca_system_score_codex":0.00034946974,"about_ca_system_score_gemma":0.0008087403,"threshold_uncertainty_score":0.0076369047},"labels":[],"label_agreement":null},{"id":"W2144693095","doi":"10.3390/s7102062","title":"Quantitative Boundary Support Characterization for Cantilever MEMS","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Microfabrication; Microscale chemistry; Microsystem; Microelectromechanical systems; Cantilever; Fabrication; Mechanical engineering; Computer science; Materials science; Nanotechnology; Engineering; Mathematics","score_opus":0.016678125205869747,"score_gpt":0.26611960121719064,"score_spread":0.24944147601132088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144693095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74223053,0.00095841795,0.25314176,0.00006739098,0.000022112874,0.000073433206,0.00033823535,0.0004813276,0.0026868116],"genre_scores_gemma":[0.96223414,0.00018594868,0.036525305,0.0000118517355,0.000007860144,0.00008326139,0.00027532614,0.00003373419,0.0006425418],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932253,0.00006584036,0.000024046029,0.000064925145,0.000490462,0.00003215199],"domain_scores_gemma":[0.9991027,0.00037218945,0.000117066535,0.00013271134,0.00024652912,0.000028805945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054653507,0.0003934698,0.00028565872,0.0007998339,0.00027725668,0.00035633126,0.0004516052,0.00058271695,0.001475433],"category_scores_gemma":[0.0020143134,0.00019530227,0.00011589171,0.00027966066,0.00051981123,0.0006807532,0.00041552092,0.00023263293,0.00027509703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054842832,0.00001794483,0.001098737,0.000085168635,0.0000044120243,0.00004800482,0.00006144379,0.0037179063,0.9762288,0.00089205074,0.00009174528,0.017698891],"study_design_scores_gemma":[0.000012430333,0.00022532688,0.009462819,0.000021058839,0.0000072376233,0.00019029513,0.0000975757,0.123338565,0.8636901,0.0012325239,0.0016901061,0.000031933345],"about_ca_topic_score_codex":0.0004082149,"about_ca_topic_score_gemma":0.0005163786,"teacher_disagreement_score":0.001475433,"about_ca_system_score_codex":0.00025790778,"about_ca_system_score_gemma":0.0001407464,"threshold_uncertainty_score":0.004935801},"labels":[],"label_agreement":null},{"id":"W2145616990","doi":"10.3390/s130100975","title":"Applications of Flexible Ultrasonic Transducer Array for Defect Detection at 150 °C","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McGill University","funders":"National Science Council","keywords":"Ultrasonic sensor; Transducer; Materials science; Piezoelectricity; Ultrasonic testing; Acoustics; Nondestructive testing; Composite material","score_opus":0.006279111605579517,"score_gpt":0.2004480981576448,"score_spread":0.1941689865520653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145616990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86727107,0.0019292741,0.12717992,0.00025476364,0.000121120385,0.00004895321,0.0001952603,0.0007012882,0.0022982573],"genre_scores_gemma":[0.95162535,0.00032950702,0.046550084,0.0000921341,0.000018732606,0.000040331557,0.000119021,0.000027685473,0.0011971479],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966574,0.000027985385,0.000018478431,0.00008437912,0.00016434894,0.000039069848],"domain_scores_gemma":[0.9996593,0.00010441348,0.00007009995,0.00003610526,0.000101997655,0.000028075316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002583838,0.0003417701,0.00037586348,0.00031574228,0.00010808274,0.0002663694,0.00048681354,0.00051511615,0.00089168944],"category_scores_gemma":[0.00062667154,0.00026266347,0.00017372808,0.00022150253,0.0003550092,0.00047139855,0.00031664997,0.0002464957,0.0003250467],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025687552,0.0000034226427,0.00030673208,0.000021544474,0.0000026010568,0.000043733726,0.000024791603,0.00030846897,0.99621993,0.000063730586,0.000036020247,0.0029433796],"study_design_scores_gemma":[0.000007950114,0.00021403689,0.0047373194,0.000007751818,0.00001781321,0.0004877645,0.00005414188,0.011589588,0.9810705,0.0001624606,0.0016213532,0.000029274142],"about_ca_topic_score_codex":0.00045992038,"about_ca_topic_score_gemma":0.0007007779,"teacher_disagreement_score":0.00089168944,"about_ca_system_score_codex":0.00022166267,"about_ca_system_score_gemma":0.00015864438,"threshold_uncertainty_score":0.002982974},"labels":[],"label_agreement":null},{"id":"W2146449740","doi":"10.3390/s8021321","title":"The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Lethbridge","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Endmember; Hyperspectral imaging; Pixel; Algorithm; Adjacency list; Computer science; Spatial analysis; Projection (relational algebra); Pattern recognition (psychology); Mathematics; Artificial intelligence; Remote sensing; Geography","score_opus":0.04154505271190751,"score_gpt":0.27873511034969173,"score_spread":0.23719005763778422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146449740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030020296,0.00009750649,0.9961008,0.000036857975,0.000011655545,0.000041100084,0.000023732273,0.00039451697,0.00029176736],"genre_scores_gemma":[0.02302129,0.00013950432,0.975593,0.00004027227,0.00001717713,0.00011801364,0.00015521672,0.00016040013,0.0007550406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872285,0.0003916061,0.00007298482,0.00026241143,0.0004939382,0.000056107263],"domain_scores_gemma":[0.99812883,0.0011243435,0.00018948923,0.00020383777,0.00030069845,0.000052765685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019585832,0.0016253808,0.0011744922,0.0012092233,0.00082635914,0.001130324,0.0012510445,0.0009994978,0.002075485],"category_scores_gemma":[0.0045374776,0.00081682997,0.0011743943,0.0012874526,0.00134632,0.0016077445,0.0018934717,0.0018047842,0.000989898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021679007,0.000117252566,0.002215214,0.0003741473,0.00035965646,0.00016778268,0.0003413893,0.23914903,0.04121431,0.030163608,0.0055114655,0.68016934],"study_design_scores_gemma":[0.000026750326,0.00009210368,0.00060269487,0.000022843395,0.000028332115,0.00020461317,0.000045164277,0.9640186,0.017167239,0.010887681,0.0068679694,0.000036150897],"about_ca_topic_score_codex":0.0038215145,"about_ca_topic_score_gemma":0.006864223,"teacher_disagreement_score":0.0038215145,"about_ca_system_score_codex":0.00046760697,"about_ca_system_score_gemma":0.0017213827,"threshold_uncertainty_score":0.010358095},"labels":[],"label_agreement":null},{"id":"W2146645857","doi":"10.3390/s7102028","title":"A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences; BIOCAP Canada; National Aeronautics and Space Administration","keywords":"Normalized Difference Vegetation Index; Environmental science; Vegetation (pathology); Elevation (ballistics); Terrain; Topographic Wetness Index; Enhanced vegetation index; Water content; Digital elevation model; Atmospheric sciences; Atmosphere (unit); Spatial distribution; Spatial variability; Leaf area index; Hydrology (agriculture); Remote sensing; Meteorology; Geology; Vegetation Index; Geography; Mathematics","score_opus":0.018256867882926364,"score_gpt":0.23148966521320113,"score_spread":0.21323279733027478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146645857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9835535,0.00072073715,0.009346912,0.000037050177,0.000009956061,0.00014170598,0.0021225545,0.00037831315,0.0036891764],"genre_scores_gemma":[0.9630441,0.00058992347,0.03205269,0.000020358251,0.000006622809,0.00006562067,0.0022809254,0.00006456279,0.0018751603],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99950933,0.0000329115,0.000028509434,0.000073054136,0.00030677114,0.000049410493],"domain_scores_gemma":[0.99912757,0.00011086735,0.000104889245,0.00004621357,0.00053227594,0.000078097735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085860724,0.000514569,0.00025773302,0.0016892449,0.00076792843,0.0010813773,0.0006632206,0.00019396406,0.0007334984],"category_scores_gemma":[0.0014376718,0.0002043843,0.00028960838,0.0020933682,0.00026566585,0.00038038954,0.00042767506,0.00016258519,0.00013902201],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048636037,0.00015721862,0.7418243,0.00026614283,0.00035026277,0.00018869183,0.0005228679,0.015369141,0.019194994,0.00038249622,0.0012753813,0.2199821],"study_design_scores_gemma":[0.000040136736,0.00011288221,0.91496724,0.000023170001,0.00011499111,0.00013820136,0.0005002542,0.0733384,0.0078065316,0.00010516854,0.002789082,0.000064037595],"about_ca_topic_score_codex":0.82150584,"about_ca_topic_score_gemma":0.92288554,"teacher_disagreement_score":0.17849416,"about_ca_system_score_codex":0.0042007435,"about_ca_system_score_gemma":0.0036229969,"threshold_uncertainty_score":0.35909063},"labels":[],"label_agreement":null},{"id":"W2146927674","doi":"10.3390/s91007988","title":"A Coupled Field Multiphysics Modeling Approach to Investigate RF MEMS Switch Failure Modes under Various Operational Conditions","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiphysics; Microelectromechanical systems; Microfabrication; Radio frequency; Finite element method; Coupling (piping); Electronic engineering; Voltage; Residual stress; Capacitive sensing; Mechanical engineering; Field (mathematics); Materials science; Engineering; Electrical engineering; Optoelectronics; Structural engineering","score_opus":0.01884858653630221,"score_gpt":0.23900414177046156,"score_spread":0.22015555523415936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146927674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11812996,0.00031137213,0.8745063,0.00016050535,0.000057558515,0.00014975162,0.00017920887,0.00040305965,0.0061023594],"genre_scores_gemma":[0.85111207,0.0005137877,0.14092815,0.00012859657,0.000049811944,0.00055249204,0.00021710203,0.00015277411,0.006345236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998734,0.000025256293,0.0000060021976,0.00002693695,0.00005381631,0.000014516504],"domain_scores_gemma":[0.9997733,0.00010609777,0.000036549558,0.000034752422,0.000040296254,0.000008948239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003664772,0.0008161939,0.00046926294,0.000862824,0.000292627,0.00047899396,0.0007330722,0.0012536896,0.0019927714],"category_scores_gemma":[0.00060718437,0.00044080516,0.0010211597,0.00028911539,0.00040282283,0.00067069527,0.00043135183,0.00053631957,0.00032841548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017246428,0.000053575037,0.00064347644,0.000045953864,0.000029716432,0.000060070124,0.000055550754,0.9669506,0.022590414,0.0030741624,0.000105982406,0.0063733365],"study_design_scores_gemma":[0.0000012774184,0.000015232528,0.00014966773,0.0000016218473,0.0000033759034,0.000009244151,0.000005821007,0.99799347,0.0011815599,0.00043121615,0.00020472064,0.0000029537828],"about_ca_topic_score_codex":0.0016105222,"about_ca_topic_score_gemma":0.0014075099,"teacher_disagreement_score":0.0019927714,"about_ca_system_score_codex":0.00041382646,"about_ca_system_score_gemma":0.0004882114,"threshold_uncertainty_score":0.0066664815},"labels":[],"label_agreement":null},{"id":"W2147041146","doi":"10.3390/s140815525","title":"Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Compute Canada","keywords":"Gradient descent; Probabilistic logic; Computer science; Computation; Orientation (vector space); Optimization problem; Set (abstract data type); Position (finance); Descent (aeronautics); Black box; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Engineering; Artificial neural network","score_opus":0.009246588122491541,"score_gpt":0.19922129455878831,"score_spread":0.18997470643629677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147041146","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004767439,0.000054822012,0.9944013,0.000055372202,0.000011380978,0.000013668614,0.000013553505,0.00023795638,0.00044456442],"genre_scores_gemma":[0.36312702,0.00019784416,0.63364655,0.00011244164,0.00006292582,0.00018901732,0.00016090584,0.00025922578,0.0022440178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993538,0.0002371364,0.000022664439,0.000101182624,0.00022202352,0.00006310091],"domain_scores_gemma":[0.9994423,0.00027378122,0.000076382545,0.000074520554,0.00010657528,0.000026369524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078322267,0.001033429,0.0012115366,0.0006899179,0.00036589667,0.0006079224,0.0011264435,0.0010307707,0.00088908366],"category_scores_gemma":[0.002103213,0.00071333494,0.00059572805,0.00087745953,0.0006042536,0.0010001118,0.0009741533,0.0007236473,0.00032421015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029610097,0.000020218424,0.00023704498,0.00002418871,0.000025396008,0.00003509904,0.00002192269,0.966618,0.003182645,0.0024171232,0.0006794526,0.02670929],"study_design_scores_gemma":[0.0000036242668,0.000008412878,0.000053918644,0.0000011318308,0.0000014454898,0.000009066861,0.0000017576341,0.9986594,0.00044432623,0.0006334614,0.00018120825,0.0000022552374],"about_ca_topic_score_codex":0.0056884848,"about_ca_topic_score_gemma":0.005237689,"teacher_disagreement_score":0.0056884848,"about_ca_system_score_codex":0.0005821133,"about_ca_system_score_gemma":0.0009811368,"threshold_uncertainty_score":0.011310756},"labels":[],"label_agreement":null},{"id":"W2148129229","doi":"10.3390/s131013609","title":"On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation; University of Ottawa","keywords":"Maximum a posteriori estimation; Diastole; Standard deviation; Blood pressure; Amplitude; Bayesian probability; Mathematics; Cardiology; Absolute deviation; Internal medicine; Statistics; Medicine; Algorithm; Maximum likelihood; Physics","score_opus":0.027805638320559812,"score_gpt":0.28539805484288683,"score_spread":0.257592416522327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148129229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029585524,0.00020185871,0.99622935,0.00008890516,0.000012285966,0.00001718691,0.000020258298,0.000092769274,0.00037877235],"genre_scores_gemma":[0.36256322,0.0014374672,0.6320437,0.00034867847,0.00024785456,0.0004251433,0.00033535538,0.00015945497,0.002439068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980963,0.0010055401,0.00009515039,0.00027470675,0.00044176498,0.00008645119],"domain_scores_gemma":[0.99569595,0.003467763,0.00023973246,0.0001476156,0.0003975057,0.00005137137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030579374,0.00095851184,0.0014722681,0.0011478734,0.0005087199,0.0014291035,0.0013126776,0.0013398947,0.0012265239],"category_scores_gemma":[0.012669624,0.00076244137,0.0009997531,0.0010750613,0.001035984,0.002107967,0.0009614327,0.0015307385,0.0005997276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019716649,0.00010612787,0.0019043256,0.00019783853,0.00017376467,0.00012250077,0.0001628512,0.796324,0.004402712,0.035219196,0.001378774,0.15981072],"study_design_scores_gemma":[0.000012737749,0.000029074501,0.00033262727,0.000024670455,0.000020595804,0.000057819292,0.000006801173,0.9881058,0.000639364,0.010143074,0.00060336135,0.00002408541],"about_ca_topic_score_codex":0.0058875005,"about_ca_topic_score_gemma":0.005096899,"teacher_disagreement_score":0.0058875005,"about_ca_system_score_codex":0.0007129187,"about_ca_system_score_gemma":0.0013888228,"threshold_uncertainty_score":0.016172111},"labels":[],"label_agreement":null},{"id":"W2148492800","doi":"10.3390/s150924343","title":"Implementation and Evaluation of Four Interoperable Open Standards for the Internet of Things","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interoperability; Computer science; Open standard; The Internet; Internet of Things; World Wide Web; Bluetooth; Operating system; Wireless","score_opus":0.17472987050224695,"score_gpt":0.40349500631119317,"score_spread":0.22876513580894622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148492800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6173611,0.00073853956,0.32751682,0.0018038965,0.0009196069,0.008894697,0.000942105,0.0061826454,0.03564062],"genre_scores_gemma":[0.5995107,0.0006450586,0.38559228,0.00042153714,0.000046256482,0.0026464022,0.005455453,0.0006053304,0.005077031],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97440016,0.0068593407,0.0031757054,0.0012686992,0.0126655055,0.0016306143],"domain_scores_gemma":[0.963817,0.006546577,0.0021608102,0.006203588,0.019834757,0.0014372523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031229822,0.0009920043,0.0006877374,0.003165335,0.0016423707,0.003947793,0.003441522,0.002157653,0.0016335197],"category_scores_gemma":[0.04558251,0.0006228458,0.0012500064,0.0021854173,0.001808517,0.0058952533,0.0025201065,0.002318404,0.00058976276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044909217,0.013357438,0.052388567,0.0025624668,0.0007239863,0.0015145568,0.005005873,0.068640254,0.11594664,0.12444957,0.014985627,0.5959341],"study_design_scores_gemma":[0.0021084822,0.015602523,0.054256268,0.0013523039,0.0013918445,0.0012743593,0.007824696,0.407643,0.36092442,0.024409298,0.12246985,0.00074309396],"about_ca_topic_score_codex":0.0055742715,"about_ca_topic_score_gemma":0.004522344,"teacher_disagreement_score":0.031229822,"about_ca_system_score_codex":0.0031480195,"about_ca_system_score_gemma":0.005653935,"threshold_uncertainty_score":0.16516107},"labels":[],"label_agreement":null},{"id":"W2149788694","doi":"10.3390/s110909069","title":"Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Remote sensing; Radiometric calibration; Calibration; Point cloud; Computer science; Reference data; Ranging; Radiometry; Environmental science; Computer vision; Mathematics; Geography; Statistics; Data mining","score_opus":0.06208141680265924,"score_gpt":0.2795674152727071,"score_spread":0.21748599847004788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149788694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15699285,0.00083462003,0.8391219,0.0002444536,0.00012972721,0.00009081067,0.00014853722,0.0006702927,0.0017668384],"genre_scores_gemma":[0.73542005,0.0006861339,0.26215956,0.000121300436,0.00006548787,0.000077047975,0.0005211823,0.00029256262,0.00065667956],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99151504,0.00203822,0.0003796266,0.0007639798,0.0050910716,0.00021204485],"domain_scores_gemma":[0.98141783,0.007586065,0.0037668075,0.0033738937,0.003784933,0.0000704869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005813201,0.0009936179,0.0005086006,0.0012638369,0.0004526279,0.0014885227,0.0010047293,0.0008467723,0.00071576977],"category_scores_gemma":[0.036032677,0.0004465775,0.00054393994,0.001768271,0.001502822,0.0015327492,0.0012002536,0.0007732165,0.00042327118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006688065,0.00024911755,0.03468056,0.0007846072,0.00037462552,0.0005645913,0.0006290025,0.24454753,0.15787002,0.011907186,0.0016009291,0.5461231],"study_design_scores_gemma":[0.00009534192,0.00091195194,0.0863172,0.00017633464,0.00028123747,0.0026965633,0.00033085962,0.5391368,0.35140094,0.009333463,0.009069095,0.00025009178],"about_ca_topic_score_codex":0.0011504731,"about_ca_topic_score_gemma":0.0013337637,"teacher_disagreement_score":0.005813201,"about_ca_system_score_codex":0.00067202,"about_ca_system_score_gemma":0.00061426556,"threshold_uncertainty_score":0.03074348},"labels":[],"label_agreement":null},{"id":"W2150207934","doi":"10.3390/s8127564","title":"Electrochemical Determination of the Antioxidant Potential of Some Less Common Fruit Species","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Botanical Studies and Applications","field":"Agricultural and Biological Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"DPPH; Flavonoid; Berry; Antioxidant; Botany; Food science; Traditional medicine; Chemistry; Horticulture; Biology; Medicine; Biochemistry","score_opus":0.021754833151419958,"score_gpt":0.21220014063605466,"score_spread":0.1904453074846347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150207934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97759914,0.0049540084,0.014334161,0.0000908853,0.000049826693,0.000047007048,0.00036532767,0.00009069751,0.0024688502],"genre_scores_gemma":[0.9779764,0.0020804966,0.01674262,0.00010872533,0.000020955868,0.00006015784,0.00046565864,0.00001270199,0.0025323231],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998429,0.000022486838,0.000011771293,0.000048613005,0.00005678278,0.000017421418],"domain_scores_gemma":[0.9998487,0.00004661591,0.000030775347,0.000009496681,0.000049620103,0.000014790076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021170342,0.00032838414,0.00015859144,0.00047825053,0.00012939371,0.00022891119,0.000283099,0.0003944479,0.00062774844],"category_scores_gemma":[0.00031230692,0.00010769776,0.00013528141,0.00032907707,0.00010858977,0.00021983514,0.00014057243,0.00032123955,0.00013423557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021964039,0.00001018729,0.0006325801,0.00004890482,0.000008821032,0.000022534212,0.000034638386,0.00002202859,0.99655163,0.000026506807,0.000015633574,0.0026046133],"study_design_scores_gemma":[0.0000065241047,0.00029142675,0.016630368,0.000016631437,0.00004931188,0.00026984923,0.00011688144,0.001098486,0.9795492,0.000079827936,0.0018824263,0.000008991929],"about_ca_topic_score_codex":0.00051559065,"about_ca_topic_score_gemma":0.0012705093,"teacher_disagreement_score":0.00062774844,"about_ca_system_score_codex":0.00011171826,"about_ca_system_score_gemma":0.00008985045,"threshold_uncertainty_score":0.0021000504},"labels":[],"label_agreement":null},{"id":"W2151055632","doi":"10.3390/s130404303","title":"Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning","year":2013,"lang":"en","type":"review","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Multipath propagation; Doppler effect; Computer science; Dead reckoning; Block (permutation group theory); Remote sensing; Sensitivity (control systems); Satellite system; SIGNAL (programming language); Real-time computing; Inertial navigation system; Electronic engineering; Global Positioning System; Telecommunications; Engineering; Geography; Physics; Inertial frame of reference","score_opus":0.1633909857902981,"score_gpt":0.3004327023398516,"score_spread":0.1370417165495535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151055632","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055177948,0.9429745,0.04049203,0.00029795765,0.0005896901,0.0000517596,0.000057604284,0.00013070543,0.009888037],"genre_scores_gemma":[0.038914178,0.9328319,0.020098336,0.00018827694,0.0003315672,0.000052800526,0.0001340737,0.000017143882,0.007431723],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995646,0.00006320427,0.000039510825,0.00008339075,0.00022341264,0.000025791596],"domain_scores_gemma":[0.9995047,0.00013314727,0.00008136305,0.000025754242,0.00024065682,0.000014305101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048299955,0.00089884625,0.00079850317,0.002152699,0.00014865055,0.00065249123,0.0009920798,0.00092737214,0.0011492497],"category_scores_gemma":[0.0006993905,0.00040750825,0.0004921094,0.0018542153,0.00030904557,0.0010526758,0.00042765285,0.00056418893,0.0015974035],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038202208,0.000042234646,0.0005700856,0.004911829,0.00005203121,0.00019931758,0.000049718554,0.0019239631,0.011197152,0.0021384694,0.0034497783,0.97542727],"study_design_scores_gemma":[0.000026141637,0.0008479893,0.0070041567,0.0032328877,0.0003800426,0.006803938,0.00034926727,0.009947626,0.058831178,0.0032275321,0.9091705,0.00017867259],"about_ca_topic_score_codex":0.00081708294,"about_ca_topic_score_gemma":0.0012144453,"teacher_disagreement_score":0.002152699,"about_ca_system_score_codex":0.00029048443,"about_ca_system_score_gemma":0.0004594474,"threshold_uncertainty_score":0.0038445592},"labels":[],"label_agreement":null},{"id":"W2151731808","doi":"10.3390/s150304605","title":"New Lower-Limb Gait Asymmetry Indices Based on a Depth Camera","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Fonds de recherche du Québec – Nature et technologies; Association Nationale de la Recherche et de la Technologie","keywords":"Gait; Asymmetry; Computer vision; Artificial intelligence; Physical medicine and rehabilitation; Computer science; Lower limb; Medicine; Physics; Surgery","score_opus":0.04103924269451657,"score_gpt":0.3576712917911321,"score_spread":0.3166320490966155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151731808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6254666,0.0030607656,0.36144155,0.00016774514,0.00020075525,0.0005819806,0.0016188831,0.0007230226,0.0067386976],"genre_scores_gemma":[0.8782941,0.0011114005,0.118455574,0.00008173415,0.000084634186,0.00028165427,0.0007216695,0.000035437988,0.000933831],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913555,0.00011841129,0.00007867878,0.00009926807,0.00052301947,0.000045089437],"domain_scores_gemma":[0.9984659,0.0002774431,0.0003921823,0.00007851575,0.0007038667,0.00008203666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071332615,0.0006315672,0.00050453836,0.0025966626,0.00016045384,0.0006018821,0.000528997,0.00041554865,0.0013446349],"category_scores_gemma":[0.0020164445,0.00023429908,0.0003990318,0.0011611378,0.00030124036,0.0009768035,0.00058848166,0.00033155765,0.00031745553],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018515446,0.00043050045,0.1400243,0.0012087417,0.000389281,0.00028386043,0.00028158023,0.006835832,0.21784246,0.0020477977,0.0031844205,0.6256197],"study_design_scores_gemma":[0.00030934802,0.0025521775,0.6018598,0.00029558592,0.00078005996,0.0052796626,0.0005596331,0.24958493,0.12816876,0.0024748868,0.007734797,0.00040033335],"about_ca_topic_score_codex":0.00096111395,"about_ca_topic_score_gemma":0.0015282438,"teacher_disagreement_score":0.0025966626,"about_ca_system_score_codex":0.0004018178,"about_ca_system_score_gemma":0.00030424527,"threshold_uncertainty_score":0.0044981837},"labels":[],"label_agreement":null},{"id":"W2151958794","doi":"10.3390/s150203154","title":"Research on Initial Alignment and Self-Calibration of Rotary Strapdown Inertial Navigation Systems","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Observability; Inertial navigation system; Position (finance); Calibration; Control theory (sociology); Inertial frame of reference; Process (computing); Inertial measurement unit; Computer science; Rotation (mathematics); Engineering; Control engineering; Artificial intelligence; Mathematics; Physics","score_opus":0.04766979275241758,"score_gpt":0.31024455597284295,"score_spread":0.2625747632204254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151958794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039523844,0.0049600536,0.94822466,0.0002485264,0.00014654292,0.00007007042,0.00001855757,0.000316288,0.006491551],"genre_scores_gemma":[0.8828685,0.009182279,0.10285389,0.0001198678,0.00023427115,0.00011286858,0.00010698143,0.000048762944,0.004472662],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985037,0.0002359765,0.00011936933,0.00034820315,0.0007024348,0.00009031935],"domain_scores_gemma":[0.99880314,0.00026050946,0.00017464161,0.00012621646,0.0005928989,0.00004249404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011479864,0.00060061394,0.00050371164,0.00070564495,0.00041990442,0.000851042,0.00081927486,0.0005009883,0.0010568016],"category_scores_gemma":[0.0028535095,0.00034704758,0.0005153865,0.00084719434,0.0006005229,0.0021089069,0.00063862785,0.0006929798,0.0002554006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002832983,0.00010952016,0.009541497,0.0012584348,0.00011582062,0.0002830428,0.0011501375,0.15735108,0.08270265,0.050494954,0.0020399247,0.6946697],"study_design_scores_gemma":[0.00005914595,0.0011045794,0.008837971,0.00017198942,0.00012514417,0.00054641656,0.00034849535,0.89427316,0.05659585,0.009512712,0.028304137,0.00012031968],"about_ca_topic_score_codex":0.00268831,"about_ca_topic_score_gemma":0.0007574871,"teacher_disagreement_score":0.00268831,"about_ca_system_score_codex":0.00053049816,"about_ca_system_score_gemma":0.00086701923,"threshold_uncertainty_score":0.00607121},"labels":[],"label_agreement":null},{"id":"W2152212928","doi":"10.3390/s100807514","title":"A Software Architecture for Adaptive Modular Sensing Systems","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modular design; Computer science; Software; Embedded system; Middleware (distributed applications); Kernel (algebra); Robotics; Computer architecture; Key (lock); Simple (philosophy); Representation (politics); Computer hardware; Distributed computing; Artificial intelligence; Robot; Operating system","score_opus":0.009965094354192086,"score_gpt":0.21232536284960907,"score_spread":0.202360268495417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152212928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067090355,0.00008590096,0.9879469,0.00010940121,0.000025772522,0.000053012816,0.000020844609,0.0034583423,0.001590754],"genre_scores_gemma":[0.2077053,0.0003167388,0.7857007,0.00014176557,0.000057409226,0.0003002978,0.00023890875,0.0003744139,0.005164488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993135,0.00010404084,0.00007416103,0.00017832243,0.000263999,0.00006601858],"domain_scores_gemma":[0.9990094,0.00025581828,0.00009590844,0.0003498379,0.00020606845,0.00008310473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010128712,0.00057992776,0.00044744636,0.00063365,0.00055081473,0.0018476036,0.0023101817,0.0011154558,0.0022573962],"category_scores_gemma":[0.0021367446,0.0005344552,0.00077941397,0.00045189093,0.0012674865,0.0026090231,0.0017602411,0.0014461633,0.0010207702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028109108,0.00022436025,0.0021323639,0.00049110194,0.00015669318,0.00061103207,0.0013570872,0.19367069,0.102443814,0.32677433,0.006272496,0.3655848],"study_design_scores_gemma":[0.00006586581,0.0002697327,0.0005561391,0.000073547046,0.00009519655,0.00042209087,0.00010199978,0.82428837,0.03500311,0.08420276,0.05485232,0.000068815985],"about_ca_topic_score_codex":0.0014465945,"about_ca_topic_score_gemma":0.0011228882,"teacher_disagreement_score":0.0023101817,"about_ca_system_score_codex":0.0006312843,"about_ca_system_score_gemma":0.00090360135,"threshold_uncertainty_score":0.0075517893},"labels":[],"label_agreement":null},{"id":"W2152429996","doi":"10.3390/s150820511","title":"Considerations on Circuit Design and Data Acquisition of a Portable Surface Plasmon Resonance Biosensing System","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ste. Anne's Hospital","funders":"State Key Laboratory of Wheat and Maize Crop Science; National Natural Science Foundation of China","keywords":"Biosensor; Surface plasmon resonance; Refractive index; Amplifier; Materials science; Sensitivity (control systems); SIGNAL (programming language); Data acquisition; Optoelectronics; Computer science; Electronic engineering; Nanotechnology; CMOS; Engineering","score_opus":0.061027561200282854,"score_gpt":0.2966959933784121,"score_spread":0.23566843217812927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152429996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039475307,0.0010518268,0.93932396,0.0011384829,0.00034905836,0.00124294,0.00046929476,0.0074218987,0.009527186],"genre_scores_gemma":[0.35110974,0.0011975892,0.62619233,0.0013241168,0.00039138616,0.0017214688,0.0008487224,0.00067448156,0.01654009],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990508,0.0001315669,0.000058751095,0.00024177243,0.00043287865,0.0000842703],"domain_scores_gemma":[0.99889284,0.0003717142,0.000078795114,0.000101516205,0.0005115796,0.000043550997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006642431,0.0007884732,0.0005774466,0.00067681936,0.00042323992,0.0013185223,0.002644725,0.00075856055,0.009327176],"category_scores_gemma":[0.0016209587,0.0004975866,0.00030530992,0.0005889629,0.00026567295,0.001237143,0.00049309403,0.00092887675,0.0026980154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005632572,0.00023646848,0.0026872125,0.0016462954,0.0001201574,0.0007872652,0.0003529077,0.008671599,0.6310475,0.011856119,0.012362671,0.32966852],"study_design_scores_gemma":[0.0002753605,0.0023134293,0.005681592,0.00022672537,0.0003086613,0.0024730647,0.00028410897,0.15593803,0.66273224,0.003983147,0.16565034,0.0001333633],"about_ca_topic_score_codex":0.001349764,"about_ca_topic_score_gemma":0.0014681652,"teacher_disagreement_score":0.009327176,"about_ca_system_score_codex":0.00096859963,"about_ca_system_score_gemma":0.00083524385,"threshold_uncertainty_score":0.031202495},"labels":[],"label_agreement":null},{"id":"W2153205039","doi":"10.3390/s150922291","title":"An Apta-Biosensor for Colon Cancer Diagnostics","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science Research and Technology","keywords":"Aptamer; Detection limit; Biosensor; Nanotechnology; Colorectal cancer; Cyclic voltammetry; Flow cytometry; Cancer; Chemistry; Materials science; Electrode; Computer science; Chromatography; Electrochemistry; Molecular biology; Medicine; Biology; Internal medicine","score_opus":0.027959622856826936,"score_gpt":0.34096267188006335,"score_spread":0.3130030490232364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153205039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33275935,0.057606217,0.5788317,0.0033261096,0.0022044852,0.00079861574,0.0013723625,0.0058670226,0.017234176],"genre_scores_gemma":[0.63967,0.014418484,0.31857607,0.0012423804,0.00023441906,0.000287365,0.0010763827,0.00010013638,0.024394734],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935764,0.00011570592,0.000033724467,0.00014111085,0.0002918102,0.000060043014],"domain_scores_gemma":[0.9998084,0.000040426115,0.00003311434,0.000013858918,0.000065220025,0.000039002414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038812993,0.000778162,0.00040532427,0.00046689433,0.00024430524,0.00039696478,0.0007538685,0.0011969745,0.001059799],"category_scores_gemma":[0.00035997693,0.0003831429,0.00039190854,0.00029275674,0.00025891178,0.00044536358,0.00032871525,0.0009430728,0.0016536383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022713399,0.000014718451,0.00013199658,0.000099154044,0.000006562353,0.00005182427,0.000010352199,0.00010931198,0.99349904,0.0001988151,0.00021009595,0.0056454185],"study_design_scores_gemma":[0.000008183858,0.00019726052,0.00063715235,0.000012458275,0.000020162637,0.000850985,0.000011874591,0.0028546106,0.98422766,0.00010307987,0.011062464,0.000014058094],"about_ca_topic_score_codex":0.0005950456,"about_ca_topic_score_gemma":0.0010976582,"teacher_disagreement_score":0.0011969745,"about_ca_system_score_codex":0.00065008755,"about_ca_system_score_gemma":0.00051336916,"threshold_uncertainty_score":0.004716754},"labels":[],"label_agreement":null},{"id":"W2153710474","doi":"10.3390/s110504572","title":"Recent Advances in Neural Recording Microsystems","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Interfacing; Microsystem; Computer science; Power management; Embedded system; Computer hardware; Power (physics); Nanotechnology","score_opus":0.05507193891870061,"score_gpt":0.3036286557852001,"score_spread":0.2485567168664995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153710474","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006116917,0.9890859,0.004035383,0.00047553625,0.00040267137,0.000017196815,0.000025429792,0.000046455232,0.0052996706],"genre_scores_gemma":[0.0032326235,0.98641753,0.005723094,0.00038177875,0.00055304775,0.000028053168,0.000053159758,0.000012043995,0.0035985978],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994748,0.0000637142,0.00006882809,0.00011038535,0.00025096542,0.000031366206],"domain_scores_gemma":[0.9986511,0.0007326786,0.00011226881,0.00005801063,0.00039586026,0.00005013488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010087813,0.0009205416,0.00086795,0.001965546,0.00033103174,0.00095693424,0.0011256471,0.0014061475,0.004974156],"category_scores_gemma":[0.0014719246,0.00054283673,0.00047854581,0.0027008783,0.00060779025,0.0022511766,0.0006933774,0.00178909,0.0034787084],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006847402,0.000090420464,0.00025136396,0.011463775,0.000036074463,0.00019496838,0.0000922963,0.0011591,0.013232472,0.011770996,0.014763367,0.9468767],"study_design_scores_gemma":[0.000009864814,0.00016264757,0.0006264747,0.001593656,0.00005636248,0.0017204133,0.000058500715,0.00078015646,0.00759402,0.0049866913,0.98237485,0.00003623541],"about_ca_topic_score_codex":0.00076334283,"about_ca_topic_score_gemma":0.0009577113,"teacher_disagreement_score":0.004974156,"about_ca_system_score_codex":0.00065772294,"about_ca_system_score_gemma":0.0008573213,"threshold_uncertainty_score":0.016640186},"labels":[],"label_agreement":null},{"id":"W2153809210","doi":"10.3390/s140917275","title":"Raman Spectroscopy for In-Line Water Quality Monitoring—Instrumentation and Potential","year":2014,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; FedDev Ontario; CMC Microsystems","keywords":"Raman spectroscopy; Instrumentation (computer programming); Nanotechnology; Water quality; Environmental science; Spectrometer; Quality (philosophy); Computer science; Biochemical engineering; Process engineering; Engineering; Materials science; Physics; Optics","score_opus":0.03595179127420179,"score_gpt":0.42855907096268125,"score_spread":0.39260727968847947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153809210","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040495,0.9886982,0.0028951997,0.00089394243,0.00062456506,0.000015457183,0.000025462721,0.00003498641,0.006407307],"genre_scores_gemma":[0.0041250745,0.9873832,0.0031042523,0.00043389658,0.000488059,0.000022127257,0.00003826957,0.000011059529,0.004394173],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992642,0.000121594465,0.000029723957,0.00011999037,0.00040311413,0.000061327475],"domain_scores_gemma":[0.99948186,0.00022575817,0.000051316252,0.000026413254,0.00017657092,0.000038025148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015448977,0.0010540903,0.0009842115,0.0024130174,0.00053604157,0.0012879457,0.0012772487,0.0020164337,0.004517656],"category_scores_gemma":[0.00083217066,0.0004987928,0.0007159024,0.0024329708,0.001068857,0.002599773,0.0010357669,0.0029330854,0.00402497],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038386952,0.00011543173,0.00024693046,0.008541709,0.000051342737,0.00021108054,0.00011214884,0.0006735428,0.018954948,0.01777781,0.02766504,0.9256116],"study_design_scores_gemma":[0.000005966041,0.0000807851,0.00041790766,0.0012818701,0.000027085773,0.0010580008,0.00008003307,0.0003711489,0.006471511,0.006785177,0.9833878,0.000032578646],"about_ca_topic_score_codex":0.00093079614,"about_ca_topic_score_gemma":0.0012514902,"teacher_disagreement_score":0.004517656,"about_ca_system_score_codex":0.00081511936,"about_ca_system_score_gemma":0.0010128644,"threshold_uncertainty_score":0.015113056},"labels":[],"label_agreement":null},{"id":"W2155844662","doi":"10.3390/s130201763","title":"Recent Advances in Bacteriophage Based Biosensors for Food-Borne Pathogen Detection","year":2013,"lang":"en","type":"review","venue":"Sensors","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":344,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Alberta Livestock and Meat Agency; University of Alberta","keywords":"Identification (biology); Bacteriophage; Foodborne pathogen; Biosensor; Biochemical engineering; Nanotechnology; Computational biology; Computer science; Biotechnology; Risk analysis (engineering); Biology; Engineering; Medicine; Gene","score_opus":0.027514447605168788,"score_gpt":0.2920268609057639,"score_spread":0.2645124133005951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155844662","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028268227,0.9974916,0.0005567233,0.00015046309,0.00019400472,0.000006967707,0.000015000855,0.000014334557,0.001288148],"genre_scores_gemma":[0.0010895312,0.99714017,0.0007577822,0.0001300636,0.00012587539,0.000008839103,0.000026725635,0.00000231735,0.00071867375],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958235,0.000048590136,0.000041471474,0.000080119695,0.00021194378,0.00003551999],"domain_scores_gemma":[0.9994723,0.00023497062,0.00006597324,0.000016271042,0.00017014473,0.000040358376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010149847,0.0012022285,0.0013265117,0.0031073105,0.00033775927,0.00085477467,0.001042675,0.001274288,0.0027378271],"category_scores_gemma":[0.0009966732,0.0004870997,0.0006960618,0.0033866903,0.00054641993,0.0015939919,0.0008474668,0.0016383647,0.0020270552],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006922909,0.00012891876,0.00028620486,0.02661075,0.0000926536,0.00026709074,0.0001086479,0.0006932387,0.017192673,0.0048406753,0.014084784,0.9356252],"study_design_scores_gemma":[0.000012250286,0.00018979759,0.0008505538,0.0020976367,0.00011352111,0.0012735702,0.000077992525,0.0002560549,0.0055308947,0.0020161958,0.9875358,0.00004568868],"about_ca_topic_score_codex":0.00090543006,"about_ca_topic_score_gemma":0.0011998152,"teacher_disagreement_score":0.0031073105,"about_ca_system_score_codex":0.0006968875,"about_ca_system_score_gemma":0.0010178995,"threshold_uncertainty_score":0.009158909},"labels":[],"label_agreement":null},{"id":"W2156167948","doi":"10.3390/s91109196","title":"Microfabricated Formaldehyde Gas Sensors","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Asian Office of Aerospace Research and Development","keywords":"Formaldehyde; Outgassing; Materials science; Nanotechnology; Volatile organic compound; Polymer; Microelectromechanical systems; Silicon nanowires; Nanowire; Process engineering; Composite material; Chemistry; Engineering; Organic chemistry","score_opus":0.006625088777805386,"score_gpt":0.18886762256889345,"score_spread":0.18224253379108807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156167948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49736595,0.016447566,0.43679833,0.0013822638,0.0014400291,0.00049113436,0.0025216264,0.008704055,0.034849036],"genre_scores_gemma":[0.5804087,0.004808059,0.36546284,0.0009760776,0.0002525025,0.00045062747,0.0015716524,0.00019187955,0.045877673],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940765,0.000027220898,0.000017271397,0.0001503445,0.00033530814,0.00006220744],"domain_scores_gemma":[0.99980336,0.000043430606,0.00005176237,0.000027674394,0.000059048445,0.000014876231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019742356,0.0005743078,0.00041728574,0.00028854373,0.00020845633,0.00036404288,0.001317035,0.0008823129,0.0026317267],"category_scores_gemma":[0.00038109423,0.00037137518,0.00033890703,0.00025446215,0.0002519841,0.00045629503,0.0004198578,0.00055020663,0.001626467],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020900445,0.000017104809,0.000089493136,0.00005976722,0.000005031812,0.000052144613,0.000018106124,0.00028681068,0.99132854,0.00028328036,0.0006164812,0.0072222953],"study_design_scores_gemma":[0.000009608602,0.00016432504,0.0009942055,0.000004775329,0.0000115694265,0.0003263923,0.000012315296,0.004051033,0.9810858,0.0000800912,0.013236947,0.000023041488],"about_ca_topic_score_codex":0.0007426087,"about_ca_topic_score_gemma":0.0021009014,"teacher_disagreement_score":0.0026317267,"about_ca_system_score_codex":0.00065788656,"about_ca_system_score_gemma":0.00017589847,"threshold_uncertainty_score":0.008803964},"labels":[],"label_agreement":null},{"id":"W2156337551","doi":"10.3390/s140406584","title":"Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Wireless; Wireless sensor network; Logarithm; Computer science; Data mining; Interval (graph theory); Real-time computing; Artificial intelligence; Mathematics; Computer network; Telecommunications","score_opus":0.01883652301916091,"score_gpt":0.25180778365389683,"score_spread":0.23297126063473592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156337551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010505188,0.0003712005,0.9884735,0.00007263079,0.000021556612,0.000018501072,0.000027507507,0.0002379926,0.0002720349],"genre_scores_gemma":[0.6083431,0.0007658555,0.3894378,0.0000896959,0.00008738253,0.00014657312,0.0002823236,0.00008579649,0.0007614124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772877,0.00062312384,0.00019798537,0.0004841406,0.00088717265,0.000078806035],"domain_scores_gemma":[0.99670655,0.0015442052,0.0005044717,0.00047576916,0.00070019026,0.000068839436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016614848,0.0007021263,0.0009098991,0.0017014982,0.00058242126,0.00077571394,0.0017990017,0.00082003564,0.00030113326],"category_scores_gemma":[0.0077433493,0.00043344224,0.00055644277,0.0023898897,0.00068478845,0.0019012197,0.0012169952,0.00075721316,0.0001793083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103388906,0.00002522056,0.0015567349,0.00013427236,0.00006656155,0.0000794154,0.00015182293,0.84044343,0.004302429,0.0074821506,0.000754147,0.14490043],"study_design_scores_gemma":[0.0000024375297,0.000016106349,0.00036358982,0.000005853456,0.0000065163035,0.00003821205,0.000023052566,0.99306893,0.0020978113,0.003833413,0.0005293425,0.000014738789],"about_ca_topic_score_codex":0.0033210386,"about_ca_topic_score_gemma":0.0016961665,"teacher_disagreement_score":0.0033210386,"about_ca_system_score_codex":0.00089236005,"about_ca_system_score_gemma":0.0005123216,"threshold_uncertainty_score":0.008786857},"labels":[],"label_agreement":null},{"id":"W2156908521","doi":"10.3390/s140101474","title":"An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar National Research Fund; Fonds National de la Recherche Luxembourg","keywords":"Compressed sensing; Compression (physics); Energy (signal processing); Computer science; Electroencephalography; Data compression; Speech recognition; Artificial intelligence; Materials science; Mathematics; Neuroscience; Psychology; Statistics","score_opus":0.023937601308123158,"score_gpt":0.29243758760849053,"score_spread":0.26849998630036737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156908521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002908928,0.00032439924,0.9958616,0.00011185863,0.00003377137,0.000018344252,0.000030785162,0.000072515504,0.00063783315],"genre_scores_gemma":[0.21892913,0.002038704,0.7739665,0.00016226505,0.00030612916,0.00015393597,0.00029146354,0.000078711775,0.004073121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997464,0.00007314215,0.000011457594,0.000027202768,0.00012663697,0.0000151334025],"domain_scores_gemma":[0.99973005,0.00014473556,0.000027830576,0.000026097674,0.000059900758,0.0000113265805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049835845,0.00059141347,0.00032658115,0.00050446985,0.00019681273,0.00037992003,0.00058254116,0.0005590051,0.0013753768],"category_scores_gemma":[0.00122157,0.00014558356,0.0003580613,0.00062687194,0.00046884327,0.00067236566,0.0005116606,0.0008829945,0.0002594381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018953231,0.00011592328,0.00035089758,0.0002964883,0.00006161549,0.0003528008,0.00018606072,0.4345333,0.078539856,0.1430686,0.005159506,0.3371454],"study_design_scores_gemma":[0.000009266095,0.00006081488,0.00012187889,0.000010905422,0.0000069643233,0.00013764654,0.000012445786,0.9839708,0.00545591,0.0073788264,0.0028225125,0.000012075449],"about_ca_topic_score_codex":0.0017096814,"about_ca_topic_score_gemma":0.001553281,"teacher_disagreement_score":0.0017096814,"about_ca_system_score_codex":0.00027928228,"about_ca_system_score_gemma":0.0005166528,"threshold_uncertainty_score":0.004601121},"labels":[],"label_agreement":null},{"id":"W2157381189","doi":"10.3390/s110403687","title":"Fiber Optic Sensors for Structural Health Monitoring of Air Platforms","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":302,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence; National Research Council Canada; Institute for Microstructural Sciences; University of Ottawa","funders":"National Research Council Canada; Ministère de la Défense Nationale","keywords":"Structural health monitoring; Aerospace; Fiber Bragg grating; Fiber optic sensor; Optical fiber; Systems engineering; Computer science; Electro-optical sensor; Engineering; Reliability engineering; Telecommunications; Electronic engineering; Electrical engineering; Aerospace engineering","score_opus":0.02955427392044469,"score_gpt":0.2612523884012362,"score_spread":0.23169811448079153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157381189","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26268128,0.26085284,0.40856323,0.0029466148,0.0012039632,0.00049158954,0.001077087,0.001571112,0.060612213],"genre_scores_gemma":[0.67918,0.07147133,0.22060318,0.000613308,0.0004464792,0.00017315685,0.00057416665,0.000065442975,0.026872853],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958676,0.000050526214,0.000010325897,0.000052627212,0.00027933673,0.0000203962],"domain_scores_gemma":[0.9998585,0.00003433383,0.000028763832,0.000012437879,0.000056450255,0.00000943301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040498519,0.00047801013,0.00027779516,0.0006018967,0.00024358096,0.0004258927,0.0003629968,0.0006710552,0.0014841761],"category_scores_gemma":[0.00035149523,0.0001628887,0.0002065018,0.0004574081,0.00020136173,0.0005604054,0.00028464384,0.00043375386,0.0006155372],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001348381,0.00006409676,0.0023377915,0.0007543889,0.00003203981,0.00021445018,0.00012391743,0.0020428798,0.74535596,0.004855079,0.0034461073,0.24063832],"study_design_scores_gemma":[0.00003082591,0.0008907376,0.015444615,0.00038981638,0.00012197956,0.002109334,0.00022882558,0.032571524,0.73316467,0.006347195,0.20861231,0.000088092245],"about_ca_topic_score_codex":0.00071208493,"about_ca_topic_score_gemma":0.0016404454,"teacher_disagreement_score":0.0014841761,"about_ca_system_score_codex":0.00036177368,"about_ca_system_score_gemma":0.00036587138,"threshold_uncertainty_score":0.004965067},"labels":[],"label_agreement":null},{"id":"W2158607861","doi":"10.3390/s8085081","title":"A Polypyrrole-based Strain Sensor Dedicated to Measure Bladder Volume in Patients with Urinary Dysfunction","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polypyrrole; Measure (data warehouse); Urinary system; Strain (injury); Urinary bladder; Volume (thermodynamics); Urology; Medicine; Biomedical engineering; Computer science; Internal medicine; Materials science; Data mining; Physics; Composite material","score_opus":0.009614060678812828,"score_gpt":0.17737802186525403,"score_spread":0.1677639611864412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158607861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9469989,0.0052176286,0.042602096,0.0005636022,0.00024271039,0.00013904604,0.00058652274,0.0006439907,0.003005454],"genre_scores_gemma":[0.9700301,0.0009556178,0.027255882,0.00021245876,0.00010574195,0.000059696642,0.00019844077,0.00003394484,0.0011481437],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974614,0.000053316173,0.00001762991,0.00006361369,0.0001029077,0.000016449458],"domain_scores_gemma":[0.9995254,0.00015972216,0.00015168452,0.000030122903,0.00006564142,0.00006744055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002453627,0.00043841318,0.00040426716,0.00038002036,0.00013451785,0.0003257369,0.0003618373,0.0005694529,0.0005208741],"category_scores_gemma":[0.0008411654,0.00013614872,0.00017871601,0.00022742491,0.0001718425,0.00028551003,0.00015162303,0.00040846656,0.00018387221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008826362,0.00022156577,0.016689736,0.00029836837,0.00004348512,0.0010489738,0.00015571892,0.00046871047,0.9086186,0.0001480744,0.0005772444,0.07084686],"study_design_scores_gemma":[0.00011125997,0.0042530145,0.09192723,0.000052938954,0.00018558501,0.014104814,0.000099498335,0.013613982,0.8699034,0.00016406692,0.005506994,0.00007725061],"about_ca_topic_score_codex":0.00015976181,"about_ca_topic_score_gemma":0.00030920323,"teacher_disagreement_score":0.0005694529,"about_ca_system_score_codex":0.00014250426,"about_ca_system_score_gemma":0.0001308021,"threshold_uncertainty_score":0.0017425418},"labels":[],"label_agreement":null},{"id":"W2159613333","doi":"10.3390/s8096055","title":"An Evaluation of Radarsat-1 and ASTER Data for Mapping Veredas (Palm Swamps)","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Space Agency","keywords":"Advanced Spaceborne Thermal Emission and Reflection Radiometer; Remote sensing; VNIR; Wetland; Swamp; Contextual image classification; Environmental science; Geography; Cartography; Computer science; Artificial intelligence; Hyperspectral imaging; Image (mathematics)","score_opus":0.21450376149174274,"score_gpt":0.30181999729572023,"score_spread":0.08731623580397749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159613333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98918754,0.00041153887,0.0073884246,0.0000608651,0.000030243462,0.00008465286,0.00052861735,0.00023684604,0.002071307],"genre_scores_gemma":[0.98045427,0.00022806067,0.017094776,0.000022066708,0.000017880751,0.00002741758,0.0015892637,0.000024300547,0.0005418887],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998386,0.0005133967,0.00010952457,0.00032594815,0.0005569994,0.00010816745],"domain_scores_gemma":[0.99787676,0.00094362197,0.0001360528,0.00021651394,0.00065860513,0.00016830729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031409662,0.0007519664,0.0003933181,0.001992831,0.0002534324,0.0008500152,0.0004344151,0.00057748985,0.00040217378],"category_scores_gemma":[0.003178893,0.00018419174,0.00037207792,0.0008872954,0.00021227683,0.00086165895,0.00042120795,0.0002496242,0.00025710915],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030383847,0.0017411747,0.19797914,0.0006928511,0.0007734711,0.00083089306,0.0007193798,0.091683805,0.14929783,0.0007800216,0.0022305448,0.55023247],"study_design_scores_gemma":[0.00023965795,0.0030616932,0.45194602,0.000086339176,0.00038901216,0.0005196213,0.0014313,0.47893402,0.058895264,0.00031052373,0.004093877,0.00009273775],"about_ca_topic_score_codex":0.005730537,"about_ca_topic_score_gemma":0.007910962,"teacher_disagreement_score":0.005730537,"about_ca_system_score_codex":0.00028767902,"about_ca_system_score_gemma":0.00027492765,"threshold_uncertainty_score":0.016611218},"labels":[],"label_agreement":null},{"id":"W2160331134","doi":"10.3390/s150717572","title":"Wireless Sensor Network Optimization: Multi-Objective Paradigm","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Optimization problem; Computer science; Software deployment; Key distribution in wireless sensor networks; Wireless network; Wireless; Distributed computing; Mathematical optimization; Computer network; Telecommunications; Mathematics","score_opus":0.05381203925768556,"score_gpt":0.33002241792906734,"score_spread":0.27621037867138176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160331134","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024836888,0.42548287,0.53391844,0.0038497758,0.0008591539,0.00012377114,0.00016818321,0.00017122398,0.032942872],"genre_scores_gemma":[0.09105763,0.6893817,0.20359963,0.0013770811,0.0018931491,0.00041312983,0.00029027977,0.00011941838,0.01186791],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992311,0.0002617739,0.00005025862,0.000096408476,0.00032741405,0.000032964308],"domain_scores_gemma":[0.99932647,0.0004000787,0.000082849976,0.0000341391,0.0001378414,0.000018709112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012840629,0.0014210003,0.0013056147,0.0013288998,0.00024614882,0.0015744338,0.0015015103,0.0014476017,0.0023235537],"category_scores_gemma":[0.0014685378,0.0004501649,0.0008959865,0.003251953,0.00089506834,0.001900314,0.0009231863,0.002140465,0.00084253994],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050500563,0.00012592907,0.0005383589,0.007565465,0.00029584463,0.00021541759,0.000109608874,0.18067732,0.002806165,0.22832577,0.018775247,0.56051445],"study_design_scores_gemma":[0.000035663583,0.00025147456,0.0015431255,0.00286799,0.00019406113,0.0010802967,0.00018940234,0.2980354,0.0039504087,0.26839408,0.42334762,0.00011042895],"about_ca_topic_score_codex":0.00065502303,"about_ca_topic_score_gemma":0.00074511813,"teacher_disagreement_score":0.0023235537,"about_ca_system_score_codex":0.0009287029,"about_ca_system_score_gemma":0.0008955823,"threshold_uncertainty_score":0.0077731013},"labels":[],"label_agreement":null},{"id":"W2161525536","doi":"10.3390/s110504512","title":"Design and Fabrication of Vertically-Integrated CMOS Image Sensors","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"University of Alberta; CMC Microsystems","keywords":"Image sensor; CMOS; Microsystem; Photodetector; CMOS sensor; Integrated circuit; USB; Dot pitch; Die (integrated circuit); Pixel; Computer science; Computer hardware; Electrical engineering; Electronic engineering; Materials science; Engineering; Optoelectronics; Artificial intelligence; Nanotechnology; Software","score_opus":0.0163973620600209,"score_gpt":0.19980596267913459,"score_spread":0.18340860061911368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161525536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26283666,0.0025431584,0.7129194,0.00053687947,0.00047255575,0.0007124241,0.0010918103,0.0016280537,0.017259017],"genre_scores_gemma":[0.4223242,0.0011975605,0.5692293,0.00022847494,0.00006402714,0.0004319498,0.0005384188,0.000071651826,0.005914251],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975616,0.000015000837,0.000015156891,0.000054104134,0.00013052202,0.000029062721],"domain_scores_gemma":[0.9998087,0.000018117968,0.000037228267,0.000014357718,0.000106380656,0.000015241327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015937716,0.0003713456,0.00024039474,0.0001860921,0.0001820419,0.0003741739,0.0007939829,0.00038572258,0.000791636],"category_scores_gemma":[0.00031015745,0.00035965673,0.00019340559,0.00019153867,0.00016475756,0.00026313117,0.00022043478,0.0002990958,0.00044969638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025583027,0.000014895149,0.00044059337,0.00009934764,0.00000797829,0.00005752318,0.000028452585,0.0018903533,0.97943234,0.0019298748,0.0005538441,0.015519216],"study_design_scores_gemma":[0.000022606735,0.0003764578,0.0020665035,0.000015557574,0.000024697007,0.0002492545,0.000028900338,0.025076738,0.95240575,0.00038929787,0.01932208,0.000022128861],"about_ca_topic_score_codex":0.00083820743,"about_ca_topic_score_gemma":0.0016005291,"teacher_disagreement_score":0.00083820743,"about_ca_system_score_codex":0.0005042746,"about_ca_system_score_gemma":0.000501505,"threshold_uncertainty_score":0.0036587715},"labels":[],"label_agreement":null},{"id":"W2161583192","doi":"10.3390/s150819429","title":"Electrochemical Characterization of Protein Adsorption onto YNGRT-Au and VLGXE-Au Surfaces","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Molecular Junctions and Nanostructures","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Peptide; Dielectric spectroscopy; Adsorption; Chemistry; Electrochemistry; Butylamine; Protein adsorption; Materials science; Combinatorial chemistry; Biochemistry; Electrode; Organic chemistry; Amine gas treating; Physical chemistry","score_opus":0.006730798728978887,"score_gpt":0.18785538156359627,"score_spread":0.18112458283461738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161583192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99058455,0.0008962931,0.007331521,0.00007651299,0.000021895434,0.000030105735,0.00020872941,0.00010140405,0.0007490395],"genre_scores_gemma":[0.97788393,0.0012363053,0.017748192,0.00010644915,0.000010713043,0.00007613123,0.00035144656,0.000029841067,0.002556893],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972016,0.000033332784,0.000016365257,0.000054761746,0.00013123886,0.000044137465],"domain_scores_gemma":[0.9998117,0.00006529623,0.000028905944,0.000011402639,0.00006471658,0.000017982273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028468127,0.0003219656,0.00039671906,0.0001931498,0.0001296924,0.0002872345,0.00043877945,0.00048108443,0.0006406158],"category_scores_gemma":[0.0006026576,0.00019219304,0.00019159766,0.00026207886,0.00018183424,0.00021173955,0.00024637446,0.00041906146,0.00025769247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015750084,0.00000489611,0.00010291169,0.000027493792,0.0000028749303,0.000026206977,0.000022245667,0.000055143264,0.9989786,0.000022972908,0.000012560403,0.0007284123],"study_design_scores_gemma":[0.0000031554566,0.00005009279,0.0019355591,0.000004082919,0.000005371986,0.000076083874,0.000040043018,0.0022974564,0.9951982,0.000019963974,0.0003659797,0.0000040985005],"about_ca_topic_score_codex":0.0010482057,"about_ca_topic_score_gemma":0.001205797,"teacher_disagreement_score":0.0010482057,"about_ca_system_score_codex":0.0003124561,"about_ca_system_score_gemma":0.000116461015,"threshold_uncertainty_score":0.0022670627},"labels":[],"label_agreement":null},{"id":"W2161654509","doi":"10.3390/s90402621","title":"Automatic Registration of Terrestrial Laser Scanning Point Clouds using Panoramic Reflectance Images","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Artificial intelligence; Computer vision; Pixel; Image registration; Computer science; Rigid transformation; Laser scanning; Bundle adjustment; Iterative closest point; Process (computing); Remote sensing; Photogrammetry; Laser; Optics; Image (mathematics); Geography; Physics","score_opus":0.03088121675195846,"score_gpt":0.2654694744419135,"score_spread":0.23458825768995503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161654509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020163285,0.00015610576,0.9743887,0.000040907787,0.00003871919,0.000086851556,0.00021836917,0.0030607078,0.0018462532],"genre_scores_gemma":[0.16036078,0.00030775432,0.8356394,0.00003845834,0.00004796092,0.00013918789,0.0013066636,0.0004917396,0.0016680604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817514,0.0002513392,0.000085207605,0.00040234238,0.0009803375,0.00010556229],"domain_scores_gemma":[0.99898607,0.00014688021,0.00016493644,0.00041303167,0.00025653935,0.000032520715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068798196,0.0008101368,0.0010284083,0.0028141486,0.0006755138,0.0012129736,0.0012852565,0.0006605863,0.0022625173],"category_scores_gemma":[0.0021200185,0.0008258817,0.0013262831,0.0028864325,0.0005583229,0.0016144918,0.0016734026,0.000995138,0.001860658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021704861,0.00015093994,0.0038063235,0.00026734924,0.00021650323,0.00025600698,0.0003956269,0.059877716,0.19407997,0.0044708066,0.0033069837,0.7329546],"study_design_scores_gemma":[0.000086961525,0.00031736272,0.017690128,0.00007798497,0.00013411866,0.0014143379,0.0005824399,0.7550726,0.18475226,0.012152197,0.0274965,0.00022309441],"about_ca_topic_score_codex":0.0024461418,"about_ca_topic_score_gemma":0.0038780777,"teacher_disagreement_score":0.0028141486,"about_ca_system_score_codex":0.00033227974,"about_ca_system_score_gemma":0.0007213964,"threshold_uncertainty_score":0.007568836},"labels":[],"label_agreement":null},{"id":"W2162399951","doi":"10.3390/s100100241","title":"A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Principal component analysis; Fault (geology); Artificial neural network; Component (thermodynamics); Fault detection and isolation; Computer science; Soft sensor; Mean squared error; Pattern recognition (psychology); Engineering; Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.01765621879095864,"score_gpt":0.2834185802252999,"score_spread":0.2657623614343413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162399951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014788802,0.00024774976,0.99742067,0.000041971485,0.00004133769,0.0000364624,0.000016933998,0.00046792915,0.0002481444],"genre_scores_gemma":[0.20063543,0.0006887478,0.79606193,0.00009466765,0.0001024451,0.00024396919,0.00012830907,0.00006782272,0.001976632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928635,0.00012294957,0.000036215777,0.00015598755,0.00036567237,0.0000328454],"domain_scores_gemma":[0.9995079,0.00014680074,0.00006511778,0.00004571613,0.00021409552,0.000020317582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062511134,0.0011474179,0.0012550075,0.0012245954,0.00057137996,0.00068235066,0.0010606303,0.0010121969,0.001311553],"category_scores_gemma":[0.0011120124,0.0004997172,0.00084259256,0.00085389306,0.00047532958,0.0012741284,0.0005614367,0.0011941304,0.0005908596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020661543,0.00016885684,0.0013936219,0.00066064874,0.0002420812,0.00030358095,0.0001534471,0.1900279,0.048436,0.010623731,0.0033419104,0.7444416],"study_design_scores_gemma":[0.000015177084,0.00012594112,0.00065320765,0.000016493135,0.000031804935,0.00023437504,0.000009035355,0.9873598,0.00727295,0.0020658083,0.0021780191,0.000037382277],"about_ca_topic_score_codex":0.0029076005,"about_ca_topic_score_gemma":0.0029085432,"teacher_disagreement_score":0.0029076005,"about_ca_system_score_codex":0.00060994294,"about_ca_system_score_gemma":0.0008529657,"threshold_uncertainty_score":0.005781293},"labels":[],"label_agreement":null},{"id":"W2162873323","doi":"10.3390/s120303669","title":"Vapochromic Behaviour of M[Au(CN)2]2-Based Coordination Polymers (M = Co, Ni)","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Metal-Organic Frameworks: Synthesis and Applications","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Pyridine; Chemistry; Isostructural; Aqueous solution; Dimethylformamide; Crystallography; Nickel; Absorption (acoustics); Crystal structure; Medicinal chemistry; Physical chemistry; Organic chemistry; Materials science","score_opus":0.015653793243151422,"score_gpt":0.2588524608290025,"score_spread":0.24319866758585107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162873323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99395955,0.0006765746,0.0029174306,0.000029375555,0.000014258457,0.000022077362,0.00010259336,0.00012732724,0.0021508553],"genre_scores_gemma":[0.9951047,0.0003335146,0.0030782348,0.000026532181,0.000007575797,0.000020151845,0.00008400102,0.000029110875,0.0013162842],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998604,0.000019090456,0.000005556689,0.00004129603,0.00004276853,0.00003074215],"domain_scores_gemma":[0.9998723,0.000024941104,0.000046865072,0.000008990992,0.00001950215,0.000027285392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008116221,0.00035281564,0.00015163141,0.00011883668,0.00011180128,0.0001986244,0.00025095316,0.00022662574,0.0010828133],"category_scores_gemma":[0.00024755648,0.00016022314,0.000111192814,0.00011176206,0.00019930625,0.00017421761,0.000201317,0.00030910745,0.0002320596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037388934,0.000006358819,0.00008533123,0.00004219898,0.000003564016,0.000018347391,0.000015353098,0.00006872845,0.9985514,0.000027691241,0.00002232608,0.0011212676],"study_design_scores_gemma":[0.000003605631,0.000090404385,0.0008081528,0.0000017049556,0.0000040842697,0.00005282865,0.000007014539,0.00034913613,0.998195,0.000005617253,0.0004797805,0.0000028492493],"about_ca_topic_score_codex":0.00063266006,"about_ca_topic_score_gemma":0.0009833355,"teacher_disagreement_score":0.0010828133,"about_ca_system_score_codex":0.00026284883,"about_ca_system_score_gemma":0.00011407222,"threshold_uncertainty_score":0.003622353},"labels":[],"label_agreement":null},{"id":"W2163049891","doi":"10.3390/s120708507","title":"Step Length Estimation Using Handheld Inertial Sensors","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Accelerometer; Mobile device; Inertial measurement unit; Short-time Fourier transform; Computer science; Two step; Process (computing); Set (abstract data type); Acoustics; SIGNAL (programming language); Artificial intelligence; Simulation; Fourier transform; Mathematics; Fourier analysis","score_opus":0.016409096296125292,"score_gpt":0.23524747484331776,"score_spread":0.21883837854719246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163049891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17571473,0.0005473227,0.8205048,0.000057692505,0.000061003513,0.00007546756,0.00031430562,0.0011116237,0.0016130945],"genre_scores_gemma":[0.8887819,0.0005337274,0.107070275,0.000040014635,0.00002436277,0.00009903009,0.00047565254,0.000040597395,0.0029343928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999892,0.000017408222,0.0000057239918,0.00003484184,0.000041510164,0.000008488111],"domain_scores_gemma":[0.99991214,0.000030170591,0.000015801754,0.000015208519,0.000021897551,0.0000046516743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009816229,0.0005034104,0.0003662481,0.00039879832,0.00011769733,0.0002895504,0.0004923578,0.00040817188,0.00096719235],"category_scores_gemma":[0.00042338236,0.00017848339,0.0003851395,0.00031649033,0.000104268,0.0003516613,0.00022723027,0.00022871027,0.0005303508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005640246,0.00016476725,0.017062781,0.00044930802,0.0002664969,0.0005124151,0.00017897566,0.32123095,0.17300777,0.0012020625,0.0017168216,0.48364356],"study_design_scores_gemma":[0.00003052875,0.00035768052,0.025489537,0.000038055474,0.00008731936,0.00048579994,0.000062737425,0.93613034,0.03362954,0.000746426,0.0028995078,0.000042566695],"about_ca_topic_score_codex":0.0025217852,"about_ca_topic_score_gemma":0.003069097,"teacher_disagreement_score":0.0025217852,"about_ca_system_score_codex":0.00014244707,"about_ca_system_score_gemma":0.0002031653,"threshold_uncertainty_score":0.0050142407},"labels":[],"label_agreement":null},{"id":"W2163354853","doi":"10.3390/s130303530","title":"Deployment of a Fully-Automated Green Fluorescent Protein Imaging System in a High Arctic Autonomous Greenhouse","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Light effects on plants","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; University of Guelph; École de Technologie Supérieure; Canadian Space Agency","funders":"Canadian Space Agency; Secretário de Ciência, Tecnologia e Ensino Superior, Governo do Estado de Parana; Simon Fraser University","keywords":"Greenhouse; Software deployment; Mars Exploration Program; Computer science; Remote sensing; International Space Station; Real-time computing; Engineering; Environmental science; Embedded system; Aerospace engineering; Geography; Astrobiology; Operating system; Biology","score_opus":0.006992249181905673,"score_gpt":0.1866770121950393,"score_spread":0.17968476301313363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163354853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8556276,0.00014724706,0.13273893,0.00026312133,0.00010376994,0.00065645913,0.0004517405,0.005283646,0.004727554],"genre_scores_gemma":[0.76948446,0.00014210679,0.2235106,0.00009355585,0.000022140825,0.00026928453,0.00044800935,0.00013390665,0.005895966],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997055,0.000030439694,0.000009460267,0.00009089721,0.00011256208,0.000051078798],"domain_scores_gemma":[0.9996221,0.000048653976,0.00003060665,0.00006421762,0.00013377734,0.00010049547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042799764,0.00036821404,0.0003315222,0.00020616913,0.0006639624,0.0004993047,0.00071229873,0.00047149535,0.0010530179],"category_scores_gemma":[0.00034350282,0.00021383453,0.00024583595,0.00011268482,0.00034408015,0.0003579695,0.00045867564,0.00033587695,0.0003931522],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033305725,0.00019129609,0.0039029648,0.00009499892,0.000022464637,0.00067064044,0.000543581,0.0075903744,0.9318371,0.0004306133,0.0012430334,0.053139996],"study_design_scores_gemma":[0.00022062639,0.0048270393,0.057054263,0.000057264428,0.00011185665,0.0017924224,0.0010557395,0.119965926,0.76992863,0.00041491847,0.04434541,0.0002259045],"about_ca_topic_score_codex":0.015702521,"about_ca_topic_score_gemma":0.015634913,"teacher_disagreement_score":0.015702521,"about_ca_system_score_codex":0.00068618776,"about_ca_system_score_gemma":0.0012784566,"threshold_uncertainty_score":0.031222224},"labels":[],"label_agreement":null},{"id":"W2164281158","doi":"10.3390/s120607778","title":"Satellite Remote Sensing of Harmful Algal Blooms (HABs) and a Potential Synthesized Framework","year":2012,"lang":"en","type":"review","venue":"Sensors","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Chinese Polar Environment Comprehensive Investigation and Assessment Programmes; State Oceanic Administration; Tongji University","keywords":"Algal bloom; Remote sensing; Satellite; Scale (ratio); Environmental science; Computer science; Satellite imagery; Geography; Ecology; Engineering; Cartography; Phytoplankton; Biology","score_opus":0.0255947581426659,"score_gpt":0.24422477290086675,"score_spread":0.21863001475820085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164281158","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093383377,0.9876754,0.006835909,0.00076896534,0.00035693514,0.000025715166,0.00007084817,0.000020545214,0.0033118126],"genre_scores_gemma":[0.008803855,0.9813654,0.008390932,0.0002484729,0.00028050045,0.00004121149,0.00012022332,0.0000035854455,0.0007458044],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999764,0.0000686345,0.000026890219,0.000048188453,0.000077218996,0.000015028248],"domain_scores_gemma":[0.9996439,0.00015814662,0.000050111405,0.00001193269,0.00011959765,0.000016164186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000943031,0.0011287546,0.0011303853,0.0027870303,0.00023160137,0.0010537946,0.0012261268,0.0010326029,0.0011526622],"category_scores_gemma":[0.00096412,0.00028300643,0.0007069511,0.0025171714,0.00060554163,0.0014703206,0.00074921397,0.00086708,0.00042344394],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049122504,0.00006955955,0.0012230877,0.021776685,0.0002957509,0.00029718693,0.00023335147,0.0034171136,0.0054216636,0.03793191,0.006693693,0.9225909],"study_design_scores_gemma":[0.000023374356,0.00044952464,0.0077810315,0.012141119,0.0010162421,0.0022329423,0.00083511526,0.006609371,0.0046125953,0.044494424,0.91963583,0.00016838784],"about_ca_topic_score_codex":0.0024967124,"about_ca_topic_score_gemma":0.0025306358,"teacher_disagreement_score":0.0027870303,"about_ca_system_score_codex":0.00074908906,"about_ca_system_score_gemma":0.0012494547,"threshold_uncertainty_score":0.005435109},"labels":[],"label_agreement":null},{"id":"W2164768617","doi":"10.3390/s7071028","title":"Development of a Fully Automated, GPS Based Monitoring System for Disaster Prevention and Emergency Preparedness: PPMS+RT","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Global Positioning System; Real-time computing; Ethernet; ALARM; Computer science; Positioning technology; Displacement (psychology); Embedded system; Engineering; Telecommunications; Computer hardware; Electrical engineering","score_opus":0.01720451476940085,"score_gpt":0.26517021533404356,"score_spread":0.2479657005646427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164768617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14176707,0.00030025453,0.77700543,0.00043888387,0.00025948064,0.0012255614,0.0021414037,0.05765437,0.01920751],"genre_scores_gemma":[0.5492117,0.0002453619,0.4129843,0.00042266402,0.00016948284,0.00084281375,0.0032868814,0.00046224374,0.032374527],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995028,0.00005533977,0.000029855673,0.000109973604,0.0002651394,0.000036928413],"domain_scores_gemma":[0.99961436,0.00004316904,0.000034686174,0.00009112325,0.00018790372,0.00002872099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051465654,0.0003583362,0.0005028644,0.00042982964,0.00019052665,0.0004831603,0.00094263937,0.0004029127,0.008443451],"category_scores_gemma":[0.0005877709,0.00021769798,0.00019905629,0.00024740995,0.00021449411,0.0005438267,0.0004111571,0.00038033279,0.0031498473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068082777,0.00023237646,0.0049497373,0.00041842443,0.00006717546,0.0002675777,0.000208928,0.011242777,0.3818277,0.0037878884,0.018867908,0.57744867],"study_design_scores_gemma":[0.00070517394,0.004406089,0.029187886,0.0001418069,0.00017773702,0.0020645037,0.00011917017,0.29886597,0.45887843,0.0023099252,0.20295888,0.00018451821],"about_ca_topic_score_codex":0.0012680431,"about_ca_topic_score_gemma":0.0010528628,"teacher_disagreement_score":0.008443451,"about_ca_system_score_codex":0.00026455295,"about_ca_system_score_gemma":0.0007205142,"threshold_uncertainty_score":0.028246164},"labels":[],"label_agreement":null},{"id":"W2165309702","doi":"10.3390/s8020830","title":"An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Hazard; Context (archaeology); Geographic information system; Data mining; Reliability (semiconductor); Domain (mathematical analysis); Raw data; Software; Representation (politics); Hazard analysis; Data science; Reliability engineering; Engineering","score_opus":0.031083822619370066,"score_gpt":0.2761710704695864,"score_spread":0.24508724785021632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165309702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011299843,0.00012833341,0.97965115,0.00027907925,0.000039418366,0.00023547807,0.00061204744,0.013702215,0.004222288],"genre_scores_gemma":[0.024757497,0.00021458842,0.9675387,0.00025022574,0.000034521752,0.000395522,0.0024743653,0.00051764364,0.0038169902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980165,0.00057706435,0.0002226991,0.0003219142,0.00077195815,0.00008978391],"domain_scores_gemma":[0.9979202,0.0008959645,0.00012833136,0.00033410182,0.0005428581,0.00017858487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004654613,0.0011463866,0.0009474651,0.0020423809,0.0008963413,0.0036598204,0.0045610256,0.0016969588,0.012775908],"category_scores_gemma":[0.0053242524,0.00072943623,0.0010703064,0.0013542726,0.0010254927,0.0036651185,0.0023565276,0.0017129488,0.00495955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042551424,0.00095864956,0.0025524294,0.0018396586,0.00039576142,0.0020429504,0.0022750834,0.15125184,0.020021586,0.2552174,0.09325292,0.46976623],"study_design_scores_gemma":[0.00013732511,0.000104811435,0.0007532295,0.00025277256,0.000101601836,0.0007924687,0.0003913439,0.6028363,0.009866911,0.16718106,0.2174755,0.00010672435],"about_ca_topic_score_codex":0.0062934435,"about_ca_topic_score_gemma":0.011282419,"teacher_disagreement_score":0.012775908,"about_ca_system_score_codex":0.0015478277,"about_ca_system_score_gemma":0.0028473618,"threshold_uncertainty_score":0.04273963},"labels":[],"label_agreement":null},{"id":"W2166202080","doi":"10.3390/s131216075","title":"A Label-Free Microfluidic Biosensor for Activity Detection of Single Microalgae Cells Based on Chlorophyll Fluorescence","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Marine Ecology and Invasive Species","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biosensor; Microfluidics; Ballast; Fluorescence; Chlorella vulgaris; Chlorophyll fluorescence; Photosynthesis; Chlorophyll; Dunaliella salina; Algae; Chemistry; Biology; Botany; Nanotechnology; Materials science; Ecology; Optics","score_opus":0.011296656766106845,"score_gpt":0.19587334152268288,"score_spread":0.18457668475657604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166202080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6195491,0.009817448,0.36373353,0.0011905538,0.0009197645,0.0003979469,0.0007973247,0.0015445683,0.0020497781],"genre_scores_gemma":[0.67994744,0.0028362079,0.31210536,0.00056565553,0.00015133129,0.00038107025,0.00055043574,0.00004075548,0.0034217755],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968195,0.000030849136,0.000028304818,0.000090223584,0.0001381815,0.00003036403],"domain_scores_gemma":[0.9998141,0.000046449437,0.000047172045,0.000017072172,0.000049157938,0.000026145606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030814207,0.00052122824,0.0004326622,0.00037923577,0.00026121645,0.0002939462,0.000828078,0.0007414437,0.00039876596],"category_scores_gemma":[0.0003674289,0.00031496602,0.00042005914,0.00021145667,0.00027981587,0.0005177038,0.00043777862,0.00057907304,0.00024967917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011419221,0.000013719462,0.000136183,0.00005205521,0.000003978114,0.000018813687,0.000008321298,0.00008261492,0.9960323,0.000115843395,0.0000533804,0.0034713354],"study_design_scores_gemma":[0.000013896988,0.00013054708,0.0009835367,0.0000054430516,0.000015594107,0.00017907683,0.000008546904,0.0037851678,0.9923064,0.00007928666,0.0024710293,0.000021517468],"about_ca_topic_score_codex":0.0005802399,"about_ca_topic_score_gemma":0.0010732911,"teacher_disagreement_score":0.000828078,"about_ca_system_score_codex":0.0005165337,"about_ca_system_score_gemma":0.0005149409,"threshold_uncertainty_score":0.0037477612},"labels":[],"label_agreement":null},{"id":"W2167827646","doi":"10.3390/s8042642","title":"High Sensitivity MEMS Strain Sensor: Design and Simulation","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Syncrude","keywords":"Piezoresistive effect; Sensitivity (control systems); Microelectromechanical systems; Finite element method; Electronic engineering; SIGNAL (programming language); Microfabrication; Materials science; Chip; Computer science; Electrical engineering; Engineering; Optoelectronics","score_opus":0.022379945765260603,"score_gpt":0.22776478521992857,"score_spread":0.20538483945466796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167827646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044803817,0.00097361923,0.93043506,0.00034955272,0.00007214687,0.00018013897,0.00032498976,0.0012591262,0.021601541],"genre_scores_gemma":[0.5775274,0.001224306,0.40522304,0.00012459476,0.00003979733,0.00077706086,0.00041839093,0.00025245966,0.01441288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998185,0.000032988057,0.0000054197953,0.000020763524,0.0001083954,0.000014014457],"domain_scores_gemma":[0.9999002,0.00004164095,0.000011412962,0.0000090596,0.000032109885,0.00000553646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031978934,0.0004179422,0.00043660795,0.00027454286,0.00021029233,0.00041489396,0.00071971095,0.0009073936,0.0034568089],"category_scores_gemma":[0.0004517068,0.00031412425,0.00036124844,0.00031104434,0.00025781707,0.00047704077,0.00029028783,0.0003434943,0.0007707716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031324787,0.000026132511,0.00047770524,0.000118792865,0.000017003062,0.00008076262,0.000039533377,0.96778464,0.010425407,0.005203319,0.0007745141,0.015020986],"study_design_scores_gemma":[0.000008776965,0.000015300395,0.00012114887,0.0000058164987,0.0000029161463,0.000021694792,0.0000036904771,0.9960742,0.0016455611,0.0004679178,0.0016286535,0.0000044019102],"about_ca_topic_score_codex":0.0025932356,"about_ca_topic_score_gemma":0.0015700556,"teacher_disagreement_score":0.0034568089,"about_ca_system_score_codex":0.00042758556,"about_ca_system_score_gemma":0.00058000686,"threshold_uncertainty_score":0.011564136},"labels":[],"label_agreement":null},{"id":"W2168055397","doi":"10.3390/s8084948","title":"Ship Detection in SAR Image Based on the Alpha-stable Distribution","year":2008,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Clutter; Constant false alarm rate; Synthetic aperture radar; Computer science; Artificial intelligence; Gaussian; False alarm; Pixel; Algorithm; Gaussian process; K-distribution; Radar imaging; Computer vision; Radar; Remote sensing; Pattern recognition (psychology); Probability distribution; Mathematics; Statistics; Physics; Geography; Telecommunications","score_opus":0.012616646421255526,"score_gpt":0.22057182376528173,"score_spread":0.2079551773440262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168055397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017921936,0.0002120779,0.98035836,0.000039999675,0.000026117827,0.000011412612,0.000021411835,0.000536864,0.0008718609],"genre_scores_gemma":[0.45857742,0.0008287239,0.5370424,0.00012252126,0.0000837017,0.000029919736,0.00020475054,0.00014065618,0.0029699474],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994609,0.00010124888,0.000021750771,0.00012987007,0.00025045694,0.000035743265],"domain_scores_gemma":[0.99913305,0.0003198866,0.00009388437,0.000117080024,0.0003031839,0.00003308304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009949271,0.0005167021,0.0004662481,0.0010923742,0.00022505342,0.00054697413,0.0004787422,0.00045324038,0.0005456429],"category_scores_gemma":[0.0018315215,0.00025058162,0.0006220624,0.0008252446,0.00055384636,0.0010197898,0.00033850005,0.00046947275,0.00062108936],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004904444,0.00011057971,0.008936049,0.0002119583,0.00014113057,0.0005496128,0.00028763193,0.13613133,0.1527918,0.023629151,0.0031717548,0.6735485],"study_design_scores_gemma":[0.000021222668,0.00012771973,0.0039510787,0.000011770272,0.000040610248,0.00074555364,0.000027657148,0.9508567,0.035853453,0.0038650313,0.0044572335,0.000042008785],"about_ca_topic_score_codex":0.0008430098,"about_ca_topic_score_gemma":0.000836932,"teacher_disagreement_score":0.0010923742,"about_ca_system_score_codex":0.0003737135,"about_ca_system_score_gemma":0.00034083155,"threshold_uncertainty_score":0.005261719},"labels":[],"label_agreement":null},{"id":"W2168155478","doi":"10.3390/s140611204","title":"A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Support vector machine; Respiratory rate; Computer science; Respiration; Artificial intelligence; Respiratory monitoring; Naive Bayes classifier; Machine learning; Spirometer; Simulation; Medicine; Respiratory system; Heart rate; Internal medicine","score_opus":0.026477208513555558,"score_gpt":0.23981586890629855,"score_spread":0.213338660392743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168155478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16980287,0.0029470946,0.6816061,0.0026106,0.0012809172,0.0028257456,0.015248315,0.11169425,0.011984116],"genre_scores_gemma":[0.7836275,0.00082555483,0.19433719,0.0014424905,0.0003990343,0.0009847537,0.010449611,0.0008787563,0.0070550456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992268,0.00010542046,0.00007412483,0.00024539715,0.00025414047,0.00009412988],"domain_scores_gemma":[0.99894947,0.00015477932,0.00015303752,0.00023662533,0.00027911313,0.00022703662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007220457,0.000812449,0.0010827075,0.0011290449,0.000415371,0.00093593047,0.0018511288,0.00071029697,0.008113934],"category_scores_gemma":[0.0017511086,0.00032179744,0.0005064302,0.0009237468,0.00023998116,0.0009775581,0.0014316645,0.0007372371,0.0038929603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008692716,0.0020371175,0.040267657,0.0013170177,0.0006479075,0.0029675404,0.0006780784,0.025429757,0.1322429,0.0066318205,0.12579396,0.6532935],"study_design_scores_gemma":[0.0012899277,0.0014837459,0.049217187,0.00023519687,0.0002777983,0.002137715,0.00032377342,0.8001058,0.07092428,0.0058144843,0.06785892,0.00033112767],"about_ca_topic_score_codex":0.0026040818,"about_ca_topic_score_gemma":0.0021072289,"teacher_disagreement_score":0.008113934,"about_ca_system_score_codex":0.0007077678,"about_ca_system_score_gemma":0.00087394,"threshold_uncertainty_score":0.027143776},"labels":[],"label_agreement":null},{"id":"W2168974696","doi":"10.3390/s110706771","title":"Data Fusion Algorithms for Multiple Inertial Measurement Units","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Filter (signal processing); Global Positioning System; Sensor fusion; Computer science; Frame (networking); Inertial navigation system; Context (archaeology); Computer vision; Kalman filter; Extended Kalman filter; Artificial intelligence; Units of measurement; Real-time computing; Inertial frame of reference; Telecommunications; Geography","score_opus":0.19389198161626547,"score_gpt":0.25519908338327557,"score_spread":0.0613071017670101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168974696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015235035,0.0005855436,0.99696845,0.00007506853,0.0000673861,0.000022809678,0.000024157023,0.00026412157,0.00046896227],"genre_scores_gemma":[0.13382527,0.0020638846,0.85961264,0.00013932091,0.00020275249,0.00027695225,0.00031939088,0.00009137917,0.0034683987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985071,0.00026176317,0.00015526652,0.0003296646,0.00066313194,0.000083139894],"domain_scores_gemma":[0.9987099,0.00036525953,0.000160742,0.00016336553,0.0005766135,0.000024100646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023509022,0.0010199924,0.0011763155,0.0015825892,0.0007435286,0.0014274156,0.0013720315,0.00115193,0.0018754443],"category_scores_gemma":[0.0049535176,0.00056288583,0.0011863343,0.0022433428,0.00047918942,0.0026222565,0.0014806018,0.0012466373,0.001080798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014825327,0.000048259484,0.001241995,0.00028022268,0.00020312051,0.0000866736,0.00022972822,0.19409862,0.009999226,0.038548492,0.0034716937,0.75164366],"study_design_scores_gemma":[0.000027635537,0.0001091055,0.0010425361,0.00007315991,0.00008656477,0.0001361712,0.00006134422,0.9439055,0.012203347,0.023506785,0.018793236,0.000054599568],"about_ca_topic_score_codex":0.003513568,"about_ca_topic_score_gemma":0.0026474344,"teacher_disagreement_score":0.003513568,"about_ca_system_score_codex":0.00089275383,"about_ca_system_score_gemma":0.0010097197,"threshold_uncertainty_score":0.012432873},"labels":[],"label_agreement":null},{"id":"W2171727020","doi":"10.3390/s140813661","title":"A Novel Optimal Configuration form Redundant MEMS Inertial Sensors Based on the Orthogonal Rotation Method","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Rotation (mathematics); Inertial navigation system; Realization (probability); Reliability (semiconductor); Inertial frame of reference; Microelectromechanical systems; Inertial measurement unit; Engineering; Fault detection and isolation; Fault (geology); Computer science; Control theory (sociology); Electronic engineering; Aerospace engineering; Artificial intelligence; Mathematics; Actuator; Physics; Electrical engineering","score_opus":0.011384313384183749,"score_gpt":0.23295310305676875,"score_spread":0.221568789672585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171727020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11753821,0.0009605727,0.8698414,0.00017011055,0.00018487916,0.000089678986,0.00012884435,0.00084162987,0.010244519],"genre_scores_gemma":[0.8100886,0.00031269706,0.18746997,0.000052485117,0.000057641642,0.000099466364,0.00011336083,0.000036261816,0.0017695772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996264,0.000083613384,0.000027679947,0.00010156566,0.00011792119,0.00004281385],"domain_scores_gemma":[0.999772,0.000028516437,0.00007068151,0.000047084577,0.000064526175,0.000017192539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018383084,0.0007372539,0.00052763504,0.0005877278,0.00033426267,0.0003662137,0.0007306618,0.00035689867,0.0014164938],"category_scores_gemma":[0.00038641706,0.00027235603,0.00029412506,0.00049452804,0.00032650054,0.00066539913,0.00045204168,0.00021703228,0.000436125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000757651,0.00012140168,0.0044120373,0.0004960946,0.00013512863,0.0009087395,0.00024124111,0.15099959,0.35990575,0.035925444,0.0058216564,0.44027528],"study_design_scores_gemma":[0.000300104,0.0023015589,0.0079371305,0.00010355549,0.0002242422,0.0041781566,0.00020660051,0.7714064,0.1671919,0.0124478005,0.033459913,0.0002426399],"about_ca_topic_score_codex":0.00044487987,"about_ca_topic_score_gemma":0.00052726857,"teacher_disagreement_score":0.0014164938,"about_ca_system_score_codex":0.000245479,"about_ca_system_score_gemma":0.0003266839,"threshold_uncertainty_score":0.0047386885},"labels":[],"label_agreement":null},{"id":"W2172251621","doi":"10.3390/s90705351","title":"Multivalent Anchoring and Oriented Display of Single-Domain Antibodies on Cellulose","year":2009,"lang":"en","type":"article","venue":"Sensors","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Institute for Biological Sciences; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pentamer; Biosensor; Avidity; Cellulose; Chemistry; Antibody; Phage display; Single-domain antibody; Bispecific antibody; Combinatorial chemistry; Biochemistry; Peptide; Biology; Monoclonal antibody; Immunology","score_opus":0.026432650253450287,"score_gpt":0.30324418678555115,"score_spread":0.2768115365321009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172251621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9726178,0.0006897704,0.024197476,0.00005365064,0.000029929759,0.00002542973,0.00012664364,0.000077965466,0.0021813032],"genre_scores_gemma":[0.9699506,0.0006008346,0.02667743,0.00007343205,0.000009231772,0.000030724805,0.00020165094,0.000029040544,0.0024271088],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999861,0.000024743098,0.000008882635,0.00003670345,0.000038354083,0.000030247262],"domain_scores_gemma":[0.9999237,0.000012389157,0.00002372319,0.000010855904,0.00001025447,0.000019035815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013576382,0.00033633402,0.00014984656,0.00015690485,0.00008536449,0.00023734635,0.00025271578,0.00023628176,0.0005890451],"category_scores_gemma":[0.00015713017,0.00012615863,0.00018756746,0.00019436951,0.00010802659,0.00014955962,0.00025464103,0.00032418355,0.00025371867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013368317,0.000006385771,0.0000655195,0.0000091308,0.0000023372363,0.000018094925,0.0000048176776,0.00012714206,0.9983676,0.00011163008,0.000014557855,0.0012594583],"study_design_scores_gemma":[0.000006180887,0.0000646415,0.0006277671,0.0000025239092,0.000005380931,0.00006769288,0.0000054591824,0.0015882698,0.99636394,0.000039266324,0.0012251007,0.0000037554814],"about_ca_topic_score_codex":0.0007791381,"about_ca_topic_score_gemma":0.0009552135,"teacher_disagreement_score":0.0007791381,"about_ca_system_score_codex":0.00035149857,"about_ca_system_score_gemma":0.00015048559,"threshold_uncertainty_score":0.0025503635},"labels":[],"label_agreement":null},{"id":"W2173608195","doi":"10.3390/s151229772","title":"Improved PPP Ambiguity Resolution Considering the Stochastic Characteristics of Atmospheric Corrections from Regional Networks","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Stochastic modelling; Stochastic process; Environmental science; Ambiguity; Computer science; Meteorology; Point (geometry); Algorithm; Remote sensing; Mathematics; Statistics; Physics; Geology; Geometry","score_opus":0.02345504271534011,"score_gpt":0.21249258905299842,"score_spread":0.18903754633765832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2173608195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076696046,0.00031237953,0.92044175,0.00008819712,0.000044049615,0.000018138298,0.0001343365,0.00041076832,0.0018543457],"genre_scores_gemma":[0.8670809,0.00048688645,0.13079575,0.000041384672,0.000050396684,0.000030359353,0.00045139572,0.0000887577,0.00097422383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892646,0.00031394875,0.000047880487,0.00019553326,0.00039509241,0.00012106274],"domain_scores_gemma":[0.9988575,0.00049484946,0.0001435893,0.00022941806,0.00025021718,0.00002447086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013628373,0.0006797243,0.00052231346,0.00065305206,0.0002999842,0.0008848146,0.00051291945,0.0004354691,0.00055825774],"category_scores_gemma":[0.0054870597,0.0002915465,0.0008020202,0.0013339472,0.00035005444,0.0014949441,0.0010372077,0.00073805114,0.00022908738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008036868,0.000019227213,0.004723971,0.000055270044,0.00006668754,0.0001476869,0.000080115155,0.91778415,0.0067845397,0.006707483,0.0004767619,0.06307371],"study_design_scores_gemma":[0.0000069342,0.000026015294,0.0028226948,0.000007677276,0.00003178278,0.0000854119,0.00001809599,0.98948205,0.003516903,0.0030307684,0.00095202925,0.000019702107],"about_ca_topic_score_codex":0.0069976663,"about_ca_topic_score_gemma":0.0050950423,"teacher_disagreement_score":0.0069976663,"about_ca_system_score_codex":0.00038787894,"about_ca_system_score_gemma":0.0008774005,"threshold_uncertainty_score":0.01391387},"labels":[],"label_agreement":null},{"id":"W2174187647","doi":"10.3390/s151128889","title":"Power Approaches for Implantable Medical Devices","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":422,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biotelemetry; Reliability (semiconductor); Wireless; Health care; Risk analysis (engineering); Medical device; Systems engineering; Reliability engineering; Power (physics); Engineering; Medicine; Biomedical engineering; Telecommunications","score_opus":0.0808946386945561,"score_gpt":0.29860772022843673,"score_spread":0.21771308153388064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2174187647","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044315,0.98710036,0.0033891501,0.00039686752,0.00041815446,0.00002187142,0.000027856142,0.00002619714,0.008176413],"genre_scores_gemma":[0.0054391674,0.9847127,0.0033137917,0.00033571295,0.00033797996,0.00003878485,0.00004852986,0.0000108594695,0.005762542],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996642,0.00004087016,0.000038110655,0.000062945765,0.00017183939,0.000021980552],"domain_scores_gemma":[0.99972886,0.00014705588,0.000037006932,0.000015416945,0.00006145203,0.000010044654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043605288,0.00089641346,0.00081567717,0.002606359,0.00042275424,0.0011751978,0.00079874095,0.0015373806,0.006103182],"category_scores_gemma":[0.000596881,0.00043952878,0.0006769203,0.002021706,0.0007031548,0.0019007454,0.000913264,0.001964695,0.0032531377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039550974,0.000092338945,0.0001549269,0.017953828,0.00006309441,0.00039102853,0.00017160542,0.0012280876,0.01722079,0.04476867,0.017065244,0.9008508],"study_design_scores_gemma":[0.000004798901,0.00007907814,0.0002549436,0.0019410849,0.00003123677,0.0012626527,0.000073040115,0.0003660434,0.003907897,0.009922425,0.98213345,0.000023270426],"about_ca_topic_score_codex":0.00037381574,"about_ca_topic_score_gemma":0.00047646184,"teacher_disagreement_score":0.006103182,"about_ca_system_score_codex":0.00051990314,"about_ca_system_score_gemma":0.000501845,"threshold_uncertainty_score":0.020417213},"labels":[],"label_agreement":null},{"id":"W2178090035","doi":"10.3390/s151129149","title":"An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Alberta","funders":"King Saud University","keywords":"Computer network; Routing protocol; Computer science; Default gateway; Wireless sensor network; Energy consumption; Interior gateway protocol; Real-time computing; Distributed computing; Routing (electronic design automation); Dynamic Source Routing; Engineering","score_opus":0.09152713343202921,"score_gpt":0.315430582093774,"score_spread":0.22390344866174478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178090035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010916655,0.006045997,0.96894497,0.0005981215,0.00058779144,0.0007015967,0.00044239956,0.0012443057,0.010518218],"genre_scores_gemma":[0.29138538,0.012085778,0.6671289,0.00067023287,0.00031573066,0.0028600334,0.0030801496,0.00022520011,0.022248616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999539,0.00011403036,0.000042363034,0.000053973865,0.00022817917,0.00002235545],"domain_scores_gemma":[0.99978167,0.000052709205,0.000040993204,0.000028455968,0.00008570525,0.000010535729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004040918,0.00065405015,0.0004950301,0.000680856,0.00059133174,0.00049229775,0.0009711712,0.00045649143,0.0010195752],"category_scores_gemma":[0.001047168,0.00018687821,0.00036292497,0.0010805854,0.00029435373,0.00095900154,0.00084123557,0.00068350957,0.00058000523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000173548,0.00014642108,0.00067413144,0.0016582169,0.0001591749,0.00068081904,0.0003922845,0.1413059,0.09245471,0.08274005,0.033818655,0.645796],"study_design_scores_gemma":[0.000080760605,0.00063243695,0.0010425656,0.00018011358,0.00014657702,0.0011756669,0.00020868727,0.6511759,0.03756855,0.02879211,0.27888322,0.000113486414],"about_ca_topic_score_codex":0.0010554604,"about_ca_topic_score_gemma":0.0020528124,"teacher_disagreement_score":0.0010554604,"about_ca_system_score_codex":0.00038847286,"about_ca_system_score_gemma":0.0007590508,"threshold_uncertainty_score":0.0034108162},"labels":[],"label_agreement":null},{"id":"W2181682338","doi":"10.3390/s151229783","title":"Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":552,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microfluidics; Biosensor; Software portability; Nanotechnology; Lab-on-a-chip; Computer science; Analyte; Materials science; Chemistry; Chromatography","score_opus":0.02372212719923213,"score_gpt":0.2748959132329128,"score_spread":0.2511737860336807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181682338","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010675291,0.9672819,0.022151945,0.0011975637,0.0012527629,0.0000719381,0.0000910115,0.00025788814,0.0066275327],"genre_scores_gemma":[0.007635615,0.96617025,0.018594852,0.0010621888,0.000724622,0.00010615958,0.00017526308,0.000027974645,0.0055030845],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989718,0.00011666582,0.000080936996,0.00020375171,0.0005453735,0.000081358994],"domain_scores_gemma":[0.999458,0.00017514837,0.00008019702,0.00002543864,0.00021084075,0.00005029579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010986306,0.0017079578,0.0018084602,0.0034889935,0.0004864513,0.0017634244,0.0017290052,0.0018086426,0.0022318487],"category_scores_gemma":[0.0010539041,0.0007021637,0.00083428493,0.0028680381,0.0012512965,0.0021906851,0.0015305781,0.0025773381,0.0026177408],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006154374,0.00012314308,0.0004017349,0.013517802,0.00011059486,0.00041826142,0.00018328468,0.0008481991,0.075892344,0.021497292,0.022927027,0.8640188],"study_design_scores_gemma":[0.000011738876,0.00014887405,0.00045565536,0.0009354328,0.00007302927,0.0012179628,0.00005836695,0.0007747648,0.027004413,0.004112244,0.96514755,0.0000600459],"about_ca_topic_score_codex":0.0007766396,"about_ca_topic_score_gemma":0.0007677872,"teacher_disagreement_score":0.0034889935,"about_ca_system_score_codex":0.0011754306,"about_ca_system_score_gemma":0.001839123,"threshold_uncertainty_score":0.008528411},"labels":[],"label_agreement":null},{"id":"W2191023362","doi":"10.3390/s151229820","title":"A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Chongqing Technology and Business University; Chongqing Science and Technology Commission","keywords":"Wireless sensor network; Computer science; Optimization problem; RADIUS; Mathematical optimization; Algorithm; Mathematics; Computer network","score_opus":0.013971853347896702,"score_gpt":0.22725252785692845,"score_spread":0.21328067450903176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191023362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019126253,0.0002454628,0.97704595,0.00016395771,0.000053267868,0.000052193554,0.000029212695,0.00026479168,0.003018841],"genre_scores_gemma":[0.7566994,0.00034141474,0.23927055,0.00014515115,0.000038299007,0.00037764543,0.00013100351,0.00006449126,0.002932078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972516,0.00006782745,0.000020173546,0.00007408001,0.00007858682,0.000034290708],"domain_scores_gemma":[0.99922264,0.00045075978,0.0000751778,0.00005403907,0.00015211395,0.000045285702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067482307,0.0006987559,0.0007401194,0.00051277695,0.00044183506,0.0007144848,0.001300366,0.000993166,0.0016398748],"category_scores_gemma":[0.0027577535,0.00023926972,0.0005805143,0.0003890004,0.000695541,0.0007150345,0.001199088,0.0010442596,0.0002282443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041616444,0.000031632353,0.00063487585,0.00004803367,0.000031849337,0.00006104936,0.00006769604,0.9345254,0.0020425443,0.012412888,0.0008649835,0.04923752],"study_design_scores_gemma":[0.000004558548,0.000015575217,0.00003209645,0.0000025817574,0.0000031474071,0.000009651478,0.0000035523788,0.99846053,0.00016121949,0.0011067827,0.00019775511,0.0000024845715],"about_ca_topic_score_codex":0.006382585,"about_ca_topic_score_gemma":0.0053108423,"teacher_disagreement_score":0.006382585,"about_ca_system_score_codex":0.0007887802,"about_ca_system_score_gemma":0.0009185843,"threshold_uncertainty_score":0.012690842},"labels":[],"label_agreement":null},{"id":"W2195769232","doi":"10.3390/s151229852","title":"Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"RSS; SIGNAL (programming language); Wireless sensor network; Computer science; Radio propagation; Tracking (education); Path loss; Received signal strength indication; Wireless; Control theory (sociology); Algorithm; Real-time computing; Artificial intelligence; Telecommunications; Control (management)","score_opus":0.009773832084039032,"score_gpt":0.1820926492150203,"score_spread":0.17231881713098127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195769232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028468829,0.00006998761,0.9702688,0.00004126155,0.000008156167,0.0000101485775,0.0000056679933,0.00016007478,0.00096704875],"genre_scores_gemma":[0.8146974,0.00018773192,0.18290462,0.000036894053,0.000015906755,0.000046526326,0.00003136881,0.00002960091,0.0020498936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997547,0.00004255116,0.00000956696,0.000073262425,0.00010298614,0.000016967284],"domain_scores_gemma":[0.9996118,0.00016001178,0.00008550359,0.0000586939,0.00007539458,0.000008501617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003497814,0.00044323853,0.0002573598,0.00030896722,0.00017481216,0.00034747165,0.00067660783,0.00042863216,0.00029115402],"category_scores_gemma":[0.0013832287,0.000213214,0.00031982744,0.00036104152,0.00046618414,0.00085126946,0.0006550365,0.0003386687,0.00015829488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008941455,0.000047300226,0.0016490635,0.00007844826,0.000030359137,0.00021824647,0.00017881417,0.7749204,0.0727144,0.009847624,0.00037580723,0.13985008],"study_design_scores_gemma":[0.000008159041,0.00004008192,0.0003958831,0.000003563612,0.000006912928,0.00008202563,0.000014375827,0.9887504,0.008859171,0.0013553353,0.0004756647,0.000008291875],"about_ca_topic_score_codex":0.0011044261,"about_ca_topic_score_gemma":0.0010718476,"teacher_disagreement_score":0.0011044261,"about_ca_system_score_codex":0.00027108457,"about_ca_system_score_gemma":0.0002984859,"threshold_uncertainty_score":0.0021959543},"labels":[],"label_agreement":null},{"id":"W2199783880","doi":"10.3390/s151229905","title":"Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Council for Science, Technology and Innovation; Universität Bremen; European Commission; York University","keywords":"Adaptation (eye); Cloud computing; Pace; Cloud manufacturing; Product (mathematics); Systems engineering; Computer science; Manufacturing engineering; Stakeholder; Distributed manufacturing; Scale (ratio); Product design; New product development; Engineering; Industrial engineering; Data science; Process management; Business","score_opus":0.04776241329000739,"score_gpt":0.2518636134754792,"score_spread":0.20410120018547181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199783880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0727811,0.010195558,0.8666349,0.010833901,0.00027677958,0.00035958402,0.0003615944,0.000801244,0.037755433],"genre_scores_gemma":[0.5453909,0.0070080874,0.44185954,0.00090013526,0.0002062575,0.00018374632,0.00038574563,0.0001125696,0.0039530783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966186,0.001016226,0.0001718593,0.00038527363,0.001604205,0.00020365014],"domain_scores_gemma":[0.9962239,0.0011999077,0.00040558938,0.0009493721,0.0010169174,0.00020426528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00376849,0.0006031561,0.0007289005,0.0016527907,0.00055315386,0.0050924057,0.0023223371,0.001305417,0.0011505991],"category_scores_gemma":[0.0032014165,0.00044749328,0.0006254838,0.0034374455,0.001405132,0.0061657885,0.0021438827,0.001359384,0.00043641718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002859101,0.0005634703,0.007724312,0.0011384183,0.00015419573,0.0003765626,0.0011950933,0.052501228,0.02032696,0.3525382,0.0063018175,0.5568938],"study_design_scores_gemma":[0.000052920404,0.00045935117,0.008739298,0.00061913807,0.000103216255,0.0011137079,0.002293615,0.56729853,0.028116588,0.24126711,0.14979935,0.00013728],"about_ca_topic_score_codex":0.0024897854,"about_ca_topic_score_gemma":0.0027091948,"teacher_disagreement_score":0.0050924057,"about_ca_system_score_codex":0.0022536993,"about_ca_system_score_gemma":0.0026520088,"threshold_uncertainty_score":0.019929886},"labels":[],"label_agreement":null},{"id":"W2210724628","doi":"10.3390/s151229910","title":"Focusing Bistatic FMCW SAR Signal by Range Migration Algorithm Based on Fresnel Approximation","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Bistatic radar; Continuous-wave radar; Synthetic aperture radar; Computer science; Radar imaging; Transmitter; Inverse synthetic aperture radar; Algorithm; Remote sensing; Radar; Electronic engineering; Channel (broadcasting); Computer vision; Telecommunications; Geology; Engineering","score_opus":0.015582787678796941,"score_gpt":0.2375812695146973,"score_spread":0.22199848183590035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2210724628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014500358,0.00018941038,0.983659,0.00005983012,0.000028712144,0.000025040816,0.000010743105,0.00038947634,0.0011372282],"genre_scores_gemma":[0.1697493,0.00035008465,0.8268428,0.000068908805,0.000038334147,0.00013309528,0.000095373885,0.00007081639,0.0026511804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999045,0.000014411528,0.0000065062372,0.00002309263,0.000040776722,0.000010676819],"domain_scores_gemma":[0.9998772,0.000037033114,0.000021415715,0.000011837218,0.000044620225,0.000007901019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001851274,0.00056507665,0.00047285244,0.0003608469,0.00023406062,0.0003110725,0.00065534335,0.00057405443,0.0010261446],"category_scores_gemma":[0.0004582629,0.00021648625,0.0005103282,0.0003776835,0.00022059985,0.0006243147,0.0003354312,0.0005258479,0.00043527898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016792209,0.00010324356,0.0011049476,0.00015338669,0.00006698687,0.00011489304,0.00023395928,0.3208123,0.100144856,0.012777595,0.002720688,0.56159925],"study_design_scores_gemma":[0.000011648435,0.000038098548,0.00019785437,0.000004614642,0.0000072616513,0.00007088995,0.000011963217,0.9927874,0.0050552757,0.000706277,0.0010997724,0.000008922754],"about_ca_topic_score_codex":0.0023330585,"about_ca_topic_score_gemma":0.0015109149,"teacher_disagreement_score":0.0023330585,"about_ca_system_score_codex":0.00028295192,"about_ca_system_score_gemma":0.0005548635,"threshold_uncertainty_score":0.0046389103},"labels":[],"label_agreement":null},{"id":"W2213380421","doi":"10.3390/s16010026","title":"A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Wireless sensor network; Cluster analysis; Computer science; Computer network; Energy consumption; Energy harvesting; Distributed computing; Efficient energy use; Key distribution in wireless sensor networks; Node (physics); Hop (telecommunications); Energy (signal processing); Wireless network; Wireless; Engineering; Mathematics; Telecommunications","score_opus":0.023109835320227097,"score_gpt":0.22956246868103933,"score_spread":0.20645263336081224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2213380421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008400425,0.00026935418,0.9892323,0.00013525998,0.000029350074,0.0000811253,0.00002228596,0.00018804992,0.0016417835],"genre_scores_gemma":[0.33917016,0.0008142303,0.6539397,0.00020570128,0.000053261585,0.00045950804,0.00020864172,0.00013502498,0.005013786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996489,0.00009126565,0.000020426145,0.00007285698,0.00013558812,0.000031003903],"domain_scores_gemma":[0.99965465,0.00011419614,0.000042766045,0.000028381513,0.00013421016,0.000025785484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009904461,0.00071485253,0.0007346166,0.0011251159,0.0008492101,0.00064782845,0.0016264755,0.0008781383,0.0008884515],"category_scores_gemma":[0.0017267262,0.00033344023,0.00048834126,0.0012321356,0.0005332672,0.001224796,0.0010064226,0.0006569333,0.00038692963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095944546,0.00007683107,0.0004946927,0.00011752739,0.00005425615,0.00006503118,0.00019024833,0.80894816,0.008516649,0.03142085,0.0032209784,0.14679877],"study_design_scores_gemma":[0.000009206896,0.000039009832,0.00006699075,0.0000063448406,0.00000690449,0.000028210092,0.000018993986,0.99247193,0.0012101469,0.005044256,0.0010876935,0.000010358206],"about_ca_topic_score_codex":0.0025353355,"about_ca_topic_score_gemma":0.003217648,"teacher_disagreement_score":0.0025353355,"about_ca_system_score_codex":0.0010543499,"about_ca_system_score_gemma":0.0014177143,"threshold_uncertainty_score":0.007649839},"labels":[],"label_agreement":null},{"id":"W2214838567","doi":"10.3390/s151229906","title":"Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Radiometer; Radiance; Vegetation (pathology); Environmental science; Canopy; Irradiance; Scale (ratio); Satellite; Primary production; Computer science; Meteorology; Engineering; Ecology; Ecosystem; Geography; Optics; Physics; Cartography","score_opus":0.02299026044936071,"score_gpt":0.24754156203816094,"score_spread":0.22455130158880024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2214838567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5500027,0.00501122,0.3922678,0.0011401476,0.0005443066,0.00072962,0.0103002535,0.008470281,0.03153366],"genre_scores_gemma":[0.41762877,0.0017615721,0.5669444,0.0005833208,0.00033692378,0.0004630096,0.007997149,0.0003283618,0.0039564525],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985876,0.00026618157,0.000050076316,0.00046589557,0.00058226264,0.000047964313],"domain_scores_gemma":[0.9981705,0.00029975723,0.00023140879,0.00042341783,0.0007728003,0.000102067344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028168864,0.0007507542,0.0004401812,0.00085987383,0.00023930347,0.00090078695,0.001235928,0.0006521934,0.0012329274],"category_scores_gemma":[0.0017406624,0.00035528818,0.0005219812,0.0013073325,0.00021632531,0.001139007,0.00073159783,0.0010395348,0.0007437036],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000963409,0.00054991036,0.08865449,0.0009096212,0.0004933745,0.00014961326,0.00021283687,0.021563148,0.4912323,0.003891775,0.008660975,0.3827185],"study_design_scores_gemma":[0.00044437978,0.0022361886,0.38409472,0.00022565121,0.0007397475,0.0014815294,0.00019738753,0.18312457,0.2784347,0.0019736986,0.14665335,0.00039404028],"about_ca_topic_score_codex":0.002198287,"about_ca_topic_score_gemma":0.0022698627,"teacher_disagreement_score":0.0028168864,"about_ca_system_score_codex":0.0005512843,"about_ca_system_score_gemma":0.00066642754,"threshold_uncertainty_score":0.014897287},"labels":[],"label_agreement":null},{"id":"W2226437371","doi":"10.3390/s16010070","title":"Design and Analysis of a Sensor System for Cutting Force Measurement in Machining Processes","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Changsha Science and Technology Project; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Machining; Calibration; Finite element method; Mechanical engineering; Coupling (piping); Machine tool; Component (thermodynamics); Automation; Kinematics; Engineering; System of measurement; Structural engineering; Physics","score_opus":0.021591741134154043,"score_gpt":0.23352843524359418,"score_spread":0.21193669410944013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2226437371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07698501,0.00052815763,0.9180911,0.0001585871,0.00005757114,0.00021789702,0.00007265718,0.00067723583,0.0032116924],"genre_scores_gemma":[0.7827536,0.00035573135,0.21357048,0.00008495055,0.00003225328,0.00019275659,0.00010933936,0.00003152925,0.002869416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951434,0.000051839965,0.00002111141,0.00009230578,0.0002919233,0.000028544508],"domain_scores_gemma":[0.9997453,0.000053001808,0.000047882506,0.000025699734,0.00011258836,0.000015434278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039925936,0.00037893598,0.00041170107,0.00024981992,0.00022613835,0.0003753475,0.00076192786,0.00061157136,0.0010492636],"category_scores_gemma":[0.00041729547,0.00024808245,0.00025663988,0.00018329667,0.00024705048,0.00053842657,0.00022172778,0.00026991373,0.0002702701],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029342165,0.00011404159,0.0022595155,0.00055531936,0.0000743289,0.00025335304,0.00017253235,0.078189306,0.796884,0.006811846,0.00090296404,0.11348945],"study_design_scores_gemma":[0.00004838775,0.0011407671,0.0065711546,0.000029181727,0.000074812946,0.0004496791,0.00006347331,0.6072965,0.3701601,0.00088042766,0.013235205,0.000050287334],"about_ca_topic_score_codex":0.00069086335,"about_ca_topic_score_gemma":0.00080747704,"teacher_disagreement_score":0.0010492636,"about_ca_system_score_codex":0.000428385,"about_ca_system_score_gemma":0.0006928579,"threshold_uncertainty_score":0.0035101175},"labels":[],"label_agreement":null},{"id":"W2228138108","doi":"10.3390/s16010065","title":"A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Reconfigurability; Computer science; Cluster analysis; Key distribution in wireless sensor networks; Wireless ad hoc network; Node (physics); Adaptability; Wireless; Context (archaeology); Distributed computing; Wireless network; Computer network; Engineering; Telecommunications; Artificial intelligence","score_opus":0.025698974883034555,"score_gpt":0.28802749233661523,"score_spread":0.26232851745358066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2228138108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105816916,0.004402922,0.87814873,0.0005396387,0.00017991995,0.00036708463,0.00008707839,0.001065269,0.009392421],"genre_scores_gemma":[0.6892353,0.002754435,0.30524144,0.00013812067,0.00006802744,0.00026925627,0.00022002879,0.00012307703,0.0019503419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967001,0.0011587183,0.00014912266,0.00031879768,0.0014862106,0.00018697964],"domain_scores_gemma":[0.99410135,0.0032621785,0.00037014394,0.0009696556,0.001133533,0.00016323474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003057321,0.0005603552,0.0007397767,0.0014284389,0.0007913566,0.00092296995,0.0023918457,0.000953315,0.0012121057],"category_scores_gemma":[0.009285148,0.00031424945,0.0005458057,0.0021992717,0.000589256,0.0022658352,0.0012667695,0.0006687795,0.000279171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011796225,0.0003813448,0.002735354,0.00076917274,0.00024369064,0.00009970075,0.00046095994,0.44015998,0.014489679,0.022630466,0.00249663,0.51435333],"study_design_scores_gemma":[0.00020981816,0.0013214748,0.0029445118,0.000075653195,0.00012179321,0.0003562574,0.0004892076,0.9622388,0.013257572,0.0073481286,0.011573014,0.000063662344],"about_ca_topic_score_codex":0.001730288,"about_ca_topic_score_gemma":0.0029483414,"teacher_disagreement_score":0.003057321,"about_ca_system_score_codex":0.001287961,"about_ca_system_score_gemma":0.0010313926,"threshold_uncertainty_score":0.016168833},"labels":[],"label_agreement":null},{"id":"W2229559299","doi":"10.3390/s16010087","title":"Silica Bottle Resonator Sensor for Refractive Index and Temperature Measurements","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Resonator; Fiber Bragg grating; Materials science; Optics; Fiber optic sensor; Refractive index; Silica fiber; Graded-index fiber; Sensitivity (control systems); RADIUS; Optical fiber; Fiber; Optoelectronics; Whispering-gallery wave; Fiber laser; Physics; Electronic engineering; Composite material","score_opus":0.014744166865307651,"score_gpt":0.23212843208815068,"score_spread":0.21738426522284302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2229559299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58409774,0.0065431185,0.38817406,0.00079845625,0.0006752973,0.0003393965,0.0006663229,0.0027907481,0.01591485],"genre_scores_gemma":[0.78599346,0.0010496655,0.2068792,0.00023754915,0.000111720474,0.00012303653,0.00021915734,0.00007576679,0.0053104917],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910945,0.00006740933,0.000023746365,0.00018729047,0.00056846207,0.00004360371],"domain_scores_gemma":[0.99963677,0.00011882644,0.0000678203,0.000033246382,0.000114477705,0.000028815572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044046412,0.00063410186,0.0005364763,0.00027604523,0.0003025364,0.00045001283,0.0011069166,0.0010273326,0.0013145318],"category_scores_gemma":[0.0005449335,0.00042912728,0.00059069577,0.00019967296,0.00042003673,0.0010490653,0.0004179974,0.00042663774,0.0007096298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006336657,0.00002834742,0.00057293166,0.00008311819,0.000021212012,0.000073907104,0.000032143194,0.0007343445,0.9929255,0.0010745542,0.00024950644,0.0041411384],"study_design_scores_gemma":[0.000029331266,0.0006195245,0.0021769414,0.000017663368,0.00006378342,0.0005546837,0.000031322554,0.050681293,0.9383725,0.00047008504,0.0068907654,0.000092169284],"about_ca_topic_score_codex":0.0010502527,"about_ca_topic_score_gemma":0.0021191603,"teacher_disagreement_score":0.0013145318,"about_ca_system_score_codex":0.00060594286,"about_ca_system_score_gemma":0.0004721984,"threshold_uncertainty_score":0.0043975115},"labels":[],"label_agreement":null},{"id":"W2234967686","doi":"10.3390/s16010069","title":"Lead-Free Piezoelectric Diaphragm Biosensors Based on Micro-Machining Technology and Chemical Solution Deposition","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Piezoelectricity; Diaphragm (acoustics); Biosensor; Materials science; Fabrication; Deposition (geology); Machining; Nanotechnology; Layer (electronics); Optoelectronics; Silicon; Composite material; Electrical engineering; Metallurgy; Engineering","score_opus":0.004030464517411722,"score_gpt":0.1816683488809288,"score_spread":0.17763788436351707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2234967686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38200003,0.02460711,0.5822316,0.0009885356,0.0011163418,0.00043211997,0.00040321206,0.0016257353,0.00659532],"genre_scores_gemma":[0.40254733,0.007292634,0.58230156,0.00042196864,0.00012544276,0.00028957136,0.00037496968,0.00006028185,0.0065862066],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939847,0.000047003912,0.00003749946,0.00012695877,0.000356622,0.000033489217],"domain_scores_gemma":[0.9997764,0.000075472606,0.000050506023,0.000027254118,0.000051046918,0.000019314675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000342487,0.0006348142,0.0005810914,0.00037145856,0.0002298043,0.00042884264,0.0013354651,0.0009405037,0.00075271813],"category_scores_gemma":[0.00046430976,0.0005866119,0.00043449568,0.0002798452,0.00044416988,0.0008015472,0.00060941157,0.00070409087,0.00060636865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013326859,0.000012987302,0.00011272061,0.00016941261,0.000007636268,0.000042584943,0.000014969436,0.0000729318,0.99243647,0.0003502471,0.00006612936,0.006700479],"study_design_scores_gemma":[0.000011805139,0.00011233105,0.00076569384,0.00000844056,0.000015806108,0.000402889,0.000010771,0.0020506934,0.9930139,0.00012511373,0.0034653074,0.000017179964],"about_ca_topic_score_codex":0.00023335178,"about_ca_topic_score_gemma":0.00057113625,"teacher_disagreement_score":0.0013354651,"about_ca_system_score_codex":0.00028427553,"about_ca_system_score_gemma":0.00035266415,"threshold_uncertainty_score":0.0025181174},"labels":[],"label_agreement":null},{"id":"W2253626339","doi":"10.3390/s16020140","title":"Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Myongji University; York University","keywords":"Segmentation; Laser scanning; Computer science; Artificial intelligence; Correctness; Point cloud; Offset (computer science); Laser; Computer vision; Pattern recognition (psychology); Normal; Mathematics; Algorithm; Optics; Surface (topology); Geometry; Physics","score_opus":0.035431003207397206,"score_gpt":0.274507318518145,"score_spread":0.23907631531074783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253626339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052213088,0.0001649246,0.94584954,0.000041865387,0.000014997369,0.000105420055,0.00010478348,0.00097462937,0.0005308333],"genre_scores_gemma":[0.26253837,0.00014347397,0.73610365,0.00001766035,0.000013360821,0.00014971096,0.00046760147,0.0001303938,0.00043580882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989869,0.0001549866,0.00009216992,0.0002688584,0.00042471898,0.00007235169],"domain_scores_gemma":[0.9992607,0.00027365994,0.00011572427,0.00010444373,0.00022422378,0.000021255002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005664625,0.0005469749,0.00082121213,0.0029064822,0.0004949803,0.0010971732,0.00091355375,0.0006132746,0.00062773435],"category_scores_gemma":[0.0020675203,0.0003901041,0.00075683335,0.001964651,0.00059428083,0.00090162153,0.00073431147,0.00039782719,0.00035752082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002867837,0.00010571737,0.0058170375,0.00025456623,0.000089788446,0.00017942874,0.00049985497,0.14502786,0.13228069,0.0061597396,0.001414546,0.7078839],"study_design_scores_gemma":[0.000011024027,0.000076554905,0.004742741,0.000016255612,0.000021611144,0.00015723874,0.00015874243,0.9465865,0.04163343,0.0040030414,0.0025583925,0.00003440286],"about_ca_topic_score_codex":0.0037602172,"about_ca_topic_score_gemma":0.0055399304,"teacher_disagreement_score":0.0037602172,"about_ca_system_score_codex":0.00061845133,"about_ca_system_score_gemma":0.0008926538,"threshold_uncertainty_score":0.0074766874},"labels":[],"label_agreement":null},{"id":"W2255371195","doi":"10.3390/s18010045","title":"Enhanced Infrared Image Processing for Impacted Carbon/Glass Fiber-Reinforced Composite Evaluation","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Russian Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Thermography; Smoothing; Materials science; SIGNAL (programming language); Signal processing; Nondestructive testing; Image processing; Infrared; Modality (human–computer interaction); Computer science; Acoustics; Optics; Artificial intelligence; Computer vision; Image (mathematics); Digital signal processing; Computer hardware","score_opus":0.013854465921210042,"score_gpt":0.27226426840668744,"score_spread":0.2584098024854774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255371195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29353157,0.0016627104,0.695515,0.00016398019,0.0000937918,0.0001408961,0.0003294681,0.0018662603,0.0066963695],"genre_scores_gemma":[0.60838205,0.0010829163,0.38526577,0.000087970344,0.000048778682,0.00008673072,0.00020981391,0.00025853742,0.0045774463],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999752,0.000031982767,0.000008212319,0.00003370825,0.0001544978,0.00001963178],"domain_scores_gemma":[0.99963725,0.00010372439,0.000065357715,0.000041451964,0.00013830743,0.000013961841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047092472,0.00046334893,0.00020157226,0.00092371734,0.00014131026,0.00040790837,0.00036085004,0.00046878317,0.0032952074],"category_scores_gemma":[0.00072017545,0.00017679368,0.00024680494,0.00044462446,0.00027320805,0.0006280524,0.0003214779,0.00047929585,0.0005778202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026379546,0.00003539562,0.00044378996,0.00020988568,0.000010331379,0.00008322284,0.00005355023,0.0028011287,0.927718,0.0005625243,0.0003079905,0.06751044],"study_design_scores_gemma":[0.0000103358625,0.00024486668,0.0051194252,0.000028249657,0.000045064022,0.0004840392,0.000058635724,0.049320474,0.94088256,0.00027394522,0.0034945838,0.000037750448],"about_ca_topic_score_codex":0.00030835983,"about_ca_topic_score_gemma":0.00077545195,"teacher_disagreement_score":0.0032952074,"about_ca_system_score_codex":0.00021480928,"about_ca_system_score_gemma":0.00022228154,"threshold_uncertainty_score":0.011023581},"labels":[],"label_agreement":null},{"id":"W2261703500","doi":"10.3390/s16020197","title":"Evaluating Quantum Dot Performance in Homogeneous FRET Immunoassays for Prostate Specific Antigen","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Quantum Dots Synthesis And Properties","field":"Materials Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Agence Nationale de la Recherche","keywords":"Förster resonance energy transfer; Immunoassay; Quantum dot; Homogeneous; Multiplexing; Photoluminescence; Prostate-specific antigen; Materials science; Chemistry; Nanotechnology; Optoelectronics; Fluorescence; Antibody; Optics; Computer science; Prostate; Medicine; Physics; Immunology; Cancer","score_opus":0.05998056017779302,"score_gpt":0.2879223323361563,"score_spread":0.22794177215836325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2261703500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9373202,0.0015566506,0.05944459,0.000075908225,0.000035765865,0.00015365164,0.00015087114,0.00013369949,0.0011287502],"genre_scores_gemma":[0.95463717,0.0011425443,0.042326894,0.000057135305,0.000009152465,0.00018221259,0.00019307033,0.000042230826,0.001409661],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989159,0.00032246317,0.00007900446,0.00026292686,0.00029390247,0.0001258977],"domain_scores_gemma":[0.99938285,0.0003278225,0.00006721219,0.000049693237,0.00014149497,0.000030840612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017162355,0.0005978084,0.00046582476,0.00029954323,0.0003214819,0.00055752974,0.0004577643,0.0006632828,0.00065886945],"category_scores_gemma":[0.0015503132,0.0003011855,0.0003120346,0.00036284528,0.00034239088,0.00061685895,0.00036765618,0.0004318091,0.00033691924],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008328026,0.0000488915,0.00036555706,0.000048746428,0.000010894955,0.000023672617,0.00004032193,0.00049693877,0.99688464,0.0001221681,0.00001854785,0.0018562635],"study_design_scores_gemma":[0.000004545811,0.00015214643,0.0004565185,0.0000018279975,0.00001231986,0.00001777167,0.000013731991,0.0027891386,0.99626046,0.0000250458,0.00026166983,0.000004771999],"about_ca_topic_score_codex":0.0012493356,"about_ca_topic_score_gemma":0.0016908442,"teacher_disagreement_score":0.0017162355,"about_ca_system_score_codex":0.00064436876,"about_ca_system_score_gemma":0.00027568176,"threshold_uncertainty_score":0.009076476},"labels":[],"label_agreement":null},{"id":"W2279109725","doi":"10.3390/s16030284","title":"Towards Reliable and Energy-Efficient Incremental Cooperative Communication for Wireless Body Area Networks","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Alberta","funders":"","keywords":"Computer science; Computer network; Network packet; Relay; Energy consumption; Throughput; Efficient energy use; Transmission (telecommunications); Cooperative diversity; Routing protocol; Wireless; Distributed computing; Channel (broadcasting); Engineering; Telecommunications; Fading","score_opus":0.00947757342534542,"score_gpt":0.20875789501478476,"score_spread":0.19928032158943934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279109725","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05636284,0.002238896,0.9383735,0.00020062682,0.00003885747,0.00005274732,0.000018417708,0.00010362901,0.0026104262],"genre_scores_gemma":[0.917194,0.0020321964,0.079307206,0.00009104868,0.000052216787,0.00008547344,0.00003765075,0.000030732783,0.0011694025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932086,0.00024459866,0.000021995162,0.00008763364,0.0002465044,0.000078359146],"domain_scores_gemma":[0.9985405,0.0008783912,0.0001866569,0.00009451096,0.00027440317,0.000025555815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010867863,0.0006965897,0.00040756105,0.00044556783,0.00027609078,0.0007340295,0.0010288948,0.00073705864,0.00043717396],"category_scores_gemma":[0.0029919625,0.00024364043,0.00049482816,0.0005499604,0.0008161382,0.0013904189,0.0006941907,0.0006673434,0.00013952029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006829315,0.000053806623,0.001257182,0.0002721097,0.00006250435,0.00028434457,0.00033609153,0.8884694,0.025177687,0.038419474,0.0006111122,0.04498794],"study_design_scores_gemma":[0.0000050256926,0.00013726515,0.0002972305,0.00001582914,0.000020198182,0.00013295685,0.00008108675,0.9886774,0.0026517764,0.0067562144,0.0012120794,0.000013103185],"about_ca_topic_score_codex":0.0012439501,"about_ca_topic_score_gemma":0.0008240812,"teacher_disagreement_score":0.0012439501,"about_ca_system_score_codex":0.00053322304,"about_ca_system_score_gemma":0.0005084029,"threshold_uncertainty_score":0.0057475567},"labels":[],"label_agreement":null},{"id":"W2284890933","doi":"10.3390/s16020233","title":"Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Electronic nose; Principal component analysis; Interference (communication); Independent component analysis; Nose; Pattern recognition (psychology); A priori and a posteriori; Artificial intelligence; Computer science; Telecommunications; Geology","score_opus":0.02482251960113089,"score_gpt":0.26528779576565453,"score_spread":0.24046527616452365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284890933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67579144,0.0011951951,0.31913877,0.00011859828,0.00006346704,0.000050915558,0.0000666095,0.00057208224,0.0030029914],"genre_scores_gemma":[0.9125969,0.00059211353,0.084905244,0.00009685254,0.000032726268,0.000026152717,0.00008527023,0.000056989473,0.0016077963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965584,0.000057316556,0.000010778822,0.00006069987,0.00018407797,0.000031355255],"domain_scores_gemma":[0.9997069,0.00012959652,0.000030879815,0.000026908589,0.0000925293,0.00001321466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002776106,0.00036315725,0.00052445335,0.00029673096,0.00018221678,0.00030667373,0.00030116455,0.00056302536,0.00039701283],"category_scores_gemma":[0.0006249704,0.00019108973,0.00029804427,0.0002864335,0.00028895692,0.00043670952,0.00032013128,0.00029098336,0.00028764116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001835367,0.000036157202,0.0012237407,0.000071801864,0.000018267101,0.00014618538,0.000048184254,0.0016263855,0.9434795,0.000093946044,0.0000991796,0.05297314],"study_design_scores_gemma":[0.000015722917,0.0004990549,0.02194614,0.000013008331,0.000049445985,0.0007089589,0.00008193669,0.05266074,0.92197037,0.00016112448,0.0018613433,0.000032190615],"about_ca_topic_score_codex":0.0004911541,"about_ca_topic_score_gemma":0.0009539597,"teacher_disagreement_score":0.00056302536,"about_ca_system_score_codex":0.00008891458,"about_ca_system_score_gemma":0.00014507,"threshold_uncertainty_score":0.0014681816},"labels":[],"label_agreement":null},{"id":"W2286147203","doi":"10.3390/s16020249","title":"Joint Transmit Antenna Selection and Power Allocation for ISDF Relaying Mobile-to-Mobile Sensor Networks","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Relay; Nakagami distribution; Transmitter power output; Fading; Antenna (radio); Selection (genetic algorithm); Computer science; Power (physics); Outage probability; Joint (building); Electronic engineering; Computer network; Engineering; Telecommunications; Channel (broadcasting); Transmitter","score_opus":0.022634528394215907,"score_gpt":0.26070390072327304,"score_spread":0.23806937232905714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2286147203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11867867,0.0009787182,0.87652755,0.00025274928,0.000030350791,0.0000344058,0.000058345675,0.00015068133,0.0032885754],"genre_scores_gemma":[0.9766244,0.0004408988,0.022319652,0.000025034562,0.00002069631,0.000036350324,0.000024252971,0.000010648855,0.0004978869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921465,0.00040178007,0.000036463698,0.00006702052,0.00019889858,0.000081098166],"domain_scores_gemma":[0.99805224,0.0014652637,0.00020573265,0.00009054158,0.00015593076,0.000030252293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056481,0.0006009721,0.0005963021,0.00024331704,0.00029166328,0.0005607763,0.00045396676,0.00041910703,0.00056567264],"category_scores_gemma":[0.0043005473,0.0002564706,0.00024139408,0.00058000523,0.00064450054,0.00070622924,0.0006085163,0.0002845448,0.00013449104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012478071,0.00002326172,0.0007451859,0.00009095632,0.00002507815,0.00013084777,0.000102048565,0.9461364,0.0065425257,0.016307976,0.00059149816,0.029179383],"study_design_scores_gemma":[0.0000067035744,0.000041482464,0.00015935737,0.0000042824063,0.000007169759,0.000041974483,0.000020954194,0.9934174,0.0011820935,0.0048976606,0.00021533373,0.000005512359],"about_ca_topic_score_codex":0.0011138406,"about_ca_topic_score_gemma":0.0014320769,"teacher_disagreement_score":0.0011138406,"about_ca_system_score_codex":0.0006946563,"about_ca_system_score_gemma":0.00064462674,"threshold_uncertainty_score":0.00558728},"labels":[],"label_agreement":null},{"id":"W2300751374","doi":"10.3390/s16030398","title":"An Inexpensive, Stable, and Accurate Relative Humidity Measurement Method for Challenging Environments","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Hygrometer; Relative humidity; Humidity; Environmental science; Meteorology","score_opus":0.09381526639221699,"score_gpt":0.2829284774092124,"score_spread":0.1891132110169954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300751374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16356027,0.004347133,0.82586277,0.00031088645,0.0005101555,0.00025682704,0.00032969323,0.0013300475,0.0034921202],"genre_scores_gemma":[0.42601195,0.0021708054,0.5667734,0.00022492232,0.00012187343,0.00021898352,0.00031105953,0.00011803268,0.004048955],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99882144,0.00013811298,0.00004710904,0.00032603403,0.0006250097,0.00004227517],"domain_scores_gemma":[0.99924904,0.0002261889,0.00015497701,0.00012886086,0.00021659966,0.00002436737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006702052,0.00061894755,0.0005198162,0.0006631593,0.00024966846,0.00048857543,0.0012446423,0.0008264828,0.0011884177],"category_scores_gemma":[0.0011771426,0.000383638,0.0003382215,0.00051484496,0.0003854085,0.0013713602,0.0004622137,0.0007361977,0.0006503394],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006214672,0.000023856568,0.0006524907,0.00029801877,0.000012580152,0.000062059546,0.00005587599,0.0006672662,0.9552117,0.00057837385,0.00035496234,0.04202076],"study_design_scores_gemma":[0.000022519804,0.00046523503,0.0039093103,0.000029511746,0.00004929362,0.0007634301,0.000079226025,0.023489226,0.95713884,0.00025173224,0.013713843,0.000087926346],"about_ca_topic_score_codex":0.00042325025,"about_ca_topic_score_gemma":0.0013379215,"teacher_disagreement_score":0.0012446423,"about_ca_system_score_codex":0.00024312735,"about_ca_system_score_gemma":0.0003264417,"threshold_uncertainty_score":0.0039756894},"labels":[],"label_agreement":null},{"id":"W2306688771","doi":"10.3390/s16040430","title":"A Novel Tactile Sensor with Electromagnetic Induction and Its Application on Stick-Slip Interaction Detection","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"State Key Laboratory of Robotics and System; Natural Science Foundation of Zhejiang Province; State Key Laboratory of Robotics; National Natural Science Foundation of China","keywords":"Tactile sensor; Slip (aerodynamics); Robot; GRASP; Wafer; Cantilever; Proximity sensor; Robot end effector; Contact force; Engineering; Inductive sensor; Acoustics; Computer science; Mechanical engineering; Artificial intelligence; Electrical engineering; Structural engineering; Physics","score_opus":0.009554544685377002,"score_gpt":0.20843618091615498,"score_spread":0.19888163623077798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2306688771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14764473,0.0024446098,0.8423291,0.00040082404,0.0004489879,0.00014728366,0.000114539784,0.0011016192,0.005368316],"genre_scores_gemma":[0.7506259,0.0008809913,0.24455492,0.00039320526,0.00013752125,0.00012107687,0.000076241944,0.00003964886,0.0031704556],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995091,0.000060575192,0.000027639717,0.00008807345,0.0002815304,0.000033017717],"domain_scores_gemma":[0.9994905,0.00018059397,0.000067491914,0.00005769158,0.00016096678,0.0000428302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039589038,0.00033012943,0.00046846824,0.0005269357,0.00023442016,0.00031868395,0.0006470192,0.000879439,0.0011815318],"category_scores_gemma":[0.00089304906,0.0002503342,0.00030757705,0.00051583303,0.00038918643,0.0010461689,0.00057299994,0.00035433838,0.00028604324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013532642,0.00007238136,0.0010479302,0.00032725034,0.00001681971,0.00045499858,0.00008362884,0.0014738594,0.9227511,0.0026064583,0.00074798916,0.07028223],"study_design_scores_gemma":[0.0000759235,0.0013489694,0.0064321463,0.000050143146,0.000070604,0.0044634957,0.00012591231,0.103532754,0.8639109,0.0022938144,0.017574584,0.00012067643],"about_ca_topic_score_codex":0.00009052686,"about_ca_topic_score_gemma":0.00012107756,"teacher_disagreement_score":0.0011815318,"about_ca_system_score_codex":0.00021402526,"about_ca_system_score_gemma":0.00020436558,"threshold_uncertainty_score":0.003952682},"labels":[],"label_agreement":null},{"id":"W2312147435","doi":"10.3390/s16020201","title":"Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Computer science; Compressed sensing; Pattern recognition (psychology); Block (permutation group theory); Artificial intelligence; Dependency (UML); Sparse approximation; Channel (broadcasting); Algorithm; Mathematics","score_opus":0.014975621328907292,"score_gpt":0.2286641557413236,"score_spread":0.2136885344124163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312147435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008472911,0.00041542022,0.9900096,0.00016396979,0.000040124967,0.00001775934,0.00005662981,0.0001226966,0.00070086354],"genre_scores_gemma":[0.34990296,0.00220511,0.64277196,0.0003170822,0.00034654298,0.00013746075,0.00076010404,0.0001121709,0.0034465983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969065,0.00007141509,0.000016821998,0.000056552097,0.00014234397,0.00002236635],"domain_scores_gemma":[0.9995028,0.00026947222,0.00006724321,0.00005113067,0.00009183504,0.00001750788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033585037,0.0006699095,0.0005097754,0.00046107813,0.0001754564,0.0003184452,0.0005919016,0.0005630871,0.00077971857],"category_scores_gemma":[0.0018219816,0.0002634462,0.00045752962,0.00060043996,0.00040140026,0.0010122322,0.0005757245,0.00080733007,0.000246796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023396492,0.00011484986,0.0009286117,0.00038698225,0.00010298277,0.00019090617,0.00016985336,0.3517823,0.072880186,0.02506406,0.0046645575,0.5434808],"study_design_scores_gemma":[0.00000918977,0.000041697775,0.00029781973,0.000010197412,0.0000113175365,0.00007914455,0.000012198211,0.98924667,0.0052017937,0.0035573107,0.0015213327,0.000011256869],"about_ca_topic_score_codex":0.0027130211,"about_ca_topic_score_gemma":0.0030214796,"teacher_disagreement_score":0.0027130211,"about_ca_system_score_codex":0.00020266263,"about_ca_system_score_gemma":0.0005392765,"threshold_uncertainty_score":0.005394459},"labels":[],"label_agreement":null},{"id":"W2313709582","doi":"10.3390/s16040501","title":"A Low Cost Compact Measurement System Constructed Using a Smart Electrochemical Sensor for the Real-Time Discrimination of Fruit Ripening","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"State Key Laboratory of Wheat and Maize Crop Science; Education Department of Henan Province; National Natural Science Foundation of China","keywords":"Ripening; Electrochemical gas sensor; Embedded system; Real-time computing; System of measurement; Automotive engineering; Computer science; Engineering; Process engineering; Electronic engineering; Electrochemistry; Chemistry; Food science; Physics; Electrode","score_opus":0.036059239913836465,"score_gpt":0.2782616449245679,"score_spread":0.24220240501073143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313709582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18799926,0.0051123127,0.79620856,0.00039384197,0.00072841794,0.00073434605,0.0011005499,0.004472454,0.0032503617],"genre_scores_gemma":[0.48214045,0.0013832821,0.5060394,0.00068621873,0.00020544032,0.0008725249,0.0012663784,0.00011634781,0.007289948],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888724,0.00009263745,0.0000687184,0.00038833468,0.0005228957,0.00004022884],"domain_scores_gemma":[0.9994516,0.00012335413,0.00009166681,0.00008095949,0.00019804561,0.000054450968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007436817,0.00089827523,0.0014587889,0.0007589137,0.0002383839,0.00049362826,0.0014945207,0.0012484881,0.0017850575],"category_scores_gemma":[0.0009276289,0.00054196746,0.00037794694,0.00058483524,0.0002759968,0.0011622327,0.0006903342,0.00067731104,0.0009033319],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110312925,0.00006211947,0.001110102,0.00033858058,0.00003002727,0.00009542446,0.000028420412,0.00030570268,0.94399863,0.00030571464,0.0008239502,0.05279099],"study_design_scores_gemma":[0.00011305167,0.0016094301,0.019673137,0.000034556437,0.000203633,0.0032700438,0.000057511537,0.032670345,0.9172107,0.00031160985,0.024702331,0.00014373627],"about_ca_topic_score_codex":0.00026669799,"about_ca_topic_score_gemma":0.0005342387,"teacher_disagreement_score":0.0017850575,"about_ca_system_score_codex":0.0003692299,"about_ca_system_score_gemma":0.00037498734,"threshold_uncertainty_score":0.00597167},"labels":[],"label_agreement":null},{"id":"W2338755356","doi":"10.3390/s16040559","title":"Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Higher Education and Scientific Research","keywords":"Nondestructive testing; Feature selection; Microwave; Feature (linguistics); Artificial intelligence; Support vector machine; Machine learning; Random forest; Computer science; Feature extraction; Electronic engineering; Acoustics; Engineering; Telecommunications","score_opus":0.0158790978327363,"score_gpt":0.21324607816078742,"score_spread":0.1973669803280511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338755356","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15465844,0.00061244896,0.84235734,0.00014852508,0.000053365056,0.00013681492,0.00020470578,0.0010891045,0.0007393078],"genre_scores_gemma":[0.6848507,0.00036416374,0.31235015,0.000079955505,0.000050567494,0.00025353773,0.00084041664,0.00007013073,0.0011404286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995321,0.00008553935,0.00004438764,0.00009968835,0.00018616974,0.000052122814],"domain_scores_gemma":[0.99882966,0.0006132218,0.00011409808,0.000075851545,0.000346986,0.000020268622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009370793,0.0008105326,0.0008789221,0.0012487159,0.00025810016,0.000535549,0.0004648323,0.00048456283,0.00064675964],"category_scores_gemma":[0.002471285,0.00017386679,0.0007663632,0.000961992,0.0002306474,0.00050110713,0.0003190393,0.00062238827,0.00027899406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002785349,0.0003185733,0.005919792,0.0002052321,0.00009506517,0.00022327376,0.0001304751,0.09056234,0.061340883,0.000665161,0.0017117548,0.83854896],"study_design_scores_gemma":[0.000032623928,0.000403834,0.011981906,0.00003204465,0.00010599195,0.0002304073,0.00010460178,0.94261414,0.04080441,0.0013214601,0.0023285109,0.00004010899],"about_ca_topic_score_codex":0.0020892832,"about_ca_topic_score_gemma":0.0015324989,"teacher_disagreement_score":0.0020892832,"about_ca_system_score_codex":0.00022475663,"about_ca_system_score_gemma":0.0004555295,"threshold_uncertainty_score":0.0049557686},"labels":[],"label_agreement":null},{"id":"W2339569074","doi":"10.3390/s16040567","title":"A Novel Method to Enhance Pipeline Trajectory Determination Using Pipeline Junctions","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pigging; Pipeline (software); Pipeline transport; Inertial measurement unit; Extended Kalman filter; Engineering; Computer science; Marine engineering; Real-time computing; Kalman filter; Artificial intelligence; Mechanical engineering","score_opus":0.03261315013742978,"score_gpt":0.3522789987608311,"score_spread":0.3196658486234013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339569074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011702776,0.00011136974,0.98656297,0.000030784664,0.000049519003,0.000028683493,0.00003955925,0.00073291006,0.0007414659],"genre_scores_gemma":[0.15027301,0.00020440802,0.846749,0.00002780244,0.000039214236,0.000058133013,0.00012933252,0.00010791832,0.0024111683],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995977,0.000044264747,0.000018329118,0.00012975489,0.00017695736,0.000032947824],"domain_scores_gemma":[0.9996209,0.00006649739,0.00005425387,0.000051939427,0.00018733507,0.000019019255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035966738,0.0006840993,0.0005129559,0.0011182314,0.00035515055,0.00045814755,0.0006819961,0.00062684476,0.0014321315],"category_scores_gemma":[0.0010302082,0.00038165067,0.0004217025,0.00094627566,0.00025929295,0.0010580422,0.00065429584,0.0005285302,0.0009775558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002164628,0.00007609662,0.0024729718,0.00019547078,0.000048189282,0.00017588837,0.00030719273,0.025761003,0.2582252,0.0037277837,0.0025055623,0.7062881],"study_design_scores_gemma":[0.000040338073,0.0003257066,0.007281601,0.000037049987,0.000092709415,0.000783909,0.0001436654,0.80233485,0.16372994,0.0015215157,0.02361092,0.00009778824],"about_ca_topic_score_codex":0.00182944,"about_ca_topic_score_gemma":0.002303037,"teacher_disagreement_score":0.00182944,"about_ca_system_score_codex":0.00026475053,"about_ca_system_score_gemma":0.0006172951,"threshold_uncertainty_score":0.004790902},"labels":[],"label_agreement":null},{"id":"W2342843801","doi":"10.3390/s16050605","title":"Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Wearable computer; Computer science; Motion capture; Gesture recognition; Motion (physics); Artificial intelligence; Interactivity; Inertial measurement unit; Quaternion; Support vector machine; Wireless; Computer vision; Population; Embedded system; Mathematics; Multimedia","score_opus":0.03153256442997454,"score_gpt":0.2435401714950088,"score_spread":0.21200760706503427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342843801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19585861,0.0008421231,0.79706454,0.00017607214,0.0001527738,0.00023774624,0.0006272022,0.0022465088,0.0027943512],"genre_scores_gemma":[0.7682871,0.00057024235,0.22594604,0.00013890695,0.00007371687,0.00021041099,0.0007227875,0.000070280046,0.003980457],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994992,0.00008784618,0.00004278791,0.00014086286,0.00018769581,0.000041679385],"domain_scores_gemma":[0.9996226,0.00008901745,0.00007419571,0.000057598812,0.00013063583,0.000025979256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004462739,0.0005795648,0.00052294956,0.00048643388,0.00016340501,0.00054221007,0.00050936086,0.000329532,0.0021385776],"category_scores_gemma":[0.0011894391,0.00020221021,0.00022759865,0.0004903359,0.00023762711,0.0007658701,0.00045995216,0.00023962058,0.0007389917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059622055,0.00015339124,0.008941645,0.0003216071,0.0001338843,0.00019699283,0.00023816091,0.0139969215,0.32668045,0.0013666804,0.0024137136,0.64496034],"study_design_scores_gemma":[0.00016388953,0.0015163689,0.08717425,0.0001311479,0.00017603568,0.0012919994,0.0003202351,0.547382,0.34404066,0.0023077354,0.015305114,0.000190597],"about_ca_topic_score_codex":0.0014991467,"about_ca_topic_score_gemma":0.0027516938,"teacher_disagreement_score":0.0021385776,"about_ca_system_score_codex":0.00021039923,"about_ca_system_score_gemma":0.00021822854,"threshold_uncertainty_score":0.007154286},"labels":[],"label_agreement":null},{"id":"W2343107556","doi":"10.3390/s16050596","title":"Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":446,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Beacon; Bluetooth Low Energy; Bluetooth; Molecular beacon; Embedded system; Computer science; Energy (signal processing); Real-time computing; Telecommunications; Wireless; Chemistry; Physics","score_opus":0.0038498416426480998,"score_gpt":0.16521117629611484,"score_spread":0.16136133465346675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343107556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088844486,0.0011155998,0.9028146,0.00012373972,0.00011963155,0.000055166314,0.00012408337,0.004209061,0.002593612],"genre_scores_gemma":[0.88665223,0.00058914826,0.10991229,0.00008692149,0.0000572411,0.000065836495,0.0002892011,0.000040776133,0.0023062984],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954706,0.000085082735,0.000027730539,0.00008425445,0.00020867679,0.00004716741],"domain_scores_gemma":[0.99951184,0.00008993791,0.00009224835,0.00010301829,0.00018039402,0.000022573317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034785148,0.0005483692,0.0005443728,0.00068902003,0.00016211737,0.0003304629,0.0006984946,0.0004693374,0.0005908033],"category_scores_gemma":[0.0010376885,0.00021419919,0.0004062821,0.0006190785,0.00015877347,0.0005870101,0.0005840548,0.00033029597,0.00055949064],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069473917,0.00011956641,0.02101139,0.00049079244,0.00015228102,0.00073738117,0.00031856017,0.08636799,0.11810586,0.002443076,0.005129048,0.7644294],"study_design_scores_gemma":[0.000094818875,0.00068607356,0.013977185,0.00006553209,0.00012208482,0.001668093,0.0001610022,0.91712683,0.0524507,0.0009992903,0.0125598805,0.00008848651],"about_ca_topic_score_codex":0.0020952837,"about_ca_topic_score_gemma":0.002583536,"teacher_disagreement_score":0.0020952837,"about_ca_system_score_codex":0.00019406581,"about_ca_system_score_gemma":0.00024723358,"threshold_uncertainty_score":0.004166186},"labels":[],"label_agreement":null},{"id":"W2346096354","doi":"10.3390/s16050624","title":"Implementation Strategies for a Universal Acquisition and Tracking Channel Applied to Real GNSS Signals","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Channel (broadcasting); Computer science; Discriminator; SIGNAL (programming language); Electronic engineering; Global Positioning System; Overhead (engineering); Real-time computing; Computer hardware; Detector; Engineering; Telecommunications","score_opus":0.017551172894637364,"score_gpt":0.25876996050566436,"score_spread":0.241218787611027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346096354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10520005,0.00048001512,0.8758483,0.00024185987,0.00016011746,0.00027474744,0.00008851486,0.0028406873,0.014865603],"genre_scores_gemma":[0.70240873,0.0003292812,0.2883294,0.00023847091,0.00008770407,0.0001916534,0.00013426693,0.00016294897,0.008117713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944276,0.00009872761,0.000034325938,0.00011751697,0.00016030896,0.0001462998],"domain_scores_gemma":[0.999466,0.00009570206,0.00005991584,0.0001413778,0.00020315364,0.00003385019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004923982,0.00051820045,0.00023685714,0.0005651601,0.0004312743,0.00079543196,0.0009048877,0.00043135305,0.003919691],"category_scores_gemma":[0.0008531819,0.0002044147,0.00017865094,0.00032623325,0.0005440346,0.00095486315,0.00061304314,0.0004926822,0.0009737882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005526749,0.00018857197,0.004681062,0.0005117987,0.00008600296,0.00039598392,0.0007693234,0.034334734,0.40099552,0.07272107,0.005787749,0.47897547],"study_design_scores_gemma":[0.00012632212,0.0015040404,0.00414583,0.00016948418,0.00016895162,0.0015184793,0.00035805948,0.1992699,0.6661206,0.011794512,0.11469055,0.00013317334],"about_ca_topic_score_codex":0.0012613072,"about_ca_topic_score_gemma":0.0018403629,"teacher_disagreement_score":0.003919691,"about_ca_system_score_codex":0.0005959728,"about_ca_system_score_gemma":0.00081577245,"threshold_uncertainty_score":0.013112664},"labels":[],"label_agreement":null},{"id":"W2362879822","doi":"10.3390/s16050662","title":"An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Fuse (electrical); Gaussian; Computer science; Low altitude; Brightness; Computer vision; Image (mathematics); Image enhancement; Transformation (genetics); Artificial intelligence; Remote sensing; Altitude (triangle); Algorithm; Mathematics; Geography; Engineering; Optics","score_opus":0.010162502528044256,"score_gpt":0.3084369328383625,"score_spread":0.29827443031031825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2362879822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025629753,0.00017635821,0.97319716,0.000037033416,0.00003245009,0.000042561507,0.000017412172,0.00023762073,0.0006297494],"genre_scores_gemma":[0.17873615,0.00030280146,0.8179264,0.000048559436,0.000026407854,0.000054540433,0.00011409047,0.00007791618,0.0027132013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996848,0.000031273383,0.000021272379,0.00006702604,0.00016563604,0.000029965922],"domain_scores_gemma":[0.99970263,0.00006838026,0.000037971648,0.000051624644,0.00012090644,0.00001840484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040135512,0.00057627464,0.0004943464,0.0009777835,0.00030353083,0.0004989462,0.0007188733,0.00046469874,0.00092002423],"category_scores_gemma":[0.0009677339,0.00028861634,0.00073960045,0.0007060062,0.00038238993,0.0010802303,0.0007434609,0.00062170025,0.00033433182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020003211,0.00010465705,0.0019390836,0.00021091684,0.000075151955,0.00023386745,0.00034358737,0.050267953,0.16589315,0.00981151,0.0013892861,0.7695308],"study_design_scores_gemma":[0.00004179971,0.00024781705,0.0037974631,0.00002109424,0.00007637035,0.00079548237,0.00016144983,0.85546243,0.12679711,0.003195701,0.009333579,0.00006977792],"about_ca_topic_score_codex":0.0017018183,"about_ca_topic_score_gemma":0.0023775604,"teacher_disagreement_score":0.0017018183,"about_ca_system_score_codex":0.00025973486,"about_ca_system_score_gemma":0.0005292716,"threshold_uncertainty_score":0.003383875},"labels":[],"label_agreement":null},{"id":"W2396225578","doi":"10.3390/s16060778","title":"Towards a Multifunctional Electrochemical Sensing and Niosome Generation Lab-on-Chip Platform Based on a Plug-and-Play Concept","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier universitaire de Québec; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Potentiostat; Microfluidics; Modular design; Plug and play; Nanotechnology; Drug delivery; Materials science; Electrode; Computer science; Chemistry; Electrochemistry","score_opus":0.009976777495752426,"score_gpt":0.20172290461033388,"score_spread":0.19174612711458144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396225578","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15839514,0.0030691344,0.8306627,0.0005786289,0.00037601043,0.00076669996,0.00021218206,0.0026342457,0.0033051337],"genre_scores_gemma":[0.3550641,0.0016109084,0.6320793,0.0007024746,0.00012265988,0.0007272391,0.0003081939,0.000124437,0.009260616],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959236,0.000064266555,0.000019043431,0.00010755892,0.00017680883,0.000039868635],"domain_scores_gemma":[0.9998197,0.000042653184,0.00002501373,0.00001631043,0.000058647238,0.000037719965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064926775,0.0009540388,0.0004970604,0.00035422944,0.000241233,0.00082550023,0.0017692476,0.000882001,0.0017970351],"category_scores_gemma":[0.00037520495,0.00050116616,0.00029298346,0.00016289197,0.00057170296,0.000972222,0.00055083603,0.0006899235,0.000918082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011001984,0.00008343831,0.00017249047,0.00020087253,0.000023065106,0.00013016637,0.00006143109,0.0006305063,0.9782859,0.0022189443,0.00041863392,0.017664531],"study_design_scores_gemma":[0.000030334731,0.00065596326,0.00050173484,0.0000134874645,0.000058130725,0.0004834192,0.000034232366,0.016880255,0.9666413,0.00036312584,0.01430084,0.00003728127],"about_ca_topic_score_codex":0.00036667517,"about_ca_topic_score_gemma":0.0005937798,"teacher_disagreement_score":0.0017970351,"about_ca_system_score_codex":0.0004193092,"about_ca_system_score_gemma":0.00044783312,"threshold_uncertainty_score":0.0060116053},"labels":[],"label_agreement":null},{"id":"W2401759074","doi":"10.3390/s16060765","title":"Instantaneous Observability of Tightly Coupled SINS/GPS during Maneuvers","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Observability; Control theory (sociology); Global Positioning System; Inertial navigation system; Observable; Channel (broadcasting); Computer science; Inertial frame of reference; Mathematics; Physics; Artificial intelligence; Classical mechanics; Applied mathematics; Telecommunications","score_opus":0.012706327896922694,"score_gpt":0.21113609832303817,"score_spread":0.19842977042611548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401759074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30514088,0.00013298859,0.6917556,0.00007671594,0.00002155083,0.000017441047,0.000083529216,0.00023244752,0.0025389574],"genre_scores_gemma":[0.9970092,0.000040597854,0.0026464446,0.0000067651094,0.0000044883186,0.0000066050375,0.000041003026,0.000007948966,0.0002369063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996063,0.00005194912,0.00001784745,0.000103829254,0.0001357653,0.000084327774],"domain_scores_gemma":[0.9993061,0.00027166566,0.00017523217,0.00008477255,0.00013293243,0.000029350944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044560502,0.00041536448,0.0003236626,0.00033646775,0.0002627346,0.00041748426,0.0003193555,0.00026125732,0.0005308975],"category_scores_gemma":[0.0020564406,0.00018010888,0.00040982442,0.00023034897,0.0008348942,0.00068985217,0.0007284363,0.0005384363,0.000058004305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031387253,0.000033089127,0.015871517,0.00012630822,0.00008807827,0.00064575707,0.00042031897,0.87956494,0.033743784,0.02545372,0.00039288602,0.04334577],"study_design_scores_gemma":[0.0000050914077,0.000060661194,0.0048519867,0.000004555633,0.0000158849,0.00008602147,0.00005509266,0.98686033,0.0036415397,0.004150589,0.00025463343,0.0000135111995],"about_ca_topic_score_codex":0.0054810937,"about_ca_topic_score_gemma":0.0023856224,"teacher_disagreement_score":0.0054810937,"about_ca_system_score_codex":0.00037719554,"about_ca_system_score_gemma":0.00058571965,"threshold_uncertainty_score":0.010898352},"labels":[],"label_agreement":null},{"id":"W2403662403","doi":"10.3390/s16060779","title":"Precise Point Positioning Using Triple GNSS Constellations in Various Modes","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Constellation; Precise Point Positioning; Computer science; Global Positioning System; Point (geometry); Hybrid positioning system; Remote sensing; Real-time computing; Telecommunications; Geodesy; Positioning system; Geography; Physics; Astronomy","score_opus":0.015970899158432604,"score_gpt":0.22470711410156824,"score_spread":0.20873621494313563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403662403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110393226,0.00033500342,0.87863,0.00020093248,0.00010700529,0.00006062155,0.0007650927,0.0011564806,0.008351593],"genre_scores_gemma":[0.8406687,0.000551707,0.15193711,0.0000973054,0.000044258482,0.00009900433,0.0015910763,0.00013823847,0.004872644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992342,0.00012121419,0.000027295559,0.00019461666,0.00035804426,0.00006466354],"domain_scores_gemma":[0.99974185,0.00004349956,0.000039140104,0.00008972615,0.00006969145,0.000016060998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059259724,0.0006797101,0.00051418017,0.00073368294,0.00026023408,0.0009447162,0.0009208017,0.00062097755,0.0010162033],"category_scores_gemma":[0.0011391198,0.0003349945,0.00059025205,0.0015283237,0.0005476051,0.0013926737,0.001182886,0.0006191839,0.0007113995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020282873,0.00003672563,0.012957341,0.00010112159,0.000088591565,0.00021658423,0.00023107328,0.8452839,0.014331292,0.014062399,0.001841735,0.11064644],"study_design_scores_gemma":[0.000023928305,0.00013200378,0.0058788257,0.000028100696,0.000033384214,0.00020658308,0.00009167777,0.97280324,0.006491075,0.006975172,0.007286785,0.00004924663],"about_ca_topic_score_codex":0.007364884,"about_ca_topic_score_gemma":0.0066210395,"teacher_disagreement_score":0.007364884,"about_ca_system_score_codex":0.00067839184,"about_ca_system_score_gemma":0.00066690886,"threshold_uncertainty_score":0.014644027},"labels":[],"label_agreement":null},{"id":"W2405157800","doi":"10.3390/s16050743","title":"Experimental Evaluation of Pulsed Thermography, Lock-in Thermography and Vibrothermography on Foreign Object Defect (FOD) in CFRP","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Thermography; Delamination (geology); Materials science; Image subtraction; Nondestructive testing; Optics; Acoustics; Image processing; Infrared; Artificial intelligence; Computer science; Image (mathematics); Physics; Radiology; Medicine","score_opus":0.012397760762923048,"score_gpt":0.23444770008656887,"score_spread":0.22204993932364583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405157800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98818874,0.00053641305,0.010628076,0.000016662789,0.000025463973,0.000027636399,0.00005342257,0.000049083326,0.0004744816],"genre_scores_gemma":[0.99066293,0.00042376787,0.008185893,0.000014621113,0.00001307158,0.000022922792,0.00003639346,0.000017019052,0.00062326295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997414,0.00005193548,0.000015406307,0.00007109192,0.00008630924,0.000033986496],"domain_scores_gemma":[0.99914086,0.0003626409,0.00013188519,0.00010775494,0.00021226758,0.000044596123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043199788,0.00033484536,0.00025379827,0.0003597804,0.00015690798,0.00013804989,0.00021621499,0.00039623378,0.0011933277],"category_scores_gemma":[0.0009527471,0.00019012541,0.00016581191,0.00026880173,0.0004981575,0.00033094242,0.00022827889,0.0002273306,0.00012613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000350973,0.00006833072,0.0010018802,0.00012977136,0.000007097432,0.00008339098,0.00015124599,0.0004984186,0.9880211,0.000052597698,0.000034414792,0.009600756],"study_design_scores_gemma":[0.000016061784,0.0019190487,0.010471641,0.000013831156,0.000046678557,0.0003542382,0.000111487476,0.0034529585,0.9831646,0.000032825337,0.00039848872,0.00001818753],"about_ca_topic_score_codex":0.00034543907,"about_ca_topic_score_gemma":0.00046390915,"teacher_disagreement_score":0.0011933277,"about_ca_system_score_codex":0.00011238789,"about_ca_system_score_gemma":0.00009827674,"threshold_uncertainty_score":0.003992021},"labels":[],"label_agreement":null},{"id":"W2410675826","doi":"10.3390/s16060850","title":"A Multidisciplinary Approach to High Throughput Nuclear Magnetic Resonance Spectroscopy","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interfacing; CMOS; Throughput; Computer science; Spectrometer; Nanotechnology; Nuclear magnetic resonance spectroscopy; Microelectronics; Computer architecture; Electronic engineering; Materials science; Computer hardware; Engineering; Nuclear magnetic resonance; Physics; Telecommunications","score_opus":0.015291816354965402,"score_gpt":0.2925134288208586,"score_spread":0.2772216124658932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2410675826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031823046,0.034113303,0.9246968,0.0052610207,0.0012018736,0.00021296894,0.00015529679,0.0009837104,0.030192688],"genre_scores_gemma":[0.09782046,0.068882056,0.79504275,0.003845466,0.0027724095,0.0011825551,0.0004722675,0.00041495595,0.029567122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99502087,0.0013921575,0.00023444403,0.0010973144,0.0019883453,0.00026683146],"domain_scores_gemma":[0.9974105,0.00084122317,0.00015565228,0.00062242866,0.0007289427,0.00024122844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048779976,0.001204957,0.0016249137,0.0026465042,0.0011924254,0.0051209005,0.0030721405,0.0027310564,0.005961283],"category_scores_gemma":[0.005210853,0.0008051321,0.001271335,0.002865598,0.0032168864,0.005515977,0.00467758,0.0039736982,0.0043819747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013523719,0.00020909103,0.00073972915,0.0013238043,0.00022198228,0.00031076724,0.0007229752,0.0101328995,0.03415463,0.6806441,0.015831927,0.2555728],"study_design_scores_gemma":[0.00005178264,0.000490893,0.0006239829,0.0005541563,0.00013634065,0.0009070884,0.0003856074,0.04259043,0.034164384,0.39340812,0.5265191,0.00016816455],"about_ca_topic_score_codex":0.000470272,"about_ca_topic_score_gemma":0.00040726218,"teacher_disagreement_score":0.005961283,"about_ca_system_score_codex":0.002305009,"about_ca_system_score_gemma":0.0021912104,"threshold_uncertainty_score":0.025797606},"labels":[],"label_agreement":null},{"id":"W2417171554","doi":"10.3390/s16060854","title":"An IMU Evaluation Method Using a Signal Grafting Scheme","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National High-tech Research and Development Program; China Postdoctoral Science Foundation","keywords":"Inertial measurement unit; Units of measurement; Computer science; Artificial intelligence","score_opus":0.030278137262142374,"score_gpt":0.3196858825480292,"score_spread":0.28940774528588686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417171554","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076738335,0.0001228388,0.9182383,0.000055327826,0.00011053157,0.00034966198,0.00007335173,0.0016232121,0.0026884275],"genre_scores_gemma":[0.42673445,0.00014312397,0.56957513,0.0000965641,0.00006678697,0.00029143933,0.00023495039,0.00016604968,0.0026915215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99834,0.0003241353,0.00010193243,0.0002760282,0.0008782203,0.00007979356],"domain_scores_gemma":[0.99835074,0.00029703826,0.00016579784,0.00037084203,0.000756881,0.00005873126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013369853,0.0011864003,0.0005540681,0.0019306411,0.00043371416,0.0006910504,0.0007845594,0.0006212202,0.0022781726],"category_scores_gemma":[0.0031558794,0.00030721014,0.00056124333,0.0010666192,0.0005491281,0.001356451,0.0011855514,0.0006333047,0.0011154854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000581403,0.00025113855,0.003599439,0.00022809752,0.0000924653,0.00011010307,0.00023624218,0.018436639,0.2777798,0.004045038,0.001179752,0.69345987],"study_design_scores_gemma":[0.000116732575,0.0018213654,0.012472291,0.000056094377,0.00017219773,0.0007692505,0.00020533505,0.4885776,0.4826691,0.0030564838,0.009856072,0.00022744562],"about_ca_topic_score_codex":0.00063421886,"about_ca_topic_score_gemma":0.00069889514,"teacher_disagreement_score":0.0022781726,"about_ca_system_score_codex":0.00031947272,"about_ca_system_score_gemma":0.0004159077,"threshold_uncertainty_score":0.0076212883},"labels":[],"label_agreement":null},{"id":"W2417520008","doi":"10.3390/s16060798","title":"Use of a Force-Torque Sensor for Self-Calibration of a 6-DOF Medical Robot","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Aeronautics and Space Administration","keywords":"Calibration; Torque; Robot; Computer science; Simulation; Engineering; Control engineering; Physics; Artificial intelligence","score_opus":0.02170133313973651,"score_gpt":0.23582801160994238,"score_spread":0.21412667847020586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417520008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15589307,0.0013551989,0.838426,0.00037767593,0.00022587231,0.00009504515,0.00006393542,0.0011965345,0.0023666597],"genre_scores_gemma":[0.79478174,0.00037325214,0.20314293,0.00014365444,0.00005748756,0.000037081663,0.00005005416,0.000043737986,0.001370016],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903715,0.00018737518,0.00006509183,0.00013666056,0.00053088035,0.000042824126],"domain_scores_gemma":[0.9990864,0.00027253508,0.00019790286,0.0001580269,0.00023884553,0.00004630567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008074686,0.00050565053,0.00043442985,0.00046964016,0.00024611058,0.0003615775,0.00079299917,0.00081651594,0.00084481225],"category_scores_gemma":[0.0019407786,0.0002844103,0.00029847192,0.00027932943,0.0003522616,0.00061240775,0.0005974068,0.00043160314,0.00037366146],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003448657,0.00010155125,0.0027557109,0.00039780696,0.000041092168,0.00038373828,0.000224612,0.00959376,0.8380254,0.0011314506,0.0005468489,0.14645322],"study_design_scores_gemma":[0.000054515054,0.001006188,0.011266794,0.00007934004,0.000096524556,0.0037407207,0.00006591537,0.11970732,0.8529385,0.0005187165,0.010409338,0.0001161989],"about_ca_topic_score_codex":0.00026593497,"about_ca_topic_score_gemma":0.00038408276,"teacher_disagreement_score":0.00084481225,"about_ca_system_score_codex":0.00019446517,"about_ca_system_score_gemma":0.00036167694,"threshold_uncertainty_score":0.004270315},"labels":[],"label_agreement":null},{"id":"W2433003374","doi":"10.3390/s16060821","title":"A New Cellular Architecture for Information Retrieval from Sensor Networks through Embedded Service and Security Protocols","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Wonkwang University","keywords":"SCADA; Computer science; Interconnectivity; Protocol (science); Computer network; Communications protocol; Cellular network; Information exchange; Computer security; Embedded system; Telecommunications; Engineering","score_opus":0.016762503421757086,"score_gpt":0.24514282150330347,"score_spread":0.22838031808154638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2433003374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013884009,0.003097622,0.94967484,0.0010730668,0.00066433195,0.00031655582,0.00014473037,0.004162697,0.026982252],"genre_scores_gemma":[0.48308912,0.0071349614,0.42620686,0.001295311,0.00068701565,0.0008320592,0.0010581105,0.00036895685,0.079327606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912494,0.000147941,0.00009021813,0.00014020578,0.00040614096,0.00009055974],"domain_scores_gemma":[0.99916315,0.00010605162,0.000057658497,0.0002873584,0.00033707122,0.000048775535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000921564,0.0005307566,0.00044457975,0.0013336532,0.00088875927,0.0029829934,0.0016039304,0.0011194785,0.0037726525],"category_scores_gemma":[0.0015774289,0.0003259168,0.0004200968,0.0013695674,0.00086230243,0.003779388,0.0017553204,0.0014049768,0.0024413501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037352418,0.00015731379,0.0010780816,0.00041475997,0.00009617251,0.00052947877,0.00073689024,0.021142866,0.053370066,0.5661326,0.023396777,0.3325715],"study_design_scores_gemma":[0.000070514805,0.00050636864,0.0008089208,0.00016542061,0.00014421943,0.001224108,0.00031927085,0.32049248,0.0400677,0.11024211,0.52583385,0.00012508343],"about_ca_topic_score_codex":0.0030578964,"about_ca_topic_score_gemma":0.0029268044,"teacher_disagreement_score":0.0037726525,"about_ca_system_score_codex":0.0013842487,"about_ca_system_score_gemma":0.0014968963,"threshold_uncertainty_score":0.012620747},"labels":[],"label_agreement":null},{"id":"W2435125193","doi":"10.3390/s16101570","title":"Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Computer science; Wireless; Context (archaeology); Data mining; Real-time computing; Set (abstract data type); Algorithm; Telecommunications","score_opus":0.0072288445128980425,"score_gpt":0.22608829423047042,"score_spread":0.21885944971757237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2435125193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08833195,0.0003163081,0.9084672,0.00020834837,0.000017689423,0.000040393334,0.000084413485,0.00019009235,0.002343642],"genre_scores_gemma":[0.8553702,0.00029521468,0.14336583,0.00005069848,0.000024557414,0.000052550975,0.00019130032,0.000029216237,0.00062047597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998982,0.00033856896,0.00004547628,0.00012667828,0.00043970253,0.00006750005],"domain_scores_gemma":[0.99710435,0.001987959,0.00024144666,0.00023639084,0.00037417538,0.00005573168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002426636,0.000688506,0.00054210075,0.00073953666,0.00028511262,0.00068933424,0.00067443826,0.00066353515,0.0009988329],"category_scores_gemma":[0.012138773,0.00034060545,0.00038196548,0.0005795045,0.0007927283,0.0018067722,0.0011226892,0.0005793988,0.00031012078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004575627,0.00011714013,0.012342906,0.00028411555,0.00013890293,0.0003131396,0.00029777837,0.6885198,0.04719802,0.05240486,0.0010676115,0.19685818],"study_design_scores_gemma":[0.000008415866,0.00011309917,0.0030676639,0.00002391164,0.000017553457,0.00010271276,0.000028401111,0.9807262,0.007413375,0.00798979,0.0004782351,0.000030713112],"about_ca_topic_score_codex":0.0023928327,"about_ca_topic_score_gemma":0.002306943,"teacher_disagreement_score":0.002426636,"about_ca_system_score_codex":0.0006343796,"about_ca_system_score_gemma":0.00087067724,"threshold_uncertainty_score":0.012833476},"labels":[],"label_agreement":null},{"id":"W2460242289","doi":"10.3390/s16060932","title":"Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Computer science; Matching (statistics); Artificial intelligence; Aerial image; Context (archaeology); Computer vision; Similarity (geometry); Feature (linguistics); 3D city models; Raised-relief map; Pattern recognition (psychology); Building model; Similarity measure; Hash function; Data mining; Image (mathematics); Terrain; Mathematics; Statistics; Geography; Visualization","score_opus":0.038467306137740666,"score_gpt":0.30405719222415734,"score_spread":0.26558988608641665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460242289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107468456,0.00033333062,0.88820004,0.00006253189,0.00008390626,0.00012384845,0.00024024387,0.0023040625,0.0011835963],"genre_scores_gemma":[0.5830898,0.00031334342,0.41460654,0.00007041491,0.00005699204,0.000097850694,0.00083425397,0.00013888103,0.00079193787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940276,0.000062695566,0.00002760672,0.00020564652,0.0002335326,0.00006773177],"domain_scores_gemma":[0.9996191,0.000040944466,0.000061477,0.00016941158,0.00008400602,0.00002503312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026979152,0.0006813435,0.0007535772,0.0015203267,0.0002714345,0.00063429837,0.0009583599,0.00054604147,0.0015793901],"category_scores_gemma":[0.0014778783,0.0004176857,0.00078081863,0.001488826,0.00033238553,0.0012234587,0.0013694357,0.00052482367,0.0007962646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003792221,0.0001737204,0.004846547,0.00017691121,0.00017208255,0.00035829342,0.0002631954,0.122014984,0.11473267,0.004934349,0.0037659393,0.74818194],"study_design_scores_gemma":[0.000031400617,0.00016071314,0.0047325594,0.000011974566,0.000049331138,0.00040639352,0.00013324808,0.95585245,0.032846235,0.0027194687,0.0030118388,0.000044464145],"about_ca_topic_score_codex":0.0024599605,"about_ca_topic_score_gemma":0.0026214498,"teacher_disagreement_score":0.0024599605,"about_ca_system_score_codex":0.00033375729,"about_ca_system_score_gemma":0.0005617681,"threshold_uncertainty_score":0.0052836537},"labels":[],"label_agreement":null},{"id":"W2461167469","doi":"10.3390/s16071076","title":"Exploiting Outage and Error Probability of Cooperative Incremental Relaying in Underwater Wireless Sensor Networks","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Alberta","funders":"Medical Research Council; King Saud University","keywords":"Retransmission; Relay; Computer science; Computer network; Throughput; Transmission (telecommunications); Underwater acoustic communication; Routing protocol; Underwater; Wireless; Real-time computing; Routing (electronic design automation); Telecommunications","score_opus":0.027593302238021256,"score_gpt":0.22831916710024372,"score_spread":0.20072586486222246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461167469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07999214,0.0010185922,0.9160847,0.00020724925,0.00003052196,0.000019773795,0.000052649266,0.00015448352,0.0024398933],"genre_scores_gemma":[0.98314214,0.00084620086,0.015215102,0.000031377047,0.0000422786,0.00002570997,0.00004064079,0.000018639743,0.0006378672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900633,0.00027394222,0.000049580853,0.00014176793,0.0004251418,0.00010334139],"domain_scores_gemma":[0.9942638,0.004454092,0.0004665999,0.00030936403,0.00045754097,0.000048516835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013478248,0.000687684,0.00052313705,0.0005439208,0.00032001533,0.000625705,0.0010532998,0.0005434311,0.00042033973],"category_scores_gemma":[0.007832442,0.0002627719,0.00028704476,0.00061845017,0.0010182506,0.0013952478,0.0009062682,0.0006013128,0.00007216083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074757496,0.000024612706,0.0019454747,0.00016676605,0.000042748634,0.00035363782,0.00024306492,0.9217949,0.006618005,0.037671782,0.00048024819,0.030584037],"study_design_scores_gemma":[0.0000027487467,0.00004348286,0.00050763245,0.0000085562515,0.000017817289,0.00012463736,0.00003150644,0.9864378,0.0016464257,0.010885027,0.00028345574,0.000010913627],"about_ca_topic_score_codex":0.0018688926,"about_ca_topic_score_gemma":0.0011401322,"teacher_disagreement_score":0.0018688926,"about_ca_system_score_codex":0.0007892502,"about_ca_system_score_gemma":0.0005638384,"threshold_uncertainty_score":0.00712806},"labels":[],"label_agreement":null},{"id":"W2461329045","doi":"10.3390/s16071071","title":"An Optically-Transparent Aptamer-Based Detection System for Colon Cancer Applications Using Gold Nanoparticles Electrodeposited on Indium Tin Oxide","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Aptamer; Indium tin oxide; Colloidal gold; Cyclic voltammetry; Nanotechnology; Nanoparticle; Monolayer; Materials science; Chemistry; Nuclear chemistry; Electrochemistry; Analytical Chemistry (journal); Electrode; Chromatography; Molecular biology; Thin film","score_opus":0.01673693675643983,"score_gpt":0.30078991881889416,"score_spread":0.28405298206245433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461329045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8379204,0.008652694,0.14676197,0.000595069,0.0004388906,0.00022865704,0.00032136086,0.001165801,0.003915126],"genre_scores_gemma":[0.89431095,0.0023558468,0.095700204,0.00035132826,0.00007110978,0.00010023354,0.0002646843,0.000037717065,0.0068079326],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997632,0.000026039219,0.000015896701,0.00007578933,0.00009727868,0.000021933747],"domain_scores_gemma":[0.99986434,0.000029658706,0.00003716723,0.000013639706,0.00003414692,0.000021042484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021196922,0.0004597536,0.00024638433,0.0002825018,0.00012493324,0.00028831133,0.0005175087,0.0006456182,0.0004288588],"category_scores_gemma":[0.00025915971,0.00026470787,0.00022936167,0.00013539988,0.00018456359,0.00040839278,0.00026612877,0.0004596712,0.00026125906],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010368699,0.0000064351316,0.00005026488,0.00003475808,0.0000024858834,0.000025404443,0.0000081797325,0.000029671246,0.99851626,0.00003703904,0.00001780909,0.0012612317],"study_design_scores_gemma":[0.000003428001,0.00006755938,0.00034973666,0.0000018611697,0.000009572431,0.00015227485,0.000006174075,0.000910701,0.99754936,0.000015483893,0.0009285323,0.0000053627537],"about_ca_topic_score_codex":0.00030462636,"about_ca_topic_score_gemma":0.00076268153,"teacher_disagreement_score":0.0006456182,"about_ca_system_score_codex":0.00033293138,"about_ca_system_score_gemma":0.00019621072,"threshold_uncertainty_score":0.002415657},"labels":[],"label_agreement":null},{"id":"W2463396900","doi":"10.3390/s16071102","title":"Sensors for Entertainment","year":2016,"lang":"en","type":"editorial","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Entertainment; Field (mathematics); State (computer science); Computer science; Engineering; Art; Visual arts","score_opus":0.01489137773368058,"score_gpt":0.3137583352210051,"score_spread":0.2988669574873245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2463396900","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000032438857,0.026798166,0.00031238014,0.026223682,0.94050443,0.000018502642,0.00004668959,0.00006674493,0.0059970096],"genre_scores_gemma":[0.0007808578,0.033062063,0.00032938624,0.027563265,0.8931372,0.00003323244,0.00006851042,0.00008252026,0.04494302],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99609226,0.0005086257,0.00041505994,0.0004896776,0.002323564,0.00017079677],"domain_scores_gemma":[0.99279046,0.0025426908,0.0004846844,0.0003020999,0.0028652332,0.0010148376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035461818,0.002752674,0.0018560066,0.0023018988,0.0018968876,0.0056354543,0.0022240472,0.01040994,0.015354863],"category_scores_gemma":[0.010873165,0.0007694447,0.0013625791,0.0010137757,0.0022812886,0.004491036,0.0020365887,0.016585056,0.016030472],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017819137,0.000006930132,0.000012295314,0.00022347955,0.000007096972,0.00004734382,0.000012375981,0.000018557706,0.00015334395,0.0016719077,0.98255426,0.015274519],"study_design_scores_gemma":[0.0000066804478,0.000011716056,0.000048868325,0.000121450874,0.0000053572944,0.00009389391,0.0000088376055,0.000024935634,0.0000689549,0.0007958174,0.9988097,0.000003863007],"about_ca_topic_score_codex":0.0007549994,"about_ca_topic_score_gemma":0.0026104578,"teacher_disagreement_score":0.015354863,"about_ca_system_score_codex":0.0017485514,"about_ca_system_score_gemma":0.0021075194,"threshold_uncertainty_score":0.051367164},"labels":[],"label_agreement":null},{"id":"W2464279077","doi":"10.3390/s16071016","title":"Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"U.S. Geological Survey; Shell","keywords":"Accelerometer; Natural frequency; Structural engineering; Acceleration; Field (mathematics); Vibration; Ambient vibration; Engineering; Structural system; Foundation (evidence); Computer science; Acoustics; Physics; Geography; Finite element method; Mathematics","score_opus":0.016914219200251875,"score_gpt":0.2544978156760425,"score_spread":0.23758359647579064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464279077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94958574,0.000036558286,0.049239334,0.000035907233,0.0000052864566,0.000014724906,0.000113270056,0.00015963253,0.00080962817],"genre_scores_gemma":[0.99738675,0.000011671049,0.0023901076,0.0000022885663,9.148387e-7,0.0000065492814,0.000049580794,0.0000014175733,0.0001507772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99994135,0.00001089071,0.0000020395528,0.000019454006,0.000017345626,0.00000892186],"domain_scores_gemma":[0.99992335,0.000024249195,0.000016255502,0.000011219553,0.000018747154,0.00000627878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012928198,0.0002810101,0.00022940389,0.0002671971,0.00013917733,0.00018250717,0.00017170739,0.0002578729,0.00034324895],"category_scores_gemma":[0.0002612683,0.0001208584,0.000112239126,0.00019572041,0.0001093798,0.00018215129,0.00014106362,0.000139306,0.00009610818],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035513137,0.000307818,0.092692316,0.00014116251,0.00007843492,0.00038119807,0.00036932155,0.5819569,0.20838213,0.00077525043,0.0009988924,0.11356142],"study_design_scores_gemma":[0.0000067963683,0.0001831026,0.053514685,0.0000047448652,0.000013939831,0.00005514856,0.00007699279,0.9373424,0.008277541,0.00014447303,0.00037137803,0.000008813257],"about_ca_topic_score_codex":0.004514835,"about_ca_topic_score_gemma":0.010514356,"teacher_disagreement_score":0.004514835,"about_ca_system_score_codex":0.0001886429,"about_ca_system_score_gemma":0.00017322307,"threshold_uncertainty_score":0.008977115},"labels":[],"label_agreement":null},{"id":"W2465181174","doi":"10.3390/s16070959","title":"Experimental Analysis of Bisbenzocyclobutene Bonded Capacitive Micromachined Ultrasonic Transducers","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence; CMC Microsystems","keywords":"Capacitive micromachined ultrasonic transducers; Materials science; Laser Doppler vibrometer; Ultrasonic sensor; Electromechanical coupling coefficient; Capacitive sensing; Transducer; Diaphragm (acoustics); Acoustics; Optoelectronics; Optics; Vibration; Piezoelectricity; Electrical engineering; Composite material","score_opus":0.007481537455160886,"score_gpt":0.2211634179171594,"score_spread":0.2136818804619985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465181174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92882586,0.002898647,0.061539374,0.00031395032,0.0003397681,0.00043810252,0.00076945685,0.0006260256,0.004248712],"genre_scores_gemma":[0.9299543,0.001314329,0.064613216,0.00012824789,0.00006216899,0.00033592843,0.00036870674,0.000040745963,0.0031823998],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890435,0.000080678314,0.000053175496,0.00023987067,0.000632721,0.000089154004],"domain_scores_gemma":[0.99925107,0.00018302232,0.00016227673,0.00006199634,0.00028706645,0.00005455074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047694801,0.0006960124,0.00051957375,0.00045253173,0.0003088703,0.00043026192,0.0010525635,0.0007893393,0.0020909372],"category_scores_gemma":[0.0011653535,0.0003835279,0.00017331759,0.00075013563,0.00033341194,0.00041619304,0.0003840621,0.00041444885,0.00060363574],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057960217,0.000027529539,0.00030809004,0.00015137321,0.000008037318,0.00011786573,0.000088872795,0.0007083875,0.99336064,0.00015513961,0.00015839496,0.004857652],"study_design_scores_gemma":[0.000014620752,0.0004784983,0.0020583174,0.000014862514,0.000017221066,0.00015724743,0.00007981902,0.005899532,0.98819554,0.000047812802,0.0030179862,0.000018601848],"about_ca_topic_score_codex":0.00085671,"about_ca_topic_score_gemma":0.0017284416,"teacher_disagreement_score":0.0020909372,"about_ca_system_score_codex":0.0005791721,"about_ca_system_score_gemma":0.00036039055,"threshold_uncertainty_score":0.0069948435},"labels":[],"label_agreement":null},{"id":"W2466213500","doi":"10.3390/s16071080","title":"Helium Ion Microscope-Assisted Nanomachining of Resonant Nanostrings","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microscope; Helium; Field ion microscope; Materials science; Nanotechnology; Ion; Optoelectronics; Optics; Chemistry; Atomic physics; Physics","score_opus":0.008358127149752332,"score_gpt":0.2565602656874513,"score_spread":0.24820213853769899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466213500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9710943,0.0002527395,0.026005417,0.00007926489,0.00008088438,0.000034495766,0.00009835404,0.00021416282,0.0021403146],"genre_scores_gemma":[0.9750357,0.0001683098,0.022479704,0.00003770791,0.0000118158505,0.00003681822,0.000070450136,0.0000449465,0.002114347],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998685,0.000014530048,0.000010209273,0.000035557758,0.000049790124,0.000021264197],"domain_scores_gemma":[0.99982613,0.000071994975,0.000034680455,0.00004280419,0.00001360241,0.000010871275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019914856,0.00016450153,0.00020527116,0.00014641718,0.000115324976,0.00018625187,0.00026457311,0.00019363494,0.0009296896],"category_scores_gemma":[0.00028061628,0.00016407121,0.00014975299,0.0001133664,0.00032748719,0.0002343556,0.00030917552,0.0002917153,0.00017965538],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017602948,0.0000054137163,0.00006507339,0.00002154575,0.0000018055989,0.000021658912,0.000024654099,0.00035255522,0.9974425,0.00029922227,0.00003096073,0.0017170759],"study_design_scores_gemma":[0.0000042559473,0.00003169414,0.0005439667,0.000001190642,0.0000011321697,0.000029846326,0.0000070747583,0.0019674478,0.99645114,0.00005024998,0.0009073966,0.0000044495605],"about_ca_topic_score_codex":0.00014846456,"about_ca_topic_score_gemma":0.000403672,"teacher_disagreement_score":0.0009296896,"about_ca_system_score_codex":0.00019680128,"about_ca_system_score_gemma":0.00009855208,"threshold_uncertainty_score":0.0031101108},"labels":[],"label_agreement":null},{"id":"W2466300307","doi":"10.3390/s16071037","title":"Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université du Québec à Montréal; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Orientation (vector space); Joint (building); Inertial measurement unit; Inertial frame of reference; Quality (philosophy); Engineering; Computer science; Aerospace engineering; Physics; Structural engineering; Mathematics","score_opus":0.014042954541674575,"score_gpt":0.22557328304142552,"score_spread":0.21153032849975095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466300307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6029494,0.00060147815,0.39305237,0.0000989457,0.000089190806,0.00016439421,0.00031776485,0.0007682732,0.0019581153],"genre_scores_gemma":[0.9541247,0.00017113877,0.044832267,0.000032488464,0.00004195164,0.000060135146,0.00026524725,0.000048881597,0.000423082],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986047,0.0003826896,0.000106881336,0.00024611264,0.0005859092,0.00007372678],"domain_scores_gemma":[0.99687386,0.00091421366,0.0007279514,0.00036917513,0.0010414295,0.00007323749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014776734,0.0006077793,0.00046639258,0.00077583705,0.00022185784,0.00073197024,0.00038448776,0.0003265578,0.00046915736],"category_scores_gemma":[0.0059536817,0.00016562987,0.00024509427,0.00071705016,0.0004781637,0.00049255736,0.00039196984,0.0002496611,0.00025587954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014444028,0.00043735668,0.17943706,0.00045229716,0.00022072357,0.00015696144,0.0008600495,0.024344737,0.27523363,0.00079550454,0.001200289,0.515417],"study_design_scores_gemma":[0.00010744685,0.0020285917,0.56638724,0.000079909805,0.00020513448,0.0004904403,0.0005399761,0.2831477,0.14246307,0.0013482807,0.003081253,0.00012091167],"about_ca_topic_score_codex":0.0019179209,"about_ca_topic_score_gemma":0.002128895,"teacher_disagreement_score":0.0019179209,"about_ca_system_score_codex":0.0001862137,"about_ca_system_score_gemma":0.0002892731,"threshold_uncertainty_score":0.007814765},"labels":[],"label_agreement":null},{"id":"W2481431266","doi":"10.3390/s16081190","title":"Practical Application of Electrochemical Nitrate Sensor under Laboratory and Forest Nursery Conditions","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Ministère des Forêts, de la Faune et des Parcs","keywords":"Nitrate; Environmental science; Leaching (pedology); Environmental chemistry; Chemistry; Soil science; Soil water","score_opus":0.00992329895249822,"score_gpt":0.2633221431580041,"score_spread":0.2533988442055059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2481431266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88448715,0.001749207,0.10967672,0.00035928862,0.0001548836,0.0001695442,0.0004499165,0.0006289769,0.0023241518],"genre_scores_gemma":[0.877866,0.001198468,0.1172216,0.0001708044,0.000033960685,0.00016960825,0.00031088846,0.000041783274,0.002986774],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992367,0.00013678877,0.00004344185,0.00021127083,0.00032433082,0.000047403184],"domain_scores_gemma":[0.9996437,0.00013894224,0.000046356785,0.00003722475,0.00011260097,0.000021177402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068724086,0.00045133987,0.0004934744,0.0001908472,0.00024874753,0.00032928828,0.0007891979,0.00085015537,0.0006528896],"category_scores_gemma":[0.00080355135,0.00021369025,0.00022378349,0.00020997436,0.00021731287,0.00042263194,0.00036465263,0.00034755166,0.00028252118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033614055,0.000015708021,0.00031090513,0.000042277992,0.0000024670308,0.00004076411,0.000025756794,0.000082229766,0.99673754,0.000021503065,0.000044730838,0.002642477],"study_design_scores_gemma":[0.000008722944,0.00027014615,0.0019411967,0.000006348371,0.000013173224,0.00035177582,0.00006478976,0.003200758,0.9926813,0.00004787135,0.0013993129,0.000014576509],"about_ca_topic_score_codex":0.00095580757,"about_ca_topic_score_gemma":0.0020380022,"teacher_disagreement_score":0.00095580757,"about_ca_system_score_codex":0.0002360156,"about_ca_system_score_gemma":0.0002961304,"threshold_uncertainty_score":0.0036345124},"labels":[],"label_agreement":null},{"id":"W2484617990","doi":"10.3390/s16071101","title":"Portable Wind Energy Harvesters for Low-Power Applications: A Survey","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Energy harvesting; Wind power; Mechanical energy; Aeroelasticity; Energy (signal processing); Electric potential energy; Renewable energy; Power (physics); Automotive engineering; Electrical engineering; Aerospace engineering; Computer science; Engineering; Marine engineering; Environmental science; Aerodynamics; Physics","score_opus":0.03614184217706178,"score_gpt":0.28482682835899525,"score_spread":0.24868498618193347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484617990","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020529283,0.99132323,0.0022271983,0.00024515085,0.00019830838,0.000016505892,0.000048215647,0.000029936176,0.0038583959],"genre_scores_gemma":[0.0071009025,0.98780525,0.0018627977,0.00014697298,0.00013791487,0.000017735601,0.000072213,0.000007152279,0.0028490706],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997924,0.000019745967,0.00002487332,0.000048114147,0.00010057606,0.000014240809],"domain_scores_gemma":[0.999683,0.00014705391,0.000053478452,0.000012294896,0.00009118949,0.000012935814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043626723,0.000648061,0.00078572653,0.0020796787,0.00022394354,0.0006888329,0.0006029857,0.00067777437,0.0031480032],"category_scores_gemma":[0.00050289725,0.00034289327,0.0005058653,0.0027159166,0.00023946438,0.001266325,0.0003372594,0.0007231051,0.0015392936],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042174186,0.00008716678,0.0005103906,0.018818233,0.00006131768,0.00023334872,0.0000858808,0.0005557124,0.015369543,0.00305844,0.0072945203,0.9538833],"study_design_scores_gemma":[0.00000957976,0.00036141317,0.0031308786,0.003683666,0.00019285205,0.0023655728,0.0002779181,0.0008040845,0.013531499,0.0023953458,0.9732001,0.000047039466],"about_ca_topic_score_codex":0.00034292872,"about_ca_topic_score_gemma":0.000728227,"teacher_disagreement_score":0.0031480032,"about_ca_system_score_codex":0.00018581496,"about_ca_system_score_gemma":0.00043094103,"threshold_uncertainty_score":0.010531068},"labels":[],"label_agreement":null},{"id":"W2506471755","doi":"10.3390/s16071111","title":"Preservation Mechanism of Chitosan-Based Coating with Cinnamon Oil for Fruits Storage Based on Sensor Data","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Nanocomposite Films for Food Packaging","field":"Materials Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Xihua University; National Natural Science Foundation of China","keywords":"Chitosan; Coating; Aspergillus flavus; Penicillium citrinum; Cinnamaldehyde; Essential oil; Antimicrobial; Chemistry; Food science; Ripening; Materials science; Chemical engineering; Organic chemistry","score_opus":0.034440444889316205,"score_gpt":0.262143318917493,"score_spread":0.2277028740281768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506471755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801087,0.0022708594,0.014559485,0.00014019069,0.00006749556,0.00006333091,0.0002583631,0.00024771018,0.0022838332],"genre_scores_gemma":[0.9930172,0.0006865867,0.004047082,0.000048719245,0.000006744431,0.00002048624,0.000119288925,0.00001877599,0.0020351373],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998652,0.000006371595,0.000008245755,0.00003273537,0.000056709418,0.0000307209],"domain_scores_gemma":[0.9998667,0.000018830919,0.00003772012,0.000011782559,0.0000518265,0.000013108442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001561325,0.00040499985,0.00016928528,0.00028141844,0.00012993377,0.00022400015,0.0005180506,0.00029667155,0.00093939505],"category_scores_gemma":[0.0002499848,0.00013987807,0.00028535983,0.00021701987,0.00016795179,0.00039801552,0.0001654157,0.00025292885,0.00016753194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003470443,0.000004608703,0.00012037022,0.000047634298,0.0000036725075,0.00003714652,0.0000107607575,0.00008563583,0.9978102,0.000059843336,0.000028800165,0.0017565661],"study_design_scores_gemma":[0.000001323798,0.000033108678,0.000713614,0.0000016711982,0.0000069678563,0.000033909833,0.0000072330663,0.00079131086,0.9981153,0.0000091884785,0.00028284013,0.0000035314276],"about_ca_topic_score_codex":0.002370696,"about_ca_topic_score_gemma":0.0025696591,"teacher_disagreement_score":0.002370696,"about_ca_system_score_codex":0.00051195937,"about_ca_system_score_gemma":0.00020127575,"threshold_uncertainty_score":0.0047138333},"labels":[],"label_agreement":null},{"id":"W2508764147","doi":"10.3390/s16081310","title":"Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":298,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Canadian Forest Service; York University","funders":"Ontario Centres of Excellence","keywords":"Drone; Remote sensing; Fire detection; Hyperspectral imaging; Context (archaeology); Computer science; Systems engineering; Environmental science; Environmental monitoring; Engineering; Architectural engineering; Geography","score_opus":0.022143959722690582,"score_gpt":0.2603073152471543,"score_spread":0.2381633555244637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508764147","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074023375,0.98819494,0.0027608732,0.0003377686,0.0002852741,0.000019672822,0.000052939973,0.000022360191,0.0075858906],"genre_scores_gemma":[0.0075803953,0.9858159,0.0025614912,0.00021336856,0.0002327255,0.000017424492,0.00008255609,0.00000723859,0.0034890275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995981,0.00005124842,0.000028501308,0.000082492814,0.0002088031,0.000030917494],"domain_scores_gemma":[0.9995542,0.00019775567,0.00006880725,0.000020256513,0.00013962314,0.000019323556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076685817,0.00090601086,0.00076668756,0.0024812946,0.0002888743,0.0009432197,0.0007673823,0.00121045,0.0053311307],"category_scores_gemma":[0.00070539286,0.00031435644,0.00069058524,0.00257231,0.00047849835,0.0013541412,0.0006972605,0.0010766576,0.0028668083],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029903189,0.00009649094,0.00048696675,0.012727814,0.000078857694,0.00012695341,0.00006119776,0.0011294396,0.008632372,0.009132486,0.01335736,0.9541402],"study_design_scores_gemma":[0.0000048461784,0.00014042376,0.0016533871,0.0034828025,0.00010378002,0.0009906552,0.00010338647,0.0007648703,0.005177899,0.0049291397,0.98261285,0.000035933856],"about_ca_topic_score_codex":0.0010198812,"about_ca_topic_score_gemma":0.0018248109,"teacher_disagreement_score":0.0053311307,"about_ca_system_score_codex":0.00045420096,"about_ca_system_score_gemma":0.00075846654,"threshold_uncertainty_score":0.017834425},"labels":[],"label_agreement":null},{"id":"W2509557266","doi":"10.3390/s16091421","title":"Performance Optimization of Priority Assisted CSMA/CA Mechanism of 802.15.6 under Saturation Regime","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"King Saud University","keywords":"Computer science; Computer network; Prioritization; Node (physics); Throughput; Wireless sensor network; Body area network; Carrier sense multiple access with collision avoidance; Wireless; Channel (broadcasting); Bandwidth (computing); Energy consumption; Real-time computing; Engineering; Telecommunications; Electrical engineering","score_opus":0.009642954055909902,"score_gpt":0.19668363841103637,"score_spread":0.18704068435512647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509557266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69109213,0.0028885182,0.28744066,0.0007991827,0.00033126416,0.00023012442,0.00011800711,0.0021486902,0.0149514135],"genre_scores_gemma":[0.99273187,0.00017313795,0.0063922824,0.00003906499,0.000014240194,0.000023395723,0.000019797815,0.000012128793,0.00059422053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892837,0.00020309647,0.00005579861,0.00017719272,0.00032429383,0.00031115793],"domain_scores_gemma":[0.99772257,0.00094416837,0.00024188845,0.00017147668,0.00078239606,0.00013743434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012886415,0.0007554888,0.00056496385,0.00078003627,0.0007323215,0.0008024976,0.0011209879,0.00071817485,0.0015127728],"category_scores_gemma":[0.003895927,0.00017324902,0.00024049127,0.00038992526,0.00047672575,0.0007163689,0.0004376017,0.0006144728,0.00027334638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004513525,0.0009966019,0.014183466,0.0006493373,0.00024401875,0.0009546356,0.0010926356,0.37910137,0.37797132,0.025339345,0.0056333067,0.18932046],"study_design_scores_gemma":[0.000089865964,0.00088628486,0.0017618426,0.00001927663,0.000046496014,0.00028162354,0.00010230562,0.95917535,0.034295943,0.002200819,0.0011054457,0.00003468804],"about_ca_topic_score_codex":0.0029740257,"about_ca_topic_score_gemma":0.0020917421,"teacher_disagreement_score":0.0029740257,"about_ca_system_score_codex":0.00062252,"about_ca_system_score_gemma":0.0011210531,"threshold_uncertainty_score":0.006815076},"labels":[],"label_agreement":null},{"id":"W2511174319","doi":"10.3390/s16091370","title":"Label-Free Ag+ Detection by Enhancing DNA Sensitized Tb3+ Luminescence","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Luminescence; DNA; Deoxyribozyme; Oligonucleotide; Detection limit; Quenching (fluorescence); Biosensor; Chemistry; G-quadruplex; Fluorescence; Nanotechnology; Materials science; Biochemistry; Optoelectronics; Physics","score_opus":0.005371354354715841,"score_gpt":0.235219351485692,"score_spread":0.22984799713097617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511174319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93555194,0.0028482694,0.05866788,0.00029210592,0.00010748907,0.00009050468,0.00014075724,0.0004653239,0.0018357806],"genre_scores_gemma":[0.937596,0.0017190077,0.057400256,0.00016401205,0.00003279171,0.00006918246,0.00016385446,0.000057859863,0.002797079],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959785,0.00010580514,0.000019703188,0.00009402591,0.00012598292,0.00005677076],"domain_scores_gemma":[0.99976915,0.00009566857,0.000046143505,0.000014504958,0.000050166796,0.000024345598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033250218,0.0005168001,0.00047407096,0.00016238765,0.000117145544,0.00040712307,0.0005990392,0.0006590968,0.0007785585],"category_scores_gemma":[0.00041853308,0.00031108825,0.00027929692,0.00015405782,0.00043882255,0.00041348222,0.00029283072,0.0005835337,0.0006319772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020433663,0.0000044443423,0.000023672197,0.000039151804,0.0000017502982,0.000017359265,0.000009052815,0.000042992015,0.9990827,0.000033303717,0.000008175761,0.0007169656],"study_design_scores_gemma":[0.0000030278748,0.000052341515,0.000058948623,0.0000013494632,0.0000028292775,0.000023910661,0.0000033814226,0.00041254805,0.99915326,0.000011460837,0.0002741487,0.0000027865906],"about_ca_topic_score_codex":0.0003028238,"about_ca_topic_score_gemma":0.00052580104,"teacher_disagreement_score":0.0007785585,"about_ca_system_score_codex":0.00043789676,"about_ca_system_score_gemma":0.00018055885,"threshold_uncertainty_score":0.0031772256},"labels":[],"label_agreement":null},{"id":"W2514362252","doi":"10.3390/s16091391","title":"On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Alberta","funders":"","keywords":"Energy consumption; Computer science; Routing protocol; Wireless sensor network; Reliability (semiconductor); Computer network; Routing (electronic design automation); Underwater; Efficient energy use; Wireless Routing Protocol; Sink (geography); Distributed computing; Engineering","score_opus":0.022355224797719998,"score_gpt":0.2192269692136349,"score_spread":0.1968717444159149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514362252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06676551,0.006215845,0.92001677,0.0007212246,0.00019740891,0.00009676668,0.000054249413,0.00022165474,0.005710547],"genre_scores_gemma":[0.8471556,0.0085186325,0.13789436,0.00019603872,0.00024702027,0.0001229543,0.00014660416,0.00006484139,0.005653962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995394,0.00016290744,0.000032388078,0.00004999891,0.00017403983,0.000041244126],"domain_scores_gemma":[0.9994336,0.00028409692,0.000057553334,0.00008452558,0.00012222225,0.000018016559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006999069,0.0005232876,0.000374577,0.0006759398,0.00033378054,0.00037280272,0.00080611516,0.0003908497,0.00047977647],"category_scores_gemma":[0.0012716721,0.00016822618,0.00024808175,0.0007780307,0.00047647365,0.0009491538,0.000616301,0.00046618414,0.00016330673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033620413,0.00016611019,0.0018249864,0.00074069726,0.00012488745,0.000664252,0.0005459461,0.5062202,0.09288852,0.070804395,0.005209715,0.3204741],"study_design_scores_gemma":[0.000016909391,0.0005149387,0.0006907544,0.000044580745,0.00004195561,0.0003493311,0.00014053509,0.95102066,0.02004347,0.016549751,0.010554367,0.00003275168],"about_ca_topic_score_codex":0.00079911324,"about_ca_topic_score_gemma":0.0008911637,"teacher_disagreement_score":0.00080611516,"about_ca_system_score_codex":0.00023298408,"about_ca_system_score_gemma":0.0003459177,"threshold_uncertainty_score":0.003701508},"labels":[],"label_agreement":null},{"id":"W2517517923","doi":"10.3390/s16081271","title":"Mathematical Model and Calibration Experiment of a Large Measurement Range Flexible Joints 6-UPUR Six-Axis Force Sensor","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Jacobian matrix and determinant; Revolute joint; Calibration; Thrust; Correctness; Control theory (sociology); Torsion (gastropod); Range (aeronautics); Computer science; Engineering; Simulation; Acoustics; Mechanical engineering; Algorithm; Aerospace engineering; Mathematics; Robot; Physics; Artificial intelligence","score_opus":0.027191688860194855,"score_gpt":0.233367124724468,"score_spread":0.20617543586427314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517517923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036657006,0.00041646216,0.9543599,0.00021757522,0.000081294784,0.00016416893,0.00009852424,0.0007172813,0.007287784],"genre_scores_gemma":[0.86750656,0.00065715576,0.12553585,0.000054614935,0.00003142625,0.0003226545,0.000119075616,0.00003177966,0.005740871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923074,0.00010200003,0.000043853142,0.00021331519,0.00037422276,0.00003582341],"domain_scores_gemma":[0.99966776,0.000079336736,0.000055233744,0.000059905648,0.00012553237,0.000012345158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007105586,0.0006541013,0.0005286625,0.00053124654,0.00047674577,0.00053856615,0.0015427881,0.0010289164,0.0023349784],"category_scores_gemma":[0.00074463256,0.0004733629,0.00064269407,0.000416076,0.00066612783,0.0014365133,0.00076182873,0.0004927203,0.00044275797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031959976,0.00013867702,0.0037177026,0.0010864966,0.000115966825,0.0013455282,0.0006886476,0.61126673,0.19524968,0.05228129,0.0020905137,0.13169914],"study_design_scores_gemma":[0.000027178427,0.00028357812,0.0013267536,0.000023337327,0.000025260439,0.00040078358,0.00004471987,0.9710377,0.020860918,0.0027590399,0.0031667878,0.000043871645],"about_ca_topic_score_codex":0.0018052069,"about_ca_topic_score_gemma":0.0008621406,"teacher_disagreement_score":0.0023349784,"about_ca_system_score_codex":0.00042631046,"about_ca_system_score_gemma":0.00062363315,"threshold_uncertainty_score":0.007811308},"labels":[],"label_agreement":null},{"id":"W2519626547","doi":"10.3390/s16091463","title":"Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"State Key Laboratory of Industrial Control Technology; Zhejiang University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential privacy; Data aggregator; Computer science; Overhead (engineering); Encryption; Scheme (mathematics); Cloud computing; Information privacy; Outsourcing; Server; Privacy software; Computer security; Secret sharing; Computer network; Wireless sensor network; Cryptography; Data mining","score_opus":0.025211885911436218,"score_gpt":0.25102999708697404,"score_spread":0.22581811117553782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519626547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049352456,0.0003277052,0.94770235,0.00035325362,0.0000538866,0.000079755526,0.00011578671,0.00035752775,0.001657265],"genre_scores_gemma":[0.937587,0.00019270129,0.060538612,0.00014698258,0.00009318266,0.00005392587,0.00012990824,0.000016423923,0.0012412027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972596,0.0006608259,0.00022222822,0.0005138621,0.001020262,0.00032328928],"domain_scores_gemma":[0.99642414,0.000982138,0.00045313296,0.0014033074,0.0005960995,0.00014120017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018517853,0.0005233819,0.00078304054,0.0005743207,0.00071386405,0.0010724309,0.0012902631,0.00077856623,0.000707813],"category_scores_gemma":[0.0049478076,0.00022441852,0.0006400302,0.001292682,0.00079449033,0.002946234,0.0027541982,0.0009160702,0.00020066446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017475064,0.0003918107,0.0091183055,0.0004723321,0.0003315043,0.0015157289,0.0012989586,0.25803697,0.1352542,0.16730914,0.0070575313,0.417466],"study_design_scores_gemma":[0.00006368999,0.0003789778,0.002379783,0.000018278944,0.0000983341,0.0014011754,0.00021812545,0.9025505,0.04163033,0.045305006,0.00590326,0.00005249579],"about_ca_topic_score_codex":0.00053854665,"about_ca_topic_score_gemma":0.00037791827,"teacher_disagreement_score":0.0018517853,"about_ca_system_score_codex":0.00080197206,"about_ca_system_score_gemma":0.0008484016,"threshold_uncertainty_score":0.009793282},"labels":[],"label_agreement":null},{"id":"W2520821548","doi":"10.3390/s16091506","title":"Decoupling Principle Analysis and Development of a Parallel Three-Dimensional Force Sensor","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Decoupling (probability); Calibration; Coupling (piping); Dynamical decoupling; Dimension (graph theory); Finite element method; Control theory (sociology); Accuracy and precision; Computer science; Engineering; Mechanical engineering; Control engineering; Structural engineering; Physics; Mathematics; Artificial intelligence","score_opus":0.012226343343970776,"score_gpt":0.21976700613211772,"score_spread":0.20754066278814695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520821548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023270017,0.00035033852,0.9731521,0.00016652994,0.000053375305,0.0001042394,0.00004913273,0.00037635406,0.0024778752],"genre_scores_gemma":[0.49465612,0.00093648094,0.50033927,0.00016831605,0.000044966477,0.00022282502,0.00017040865,0.000046715882,0.0034149059],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991787,0.000053041986,0.00003784303,0.00016190637,0.00053417974,0.000034244196],"domain_scores_gemma":[0.99964416,0.000058594513,0.00004518477,0.0000539105,0.00017683314,0.000021337057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060662243,0.0005957332,0.0005298407,0.0006063886,0.0003072028,0.0005155612,0.0010137106,0.00079782924,0.0012233339],"category_scores_gemma":[0.0008175841,0.0005263955,0.0005858305,0.0004532168,0.0004628896,0.0019686767,0.00070206035,0.0005342873,0.00028195826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017762311,0.00013582803,0.003262381,0.0006658196,0.00007975953,0.0004465168,0.00038304986,0.061562866,0.7143479,0.033155438,0.0017336565,0.18404908],"study_design_scores_gemma":[0.00004521,0.00049800077,0.002433445,0.000038935304,0.00005559896,0.0010357761,0.000107062755,0.69068646,0.28164613,0.0062925983,0.0170604,0.00010041006],"about_ca_topic_score_codex":0.0011204969,"about_ca_topic_score_gemma":0.00078192394,"teacher_disagreement_score":0.0012233339,"about_ca_system_score_codex":0.00046958265,"about_ca_system_score_gemma":0.0008659666,"threshold_uncertainty_score":0.004092455},"labels":[],"label_agreement":null},{"id":"W2520903308","doi":"10.3390/s16091486","title":"Automated As-Built Model Generation of Subway Tunnels from Mobile LiDAR Data","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ellipse; Ranging; Standard deviation; Curvature; Lidar; Orientation (vector space); Residual; Outlier; Cross section (physics); Geodesy; Geology; Computer science; Geometry; Remote sensing; Mathematics; Artificial intelligence; Algorithm; Statistics; Physics","score_opus":0.04396365722627404,"score_gpt":0.2852862949241122,"score_spread":0.24132263769783818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520903308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04035269,0.00014873549,0.9497401,0.000049411992,0.000030652584,0.00011340544,0.0008148055,0.008181132,0.0005689678],"genre_scores_gemma":[0.3641254,0.00024931677,0.62550604,0.000057789515,0.000026105321,0.00025221572,0.0071440744,0.0009226922,0.0017163978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993617,0.00009270992,0.000034803437,0.00021888096,0.00019816062,0.00009386243],"domain_scores_gemma":[0.9992505,0.00017871722,0.00009764722,0.00020718556,0.0002023232,0.000063691754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067353254,0.0015075031,0.0014375555,0.0014174216,0.00044531754,0.001483269,0.002137303,0.001036138,0.002327427],"category_scores_gemma":[0.0016260195,0.0008634721,0.0023286676,0.0008938633,0.00040506935,0.0012740132,0.0017092444,0.0011076042,0.0017873904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027876804,0.00023973202,0.007512869,0.00032891118,0.00027042342,0.0003506499,0.00039177953,0.52672344,0.029263943,0.0020873637,0.0049954555,0.42755663],"study_design_scores_gemma":[0.000012861182,0.00003334534,0.00093389733,0.000012496074,0.000016129366,0.000063726846,0.000080053964,0.9923981,0.0038588664,0.0010190901,0.0015519466,0.00001945085],"about_ca_topic_score_codex":0.00826311,"about_ca_topic_score_gemma":0.015612047,"teacher_disagreement_score":0.00826311,"about_ca_system_score_codex":0.00044345637,"about_ca_system_score_gemma":0.0015321091,"threshold_uncertainty_score":0.01643002},"labels":[],"label_agreement":null},{"id":"W2521692127","doi":"10.3390/s16091549","title":"Underdetermined DOA Estimation Using MVDR-Weighted LASSO","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Underdetermined system; Algorithm; Singular value decomposition; Lasso (programming language); Orthogonality; Direction of arrival; Mathematics; Noise (video); Computer science; Compressed sensing; Antenna (radio); Artificial intelligence","score_opus":0.023463364070389632,"score_gpt":0.28322431485317,"score_spread":0.2597609507827804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521692127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021051676,0.00011846256,0.9970709,0.00006288555,0.00001993107,0.00001032607,0.000039606308,0.00015369689,0.0004189358],"genre_scores_gemma":[0.15553337,0.00069343677,0.8390199,0.00020047612,0.00020083072,0.00023821255,0.00069772004,0.00020052375,0.003215566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887985,0.00044552234,0.00007523218,0.00026746807,0.00026822905,0.00006365495],"domain_scores_gemma":[0.9986016,0.00068389,0.00030116754,0.0001593674,0.00020752783,0.000046434314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011816066,0.0013901308,0.001674177,0.0006985758,0.00042414403,0.0010010154,0.0009875454,0.0009829632,0.0015030088],"category_scores_gemma":[0.0044447617,0.0005533255,0.0011224824,0.0010990347,0.00056457706,0.001100179,0.0014466406,0.0017875369,0.0007540481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022044257,0.00007841801,0.0011047133,0.00034731222,0.00024919113,0.0002286823,0.00023823345,0.63982666,0.024117593,0.030324796,0.0058484236,0.29741558],"study_design_scores_gemma":[0.000010991171,0.000032065436,0.0001959647,0.000012594363,0.000012679061,0.000089623056,0.000015357984,0.99020994,0.0017616887,0.005636984,0.0020025913,0.000019510531],"about_ca_topic_score_codex":0.001486744,"about_ca_topic_score_gemma":0.0017375139,"teacher_disagreement_score":0.001674177,"about_ca_system_score_codex":0.0002596955,"about_ca_system_score_gemma":0.000921629,"threshold_uncertainty_score":0.0062490106},"labels":[],"label_agreement":null},{"id":"W2529830695","doi":"10.3390/s16101639","title":"Quasi-3D Modeling and Efficient Simulation of Laminar Flows in Microfluidic Devices","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laminar flow; Microfluidics; Solver; Flow (mathematics); Reynolds number; Computation; Mechanics; Fourier series; Channel (broadcasting); Computational fluid dynamics; Computer science; Simulation; Planar; Computational science; Materials science; Algorithm; Physics; Computer graphics (images); Mathematics; Nanotechnology; Turbulence; Mathematical analysis; Telecommunications","score_opus":0.013065513865245204,"score_gpt":0.21693104092697715,"score_spread":0.20386552706173194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529830695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049996972,0.0004602751,0.9379316,0.0003220971,0.00013028794,0.00017032697,0.0007436579,0.0011286316,0.009116098],"genre_scores_gemma":[0.47902945,0.0014901353,0.50984097,0.00022826156,0.000058590336,0.0011070477,0.0008881858,0.00038696828,0.0069704005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998336,0.000036461137,0.000010409147,0.000017928485,0.00007833328,0.00002329146],"domain_scores_gemma":[0.9996884,0.00014271324,0.000037143396,0.000043187065,0.00006397028,0.000024609126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033241312,0.00053818844,0.0004971552,0.00039058787,0.00058955624,0.0010480988,0.00094688387,0.0011996491,0.0023619123],"category_scores_gemma":[0.0007236387,0.00062791206,0.00085377804,0.00039309377,0.00057994877,0.0005301494,0.0006049561,0.00068742456,0.0005840264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024912459,0.00003812215,0.000534565,0.00008923196,0.000015575715,0.0001045231,0.00007728136,0.9671391,0.016380044,0.009097161,0.0006063533,0.005893018],"study_design_scores_gemma":[0.00000626806,0.000009889125,0.00011682364,0.000005111228,0.0000024077417,0.000022729582,0.000005971279,0.9964432,0.0013068581,0.0007403689,0.0013324945,0.000007960796],"about_ca_topic_score_codex":0.006580024,"about_ca_topic_score_gemma":0.004402574,"teacher_disagreement_score":0.006580024,"about_ca_system_score_codex":0.00090600224,"about_ca_system_score_gemma":0.0019180769,"threshold_uncertainty_score":0.013083458},"labels":[],"label_agreement":null},{"id":"W2530703124","doi":"10.3390/s16101697","title":"Selectivity Enhancement in Molecularly Imprinted Polymers for Binding of Bisphenol A","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Carleton University","keywords":"Molecularly imprinted polymer; Ethylene glycol dimethacrylate; Chemistry; Selectivity; Methacrylic acid; Bisphenol A; Combinatorial chemistry; Ethylene glycol; Capillary electrophoresis; Monomer; Chromatography; Polymer; Organic chemistry","score_opus":0.01943231459768242,"score_gpt":0.2859184969998625,"score_spread":0.2664861824021801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530703124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8914736,0.0086176405,0.09262513,0.00048050054,0.00021240942,0.00013917999,0.00023038054,0.00079492695,0.0054261447],"genre_scores_gemma":[0.91029394,0.0045342976,0.0794015,0.0004964756,0.00007924986,0.00014691333,0.0002255028,0.000071783135,0.0047503626],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994479,0.00012240934,0.000023756582,0.00013192307,0.00019912135,0.000074957825],"domain_scores_gemma":[0.9997303,0.00010635476,0.000060173425,0.000017573297,0.00006461289,0.000021042513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039676955,0.0006800201,0.00024400752,0.000375379,0.0001394272,0.00028787108,0.0004230346,0.0006891224,0.0006186497],"category_scores_gemma":[0.00059369544,0.00044095272,0.00038853308,0.00020566082,0.00025952022,0.0004884099,0.00029488036,0.00066243095,0.0006621905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025857136,0.000010580058,0.000041799827,0.00002193659,0.000003969206,0.000017728522,0.000006046582,0.000051555882,0.99758875,0.000034172786,0.000025268782,0.0021724673],"study_design_scores_gemma":[0.0000021444014,0.00004290955,0.00025609284,0.0000014050968,0.0000048904685,0.000045133118,0.0000026463817,0.0007697982,0.9982339,0.0000079676465,0.0006297252,0.00000337484],"about_ca_topic_score_codex":0.00042582184,"about_ca_topic_score_gemma":0.00067658693,"teacher_disagreement_score":0.0006891224,"about_ca_system_score_codex":0.00023996973,"about_ca_system_score_gemma":0.00019711754,"threshold_uncertainty_score":0.0020983815},"labels":[],"label_agreement":null},{"id":"W2536525838","doi":"10.3390/s16111783","title":"Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"Canadian Space Agency","keywords":"Moderate-resolution imaging spectroradiometer; Land cover; Random forest; Remote sensing; Outlier; Computer science; Feature selection; Data mining; Scale (ratio); Time series; Identification (biology); Missing data; Artificial intelligence; Satellite; Machine learning; Land use; Geography; Cartography; Engineering","score_opus":0.023999515642364164,"score_gpt":0.21974266754481067,"score_spread":0.1957431519024465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536525838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05528557,0.0001363061,0.9437049,0.00008994096,0.000017567943,0.000069616544,0.00007070024,0.00037419153,0.00025129053],"genre_scores_gemma":[0.7033496,0.00014499834,0.2947939,0.00005553196,0.000050626117,0.0002899006,0.0004158854,0.00008134601,0.0008182416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925894,0.00041910424,0.00003433917,0.00011264514,0.00010510201,0.000069915084],"domain_scores_gemma":[0.99822193,0.0012628854,0.00015616667,0.000109138324,0.00020584476,0.00004407939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027255605,0.00080244825,0.0009622579,0.000747501,0.00034721868,0.0005645811,0.0011170907,0.00071084715,0.00055549026],"category_scores_gemma":[0.0049272403,0.0004238211,0.0013098951,0.00067743467,0.00053271477,0.0007474013,0.00047334662,0.000798322,0.00016866626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001118877,0.000080710444,0.001826494,0.000042051466,0.000052986554,0.00007402057,0.00007581552,0.93977875,0.0015832687,0.0018727276,0.00048701258,0.054014307],"study_design_scores_gemma":[0.0000019123122,0.0000064348355,0.00012577242,0.0000012558771,0.000002245412,0.0000032237003,0.0000030591086,0.9987656,0.00021472223,0.0008334777,0.000040329916,0.0000021108463],"about_ca_topic_score_codex":0.0048523145,"about_ca_topic_score_gemma":0.003800591,"teacher_disagreement_score":0.0048523145,"about_ca_system_score_codex":0.0005013563,"about_ca_system_score_gemma":0.00065560575,"threshold_uncertainty_score":0.01441431},"labels":[],"label_agreement":null},{"id":"W2537105705","doi":"10.3390/s16101752","title":"Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council","keywords":"Wearable computer; Inertial measurement unit; Foot (prosody); Computer science; Wearable technology; Inertial frame of reference; Embedded system; Human–computer interaction; Simulation; Engineering; Artificial intelligence; Physics","score_opus":0.008072346701900332,"score_gpt":0.2502608991694376,"score_spread":0.2421885524675373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537105705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06498293,0.00069705944,0.918379,0.00030498547,0.0004520468,0.00076054526,0.0003771872,0.0032261612,0.010820115],"genre_scores_gemma":[0.60906875,0.00091642636,0.3694649,0.00040603318,0.00012613805,0.00076914375,0.0005159091,0.000090714704,0.018642003],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999723,0.00003628703,0.0000298095,0.00007319138,0.0001054165,0.000032215958],"domain_scores_gemma":[0.9998367,0.000016460945,0.000020615365,0.000016570646,0.00009289216,0.000016759312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021861754,0.0005531818,0.00038969095,0.0005815782,0.000203582,0.00040887212,0.0011499571,0.0005313192,0.004197085],"category_scores_gemma":[0.00035926557,0.0002314076,0.00025356462,0.00026173776,0.000136233,0.0004435773,0.0004148971,0.00024390285,0.0015228862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039806758,0.00021717,0.00552826,0.000806321,0.000084738975,0.0009934938,0.0003430348,0.003103273,0.6749361,0.0035226515,0.00771307,0.30235383],"study_design_scores_gemma":[0.00026298454,0.005102388,0.02942957,0.0002752099,0.0003305389,0.0052896617,0.0003493848,0.1337974,0.6851994,0.0011344589,0.13862169,0.00020736048],"about_ca_topic_score_codex":0.00048057473,"about_ca_topic_score_gemma":0.0006436166,"teacher_disagreement_score":0.004197085,"about_ca_system_score_codex":0.00014805487,"about_ca_system_score_gemma":0.00033806273,"threshold_uncertainty_score":0.014040649},"labels":[],"label_agreement":null},{"id":"W2544526767","doi":"10.3390/s16111824","title":"Millimetre Level Accuracy GNSS Positioning with the Blind Adaptive Beamforming Method in Interference Environments","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Beamforming; Satellite system; Interference (communication); Adaptive beamformer; Electronic engineering; Precise Point Positioning; Global Positioning System; Engineering; Telecommunications","score_opus":0.02917107027002192,"score_gpt":0.2476203781270185,"score_spread":0.2184493078569966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544526767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019517155,0.00012672698,0.9789007,0.000035500114,0.000021665901,0.000014205308,0.000016759284,0.00036685652,0.00100044],"genre_scores_gemma":[0.3808103,0.00021683153,0.61654186,0.000055416436,0.000017596156,0.000055314795,0.00007946028,0.000064938075,0.0021583536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956983,0.00008079206,0.000019269366,0.000052131396,0.00025284206,0.00002511515],"domain_scores_gemma":[0.9995789,0.00015358969,0.0000758876,0.000075226475,0.00009993956,0.000016401611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045306,0.00050113176,0.00029015116,0.0003594148,0.00024396261,0.00037724734,0.000459859,0.0005707023,0.0006893093],"category_scores_gemma":[0.0016542721,0.00022518048,0.0002650776,0.0005216457,0.00046280117,0.00051769346,0.0005000815,0.0003999536,0.00032501106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038031331,0.00006969828,0.0020797122,0.00018689157,0.000075797165,0.00012498604,0.00024584596,0.5737218,0.13030355,0.007947183,0.0009923469,0.28387177],"study_design_scores_gemma":[0.00001833842,0.000101114325,0.0012359591,0.000011674743,0.000019284815,0.00015522476,0.00002138351,0.94577307,0.048758853,0.0019582587,0.0019106977,0.0000361349],"about_ca_topic_score_codex":0.0028781209,"about_ca_topic_score_gemma":0.0036377665,"teacher_disagreement_score":0.0028781209,"about_ca_system_score_codex":0.00042212903,"about_ca_system_score_gemma":0.0005559516,"threshold_uncertainty_score":0.005722761},"labels":[],"label_agreement":null},{"id":"W2545698454","doi":"10.3390/s16111792","title":"Skeleton-Based Abnormal Gait Detection","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Silhouette; Artificial intelligence; Gait analysis; Computer science; Pattern recognition (psychology); Computer vision; Human skeleton; Feature (linguistics); Skeleton (computer programming); Cluster analysis; Physical medicine and rehabilitation; Medicine","score_opus":0.005784644775524958,"score_gpt":0.18179062251965522,"score_spread":0.17600597774413027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545698454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2459244,0.000855117,0.7400489,0.000111432535,0.00011996446,0.00020691067,0.001688507,0.00798461,0.0030601225],"genre_scores_gemma":[0.74751055,0.00062599924,0.24414556,0.000052854186,0.000049133338,0.000081133745,0.0031964588,0.00021876759,0.0041195787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999446,0.00007216417,0.000050211213,0.00014472398,0.0002355873,0.00005146826],"domain_scores_gemma":[0.999335,0.00008281182,0.00012537956,0.00009703007,0.00030984104,0.000049951374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030541996,0.0007087458,0.0007236387,0.0034160255,0.00017420748,0.00035890046,0.0005135309,0.00045599,0.0019046647],"category_scores_gemma":[0.001334399,0.00026663824,0.0004610975,0.00115781,0.00024851592,0.00051758316,0.00046104647,0.00020944412,0.001278375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061721075,0.00022770472,0.026599118,0.0003284146,0.00015208093,0.00064881146,0.00009633241,0.0287338,0.17281766,0.0014006303,0.006660021,0.7617181],"study_design_scores_gemma":[0.00003061041,0.00029644233,0.037037827,0.00003852587,0.00006326446,0.0027379957,0.000066310444,0.8956024,0.05787109,0.001808103,0.0043882844,0.000059200036],"about_ca_topic_score_codex":0.0027385487,"about_ca_topic_score_gemma":0.0042118845,"teacher_disagreement_score":0.0034160255,"about_ca_system_score_codex":0.0002113414,"about_ca_system_score_gemma":0.00037103493,"threshold_uncertainty_score":0.0063717365},"labels":[],"label_agreement":null},{"id":"W2547544609","doi":"10.3390/s16111835","title":"Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Square (algebra); Filter (signal processing); Sensor fusion; Fusion; Test (biology); Computer science; Mathematics; Artificial intelligence; Biology; Computer vision","score_opus":0.023488309061566348,"score_gpt":0.23692258547492206,"score_spread":0.21343427641335572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547544609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004223637,0.00013529493,0.99507976,0.000059535705,0.000027990896,0.000011271219,0.000012191498,0.0001423997,0.00030786567],"genre_scores_gemma":[0.6777775,0.0005278181,0.31780565,0.0002181088,0.00012255968,0.00017812169,0.00023309156,0.00007769659,0.0030593849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984358,0.00041331074,0.00008032846,0.00036977703,0.0005805316,0.00012026079],"domain_scores_gemma":[0.99871206,0.000624537,0.00015431707,0.00015227753,0.0003151351,0.000041703715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016487961,0.00094001234,0.0010998878,0.0006214393,0.00043758337,0.0009047506,0.0012185647,0.0012822261,0.0009728104],"category_scores_gemma":[0.004546185,0.0004680344,0.0011869831,0.0010092811,0.00076116004,0.0014413409,0.0012737153,0.0015667024,0.00037338713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003569042,0.00009695422,0.0012989726,0.00018554834,0.0002091469,0.00017160122,0.00021844765,0.7926665,0.013694709,0.027563546,0.0011763454,0.16236132],"study_design_scores_gemma":[0.000007008794,0.00004060836,0.00017000812,0.0000038082392,0.00001087033,0.000027704486,0.000004920455,0.9948704,0.0020513048,0.0024157544,0.00038616362,0.000011540756],"about_ca_topic_score_codex":0.0045181024,"about_ca_topic_score_gemma":0.0027823125,"teacher_disagreement_score":0.0045181024,"about_ca_system_score_codex":0.00091244676,"about_ca_system_score_gemma":0.0010310762,"threshold_uncertainty_score":0.008983612},"labels":[],"label_agreement":null},{"id":"W2548883819","doi":"10.3390/s16111841","title":"Optical Microbottle Resonators for Sensing","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Whispering-gallery wave; Resonator; Fabrication; Optical sensing; Whispering gallery; Signature (topology); SPHERES; Optoelectronics; Materials science; Nanotechnology; Optics; Physics; Engineering; Aerospace engineering","score_opus":0.02797500404091358,"score_gpt":0.28828203835098853,"score_spread":0.26030703431007496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548883819","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006133688,0.9907788,0.0024010069,0.00023792988,0.00045566124,0.000017050206,0.000040314193,0.000034017117,0.005421905],"genre_scores_gemma":[0.0069176797,0.98219556,0.003538737,0.00031259918,0.00031531337,0.000038162543,0.00010241589,0.000008668533,0.0065708268],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99969804,0.000031061063,0.00002305785,0.000063672116,0.00015312665,0.000031125113],"domain_scores_gemma":[0.99987376,0.00004212016,0.000021243897,0.000007602803,0.00004294972,0.000012327818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042520065,0.001037728,0.0007501145,0.001969682,0.00026948302,0.000736947,0.0007478704,0.0011448981,0.0040312293],"category_scores_gemma":[0.0004069211,0.00035090462,0.0005031626,0.0016143833,0.00038121906,0.001263294,0.0006564016,0.0014428172,0.0039233696],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038350503,0.00010961276,0.00022573832,0.018708685,0.000067494926,0.0003874202,0.00013734288,0.0005680857,0.07209731,0.017244814,0.028250838,0.8621643],"study_design_scores_gemma":[0.0000073110227,0.00008134468,0.00038830997,0.0010324789,0.000041349675,0.0012778662,0.000049078153,0.0003145818,0.015391028,0.002139601,0.97924876,0.00002840491],"about_ca_topic_score_codex":0.0005569832,"about_ca_topic_score_gemma":0.000982511,"teacher_disagreement_score":0.0040312293,"about_ca_system_score_codex":0.00043566004,"about_ca_system_score_gemma":0.0006092948,"threshold_uncertainty_score":0.013485849},"labels":[],"label_agreement":null},{"id":"W2552083027","doi":"10.3390/s16111926","title":"Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Changsha Science and Technology Project; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Machinability; Machining; Component (thermodynamics); Tool wear; Machine tool; Cutting tool; Mechanical engineering; Numerical control; Field (mathematics); Engineering; Computer science","score_opus":0.2700676315064661,"score_gpt":0.47101407950940205,"score_spread":0.20094644800293593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552083027","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001207058,0.96499246,0.025463738,0.0006026752,0.00060501375,0.00004523656,0.000041819065,0.000053907166,0.006988025],"genre_scores_gemma":[0.01389767,0.95361936,0.025792079,0.00051861146,0.00056158175,0.000110284884,0.000095095005,0.000020849497,0.0053845043],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991731,0.00009976342,0.000067900386,0.00020100901,0.00040887168,0.000049287948],"domain_scores_gemma":[0.99917823,0.0003834034,0.00008862413,0.000055318375,0.00026416074,0.000030188265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011897518,0.0011071728,0.0010127616,0.0028181472,0.0003864808,0.0014336469,0.0015971413,0.0018369019,0.003619501],"category_scores_gemma":[0.0010652285,0.00062637054,0.00088821654,0.002987964,0.0011240082,0.0024879812,0.0009958892,0.002243058,0.002211298],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065013366,0.00017068535,0.000462226,0.017322255,0.00008856374,0.000261827,0.0001944368,0.0021598113,0.026230002,0.037020475,0.0070489417,0.9089757],"study_design_scores_gemma":[0.000013933953,0.00033825892,0.001601782,0.0029314088,0.00011993235,0.0020569828,0.00017036202,0.0032707762,0.022359999,0.015575663,0.95146143,0.00009953139],"about_ca_topic_score_codex":0.0006375475,"about_ca_topic_score_gemma":0.00066046574,"teacher_disagreement_score":0.003619501,"about_ca_system_score_codex":0.0007228468,"about_ca_system_score_gemma":0.0012077643,"threshold_uncertainty_score":0.012108386},"labels":[],"label_agreement":null},{"id":"W2556354280","doi":"10.3390/s16111879","title":"Fast and Inexpensive Detection of Bacterial Viability and Drug Effectiveness through Metabolic Monitoring","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Grand Challenges Canada; Canada Research Chairs; McMaster University","keywords":"Bacteria; Turnaround time; Fluorophore; Drug detection; Microbiological culture; Sample preparation; Chromatography; Chemistry; Computer science; Biology; Fluorescence","score_opus":0.006572264284943967,"score_gpt":0.2093147738895686,"score_spread":0.20274250960462462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556354280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5442969,0.017141396,0.42129487,0.0014839122,0.0005283796,0.0003291903,0.0023798556,0.0029381462,0.009607346],"genre_scores_gemma":[0.73334384,0.012206094,0.244965,0.00066221063,0.000175238,0.00051337207,0.0016538665,0.00020828372,0.0062720077],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99874556,0.00015945543,0.00005638893,0.00029382878,0.00061136595,0.00013330168],"domain_scores_gemma":[0.9992867,0.00026619216,0.0001916261,0.0000577701,0.0001509151,0.000046744943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066698354,0.0007245823,0.00062309665,0.0008748298,0.00023868299,0.000759113,0.0006634627,0.0011426797,0.0014090488],"category_scores_gemma":[0.0011558733,0.000458112,0.00046813546,0.00063136406,0.0005065734,0.0011582562,0.0007635364,0.0014917075,0.00097145303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004574056,0.0000261929,0.00041071058,0.00013753855,0.00000814333,0.000029383235,0.000021529875,0.00023130534,0.9905653,0.00023038467,0.00028103119,0.008012868],"study_design_scores_gemma":[0.000008240347,0.0001576897,0.0019941127,0.00001860309,0.000015432097,0.000145907,0.000036143425,0.005243539,0.98942906,0.00018752224,0.0027352388,0.000028436965],"about_ca_topic_score_codex":0.0005483373,"about_ca_topic_score_gemma":0.00081172166,"teacher_disagreement_score":0.0014090488,"about_ca_system_score_codex":0.00045381577,"about_ca_system_score_gemma":0.00025671942,"threshold_uncertainty_score":0.0047137737},"labels":[],"label_agreement":null},{"id":"W2556487696","doi":"10.3390/s16111937","title":"Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Pseudorange; Computer science; Beamforming; Electronic engineering; Antenna array; Antenna (radio); Narrowband; Global Positioning System; Remote sensing; Engineering; Telecommunications; Geography","score_opus":0.02006456470940539,"score_gpt":0.2335674101734429,"score_spread":0.21350284546403753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556487696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89434695,0.0006836668,0.098198324,0.00011329359,0.00002752664,0.000036299145,0.0002511585,0.0007400687,0.0056026736],"genre_scores_gemma":[0.9929438,0.00017136168,0.005832888,0.00002012925,0.000009737518,0.000014560964,0.00014475241,0.000033525608,0.0008293135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993376,0.00013948562,0.000034611683,0.00010346333,0.00026460027,0.000120152756],"domain_scores_gemma":[0.99766195,0.0012138301,0.00029871808,0.00024823076,0.0005222446,0.000054998887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058268034,0.0006621182,0.00052538526,0.00085121026,0.00026486817,0.0003914146,0.00047299822,0.0009087065,0.0012752624],"category_scores_gemma":[0.0027124933,0.00019028393,0.00038233746,0.0007664319,0.00026346053,0.00044278352,0.00036313402,0.000334801,0.00062568823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021503612,0.00014225299,0.019949233,0.00044168875,0.00028940136,0.0007288442,0.0002617164,0.6162796,0.26891682,0.001959058,0.00052779453,0.08835321],"study_design_scores_gemma":[0.000026308711,0.0010711479,0.036277458,0.000032180236,0.0001284759,0.0010426521,0.000108677516,0.84995836,0.11003397,0.0005021494,0.0007708227,0.000047777285],"about_ca_topic_score_codex":0.00093241385,"about_ca_topic_score_gemma":0.00067031087,"teacher_disagreement_score":0.0012752624,"about_ca_system_score_codex":0.00038911778,"about_ca_system_score_gemma":0.00016266211,"threshold_uncertainty_score":0.0042662024},"labels":[],"label_agreement":null},{"id":"W2558099083","doi":"10.3390/s16122017","title":"MEMS IMU Error Mitigation Using Rotation Modulation Technique","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Inertial measurement unit; GNSS applications; Inertial navigation system; Rotation (mathematics); Units of measurement; Inertial reference unit; Global Positioning System; Computer science; Microelectromechanical systems; Accelerometer; Inertial frame of reference; Engineering; Artificial intelligence; Telecommunications; Physics","score_opus":0.013557286648345704,"score_gpt":0.24128839443855737,"score_spread":0.22773110779021166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558099083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21186195,0.0019158092,0.77703804,0.00027042822,0.00018374542,0.000110569614,0.000058898684,0.001197877,0.0073627885],"genre_scores_gemma":[0.8766152,0.0007083136,0.1199826,0.00005571924,0.00006669254,0.00004599997,0.00006760287,0.000033271474,0.0024246734],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997439,0.000038473998,0.000013442549,0.000038182632,0.00013504027,0.000030907882],"domain_scores_gemma":[0.9997352,0.000039115625,0.00006608399,0.000046335033,0.000107434855,0.0000059559143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021010255,0.0006377064,0.00030290004,0.0004847495,0.00024117298,0.00022034695,0.00033883436,0.00030756183,0.00075414794],"category_scores_gemma":[0.0007007028,0.00013567129,0.00028796852,0.00038641857,0.00018973676,0.00033975093,0.00027607998,0.00020066924,0.00030838276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038335094,0.00006208438,0.004866724,0.00023402552,0.00007675601,0.00021763249,0.00020712217,0.032825164,0.5053822,0.0034588396,0.0013409388,0.45094517],"study_design_scores_gemma":[0.000052522442,0.0011107762,0.014784676,0.00005040515,0.00015972258,0.00080854044,0.00014272057,0.29783255,0.66476715,0.00076919864,0.019458538,0.00006322741],"about_ca_topic_score_codex":0.00087829976,"about_ca_topic_score_gemma":0.0008803013,"teacher_disagreement_score":0.00087829976,"about_ca_system_score_codex":0.0001660749,"about_ca_system_score_gemma":0.0002235229,"threshold_uncertainty_score":0.0025228858},"labels":[],"label_agreement":null},{"id":"W2558114395","doi":"10.3390/s16122027","title":"A Multi-Pumping Flow System for In Situ Measurements of Dissolved Manganese in Aquatic Systems","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Geochemistry and Elemental Analysis","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Absorbance; Manganese; Detection limit; Chemistry; Flow injection analysis; In situ; Dissolved organic carbon; Inductively coupled plasma; Seawater; Analytical Chemistry (journal); Spectrophotometry; Environmental chemistry; Environmental science; Chromatography; Geology; Plasma","score_opus":0.03533889297038045,"score_gpt":0.22861360923898755,"score_spread":0.1932747162686071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558114395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5444534,0.00078581076,0.43580356,0.00029040402,0.00018048954,0.0014239192,0.006538713,0.007690473,0.002833191],"genre_scores_gemma":[0.55329293,0.00072856946,0.43220866,0.00026677834,0.00009235334,0.0034965547,0.0020051613,0.00023097222,0.007677979],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992811,0.0000760706,0.000053077078,0.00023461378,0.00031462772,0.000040509392],"domain_scores_gemma":[0.99966836,0.00008272371,0.000068835056,0.000044518212,0.000097272794,0.000038236558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008419866,0.00085173314,0.00079809356,0.0010356851,0.0007090235,0.00028034326,0.0011898964,0.00057616155,0.0029000537],"category_scores_gemma":[0.00047060344,0.00046294433,0.0002461169,0.0006948109,0.00027133359,0.00060777494,0.0006069723,0.0005372454,0.0006226111],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012850808,0.00006458988,0.002863591,0.000110091474,0.000013779353,0.000021392007,0.000049844843,0.00023087136,0.9736744,0.000112704685,0.00052646216,0.022203797],"study_design_scores_gemma":[0.00014538153,0.0014549728,0.036707338,0.000029720193,0.00013033283,0.00059767795,0.00004159142,0.03274936,0.9073009,0.00023363926,0.02052104,0.00008816297],"about_ca_topic_score_codex":0.0015512169,"about_ca_topic_score_gemma":0.0034117347,"teacher_disagreement_score":0.0029000537,"about_ca_system_score_codex":0.0005420442,"about_ca_system_score_gemma":0.0007116646,"threshold_uncertainty_score":0.009701669},"labels":[],"label_agreement":null},{"id":"W2559732065","doi":"10.3390/s16122023","title":"One-Step Fabrication of Microchannels with Integrated Three Dimensional Features by Hot Intrusion Embossing","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Nanofabrication and Lithography Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems; Itä-Suomen Yliopisto","keywords":"Fabrication; Microfluidics; Thermoplastic; Microchannel; Materials science; Polycarbonate; Template; Polystyrene; Embossing; Nanotechnology; Mechanical engineering; Engineering drawing; Composite material; Polymer; Engineering","score_opus":0.006970313330835337,"score_gpt":0.2016691184351267,"score_spread":0.19469880510429136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559732065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7222863,0.002170716,0.26691836,0.00034661934,0.00021890861,0.00035856577,0.00047711568,0.0018793489,0.005344138],"genre_scores_gemma":[0.72022885,0.0009947249,0.27541342,0.00009403592,0.000033120898,0.00021745103,0.00020640575,0.0000949678,0.0027170146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996499,0.000023554538,0.00003201343,0.00007949983,0.00015581098,0.000059284583],"domain_scores_gemma":[0.99955577,0.00012985365,0.00013379143,0.00008939525,0.000047573863,0.000043648113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034032518,0.0004246182,0.00032026347,0.0002814442,0.00020514397,0.00046426218,0.0006237205,0.00039345122,0.0005999228],"category_scores_gemma":[0.0005684483,0.00029448562,0.00041138567,0.0001444884,0.0005459405,0.00048376678,0.000552938,0.00063804747,0.00032957815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016758137,0.000024263622,0.00012896855,0.00007324709,0.0000054535567,0.000048187983,0.00004904098,0.00041223518,0.99259585,0.000785764,0.00006851239,0.0057917554],"study_design_scores_gemma":[0.0000031793145,0.000038874856,0.00021652938,0.0000018744382,0.000002402201,0.00007398105,0.000004662912,0.001063645,0.9973666,0.000047657504,0.0011719302,0.000008751611],"about_ca_topic_score_codex":0.0002893349,"about_ca_topic_score_gemma":0.00085399277,"teacher_disagreement_score":0.0006237205,"about_ca_system_score_codex":0.0003400479,"about_ca_system_score_gemma":0.0004652787,"threshold_uncertainty_score":0.002467215},"labels":[],"label_agreement":null},{"id":"W2560218054","doi":"10.3390/s16122069","title":"Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Cluster analysis; Fault detection and isolation; Computer science; Sample (material); Data mining; Pattern recognition (psychology); Fault (geology); Set (abstract data type); k-nearest neighbors algorithm; Artificial intelligence","score_opus":0.01679558251196182,"score_gpt":0.23687302691040682,"score_spread":0.220077444398445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560218054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047300067,0.0003801472,0.99371874,0.00006294955,0.000035757323,0.000026443178,0.00002097907,0.00038832368,0.0006366525],"genre_scores_gemma":[0.3940772,0.00077408605,0.6032057,0.000169927,0.00009379536,0.00016825687,0.00018747187,0.00012424658,0.0011992955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740547,0.0005828979,0.00019231833,0.00054281123,0.0011737142,0.00010292115],"domain_scores_gemma":[0.99667835,0.0015514158,0.00034123388,0.00030344064,0.001063179,0.00006245903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015496735,0.00077269395,0.0013587951,0.0015358638,0.00082920317,0.00090215437,0.001604274,0.0013275456,0.00046076355],"category_scores_gemma":[0.008135306,0.0003533195,0.000783739,0.0013618201,0.0011799791,0.0011829023,0.0006644255,0.001101588,0.00032733704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014743858,0.000058493068,0.0027555374,0.0002255979,0.00009560227,0.00021638509,0.00015146779,0.7484855,0.009776046,0.017185671,0.0024261822,0.21847603],"study_design_scores_gemma":[0.0000041431513,0.000019477753,0.00023289076,0.000007642604,0.0000075864714,0.000082308485,0.000010376322,0.98911405,0.003568338,0.0061267912,0.000810559,0.000015848158],"about_ca_topic_score_codex":0.00597468,"about_ca_topic_score_gemma":0.0040214793,"teacher_disagreement_score":0.00597468,"about_ca_system_score_codex":0.0010099828,"about_ca_system_score_gemma":0.0008915616,"threshold_uncertainty_score":0.011879802},"labels":[],"label_agreement":null},{"id":"W2560246550","doi":"10.3390/s16122075","title":"Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Remote sensing; Environmental science; Ocean color; Satellite; Chlorophyll a; Atmospheric correction; Phytoplankton; Reflectivity; Satellite imagery; In situ; Chlorophyll; Oceanography; Meteorology; Geology; Geography; Chemistry; Physics; Nutrient","score_opus":0.018585160991017733,"score_gpt":0.23029483413871402,"score_spread":0.2117096731476963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560246550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964527,0.00013239228,0.0015264096,0.00004951306,0.000008898799,0.000014279901,0.00048097398,0.00010290211,0.0012319435],"genre_scores_gemma":[0.9931283,0.00015247865,0.0051176124,0.000018608556,0.000008297558,0.000010291479,0.0012194582,0.000021441636,0.00032354376],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998741,0.000026880512,0.00001033738,0.00003307905,0.0000314883,0.000024094548],"domain_scores_gemma":[0.999653,0.000069112444,0.000053798445,0.0000609088,0.00013169054,0.000031553474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004477083,0.0005585843,0.00020647582,0.0014008539,0.00030886088,0.0006901027,0.00029068167,0.00037942163,0.0005007653],"category_scores_gemma":[0.00074160204,0.00018700583,0.00042323407,0.0011033908,0.00018998171,0.00048566156,0.0002089062,0.00018117609,0.00034720512],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032303025,0.00027094418,0.81294507,0.00015092197,0.00023060219,0.00045747522,0.00035344655,0.03136586,0.040760092,0.0002631014,0.0015514437,0.11132791],"study_design_scores_gemma":[0.00003323233,0.00012131641,0.8542803,0.00003660845,0.00012636789,0.00015960967,0.00076339906,0.1321283,0.010291185,0.00026120586,0.0017628665,0.000035646284],"about_ca_topic_score_codex":0.02315858,"about_ca_topic_score_gemma":0.02788962,"teacher_disagreement_score":0.02315858,"about_ca_system_score_codex":0.00029010195,"about_ca_system_score_gemma":0.00039634842,"threshold_uncertainty_score":0.04604757},"labels":[],"label_agreement":null},{"id":"W2560330288","doi":"10.3390/s16122061","title":"Integrating Deoxyribozymes into Colorimetric Sensing Platforms","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deoxyribozyme; Biosensor; Biodefense; Aptamer; Nanotechnology; Analyte; Nucleic acid; Naked eye; Chemistry; Biochemical engineering; DNA; Biochemistry; Materials science; Biology; Detection limit; Engineering; Molecular biology","score_opus":0.020583513102977284,"score_gpt":0.3321380887619696,"score_spread":0.3115545756589923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560330288","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012632563,0.9899436,0.0036345366,0.00017644116,0.00032959494,0.00002272425,0.000031914205,0.000045288343,0.0045525935],"genre_scores_gemma":[0.007883363,0.98418325,0.0041386764,0.00026365244,0.00016162156,0.00003368314,0.00008582151,0.000007705674,0.0032421981],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99970335,0.000034530894,0.000029502482,0.000066261746,0.0001351965,0.00003116172],"domain_scores_gemma":[0.999846,0.000063728614,0.000026840666,0.0000064818028,0.00004411705,0.000012924616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051813235,0.001001887,0.0009839245,0.0017783764,0.00014859103,0.0008990891,0.00087554153,0.0011222669,0.0014029244],"category_scores_gemma":[0.0004734895,0.0005342051,0.0004943949,0.0014789829,0.0003811004,0.0011193223,0.00064434326,0.0013271593,0.0017427572],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006208247,0.00011540683,0.00029203374,0.01733933,0.00008330767,0.00060952076,0.00011313377,0.00092980306,0.1077862,0.010656017,0.008067095,0.8539461],"study_design_scores_gemma":[0.000015509035,0.00021294979,0.0006202895,0.0012255858,0.00010013744,0.0019272981,0.000040503157,0.00054484117,0.051201805,0.002048594,0.9420178,0.000044609762],"about_ca_topic_score_codex":0.00034028527,"about_ca_topic_score_gemma":0.00048022377,"teacher_disagreement_score":0.0017783764,"about_ca_system_score_codex":0.00042465483,"about_ca_system_score_gemma":0.00039539477,"threshold_uncertainty_score":0.00469321},"labels":[],"label_agreement":null},{"id":"W2563479632","doi":"10.3390/s16122121","title":"Design and Optimization of a Hybrid-Driven Waist Rehabilitation Robot","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Waist; Rehabilitation; Robot; Computer science; Physical medicine and rehabilitation; Engineering; Human–computer interaction; Simulation; Physical therapy; Artificial intelligence; Medicine","score_opus":0.013479576341746308,"score_gpt":0.25383976461831287,"score_spread":0.24036018827656655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563479632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06412692,0.00021626188,0.92888457,0.00007961583,0.000029494375,0.00014692762,0.00005376329,0.00038081515,0.0060815825],"genre_scores_gemma":[0.7912041,0.0002344868,0.20336437,0.000046790454,0.00001477733,0.00053539977,0.00009333246,0.000040082272,0.0044665267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998241,0.00003109545,0.000010553153,0.00004365292,0.00006781373,0.000022705224],"domain_scores_gemma":[0.9998821,0.000026039685,0.000031978787,0.000011285667,0.000036732275,0.000011905725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003164351,0.00047861057,0.00043940186,0.00032418204,0.00023512774,0.00039811936,0.0005857425,0.00048433171,0.0016558606],"category_scores_gemma":[0.0003160977,0.0003287983,0.00036891183,0.00018123673,0.00027558347,0.00023818696,0.00044524347,0.00021123624,0.0003693441],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017147991,0.000074531075,0.0012433295,0.00030621362,0.00006348886,0.00026091744,0.00010082688,0.87766004,0.056818083,0.0038554426,0.00059947034,0.058846146],"study_design_scores_gemma":[0.000039891158,0.00031750198,0.0009069151,0.000015480387,0.000025016467,0.00008647732,0.000028567467,0.992529,0.0039858725,0.00045190775,0.0015993584,0.000014003262],"about_ca_topic_score_codex":0.0013348293,"about_ca_topic_score_gemma":0.0010873755,"teacher_disagreement_score":0.0016558606,"about_ca_system_score_codex":0.00025028124,"about_ca_system_score_gemma":0.0006359512,"threshold_uncertainty_score":0.0055393577},"labels":[],"label_agreement":null},{"id":"W2564723404","doi":"10.3390/s16122171","title":"Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial intelligence; Grayscale; Computer science; RGB color model; Noise (video); Computer vision; Gesture; Motion (physics); Pattern recognition (psychology); Feature extraction; Feature (linguistics); Gesture recognition; Image (mathematics)","score_opus":0.1352260324214267,"score_gpt":0.2952681514191982,"score_spread":0.16004211899777152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564723404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06539077,0.0007500405,0.9295839,0.00007765585,0.00006826029,0.000085717824,0.00062163436,0.0023423003,0.0010796572],"genre_scores_gemma":[0.7248795,0.0006210421,0.26963615,0.00011086419,0.00006331156,0.00021418129,0.0023369275,0.00009937564,0.0020385182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967957,0.000029397666,0.000024783665,0.0001008187,0.00012689471,0.00003858697],"domain_scores_gemma":[0.9997098,0.00007391402,0.00004120229,0.00007184669,0.000079054735,0.00002412367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030443765,0.00064281566,0.0008879178,0.0010441104,0.00019920192,0.0003822797,0.0008422438,0.0004597551,0.0014503621],"category_scores_gemma":[0.001219662,0.00017892661,0.00054920715,0.0011528202,0.00027721096,0.0006889156,0.0006844576,0.00045630505,0.00066948927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028938704,0.0002060034,0.004048656,0.00017932944,0.00008141631,0.000208885,0.00007437193,0.034743045,0.07494338,0.0011774388,0.0035841034,0.88046396],"study_design_scores_gemma":[0.000016993516,0.00016970416,0.010452976,0.000027740532,0.00004722065,0.00037682662,0.00010443221,0.9386544,0.045258608,0.002123746,0.002733958,0.000033336994],"about_ca_topic_score_codex":0.0037242516,"about_ca_topic_score_gemma":0.006705407,"teacher_disagreement_score":0.0037242516,"about_ca_system_score_codex":0.00030241613,"about_ca_system_score_gemma":0.00047400632,"threshold_uncertainty_score":0.007405162},"labels":[],"label_agreement":null},{"id":"W2565625374","doi":"10.3390/s17010074","title":"CMOS Electrochemical Instrumentation for Biosensor Microsystems: A Review","year":2016,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":192,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Division of Electrical, Communications and Cyber Systems; National Institute for Occupational Safety and Health; National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; National Institutes of Health; National Science Foundation","keywords":"CMOS; Microsystem; Instrumentation (computer programming); Miniaturization; Biosensor; Electronic circuit; Integrated circuit; Computer science; Nanotechnology; Electrical engineering; Electronic engineering; Engineering; Materials science","score_opus":0.029294479659653094,"score_gpt":0.3221756165427109,"score_spread":0.2928811368830578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565625374","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017835606,0.9947463,0.0019077508,0.00037889316,0.00055989885,0.000019435538,0.000027399095,0.000026448672,0.002155567],"genre_scores_gemma":[0.0011695047,0.99334335,0.0025980743,0.00028866707,0.0004619966,0.000027384938,0.00005540209,0.000006863859,0.0020486873],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948716,0.00007034134,0.00006027341,0.000092964685,0.00025472895,0.00003452007],"domain_scores_gemma":[0.9993887,0.00021802478,0.00007570547,0.000022451475,0.00026072783,0.000034528915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086869136,0.0014172015,0.0012446785,0.0022464625,0.00039944,0.0011943164,0.001435662,0.0016103488,0.005139682],"category_scores_gemma":[0.0009710167,0.0006181175,0.00058327714,0.003214589,0.00050347595,0.002233969,0.0008089134,0.0017289262,0.0054732366],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003738974,0.0000828209,0.0001773198,0.015357419,0.00005234879,0.0002146593,0.00006208959,0.00042838164,0.008397438,0.0063175154,0.029602438,0.9392702],"study_design_scores_gemma":[0.000006163997,0.00009293473,0.00030148524,0.0015401919,0.000054036,0.0011583846,0.000036818827,0.00026556975,0.0032119083,0.001961077,0.99134314,0.000028210783],"about_ca_topic_score_codex":0.0008670853,"about_ca_topic_score_gemma":0.001215712,"teacher_disagreement_score":0.005139682,"about_ca_system_score_codex":0.0006527503,"about_ca_system_score_gemma":0.0012530128,"threshold_uncertainty_score":0.017193913},"labels":[],"label_agreement":null},{"id":"W2565995826","doi":"10.3390/s16122112","title":"Application of Template Matching for Improving Classification of Urban Railroad Point Clouds","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Catenary; Computer science; Track (disk drive); Matching (statistics); False positive paradox; Point (geometry); Template matching; Artificial intelligence; Computer vision; Pattern recognition (psychology); Engineering; Mathematics; Statistics; Structural engineering; Geometry; Image (mathematics)","score_opus":0.012059767202225262,"score_gpt":0.23973845556267617,"score_spread":0.2276786883604509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565995826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07341705,0.0004592508,0.9181847,0.000070292444,0.000097904056,0.00020811179,0.0010797898,0.0052519985,0.0012308515],"genre_scores_gemma":[0.42363286,0.0004794169,0.5684277,0.000071773924,0.000049714068,0.00016246142,0.00549827,0.00038890418,0.0012889287],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983107,0.0001533584,0.00012066708,0.0004974072,0.0006907017,0.0002271012],"domain_scores_gemma":[0.99881613,0.00021920797,0.00012537843,0.000306395,0.00048890413,0.000044036613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011490067,0.0010072744,0.0014662148,0.0040635653,0.0004822228,0.0016378876,0.0019400978,0.0011217684,0.0011512723],"category_scores_gemma":[0.002674458,0.00047369424,0.0019895043,0.0038422735,0.00032518708,0.0014137707,0.0012561058,0.0007116799,0.0016220115],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019898723,0.00022913294,0.014389507,0.00024706658,0.0002561348,0.00021249556,0.00014463256,0.09045956,0.056908853,0.0017568216,0.0045347977,0.8306621],"study_design_scores_gemma":[0.000018161036,0.00008845763,0.00928636,0.00002703438,0.00007076786,0.00031463645,0.00015713193,0.9354643,0.048074294,0.0019766141,0.0044867177,0.000035386205],"about_ca_topic_score_codex":0.009159767,"about_ca_topic_score_gemma":0.0079496475,"teacher_disagreement_score":0.009159767,"about_ca_system_score_codex":0.0005754623,"about_ca_system_score_gemma":0.0009819991,"threshold_uncertainty_score":0.018212914},"labels":[],"label_agreement":null},{"id":"W2568988523","doi":"10.3390/s17010108","title":"A Wirelessly Powered Smart Contact Lens with Reconfigurable Wide Range and Tunable Sensitivity Sensor Readout Circuitry","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan; Canadian Institute of Steel Construction","keywords":"Capacitive sensing; Sensitivity (control systems); Contact lens; Chip; Electro-optical sensor; Electrical engineering; Pressure sensor; Interface (matter); Lens (geology); Electronic engineering; Computer hardware; Engineering; Materials science; Computer science; Optics","score_opus":0.016782303627869827,"score_gpt":0.21224895129244975,"score_spread":0.1954666476645799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568988523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7420851,0.0056817974,0.23858693,0.00042883528,0.0004251685,0.00029087954,0.00058978016,0.0029044396,0.009007103],"genre_scores_gemma":[0.91324687,0.00076562766,0.079694636,0.00031020504,0.00010123383,0.00007703124,0.00016475999,0.000057571822,0.0055820197],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997253,0.000021693064,0.000016633063,0.00008238036,0.00012307857,0.00003085418],"domain_scores_gemma":[0.99977595,0.00004258048,0.00006255029,0.000031864947,0.00005769964,0.000029382225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015338781,0.00035297105,0.0002998967,0.00030481897,0.00013362907,0.00045038492,0.0008831873,0.0004241845,0.0012419109],"category_scores_gemma":[0.00030694928,0.00019564577,0.0002486157,0.0002964168,0.00019629416,0.0006469162,0.00037939975,0.00023009961,0.0004719666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008783315,0.000024004488,0.00045250033,0.00010286506,0.000012195967,0.0001592543,0.000031233616,0.00026285255,0.97791094,0.00029845035,0.00025468212,0.02040328],"study_design_scores_gemma":[0.000034424018,0.0005039861,0.0028347988,0.000008883509,0.000043922766,0.0010317372,0.000031315372,0.009750455,0.97624594,0.00012313253,0.009348763,0.000042691503],"about_ca_topic_score_codex":0.00042475725,"about_ca_topic_score_gemma":0.00070665067,"teacher_disagreement_score":0.0012419109,"about_ca_system_score_codex":0.0003484164,"about_ca_system_score_gemma":0.00020750564,"threshold_uncertainty_score":0.0041546226},"labels":[],"label_agreement":null},{"id":"W2569064097","doi":"10.3390/s17010112","title":"Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Iran National Science Foundation; National Science Foundation","keywords":"Trunk; Wearable computer; Motion capture; Computer science; Biomechanics; 3d printed; Motion analysis; Simulation; Kinematics; Artificial intelligence; Motion (physics); Computer vision; Biomedical engineering; Engineering; Medicine; Physics; Embedded system; Anatomy","score_opus":0.04864932601820254,"score_gpt":0.2616491085247978,"score_spread":0.21299978250659524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569064097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123357885,0.00066447933,0.87162906,0.00011971806,0.00014502129,0.00010323502,0.00024718733,0.0012191635,0.0025141868],"genre_scores_gemma":[0.79829395,0.0005276465,0.19842266,0.00010758888,0.00006586743,0.00016083526,0.00048070602,0.000036124675,0.0019046565],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994796,0.000072835755,0.000042776177,0.00014703294,0.00022967022,0.000028130433],"domain_scores_gemma":[0.9997032,0.000056561006,0.00005412841,0.000032635126,0.0001378847,0.000015522106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000400898,0.000616479,0.0004925515,0.0008626201,0.00019763244,0.00033628757,0.00041371898,0.00055453595,0.00083315495],"category_scores_gemma":[0.0007892118,0.00018901881,0.00043411748,0.00081770844,0.00019172448,0.000639903,0.00047941742,0.00030202168,0.00034344944],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055939954,0.00017943325,0.009069401,0.00061945163,0.0002439822,0.00038922968,0.00035906743,0.048566975,0.3029963,0.0015571468,0.0020530103,0.63340664],"study_design_scores_gemma":[0.00007130189,0.0012515978,0.03837396,0.0001013581,0.00029599253,0.0010330494,0.00021349757,0.77955884,0.16782641,0.0026787382,0.008476038,0.00011914292],"about_ca_topic_score_codex":0.0008287999,"about_ca_topic_score_gemma":0.0009624633,"teacher_disagreement_score":0.0008626201,"about_ca_system_score_codex":0.0001947901,"about_ca_system_score_gemma":0.00021544556,"threshold_uncertainty_score":0.0027871728},"labels":[],"label_agreement":null},{"id":"W2571530089","doi":"10.3390/s17010118","title":"Design and Integration for High Performance Robotic Systems Based on Decomposition and Hybridization Approaches","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Decomposition; Computer science; Artificial intelligence; Engineering; Computer architecture; Systems engineering; Chemistry","score_opus":0.030778667101672107,"score_gpt":0.2164218236531759,"score_spread":0.1856431565515038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571530089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013484039,0.00019521912,0.98026526,0.000050914536,0.000024202067,0.00005018387,0.00000681101,0.0003498696,0.0055734874],"genre_scores_gemma":[0.42998952,0.0004086243,0.5647872,0.000090700705,0.000025724134,0.00035014277,0.0000567109,0.00010533795,0.004186081],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996061,0.00007978176,0.000021083244,0.000086566615,0.00015624057,0.000050276954],"domain_scores_gemma":[0.9998172,0.000043204782,0.000039566858,0.00003422499,0.000047876223,0.000017913766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005426037,0.0005741455,0.0004368817,0.00048707996,0.0003743414,0.0006439452,0.000650517,0.00050118595,0.0021286285],"category_scores_gemma":[0.00052567926,0.00037665782,0.0006752492,0.00030547616,0.0005535017,0.00074018247,0.001032806,0.00062966463,0.0007323927],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016035845,0.00014934043,0.00094158866,0.00041352297,0.00010507461,0.00027123236,0.00044133037,0.33729422,0.3410334,0.12270901,0.0011875838,0.19529337],"study_design_scores_gemma":[0.000049597,0.0007071945,0.00074935245,0.00007038971,0.00007698756,0.00033003223,0.00010327817,0.8929514,0.048803844,0.03470939,0.021404421,0.000044051274],"about_ca_topic_score_codex":0.0004018442,"about_ca_topic_score_gemma":0.0003952041,"teacher_disagreement_score":0.0021286285,"about_ca_system_score_codex":0.00044164786,"about_ca_system_score_gemma":0.00047323096,"threshold_uncertainty_score":0.007120967},"labels":[],"label_agreement":null},{"id":"W2573003069","doi":"10.3390/s17010130","title":"Wearable Sensors for Remote Health Monitoring","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1332,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Life expectancy; Health care; Risk analysis (engineering); Wearable technology; Computer science; SAFER; Business; Computer security; Medicine; Embedded system; Environmental health","score_opus":0.27639891504552916,"score_gpt":0.43359654423533045,"score_spread":0.1571976291898013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573003069","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091438316,0.9829281,0.005941525,0.00052793504,0.00095261604,0.0000414769,0.00011394054,0.000092944574,0.008487052],"genre_scores_gemma":[0.011854083,0.97286993,0.005503649,0.0006906174,0.00082480564,0.00006998809,0.00023190082,0.000017633294,0.007937383],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999241,0.000112225796,0.0000691382,0.0001599515,0.00037221506,0.00004549761],"domain_scores_gemma":[0.9993623,0.00026587007,0.00009339505,0.000035940375,0.00021676309,0.000025696845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007706129,0.0010458984,0.00091000006,0.0023739573,0.00022521568,0.0009646666,0.0010366056,0.0015687981,0.006018108],"category_scores_gemma":[0.0012948847,0.00029824892,0.00082697324,0.0028099888,0.0004406111,0.0015204439,0.0007744296,0.0014009211,0.0040514567],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051038834,0.00008556498,0.00048589494,0.010745362,0.00007585721,0.00019321647,0.00007857188,0.0005157712,0.011178435,0.0045457655,0.01985291,0.9521917],"study_design_scores_gemma":[0.00001115387,0.0001992467,0.0024682283,0.0036498562,0.00011850467,0.0022792597,0.0001161779,0.0007303229,0.007285678,0.0037675041,0.9793281,0.000046027555],"about_ca_topic_score_codex":0.00045253994,"about_ca_topic_score_gemma":0.0005304109,"teacher_disagreement_score":0.006018108,"about_ca_system_score_codex":0.00032596028,"about_ca_system_score_gemma":0.0005364272,"threshold_uncertainty_score":0.020132542},"labels":[],"label_agreement":null},{"id":"W2575789609","doi":"10.3390/s17010181","title":"Value-Based Caching in Information-Centric Wireless Body Area Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; King Saud University; Institute of Population and Public Health; Society for Radiological Protection","keywords":"Computer science; Cache; Exploit; Node (physics); Computer network; Wireless; Distributed computing; Real-time computing; Computer security; Operating system","score_opus":0.012360375719023608,"score_gpt":0.2196896327202351,"score_spread":0.2073292570012115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575789609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11338271,0.005663346,0.8750829,0.00048898463,0.00015855224,0.0001398789,0.00008920775,0.00077741226,0.0042169997],"genre_scores_gemma":[0.95354086,0.0009987919,0.044175696,0.00007499545,0.00005306944,0.00005288105,0.000047786987,0.000027929145,0.0010280915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925476,0.0002939021,0.00004878131,0.00009049001,0.00020621895,0.0001058334],"domain_scores_gemma":[0.9976101,0.0011834743,0.00034255136,0.00037173816,0.00040908981,0.000083105304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012206481,0.00047799852,0.0008033656,0.00071364024,0.00068866124,0.0011005544,0.0016844968,0.00073807576,0.00041972112],"category_scores_gemma":[0.0038744705,0.0002970409,0.00032120314,0.0011873902,0.00082373456,0.0018140145,0.00063183333,0.0004330484,0.00012676182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058405765,0.0001927802,0.007948348,0.0005408488,0.00018673766,0.00087054976,0.0006248748,0.6673079,0.028808685,0.091293186,0.0046267956,0.1970153],"study_design_scores_gemma":[0.000020719648,0.00015403652,0.00070254825,0.00003362044,0.000053818534,0.00030663752,0.00006955814,0.97577137,0.0062629594,0.0131142745,0.003480137,0.000030372115],"about_ca_topic_score_codex":0.0038000545,"about_ca_topic_score_gemma":0.0035510513,"teacher_disagreement_score":0.0038000545,"about_ca_system_score_codex":0.001168615,"about_ca_system_score_gemma":0.00083627005,"threshold_uncertainty_score":0.00847888},"labels":[],"label_agreement":null},{"id":"W2576558781","doi":"10.3390/s17010191","title":"Au-Graphene Hybrid Plasmonic Nanostructure Sensor Based on Intensity Shift","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Plasmonic and Surface Plasmon Research","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Figure of merit; Surface plasmon resonance; Refractive index; Materials science; Graphene; Plasmon; Optoelectronics; Nanostructure; Localized surface plasmon; Resonance (particle physics); Surface plasmon; Nanotechnology; Optics; Nanoparticle; Physics","score_opus":0.014426809007222033,"score_gpt":0.2365286987054759,"score_spread":0.22210188969825387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576558781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9062102,0.0029896807,0.082305536,0.00032834167,0.00024399257,0.00010244749,0.0004571583,0.0014642319,0.0058983453],"genre_scores_gemma":[0.935269,0.00078639435,0.059730157,0.00014036139,0.000023534427,0.00004861569,0.0001570103,0.00001632041,0.0038285705],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997696,0.000033582874,0.000008667838,0.000054057822,0.000114398616,0.00001959552],"domain_scores_gemma":[0.9999101,0.000024331017,0.000017387267,0.000009471955,0.000028214246,0.000010395053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000143399,0.00043926784,0.00029696786,0.00038163303,0.00018757123,0.00025994246,0.0007284835,0.00074945507,0.0008375711],"category_scores_gemma":[0.00020559259,0.0001883916,0.00023409475,0.00029220353,0.00021648749,0.0004002074,0.00027332085,0.00026641184,0.00030048343],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039245682,0.000016390424,0.00012266375,0.00005033448,0.000009493634,0.000044139542,0.000011155202,0.00019954299,0.99481213,0.00010521015,0.000109377776,0.0044803377],"study_design_scores_gemma":[0.0000043232885,0.00012212215,0.0008025469,0.000003809174,0.000016510956,0.00019093254,0.000011492435,0.006959777,0.9906935,0.00005835338,0.001122526,0.000014001085],"about_ca_topic_score_codex":0.00062987895,"about_ca_topic_score_gemma":0.0013010715,"teacher_disagreement_score":0.0008375711,"about_ca_system_score_codex":0.00034569963,"about_ca_system_score_gemma":0.00013223804,"threshold_uncertainty_score":0.0028019547},"labels":[],"label_agreement":null},{"id":"W2577035907","doi":"10.3390/s17010195","title":"Correction: Liu, B., et al. Quantitative Evaluation of Pulsed Thermography, Lock-In Thermography and Vibrothermography on Foreign Object Defect (FOD) in CFRP. Sensors 2016, 16, doi:10.3390/s16050743","year":2017,"lang":"en","type":"erratum","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Thermography; Object (grammar); Lock (firearm); Materials science; Artificial intelligence; Engineering; Computer science; Optics; Mechanical engineering; Physics; Infrared","score_opus":0.020288245386773294,"score_gpt":0.2806888921330642,"score_spread":0.2604006467462909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577035907","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017336021,0.0016188233,0.0009273328,0.019276492,0.9743557,0.000025482434,0.0014275169,0.00034195904,0.0018533563],"genre_scores_gemma":[0.028239064,0.02252341,0.013959034,0.06254704,0.46911722,0.00046145872,0.012108152,0.0035019424,0.38754272],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957306,0.00044670844,0.00079771626,0.00053978496,0.0021813782,0.00030390502],"domain_scores_gemma":[0.9697684,0.004274146,0.0014757073,0.0018863373,0.021505475,0.0010899432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003676454,0.0022956184,0.0019116629,0.0046590883,0.00218049,0.0031246794,0.0036780701,0.0052651134,0.0679417],"category_scores_gemma":[0.05472598,0.0012229186,0.0016821952,0.0023673885,0.00231831,0.0022350706,0.0024752861,0.0076879277,0.04083059],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003633896,0.0000071236113,0.00008301222,0.0002926269,0.00001059543,0.00023691256,0.000030353505,0.000045495886,0.00010583134,0.00043149674,0.98712033,0.011599979],"study_design_scores_gemma":[0.000028105913,0.000023303975,0.0008309067,0.00037770523,0.00003193963,0.0009784244,0.00007264164,0.00019134379,0.00059290417,0.0006512058,0.9961915,0.00002997025],"about_ca_topic_score_codex":0.011494,"about_ca_topic_score_gemma":0.01114331,"teacher_disagreement_score":0.0679417,"about_ca_system_score_codex":0.0030888447,"about_ca_system_score_gemma":0.0043286444,"threshold_uncertainty_score":0.22728759},"labels":[],"label_agreement":null},{"id":"W2578927478","doi":"10.3390/s17010134","title":"On Connectivity of Wireless Sensor Networks with Directional Antennas","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; National Key Research and Development Program of China; Fundo para o Desenvolvimento das Ciências e da Tecnologia; King Saud University; National Natural Science Foundation of China","keywords":"Computer science; Omnidirectional antenna; Directional antenna; Antenna (radio); Antenna array; Electronic engineering; Wireless sensor network; Wireless; Computer network; Telecommunications; Engineering","score_opus":0.008246314191990421,"score_gpt":0.21558595918957604,"score_spread":0.20733964499758561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578927478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10628136,0.0029163233,0.873434,0.0015226167,0.0000891593,0.000071523005,0.000269497,0.00016399469,0.015251563],"genre_scores_gemma":[0.9575838,0.005587174,0.032886323,0.0002724532,0.00022352072,0.00018544504,0.0002620182,0.000110089444,0.002889327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990778,0.00051028404,0.0000238714,0.00011683174,0.00018357633,0.00008770687],"domain_scores_gemma":[0.99512476,0.0036983253,0.00054029445,0.00018768413,0.00035337408,0.00009550281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001202681,0.0010510645,0.0006438921,0.0015593502,0.0006054109,0.0007446876,0.0009381554,0.0008404063,0.0017043242],"category_scores_gemma":[0.008315853,0.00036220468,0.0006811812,0.0017365194,0.001408794,0.0030141282,0.0010605742,0.000930332,0.00022368963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003423516,0.000015961237,0.0014474426,0.00006792773,0.000022683067,0.00012543272,0.00008675314,0.9068584,0.0011168465,0.081243984,0.00087641546,0.008103884],"study_design_scores_gemma":[0.000003980537,0.000037721144,0.0004537481,0.000017679444,0.000010121882,0.00007607747,0.00004413381,0.9618573,0.00021574688,0.03647206,0.00080336933,0.000007949827],"about_ca_topic_score_codex":0.0018460882,"about_ca_topic_score_gemma":0.0015840696,"teacher_disagreement_score":0.0018460882,"about_ca_system_score_codex":0.0010539833,"about_ca_system_score_gemma":0.00037003277,"threshold_uncertainty_score":0.007647276},"labels":[],"label_agreement":null},{"id":"W2584946974","doi":"10.3390/s17020280","title":"A Versatile and Reproducible Multi-Frequency Electrical Impedance Tomography System","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Medical Research Council Canada","keywords":"Electrical impedance tomography; Resistor; Electrical impedance; Computer science; Current source; Software; Electrode; Voltage source; Voltage; Bandwidth (computing); Electronic engineering; Computer hardware; Materials science; Biomedical engineering; Electrical engineering; Physics; Engineering; Telecommunications","score_opus":0.011147424619049856,"score_gpt":0.2200477197013598,"score_spread":0.20890029508230995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584946974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042331375,0.00072500913,0.9460048,0.00029757494,0.0001997858,0.00091524486,0.0010721338,0.006006188,0.002447814],"genre_scores_gemma":[0.19088463,0.0010720099,0.7901553,0.0004221399,0.00014350921,0.0027965147,0.0017540634,0.00069847447,0.012073302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982008,0.00020955222,0.00015861639,0.00050024776,0.00083647127,0.000094360206],"domain_scores_gemma":[0.9988575,0.00023217619,0.0001594432,0.00034881986,0.00028696848,0.00011508251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016214981,0.0007531924,0.000852204,0.0010475457,0.00041515075,0.0006885395,0.0013061216,0.0009102833,0.006304726],"category_scores_gemma":[0.0017831662,0.0005847917,0.0004043938,0.00074125367,0.0006092588,0.0009642071,0.0017237089,0.0010202467,0.0029653187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015901114,0.000049557762,0.00075719535,0.00019148842,0.000018477649,0.00017241067,0.00008439937,0.00047514102,0.94725156,0.0009800064,0.0022279746,0.047632802],"study_design_scores_gemma":[0.00014956319,0.001073675,0.01344306,0.00009599972,0.00011056646,0.006088552,0.00010100696,0.016103715,0.85597545,0.00089467195,0.10579682,0.00016679315],"about_ca_topic_score_codex":0.00032713488,"about_ca_topic_score_gemma":0.0007446365,"teacher_disagreement_score":0.006304726,"about_ca_system_score_codex":0.00039531357,"about_ca_system_score_gemma":0.0008702957,"threshold_uncertainty_score":0.021091342},"labels":[],"label_agreement":null},{"id":"W2587098083","doi":"10.3390/s17020316","title":"Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; Qinglan Project of Jiangsu Province of China; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Probabilistic logic; Computer science; Data collection; Real-time computing; Algorithm; Underwater acoustic communication; Latency (audio); Underwater; Traverse; Data mining; Distributed computing; Artificial intelligence; Telecommunications","score_opus":0.06257437095194471,"score_gpt":0.28016344153210093,"score_spread":0.21758907058015622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587098083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011205329,0.00041664048,0.9872844,0.00007976771,0.000026167065,0.00005562559,0.000059962047,0.00030885648,0.00056325505],"genre_scores_gemma":[0.43659523,0.00091466284,0.55999184,0.0000845955,0.000056373774,0.00046501315,0.00048575335,0.00009333066,0.0013131612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921536,0.00015958643,0.00006107811,0.00017534797,0.0003273936,0.00006111377],"domain_scores_gemma":[0.9986884,0.0005707868,0.00020733736,0.00015216242,0.00032197862,0.000059378228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008996512,0.0007614002,0.0010169656,0.0011361863,0.0010928223,0.0007176871,0.0021879913,0.00058291276,0.0007619195],"category_scores_gemma":[0.0034656853,0.0005265895,0.0008343919,0.0018083198,0.0005586583,0.0019577257,0.0015418233,0.000737851,0.00020958594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010341176,0.000050242947,0.0014570376,0.00009755513,0.00005247649,0.000041826657,0.00016029623,0.8693433,0.0024469907,0.008500163,0.0012928387,0.11645389],"study_design_scores_gemma":[0.000006357723,0.000024078725,0.00014259298,0.0000035126086,0.00000795303,0.000025384006,0.00002660112,0.9958878,0.0007520247,0.0024812147,0.00063508307,0.0000072712382],"about_ca_topic_score_codex":0.008464936,"about_ca_topic_score_gemma":0.008462683,"teacher_disagreement_score":0.008464936,"about_ca_system_score_codex":0.0010750265,"about_ca_system_score_gemma":0.0015853908,"threshold_uncertainty_score":0.016831338},"labels":[],"label_agreement":null},{"id":"W2592175580","doi":"10.3390/s17030528","title":"Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Geographic information system; Real estate; Principal component analysis; Environmental quality; Urban morphology; Population; Geography; Census; Regression analysis; Land use; Computer science; Urban planning; Environmental resource management; Cartography; Environmental science; Civil engineering; Engineering; Machine learning; Business; Artificial intelligence","score_opus":0.022701362497860495,"score_gpt":0.29525948616330266,"score_spread":0.2725581236654422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592175580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15891224,0.00024188262,0.8346766,0.00009621086,0.00003365827,0.00008230191,0.00056119595,0.002460602,0.0029352843],"genre_scores_gemma":[0.7126486,0.00022648438,0.28516725,0.000026715601,0.000013870158,0.000058889596,0.00081609876,0.00023593045,0.00080610334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978382,0.0008765642,0.00018818997,0.0004564553,0.0005460331,0.00009459394],"domain_scores_gemma":[0.9956988,0.0018578582,0.00047936226,0.00062623975,0.0013084935,0.000029236684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036522506,0.0009054306,0.0007279127,0.0030212123,0.0003044719,0.0011417842,0.0007984682,0.00035911438,0.0012054289],"category_scores_gemma":[0.015773771,0.00032412144,0.00073920225,0.0035116153,0.0002314941,0.0011671886,0.000886078,0.0005406402,0.00087692623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002034221,0.00016144043,0.11133401,0.00035258033,0.00057044445,0.00017425866,0.00073581043,0.24751914,0.025682833,0.003795682,0.0018543877,0.60761595],"study_design_scores_gemma":[0.000014775826,0.00005087715,0.05561838,0.00004503685,0.000107707856,0.00008900406,0.0003770382,0.928864,0.009308253,0.0019805029,0.0034817816,0.00006265438],"about_ca_topic_score_codex":0.021024702,"about_ca_topic_score_gemma":0.022374755,"teacher_disagreement_score":0.021024702,"about_ca_system_score_codex":0.0003289629,"about_ca_system_score_gemma":0.0005380469,"threshold_uncertainty_score":0.04180467},"labels":[],"label_agreement":null},{"id":"W2592878160","doi":"10.3390/s17030478","title":"Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"China Scholarship Council","keywords":"Gait; Discriminative model; Convolutional neural network; Computer science; Artificial intelligence; Wearable computer; Computer vision; Pattern recognition (psychology); Inertial measurement unit; Gait analysis; Identification (biology); Physical medicine and rehabilitation; Embedded system","score_opus":0.02455750411681139,"score_gpt":0.24973541557368903,"score_spread":0.22517791145687763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592878160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36624104,0.0010336128,0.6226672,0.0001898628,0.00026433222,0.00023845764,0.0017590338,0.0037438583,0.003862609],"genre_scores_gemma":[0.84380674,0.00065682887,0.14751616,0.00012261447,0.000055134904,0.00012242307,0.003299518,0.00004785021,0.0043727253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985206,0.000013194766,0.000010114463,0.00005314915,0.000046232217,0.00002520453],"domain_scores_gemma":[0.99991024,0.000011494661,0.00001855577,0.000016211956,0.00003263429,0.000010842308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017840973,0.000675838,0.0004990039,0.00083553087,0.0001110232,0.00024949334,0.0005239487,0.00030683723,0.00096033595],"category_scores_gemma":[0.00039962068,0.00021750916,0.0003428148,0.0005827656,0.00013824816,0.00035766044,0.00037498234,0.00027747857,0.00042914093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005572153,0.00049585925,0.010208734,0.0001573861,0.0001787358,0.0003258591,0.00003852232,0.05130483,0.0906282,0.00077890215,0.0056322655,0.8396936],"study_design_scores_gemma":[0.000021022792,0.00019070256,0.01548096,0.000019694806,0.000053337993,0.0003149919,0.00002146165,0.9578426,0.02399432,0.00055102655,0.0014868787,0.000022976843],"about_ca_topic_score_codex":0.0047442717,"about_ca_topic_score_gemma":0.009833268,"teacher_disagreement_score":0.0047442717,"about_ca_system_score_codex":0.00031731723,"about_ca_system_score_gemma":0.0002726179,"threshold_uncertainty_score":0.0094332695},"labels":[],"label_agreement":null},{"id":"W2593796416","doi":"10.3390/s17030529","title":"A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wearable computer; Activity recognition; Accelerometer; Computer science; Artificial intelligence; Machine learning; Support vector machine; Principal component analysis; Feature (linguistics); Motion (physics); Wearable technology; Dimension (graph theory); Pattern recognition (psychology); Human–computer interaction; Data mining","score_opus":0.08897369318164437,"score_gpt":0.3240306732050064,"score_spread":0.235056980023362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593796416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23183283,0.14264585,0.59512246,0.0038410488,0.0011239726,0.0005193923,0.0053272094,0.0013306058,0.018256664],"genre_scores_gemma":[0.7383709,0.0962373,0.14502195,0.00096040725,0.0013291009,0.00043015036,0.010736774,0.00016254254,0.006750813],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984688,0.00037104488,0.00013635037,0.00033232936,0.0006134635,0.00007792279],"domain_scores_gemma":[0.9970145,0.0014165296,0.0002141192,0.00035641278,0.00092941045,0.000068978625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013316256,0.0010278504,0.0007637743,0.0019880654,0.00034067352,0.0009420836,0.00046532726,0.00076321844,0.0010974178],"category_scores_gemma":[0.004414701,0.00035507523,0.0009597689,0.0028472915,0.00035892802,0.0013661211,0.00046142327,0.00081421336,0.0006019888],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027524985,0.0002405633,0.015315195,0.0015605587,0.00033181402,0.00025951033,0.00016162213,0.022534896,0.021827126,0.0055735284,0.008428435,0.9234915],"study_design_scores_gemma":[0.000046532045,0.0028665266,0.3162208,0.002603644,0.0011493821,0.0041196146,0.00080415694,0.3632089,0.08945254,0.01992451,0.19922346,0.00037998796],"about_ca_topic_score_codex":0.0014041275,"about_ca_topic_score_gemma":0.0014869269,"teacher_disagreement_score":0.0019880654,"about_ca_system_score_codex":0.00030033218,"about_ca_system_score_gemma":0.00045823885,"threshold_uncertainty_score":0.007042408},"labels":[],"label_agreement":null},{"id":"W2594248285","doi":"10.3390/s17030500","title":"PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Scalability; Computer science; Data aggregator; Scheme (mathematics); Computer security; Information privacy; Variance (accounting); Risk analysis (engineering); Business; Wireless sensor network; Computer network; Database","score_opus":0.13392171216356843,"score_gpt":0.33370271966841547,"score_spread":0.19978100750484704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594248285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016685074,0.00058254314,0.9789507,0.0003741139,0.00013655459,0.0001435358,0.000363346,0.001412704,0.0013513458],"genre_scores_gemma":[0.78274256,0.0006488658,0.21131867,0.0004070348,0.00023512336,0.0002684152,0.0009724451,0.000076536155,0.0033303676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679774,0.0009533772,0.00029513412,0.0006268365,0.0009670497,0.0003599125],"domain_scores_gemma":[0.9976369,0.000629859,0.00029654914,0.0009146639,0.00040558635,0.000116478375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026250402,0.00076010835,0.0013529932,0.0009388323,0.00096254906,0.0014606433,0.0023743482,0.0010617505,0.0013549699],"category_scores_gemma":[0.005389772,0.00042524017,0.0010676704,0.001964825,0.0009310109,0.003482481,0.003960575,0.0015136249,0.0006093116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016114911,0.00028792978,0.00435002,0.0005650633,0.00035986316,0.0006815632,0.000644577,0.35884506,0.0397319,0.09209133,0.022830721,0.47800034],"study_design_scores_gemma":[0.000057129317,0.00029141852,0.00076854014,0.000016769305,0.00004894887,0.0004151706,0.000085213316,0.9508401,0.0076363063,0.02713722,0.012655723,0.00004740086],"about_ca_topic_score_codex":0.0014483946,"about_ca_topic_score_gemma":0.0009715817,"teacher_disagreement_score":0.0026250402,"about_ca_system_score_codex":0.0008509435,"about_ca_system_score_gemma":0.0015272778,"threshold_uncertainty_score":0.013882697},"labels":[],"label_agreement":null},{"id":"W2595932228","doi":"10.3390/s17030583","title":"Localization and Tracking of Implantable Biomedical Sensors","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wearable computer; Computer science; Wireless sensor network; Tracking (education); Tracking system; Real-time computing; Artificial intelligence; Embedded system; Kalman filter","score_opus":0.04557819989876268,"score_gpt":0.3036044198111994,"score_spread":0.25802621991243674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595932228","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068333076,0.98587275,0.008414817,0.0002211353,0.00039029703,0.000023985827,0.000030095698,0.000042616713,0.0043209423],"genre_scores_gemma":[0.0070157037,0.9832345,0.005179653,0.00019291448,0.00032649536,0.00003730645,0.000066594344,0.00000751421,0.00393927],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958915,0.00005150529,0.000045718625,0.00009388782,0.00019261958,0.000027123786],"domain_scores_gemma":[0.9995901,0.00016188886,0.00006208204,0.000018954552,0.00015187224,0.00001501091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054218806,0.00092937826,0.0008520449,0.0025901096,0.00022611355,0.00087290094,0.0011600477,0.0011977649,0.0017049124],"category_scores_gemma":[0.00084633625,0.00037217513,0.0005506465,0.0021297988,0.00038629497,0.0014938249,0.0005297003,0.0007512791,0.001417998],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042724416,0.000056906785,0.0003225659,0.013992501,0.000055662807,0.00025832158,0.000082203595,0.0018354194,0.010743705,0.008170515,0.0098144645,0.954625],"study_design_scores_gemma":[0.000008322305,0.00020586522,0.0013434511,0.0038650623,0.00013374064,0.002582892,0.0001278251,0.0029947916,0.013394271,0.0042021726,0.97107637,0.00006529684],"about_ca_topic_score_codex":0.0006730202,"about_ca_topic_score_gemma":0.00061636034,"teacher_disagreement_score":0.0025901096,"about_ca_system_score_codex":0.00042785835,"about_ca_system_score_gemma":0.0006025511,"threshold_uncertainty_score":0.0057034492},"labels":[],"label_agreement":null},{"id":"W2596854549","doi":"10.3390/s17040908","title":"Enhancing the Responsivity of Uncooled Infrared Detectors Using Plasmonics for High-Performance Infrared Spectroscopy","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Plasmonic and Surface Plasmon Research","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Asylum, Migration and Integration Fund; Korea Institute of Science and Technology; University of Calgary","keywords":"Responsivity; Materials science; Plasmon; Optoelectronics; Infrared; Spectroscopy; Detector; Infrared spectroscopy; Infrared detector; Optics; Photodetector; Chemistry; Physics","score_opus":0.021770199634180077,"score_gpt":0.26815781477647094,"score_spread":0.24638761514229085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596854549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96750224,0.0009001686,0.02974481,0.000085221014,0.000048779093,0.00002960326,0.00006815191,0.00027418922,0.0013467646],"genre_scores_gemma":[0.96835726,0.00036603803,0.029505944,0.000053873344,0.0000113675505,0.000024315385,0.000073127354,0.00003765681,0.0015705304],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997335,0.00003007919,0.000010197113,0.000049112834,0.00014086512,0.000036184123],"domain_scores_gemma":[0.999777,0.00007271658,0.00004907981,0.000027069107,0.000055828066,0.000018295363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003551216,0.00040577742,0.0003038319,0.000201246,0.00012767977,0.00042486333,0.0006237833,0.0005080693,0.00033527406],"category_scores_gemma":[0.0004498893,0.00034275305,0.00026316906,0.0001441955,0.00029974006,0.00038418183,0.00037284842,0.00038120215,0.00023948468],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000108135355,0.000008706629,0.00008593306,0.000014219736,0.0000025354823,0.000016541859,0.000008037317,0.00018457219,0.9988939,0.00006385108,0.000011691193,0.0006992351],"study_design_scores_gemma":[0.0000024616857,0.000041744723,0.00027289233,9.855861e-7,0.0000036793504,0.000023815204,0.000005481876,0.003809855,0.9955479,0.000011506214,0.00027549468,0.000004175999],"about_ca_topic_score_codex":0.0004891586,"about_ca_topic_score_gemma":0.0011032153,"teacher_disagreement_score":0.0006237833,"about_ca_system_score_codex":0.00042610854,"about_ca_system_score_gemma":0.00021144701,"threshold_uncertainty_score":0.003091693},"labels":[],"label_agreement":null},{"id":"W2598497527","doi":"10.3390/s17030631","title":"On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Popularity; Computer science; Social media; Context (archaeology); Semantics (computer science); Information retrieval; Data science; World Wide Web; Internet privacy; Psychology","score_opus":0.06589116386321922,"score_gpt":0.3298113893779653,"score_spread":0.26392022551474603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598497527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90459937,0.0026602247,0.083740264,0.00067952496,0.00013985462,0.00020490435,0.003555082,0.0008205066,0.003600274],"genre_scores_gemma":[0.97819215,0.00050423265,0.016956946,0.00008278674,0.00010322508,0.000056617937,0.0029350754,0.000033285494,0.0011357894],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990827,0.00022218248,0.00006593414,0.0002644889,0.00021769089,0.000146915],"domain_scores_gemma":[0.9946445,0.0035247486,0.0006859551,0.00023502277,0.0007281531,0.0001816661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011732264,0.0012052526,0.0008169525,0.0041455682,0.00046895636,0.0007498576,0.00069547765,0.0009512156,0.00086929946],"category_scores_gemma":[0.0065088095,0.00029515458,0.00069411396,0.002357965,0.0005184191,0.0015483659,0.00056156947,0.00063612027,0.0007589694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015178431,0.0010947732,0.4563396,0.00074834033,0.00049958157,0.001284361,0.00050332915,0.2322382,0.015615893,0.0028212646,0.01062663,0.27671024],"study_design_scores_gemma":[0.00001535102,0.0001372068,0.037569214,0.00002426373,0.00003293906,0.00027998706,0.0001703276,0.95862216,0.0015335926,0.0008521352,0.0007300637,0.000032708638],"about_ca_topic_score_codex":0.03377871,"about_ca_topic_score_gemma":0.050645,"teacher_disagreement_score":0.03377871,"about_ca_system_score_codex":0.0007592928,"about_ca_system_score_gemma":0.0005461429,"threshold_uncertainty_score":0.06716418},"labels":[],"label_agreement":null},{"id":"W2600656202","doi":"10.3390/s17040698","title":"Improving Observability of an Inertial System by Rotary Motions of an IMU","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Observability; Inertial measurement unit; Accelerometer; Inertial navigation system; Azimuth; Control theory (sociology); Observable; Inertial frame of reference; Rotation (mathematics); Computer science; Inertial reference unit; Unobservable; Gyroscope; Engineering; Physics; Aerospace engineering; Mathematics; Artificial intelligence; Classical mechanics; Optics","score_opus":0.009681718864630049,"score_gpt":0.22311825640616498,"score_spread":0.21343653754153494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600656202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1363025,0.00023632664,0.86197615,0.00008182807,0.000017834149,0.000015493113,0.000015905798,0.00029264466,0.0010612647],"genre_scores_gemma":[0.98484147,0.0000955849,0.014788632,0.000011752732,0.0000096578215,0.000010391857,0.000015261136,0.0000081349035,0.00021917485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994474,0.00012775775,0.000037272177,0.00011722178,0.00021066183,0.00005970642],"domain_scores_gemma":[0.9989759,0.00041575747,0.00023752487,0.00019383682,0.00015676334,0.000020317111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006992442,0.0005795375,0.00040769394,0.00026588325,0.00025075296,0.00037169803,0.000320236,0.00025324064,0.00036151838],"category_scores_gemma":[0.00298287,0.00021357351,0.00031946602,0.00017169888,0.0005864305,0.0006731087,0.0007039748,0.00042033213,0.00006659264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048396873,0.000072708215,0.010779521,0.00031246507,0.00012015256,0.00042594448,0.0005921981,0.6040119,0.18366665,0.017445687,0.00039792762,0.18169087],"study_design_scores_gemma":[0.000013141122,0.00022152478,0.005070431,0.000012604883,0.00003710736,0.00008152248,0.00003656259,0.97115326,0.020600198,0.0019131913,0.000843604,0.000016933356],"about_ca_topic_score_codex":0.0024572099,"about_ca_topic_score_gemma":0.0018459967,"teacher_disagreement_score":0.0024572099,"about_ca_system_score_codex":0.00025910808,"about_ca_system_score_gemma":0.00043961153,"threshold_uncertainty_score":0.0048857927},"labels":[],"label_agreement":null},{"id":"W2602540228","doi":"10.3390/s17030621","title":"Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Point cloud; 3D city models; Building model; Lidar; Regularization (linguistics); Model building; Data mining; Artificial intelligence; Computer vision; Simulation; Remote sensing; Geography; Visualization","score_opus":0.06741739782608328,"score_gpt":0.31054053313899266,"score_spread":0.24312313531290938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602540228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025195878,0.00006376219,0.9738574,0.00005799405,0.0000072086536,0.000026814374,0.00009628227,0.0003941576,0.00030052234],"genre_scores_gemma":[0.4050917,0.00021736773,0.59169495,0.00009572954,0.000035199606,0.00018888965,0.0014241635,0.0002418575,0.0010100603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994898,0.000120917495,0.00002624623,0.00010836782,0.00020773838,0.00004695212],"domain_scores_gemma":[0.99916494,0.0003823411,0.00012663746,0.00015764766,0.00013318658,0.000035264555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009077737,0.00070294505,0.00059126556,0.0009919056,0.00032030744,0.00075511844,0.001358997,0.0009372543,0.00051125715],"category_scores_gemma":[0.002584968,0.0007774949,0.0011290067,0.0008170545,0.0007880459,0.00094687665,0.001189727,0.0013067413,0.00034460885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068978035,0.00005496855,0.0012542675,0.00010879966,0.000043750086,0.00015719862,0.000119174154,0.8998039,0.018396193,0.0049343253,0.00084229995,0.07421617],"study_design_scores_gemma":[0.0000014279653,0.0000050906133,0.00008229117,0.0000018110318,0.0000017152947,0.000010778689,0.0000054578277,0.99839634,0.0007633521,0.00058219157,0.00014635932,0.000003192434],"about_ca_topic_score_codex":0.0066291955,"about_ca_topic_score_gemma":0.0069943042,"teacher_disagreement_score":0.0066291955,"about_ca_system_score_codex":0.0005829492,"about_ca_system_score_gemma":0.0009879217,"threshold_uncertainty_score":0.01318121},"labels":[],"label_agreement":null},{"id":"W2602721261","doi":"10.3390/s17040684","title":"Rapid and Low-Cost CRP Measurement by Integrating a Paper-Based Microfluidic Immunoassay with Smartphone (CRP-Chip)","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seven Oaks General Hospital; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Immunoassay; Microfluidics; Chip; Lab-on-a-chip; Microfluidic chip; Embedded system; Computer science; Computer hardware; Nanotechnology; Medicine; Materials science; Telecommunications; Immunology; Antibody","score_opus":0.011682649960053767,"score_gpt":0.19372104397423826,"score_spread":0.18203839401418448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602721261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29563874,0.017444987,0.6710817,0.0010921985,0.0016630231,0.00093563437,0.0012759161,0.0043813842,0.0064863386],"genre_scores_gemma":[0.41828454,0.005779438,0.5647589,0.0010651014,0.00032106138,0.0007822477,0.00072934746,0.00007481253,0.008204659],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889153,0.00015656426,0.00008375887,0.00025327312,0.0005462957,0.00006857623],"domain_scores_gemma":[0.999524,0.00016446234,0.000092556045,0.00004716214,0.00014112882,0.000030758663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006412969,0.00095318846,0.00049086555,0.0008095017,0.00015993907,0.0005385526,0.0007408339,0.0009475739,0.0010062965],"category_scores_gemma":[0.0010807336,0.00040188694,0.00040397994,0.0003625885,0.0002738895,0.00039451863,0.00083968876,0.00039257432,0.0008821625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070624425,0.00008306748,0.0011375281,0.00028156652,0.000029800864,0.00015521415,0.000022007163,0.00042246585,0.9533004,0.00030994808,0.0007827356,0.043404758],"study_design_scores_gemma":[0.000027569758,0.00062239036,0.004861361,0.000044483433,0.000055013115,0.0008551052,0.000024181234,0.012098847,0.9668465,0.0001923715,0.01431091,0.00006137636],"about_ca_topic_score_codex":0.0005252085,"about_ca_topic_score_gemma":0.00084566115,"teacher_disagreement_score":0.0010062965,"about_ca_system_score_codex":0.00031982196,"about_ca_system_score_gemma":0.00033258335,"threshold_uncertainty_score":0.003391564},"labels":[],"label_agreement":null},{"id":"W2606404677","doi":"10.3390/s17040877","title":"Out-of-Plane Continuous Electrostatic Micro-Power Generators","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Electret; Wideband; Electrical engineering; Acceleration; Bandwidth (computing); Power (physics); Engineering; Generator (circuit theory); Amplitude; Topology (electrical circuits); Capacitance; Acoustics; Physics; Electronic engineering; Telecommunications; Optics; Classical mechanics","score_opus":0.01275870630912511,"score_gpt":0.23306537923961917,"score_spread":0.22030667293049405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606404677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5070776,0.0011363479,0.46401015,0.00040917518,0.00028263798,0.00013719675,0.00032990085,0.0010876592,0.02552926],"genre_scores_gemma":[0.93114966,0.0002706433,0.060572594,0.000063019266,0.000059254387,0.000037408536,0.00010587129,0.000037663445,0.007703869],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992585,0.000006563713,0.0000036074525,0.000017431903,0.000039265426,0.0000073464407],"domain_scores_gemma":[0.9998969,0.000029297467,0.000019499577,0.000017174116,0.000026457357,0.000010643819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007009353,0.00019040098,0.00014457438,0.00014537506,0.0000965005,0.00023738413,0.00035608676,0.00021178497,0.0018509526],"category_scores_gemma":[0.00012956349,0.00010456632,0.00009710568,0.00015113587,0.00016119498,0.00036209784,0.00023014218,0.000215463,0.00048996985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014727144,0.0000579503,0.0009364646,0.00019297028,0.000022904436,0.00043451285,0.000101067024,0.0038339286,0.8655248,0.00607305,0.0015920104,0.12108308],"study_design_scores_gemma":[0.0000490232,0.0006522534,0.0041105244,0.00001849362,0.000024159019,0.0016397345,0.000053986856,0.049087882,0.89301956,0.0023556869,0.048958052,0.000030602634],"about_ca_topic_score_codex":0.00005480407,"about_ca_topic_score_gemma":0.00013867712,"teacher_disagreement_score":0.0018509526,"about_ca_system_score_codex":0.00008769144,"about_ca_system_score_gemma":0.00007427479,"threshold_uncertainty_score":0.006192088},"labels":[],"label_agreement":null},{"id":"W2606663270","doi":"10.3390/s17040810","title":"Miniaturized FDDA and CMOS Based Potentiostat for Bio-Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Potentiostat; CMOS; Resistor; Electrical engineering; Amplifier; Electronic engineering; Capacitor; Differential amplifier; Buffer amplifier; Engineering; Materials science; Voltage; Chemistry; Electrode","score_opus":0.014260432420028352,"score_gpt":0.23273443475491173,"score_spread":0.21847400233488337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606663270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12436448,0.009954975,0.8483271,0.00087687746,0.001270383,0.0004478545,0.001201192,0.004046341,0.009510802],"genre_scores_gemma":[0.42419115,0.0030666143,0.56009805,0.0005901295,0.00023745891,0.00042759726,0.00063895166,0.0001004515,0.010649516],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995722,0.000038200877,0.000026573258,0.00013477779,0.00019396012,0.000034272318],"domain_scores_gemma":[0.9998061,0.000053056265,0.000027896496,0.000033974633,0.000063776235,0.000015280451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003025459,0.00053946394,0.0004686018,0.0003173387,0.00019697887,0.000502427,0.0013700089,0.00073086924,0.0024519442],"category_scores_gemma":[0.00049755344,0.0003782333,0.0003018337,0.00029300022,0.00026536206,0.0006320031,0.00049197057,0.00061704364,0.0011582524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068894995,0.00001793429,0.0001795802,0.00024351577,0.000011112029,0.00012088244,0.00003415061,0.00034294734,0.9718868,0.0010084293,0.0006765101,0.025409086],"study_design_scores_gemma":[0.000027872635,0.00048264212,0.0018508994,0.000033695636,0.000048191476,0.001776549,0.000030719224,0.010534584,0.9327795,0.00052136515,0.051869124,0.000044901535],"about_ca_topic_score_codex":0.00022678936,"about_ca_topic_score_gemma":0.00046623746,"teacher_disagreement_score":0.0024519442,"about_ca_system_score_codex":0.00040876886,"about_ca_system_score_gemma":0.00028960477,"threshold_uncertainty_score":0.008202612},"labels":[],"label_agreement":null},{"id":"W2607060375","doi":"10.3390/s17040732","title":"Bench-Top Fabrication of an All-PDMS Microfluidic Electrochemical Cell Sensor Integrating Micro/Nanostructured Electrodes","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Microfabrication; Fabrication; Nanotechnology; Materials science; Microfluidics; Electrode; Lab-on-a-chip; PDMS stamp; Chemistry","score_opus":0.01806708133704149,"score_gpt":0.2673396930574345,"score_spread":0.249272611720393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607060375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6306182,0.0043893964,0.33277723,0.00090224727,0.001520851,0.0016802192,0.008820914,0.004910081,0.0143808415],"genre_scores_gemma":[0.49491972,0.0030742951,0.4917726,0.00038103625,0.0001427558,0.0015061124,0.0022311243,0.00014907598,0.005823323],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996222,0.000017176113,0.000030433675,0.00011869764,0.00017148869,0.000040004274],"domain_scores_gemma":[0.999816,0.000051967694,0.00002971565,0.00004004356,0.00003776666,0.000024557074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035588688,0.0007918087,0.00059618644,0.00035840127,0.0003854921,0.00041349998,0.0012809333,0.00068372063,0.002041449],"category_scores_gemma":[0.00030375805,0.00055262825,0.000441926,0.00027127797,0.00037984634,0.0004741799,0.00037441403,0.00055248314,0.0012123379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022071568,0.000031920088,0.00010925902,0.00013764808,0.0000075117655,0.00008433009,0.000022173279,0.00032948278,0.99456066,0.00031073572,0.00030441472,0.0040799202],"study_design_scores_gemma":[0.000010145127,0.000094477204,0.0005173072,0.0000052141017,0.00000808641,0.00010440434,0.000008706766,0.002790321,0.9908496,0.00007664106,0.005518422,0.000016687243],"about_ca_topic_score_codex":0.00060235645,"about_ca_topic_score_gemma":0.0014067132,"teacher_disagreement_score":0.002041449,"about_ca_system_score_codex":0.00046975724,"about_ca_system_score_gemma":0.000871587,"threshold_uncertainty_score":0.006829381},"labels":[],"label_agreement":null},{"id":"W2608675201","doi":"10.3390/s17050958","title":"Multispectral LiDAR Data for Land Cover Classification of Urban Areas","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Lidar; Remote sensing; Land cover; Ranging; Terrain; Contextual image classification; Environmental science; Computer science; Geography; Artificial intelligence; Land use; Cartography; Geodesy; Engineering","score_opus":0.04394703609703291,"score_gpt":0.2948531168515954,"score_spread":0.25090608075456244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608675201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67418617,0.0051066466,0.12384311,0.0008009071,0.00017919444,0.00092217606,0.15299338,0.0044298866,0.037538495],"genre_scores_gemma":[0.7618309,0.0015022727,0.105868146,0.00014512926,0.000030537332,0.00029633316,0.1200464,0.00013051418,0.010149712],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996947,0.000024225554,0.00001717487,0.000042806976,0.00018198715,0.0000391374],"domain_scores_gemma":[0.9996799,0.00002352339,0.000028862334,0.00004903551,0.000199647,0.000019058525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021057644,0.00035683095,0.00034375113,0.001966087,0.00044745384,0.0005082677,0.0004320978,0.00033660218,0.003681852],"category_scores_gemma":[0.00048480448,0.00013213354,0.00030218356,0.0025873627,0.000106023086,0.00038291214,0.00032645353,0.00026586273,0.0022242789],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033353112,0.00039827908,0.075305626,0.001026158,0.00017462678,0.00044305655,0.00034448595,0.017063184,0.097621836,0.0015927001,0.04178133,0.7639153],"study_design_scores_gemma":[0.00009131591,0.00020361067,0.56192374,0.0002998816,0.00020341511,0.00092300243,0.0016535373,0.18416965,0.07083673,0.002708056,0.17685582,0.0001312138],"about_ca_topic_score_codex":0.0956098,"about_ca_topic_score_gemma":0.20135517,"teacher_disagreement_score":0.0956098,"about_ca_system_score_codex":0.0008769801,"about_ca_system_score_gemma":0.0011086868,"threshold_uncertainty_score":0.19010657},"labels":[],"label_agreement":null},{"id":"W2609876456","doi":"10.3390/s17040934","title":"Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stan Cassidy Foundation; University of New Brunswick","funders":"Canadian Institutes of Health Research; Atlantic Canada Opportunities Agency","keywords":"Wearable computer; Task (project management); Test (biology); Convergent validity; Computer science; Wearable technology; Human–computer interaction; Simulation; Embedded system; Engineering; Psychology; Psychometrics; Systems engineering; Developmental psychology","score_opus":0.05525571120027372,"score_gpt":0.3190879541336349,"score_spread":0.26383224293336116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609876456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9859139,0.00040652364,0.008324806,0.00008767267,0.00017448186,0.00024127662,0.00057782436,0.00007469912,0.004198783],"genre_scores_gemma":[0.9941214,0.00013489086,0.0046417206,0.00007100004,0.000032855874,0.00017527486,0.00050482753,0.000020490408,0.00029751778],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928479,0.0020648576,0.0007995308,0.0011126129,0.002948979,0.00022611023],"domain_scores_gemma":[0.97338957,0.01240458,0.0038375298,0.0019528351,0.007842931,0.0005724458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008829644,0.0006605913,0.00061553204,0.0015357439,0.0003748553,0.0012550163,0.0007842608,0.0009912438,0.0009903167],"category_scores_gemma":[0.03075666,0.00035318633,0.0010468359,0.00066892925,0.0006623817,0.0006575468,0.0010813269,0.0005417975,0.00071216974],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010071755,0.00029047733,0.9505562,0.00023669052,0.00072028337,0.00008326062,0.0013019575,0.0009772115,0.0052841315,0.00028491468,0.000562516,0.038695183],"study_design_scores_gemma":[0.00006167423,0.0012406233,0.98755187,0.00015084783,0.00019483459,0.00022588213,0.0008053512,0.0065252925,0.0020318192,0.00032903324,0.00084418186,0.000038600785],"about_ca_topic_score_codex":0.0013163766,"about_ca_topic_score_gemma":0.00244268,"teacher_disagreement_score":0.008829644,"about_ca_system_score_codex":0.00045224148,"about_ca_system_score_gemma":0.0004526702,"threshold_uncertainty_score":0.046696126},"labels":[],"label_agreement":null},{"id":"W2610496644","doi":"10.3390/s17051050","title":"Wearable Contactless Respiration Sensor Based on Multi-Material Fibers Integrated into Textile","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Wearable computer; Textile; Wearable technology; Respiration; Computer science; Materials science; Engineering; Embedded system; Composite material; Biology","score_opus":0.015409263015167075,"score_gpt":0.24045393238670273,"score_spread":0.22504466937153567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610496644","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8446429,0.004636292,0.14724065,0.00022955521,0.00033423363,0.000109496024,0.00027708663,0.0005749785,0.0019548961],"genre_scores_gemma":[0.92576075,0.0016143472,0.069430955,0.0001663941,0.000091321155,0.000049387363,0.00014128687,0.000030764815,0.0027148149],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975055,0.000048698388,0.000014223356,0.000082853294,0.00008487045,0.000018901124],"domain_scores_gemma":[0.9997348,0.00006868134,0.00007971172,0.000031140153,0.000057462512,0.000028157505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017763178,0.00044286583,0.0003698501,0.00027895867,0.00012188729,0.0002788632,0.00042821254,0.0005831031,0.0006789387],"category_scores_gemma":[0.00036417219,0.00017115683,0.0002686472,0.00023723728,0.00015836765,0.000624606,0.00028831876,0.00019820422,0.00029657202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016620377,0.000035385576,0.0014822519,0.00014157213,0.000017122937,0.00023827353,0.000046943947,0.0002513933,0.97790354,0.000052777326,0.00010646946,0.019558063],"study_design_scores_gemma":[0.000030118013,0.0021652568,0.024833018,0.000043206786,0.00011654179,0.0038814973,0.00010756287,0.012945878,0.94965386,0.00013575013,0.00602864,0.000058739482],"about_ca_topic_score_codex":0.00012737345,"about_ca_topic_score_gemma":0.00026655028,"teacher_disagreement_score":0.0006789387,"about_ca_system_score_codex":0.00009690615,"about_ca_system_score_gemma":0.0000691552,"threshold_uncertainty_score":0.002271235},"labels":[],"label_agreement":null},{"id":"W2611201989","doi":"10.3390/s17050994","title":"Scalability Issues for Remote Sensing Infrastructure: A Case Study","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Sensor web; Benchmarking; Computer science; Bottleneck; Scalability; The Internet; Data stream mining; Data collection; Wireless sensor network; Data sharing; Data science; World Wide Web; Database; Telecommunications; Computer network; Data mining; Embedded system","score_opus":0.03287701202170779,"score_gpt":0.3137112260240165,"score_spread":0.28083421400230874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611201989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9175858,0.0025509512,0.039873485,0.006805486,0.00011954316,0.0007102772,0.0005790728,0.0020129625,0.029762425],"genre_scores_gemma":[0.9753906,0.0007250422,0.02140429,0.00023154731,0.00007751641,0.000109909415,0.00031738202,0.00019925194,0.0015443268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"case_report","domain_scores_codex":[0.99300003,0.0028659445,0.00040750921,0.00057261495,0.002098108,0.001055775],"domain_scores_gemma":[0.9661392,0.02347966,0.001411744,0.0029500131,0.004779473,0.0012398736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074351644,0.0008490979,0.0006823891,0.0011908613,0.0022572812,0.0025310994,0.0027447897,0.0021966584,0.0018981654],"category_scores_gemma":[0.022410084,0.0005404786,0.0006430439,0.003573552,0.0016842666,0.0045600384,0.0017117955,0.0020918348,0.0002995598],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002161758,0.003195368,0.12651984,0.0035995042,0.00057402777,0.018737,0.0145958625,0.39691642,0.058449857,0.07783538,0.061236523,0.23617849],"study_design_scores_gemma":[0.00041962072,0.0015449843,0.040089853,0.00035091868,0.00031388603,0.006169352,0.012743143,0.8151923,0.04426903,0.024325179,0.054369915,0.00021184377],"about_ca_topic_score_codex":0.025129365,"about_ca_topic_score_gemma":0.023888158,"teacher_disagreement_score":0.025129365,"about_ca_system_score_codex":0.0042712083,"about_ca_system_score_gemma":0.001945453,"threshold_uncertainty_score":0.049966216},"labels":[],"label_agreement":null},{"id":"W2611610561","doi":"10.3390/s17051027","title":"Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Defense Pre-Research Foundation of China; National Natural Science Foundation of China","keywords":"Wireless sensor network; Computer science; Real-time computing; Jitter; Synchronization (alternating current); Offset (computer science); Structural health monitoring; Clock drift; Bridge (graph theory); Clock synchronization; Time synchronization; Wireless; Engineering; Computer network; Channel (broadcasting); Telecommunications","score_opus":0.017200000849121343,"score_gpt":0.25382547761059226,"score_spread":0.23662547676147092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611610561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017258475,0.00047525731,0.98069394,0.00009947002,0.000098468045,0.000051919847,0.00002188428,0.00047562097,0.00082489377],"genre_scores_gemma":[0.7665662,0.00055704237,0.22933176,0.00011957919,0.000106301064,0.0002737289,0.00019320456,0.000055802437,0.002796398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910563,0.000262772,0.00009578769,0.00019439297,0.0002915594,0.000049851493],"domain_scores_gemma":[0.9992643,0.00027588304,0.00015629726,0.0001144988,0.00015347707,0.00003560954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007783874,0.00044047378,0.0004574849,0.00053465716,0.0005176895,0.00048195396,0.0006709277,0.00048679544,0.0008294675],"category_scores_gemma":[0.0028379492,0.0001496839,0.00025150288,0.0005904321,0.00024374778,0.001037431,0.00072897377,0.000517301,0.00018078263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006398467,0.00013352915,0.0038170046,0.00028982505,0.00011077802,0.0002761524,0.00050853175,0.15814257,0.07643488,0.031707894,0.0047568763,0.7231821],"study_design_scores_gemma":[0.00008064675,0.00034857294,0.0015338234,0.000025902418,0.00004525504,0.00037447485,0.00007586791,0.9438423,0.03451618,0.0071628396,0.011953651,0.000040508676],"about_ca_topic_score_codex":0.0006048927,"about_ca_topic_score_gemma":0.0006290166,"teacher_disagreement_score":0.0008294675,"about_ca_system_score_codex":0.00029589448,"about_ca_system_score_gemma":0.0005866429,"threshold_uncertainty_score":0.004116595},"labels":[],"label_agreement":null},{"id":"W2611740915","doi":"10.3390/s17051060","title":"A Novel Real-Time Reference Key Frame Scan Matching Method","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Key (lock); Matching (statistics); Computer science; Frame (networking); Key frame; Reference frame; Computer vision; Real-time computing; Artificial intelligence; Mathematics; Computer security; Telecommunications; Statistics","score_opus":0.027211861570527067,"score_gpt":0.2918743676559124,"score_spread":0.26466250608538533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611740915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050282883,0.00025678187,0.992675,0.000054245003,0.000084248975,0.00004420593,0.000045438603,0.0005401927,0.0012715844],"genre_scores_gemma":[0.12918928,0.0005382325,0.86368597,0.00012522911,0.00013533859,0.00015424874,0.00039038577,0.00018578027,0.0055955686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999164,0.00009025487,0.000035707577,0.00017201978,0.00047418565,0.0000638438],"domain_scores_gemma":[0.9995714,0.00006835492,0.000056806082,0.00008021607,0.0002003107,0.000022847169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036164504,0.0006779191,0.00077576685,0.0014906554,0.0004010142,0.0008188377,0.0014124957,0.0009626022,0.0037230558],"category_scores_gemma":[0.0015827745,0.0003490815,0.0006828972,0.001437431,0.0003357393,0.0014483684,0.0008930609,0.00076289085,0.0021241351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021351434,0.0000602091,0.00074248086,0.00012698317,0.000049086244,0.00018404053,0.000100258156,0.017844459,0.081823,0.008398728,0.0049872776,0.88547],"study_design_scores_gemma":[0.00006978874,0.00026622877,0.0018024613,0.00003539542,0.00006217406,0.0018070076,0.000093069975,0.87395155,0.08278842,0.0039603016,0.035089266,0.00007427251],"about_ca_topic_score_codex":0.0021532592,"about_ca_topic_score_gemma":0.0018886566,"teacher_disagreement_score":0.0037230558,"about_ca_system_score_codex":0.00043817674,"about_ca_system_score_gemma":0.0009834451,"threshold_uncertainty_score":0.012454867},"labels":[],"label_agreement":null},{"id":"W2612017324","doi":"10.3390/s17051076","title":"A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Key Science and Technology Program of Shaanxi Province; Education Department of Shaanxi Province; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Rammed earth; Pruning; Sequence (biology); Air temperature; Computer science; Tree (set theory); Geotechnical engineering; Geology; Mathematics; Atmospheric sciences","score_opus":0.04735966214753661,"score_gpt":0.2814200424860028,"score_spread":0.23406038033846616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612017324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8349232,0.001652666,0.15619577,0.00079812645,0.0001305949,0.0001698406,0.0026757608,0.00048324082,0.002970796],"genre_scores_gemma":[0.9437768,0.0007441538,0.051750045,0.00006397079,0.00004282983,0.00007267778,0.002809331,0.000021480582,0.0007186896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885094,0.00018195846,0.00014084842,0.00036804497,0.00036321097,0.00009496206],"domain_scores_gemma":[0.99668175,0.0017450044,0.00054710684,0.0003694782,0.0005642473,0.00009239113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011666107,0.00052075,0.0005227074,0.001956487,0.0004734976,0.00078150834,0.000626213,0.00044719563,0.00038321118],"category_scores_gemma":[0.0054589873,0.00019909863,0.00068432523,0.0028899836,0.00032490946,0.001272158,0.00034818292,0.00055148033,0.00012202648],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059187354,0.00041137036,0.46111172,0.0010120024,0.00045587888,0.004788697,0.00096919155,0.12776746,0.019480478,0.0064854315,0.0061267996,0.3707991],"study_design_scores_gemma":[0.000014642081,0.00018958193,0.12585066,0.00007264823,0.00012658241,0.0017019666,0.00069175905,0.8520554,0.00964967,0.004827714,0.0047807703,0.00003853457],"about_ca_topic_score_codex":0.006795388,"about_ca_topic_score_gemma":0.008549669,"teacher_disagreement_score":0.006795388,"about_ca_system_score_codex":0.0004358684,"about_ca_system_score_gemma":0.0005154268,"threshold_uncertainty_score":0.013511658},"labels":[],"label_agreement":null},{"id":"W2612480714","doi":"10.3390/s17051079","title":"GryphSens: A Smartphone-Based Portable Diagnostic Reader for the Rapid Detection of Progesterone in Milk","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Software portability; Computer science; Android (operating system); Smartphone application; Raspberry pi; Embedded system; Usability; Mobile device; Computer hardware; Point-of-care testing; Operating system; Internet of Things; Multimedia; Medicine","score_opus":0.017482825150391516,"score_gpt":0.22760142785194323,"score_spread":0.21011860270155172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612480714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24047886,0.046230283,0.6102755,0.002252292,0.003188283,0.0040371665,0.009471368,0.058425013,0.025641158],"genre_scores_gemma":[0.39278933,0.010607415,0.54564613,0.0024129325,0.0006523137,0.00204163,0.0052245297,0.00084605016,0.039779723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978053,0.00034348803,0.00017081383,0.00049598835,0.0010856775,0.000098791796],"domain_scores_gemma":[0.9991943,0.00027123868,0.00016847087,0.000092173585,0.0002032827,0.00007054662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011305053,0.001444868,0.0011001518,0.0016039723,0.00030836128,0.00057856797,0.0019933225,0.0016881548,0.005493678],"category_scores_gemma":[0.0021963676,0.0005609082,0.0006090367,0.0007253509,0.00043759632,0.00068369816,0.001031509,0.0008154658,0.003702277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082448934,0.00018512455,0.0029153458,0.0021721034,0.00014320413,0.0007950067,0.00024343102,0.00039061726,0.74805516,0.0008953315,0.01754305,0.22583719],"study_design_scores_gemma":[0.00036420673,0.0026009723,0.021124516,0.00025685906,0.0004055623,0.014233708,0.00014510172,0.018054657,0.7785551,0.0007532137,0.16316552,0.00034059273],"about_ca_topic_score_codex":0.0007793586,"about_ca_topic_score_gemma":0.0015899658,"teacher_disagreement_score":0.005493678,"about_ca_system_score_codex":0.00032876665,"about_ca_system_score_gemma":0.0005609377,"threshold_uncertainty_score":0.018378139},"labels":[],"label_agreement":null},{"id":"W2612726895","doi":"10.3390/s17051084","title":"A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"University of Pretoria; National Research Foundation","keywords":"Routing protocol; Computer science; Energy consumption; Computer network; Wireless sensor network; Link-state routing protocol; Efficient energy use; Interior gateway protocol; Routing (electronic design automation); Routing domain; Dynamic Source Routing; Software deployment; Distributed computing; Engineering; Electrical engineering","score_opus":0.02907300192678748,"score_gpt":0.2899468671399145,"score_spread":0.26087386521312705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612726895","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034312212,0.8374483,0.105515964,0.0024281384,0.0034110448,0.00031661286,0.00047564405,0.0006328167,0.046340387],"genre_scores_gemma":[0.020633355,0.90481573,0.052173484,0.0016879541,0.0019584643,0.00038160698,0.0014377524,0.00017039977,0.016741285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988531,0.00019905446,0.00018121628,0.0001424601,0.0005463784,0.000077766854],"domain_scores_gemma":[0.99911135,0.00034001135,0.00008053727,0.00007129962,0.0003616759,0.000035105128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009045446,0.0012876231,0.0010618055,0.0024654286,0.0006624313,0.0014327604,0.0016506261,0.0014019294,0.0048702983],"category_scores_gemma":[0.0021058396,0.0006201176,0.0008133147,0.005644014,0.0004568647,0.0039000744,0.0010616765,0.0018698517,0.0035920131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000966109,0.00013882706,0.0006390462,0.007591054,0.00007226857,0.00029493606,0.00019829889,0.0059060077,0.005890626,0.03335082,0.06249709,0.88332444],"study_design_scores_gemma":[0.000010634425,0.00016855897,0.00056498515,0.0014403067,0.00008300194,0.0011904843,0.00013109684,0.007096862,0.0021810115,0.011186433,0.9758834,0.00006313479],"about_ca_topic_score_codex":0.0010710021,"about_ca_topic_score_gemma":0.0010882855,"teacher_disagreement_score":0.0048702983,"about_ca_system_score_codex":0.0006435531,"about_ca_system_score_gemma":0.0012984792,"threshold_uncertainty_score":0.01629275},"labels":[],"label_agreement":null},{"id":"W2612742916","doi":"10.3390/s17051083","title":"Acquisition and Neural Network Prediction of 3D Deformable Object Shape Using a Kinect and a Force-Torque Sensor","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Artificial neural network; Point cloud; Computer science; Computer vision; Object (grammar); Similarity (geometry); Representation (politics); Torque; Cluster analysis; Pattern recognition (psychology); Physics; Image (mathematics)","score_opus":0.04145413771092277,"score_gpt":0.2674041890435758,"score_spread":0.22595005133265306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612742916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3881601,0.00020165123,0.6082623,0.0001139448,0.00007679408,0.000087233384,0.00026846846,0.0015159084,0.0013135975],"genre_scores_gemma":[0.8876627,0.00011820018,0.11060148,0.00003779912,0.00001135402,0.00008340154,0.00030527226,0.00004222209,0.0011376712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.000016367101,0.000007702187,0.000047348844,0.00006205645,0.00001730297],"domain_scores_gemma":[0.9997578,0.00007879524,0.000039016177,0.000037132308,0.00006515748,0.000022080163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032496738,0.00073046854,0.00037851703,0.0005621931,0.00017957711,0.0003660467,0.00053131033,0.0006914605,0.0005509794],"category_scores_gemma":[0.0011969011,0.00033139202,0.0003823174,0.00050458167,0.00028999298,0.0005486092,0.0004500197,0.00048637897,0.00020969106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026867155,0.0002168605,0.007212408,0.00005978863,0.000047158337,0.00019605187,0.000077256474,0.7883578,0.04890125,0.00082862715,0.00065745204,0.15317667],"study_design_scores_gemma":[0.0000011526022,0.0000102555,0.00092231436,0.0000011585222,0.0000012760187,0.0000072455714,0.000004098799,0.99628973,0.002607721,0.0001057545,0.000046824378,0.0000023864961],"about_ca_topic_score_codex":0.007285669,"about_ca_topic_score_gemma":0.010436647,"teacher_disagreement_score":0.007285669,"about_ca_system_score_codex":0.00054603646,"about_ca_system_score_gemma":0.0004291207,"threshold_uncertainty_score":0.014486492},"labels":[],"label_agreement":null},{"id":"W2613009272","doi":"10.3390/s17051121","title":"Nonlinear Parameter Identification of a Resonant Electrostatic MEMS Actuator","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Actuator; Nonlinear system; Laser Doppler vibrometer; Microelectromechanical systems; Subharmonic function; Voltage; Noise (video); Control theory (sociology); Duffing equation; Acoustics; Physics; Laser; Materials science; Optics; Engineering; Optoelectronics; Electrical engineering; Computer science; Laser power scaling; Mathematics; Mathematical analysis","score_opus":0.012830393305799504,"score_gpt":0.2590419133944929,"score_spread":0.2462115200886934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613009272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9435235,0.00009119938,0.054339368,0.00006189525,0.0000072952776,0.000024839415,0.00004520267,0.00011182551,0.0017948847],"genre_scores_gemma":[0.9951841,0.000020551044,0.0044783745,0.000004731322,8.809032e-7,0.0000136723065,0.000015160848,0.00000384371,0.00027865515],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998311,0.000029035615,0.0000071580016,0.000049530536,0.000065586144,0.00001761361],"domain_scores_gemma":[0.9998642,0.00006318536,0.000024936597,0.000023832437,0.000017177357,0.000006630866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023460355,0.00027468216,0.00026161002,0.00015599515,0.00016902448,0.00018662588,0.0003803946,0.00045686716,0.0006300789],"category_scores_gemma":[0.00054928544,0.0001575226,0.0001597356,0.000072919465,0.00024514546,0.00026201067,0.0003056501,0.00019645895,0.00009428874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011630531,0.000050920284,0.0019505534,0.000109319386,0.000024423061,0.00021072605,0.00014029005,0.07838381,0.90357554,0.0012382437,0.000074736636,0.01412512],"study_design_scores_gemma":[0.000014391705,0.00015955702,0.0048376694,0.0000074981367,0.000012550814,0.00014923299,0.000048336336,0.8345379,0.15909208,0.0005926903,0.00052619335,0.000021930755],"about_ca_topic_score_codex":0.00074409036,"about_ca_topic_score_gemma":0.00081184774,"teacher_disagreement_score":0.00074409036,"about_ca_system_score_codex":0.00025564668,"about_ca_system_score_gemma":0.00023462165,"threshold_uncertainty_score":0.0021078587},"labels":[],"label_agreement":null},{"id":"W2616510615","doi":"10.3390/s17051161","title":"An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Gradient descent; Control theory (sociology); Magnetometer; Orientation (vector space); Gyroscope; Sensor fusion; Computer science; Inertial measurement unit; Accelerometer; Disturbance (geology); Mathematics; Magnetic field; Artificial intelligence; Physics","score_opus":0.013333425973518399,"score_gpt":0.28687065992571614,"score_spread":0.27353723395219776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616510615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018619996,0.00025615242,0.9797575,0.000061275634,0.000099029436,0.000029156892,0.00001341275,0.00036762227,0.00079582824],"genre_scores_gemma":[0.549412,0.00040059464,0.44630674,0.000103509396,0.00010658248,0.00010725852,0.00009754226,0.000079887745,0.0033858877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999579,0.000060535425,0.000029010554,0.00010967154,0.00018361343,0.000038188973],"domain_scores_gemma":[0.9995832,0.000065289336,0.000064870146,0.000043175478,0.00022499092,0.000018501469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045233095,0.00067984324,0.0006490515,0.00049012987,0.00039800702,0.000505941,0.00076781464,0.0005711684,0.0005643659],"category_scores_gemma":[0.0014672882,0.00032885722,0.0004700468,0.0005094255,0.00028211423,0.0005950456,0.0004422033,0.0006274269,0.00035084225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025416687,0.00010317114,0.004022622,0.0001811957,0.00009324239,0.0002238766,0.00017880791,0.1581494,0.085620336,0.0029927948,0.0021194282,0.7460609],"study_design_scores_gemma":[0.00001574126,0.000098071585,0.0025386785,0.000011954437,0.000026217062,0.00014770878,0.000026900334,0.9818887,0.012623218,0.0004772256,0.0021183172,0.000027206785],"about_ca_topic_score_codex":0.0039274227,"about_ca_topic_score_gemma":0.0043901624,"teacher_disagreement_score":0.0039274227,"about_ca_system_score_codex":0.0003163015,"about_ca_system_score_gemma":0.0006372286,"threshold_uncertainty_score":0.0078091025},"labels":[],"label_agreement":null},{"id":"W2618337305","doi":"10.3390/s17061217","title":"A Smart Power Electronic Multiconverter for the Residential Sector","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hydro-Québec","keywords":"Smart grid; Renewable energy; Distributed generation; Photovoltaic system; Energy management system; Energy storage; Energy management; Electric power system; Demand response; Computer science; Maximum power point tracking; Stand-alone power system; Engineering; Automotive engineering; Reliability engineering; Electricity; Electrical engineering; Power (physics); Energy (signal processing)","score_opus":0.017167731756564112,"score_gpt":0.2763503768628993,"score_spread":0.2591826451063352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618337305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27670708,0.002180421,0.61802083,0.0012475167,0.0007960727,0.00087464263,0.0014375079,0.020456815,0.07827902],"genre_scores_gemma":[0.91342777,0.00059450197,0.045763467,0.00031579,0.00008056305,0.00014172411,0.00056736055,0.00021794405,0.03889097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999765,0.00005301165,0.0000117087,0.000061364706,0.000091167756,0.00001778086],"domain_scores_gemma":[0.9998005,0.00002110634,0.00001657999,0.00008406502,0.000054457145,0.0000232775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026101532,0.00045022758,0.00040343156,0.00027332347,0.00041942313,0.0007009966,0.0010381215,0.0005665619,0.01219353],"category_scores_gemma":[0.00024252334,0.00013132763,0.0002642099,0.0003799646,0.00023822488,0.0010149867,0.00068500446,0.00052432664,0.0037802465],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013927608,0.0005058143,0.0058468,0.0010492651,0.00009770596,0.0013970283,0.00049119594,0.020048622,0.28987476,0.020653326,0.031640008,0.62700284],"study_design_scores_gemma":[0.00063697784,0.005838446,0.019897217,0.00024325082,0.0002526725,0.0059018363,0.00036343848,0.2226038,0.30609983,0.011379625,0.42660132,0.00018157052],"about_ca_topic_score_codex":0.00043698688,"about_ca_topic_score_gemma":0.0007747539,"teacher_disagreement_score":0.01219353,"about_ca_system_score_codex":0.00028024678,"about_ca_system_score_gemma":0.00028478962,"threshold_uncertainty_score":0.040791452},"labels":[],"label_agreement":null},{"id":"W2618844859","doi":"10.3390/s17061227","title":"A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Intrusion detection system; Node (physics); Computer network; Protocol (science); Metric (unit); Wireless sensor network; Layer (electronics); Application layer; Physical layer; Trustworthiness; Network layer; Wireless; Computer security; Engineering; Telecommunications","score_opus":0.028498599728918605,"score_gpt":0.29823894061335654,"score_spread":0.26974034088443793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618844859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025733635,0.0005796573,0.9711412,0.00024421583,0.00011571475,0.00020190886,0.000033131524,0.0008971703,0.0010534155],"genre_scores_gemma":[0.8369173,0.00046989776,0.1607388,0.00017572701,0.00006890066,0.00018902317,0.00009178404,0.000032114316,0.001316356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974279,0.00068316044,0.00030384673,0.00033961525,0.0010745019,0.00017096302],"domain_scores_gemma":[0.9964393,0.0010103707,0.0006696366,0.00065941893,0.0010638514,0.00015750875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025964652,0.0006601319,0.0009988134,0.0010520533,0.0007816412,0.0010430013,0.0015496343,0.0010808381,0.00048603295],"category_scores_gemma":[0.0084212115,0.00029017855,0.0007026798,0.0008743727,0.00090640073,0.0032472627,0.0018193559,0.0012648953,0.00023702634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00101099,0.0005195428,0.007260551,0.00065828173,0.0005456868,0.0009492799,0.0010914054,0.22418803,0.12357618,0.092306666,0.0064094216,0.541484],"study_design_scores_gemma":[0.000029453513,0.00039089684,0.0006094921,0.000021151258,0.00008088629,0.00061339844,0.000049650527,0.9680158,0.018859189,0.0075820666,0.0036999823,0.000048091606],"about_ca_topic_score_codex":0.00073319825,"about_ca_topic_score_gemma":0.0005254348,"teacher_disagreement_score":0.0025964652,"about_ca_system_score_codex":0.0009798491,"about_ca_system_score_gemma":0.0010338367,"threshold_uncertainty_score":0.013731599},"labels":[],"label_agreement":null},{"id":"W2619146642","doi":"10.3390/s17061215","title":"Internal Model-Based Robust Tracking Control Design for the MEMS Electromagnetic Micromirror","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Microelectromechanical systems; Controller (irrigation); Digital micromirror device; Tracking (education); SIGNAL (programming language); Track (disk drive); Electronic engineering; Computer science; Control theory (sociology); Engineering; Physics; Artificial intelligence; Control (management); Electrical engineering; Mechanical engineering","score_opus":0.034115022077709806,"score_gpt":0.2503274267239405,"score_spread":0.21621240464623068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619146642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010768252,0.0002760587,0.9839293,0.00011025538,0.00006323564,0.000040362735,0.000019451545,0.00039880417,0.0043942844],"genre_scores_gemma":[0.94607824,0.00037083734,0.050317373,0.0000884758,0.00003613839,0.00018303526,0.00006163073,0.000058175443,0.0028062756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936205,0.0000916225,0.00003608826,0.00018064551,0.00026061822,0.00006890394],"domain_scores_gemma":[0.9995915,0.000086528205,0.00010760161,0.000042488904,0.00015492254,0.000016967846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094424025,0.0009591258,0.001016969,0.00036269636,0.00047353373,0.001320142,0.0010684076,0.000927829,0.0013168738],"category_scores_gemma":[0.0010672682,0.00042731361,0.00082893536,0.00023417913,0.0007409316,0.00063324644,0.0009815014,0.0009295643,0.00037909028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001522552,0.000067958055,0.0005280129,0.00040745063,0.00009959643,0.00021705452,0.00026864096,0.8494371,0.060608182,0.019452792,0.0012416592,0.06751931],"study_design_scores_gemma":[0.000018621837,0.0001232706,0.00013892625,0.000011464028,0.000016235781,0.000029696374,0.000011266465,0.9930327,0.005062384,0.000706181,0.0008369452,0.000012231428],"about_ca_topic_score_codex":0.0027628278,"about_ca_topic_score_gemma":0.0017082725,"teacher_disagreement_score":0.0027628278,"about_ca_system_score_codex":0.0006643175,"about_ca_system_score_gemma":0.00091835856,"threshold_uncertainty_score":0.0054935217},"labels":[],"label_agreement":null},{"id":"W2619706669","doi":"10.3390/s17061187","title":"Multimodal Bio-Inspired Tactile Sensing Module for Surface Characterization","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; University of Ottawa","funders":"Ciência sem Fronteiras; Natural Sciences and Engineering Research Council of Canada; Ministério da Educação","keywords":"Characterization (materials science); Computer science; Human–computer interaction; Biomimetics; Biomimetic materials; Tactile sensor; Nanotechnology; Artificial intelligence; Computer vision; Materials science; Robot","score_opus":0.01855630081342622,"score_gpt":0.24428182628681547,"score_spread":0.22572552547338925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619706669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7125858,0.0010764756,0.28217056,0.00019502544,0.000074650496,0.00008356369,0.0002946323,0.0009400656,0.002579247],"genre_scores_gemma":[0.9258483,0.00021242368,0.07200942,0.00015063238,0.000026074953,0.00005628549,0.00011482974,0.000027307533,0.0015546363],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998043,0.00002656093,0.0000074932977,0.000045733093,0.00009813773,0.000017780321],"domain_scores_gemma":[0.9998246,0.00004605932,0.000047716265,0.000029599094,0.000037604277,0.000014481436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018093266,0.00027359775,0.0002465467,0.00029898886,0.0000816649,0.00022819359,0.00041039407,0.0005223244,0.0013043265],"category_scores_gemma":[0.00038130063,0.0001148879,0.00018535825,0.0002611826,0.00019553052,0.00039324985,0.0004337334,0.00019184701,0.00033488625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051144387,0.00001843948,0.00054085476,0.000058011967,0.0000068961676,0.000045687317,0.000031409294,0.00046430336,0.9738898,0.00015631194,0.00014475972,0.024592346],"study_design_scores_gemma":[0.0000161975,0.0009670218,0.023156922,0.000029046241,0.000048842296,0.0010929805,0.00012289813,0.06920891,0.8982867,0.0007072275,0.0063077738,0.000055599612],"about_ca_topic_score_codex":0.000077495606,"about_ca_topic_score_gemma":0.000219705,"teacher_disagreement_score":0.0013043265,"about_ca_system_score_codex":0.00012965173,"about_ca_system_score_gemma":0.00007989959,"threshold_uncertainty_score":0.0043634176},"labels":[],"label_agreement":null},{"id":"W2621386686","doi":"10.3390/s17061272","title":"A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Calgary","keywords":"Inertial measurement unit; Particle filter; Extended Kalman filter; Computer science; Inertial navigation system; Real-time computing; Map matching; Kalman filter; Indoor positioning system; Global Positioning System; Computer vision; Artificial intelligence; Orientation (vector space); Accelerometer; Mathematics; Telecommunications","score_opus":0.012837131267788056,"score_gpt":0.2310715795134802,"score_spread":0.21823444824569216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621386686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015443263,0.0004058109,0.9726589,0.00016480568,0.0002491291,0.000106374275,0.00022597788,0.005146299,0.0055994987],"genre_scores_gemma":[0.50453705,0.00075791014,0.47873822,0.00033672978,0.00022221317,0.0002533689,0.0011871385,0.00014380694,0.0138236005],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996915,0.000033687767,0.000017541204,0.0000672799,0.00015435253,0.000035773748],"domain_scores_gemma":[0.9998067,0.00001278169,0.00001735244,0.000037640493,0.00010712628,0.000018372662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525202,0.00063901965,0.00048347493,0.00066097226,0.00040557998,0.00067060837,0.0011959124,0.000765087,0.0032107397],"category_scores_gemma":[0.0003598771,0.00022629947,0.0003380389,0.00065697334,0.00018004929,0.0010679059,0.00079863897,0.00057141535,0.0026679274],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040663936,0.00024371235,0.00525506,0.0004168729,0.00012105492,0.0004860106,0.00020684523,0.024321798,0.11512063,0.010722909,0.016619526,0.8260791],"study_design_scores_gemma":[0.000100868725,0.00096814625,0.009180905,0.00008840206,0.00029987653,0.0014159946,0.00018777217,0.7297357,0.10796717,0.004169304,0.14574488,0.00014101532],"about_ca_topic_score_codex":0.002491213,"about_ca_topic_score_gemma":0.002674641,"teacher_disagreement_score":0.0032107397,"about_ca_system_score_codex":0.00030611083,"about_ca_system_score_gemma":0.0007870649,"threshold_uncertainty_score":0.010741055},"labels":[],"label_agreement":null},{"id":"W2621464581","doi":"10.3390/s17061287","title":"Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Inertial measurement unit; Activity recognition; Classifier (UML); Wearable computer; Pattern recognition (psychology); Support vector machine; Machine learning; Data mining","score_opus":0.2074829021052562,"score_gpt":0.38087394462685153,"score_spread":0.17339104252159535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621464581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077798218,0.00008100839,0.99146867,0.000030739226,0.000006734196,0.000051300372,0.000016503654,0.0003444379,0.00022075222],"genre_scores_gemma":[0.34697807,0.00017736547,0.6500565,0.00009091986,0.000049989114,0.0005485816,0.00038104158,0.000086593915,0.0016309508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851674,0.00037333567,0.00013116268,0.00042036586,0.0004018804,0.00015654718],"domain_scores_gemma":[0.9985018,0.0006013853,0.00016560736,0.0001446819,0.000530752,0.000055889315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022041858,0.0008251608,0.0013262054,0.0012152774,0.00046060458,0.0009537621,0.0021547403,0.001073084,0.0010591873],"category_scores_gemma":[0.004198879,0.0005130089,0.0014487837,0.0012690882,0.00083602144,0.001124118,0.0011349055,0.0011530877,0.00056084146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017052988,0.00030025668,0.0025209836,0.00011437595,0.00013649951,0.00009213701,0.00022543395,0.56818295,0.010421386,0.0067700655,0.00080206856,0.41026327],"study_design_scores_gemma":[0.0000027483782,0.0000372785,0.00019073205,0.0000033730275,0.000005578983,0.000013124415,0.000006824062,0.9972498,0.0013351542,0.0009646341,0.00018573011,0.000005121877],"about_ca_topic_score_codex":0.006039848,"about_ca_topic_score_gemma":0.004597513,"teacher_disagreement_score":0.006039848,"about_ca_system_score_codex":0.00085222995,"about_ca_system_score_gemma":0.0012546392,"threshold_uncertainty_score":0.012009382},"labels":[],"label_agreement":null},{"id":"W2621543313","doi":"10.3390/s17061321","title":"Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Accelerometer; Artificial intelligence; Random forest; Computer science; Pattern recognition (psychology); Mathematics","score_opus":0.053410952834640986,"score_gpt":0.36473512194555957,"score_spread":0.3113241691109186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621543313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874347,0.0003706227,0.010346943,0.000056110304,0.00003708,0.000072803094,0.0008139905,0.0001902045,0.0006774908],"genre_scores_gemma":[0.98742825,0.00028944787,0.010240359,0.000036160716,0.000027919075,0.00006776398,0.0011077676,0.0000081755215,0.00079419557],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986696,0.00001496813,0.000019725305,0.000035216042,0.000043525837,0.000019663514],"domain_scores_gemma":[0.9997321,0.000041984746,0.000076711774,0.000016277274,0.00010926372,0.00002364549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002714482,0.0005963234,0.0005939829,0.00093803025,0.00013184408,0.00031223008,0.0001757806,0.00033883261,0.0008083366],"category_scores_gemma":[0.0009964738,0.00011417713,0.00045522867,0.00045290607,0.00007711646,0.00026815693,0.00028016235,0.00018353065,0.00034318038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015850842,0.00035378808,0.65591186,0.0002417441,0.00027785302,0.0004220648,0.00035397973,0.00262063,0.042857442,0.00007899517,0.0022233734,0.29307327],"study_design_scores_gemma":[0.000041207568,0.0010357932,0.9514434,0.00006346433,0.00017139538,0.00096492376,0.0004940911,0.037245445,0.0074506556,0.0001842088,0.0008714708,0.000033884786],"about_ca_topic_score_codex":0.001745934,"about_ca_topic_score_gemma":0.0054883948,"teacher_disagreement_score":0.001745934,"about_ca_system_score_codex":0.000089650865,"about_ca_system_score_gemma":0.00011619591,"threshold_uncertainty_score":0.0034715533},"labels":[],"label_agreement":null},{"id":"W2621991656","doi":"10.3390/s17061302","title":"An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"GNSS applications; Precise Point Positioning; Galileo (satellite navigation); GLONASS; Global Positioning System; Convergence (economics); Constellation; Computer science; Satellite system; Geodesy; Real-time computing; Satellite; Algorithm; Telecommunications; Geography; Physics; Engineering; Aerospace engineering","score_opus":0.024337417146071308,"score_gpt":0.23227492703404615,"score_spread":0.20793750988797483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621991656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04179332,0.0003007356,0.95540196,0.000102757156,0.000099578836,0.000053087744,0.000080449514,0.00088660046,0.0012815396],"genre_scores_gemma":[0.41395113,0.00032879392,0.5835137,0.00007354149,0.00006552678,0.00011377431,0.0005206916,0.00011074778,0.0013220467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994868,0.00008961015,0.00003500874,0.000121936755,0.00020291931,0.000063643056],"domain_scores_gemma":[0.99946874,0.000080904465,0.00006919677,0.00013439472,0.00022086193,0.000025911524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009460431,0.00090481696,0.00065383955,0.0013426776,0.00036022792,0.0004914744,0.0008843257,0.0005291044,0.0012346156],"category_scores_gemma":[0.0023290052,0.0004156271,0.00072927185,0.0013198766,0.0002460831,0.0012995402,0.0018566428,0.00076956785,0.000587122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025604083,0.00011890473,0.01152357,0.00020018853,0.00015900955,0.0003239779,0.0002494044,0.32410797,0.037080683,0.008094104,0.0026854319,0.61520064],"study_design_scores_gemma":[0.0000383075,0.000119701974,0.0035454722,0.00001468442,0.000040957828,0.00016355439,0.00005333682,0.9815329,0.007582925,0.0022105537,0.0046759746,0.000021727743],"about_ca_topic_score_codex":0.005635476,"about_ca_topic_score_gemma":0.0037548305,"teacher_disagreement_score":0.005635476,"about_ca_system_score_codex":0.00027745264,"about_ca_system_score_gemma":0.00085327745,"threshold_uncertainty_score":0.011205316},"labels":[],"label_agreement":null},{"id":"W2622261466","doi":"10.3390/s17061336","title":"3D Printing-Based Integrated Water Quality Sensing System","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Volumetric flow rate; Conductivity; Water quality; Inlet; Materials science; Pressure sensor; Water flow; Flow sensor; Environmental science; Process engineering; Petroleum engineering; Environmental engineering; Mechanical engineering; Engineering; Chemistry; Acoustics; Mechanics","score_opus":0.03296611248915716,"score_gpt":0.30563964935577004,"score_spread":0.2726735368666129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622261466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124838196,0.0013503833,0.8146476,0.00058541005,0.0007839616,0.00042464252,0.0016015039,0.025447262,0.03032103],"genre_scores_gemma":[0.7128853,0.00069664355,0.254055,0.0007992261,0.00013953182,0.0005404048,0.0012181731,0.00025424923,0.02941134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993894,0.000033658,0.000035374345,0.00014911432,0.0003604854,0.000031886277],"domain_scores_gemma":[0.9997414,0.000030729403,0.0000536696,0.000068765155,0.00008786084,0.000017705479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022739779,0.0005060695,0.0006211732,0.0005002157,0.00027405776,0.00076152646,0.0017501035,0.0010623174,0.0055735526],"category_scores_gemma":[0.0003512105,0.0002829363,0.0005311222,0.00037366129,0.0002283847,0.0006062187,0.0007550281,0.00035966668,0.0026735577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002762804,0.00019513941,0.0014380153,0.00045934343,0.00006552417,0.00094757346,0.00015271832,0.018997205,0.82542026,0.002736358,0.009287385,0.14002429],"study_design_scores_gemma":[0.000106643456,0.0009916588,0.004527685,0.00004450668,0.00013957429,0.0019771825,0.000042509495,0.28436884,0.6446306,0.0013298009,0.061674636,0.00016627814],"about_ca_topic_score_codex":0.000666764,"about_ca_topic_score_gemma":0.00057312835,"teacher_disagreement_score":0.0055735526,"about_ca_system_score_codex":0.00046690082,"about_ca_system_score_gemma":0.0003484697,"threshold_uncertainty_score":0.018645346},"labels":[],"label_agreement":null},{"id":"W2622706983","doi":"10.3390/s17071486","title":"Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université du Québec à Chicoutimi","funders":"","keywords":"Bayesian network; Context (archaeology); Computer science; Dependency (UML); Domain (mathematical analysis); Process (computing); Machine learning; COPD; Data mining; Artificial intelligence; Discretization; Medicine; Mathematics","score_opus":0.05152361374359497,"score_gpt":0.2846989978691281,"score_spread":0.23317538412553312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622706983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053862415,0.0010777409,0.9386998,0.00083414186,0.00009207559,0.00015881828,0.00080992223,0.000979386,0.003485692],"genre_scores_gemma":[0.85924166,0.0013971234,0.13232778,0.00024266307,0.00013075817,0.00035370188,0.0015340493,0.000059232534,0.004712986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936825,0.00017691625,0.00004382452,0.00017993944,0.00016114446,0.000069909656],"domain_scores_gemma":[0.99937797,0.00037363305,0.000054154476,0.000023683147,0.00014520172,0.000025210025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094229775,0.0008863989,0.0007697366,0.0011958185,0.00051270047,0.0011170524,0.0012405809,0.0011137346,0.001963608],"category_scores_gemma":[0.0026863727,0.0003892757,0.00086936,0.00077103055,0.00027809007,0.0012568131,0.0005831017,0.0011777158,0.0004959071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002413128,0.00018796811,0.008981956,0.00012609152,0.0001145882,0.00027164587,0.00014329005,0.879999,0.0018027995,0.007298735,0.0026849406,0.09814759],"study_design_scores_gemma":[0.0000052472396,0.000012353231,0.0004095624,0.000006858823,0.000013842103,0.000019034962,0.000008445062,0.9968407,0.00015200423,0.0022594016,0.00026712322,0.0000054459206],"about_ca_topic_score_codex":0.0272684,"about_ca_topic_score_gemma":0.020234343,"teacher_disagreement_score":0.0272684,"about_ca_system_score_codex":0.0010894616,"about_ca_system_score_gemma":0.0010617747,"threshold_uncertainty_score":0.054219365},"labels":[],"label_agreement":null},{"id":"W2626018386","doi":"10.3390/s17061384","title":"Instrumented Compliant Wrist with Proximity and Contact Sensing for Close Robot Interaction Control","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Controller (irrigation); Contact force; Robot end effector; Engineering; Instrumentation (computer programming); Robotics; Simulation; Computer science; Control engineering; Artificial intelligence","score_opus":0.024748431550599494,"score_gpt":0.2583929078551074,"score_spread":0.23364447630450788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626018386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36114436,0.0013775898,0.62997866,0.00012914393,0.00013007702,0.00016188224,0.0000689263,0.0012900169,0.005719428],"genre_scores_gemma":[0.9093666,0.00026141905,0.08732483,0.00007359436,0.000028461367,0.000101834106,0.00004137982,0.0000317947,0.002769992],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999106,0.000112016256,0.00005641821,0.00012481381,0.00055638613,0.000044327695],"domain_scores_gemma":[0.9993237,0.00010964458,0.00024714466,0.00018277521,0.0000978999,0.00003871564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004603456,0.00037968415,0.00037296343,0.00040993578,0.00015852069,0.0004429361,0.00081221526,0.0005475482,0.0011800706],"category_scores_gemma":[0.0007620012,0.00017351007,0.00026157385,0.0003221314,0.00048506586,0.00042192347,0.0006428684,0.00026469096,0.00037298174],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001568017,0.00010083332,0.0009314359,0.00018055495,0.000016173122,0.00031920456,0.0001248882,0.003871497,0.9320279,0.001894033,0.0003303679,0.060046263],"study_design_scores_gemma":[0.00009681138,0.0038176451,0.013052373,0.00008000712,0.00006765321,0.0023566294,0.000090742804,0.104916364,0.8577743,0.0015159772,0.016116228,0.00011525464],"about_ca_topic_score_codex":0.00009380242,"about_ca_topic_score_gemma":0.00014433567,"teacher_disagreement_score":0.0011800706,"about_ca_system_score_codex":0.0001397557,"about_ca_system_score_gemma":0.00018068527,"threshold_uncertainty_score":0.0039477944},"labels":[],"label_agreement":null},{"id":"W2626063980","doi":"10.3390/s17061391","title":"TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Non-line-of-sight propagation; Computer science; Wireless; Position (finance); Line (geometry); Power (physics); Indoor positioning system; Real-time computing; Chip; Wireless network; State (computer science); Exploit; Matching (statistics); Construct (python library); Algorithm; Embedded system; Telecommunications; Computer network; Mathematics","score_opus":0.024319218418731664,"score_gpt":0.25742968275298106,"score_spread":0.2331104643342494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626063980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12611249,0.00086759386,0.85083675,0.00036953896,0.00034011723,0.00024599987,0.00047586285,0.008418714,0.012332823],"genre_scores_gemma":[0.7755403,0.00035712714,0.21321462,0.0005072865,0.00009803933,0.0002084695,0.000666429,0.00009826464,0.0093095135],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971634,0.000038485483,0.000016977572,0.00007929839,0.00010604943,0.0000428353],"domain_scores_gemma":[0.9996556,0.000047724803,0.00006339488,0.00006079208,0.00013798718,0.000034450768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002319313,0.00043397347,0.00038541254,0.00048733773,0.0003231816,0.00044637237,0.0010737261,0.0005172505,0.002951639],"category_scores_gemma":[0.00046255547,0.00014759113,0.0002307737,0.00052122877,0.00023109007,0.0009075692,0.00051693706,0.00031191026,0.00085639546],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090884155,0.00027565958,0.011626148,0.000816333,0.00011187746,0.00079614075,0.00028254685,0.03062652,0.29309052,0.013249748,0.03259923,0.61561644],"study_design_scores_gemma":[0.00038657003,0.0047330256,0.013921416,0.00011890282,0.00033178422,0.004860721,0.00024629696,0.5425939,0.25350022,0.0020341894,0.17695236,0.00032060954],"about_ca_topic_score_codex":0.0015012294,"about_ca_topic_score_gemma":0.002743156,"teacher_disagreement_score":0.002951639,"about_ca_system_score_codex":0.00034165508,"about_ca_system_score_gemma":0.00065347325,"threshold_uncertainty_score":0.009874284},"labels":[],"label_agreement":null},{"id":"W2626902424","doi":"10.3390/s17081748","title":"On-Chip High-Finesse Fabry-Perot Microcavities for Optical Sensing and Quantum Information","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fabry–Pérot interferometer; Finesse; Optoelectronics; Quantum sensor; Microelectromechanical systems; Fabrication; Metrology; Optics; Radius of curvature; Physics; Interferometry; Quantum; Curvature; Quantum information; Wavelength; Quantum network","score_opus":0.039190699483211924,"score_gpt":0.28991442998393613,"score_spread":0.2507237305007242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626902424","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048512314,0.99521035,0.0009453211,0.0002271808,0.00026966454,0.000012149384,0.000029444232,0.000018735554,0.0028020667],"genre_scores_gemma":[0.002749468,0.9922315,0.0012852158,0.00017542315,0.00012075034,0.000019365283,0.0000675071,0.0000038933654,0.0033467917],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998523,0.00001610001,0.000011378073,0.000028104501,0.00007174833,0.000020346788],"domain_scores_gemma":[0.9998534,0.000051361334,0.000024874313,0.0000070228166,0.000048117625,0.000015148191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042783064,0.00080711313,0.0006416414,0.0018196487,0.00025783796,0.0006100752,0.0007457941,0.0008017351,0.003737046],"category_scores_gemma":[0.0003844487,0.00030614733,0.00035846792,0.0016600548,0.00029419828,0.0010887929,0.0005708876,0.0009636144,0.0028308802],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029816641,0.000087568806,0.00021608848,0.011895197,0.000050994182,0.00024065019,0.00005699776,0.0005086128,0.01798039,0.008937365,0.021127239,0.9388691],"study_design_scores_gemma":[0.0000039189936,0.00004716719,0.00028603288,0.00073276664,0.000028751585,0.0006730968,0.000029341063,0.00012004106,0.003795747,0.0010235106,0.9932452,0.000014329043],"about_ca_topic_score_codex":0.0005145702,"about_ca_topic_score_gemma":0.0015191959,"teacher_disagreement_score":0.003737046,"about_ca_system_score_codex":0.0004371858,"about_ca_system_score_gemma":0.00064243324,"threshold_uncertainty_score":0.012501597},"labels":[],"label_agreement":null},{"id":"W2626973872","doi":"10.3390/s17061420","title":"Fabrication of Circuits on Flexible Substrates Using Conductive SU-8 for Sensing Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Nanomaterials and Printing Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Microfabrication; Materials science; Miniaturization; Fabrication; Electrical conductor; Capacitor; Interconnection; Electronic circuit; Optoelectronics; Resistor; Planar; Printed circuit board; Flexible electronics; Electroforming; Inductor; Layer (electronics); Nanotechnology; Electrical engineering; Computer science; Composite material; Engineering; Telecommunications","score_opus":0.06445617117034995,"score_gpt":0.29566648859875955,"score_spread":0.23121031742840958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626973872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63007385,0.0060617006,0.34407762,0.00041161856,0.0006146478,0.00033033342,0.00047823368,0.0025651942,0.015386822],"genre_scores_gemma":[0.6812114,0.0024883335,0.30909777,0.00016591452,0.00008950852,0.00016969978,0.00039441933,0.00013720774,0.006245825],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997453,0.000018494236,0.000021734522,0.00006252738,0.000117932184,0.00003400417],"domain_scores_gemma":[0.99979967,0.000054065116,0.000053520816,0.000054055323,0.000026259602,0.00001254962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016301005,0.0006226927,0.00026864646,0.00030517895,0.00019936553,0.0004363737,0.0005982432,0.00047065344,0.0010944783],"category_scores_gemma":[0.00028523715,0.0002827885,0.0003134843,0.00023246264,0.00020150002,0.00059675553,0.0003646735,0.00046770414,0.00068298535],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008158471,0.000009233446,0.00005275119,0.00006132869,0.0000040201603,0.000074128635,0.000016889693,0.0001975516,0.9909601,0.0002442356,0.00009107098,0.00828057],"study_design_scores_gemma":[0.000008540031,0.00015220717,0.00046438386,0.0000083351315,0.000008997106,0.00030409763,0.000016882497,0.0021529333,0.98918134,0.00021480583,0.0074760085,0.000011618706],"about_ca_topic_score_codex":0.000104934195,"about_ca_topic_score_gemma":0.00038273496,"teacher_disagreement_score":0.0010944783,"about_ca_system_score_codex":0.0002239709,"about_ca_system_score_gemma":0.00014908891,"threshold_uncertainty_score":0.0036614537},"labels":[],"label_agreement":null},{"id":"W2726747157","doi":"10.3390/s17071526","title":"Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Ellipsoid; Sensor fusion; Fusion; Computer science; Geodesy; Artificial intelligence; Geology","score_opus":0.04488412454803021,"score_gpt":0.2965000776262643,"score_spread":0.2516159530782341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726747157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071650026,0.00011597285,0.99171317,0.00005110356,0.000022537919,0.000019274854,0.000024741852,0.00017238082,0.00071595114],"genre_scores_gemma":[0.5695301,0.00033482365,0.4269026,0.000119015705,0.000060370676,0.00015278583,0.0003013851,0.00006612362,0.0025327478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979861,0.00043338418,0.00014172719,0.00048608106,0.0008065909,0.0001461115],"domain_scores_gemma":[0.99842095,0.00039756956,0.00017402507,0.0003649634,0.0005953511,0.000047114678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018737743,0.0010301194,0.0013152605,0.000917786,0.0007531052,0.0010831786,0.0012945086,0.0008841492,0.0010813443],"category_scores_gemma":[0.0034990963,0.0005201676,0.0010675167,0.0012931598,0.0007215487,0.002494895,0.0023988604,0.001121833,0.000364359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039578756,0.00008384402,0.0027866738,0.00019482813,0.00022135634,0.00020418709,0.0002728841,0.6264704,0.027667474,0.023616578,0.00204853,0.31603757],"study_design_scores_gemma":[0.000013852825,0.000043773915,0.00042231448,0.0000065627723,0.00001864318,0.00007950507,0.000030360106,0.9850336,0.008298532,0.00481551,0.0012153288,0.000022078097],"about_ca_topic_score_codex":0.0037018359,"about_ca_topic_score_gemma":0.0030035004,"teacher_disagreement_score":0.0037018359,"about_ca_system_score_codex":0.0008371139,"about_ca_system_score_gemma":0.001352476,"threshold_uncertainty_score":0.00990957},"labels":[],"label_agreement":null},{"id":"W2729424549","doi":"10.3390/s17071579","title":"Characterization of Signal Quality Monitoring Techniques for Multipath Detection in GNSS Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; GNSS applications; Computer science; Delay spread; Sensitivity (control systems); Pseudorange; Multipath mitigation; Discriminator; Electronic engineering; Real-time computing; Telecommunications; Engineering; Global Positioning System","score_opus":0.029048097762141782,"score_gpt":0.3010413155160019,"score_spread":0.2719932177538601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729424549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77936774,0.0015465887,0.21521801,0.0001599558,0.00002891448,0.00010870259,0.00013372507,0.0005025786,0.0029337474],"genre_scores_gemma":[0.96571,0.0003496497,0.033415504,0.000026657377,0.0000117182935,0.000026143522,0.00008157647,0.000020924612,0.00035780983],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991647,0.00016016704,0.000042317173,0.00011565181,0.0004429795,0.00007428021],"domain_scores_gemma":[0.99767977,0.00092835305,0.0004889736,0.0002334201,0.00062233425,0.000047136753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009285189,0.00048107273,0.0002918068,0.0009211229,0.00023364299,0.000435456,0.0003576555,0.00048826582,0.00042052648],"category_scores_gemma":[0.004075031,0.00016666201,0.00020017412,0.0008624833,0.000331381,0.0005510965,0.00036090732,0.00030479342,0.0001603688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077271275,0.000102057296,0.04338071,0.00053518865,0.00009826395,0.00029014266,0.00030290772,0.07593211,0.6222221,0.0017622731,0.00026171055,0.2543398],"study_design_scores_gemma":[0.00003801988,0.0022399344,0.10899101,0.00010034478,0.00013048878,0.0020359876,0.0002233027,0.34908047,0.531934,0.0015485004,0.0035858266,0.00009211457],"about_ca_topic_score_codex":0.0006717479,"about_ca_topic_score_gemma":0.00077783136,"teacher_disagreement_score":0.0009285189,"about_ca_system_score_codex":0.00039290453,"about_ca_system_score_gemma":0.0002845193,"threshold_uncertainty_score":0.0049105287},"labels":[],"label_agreement":null},{"id":"W2729693777","doi":"10.3390/s17071576","title":"Multiport Circular Polarized RFID-Tag Antenna for UHF Sensor Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"RFID technology advancements","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Patch antenna; Ultra high frequency; Microstrip antenna; Circular polarization; Antenna (radio); Electrical engineering; Resistive touchscreen; Loop antenna; Physics; Bandwidth (computing); Microstrip; Computer science; Antenna factor; Telecommunications; Engineering","score_opus":0.015374053139867323,"score_gpt":0.26354006110301065,"score_spread":0.24816600796314334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729693777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16254862,0.0010868445,0.8174624,0.00049972156,0.00055744377,0.00004924644,0.00018503917,0.0023171946,0.015293441],"genre_scores_gemma":[0.8258854,0.0005592258,0.16252162,0.00045339557,0.00018145016,0.000057176312,0.00033349643,0.000112973874,0.009895201],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997414,0.00005599617,0.000013016018,0.000064875836,0.00009055005,0.000034167086],"domain_scores_gemma":[0.9995639,0.0000664948,0.00007487614,0.00011330862,0.00015731553,0.0000241947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018215354,0.00050237094,0.00037545597,0.0002841027,0.00014544369,0.0005188895,0.00069082784,0.0008951792,0.0017465682],"category_scores_gemma":[0.00028392885,0.00024405774,0.00046133774,0.0003932911,0.00017164992,0.00047300052,0.00034531625,0.0004399876,0.0026618214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046141955,0.0000957918,0.0034121142,0.000228929,0.00006421351,0.0005665672,0.000069871094,0.004266225,0.82447714,0.0037328198,0.0051551787,0.15746975],"study_design_scores_gemma":[0.00003647506,0.00090149307,0.006727165,0.000028575292,0.00010817773,0.0060868957,0.00009077919,0.080250785,0.86184746,0.0012713304,0.042563494,0.00008734538],"about_ca_topic_score_codex":0.00006090997,"about_ca_topic_score_gemma":0.00009095943,"teacher_disagreement_score":0.0017465682,"about_ca_system_score_codex":0.00022010987,"about_ca_system_score_gemma":0.000117424934,"threshold_uncertainty_score":0.0058428645},"labels":[],"label_agreement":null},{"id":"W2731501872","doi":"10.3390/s17071588","title":"Towards a Cognitive Radar: Canada’s Third-Generation High Frequency Surface Wave Radar (HFSWR) for Surveillance of the 200 Nautical Mile Exclusive Economic Zone","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Interference (communication); Radar; Clutter; Telecommunications; Exclusive economic zone; Computer science; Engineering; Global Positioning System; Mile; Real-time computing; Electronic engineering; Electrical engineering; Channel (broadcasting); Geography","score_opus":0.018026733974391536,"score_gpt":0.2235270115921175,"score_spread":0.20550027761772596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2731501872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15625432,0.019787574,0.45800984,0.10032407,0.0032623424,0.00084546686,0.00087061524,0.004085692,0.25656006],"genre_scores_gemma":[0.671073,0.00590765,0.23340198,0.012413008,0.0003686082,0.00009012369,0.00086121174,0.00016465569,0.07571976],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989396,0.00006982478,0.000008125087,0.00007643591,0.00061148056,0.00029453935],"domain_scores_gemma":[0.9985089,0.00006759979,0.000047792648,0.000044608143,0.00089605624,0.00043503274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022310705,0.0006128668,0.00022662897,0.00077266817,0.0015712483,0.003031175,0.0015521033,0.0016357602,0.0038544335],"category_scores_gemma":[0.0013738372,0.00019919603,0.00024683037,0.00054298557,0.0022561615,0.0014126947,0.0016031753,0.0017408048,0.0012966255],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076791627,0.00042216128,0.01219469,0.00035439327,0.00008273383,0.0007433791,0.0014859042,0.015534335,0.061229933,0.12567714,0.15074225,0.63076514],"study_design_scores_gemma":[0.00032418617,0.0009982373,0.0159764,0.00037232344,0.000099941775,0.0010467308,0.0019514867,0.12427017,0.036555775,0.04072656,0.7774566,0.00022163436],"about_ca_topic_score_codex":0.51206094,"about_ca_topic_score_gemma":0.6109664,"teacher_disagreement_score":0.48793906,"about_ca_system_score_codex":0.0060820053,"about_ca_system_score_gemma":0.030124513,"threshold_uncertainty_score":0.98162526},"labels":[],"label_agreement":null},{"id":"W2734438923","doi":"10.3390/s17071622","title":"Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Wearable technology; Computer science; Sensor fusion; Artifact (error); Electronics; Reliability (semiconductor); Embedded system; Human–computer interaction; Engineering; Artificial intelligence; Electrical engineering; Power (physics)","score_opus":0.09726548301257656,"score_gpt":0.3435419764767335,"score_spread":0.24627649346415695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734438923","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016199538,0.9878307,0.004869962,0.00032305496,0.0004406007,0.000029437655,0.00005881372,0.0000388314,0.004788675],"genre_scores_gemma":[0.010612754,0.9778201,0.0048127687,0.00035210632,0.00028729203,0.000051481366,0.000120685465,0.000011912795,0.005930888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977785,0.00002494341,0.000024453964,0.00004577202,0.0001053672,0.000021625543],"domain_scores_gemma":[0.9997516,0.00010265713,0.00004213265,0.000011823862,0.000076940334,0.000014677081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004827954,0.0008671188,0.00066085986,0.0020828915,0.00019782606,0.0006718091,0.00075894914,0.0011440548,0.0022840393],"category_scores_gemma":[0.0006104064,0.0003601465,0.0005332527,0.0017803643,0.00029833338,0.001165957,0.00053754094,0.0011592833,0.0016972044],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048030797,0.00012766445,0.00033183597,0.013985767,0.00006860078,0.00041506463,0.00009049769,0.0007788959,0.031615686,0.006862634,0.012351256,0.93332404],"study_design_scores_gemma":[0.000012586282,0.0003074375,0.0015199482,0.0023521201,0.00011917616,0.0034527532,0.00008619284,0.0010853059,0.028519722,0.0031101834,0.9593736,0.000061001898],"about_ca_topic_score_codex":0.00031316502,"about_ca_topic_score_gemma":0.00061044045,"teacher_disagreement_score":0.0022840393,"about_ca_system_score_codex":0.00024058209,"about_ca_system_score_gemma":0.000400698,"threshold_uncertainty_score":0.007640898},"labels":[],"label_agreement":null},{"id":"W2734962972","doi":"10.3390/s17071640","title":"Sensing Responses Based on Transfer Characteristics of InAs Nanowire Field-Effect Transistors","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Nanowire Synthesis and Applications","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanowire; Indium arsenide; Field-effect transistor; Materials science; Electron transfer; Nanotechnology; Conductance; Transistor; Indium; Optoelectronics; Analyte; Field effect; Adsorption; Chemistry; Photochemistry; Voltage; Quantum dot; Physical chemistry; Condensed matter physics","score_opus":0.010389631084812365,"score_gpt":0.23220049622032007,"score_spread":0.2218108651355077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734962972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9814004,0.00046441585,0.015795002,0.00011854462,0.000043651115,0.000035910914,0.0004051947,0.0003367814,0.0014000104],"genre_scores_gemma":[0.99558085,0.00032290764,0.0031425888,0.00003920315,0.000010794539,0.000020673075,0.00011676985,0.000015407762,0.00075073726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999851,0.000019565443,0.000008009512,0.0000491157,0.00005311236,0.000019155676],"domain_scores_gemma":[0.9997384,0.000121208526,0.000055147793,0.00002157525,0.00005153568,0.000012114513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017919534,0.00036669598,0.00020291981,0.00029111793,0.000118227916,0.00027541982,0.00037651887,0.00037110902,0.0009209153],"category_scores_gemma":[0.00068381516,0.00014727285,0.00018164417,0.00020654238,0.00022238455,0.00046900223,0.00014973799,0.00022705442,0.0002445236],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045208202,0.000016479413,0.00040706285,0.000029370474,0.000005217278,0.000048384347,0.00003814406,0.00030666703,0.9971169,0.00009296189,0.000030789648,0.0018629438],"study_design_scores_gemma":[0.000005282495,0.00018870647,0.0024370898,0.0000044524445,0.000014182508,0.00012805186,0.00003666509,0.011043984,0.9855661,0.00014547867,0.00042155205,0.000008521178],"about_ca_topic_score_codex":0.00033176504,"about_ca_topic_score_gemma":0.0003975692,"teacher_disagreement_score":0.0009209153,"about_ca_system_score_codex":0.00037219282,"about_ca_system_score_gemma":0.000068058835,"threshold_uncertainty_score":0.0030807853},"labels":[],"label_agreement":null},{"id":"W2737873585","doi":"10.3390/s17071658","title":"Surface Estimation for Microwave Imaging","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Microwave imaging; Imaging phantom; Computer science; Microwave; Computer vision; Laser; Set (abstract data type); Noise (video); Artificial intelligence; Optics; Image (mathematics); Physics; Telecommunications","score_opus":0.012436669658724607,"score_gpt":0.2494885921977467,"score_spread":0.23705192253902208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737873585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010069296,0.00044471814,0.99712425,0.00010623302,0.000035465135,0.000013933771,0.000044608947,0.00029638034,0.0009274252],"genre_scores_gemma":[0.13042066,0.002780249,0.8580567,0.00022891504,0.0002824694,0.00021120886,0.00076650357,0.0004296824,0.0068235123],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956733,0.00013160292,0.000019055107,0.00008159716,0.00017506248,0.000025402072],"domain_scores_gemma":[0.99923384,0.0003903668,0.000067933004,0.00013130308,0.00015740613,0.000019250805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005332217,0.00075283716,0.0005728263,0.00081234646,0.00022383877,0.0008888491,0.00070840074,0.0010220498,0.004038649],"category_scores_gemma":[0.0034585656,0.0003182449,0.0006594527,0.0010220322,0.0005865965,0.0009016551,0.00092374324,0.0013399235,0.0025006605],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010288431,0.00004593822,0.00085805025,0.00039397017,0.000077637975,0.000111001464,0.00012414774,0.26853684,0.03910287,0.096197054,0.01099826,0.58345145],"study_design_scores_gemma":[0.0000065111885,0.000024942923,0.0004385632,0.000027749227,0.000011116986,0.00015037258,0.000028060616,0.9390382,0.006468919,0.038547263,0.015239739,0.000018447248],"about_ca_topic_score_codex":0.0011142531,"about_ca_topic_score_gemma":0.000938902,"teacher_disagreement_score":0.004038649,"about_ca_system_score_codex":0.0004314388,"about_ca_system_score_gemma":0.0003535158,"threshold_uncertainty_score":0.013510644},"labels":[],"label_agreement":null},{"id":"W2738805783","doi":"10.3390/s17081707","title":"Recent Advancements towards Full-System Microfluidics","year":2017,"lang":"en","type":"editorial","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Microfluidics; Microsystem; Nanotechnology; Plug and play; Automation; System integration; Engineering; Computer science; Flow control (data); Systems engineering; Mechanical engineering; Materials science","score_opus":0.00821891678627622,"score_gpt":0.23527238786627627,"score_spread":0.22705347108000004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738805783","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028181396,0.41626272,0.0018278039,0.043323092,0.52968514,0.000021794067,0.000050589482,0.00017259124,0.008374492],"genre_scores_gemma":[0.0030989142,0.42437455,0.0018795121,0.027425975,0.5216901,0.000033353626,0.00008862895,0.00010228767,0.021306809],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997884,0.00028636906,0.000280255,0.0003953465,0.0009910832,0.00016295498],"domain_scores_gemma":[0.9943041,0.0021055327,0.00032857482,0.00017763967,0.0022828656,0.0008012031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004701928,0.001366198,0.0009228017,0.0017661217,0.0008839972,0.0036376955,0.0014446981,0.00408748,0.00865552],"category_scores_gemma":[0.007621192,0.000529594,0.00071520143,0.0010149094,0.0015571269,0.003913831,0.0018939648,0.0071684574,0.005944185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091582835,0.00004132511,0.00008536858,0.0028031028,0.000033358712,0.0003071814,0.00010655279,0.00015548826,0.001985465,0.012036678,0.7984046,0.18394928],"study_design_scores_gemma":[0.000004493502,0.000012910351,0.00003397378,0.00013742033,0.0000064229575,0.00015918174,0.000012812814,0.00003811984,0.00021217969,0.0009292306,0.99844813,0.0000051597417],"about_ca_topic_score_codex":0.00030176237,"about_ca_topic_score_gemma":0.0008319265,"teacher_disagreement_score":0.00865552,"about_ca_system_score_codex":0.0015379582,"about_ca_system_score_gemma":0.0016888406,"threshold_uncertainty_score":0.028955579},"labels":[],"label_agreement":null},{"id":"W2739087954","doi":"10.3390/s17071667","title":"A Novel Physical Sensing Principle for Liquid Characterization Using Paper-Based Hygro-Mechanical Systems (PB-HMS)","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Software portability; Characterization (materials science); Cantilever; Microfluidics; Materials science; Nanotechnology; Computer science; Work (physics); Biological system; Mechanical engineering; Engineering; Composite material","score_opus":0.03004130265832822,"score_gpt":0.2664435392632551,"score_spread":0.23640223660492687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739087954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28634444,0.013858731,0.6901144,0.0011052374,0.0007431636,0.0003779616,0.0003164246,0.0013131654,0.005826536],"genre_scores_gemma":[0.69607997,0.004420727,0.29166585,0.0007962619,0.00026877926,0.0002269121,0.00018800251,0.00004168924,0.006311815],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996264,0.000052686846,0.000022870096,0.00010327711,0.00017819379,0.000016607564],"domain_scores_gemma":[0.99970883,0.00011176128,0.00006951,0.000027942533,0.00005546997,0.0000265665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037206706,0.00047285913,0.00022709576,0.0003654813,0.00019295432,0.0003040336,0.00052806974,0.0006535742,0.00086296536],"category_scores_gemma":[0.0003563268,0.00028305178,0.00029555615,0.00022659289,0.00076270296,0.0006650536,0.00044104687,0.0007548385,0.00033369844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018916067,0.000009468796,0.00010107449,0.0001411477,0.0000055279215,0.00005530993,0.000025339981,0.000065316926,0.9883449,0.00076703326,0.00009927401,0.010366614],"study_design_scores_gemma":[0.0000065180207,0.0001978328,0.0006388999,0.0000079838455,0.000007668915,0.00042712834,0.000013708567,0.0011555138,0.9923781,0.00019893369,0.0049560233,0.000011701319],"about_ca_topic_score_codex":0.00008542776,"about_ca_topic_score_gemma":0.00013320912,"teacher_disagreement_score":0.00086296536,"about_ca_system_score_codex":0.00017433742,"about_ca_system_score_gemma":0.00018921412,"threshold_uncertainty_score":0.0028868914},"labels":[],"label_agreement":null},{"id":"W2739561174","doi":"10.3390/s17081735","title":"Automated Water Quality Survey and Evaluation Using an IoT Platform with Mobile Sensor Nodes","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Real-time computing; Search engine indexing; Wireless sensor network; Motion planning; Index (typography); Wireless; Sampling (signal processing); Computer network; Telecommunications; Artificial intelligence; Robot","score_opus":0.15038580894836717,"score_gpt":0.37490963076954514,"score_spread":0.22452382182117797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739561174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42267096,0.00015595299,0.57116634,0.00010056432,0.000045986322,0.0003151671,0.00034640916,0.0017530578,0.0034455375],"genre_scores_gemma":[0.87438744,0.00008546128,0.12426951,0.000023945648,0.000007984939,0.00012911248,0.00023817844,0.00002009095,0.000838237],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963653,0.000054562937,0.000019172363,0.000078398254,0.00018060295,0.00003064062],"domain_scores_gemma":[0.9996437,0.000069422866,0.00006435658,0.000060884653,0.00012590847,0.00003571021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037485064,0.0005038264,0.00043206045,0.00073503185,0.00030472627,0.00041191778,0.00050266675,0.00033084498,0.0005790672],"category_scores_gemma":[0.0006947202,0.00019903525,0.00025887522,0.00078650226,0.000205107,0.0007998133,0.0006327177,0.00016689401,0.00018037714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006971257,0.0005695581,0.036983963,0.0002975705,0.00010346345,0.00046428907,0.0002329809,0.27641702,0.25948787,0.0025415106,0.0020276771,0.42017698],"study_design_scores_gemma":[0.00002029053,0.00031197656,0.008002531,0.000006483295,0.000018162227,0.000059634047,0.00008383517,0.95688564,0.032796867,0.0006075751,0.0011885935,0.000018433853],"about_ca_topic_score_codex":0.0030342008,"about_ca_topic_score_gemma":0.005304589,"teacher_disagreement_score":0.0030342008,"about_ca_system_score_codex":0.00047566812,"about_ca_system_score_gemma":0.0005519607,"threshold_uncertainty_score":0.006033063},"labels":[],"label_agreement":null},{"id":"W2739653208","doi":"10.3390/s17081761","title":"Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Wireless sensor network; Key (lock); Optimization problem; Management science; Mathematical optimization; Distributed computing; Engineering; Computer network; Algorithm; Mathematics; Computer security","score_opus":0.022685314824518705,"score_gpt":0.2537961891693131,"score_spread":0.2311108743447944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739653208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074021367,0.064898804,0.9166678,0.0017521549,0.0017030565,0.00009679798,0.00012039881,0.00024141303,0.013779328],"genre_scores_gemma":[0.023696654,0.22512443,0.71272576,0.003026413,0.00734793,0.0009459286,0.0006109398,0.00064356887,0.025878452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986884,0.00046026922,0.00015275841,0.00022850304,0.00041514618,0.000054917666],"domain_scores_gemma":[0.9983746,0.0012316566,0.000097499964,0.00007972269,0.00018553308,0.000031039177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025044112,0.002756689,0.0013283095,0.0016866216,0.00048734422,0.0017678931,0.0010420895,0.0019805026,0.008167524],"category_scores_gemma":[0.0036999474,0.0010595511,0.0024318125,0.0033468558,0.0012649044,0.00303334,0.0012124365,0.004908421,0.0043346807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007352424,0.0002865329,0.0005035047,0.006204819,0.00023766182,0.0005405378,0.00057060213,0.06062038,0.0068815947,0.42464486,0.0773988,0.42203707],"study_design_scores_gemma":[0.00003216079,0.00026464375,0.00093398517,0.0016555368,0.00010229562,0.0014165028,0.00014238278,0.06427433,0.0024497623,0.3407791,0.5878514,0.00009781095],"about_ca_topic_score_codex":0.0006889277,"about_ca_topic_score_gemma":0.0006878849,"teacher_disagreement_score":0.008167524,"about_ca_system_score_codex":0.0009459533,"about_ca_system_score_gemma":0.0007604314,"threshold_uncertainty_score":0.027323127},"labels":[],"label_agreement":null},{"id":"W2742211991","doi":"10.3390/s17102413","title":"Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lightning (connector); Environmental science; Lightning strike; Meteorology; Geography; Hazard; Lightning detection; Land cover; Physical geography; Land use; Thunderstorm; Engineering; Ecology","score_opus":0.015299828931147605,"score_gpt":0.2184690309659371,"score_spread":0.2031692020347895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742211991","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99413717,0.00027709754,0.0008808704,0.0000698004,0.00000474425,0.000035498397,0.0027668984,0.00004503181,0.0017828544],"genre_scores_gemma":[0.9934515,0.000267686,0.0019888459,0.000020170852,0.0000024916242,0.00001155727,0.0027140195,0.0000065173467,0.0015372083],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999694,0.000024622432,0.000021076254,0.000049296057,0.00012188319,0.000089112655],"domain_scores_gemma":[0.9988042,0.00018354601,0.00010931598,0.000027500686,0.0007165238,0.00015886885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006003364,0.00027198237,0.00025532514,0.002054201,0.0009888371,0.0007909272,0.0006398424,0.00018283653,0.0008506573],"category_scores_gemma":[0.0014703051,0.00016989719,0.00025605233,0.0037781834,0.00036417932,0.00020363403,0.00032244358,0.00018474809,0.00012957868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020228219,0.00007868868,0.9510806,0.00007183446,0.00007680944,0.00041665827,0.0004777193,0.008645448,0.0015058428,0.00023720264,0.0015680905,0.035638794],"study_design_scores_gemma":[0.000012819815,0.000021365453,0.9778569,0.0000241827,0.000039581013,0.00007095818,0.0016911293,0.018117756,0.0006193443,0.00009453943,0.0014334869,0.00001799814],"about_ca_topic_score_codex":0.9939528,"about_ca_topic_score_gemma":0.99669194,"teacher_disagreement_score":0.010657326,"about_ca_system_score_codex":0.010657326,"about_ca_system_score_gemma":0.013557411,"threshold_uncertainty_score":0.07732469},"labels":[],"label_agreement":null},{"id":"W2742596581","doi":"10.3390/s17081804","title":"A Study of Thermistor Performance within a Textile Structure","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Thermoregulation and physiological responses","field":"Medicine","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Defence Science and Technology Group; Defence Science and Technology Laboratory; Trent University; Nottingham Trent University","keywords":"Thermistor; Textile; Materials science; Engineering; Electrical engineering; Composite material","score_opus":0.04394109970532624,"score_gpt":0.3238067973203807,"score_spread":0.2798656976150545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742596581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920894,0.0010838868,0.0055417526,0.000043603926,0.00005147362,0.000039690698,0.00007547172,0.00006167557,0.0010130113],"genre_scores_gemma":[0.99279845,0.00055754354,0.004898305,0.000016903688,0.000017202563,0.000031014148,0.000089700916,0.000030799187,0.001560106],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993345,0.00013753472,0.000038131016,0.00016865383,0.00023936186,0.000081865655],"domain_scores_gemma":[0.99880064,0.00046805092,0.0002283701,0.00015995711,0.00028552525,0.000057418678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008055515,0.0003980193,0.000378552,0.00016784245,0.0002131577,0.00049050566,0.00041845525,0.0007340963,0.00079032825],"category_scores_gemma":[0.0019993607,0.00025242608,0.00028489824,0.0002473003,0.00037980505,0.00053174305,0.00021579092,0.0004783472,0.0003967026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005726986,0.000016653023,0.00012124641,0.000052658477,0.000003707749,0.000032403757,0.000070524795,0.00020555397,0.998231,0.0000303607,0.000013154969,0.0011655102],"study_design_scores_gemma":[0.000004802335,0.0014544822,0.0027099242,0.000010366902,0.000016608672,0.00010983019,0.000050190407,0.0018951582,0.99279743,0.000014166443,0.0009276539,0.000009328979],"about_ca_topic_score_codex":0.00020719763,"about_ca_topic_score_gemma":0.00023674086,"teacher_disagreement_score":0.0008055515,"about_ca_system_score_codex":0.00023648278,"about_ca_system_score_gemma":0.00014490065,"threshold_uncertainty_score":0.004260242},"labels":[],"label_agreement":null},{"id":"W2742699470","doi":"10.3390/s17081829","title":"Achieve Location Privacy-Preserving Range Query in Vehicular Sensing","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Paillier cryptosystem; Computer science; Homomorphic encryption; Range query (database); Overhead (engineering); Scheme (mathematics); Exploit; Information privacy; Range (aeronautics); Computer network; Encryption; Cryptosystem; Computer security; Information retrieval; Hybrid cryptosystem; Web search query; Search engine","score_opus":0.012512202766344313,"score_gpt":0.22920497457050037,"score_spread":0.21669277180415605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742699470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04524896,0.0006577973,0.95166004,0.0002464894,0.00004240379,0.000100006255,0.0000932399,0.00038191263,0.0015691849],"genre_scores_gemma":[0.9249383,0.0005966535,0.072469704,0.00019089645,0.000070142676,0.000105460735,0.00018772246,0.000025032832,0.0014160761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974644,0.0006623167,0.00018457543,0.00047628194,0.0008657335,0.00034670957],"domain_scores_gemma":[0.9981451,0.00072930875,0.00025281342,0.0005658663,0.00024807354,0.00005874297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018010365,0.00055815105,0.0010128065,0.00071783375,0.00077673694,0.00094388094,0.0013848831,0.0009815113,0.0006851844],"category_scores_gemma":[0.0040537007,0.00029970577,0.00055408606,0.001436066,0.0010576613,0.0037279162,0.0033905632,0.00077901,0.00028425577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001604249,0.00028069477,0.0035714973,0.0006966393,0.00020580016,0.0013239863,0.0015627854,0.3693327,0.12291441,0.19107425,0.005473481,0.3019595],"study_design_scores_gemma":[0.00011018174,0.00047372287,0.00065718056,0.000020468116,0.00006572003,0.000907009,0.00044987685,0.89909726,0.04312566,0.050244562,0.0047808215,0.000067496134],"about_ca_topic_score_codex":0.0009688949,"about_ca_topic_score_gemma":0.00055017014,"teacher_disagreement_score":0.0018010365,"about_ca_system_score_codex":0.00061022496,"about_ca_system_score_gemma":0.0009031348,"threshold_uncertainty_score":0.009524941},"labels":[],"label_agreement":null},{"id":"W2744282427","doi":"10.3390/s17081808","title":"Energy-Based Metrics for Arthroscopic Skills Assessment","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Computer science; Machine learning; Artificial intelligence; Energy (signal processing); Cross-validation; Data mining; Statistics; Mathematics","score_opus":0.0436837103034749,"score_gpt":0.3761214281951116,"score_spread":0.3324377178916367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744282427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11972822,0.0033539084,0.8685136,0.0002312037,0.00024976511,0.00035208726,0.00082543987,0.0011262677,0.005619459],"genre_scores_gemma":[0.661103,0.0015044293,0.333035,0.00011476019,0.000096320444,0.00060755346,0.0009980719,0.00016204368,0.0023787825],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797183,0.0005177298,0.00017848598,0.00019152896,0.0010846339,0.00005587979],"domain_scores_gemma":[0.9966775,0.0015125385,0.0004923889,0.00024366195,0.0009933566,0.00008051488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018923854,0.00096891297,0.00066426623,0.0025390114,0.00025053692,0.00083616923,0.0005347864,0.0007950171,0.0018212403],"category_scores_gemma":[0.008063433,0.000224831,0.00042467407,0.0020522762,0.00038257445,0.0012305551,0.0006514616,0.00048565387,0.00063903764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049090356,0.00041235817,0.028370757,0.0011094173,0.00019947546,0.0001653008,0.0002705451,0.05516931,0.09465341,0.004441503,0.0044689868,0.810248],"study_design_scores_gemma":[0.000067972775,0.002256998,0.16083443,0.00050398824,0.00018275417,0.00161551,0.00069699966,0.7166237,0.0855003,0.0103949765,0.020976903,0.00034548604],"about_ca_topic_score_codex":0.0010379633,"about_ca_topic_score_gemma":0.0015826065,"teacher_disagreement_score":0.0025390114,"about_ca_system_score_codex":0.0004315334,"about_ca_system_score_gemma":0.0004411566,"threshold_uncertainty_score":0.010008037},"labels":[],"label_agreement":null},{"id":"W2745510169","doi":"10.3390/s17092114","title":"Surface Profiling and Core Evaluation of Aluminum Honeycomb Sandwich Aircraft Panels Using Multi-Frequency Eddy Current Testing","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Royal Military College of Canada","funders":"","keywords":"Materials science; Honeycomb structure; Nondestructive testing; Profiling (computer programming); Eddy-current testing; Eddy current; Optics; Honeycomb; Acoustics; Aluminium; Sandwich-structured composite; Core (optical fiber); Composite material; Engineering; Electrical engineering; Computer science","score_opus":0.1980396527290109,"score_gpt":0.36849758624498247,"score_spread":0.17045793351597158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745510169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98674405,0.00012377545,0.01243156,0.00001358077,0.000004612704,0.000021456928,0.000048248952,0.0000996815,0.00051297963],"genre_scores_gemma":[0.99323833,0.000049490067,0.006342669,0.000010182393,0.0000020152843,0.0000074331906,0.000038170743,0.000010877873,0.00030072615],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976414,0.000022683085,0.000009072253,0.000032092965,0.00014440092,0.000027573122],"domain_scores_gemma":[0.9993956,0.000108631284,0.00010026182,0.00007594535,0.00027694294,0.000042442964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002516252,0.0002817328,0.0001762077,0.0006447926,0.00014032843,0.00024341565,0.00021849503,0.00027489255,0.0006221436],"category_scores_gemma":[0.00048014845,0.00016309539,0.00013035572,0.00020189118,0.00024019039,0.0002776543,0.00031917955,0.00017336685,0.00015579775],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068791334,0.000023418359,0.005814264,0.000036340138,0.000004668287,0.00011730842,0.00011747332,0.0010419283,0.9809725,0.00003144245,0.000046431098,0.0117254285],"study_design_scores_gemma":[0.000007756051,0.0006207788,0.12898912,0.000011572331,0.000018675326,0.0005486034,0.000247249,0.0226333,0.8460257,0.00007156097,0.0008059636,0.000019701125],"about_ca_topic_score_codex":0.00065296085,"about_ca_topic_score_gemma":0.0019434319,"teacher_disagreement_score":0.00065296085,"about_ca_system_score_codex":0.00014641845,"about_ca_system_score_gemma":0.00008564702,"threshold_uncertainty_score":0.0020813346},"labels":[],"label_agreement":null},{"id":"W2748640347","doi":"10.3390/s17081892","title":"Secure Localization in the Presence of Colluders in WSNs","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nokia (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversary; Node (physics); Adversarial system; Wireless sensor network; Computer science; Position (finance); Wireless; Range (aeronautics); Computer network; Computer security; Artificial intelligence; Engineering; Telecommunications","score_opus":0.01144697833478982,"score_gpt":0.2347161456488858,"score_spread":0.22326916731409596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748640347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10653765,0.00040962143,0.8912819,0.00019676318,0.000035630244,0.000026329659,0.000017940461,0.00024374084,0.001250375],"genre_scores_gemma":[0.953968,0.0003086455,0.044948105,0.000031635,0.00003325443,0.000029537778,0.000028372984,0.000026551808,0.0006260694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984505,0.0005465812,0.00007205971,0.0002860173,0.0004861663,0.00015869617],"domain_scores_gemma":[0.99678767,0.0017719475,0.00058764854,0.00051625434,0.00024771443,0.00008882147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014411206,0.00072993967,0.0009720507,0.0006202798,0.00071738,0.0008789318,0.0009021582,0.0009595526,0.00042079258],"category_scores_gemma":[0.006732753,0.00036318545,0.00044477024,0.00060200086,0.0018975312,0.0015020531,0.0030070394,0.000796804,0.00025233848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024152748,0.000030075655,0.0034059878,0.00008743817,0.00007087959,0.0010214992,0.00028679965,0.929782,0.01963906,0.016936423,0.00032817942,0.028170183],"study_design_scores_gemma":[0.0000146241,0.00011135051,0.00044864588,0.000010114585,0.000017518789,0.00026428563,0.000075568336,0.9809965,0.0068147685,0.010540113,0.0006915949,0.00001487908],"about_ca_topic_score_codex":0.00095476076,"about_ca_topic_score_gemma":0.00058478507,"teacher_disagreement_score":0.0014411206,"about_ca_system_score_codex":0.0005528553,"about_ca_system_score_gemma":0.00045608415,"threshold_uncertainty_score":0.007621467},"labels":[],"label_agreement":null},{"id":"W2749301731","doi":"10.3390/s17081904","title":"Dynamic Fuzzy-Logic Based Path Planning for Mobility-Assisted Localization in Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Dalhousie University","funders":"","keywords":"Fuzzy logic; Node (physics); Motion planning; Wireless sensor network; Path (computing); Mobility model; Computer science; Context (archaeology); Software deployment; Real-time computing; Computer network; Distributed computing; Engineering; Artificial intelligence","score_opus":0.015723495390129766,"score_gpt":0.2571879706592691,"score_spread":0.24146447526913936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749301731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009139062,0.00027675406,0.9879479,0.000085029664,0.000030284424,0.00003540486,0.00002685652,0.00016874114,0.0022900337],"genre_scores_gemma":[0.855147,0.0006205185,0.14136532,0.00007417949,0.000032055224,0.00014029964,0.00008455928,0.000021025939,0.002515102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976295,0.000045366225,0.000015563055,0.000056325913,0.00009547057,0.000024338618],"domain_scores_gemma":[0.9998417,0.00006293929,0.000031241707,0.000012731115,0.00004312289,0.000008203083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032518525,0.0004391912,0.0002723041,0.0004301784,0.00039213093,0.00049749366,0.00090739445,0.0005685461,0.0010394214],"category_scores_gemma":[0.0006913142,0.00018154616,0.00044907353,0.00044104643,0.0004520167,0.00067034096,0.00036078828,0.0005029931,0.00017242912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102636346,0.000059034217,0.000742761,0.00014274236,0.000033220404,0.00018796747,0.00014574619,0.85182637,0.013326158,0.02289803,0.0008882791,0.10964708],"study_design_scores_gemma":[0.000007958397,0.000052893683,0.00014987537,0.000011134302,0.000013063356,0.000044389642,0.000014295432,0.9934645,0.001637653,0.0036701683,0.0009236654,0.000010354199],"about_ca_topic_score_codex":0.0064207576,"about_ca_topic_score_gemma":0.0056716744,"teacher_disagreement_score":0.0064207576,"about_ca_system_score_codex":0.0008085161,"about_ca_system_score_gemma":0.0008677453,"threshold_uncertainty_score":0.0127667785},"labels":[],"label_agreement":null},{"id":"W2751233170","doi":"10.3390/s17092023","title":"Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Basic Research Program of Shaanxi Province; National Natural Science Foundation of China","keywords":"Relay; Computer network; Computer science; Energy harvesting; Secure communication; Node (physics); Wireless; Eavesdropping; Wireless sensor network; Jamming; Transmission (telecommunications); Energy (signal processing); Power (physics); Telecommunications; Encryption; Engineering","score_opus":0.014789629195301319,"score_gpt":0.2259031537234756,"score_spread":0.21111352452817428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751233170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10034348,0.0014712233,0.8927393,0.00039666682,0.00011658235,0.000090349335,0.000059142167,0.00020893165,0.004574291],"genre_scores_gemma":[0.97390175,0.00074701576,0.02386265,0.00008097037,0.00003334843,0.00006902308,0.00002491796,0.000008972031,0.0012713961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812156,0.0007013475,0.000098339784,0.00035578437,0.00044871386,0.00027433407],"domain_scores_gemma":[0.99696964,0.0016860032,0.00049943355,0.0003769243,0.00035863955,0.000109340195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015841699,0.0011338746,0.001200201,0.0009194849,0.0010373831,0.0017362003,0.001350659,0.0013106657,0.0008471936],"category_scores_gemma":[0.0038400772,0.0003643741,0.0006282769,0.0011041005,0.0024441842,0.0026094995,0.002241412,0.0009867009,0.00023500425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011130302,0.00008054411,0.0018560007,0.00034630077,0.0001761147,0.0021568714,0.000834852,0.72632784,0.03154562,0.18330872,0.0018401922,0.050413877],"study_design_scores_gemma":[0.000022854772,0.00015270562,0.00015608112,0.0000200543,0.00004226303,0.00044372806,0.00012860377,0.97632027,0.0035791595,0.018075662,0.001028845,0.000029744151],"about_ca_topic_score_codex":0.001738112,"about_ca_topic_score_gemma":0.0012835928,"teacher_disagreement_score":0.001738112,"about_ca_system_score_codex":0.001326194,"about_ca_system_score_gemma":0.00083445,"threshold_uncertainty_score":0.009622216},"labels":[],"label_agreement":null},{"id":"W2751539606","doi":"10.3390/s17092073","title":"The Design and Development of an Omni-Directional Mobile Robot Oriented to an Intelligent Manufacturing System","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Mobile robot; Robot; Engineering; Encoder; Robot control; Control engineering; Computer science; Simulation; Embedded system; Real-time computing; Artificial intelligence","score_opus":0.02191004134610721,"score_gpt":0.2539284461443707,"score_spread":0.2320184047982635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751539606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034261476,0.00049369695,0.955473,0.00010542254,0.00012473628,0.00028550896,0.00006213729,0.0013731197,0.00782086],"genre_scores_gemma":[0.33970663,0.000592554,0.64782536,0.00012932562,0.00003648729,0.00046059617,0.00013767192,0.000051905306,0.011059381],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998288,0.000015948634,0.000010199603,0.000041068903,0.00008252792,0.000021407655],"domain_scores_gemma":[0.99988604,0.000008985782,0.00001465698,0.000010678832,0.000060442955,0.000019181061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023808636,0.0003737021,0.00029947842,0.00026799718,0.00021789067,0.00032485276,0.0007353156,0.0004597742,0.00098598],"category_scores_gemma":[0.00017048251,0.00023679777,0.0002615968,0.00013484765,0.00018652467,0.0002490406,0.0002801172,0.0002853892,0.0006143911],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020918557,0.00014979119,0.0030378746,0.0007906961,0.00006453532,0.00066854816,0.0002582766,0.04313256,0.6331041,0.013211477,0.0026265956,0.3027464],"study_design_scores_gemma":[0.00019684849,0.0033058305,0.010692597,0.0001725482,0.00021672258,0.0025829575,0.00021022829,0.56574094,0.2509661,0.0023286857,0.16339104,0.00019545254],"about_ca_topic_score_codex":0.000995258,"about_ca_topic_score_gemma":0.0009151581,"teacher_disagreement_score":0.000995258,"about_ca_system_score_codex":0.00015117392,"about_ca_system_score_gemma":0.00064547296,"threshold_uncertainty_score":0.0032984614},"labels":[],"label_agreement":null},{"id":"W2752525213","doi":"10.3390/s17091981","title":"GPS Satellite Orbit Prediction at User End for Real-Time PPP System","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"China Scholarship Council","keywords":"Precise Point Positioning; Geodesy; Orbit (dynamics); Satellite; Satellite system; Global Positioning System; GNSS applications; Computer science; Orbit determination; Kinematics; Mean squared error; Simulation; Remote sensing; Aerospace engineering; Geology; Physics; Engineering; Mathematics; Telecommunications","score_opus":0.011613818906923803,"score_gpt":0.21564314632649786,"score_spread":0.20402932741957405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752525213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10520423,0.00023760673,0.8856898,0.00013477953,0.00015483721,0.000072743336,0.00042110356,0.0043760897,0.0037088648],"genre_scores_gemma":[0.8957565,0.00017881014,0.10001181,0.000042298852,0.000051423554,0.000054800003,0.0010016537,0.000094665054,0.0028080943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951756,0.00008159129,0.000029265526,0.00009727463,0.0002239608,0.000050252227],"domain_scores_gemma":[0.99951863,0.000038593087,0.000038129314,0.00009924715,0.00028082597,0.000024544948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005692825,0.00052107475,0.0004377346,0.0005020162,0.00033731037,0.0005976395,0.0007063083,0.00045002354,0.0012475859],"category_scores_gemma":[0.0011773154,0.00022346622,0.00035380942,0.00055220147,0.00021701283,0.0008332978,0.0006668342,0.000525388,0.00073939393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048576092,0.00008540555,0.035449766,0.00017312173,0.00006957375,0.0005327798,0.00028575005,0.56307524,0.026691437,0.0048199315,0.0076034185,0.36072776],"study_design_scores_gemma":[0.000016653017,0.000056693505,0.003290086,0.0000057714483,0.00001615398,0.00006428266,0.000032214506,0.9854595,0.008088765,0.0006440419,0.0023093864,0.000016375772],"about_ca_topic_score_codex":0.008169518,"about_ca_topic_score_gemma":0.0039455546,"teacher_disagreement_score":0.008169518,"about_ca_system_score_codex":0.00031721743,"about_ca_system_score_gemma":0.0007364082,"threshold_uncertainty_score":0.016243935},"labels":[],"label_agreement":null},{"id":"W2753662912","doi":"10.3390/s17092003","title":"Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Université du Québec à Chicoutimi","keywords":"Wearable computer; Inertial measurement unit; Artificial neural network; Context (archaeology); Computer science; Artificial intelligence; Identification (biology); Center of pressure (fluid mechanics); Wearable technology; Work (physics); Set (abstract data type); Machine learning; Pattern recognition (psychology); Engineering; Embedded system","score_opus":0.053166700512482824,"score_gpt":0.33724385633510295,"score_spread":0.2840771558226201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753662912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6509374,0.0014081588,0.34142792,0.00019199518,0.0002677329,0.00018445622,0.00070107396,0.0012804484,0.003600856],"genre_scores_gemma":[0.94949424,0.00033570218,0.047853865,0.000040418778,0.00003820676,0.00012597696,0.0003729089,0.00001425152,0.0017243585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998061,0.00003436217,0.000017056187,0.00006105888,0.000052968295,0.000028554967],"domain_scores_gemma":[0.9998111,0.00007021519,0.000035048466,0.000016195068,0.000055574194,0.000011866318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029868088,0.00066316273,0.0004863852,0.0008006766,0.00018084074,0.00034178948,0.0002641507,0.0005309552,0.0015424496],"category_scores_gemma":[0.00072380796,0.0001644377,0.0004057039,0.0006039427,0.00010604058,0.00028680486,0.0002856715,0.00026375786,0.0004485695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010566189,0.0006595362,0.03198912,0.00029853501,0.00021098198,0.00039551005,0.00015626005,0.055060998,0.091031104,0.00044681286,0.0020507104,0.81664383],"study_design_scores_gemma":[0.000035596087,0.0004304515,0.056159366,0.0000594014,0.000104719744,0.0002514553,0.00012655754,0.9216043,0.019657765,0.00063008984,0.00091028446,0.000030019395],"about_ca_topic_score_codex":0.0018071481,"about_ca_topic_score_gemma":0.0022986007,"teacher_disagreement_score":0.0018071481,"about_ca_system_score_codex":0.00022956579,"about_ca_system_score_gemma":0.0001650941,"threshold_uncertainty_score":0.0051600337},"labels":[],"label_agreement":null},{"id":"W2753754860","doi":"10.3390/s17092032","title":"An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"GNSS applications; Covariance; Computer science; Kalman filter; Noise (video); Inertial navigation system; GPS/INS; Satellite system; Multipath propagation; Real-time computing; Algorithm; Global Positioning System; Artificial intelligence; Assisted GPS; Mathematics","score_opus":0.03223895371967359,"score_gpt":0.26964219246059634,"score_spread":0.23740323874092276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753754860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017684557,0.00016082358,0.9797275,0.000050362738,0.00004046142,0.00004376195,0.00002311763,0.0007310034,0.0015383537],"genre_scores_gemma":[0.6651816,0.0002050764,0.33047727,0.00015983282,0.00006453495,0.00014533997,0.00019283527,0.000056013145,0.003517602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992526,0.00008534369,0.00004013505,0.00024125808,0.00031373993,0.00006688158],"domain_scores_gemma":[0.99961084,0.00004583946,0.0000573645,0.000073024356,0.00018889287,0.000024177893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004812695,0.0007976666,0.0006681141,0.00044066465,0.0005157649,0.00059980305,0.0016583542,0.0007433631,0.0009599728],"category_scores_gemma":[0.00088747515,0.00042886945,0.00051097875,0.0006029251,0.0003793106,0.001086319,0.0013306951,0.00069159456,0.0005928354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042932006,0.00022049452,0.0048764786,0.0002282483,0.00021764197,0.00041226816,0.00034487413,0.35152507,0.18103136,0.011291831,0.0026512956,0.44677114],"study_design_scores_gemma":[0.000027269502,0.00020959789,0.0012497789,0.000013371623,0.000072645125,0.00020589803,0.000024366205,0.97169435,0.021728266,0.0014383297,0.0032980056,0.000038205464],"about_ca_topic_score_codex":0.004546129,"about_ca_topic_score_gemma":0.0051320195,"teacher_disagreement_score":0.004546129,"about_ca_system_score_codex":0.0004475163,"about_ca_system_score_gemma":0.00085082056,"threshold_uncertainty_score":0.009039342},"labels":[],"label_agreement":null},{"id":"W2754994685","doi":"10.3390/s17092154","title":"A Generic Compliance Modeling Method for Two-Axis Elliptical-Arc-Filleted Flexure Hinges","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Piezoelectric Actuators and Control","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Hinge; Structural engineering; Finite element method; Arc (geometry); Plane (geometry); Engineering; Geometry; Mechanical engineering; Mathematics","score_opus":0.043654523322491263,"score_gpt":0.29902700278510297,"score_spread":0.2553724794626117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754994685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004782576,0.00006492165,0.993807,0.00001528996,0.00000863172,0.000012769442,0.00001854185,0.00008854067,0.0012017015],"genre_scores_gemma":[0.5821968,0.00069284986,0.41023135,0.000056883375,0.000028214581,0.00019098743,0.00018041606,0.00010765761,0.006314817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997545,0.00004650105,0.000016773043,0.00005731707,0.00010727812,0.000017648494],"domain_scores_gemma":[0.99985266,0.000034391538,0.00003568696,0.00003095508,0.000040133535,0.0000061386977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038695362,0.0006161727,0.00030539028,0.00048368514,0.00026155813,0.00037433178,0.0008891243,0.00070982095,0.001688384],"category_scores_gemma":[0.0004698244,0.00027787065,0.00077371066,0.00038497613,0.00041405,0.00083590794,0.0004201853,0.0004881302,0.00031147373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051726278,0.0000723943,0.00111486,0.00024304492,0.00003281401,0.0003652303,0.00028741168,0.70730525,0.11326805,0.081651755,0.0009604361,0.09464706],"study_design_scores_gemma":[0.0000035997598,0.000030373227,0.000162847,0.00001189175,0.0000059489635,0.00012899557,0.000019233255,0.98844004,0.0067416606,0.0019846056,0.0024583926,0.000012418349],"about_ca_topic_score_codex":0.0011065365,"about_ca_topic_score_gemma":0.00087566796,"teacher_disagreement_score":0.001688384,"about_ca_system_score_codex":0.0003334666,"about_ca_system_score_gemma":0.0004802322,"threshold_uncertainty_score":0.0056482553},"labels":[],"label_agreement":null},{"id":"W2755286355","doi":"10.3390/s17092141","title":"Using Impedance Measurements to Characterize Surface Modified with Gold Nanoparticles","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Alberta Innovates","keywords":"Dielectric spectroscopy; Biomolecule; Biosensor; Lab-on-a-chip; Materials science; Colloidal gold; Nanotechnology; Surface modification; Electrical impedance; Substrate (aquarium); Chip; Point-of-care testing; Computer science; Electrode; Biomedical engineering; Nanoparticle; Electrical engineering; Microfluidics; Chemistry; Engineering; Chemical engineering; Telecommunications","score_opus":0.07054805877403564,"score_gpt":0.3194053292366727,"score_spread":0.24885727046263706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755286355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6592246,0.0025085218,0.330995,0.0004879209,0.00032668436,0.00013447242,0.00063671084,0.0011302962,0.004555706],"genre_scores_gemma":[0.88631725,0.0021652216,0.105956435,0.00034370832,0.000059710754,0.00014339802,0.0006993031,0.000109833985,0.004205125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995109,0.000063776504,0.000039276463,0.00010105243,0.00025422897,0.000030739142],"domain_scores_gemma":[0.999663,0.00011365212,0.00007168715,0.000037753314,0.000101433274,0.000012416284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032105768,0.0005569631,0.00028602226,0.0005505655,0.00014522196,0.0004505672,0.00045416568,0.0007672226,0.00065830105],"category_scores_gemma":[0.0009852026,0.00017925394,0.0002671976,0.0004826707,0.00027424854,0.00072489487,0.00031377346,0.0004095423,0.0006055295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001941763,0.000015417523,0.0003998019,0.00004453934,0.000009228683,0.000040591876,0.000025939018,0.00032874895,0.9937557,0.00010104739,0.00006959698,0.005190009],"study_design_scores_gemma":[0.000005362013,0.00011261696,0.0021208932,0.0000055430787,0.000016428128,0.00014843325,0.000029003744,0.007691394,0.98793715,0.00025272087,0.0016671899,0.000013312077],"about_ca_topic_score_codex":0.00029614198,"about_ca_topic_score_gemma":0.00036728603,"teacher_disagreement_score":0.0007672226,"about_ca_system_score_codex":0.00032433873,"about_ca_system_score_gemma":0.000106490195,"threshold_uncertainty_score":0.002353251},"labels":[],"label_agreement":null},{"id":"W2755337709","doi":"10.3390/s17092112","title":"An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Key distribution in wireless sensor networks; Wireless WAN; Wi-Fi array; Computer network; Mobile wireless sensor network; Wireless network; Computer science; Wireless; Node (physics); Sensor node; Embedded system; Heterogeneous network; Network architecture; Engineering; Telecommunications","score_opus":0.049431894858640066,"score_gpt":0.2784471919703942,"score_spread":0.2290152971117541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755337709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07520468,0.0012300117,0.90196663,0.00061175146,0.0002296593,0.00028165078,0.00011796687,0.0016977278,0.018659972],"genre_scores_gemma":[0.7754057,0.0012609016,0.21366332,0.00018536288,0.000058628448,0.00028926542,0.00016528991,0.000028492623,0.008943092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997398,0.000073433825,0.000014504464,0.000056254506,0.00009875685,0.000017143471],"domain_scores_gemma":[0.9998547,0.000037227033,0.000029868464,0.000018425064,0.000040726576,0.000018908142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029111985,0.00030707865,0.00031908008,0.00028926125,0.00043282576,0.00065150193,0.0007781342,0.0004563346,0.0012811817],"category_scores_gemma":[0.00039071354,0.00011628047,0.00016460838,0.00036986527,0.00025391227,0.0007667139,0.00056784396,0.00036814102,0.0002826276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005378202,0.00059958687,0.0053683557,0.0007148039,0.00014878526,0.0018538367,0.000490102,0.4110417,0.12790877,0.06932393,0.008063319,0.37394902],"study_design_scores_gemma":[0.00004807858,0.000391094,0.0009101503,0.00002316169,0.000045323824,0.00031913558,0.000057881505,0.95866966,0.015097876,0.0048893075,0.019527249,0.000021083228],"about_ca_topic_score_codex":0.0009638962,"about_ca_topic_score_gemma":0.0011371484,"teacher_disagreement_score":0.0012811817,"about_ca_system_score_codex":0.00026231984,"about_ca_system_score_gemma":0.00057589746,"threshold_uncertainty_score":0.0042859316},"labels":[],"label_agreement":null},{"id":"W2755368436","doi":"10.3390/s17092115","title":"Time-Resolved Diffuse Optical Spectroscopy and Imaging Using Solid-State Detectors: Characteristics, Present Status, and Research Challenges","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Silicon photomultiplier; Detector; Diffuse optical imaging; Computer science; Photomultiplier; Optoelectronics; Optics; Physics; Scintillator","score_opus":0.1444838903398222,"score_gpt":0.4648148295274314,"score_spread":0.32033093918760924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755368436","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034270738,0.99722904,0.0007317449,0.00031764852,0.00012996384,0.0000049238115,0.000011784179,0.00001153465,0.0012206732],"genre_scores_gemma":[0.0021878555,0.9957639,0.000923713,0.00020369739,0.00021763277,0.000012224233,0.000027979971,0.000004129018,0.0006588689],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995443,0.000061996856,0.00004823892,0.00009928889,0.00020445201,0.000041676893],"domain_scores_gemma":[0.9990447,0.0005355196,0.00011103666,0.000030718944,0.0002318079,0.000046274337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012535105,0.0008848926,0.001104343,0.00250746,0.00030799874,0.0011658652,0.00097510876,0.0015094882,0.0015575613],"category_scores_gemma":[0.0011491769,0.0004820059,0.0005623019,0.0023026078,0.0009592683,0.0025198343,0.0006753692,0.00207494,0.001055377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004793313,0.00009622949,0.00042747075,0.016713386,0.0000641615,0.00024396999,0.00015194318,0.00067787606,0.012119683,0.014177767,0.010554158,0.9447254],"study_design_scores_gemma":[0.000008849766,0.00019619681,0.0010475053,0.002209589,0.000096011885,0.0031520065,0.00016059702,0.0005772308,0.009305087,0.0053558205,0.977824,0.00006708824],"about_ca_topic_score_codex":0.0008253185,"about_ca_topic_score_gemma":0.0010720213,"teacher_disagreement_score":0.00250746,"about_ca_system_score_codex":0.00070742046,"about_ca_system_score_gemma":0.0011443965,"threshold_uncertainty_score":0.006629288},"labels":[],"label_agreement":null},{"id":"W2756795309","doi":"10.3390/s17102166","title":"Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; Fundamental Research Funds for the Central Universities; King Saud University; National Natural Science Foundation of China","keywords":"Computer science; Asynchronous communication; Distributed computing; Correctness; Mobile ad hoc network; Wireless ad hoc network; Computer network; Rollback; Wireless; Algorithm; Database transaction; Network packet","score_opus":0.021093118686601218,"score_gpt":0.25364214718286626,"score_spread":0.23254902849626505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756795309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008492342,0.0002510037,0.98877114,0.00012843499,0.000028605073,0.00006137252,0.000013891908,0.00012473432,0.002128471],"genre_scores_gemma":[0.66715705,0.0008591049,0.32725435,0.00013375518,0.00006363724,0.0005202346,0.00010788185,0.00007859463,0.0038254717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994717,0.00016377385,0.0000287828,0.00009893433,0.00017945317,0.000057301353],"domain_scores_gemma":[0.9995022,0.000264438,0.000057351375,0.00003226618,0.00012399905,0.00001977747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081864354,0.00059747463,0.000550509,0.00068048853,0.0005102893,0.0008647191,0.00094430504,0.00066357624,0.0013550435],"category_scores_gemma":[0.002236166,0.00028406124,0.00044752657,0.00071653177,0.00062035635,0.0011708896,0.00082675496,0.0004188428,0.00024430547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042100506,0.000031628646,0.00060537807,0.00010126284,0.000030234483,0.000046728244,0.00005365253,0.8840681,0.0025234998,0.04310768,0.0009444115,0.06844543],"study_design_scores_gemma":[0.000004055129,0.000019486832,0.000045995024,0.0000035302132,0.0000034107024,0.000009674855,0.000009713648,0.9941912,0.00031954166,0.004768539,0.0006227986,0.0000020526045],"about_ca_topic_score_codex":0.0019416385,"about_ca_topic_score_gemma":0.0018681003,"teacher_disagreement_score":0.0019416385,"about_ca_system_score_codex":0.00096342806,"about_ca_system_score_gemma":0.0011717248,"threshold_uncertainty_score":0.006990254},"labels":[],"label_agreement":null},{"id":"W2757326413","doi":"10.3390/s17102237","title":"Improving the Accuracy of Direct Geo-referencing of Smartphone-Based Mobile Mapping Systems Using Relative Orientation and Scene Geometric Constraints","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Orientation (vector space); Computer science; Computer vision; Bundle adjustment; Global Positioning System; Gyroscope; Accelerometer; Artificial intelligence; Mobile mapping; Mobile device; Real-time computing; Photogrammetry; Engineering; Telecommunications","score_opus":0.026868152949820586,"score_gpt":0.2440016205313992,"score_spread":0.21713346758157862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757326413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047497932,0.00034217804,0.94818836,0.000079755344,0.00008308498,0.00004824966,0.00011564915,0.0014292101,0.0022155964],"genre_scores_gemma":[0.32291108,0.00035405494,0.6745276,0.000045217446,0.000043615022,0.000060998478,0.0003602872,0.00022655452,0.0014705247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987134,0.00017934303,0.00006409087,0.0002765497,0.0006761632,0.00009052909],"domain_scores_gemma":[0.99849045,0.0002333853,0.0001902751,0.00041597692,0.000627783,0.00004202396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006534527,0.0012621172,0.0006624666,0.0013316808,0.00049131847,0.0010320988,0.00086539827,0.0005742,0.0017671561],"category_scores_gemma":[0.004823602,0.0005173294,0.00057415676,0.0010289229,0.00034479616,0.0013416461,0.0016048911,0.00078855734,0.0014349403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023530154,0.00007524635,0.009044736,0.00029719778,0.00012297864,0.0002585818,0.0008034035,0.07230018,0.13288218,0.0035001815,0.002550443,0.77792954],"study_design_scores_gemma":[0.000053793745,0.00031367427,0.020169755,0.000098199125,0.00010262307,0.00079990976,0.00066370843,0.79934144,0.14631525,0.0035892676,0.028421173,0.0001311389],"about_ca_topic_score_codex":0.0048023337,"about_ca_topic_score_gemma":0.006133367,"teacher_disagreement_score":0.0048023337,"about_ca_system_score_codex":0.00036125892,"about_ca_system_score_gemma":0.0008172967,"threshold_uncertainty_score":0.009548783},"labels":[],"label_agreement":null},{"id":"W2760457636","doi":"10.3390/s17102238","title":"Error Modeling and Experimental Study of a Flexible Joint 6-UPUR Parallel Six-Axis Force Sensor","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Revolute joint; Kinematics; Correctness; Deformation (meteorology); Joint (building); Stiffness; Computer science; Range (aeronautics); Calibration; Observational error; Matrix (chemical analysis); Control theory (sociology); Simulation; Algorithm; Structural engineering; Engineering; Mathematics; Artificial intelligence; Robot; Physics; Statistics","score_opus":0.06382005645649734,"score_gpt":0.3052896852339327,"score_spread":0.24146962877743533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760457636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6983759,0.0004340472,0.29578045,0.0004168576,0.00023294958,0.00022987321,0.00029569524,0.0011387729,0.0030955425],"genre_scores_gemma":[0.94051266,0.00015716694,0.05738025,0.000059897084,0.00001782893,0.00010619012,0.00014000024,0.000038856793,0.0015870876],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972813,0.0002378349,0.00017157976,0.00040356157,0.0018005196,0.00010517919],"domain_scores_gemma":[0.9973991,0.00048625268,0.00042086796,0.00045089028,0.00115265,0.00009028468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017355897,0.0007852419,0.0004794624,0.0008196915,0.00043721768,0.00049752067,0.0016058527,0.0011792454,0.0010632453],"category_scores_gemma":[0.0025075746,0.000357413,0.00048235562,0.0005537099,0.0005461679,0.0011762505,0.00065777026,0.000417507,0.00025689008],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090503035,0.00048147445,0.010592529,0.00093186117,0.0001428272,0.0008241099,0.0013125129,0.05671848,0.8409049,0.004465807,0.0015481886,0.08117232],"study_design_scores_gemma":[0.00006395655,0.0017941891,0.010638017,0.00004236625,0.00008825877,0.00048048818,0.00025525154,0.38403392,0.5979411,0.00050583493,0.00404188,0.000114656454],"about_ca_topic_score_codex":0.0015566598,"about_ca_topic_score_gemma":0.0012258184,"teacher_disagreement_score":0.0017355897,"about_ca_system_score_codex":0.00046655626,"about_ca_system_score_gemma":0.0005208615,"threshold_uncertainty_score":0.009178758},"labels":[],"label_agreement":null},{"id":"W2762030399","doi":"10.3390/s17102294","title":"Augmented Reality as a Telemedicine Platform for Remote Procedural Training","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":249,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Telemedicine; Augmented reality; Computer science; Training (meteorology); Virtual reality; Multimedia; Human–computer interaction; Engineering; Health care; Geography","score_opus":0.12865980124232404,"score_gpt":0.38933749138520285,"score_spread":0.2606776901428788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762030399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26045427,0.0023076562,0.714805,0.0006952865,0.00033418767,0.0005206824,0.00041399038,0.0029974584,0.017471472],"genre_scores_gemma":[0.6956662,0.0012636014,0.29579473,0.00021576075,0.000094113915,0.00025484877,0.00027203409,0.00008950283,0.0063492316],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994783,0.00020672372,0.000024689227,0.00005331648,0.0002007826,0.00003623707],"domain_scores_gemma":[0.99959236,0.00015925615,0.00004918389,0.00008881133,0.000067125315,0.00004329788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003863088,0.0004685187,0.00019578423,0.0004443632,0.0002074117,0.0008462217,0.0006373449,0.00077590806,0.004034282],"category_scores_gemma":[0.000856595,0.0002508223,0.0004645875,0.0002551633,0.0002711303,0.00061719184,0.0010903645,0.00042188808,0.0007396103],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009914241,0.0004690224,0.0030231564,0.0010142456,0.000134868,0.001670259,0.0024467935,0.021969175,0.440502,0.009367853,0.0054631676,0.51294804],"study_design_scores_gemma":[0.0004278546,0.010202016,0.049307317,0.0009896989,0.0007108243,0.014827539,0.0025272751,0.27181396,0.38067704,0.008043863,0.25968304,0.0007895347],"about_ca_topic_score_codex":0.0003908551,"about_ca_topic_score_gemma":0.00059171143,"teacher_disagreement_score":0.004034282,"about_ca_system_score_codex":0.00014788583,"about_ca_system_score_gemma":0.0003041397,"threshold_uncertainty_score":0.013496041},"labels":[],"label_agreement":null},{"id":"W2762061960","doi":"10.3390/s17102271","title":"Reconfigurable Microfluidic Magnetic Valve Arrays: Towards a Radiotherapy-Compatible Spheroid Culture Platform for the Combinatorial Screening of Cancer Therapies","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Cancer Research Society","keywords":"Spheroid; Microfluidics; Biomedical engineering; 3D cell culture; Cell culture; Nanotechnology; Materials science; Cell; Chemistry; Biology; Medicine; Biochemistry","score_opus":0.03299549641535157,"score_gpt":0.30503083697648525,"score_spread":0.27203534056113365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762061960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6431313,0.0062634093,0.33719876,0.00071435136,0.0005053625,0.0004310143,0.0016471783,0.0025910551,0.0075174994],"genre_scores_gemma":[0.7637005,0.0026483536,0.22744198,0.0002923403,0.00009580958,0.0003531439,0.0006156772,0.00012396352,0.004728241],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997578,0.0000423595,0.000016512236,0.00006948518,0.0000848425,0.000028853052],"domain_scores_gemma":[0.99983716,0.00004775984,0.00005099562,0.000020458534,0.00001895526,0.000024576664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003170348,0.00039135118,0.00034857864,0.00028161114,0.00016645648,0.00057892874,0.00045828646,0.0004212692,0.00063231704],"category_scores_gemma":[0.00028640864,0.00024130296,0.00030329535,0.00016007623,0.0002579415,0.00026512082,0.00026700838,0.00042038463,0.000422826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028912118,0.000012811629,0.00006234913,0.0000406618,0.00000432556,0.00003825852,0.000016438613,0.00057492364,0.9955035,0.00033616842,0.0001178604,0.003263789],"study_design_scores_gemma":[0.000011789948,0.00014727285,0.0003644739,0.0000038362928,0.000010143284,0.00012834212,0.000007211251,0.0043762303,0.98859906,0.00010208172,0.0062377686,0.000011713436],"about_ca_topic_score_codex":0.0003095997,"about_ca_topic_score_gemma":0.0005213072,"teacher_disagreement_score":0.00063231704,"about_ca_system_score_codex":0.0003396756,"about_ca_system_score_gemma":0.00031083447,"threshold_uncertainty_score":0.0024645329},"labels":[],"label_agreement":null},{"id":"W2762757392","doi":"10.3390/s17102347","title":"Remote Sensing-Based Quantification of the Impact of Flash Flooding on the Rice Production: A Case Study over Northeastern Bangladesh","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Flooding (psychology); Flash flood; Flood myth; Environmental science; Crop; Geography; Remote sensing; Cartography; Hydrology (agriculture); Forestry; Engineering","score_opus":0.032480879683306284,"score_gpt":0.3015003441625097,"score_spread":0.26901946447920344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762757392","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99838233,0.000026580736,0.0005313846,0.000055809447,0.0000016900601,0.000018984063,0.0002579139,0.000017204551,0.00070814433],"genre_scores_gemma":[0.99855393,0.000058085698,0.00094136584,0.000007157597,0.0000023440637,0.000009651653,0.00020554809,0.0000033595359,0.00021859248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998105,0.00005078615,0.000016860926,0.000040467054,0.000039355098,0.000042131036],"domain_scores_gemma":[0.99959284,0.00017637473,0.00007410002,0.00003334498,0.00007950621,0.00004384415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044106477,0.00046882412,0.00030982852,0.00079771865,0.00038527863,0.00061766227,0.00043666954,0.0005367131,0.00058565213],"category_scores_gemma":[0.00065325934,0.00018438557,0.00037368177,0.001182219,0.00035550082,0.00045942568,0.00037173502,0.00024936517,0.00010017962],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005717655,0.0011379393,0.70651144,0.00031022585,0.00033466457,0.010165557,0.0019443462,0.19203007,0.045116767,0.0010961863,0.001496661,0.039284445],"study_design_scores_gemma":[0.00006929375,0.00042747046,0.6672081,0.000046413934,0.00017192452,0.00071443646,0.0062056608,0.31617802,0.0071174144,0.00037664096,0.0013867693,0.00009790715],"about_ca_topic_score_codex":0.05962108,"about_ca_topic_score_gemma":0.07159147,"teacher_disagreement_score":0.05962108,"about_ca_system_score_codex":0.00096641167,"about_ca_system_score_gemma":0.0004216593,"threshold_uncertainty_score":0.118548095},"labels":[],"label_agreement":null},{"id":"W2765114824","doi":"10.3390/s17112511","title":"A Review of Hybrid Fiber-Optic Distributed Simultaneous Vibration and Temperature Sensing Technology and Its Geophysical Applications","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Distributed acoustic sensing; Reflectometry; Optical time-domain reflectometer; Rayleigh scattering; Optical fiber; Brillouin scattering; Vibration; Image resolution; Dynamic range; Fiber optic sensor; Remote sensing; Acoustics; Temperature measurement; Sensitivity (control systems); Time domain; Materials science; Computer science; Optics; Electronic engineering; Engineering; Physics; Geology; Polarization-maintaining optical fiber","score_opus":0.01562918118195555,"score_gpt":0.281811009012243,"score_spread":0.26618182783028743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765114824","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049443735,0.99479514,0.0011838323,0.0002869977,0.00032229882,0.000008920418,0.00003940707,0.000020344381,0.002848562],"genre_scores_gemma":[0.0025083823,0.9941247,0.0010879127,0.00018535055,0.00021824536,0.000012583707,0.00005686737,0.0000038796948,0.001802056],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977857,0.000020146821,0.000028624358,0.000056810597,0.00009652792,0.000019372163],"domain_scores_gemma":[0.999648,0.00014803553,0.00005962797,0.000012462221,0.00010780253,0.000024049064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047541477,0.0008997858,0.0007230558,0.0023922133,0.00032538467,0.00076142343,0.00071097387,0.0008951707,0.0038887444],"category_scores_gemma":[0.0005807786,0.00036651737,0.00047509398,0.0027200603,0.00032997056,0.0015405341,0.0004991253,0.0008840755,0.0017550179],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003664252,0.00008436789,0.00039748932,0.022978393,0.00007178239,0.00024059587,0.00008769084,0.0007627221,0.010100289,0.0064462516,0.02215313,0.9366407],"study_design_scores_gemma":[0.000004026924,0.00014207207,0.0009283862,0.0017772394,0.00009045769,0.0013838805,0.00006353674,0.0002709938,0.0032716708,0.0017258538,0.99030894,0.00003292184],"about_ca_topic_score_codex":0.00082340767,"about_ca_topic_score_gemma":0.0012985695,"teacher_disagreement_score":0.0038887444,"about_ca_system_score_codex":0.00038664037,"about_ca_system_score_gemma":0.0008696694,"threshold_uncertainty_score":0.013009191},"labels":[],"label_agreement":null},{"id":"W2765275233","doi":"10.3390/s17112496","title":"Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":607,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Health care; Life expectancy; Wearable computer; Population ageing; Wearable technology; Business; Expectancy theory; Population; Internet privacy; Risk analysis (engineering); Engineering; Medicine; Computer science; Psychology; Environmental health; Economic growth","score_opus":0.4694568834817912,"score_gpt":0.4936928425368305,"score_spread":0.02423595905503928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765275233","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016290072,0.9976763,0.00033734713,0.00055398495,0.00023405324,0.0000059930762,0.0000111027775,0.000009197419,0.0010091728],"genre_scores_gemma":[0.0010625065,0.99750805,0.00046607462,0.0003000675,0.0003016384,0.000007488961,0.000019949528,0.000002312254,0.0003318991],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993838,0.00011472707,0.00008542639,0.00009567529,0.00026419671,0.000056122604],"domain_scores_gemma":[0.99805707,0.001119397,0.00015553809,0.00005177367,0.0005155926,0.00010061601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016143656,0.00084695464,0.00102151,0.0022146136,0.00038769265,0.0015386209,0.0010905468,0.0016296679,0.0040996214],"category_scores_gemma":[0.0021873072,0.00035346556,0.0007075887,0.0029133926,0.0007074175,0.0030315623,0.0011669858,0.0019250592,0.0017440867],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036530146,0.00007495449,0.0002734465,0.014809209,0.000042958884,0.000114975766,0.00012791269,0.00033464198,0.001038021,0.007916199,0.017585497,0.95764565],"study_design_scores_gemma":[0.000011623557,0.00017063369,0.0011861661,0.0105763925,0.00012129548,0.0013469482,0.00028101073,0.00039886462,0.0006441221,0.0056481925,0.97957647,0.000038311475],"about_ca_topic_score_codex":0.0010515042,"about_ca_topic_score_gemma":0.001332732,"teacher_disagreement_score":0.0040996214,"about_ca_system_score_codex":0.0006591644,"about_ca_system_score_gemma":0.0017548976,"threshold_uncertainty_score":0.013714552},"labels":[],"label_agreement":null},{"id":"W2765397667","doi":"10.3390/s17112441","title":"Chronotropic Competence Indices Extracted from Wearable Sensors for Cardiovascular Diseases Management","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Cardiovascular and exercise physiology","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Chronotropic; Wearable computer; Medicine; Competence (human resources); Rehabilitation; Physical therapy; Internal medicine; Engineering; Psychology; Embedded system; Heart rate","score_opus":0.016131595500686716,"score_gpt":0.2625919532014686,"score_spread":0.2464603577007819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765397667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6751525,0.02650516,0.27167466,0.0005347768,0.001061782,0.00071854756,0.006885762,0.0014098214,0.016057102],"genre_scores_gemma":[0.9315561,0.004817158,0.058800966,0.00024138445,0.00041813194,0.0003380673,0.0022606198,0.000057913985,0.0015096677],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99969435,0.000052250733,0.000039491733,0.000070014954,0.000120761746,0.000023208398],"domain_scores_gemma":[0.9995128,0.00016277359,0.000107779015,0.0000439333,0.00013427714,0.000038373502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046514967,0.0010252846,0.0006760313,0.0013843693,0.00015186524,0.00083485164,0.0003065875,0.00078665704,0.0011303901],"category_scores_gemma":[0.0016570024,0.00014004584,0.00042517995,0.0010003857,0.00014574538,0.00044883214,0.00038935212,0.0004032349,0.00049874146],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013709292,0.00056087266,0.121242985,0.0024192303,0.0005285161,0.00097192114,0.00040475713,0.010166853,0.2827761,0.0015215096,0.005269841,0.57276654],"study_design_scores_gemma":[0.00014560274,0.0024168342,0.7126366,0.0005647936,0.0011258526,0.006544118,0.0005355968,0.1408011,0.11384721,0.0034098774,0.017674776,0.00029754246],"about_ca_topic_score_codex":0.00044333763,"about_ca_topic_score_gemma":0.00079273287,"teacher_disagreement_score":0.0013843693,"about_ca_system_score_codex":0.0001411776,"about_ca_system_score_gemma":0.00018491904,"threshold_uncertainty_score":0.003781557},"labels":[],"label_agreement":null},{"id":"W2765415081","doi":"10.3390/s17112536","title":"All-Solid-State Sodium-Selective Electrode with a Solid Contact of Chitosan/Prussian Blue Nanocomposite","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Misericordia Community Hospital; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Prussian blue; Electrode; Materials science; Potentiometric titration; Ion selective electrode; Nanocomposite; Reference electrode; Potentiometric sensor; Analytical Chemistry (journal); Chemical engineering; Inorganic chemistry; Chemistry; Electrochemistry; Nanotechnology; Chromatography; Selectivity; Organic chemistry","score_opus":0.009038256616075038,"score_gpt":0.25514637304929944,"score_spread":0.2461081164332244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765415081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8398694,0.0039624064,0.14717908,0.0004413059,0.00020596971,0.0002002736,0.0005449886,0.0022377735,0.005358848],"genre_scores_gemma":[0.8992121,0.0015367046,0.09119869,0.00017101155,0.000046940397,0.00013886868,0.00048294,0.000093238596,0.007119399],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892044,0.0001122433,0.000058708596,0.000282283,0.00056899583,0.000057380672],"domain_scores_gemma":[0.999603,0.000074003314,0.000095930656,0.0000391356,0.00015759737,0.000030326384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037007104,0.0011091928,0.00057518046,0.00059296476,0.00025448567,0.00037286725,0.0011979971,0.0008690407,0.0008256925],"category_scores_gemma":[0.00049425167,0.0004809205,0.00043946994,0.00050144905,0.00035867814,0.00076101825,0.0004992256,0.00072653044,0.00053376786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000143653715,0.0000066197613,0.000035331075,0.000044838627,0.0000060055295,0.000033312244,0.0000059131835,0.000035653888,0.99860305,0.000031334872,0.0000267353,0.0011569246],"study_design_scores_gemma":[0.0000035011726,0.000041077914,0.00039362512,0.0000024045382,0.00000963217,0.00015769257,0.000005777723,0.001565201,0.99720556,0.000014648752,0.0005936772,0.0000071896693],"about_ca_topic_score_codex":0.0012989659,"about_ca_topic_score_gemma":0.0034038108,"teacher_disagreement_score":0.0012989659,"about_ca_system_score_codex":0.0005519897,"about_ca_system_score_gemma":0.0003712136,"threshold_uncertainty_score":0.0040049553},"labels":[],"label_agreement":null},{"id":"W2765477807","doi":"10.3390/s17112519","title":"Femtosecond FBG Written through the Coating for Sensing Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TeraXion (Canada); Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Coating; Silicone; Femtosecond; Fiber Bragg grating; Cladding (metalworking); Polyimide; Composite material; PHOSFOS; Fabrication; Laser; Acrylate; Plastic-clad silica fiber; Annealing (glass); Optoelectronics; Optics; Fiber; Plastic optical fiber; Wavelength; Fiber optic sensor; Polymer; Layer (electronics)","score_opus":0.025119905918362502,"score_gpt":0.277347902756719,"score_spread":0.2522279968383565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765477807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9401341,0.003643151,0.047678467,0.00015753892,0.00022083048,0.000058843973,0.0003271865,0.0006053332,0.0071746036],"genre_scores_gemma":[0.94162613,0.0023418134,0.048538405,0.00008263431,0.0000356443,0.0000269443,0.00020944946,0.000070773145,0.0070682154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999416,0.0000026967948,0.0000030147414,0.000016310538,0.000027318842,0.000009107014],"domain_scores_gemma":[0.9999161,0.000018859904,0.000025533831,0.000012867312,0.000019526899,0.000007130116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006126182,0.00035309372,0.00012948814,0.00016738568,0.00012848512,0.00018485523,0.00015489077,0.0002528541,0.00087252184],"category_scores_gemma":[0.00011239765,0.0001452656,0.00010934205,0.00013439271,0.00014980926,0.00018753778,0.00008513816,0.00033120724,0.00030511903],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044821845,0.0000021758926,0.00004033447,0.000013424944,7.201802e-7,0.000013008105,0.0000058900578,0.000032672786,0.99783295,0.00003168922,0.000026530764,0.0019960406],"study_design_scores_gemma":[9.336678e-7,0.000030801875,0.00068908295,0.0000019986803,0.0000028414347,0.000088145265,0.0000049317414,0.00044639592,0.99761176,0.000017265784,0.0011042637,0.0000016286547],"about_ca_topic_score_codex":0.00060250284,"about_ca_topic_score_gemma":0.0014376526,"teacher_disagreement_score":0.00087252184,"about_ca_system_score_codex":0.00023493839,"about_ca_system_score_gemma":0.00016242497,"threshold_uncertainty_score":0.002918899},"labels":[],"label_agreement":null},{"id":"W2765619188","doi":"10.3390/s17102397","title":"The Light Node Communication Framework: A New Way to Communicate Inside Smart Homes","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Scalability; Computer science; Architecture; Network packet; Node (physics); Rendering (computer graphics); Encryption; The Internet; Context (archaeology); Reliability (semiconductor); Focus (optics); Domain (mathematical analysis); Computer network; Forcing (mathematics); Computer security; Multimedia; World Wide Web; Engineering; Operating system","score_opus":0.03383975712363486,"score_gpt":0.2886628022034385,"score_spread":0.25482304507980363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765619188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012801334,0.0018701067,0.9565569,0.0019683056,0.0005737923,0.00020199675,0.00005393398,0.004151942,0.021821689],"genre_scores_gemma":[0.31616503,0.0029066203,0.644141,0.0017665004,0.00062833104,0.0004010229,0.00017975601,0.000735833,0.033075847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992884,0.00020295275,0.000029078392,0.00009934501,0.00026626422,0.000114053306],"domain_scores_gemma":[0.9995702,0.000079556434,0.000034877226,0.000108033215,0.00010070457,0.00010658729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012275681,0.00052344776,0.00036656912,0.0007055179,0.0011323645,0.0019041516,0.0015329461,0.001348474,0.0033466376],"category_scores_gemma":[0.0009983553,0.00029914654,0.00048284832,0.00038603914,0.0015577911,0.0043153847,0.0022744949,0.0019368441,0.001212123],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002664683,0.00017534473,0.0009886511,0.00036051768,0.00005663088,0.00096058677,0.0033488593,0.00955916,0.04022827,0.64994174,0.032377,0.26173678],"study_design_scores_gemma":[0.00009180187,0.00044972132,0.0007085967,0.0003134811,0.00009848518,0.0015404519,0.00081187923,0.08198736,0.021282936,0.11857085,0.7739375,0.00020706108],"about_ca_topic_score_codex":0.002965997,"about_ca_topic_score_gemma":0.0034777862,"teacher_disagreement_score":0.0033466376,"about_ca_system_score_codex":0.00071666186,"about_ca_system_score_gemma":0.0012350671,"threshold_uncertainty_score":0.01119566},"labels":[],"label_agreement":null},{"id":"W2765759611","doi":"10.3390/s17112464","title":"Electrochemical Detection of Plasma Immunoglobulin as a Biomarker for Alzheimer’s Disease","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petroleum Technology Development Fund; Engineering and Physical Sciences Research Council; Alzheimer Society","keywords":"Polyclonal antibodies; Antibody; Biomarker; Serial dilution; Albumin; Immunoglobulin G; Blood plasma; Medicine; Immunoassay; Immunology; Chemistry; Pathology; Internal medicine; Biochemistry","score_opus":0.014762292342754899,"score_gpt":0.2477063790522837,"score_spread":0.23294408670952882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765759611","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.861783,0.02917478,0.10270157,0.000617086,0.00038371145,0.00008312342,0.00027259355,0.0004300312,0.00455414],"genre_scores_gemma":[0.94684327,0.0060320306,0.044035193,0.00037081013,0.00008540874,0.000055824756,0.00013330666,0.0000097564725,0.0024344786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960166,0.00014091424,0.00002131008,0.000071342845,0.00013270772,0.000032029875],"domain_scores_gemma":[0.99978775,0.000089147106,0.000035328827,0.000011201996,0.000059048194,0.000017623079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055403716,0.0003697683,0.00025247614,0.00040814874,0.00008992511,0.00030618996,0.00037808457,0.00085948553,0.0003039652],"category_scores_gemma":[0.00069797254,0.00014436086,0.00016194895,0.00029999507,0.0001978689,0.00021329534,0.00022317174,0.00036874693,0.00020149502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072032526,0.000048486974,0.0012643893,0.00011659308,0.000018843817,0.000106934596,0.000035929723,0.000107357635,0.9865739,0.00009809034,0.00009957795,0.011457926],"study_design_scores_gemma":[0.00002762341,0.00065121666,0.01153239,0.000034554138,0.000076633994,0.0017797176,0.000096049,0.006293109,0.9758744,0.00026568936,0.0033461056,0.000022437722],"about_ca_topic_score_codex":0.0003098823,"about_ca_topic_score_gemma":0.00048029862,"teacher_disagreement_score":0.00085948553,"about_ca_system_score_codex":0.0001521769,"about_ca_system_score_gemma":0.0001597397,"threshold_uncertainty_score":0.0029301047},"labels":[],"label_agreement":null},{"id":"W2765796219","doi":"10.3390/s17102378","title":"Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landslide; Point cloud; Computer science; Change detection; Remote sensing; Computer vision; Fault scarp; Orthophoto; Point (geometry); Artificial intelligence; Image processing; Displacement (psychology); Data mining; Image (mathematics); Geology; Mathematics; Geotechnical engineering","score_opus":0.015097842045055848,"score_gpt":0.30257283819927533,"score_spread":0.28747499615421945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765796219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23689501,0.00086419797,0.7482943,0.00016985183,0.0001533654,0.00039478778,0.0045457506,0.004952767,0.0037299546],"genre_scores_gemma":[0.7170723,0.00069561996,0.2771834,0.00003194162,0.00003181148,0.0002572362,0.0036262628,0.0001229479,0.0009785006],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99976987,0.00002866022,0.000017503942,0.00003744834,0.00012507959,0.000021436463],"domain_scores_gemma":[0.9996501,0.000045963527,0.00006509575,0.00005716512,0.00016039665,0.00002128812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023463373,0.00068038114,0.00037469078,0.0018315807,0.00020489453,0.00054659444,0.0004897768,0.00041393636,0.0016268612],"category_scores_gemma":[0.0007944552,0.00023728072,0.00040223033,0.0019491202,0.000116340925,0.0005456307,0.0003000314,0.00035310604,0.00072407635],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039177772,0.00035617687,0.018527685,0.00050779356,0.00015769523,0.00054078526,0.00022975658,0.21309865,0.13922143,0.002617139,0.009320054,0.615031],"study_design_scores_gemma":[0.000017900153,0.00009317407,0.020475423,0.000038605416,0.0000360263,0.00016137263,0.00017732834,0.93893397,0.034994867,0.0008019776,0.0042354595,0.000033947796],"about_ca_topic_score_codex":0.004994608,"about_ca_topic_score_gemma":0.007658674,"teacher_disagreement_score":0.004994608,"about_ca_system_score_codex":0.0002853767,"about_ca_system_score_gemma":0.00040699073,"threshold_uncertainty_score":0.0099310875},"labels":[],"label_agreement":null},{"id":"W2766010555","doi":"10.3390/s17102377","title":"Sensor-Data Fusion for Multi-Person Indoor Location Estimation","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Sensor fusion; Computer science; Fusion; Estimation; Real-time computing; Artificial intelligence; Engineering; Systems engineering","score_opus":0.0827458121506196,"score_gpt":0.3074397182280245,"score_spread":0.22469390607740491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766010555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015518795,0.00035623022,0.9831646,0.00010734465,0.000046558645,0.000018664075,0.00009403545,0.0001912401,0.0005026502],"genre_scores_gemma":[0.752287,0.00064762647,0.24529053,0.0001063932,0.00010462033,0.000079653146,0.00031467952,0.000025412317,0.0011441287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991148,0.00032487506,0.000052883635,0.00021314323,0.00020757371,0.0000866714],"domain_scores_gemma":[0.99917716,0.00038455636,0.00012358715,0.0001561354,0.00012576403,0.000032681597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011800419,0.000677828,0.0009873332,0.00065682654,0.00051066733,0.00067540305,0.000784343,0.00096188573,0.00085571804],"category_scores_gemma":[0.0036105455,0.0002893492,0.00063865585,0.0013390356,0.00042736286,0.0012804638,0.0012802553,0.00078049797,0.0003955139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000383026,0.00016171852,0.004935035,0.00033020243,0.0002045952,0.00034772494,0.00034123275,0.7217329,0.018607454,0.013526208,0.0018830353,0.23754685],"study_design_scores_gemma":[0.0000100632415,0.0000806876,0.0016561708,0.000015808077,0.000025321822,0.00010884203,0.00007533467,0.9838463,0.005375735,0.0072980323,0.0014854486,0.000022171385],"about_ca_topic_score_codex":0.0025620088,"about_ca_topic_score_gemma":0.0037117377,"teacher_disagreement_score":0.0025620088,"about_ca_system_score_codex":0.00040144258,"about_ca_system_score_gemma":0.00050928927,"threshold_uncertainty_score":0.0062407255},"labels":[],"label_agreement":null},{"id":"W2766745697","doi":"10.3390/s17112551","title":"An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sampling (signal processing); Adaptive sampling; Computer science; Real-time computing; Continuous monitoring; Power management; Water quality; Environmental monitoring; Wireless sensor network; Algorithm; Power (physics); Environmental science; Engineering; Detector; Telecommunications; Environmental engineering; Statistics","score_opus":0.06925927908553713,"score_gpt":0.33730121714902783,"score_spread":0.2680419380634907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766745697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03491417,0.00019203323,0.96403515,0.00007260918,0.000030544878,0.000035880792,0.000016309254,0.00018818882,0.0005150262],"genre_scores_gemma":[0.6611837,0.00025192683,0.33712503,0.00007650709,0.000038836148,0.00015125726,0.0000903225,0.000022209375,0.0010601914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970275,0.00007698244,0.000024345047,0.00007322746,0.00010134593,0.000021456213],"domain_scores_gemma":[0.9996191,0.000187898,0.00004711789,0.000027016531,0.00010446953,0.000014312647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005486709,0.000365953,0.00034547003,0.0003333404,0.00026234036,0.00030522814,0.00064070645,0.00037508315,0.00030738884],"category_scores_gemma":[0.0015684307,0.00015447351,0.0002463014,0.00047759997,0.00027182736,0.00055540446,0.00030418523,0.00038666435,0.00007827067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025474656,0.00013053288,0.0032285298,0.000100832505,0.00006060134,0.00009022402,0.00011497876,0.6704044,0.03591305,0.005265251,0.000902263,0.28353456],"study_design_scores_gemma":[0.000007007514,0.000043773787,0.00024051787,0.0000023344649,0.0000045703555,0.000018779567,0.0000063227058,0.99655735,0.0022857753,0.00051149714,0.0003190957,0.0000030307008],"about_ca_topic_score_codex":0.002624028,"about_ca_topic_score_gemma":0.0029907553,"teacher_disagreement_score":0.002624028,"about_ca_system_score_codex":0.00037684888,"about_ca_system_score_gemma":0.0005783513,"threshold_uncertainty_score":0.005217552},"labels":[],"label_agreement":null},{"id":"W2768006888","doi":"10.3390/s17112562","title":"The Virtual Environment for Rapid Prototyping of the Intelligent Environment","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Rapid prototyping; Virtual prototyping; Systems engineering; Human–computer interaction; Engineering; Computer science; Virtual machine; Software engineering; Embedded system; Operating system; Simulation; Mechanical engineering","score_opus":0.035854595668966475,"score_gpt":0.24904690150520306,"score_spread":0.21319230583623658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768006888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042687976,0.0002839422,0.9717774,0.0003287088,0.00027336995,0.00041627788,0.0005466547,0.009006617,0.013098279],"genre_scores_gemma":[0.09387484,0.0006352501,0.8906589,0.0001501317,0.000058085523,0.0016480136,0.0012540546,0.0032507174,0.008470002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982982,0.0007492142,0.00013021378,0.00019276972,0.00050263276,0.00012690877],"domain_scores_gemma":[0.99623066,0.0018176901,0.00011505329,0.0009990948,0.00040057066,0.00043688115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002341325,0.0014854423,0.0008271241,0.00096471026,0.0007324688,0.0031180838,0.0027834545,0.00141198,0.037560247],"category_scores_gemma":[0.008378465,0.0011134249,0.0015375533,0.00051971414,0.0014142919,0.0026749987,0.0056362534,0.0018203288,0.0067513916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001031119,0.0005608362,0.0021395024,0.001891615,0.00022517159,0.00211962,0.0033003478,0.17822544,0.083241366,0.18432276,0.085390076,0.45755208],"study_design_scores_gemma":[0.00040175955,0.00050078455,0.001220584,0.00050285435,0.00008597722,0.0009914229,0.00034544658,0.27368325,0.028997233,0.054660328,0.6383646,0.0002458195],"about_ca_topic_score_codex":0.0007897986,"about_ca_topic_score_gemma":0.0011550115,"teacher_disagreement_score":0.037560247,"about_ca_system_score_codex":0.00042289047,"about_ca_system_score_gemma":0.001183712,"threshold_uncertainty_score":0.1256516},"labels":[],"label_agreement":null},{"id":"W2768026417","doi":"10.3390/s17112573","title":"A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Computer science; Dead reckoning; Step detection; Mobile device; Real-time computing; Artificial intelligence; Android (operating system); Task (project management); Classifier (UML); Computer vision; Match moving; Engineering; Motion (physics); Global Positioning System; Telecommunications","score_opus":0.037433047433177116,"score_gpt":0.311351734730768,"score_spread":0.2739186872975909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768026417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30027398,0.0051171365,0.6261593,0.00085218373,0.0015870999,0.001297768,0.005031086,0.025427753,0.034253743],"genre_scores_gemma":[0.75160486,0.0014808234,0.21951841,0.00077021046,0.00022786044,0.00049876113,0.0021991015,0.00013690461,0.023563102],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982315,0.000017593024,0.000012835612,0.00004299123,0.00008754517,0.000015827976],"domain_scores_gemma":[0.9997446,0.00003590581,0.00002794481,0.000035952544,0.00013167599,0.000023931296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012539071,0.0007839212,0.000570272,0.0008066772,0.00022611463,0.00033269022,0.00054210075,0.00057479384,0.0052322084],"category_scores_gemma":[0.0005119796,0.00017835581,0.00029659126,0.0004956559,0.00008958019,0.0003270276,0.00033055712,0.00025411206,0.0026193908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009038292,0.00022267872,0.015542227,0.0011504632,0.00014095764,0.0010649903,0.00015276749,0.0020145895,0.29695258,0.000976867,0.030095479,0.6507826],"study_design_scores_gemma":[0.00033362032,0.0040829834,0.13430215,0.0004186742,0.0008223877,0.013211637,0.00041618553,0.23800498,0.45285934,0.0012138523,0.1539212,0.00041295108],"about_ca_topic_score_codex":0.001291222,"about_ca_topic_score_gemma":0.0033974533,"teacher_disagreement_score":0.0052322084,"about_ca_system_score_codex":0.000107900574,"about_ca_system_score_gemma":0.0002940527,"threshold_uncertainty_score":0.0175035},"labels":[],"label_agreement":null},{"id":"W2768906510","doi":"10.3390/s17112679","title":"Nondestructive Evaluation of Carbon Fiber Bicycle Frames Using Infrared Thermography","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Thermography; Nondestructive testing; Frame (networking); Stiffness; Computer science; Infrared; Materials science; Work (physics); Engineering; Mechanical engineering; Composite material; Optics; Telecommunications","score_opus":0.026312508683771694,"score_gpt":0.2752581079545692,"score_spread":0.24894559927079749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768906510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9323935,0.0011544923,0.06398229,0.000045298435,0.00004010273,0.00005256154,0.00009715589,0.00024088388,0.0019937833],"genre_scores_gemma":[0.976102,0.00035831827,0.022321364,0.000018785298,0.000010064524,0.000023746614,0.000046062163,0.00002105399,0.001098729],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975365,0.000024109595,0.0000076185834,0.00004458179,0.00014905988,0.00002087897],"domain_scores_gemma":[0.999589,0.00013160089,0.000069349524,0.00003668119,0.00015727674,0.000016072163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031039945,0.00040247812,0.00019023131,0.0006285449,0.00013504646,0.00024441286,0.00038873628,0.0004119339,0.0006976699],"category_scores_gemma":[0.0006896996,0.00016762702,0.00010741818,0.00026809992,0.00028663484,0.0002798871,0.00018172644,0.00016536792,0.00016836305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009299615,0.000019852854,0.00093477196,0.00008211257,0.000004560142,0.00008461333,0.00008419525,0.00055012,0.98159856,0.000072897026,0.00006281966,0.016412418],"study_design_scores_gemma":[0.0000068301338,0.00036051363,0.013366156,0.000018522123,0.000021384603,0.0002355985,0.00008752323,0.013099796,0.97179776,0.00006151313,0.0009214058,0.00002295161],"about_ca_topic_score_codex":0.000654197,"about_ca_topic_score_gemma":0.0012383885,"teacher_disagreement_score":0.0006976699,"about_ca_system_score_codex":0.00019034743,"about_ca_system_score_gemma":0.000114049064,"threshold_uncertainty_score":0.002333939},"labels":[],"label_agreement":null},{"id":"W2769149776","doi":"10.3390/s17122725","title":"Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Ministerio de Economía y Competitividad; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Pattern recognition (psychology); Brain–computer interface; Electroencephalography; Similarity (geometry); Artificial intelligence; Computer science; Standard deviation; Spatial filter; Filter (signal processing); Correlation coefficient; Canonical correlation; Adaptive filter; Signal processing; Speech recognition; Computer vision; Mathematics; Statistics; Machine learning; Algorithm; Image (mathematics)","score_opus":0.062455822823802826,"score_gpt":0.305588275687199,"score_spread":0.24313245286339616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769149776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030731466,0.00018272549,0.96738386,0.000045147834,0.00006245642,0.00004708271,0.000037083304,0.0004499578,0.0010601791],"genre_scores_gemma":[0.3624657,0.000453686,0.6320419,0.00013351657,0.00011868733,0.00018849227,0.00026193104,0.00020621785,0.004129962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995235,0.0000850062,0.00002890092,0.00011076806,0.00021224158,0.000039545543],"domain_scores_gemma":[0.99937844,0.00019386815,0.000051689123,0.000077887045,0.0002754098,0.000022619844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005064988,0.0006263977,0.0004944291,0.0007801689,0.0002959049,0.00055103714,0.0006184337,0.0005396015,0.0017230781],"category_scores_gemma":[0.0016548242,0.00017674059,0.00070270797,0.00076811103,0.00030188062,0.0006127724,0.00036268792,0.0005936349,0.00056953484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043286858,0.00027332603,0.002605583,0.00016639606,0.00020651602,0.00011327612,0.00018812252,0.055570412,0.1310591,0.004950777,0.0024515218,0.8019821],"study_design_scores_gemma":[0.000027747597,0.00028451774,0.0075115976,0.000018297102,0.00010482405,0.00029005297,0.00004503867,0.940564,0.04450809,0.0012353001,0.005375341,0.00003518702],"about_ca_topic_score_codex":0.004771263,"about_ca_topic_score_gemma":0.006391545,"teacher_disagreement_score":0.004771263,"about_ca_system_score_codex":0.00041340847,"about_ca_system_score_gemma":0.0007488285,"threshold_uncertainty_score":0.009486973},"labels":[],"label_agreement":null},{"id":"W2769157021","doi":"10.3390/s17122739","title":"Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency","funders":"National Natural Science Foundation of China","keywords":"Fourier transform; Hyperplane; k-nearest neighbors algorithm; Pattern recognition (psychology); Colorectal cancer; Fourier transform infrared spectroscopy; Entropy (arrow of time); Artificial intelligence; Mathematics; Support vector machine; Physics; Cancer; Medicine; Computer science; Internal medicine; Optics","score_opus":0.02698894129614037,"score_gpt":0.3195053316165202,"score_spread":0.29251639032037985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769157021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51356477,0.0025899024,0.47855675,0.00024089639,0.00021706412,0.00019071273,0.0002943307,0.0006612194,0.0036843934],"genre_scores_gemma":[0.80269945,0.0005614322,0.1943395,0.00006877175,0.00007221569,0.00007328077,0.00031602947,0.000017566195,0.0018517866],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986601,0.00025545098,0.00012884755,0.00027548175,0.0005791774,0.00010098017],"domain_scores_gemma":[0.99931765,0.0002390982,0.00008120041,0.00005940612,0.0002655247,0.000037075522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012956526,0.00035520014,0.0007952053,0.0016488332,0.00035688403,0.0006166377,0.00045974329,0.001054479,0.00049412355],"category_scores_gemma":[0.0025340638,0.00024155524,0.0007368118,0.00078032876,0.0002837175,0.0010061614,0.00042813254,0.00042343343,0.00040245012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012779478,0.0006899267,0.060814556,0.00046578932,0.00038823002,0.0006865261,0.00022711956,0.036203332,0.16363677,0.0017813843,0.0020873677,0.7317411],"study_design_scores_gemma":[0.00006839901,0.000580432,0.068559736,0.000040633236,0.00024989058,0.0015858319,0.00016638605,0.88433576,0.039698936,0.001900015,0.0026712914,0.00014269697],"about_ca_topic_score_codex":0.002906188,"about_ca_topic_score_gemma":0.0044679283,"teacher_disagreement_score":0.002906188,"about_ca_system_score_codex":0.0004041743,"about_ca_system_score_gemma":0.00041050167,"threshold_uncertainty_score":0.00685215},"labels":[],"label_agreement":null},{"id":"W2770901093","doi":"10.3390/s17112653","title":"Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":275,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shanghai","keywords":"Tactile sensor; Smart system; Focus (optics); Modalities; Stimulus modality; Computer science; Human–computer interaction; Engineering; Sensory system; Artificial intelligence; Embedded system; Internet of Things; Neuroscience","score_opus":0.05898800608995094,"score_gpt":0.3102637036491058,"score_spread":0.2512756975591549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770901093","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004104042,0.9952454,0.0007465518,0.0002573255,0.0003699368,0.000007911705,0.000024416599,0.000015955344,0.0029221338],"genre_scores_gemma":[0.0021793372,0.99462175,0.0008320053,0.00019504728,0.00026067797,0.000013348805,0.000037989725,0.000003440191,0.001856305],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980885,0.000019517824,0.000023703959,0.00004470675,0.00008306446,0.000020113375],"domain_scores_gemma":[0.9997687,0.00010676413,0.000036779882,0.000008031462,0.000062002364,0.000017735063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033079388,0.00093420036,0.0009677809,0.0021340826,0.00036748635,0.0009944177,0.000796083,0.0012128061,0.0049147694],"category_scores_gemma":[0.00044931244,0.00038219115,0.00048992975,0.0022527399,0.00037835015,0.0017937885,0.0006097825,0.0012314017,0.0023641381],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004228465,0.00014712081,0.0002567,0.03432023,0.000071108254,0.00042912932,0.00012496322,0.0007448986,0.017602952,0.010717961,0.02969752,0.90584517],"study_design_scores_gemma":[0.000004993275,0.000097030956,0.0004438744,0.002049717,0.000058964437,0.0017412235,0.00007448425,0.00024254834,0.0033106667,0.0023764856,0.989571,0.000028981478],"about_ca_topic_score_codex":0.0004079531,"about_ca_topic_score_gemma":0.000720162,"teacher_disagreement_score":0.0049147694,"about_ca_system_score_codex":0.0003436949,"about_ca_system_score_gemma":0.00073926715,"threshold_uncertainty_score":0.016441524},"labels":[],"label_agreement":null},{"id":"W2771075583","doi":"10.3390/s17122909","title":"Extreme Environment Sensing Using Femtosecond Laser-Inscribed Fiber Bragg Gratings","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":143,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fiber Bragg grating; Femtosecond; Materials science; Laser; Optics; Inscribed figure; PHOSFOS; Optical fiber; Fiber optic sensor; Optoelectronics; Fiber laser; Polarization-maintaining optical fiber; Physics","score_opus":0.11263289901112958,"score_gpt":0.3015971353658715,"score_spread":0.18896423635474194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771075583","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010676652,0.9941596,0.0009623904,0.0001562297,0.00024079191,0.000007303087,0.000017312415,0.000017282407,0.0033714173],"genre_scores_gemma":[0.0056117363,0.9901834,0.000983429,0.00013433986,0.0001450398,0.0000083372925,0.000038406844,0.0000026248417,0.0028927296],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99983776,0.00001144429,0.000012143017,0.00002914314,0.000090814865,0.000018645065],"domain_scores_gemma":[0.9998963,0.000032799104,0.0000211385,0.000004125537,0.000036737423,0.000008922989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002863251,0.00068503746,0.00053117314,0.0016620472,0.00016965422,0.00059393595,0.00046956516,0.0007117714,0.0015157126],"category_scores_gemma":[0.00027024277,0.00025438963,0.00036348143,0.0014698593,0.00023794406,0.0009722827,0.00043510352,0.0008125199,0.0011349104],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036330966,0.00010331475,0.00039911218,0.011475138,0.00006002098,0.00024563042,0.00007879112,0.0006842016,0.039710775,0.005974742,0.010996739,0.9302352],"study_design_scores_gemma":[0.000004723849,0.00014226024,0.0011355337,0.0013707422,0.000057408488,0.0016639864,0.00008574781,0.00043265714,0.022849865,0.0019256909,0.9703001,0.00003134609],"about_ca_topic_score_codex":0.00054745394,"about_ca_topic_score_gemma":0.0010325089,"teacher_disagreement_score":0.0016620472,"about_ca_system_score_codex":0.00031295695,"about_ca_system_score_gemma":0.00035856036,"threshold_uncertainty_score":0.005070567},"labels":[],"label_agreement":null},{"id":"W2772696078","doi":"10.3390/s17122823","title":"Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"European Research Council; Horizon 2020 Framework Programme; Irish Research Council; Science Foundation Ireland; European Commission","keywords":"Microwave imaging; Robustness (evolution); Microwave; Radar; Computer science; Algorithm; Artificial intelligence; Radar imaging; Image quality; Dielectric; Computer vision; Image (mathematics); Materials science; Telecommunications","score_opus":0.04236617827314783,"score_gpt":0.3122307790545387,"score_spread":0.2698646007813909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772696078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040492654,0.0005215087,0.9574005,0.00019447549,0.000015996895,0.00006635742,0.000030539562,0.00020728716,0.0010706487],"genre_scores_gemma":[0.5340985,0.00049337506,0.4628229,0.00014248371,0.000036207206,0.0003351629,0.0001729192,0.00017383703,0.0017245865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992144,0.00042835562,0.000054139833,0.00008593105,0.00017151637,0.00004556818],"domain_scores_gemma":[0.99565095,0.0034071263,0.00027120736,0.00020886071,0.000390862,0.0000710783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035694675,0.0008931703,0.0007752916,0.0015347397,0.00029330823,0.00096252473,0.00073067844,0.0015502158,0.0009892347],"category_scores_gemma":[0.013158656,0.00031238372,0.0004930621,0.0006413192,0.000694321,0.0011182805,0.00080739125,0.00090219686,0.00024473912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084502084,0.00007750753,0.0026088078,0.000076969816,0.000064423635,0.000045600103,0.000110830086,0.8766154,0.004461912,0.0071251155,0.0004506483,0.10827828],"study_design_scores_gemma":[0.000008949831,0.00005893986,0.0004272844,0.000017010867,0.000008828512,0.00002602671,0.000016951077,0.99594444,0.0011773546,0.002038668,0.00026676623,0.000008707096],"about_ca_topic_score_codex":0.0019669873,"about_ca_topic_score_gemma":0.001438743,"teacher_disagreement_score":0.0035694675,"about_ca_system_score_codex":0.0007936614,"about_ca_system_score_gemma":0.000578429,"threshold_uncertainty_score":0.018877387},"labels":[],"label_agreement":null},{"id":"W2773096961","doi":"10.3390/s17122768","title":"Assessment of Embedded Conjugated Polymer Sensor Arrays for Potential Load Transmission Measurement in Orthopaedic Implants","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Conducting polymers and applications","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Piezoresistive effect; Fabrication; Polyaniline; Smart polymer; Repeatability; Pressure sensor; Polymer; Biomedical engineering; Computer science; Composite material; Mechanical engineering; Engineering","score_opus":0.04012338595536937,"score_gpt":0.3182776281778537,"score_spread":0.27815424222248436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773096961","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92577565,0.002645463,0.069303304,0.00016009944,0.00007876024,0.0001008697,0.00014087206,0.0003078408,0.0014870935],"genre_scores_gemma":[0.92471206,0.0013343062,0.0718232,0.00008295263,0.00002873768,0.00010654966,0.000090292226,0.000032956526,0.001788877],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995346,0.0000909427,0.000027828692,0.00009449982,0.00021897959,0.000033073462],"domain_scores_gemma":[0.9994443,0.00022088981,0.00014651407,0.000038892733,0.000121409415,0.000027894568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005806634,0.00039129858,0.00031153165,0.00022984832,0.00010514895,0.00033346532,0.0003710397,0.00056856015,0.0005385859],"category_scores_gemma":[0.00092975114,0.00029833708,0.0001636556,0.00019667504,0.00025284302,0.000656681,0.00023039004,0.00028538593,0.00023652698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019880697,0.0000126030845,0.000096115546,0.0000394439,0.0000025976676,0.00001525283,0.000016205151,0.0002227587,0.9970029,0.0000266338,0.000013100997,0.0025325334],"study_design_scores_gemma":[0.0000027338751,0.00020494458,0.00071839336,0.000003433017,0.000007924647,0.00006600941,0.000017354905,0.003293604,0.99522805,0.000027822463,0.0004229113,0.0000067924298],"about_ca_topic_score_codex":0.00009974943,"about_ca_topic_score_gemma":0.0003237175,"teacher_disagreement_score":0.0005806634,"about_ca_system_score_codex":0.00019620583,"about_ca_system_score_gemma":0.00016463036,"threshold_uncertainty_score":0.003070891},"labels":[],"label_agreement":null},{"id":"W2773322902","doi":"10.3390/s17122836","title":"A Miniaturized Impedimetric Immunosensor for the Competitive Detection of Adrenocorticotropic Hormone","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs","keywords":"Biotinylation; Immunoassay; Adrenocorticotropic hormone; Streptavidin; Chemistry; Chromatography; Dielectric spectroscopy; Materials science; Electrode; Electrochemistry; Biochemistry; Antibody; Hormone; Biotin; Biology; Immunology","score_opus":0.010663577507216939,"score_gpt":0.27767393535443274,"score_spread":0.26701035784721583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773322902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37756592,0.01316904,0.597778,0.001135947,0.0018185232,0.0007334266,0.0008594955,0.0033378247,0.0036017906],"genre_scores_gemma":[0.55625796,0.003927652,0.4342238,0.00063627656,0.00024844235,0.0005035025,0.00053500844,0.00006468754,0.0036025408],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990312,0.00014120313,0.000053107746,0.0002599824,0.00045239588,0.00006205325],"domain_scores_gemma":[0.9997552,0.00008662685,0.000033333825,0.00002262656,0.0000706655,0.000031459167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066572806,0.0010469805,0.0007319409,0.00047905173,0.00020726143,0.00048736393,0.0013480508,0.0011543016,0.0005171862],"category_scores_gemma":[0.0008497232,0.000525193,0.00047860376,0.0003159326,0.0003174979,0.0006369994,0.0005674287,0.0011618702,0.0004580015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025261328,0.000014525464,0.00006134866,0.00007076423,0.000010074688,0.00005059216,0.000011674822,0.00011341907,0.99516064,0.000101325444,0.00009563394,0.0042846752],"study_design_scores_gemma":[0.0000144066,0.00023681686,0.00062848616,0.000005931297,0.00003259694,0.00039217432,0.00001457289,0.00669239,0.98839045,0.000069533446,0.00349447,0.000028206592],"about_ca_topic_score_codex":0.00023585994,"about_ca_topic_score_gemma":0.00053070096,"teacher_disagreement_score":0.0013480508,"about_ca_system_score_codex":0.00041041223,"about_ca_system_score_gemma":0.0003858416,"threshold_uncertainty_score":0.0035207272},"labels":[],"label_agreement":null},{"id":"W2774787369","doi":"10.3390/s17122812","title":"Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Powertech Labs (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Efficient energy use; Carbon footprint; Information and Communications Technology; Energy conservation; Agile software development; Home automation; Energy management; Environmental economics; Computer science; Robustness (evolution); Energy consumption; Engineering; Energy (signal processing); Greenhouse gas; Telecommunications","score_opus":0.007553504877234909,"score_gpt":0.19202938503109016,"score_spread":0.18447588015385524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774787369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24463178,0.005198844,0.683829,0.0051850937,0.0005017905,0.00022504962,0.00036933608,0.001220785,0.058838308],"genre_scores_gemma":[0.94171923,0.0019774756,0.05042478,0.00026735337,0.00004705868,0.000053788077,0.00019294609,0.000036388446,0.005281029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998865,0.000025622841,0.0000056304434,0.000018428285,0.00004224178,0.000021635278],"domain_scores_gemma":[0.9999312,0.000020422562,0.000009487279,0.000006826383,0.000026115424,0.0000058357973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022208557,0.00028236446,0.00027165067,0.0003144964,0.0003734076,0.00060455437,0.00042712485,0.0005362301,0.0010204546],"category_scores_gemma":[0.00030169447,0.000120755074,0.0003738692,0.0008953725,0.0002299198,0.0010712603,0.00043363584,0.00040290348,0.0002029736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025440566,0.00035243575,0.009385655,0.00057146966,0.00011249985,0.0006941019,0.0003446355,0.400366,0.03413765,0.06061435,0.014427319,0.47873953],"study_design_scores_gemma":[0.00002375388,0.00013758597,0.0048023346,0.00005569483,0.000045961668,0.0002425048,0.00055313524,0.92933017,0.010703478,0.028582934,0.025475709,0.000046795914],"about_ca_topic_score_codex":0.0040240353,"about_ca_topic_score_gemma":0.0074495957,"teacher_disagreement_score":0.0040240353,"about_ca_system_score_codex":0.000420266,"about_ca_system_score_gemma":0.00057220994,"threshold_uncertainty_score":0.008001208},"labels":[],"label_agreement":null},{"id":"W2778471537","doi":"10.3390/s18010023","title":"Epigallocatechin Gallate-Modified Graphite Paste Electrode for Simultaneous Detection of Redox-Active Biomolecules","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Detection limit; Ascorbic acid; Chemistry; Electrode; Graphite; Electrochemistry; Redox; Electrochemical gas sensor; Nuclear chemistry; Analyte; Chromatography; Inorganic chemistry; Organic chemistry","score_opus":0.007239978380620234,"score_gpt":0.22539778813449318,"score_spread":0.21815780975387294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2778471537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6980572,0.04872529,0.2457668,0.00082354,0.0007760951,0.00025599304,0.00068125897,0.0012480747,0.0036657637],"genre_scores_gemma":[0.8371104,0.009907827,0.14848869,0.00050833623,0.000107980246,0.00016245457,0.00056517095,0.00004056475,0.0031085783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898785,0.0002299457,0.0000722327,0.00021620958,0.00042736725,0.000066305496],"domain_scores_gemma":[0.9997645,0.000071931936,0.000039734285,0.000023003417,0.00008307107,0.000017799173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057581597,0.00094887614,0.00060408434,0.0010034398,0.00025201222,0.00039053592,0.0009942785,0.0017969357,0.0004283756],"category_scores_gemma":[0.0005677993,0.00042936992,0.0005656985,0.0007375955,0.00023484569,0.0005538862,0.00041808764,0.0008325633,0.00035848498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046634883,0.00002258751,0.00017581535,0.00011841614,0.00002058924,0.00007246665,0.000014271504,0.000057593916,0.9939976,0.000050435632,0.000042232074,0.00538123],"study_design_scores_gemma":[0.000007590418,0.00022631379,0.0020423091,0.00001057633,0.000047698475,0.0005939311,0.000020609632,0.0026916084,0.9922956,0.000077127785,0.0019683111,0.00001837394],"about_ca_topic_score_codex":0.0005047077,"about_ca_topic_score_gemma":0.0014271126,"teacher_disagreement_score":0.0017969357,"about_ca_system_score_codex":0.00025294453,"about_ca_system_score_gemma":0.00018352382,"threshold_uncertainty_score":0.0030452013},"labels":[],"label_agreement":null},{"id":"W2779486016","doi":"10.3390/s17122934","title":"Lab-on-a-Chip Platforms for Detection of Cardiovascular Disease and Cancer Biomarkers","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seven Oaks General Hospital; Victoria General Hospital; University of Manitoba","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs; Chinese Academy of Sciences; University of Manitoba","keywords":"Software portability; Biomarker; Cancer biomarkers; Biomarker discovery; Cancer; Disease; Cancer detection; Computer science; Medicine; Bioinformatics; Risk analysis (engineering); Computational biology; Internal medicine; Proteomics; Biology","score_opus":0.05143832541838937,"score_gpt":0.29191666679639566,"score_spread":0.24047834137800628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779486016","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005863258,0.9903942,0.004475535,0.00040505858,0.00067524123,0.000038277736,0.00006173741,0.00007622331,0.003287513],"genre_scores_gemma":[0.003427991,0.98767453,0.003939495,0.00050995447,0.00035242198,0.00007825585,0.00015310783,0.000011914325,0.0038522996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940264,0.00007121765,0.00005208829,0.00010852053,0.00031242307,0.000053184016],"domain_scores_gemma":[0.9995516,0.00017668091,0.00006335837,0.00001848039,0.00015772386,0.000032028027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008881319,0.0014797576,0.001171139,0.0029155589,0.0002925567,0.0009631715,0.0013207727,0.0016772625,0.0031440686],"category_scores_gemma":[0.00090899185,0.00057908986,0.00075938646,0.0021311652,0.00043558842,0.0017188052,0.0009248891,0.0018923536,0.0031128325],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050162354,0.00014091046,0.00032973805,0.017721547,0.000110932524,0.0003040705,0.000085070176,0.0007813044,0.03861311,0.007964809,0.03580642,0.8980919],"study_design_scores_gemma":[0.000008235562,0.00013709127,0.00053142116,0.0009744125,0.00008886921,0.0010152499,0.000045037534,0.0006217362,0.013184713,0.0015580173,0.9817892,0.00004589723],"about_ca_topic_score_codex":0.000580345,"about_ca_topic_score_gemma":0.00089352607,"teacher_disagreement_score":0.0031440686,"about_ca_system_score_codex":0.0005695313,"about_ca_system_score_gemma":0.0008332903,"threshold_uncertainty_score":0.010517895},"labels":[],"label_agreement":null},{"id":"W2780098221","doi":"10.3390/s17122939","title":"Using the Kalman Algorithm to Correct Data Errors of a 24-Bit Visible Spectrometer","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Kalman filter; Computer science; Algorithm; MATLAB; Light intensity; Spec#; Spectrometer; Noise (video); Optics; Artificial intelligence; Physics","score_opus":0.12493921207669807,"score_gpt":0.34919414761021,"score_spread":0.22425493553351195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2780098221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038963666,0.00012164026,0.9939322,0.000037261787,0.000037694976,0.000023211134,0.000035876164,0.0013739828,0.00054172915],"genre_scores_gemma":[0.17715764,0.00049262523,0.8175582,0.000099675875,0.00006400975,0.0001827521,0.0004079217,0.00028415496,0.0037529261],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926525,0.00009336888,0.000064822874,0.0002102638,0.00031144102,0.00005487952],"domain_scores_gemma":[0.9991984,0.0002278681,0.000115489944,0.00010629696,0.00033345696,0.000018508337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009998274,0.0011189767,0.0008112782,0.0008511292,0.00081748475,0.0011151014,0.001044871,0.00073950004,0.001723572],"category_scores_gemma":[0.0030100471,0.00043699957,0.00056288077,0.00087739096,0.0004129321,0.0015555699,0.00064215314,0.0010146652,0.0009887796],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029450664,0.000080577505,0.005097371,0.0003681296,0.00020397417,0.00021180022,0.000344235,0.2712856,0.048362866,0.010246929,0.0049111107,0.6585929],"study_design_scores_gemma":[0.00003170491,0.00009324475,0.0020576634,0.000037805872,0.000055092587,0.00013998843,0.000055869725,0.9559029,0.027877308,0.0033029697,0.010372461,0.00007309538],"about_ca_topic_score_codex":0.010499455,"about_ca_topic_score_gemma":0.0074762134,"teacher_disagreement_score":0.010499455,"about_ca_system_score_codex":0.00064462796,"about_ca_system_score_gemma":0.0018046646,"threshold_uncertainty_score":0.020876646},"labels":[],"label_agreement":null},{"id":"W2781125408","doi":"10.3390/s18010076","title":"How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"State Key Laboratory of Fluid Power and Mechatronic Systems; Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Sensor fusion; Kalman filter; Heading (navigation); Control theory (sociology); Attitude and heading reference system; Gyroscope; Computer science; Decoupling (probability); Inertial measurement unit; Extended Kalman filter; Orientation (vector space); Compass; Inertial frame of reference; Control engineering; Engineering; Computer vision; Artificial intelligence; Mathematics; Aerospace engineering; Physics","score_opus":0.03083934489654024,"score_gpt":0.2979734942246,"score_spread":0.2671341493280598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781125408","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018762558,0.9858754,0.008503551,0.0003330105,0.00044000105,0.00002659162,0.00003308226,0.000029536879,0.0028825444],"genre_scores_gemma":[0.013332892,0.9806451,0.0037089202,0.00017280472,0.00043293426,0.000031130476,0.00006414675,0.0000074823947,0.001604681],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938047,0.0001073699,0.00009291807,0.00014190694,0.0002454969,0.00003178031],"domain_scores_gemma":[0.9991849,0.0003735673,0.00009666806,0.000026917734,0.0003001397,0.000017705146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092318153,0.0008985059,0.0009575576,0.0021417325,0.00026336533,0.0009174135,0.0009064586,0.0012662086,0.0010948704],"category_scores_gemma":[0.0018277455,0.00042002313,0.0006709606,0.0022725144,0.0005134411,0.0016504343,0.0005056011,0.00065178453,0.000682065],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005735127,0.000051880637,0.0012037071,0.01725207,0.00015015082,0.00020848424,0.00012081215,0.002844156,0.003727172,0.005613574,0.004680624,0.96409005],"study_design_scores_gemma":[0.00003146091,0.00071686215,0.013894536,0.009229925,0.0012661348,0.0052150222,0.00046401526,0.012335196,0.019789005,0.00942961,0.9273707,0.0002574988],"about_ca_topic_score_codex":0.0019981824,"about_ca_topic_score_gemma":0.0020018837,"teacher_disagreement_score":0.0021417325,"about_ca_system_score_codex":0.00048709832,"about_ca_system_score_gemma":0.0011005491,"threshold_uncertainty_score":0.0048823357},"labels":[],"label_agreement":null},{"id":"W2781356165","doi":"10.3390/s17122948","title":"Blind Compensation of I/Q Impairments in Wireless Transceivers","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Transmitter; Transceiver; Computer science; Compensation (psychology); Additive white Gaussian noise; Wireless; Image response; Bit error rate; Channel (broadcasting); Algorithm; Carrier frequency offset; Gaussian; Electronic engineering; Control theory (sociology); Telecommunications; Frequency offset; Radio frequency; Artificial intelligence; Engineering; Orthogonal frequency-division multiplexing","score_opus":0.02086179277809441,"score_gpt":0.26111996781799723,"score_spread":0.24025817503990282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781356165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03285854,0.00027853815,0.9655048,0.000070021444,0.000024913303,0.00002579052,0.000022249807,0.000519463,0.0006956581],"genre_scores_gemma":[0.7584292,0.0005439721,0.23861584,0.00015204506,0.00006529855,0.000069502785,0.00008363336,0.00008828576,0.001952154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992138,0.00018125924,0.00004217039,0.00011325771,0.00039293052,0.000056557798],"domain_scores_gemma":[0.99910873,0.0003522894,0.00027250816,0.00010301632,0.00014097498,0.00002239545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084417494,0.00070506014,0.0005301221,0.00069963816,0.00035904744,0.00064174226,0.00052146055,0.00075312564,0.0008292225],"category_scores_gemma":[0.0030383493,0.00034689196,0.0003639648,0.00045835396,0.00076475024,0.0015479708,0.00080436416,0.0005671984,0.0005155214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058407366,0.00019857082,0.004220614,0.00044938934,0.00010362584,0.00031656204,0.000343558,0.29779947,0.36521152,0.012670207,0.0011474913,0.31695494],"study_design_scores_gemma":[0.000047093068,0.00039239755,0.0030206076,0.000066053915,0.000043849708,0.00083683647,0.00006757128,0.7947446,0.1869821,0.011115703,0.0025945988,0.00008853578],"about_ca_topic_score_codex":0.00039275645,"about_ca_topic_score_gemma":0.00047572888,"teacher_disagreement_score":0.00084417494,"about_ca_system_score_codex":0.00038022763,"about_ca_system_score_gemma":0.0005031919,"threshold_uncertainty_score":0.004464507},"labels":[],"label_agreement":null},{"id":"W2781405137","doi":"10.3390/s17122955","title":"Anti-Runaway Prevention System with Wireless Sensors for Intelligent Track Skates at Railway Stations","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Track (disk drive); Real-time computing; Wireless; Engineering; Global Positioning System; Simulation; Management system; Computer security; Computer science; Embedded system; Automotive engineering; Transport engineering; Telecommunications; Operations management","score_opus":0.01259268986350682,"score_gpt":0.2324921179770997,"score_spread":0.2198994281135929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781405137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6176157,0.0012475529,0.35617468,0.0003029928,0.00031150656,0.00038716645,0.000763916,0.012515194,0.010681205],"genre_scores_gemma":[0.95922875,0.00031729302,0.034595806,0.00010718637,0.000036551475,0.00013899045,0.00038996586,0.000052415573,0.005132988],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994696,0.000066696135,0.00004500547,0.0001453942,0.00022057984,0.000052714866],"domain_scores_gemma":[0.99964535,0.000038322414,0.000057758105,0.000049450075,0.00018224893,0.000026857733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030451253,0.00060779956,0.0005606784,0.00082132214,0.0003403359,0.00043938824,0.0008586249,0.00045048408,0.0014185363],"category_scores_gemma":[0.0004652297,0.00035143126,0.00028879443,0.0005388121,0.00015272187,0.00082974497,0.00071324187,0.00026766854,0.0007200656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013858451,0.00063569774,0.08850751,0.000984495,0.0002946491,0.0011122247,0.0013622637,0.015907472,0.3934141,0.001316793,0.011483226,0.48359567],"study_design_scores_gemma":[0.00023599227,0.003062766,0.15741831,0.00020921715,0.0010672873,0.0021069567,0.0015872483,0.3881858,0.38393313,0.0011905867,0.060698275,0.0003044739],"about_ca_topic_score_codex":0.002065945,"about_ca_topic_score_gemma":0.0031253789,"teacher_disagreement_score":0.002065945,"about_ca_system_score_codex":0.00024906528,"about_ca_system_score_gemma":0.00041428948,"threshold_uncertainty_score":0.0047454834},"labels":[],"label_agreement":null},{"id":"W2782219207","doi":"10.3390/s18010091","title":"A Review of Oil Spill Remote Sensing","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":419,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Oil spill; Remote sensing; Petroleum engineering; Environmental science; Computer science; Engineering; Geology","score_opus":0.0636510376903595,"score_gpt":0.3384787190958503,"score_spread":0.2748276814054908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782219207","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025416032,0.99443346,0.00064862636,0.00039002052,0.00047665086,0.000013730292,0.00014518539,0.00002857807,0.0036096142],"genre_scores_gemma":[0.0013171672,0.9958578,0.0008031026,0.00019851945,0.00031754648,0.000010862372,0.00018400066,0.0000062578806,0.0013047942],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995621,0.000056816716,0.00006611214,0.00009559431,0.00018842972,0.00003091954],"domain_scores_gemma":[0.9990717,0.00037709024,0.00013551125,0.000033314784,0.00033356037,0.00004875568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076158816,0.0012772499,0.0014840885,0.004732632,0.00043105934,0.0012367052,0.0012657788,0.0011723394,0.008963943],"category_scores_gemma":[0.0013132554,0.00044461383,0.00088311196,0.005360842,0.0004273639,0.0023146404,0.00068321254,0.0011714686,0.004003015],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041170337,0.0000642065,0.00043030488,0.025895799,0.000094445604,0.00016945419,0.000065622575,0.00068392616,0.00197608,0.0028090395,0.040003993,0.9277659],"study_design_scores_gemma":[0.0000037521463,0.0000525372,0.0010448688,0.0041533886,0.00008872179,0.0005771723,0.00007263401,0.00011474349,0.0005322282,0.0011054486,0.99223,0.000024576419],"about_ca_topic_score_codex":0.002665211,"about_ca_topic_score_gemma":0.0031944197,"teacher_disagreement_score":0.008963943,"about_ca_system_score_codex":0.00071472954,"about_ca_system_score_gemma":0.0016832751,"threshold_uncertainty_score":0.029987395},"labels":[],"label_agreement":null},{"id":"W2782795431","doi":"10.3390/s18010205","title":"Plils: A Practical Indoor Localization System through Less Expensive Wireless Chips via Subregion Clustering","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Wireless; Computer science; Exploit; Cluster analysis; Power (physics); Support vector machine; Artificial neural network; Phase (matter); Measure (data warehouse); Signal strength; Wireless network; Real-time computing; SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Electronic engineering; Data mining; Engineering; Telecommunications","score_opus":0.02603130270641757,"score_gpt":0.2536941574232174,"score_spread":0.2276628547167998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782795431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026285443,0.00016747719,0.9616179,0.00010990389,0.000060141785,0.000077704586,0.00020133788,0.008031943,0.0034481746],"genre_scores_gemma":[0.40879023,0.00019144257,0.5791068,0.00021535721,0.000044039898,0.00020594799,0.0006097901,0.0002263138,0.010610013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966705,0.000051700157,0.00001575491,0.0000895098,0.00013548083,0.00004050761],"domain_scores_gemma":[0.999579,0.000048320246,0.00006479285,0.00012233402,0.00015278342,0.00003280668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025122103,0.00059926964,0.00057543186,0.00074150413,0.00033314218,0.00046221833,0.0016135145,0.00046128235,0.0030504616],"category_scores_gemma":[0.0005602343,0.00022180624,0.00027947352,0.0007980273,0.00029224806,0.0010772044,0.0011160026,0.00039644117,0.0028992265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057032687,0.00013828847,0.003968159,0.00035069344,0.00007666577,0.00031439227,0.00031001732,0.06271182,0.11509171,0.005881451,0.0135329515,0.7970535],"study_design_scores_gemma":[0.00014669364,0.0008050473,0.006679664,0.00004706651,0.000105405845,0.0011973465,0.00032774676,0.75894934,0.15735455,0.004896881,0.0693272,0.00016313375],"about_ca_topic_score_codex":0.0019138503,"about_ca_topic_score_gemma":0.003170771,"teacher_disagreement_score":0.0030504616,"about_ca_system_score_codex":0.00040339903,"about_ca_system_score_gemma":0.00059382844,"threshold_uncertainty_score":0.010204852},"labels":[],"label_agreement":null},{"id":"W2782986898","doi":"10.3390/s18010133","title":"A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Key distribution in wireless sensor networks; Default gateway; Energy harvesting; Sensor node; Node (physics); Computer network; Computer science; Network packet; Energy (signal processing); Energy consumption; Duty cycle; Wireless; Real-time computing; Engineering; Wireless network; Electrical engineering; Telecommunications","score_opus":0.019553731132292553,"score_gpt":0.2059744598409853,"score_spread":0.18642072870869275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782986898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012717298,0.0010578231,0.9651464,0.00026477445,0.0001425798,0.00009891965,0.00004012538,0.0006612806,0.019870665],"genre_scores_gemma":[0.4527314,0.0032951813,0.49942833,0.00042001402,0.00016909753,0.0005163911,0.00022103052,0.000114383074,0.04310419],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985135,0.000019454259,0.000007747806,0.00003928789,0.00007235431,0.000009833211],"domain_scores_gemma":[0.9999534,0.0000048300717,0.00000641397,0.000008899754,0.000021985861,0.0000045210963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012268347,0.00032284806,0.0003228145,0.0002571307,0.00027920667,0.00033451736,0.0007351163,0.00039878802,0.0014634689],"category_scores_gemma":[0.000104194645,0.00020507396,0.00041561734,0.00021462297,0.00021165475,0.0006794054,0.0003714706,0.0003037074,0.0007742842],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000798692,0.00008821857,0.0010148502,0.0008402247,0.0000846237,0.0004430788,0.00038866253,0.064795375,0.5612715,0.08628145,0.0060833003,0.27862883],"study_design_scores_gemma":[0.00005811419,0.0011137099,0.0018641831,0.00011692485,0.00014240293,0.0017241434,0.0001697608,0.5948344,0.106519535,0.024562879,0.26879486,0.000099082376],"about_ca_topic_score_codex":0.00036461977,"about_ca_topic_score_gemma":0.00051543885,"teacher_disagreement_score":0.0014634689,"about_ca_system_score_codex":0.00020303109,"about_ca_system_score_gemma":0.00029101555,"threshold_uncertainty_score":0.0048958063},"labels":[],"label_agreement":null},{"id":"W2783691976","doi":"10.3390/s18010210","title":"Multiple-Octave-Spanning Vibration Sensing Based on Simultaneous Vector Demodulation of 499 Fizeau Interference Signals from Identical Ultra-Weak Fiber Bragg Gratings Over 2.5 km","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Demodulation; Fiber Bragg grating; Acoustics; Optics; Vibration; Materials science; Ranging; Optical fiber; Physics; Computer science; Telecommunications","score_opus":0.011621388617550377,"score_gpt":0.23741630619129928,"score_spread":0.2257949175737489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783691976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9802101,0.00015314946,0.018891715,0.000039299084,0.000010234488,0.00001126061,0.000030315661,0.00009038931,0.0005634248],"genre_scores_gemma":[0.980467,0.00006338549,0.019022042,0.000026087038,0.0000044834937,0.000009295147,0.000033042525,0.000006799761,0.0003679536],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998029,0.000018481725,0.000007338766,0.00006649377,0.00007780666,0.000026958282],"domain_scores_gemma":[0.9998543,0.00003449306,0.000048499045,0.000016474536,0.000029837422,0.000016398419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020748923,0.00031465688,0.00019926197,0.000255423,0.00014499588,0.00015972479,0.00040069132,0.00029755817,0.00028420318],"category_scores_gemma":[0.00030373925,0.00016671619,0.00011388626,0.00016039908,0.00034679653,0.00043179118,0.0003258857,0.00020337383,0.0000868853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050730694,0.000018458762,0.0014384214,0.000015371888,0.0000042563634,0.000028011424,0.000038649454,0.00044554906,0.99003416,0.00010359608,0.000023893062,0.00779884],"study_design_scores_gemma":[0.000011369225,0.00026643003,0.010038595,0.00000428916,0.000011154951,0.00017108776,0.000039318224,0.01821704,0.9703776,0.0001390659,0.00070743484,0.000016692278],"about_ca_topic_score_codex":0.0011116809,"about_ca_topic_score_gemma":0.00263135,"teacher_disagreement_score":0.0011116809,"about_ca_system_score_codex":0.0002719694,"about_ca_system_score_gemma":0.00015136186,"threshold_uncertainty_score":0.0022104383},"labels":[],"label_agreement":null},{"id":"W2783768090","doi":"10.3390/s18010174","title":"Feasibility of Detecting Natural Frequencies of Hydraulic Turbines While in Operation, Using Strain Gauges","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"FP7 Energy; BC Hydro; European Commission","keywords":"Hydropower; Strain gauge; Turbine; Francis turbine; Marine engineering; Natural frequency; Engineering; Vibration; Hydraulic turbines; Accelerometer; Structural engineering; Mechanical engineering; Acoustics; Computer science; Electrical engineering","score_opus":0.03385754300477293,"score_gpt":0.26947576707145204,"score_spread":0.23561822406667912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783768090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9535517,0.00025123142,0.04464247,0.000052373736,0.000050861607,0.000049442337,0.0001891855,0.00019789708,0.0010149168],"genre_scores_gemma":[0.9837113,0.000110050394,0.015639031,0.000017898621,0.00001226758,0.000027583006,0.000082695806,0.000014243596,0.00038490127],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994493,0.00010569825,0.000026753927,0.0001491466,0.0002177932,0.00005124128],"domain_scores_gemma":[0.99912006,0.000341763,0.00011930743,0.00007301092,0.0002949448,0.000050920327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005130317,0.0005585779,0.0002844976,0.00061960536,0.00017680465,0.0003250216,0.00046629616,0.0006149922,0.00075139414],"category_scores_gemma":[0.0013663294,0.00020378108,0.00015801121,0.0002796292,0.00037107753,0.00045671215,0.00021522053,0.00026065597,0.0002837608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038885858,0.0001277054,0.032382086,0.0001329567,0.000021545786,0.0001471872,0.0002967957,0.0019273069,0.89529777,0.00012110487,0.00017479014,0.06898192],"study_design_scores_gemma":[0.000040192852,0.002418619,0.14921646,0.000037699796,0.0000937816,0.0005988649,0.0007227897,0.0404425,0.80321926,0.00030668272,0.002802942,0.00010027732],"about_ca_topic_score_codex":0.00047643913,"about_ca_topic_score_gemma":0.001587646,"teacher_disagreement_score":0.00075139414,"about_ca_system_score_codex":0.0001224823,"about_ca_system_score_gemma":0.00015630574,"threshold_uncertainty_score":0.0027132034},"labels":[],"label_agreement":null},{"id":"W2784014468","doi":"10.3390/s18020479","title":"Analysis of Dark Current in BRITE Nanostellite CCD Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; Karl-Franzens-Universität Graz; Universität Wien; University of Toronto; Österreichische Forschungsförderungsgesellschaft; Ministerstwo Edukacji i Nauki; Fundacja na rzecz Nauki Polskiej","keywords":"Dark current; Pixel; Physics; Sky; Charge-coupled device; Proton; Normalization (sociology); Optics; Detector; Optoelectronics; Astronomy; Nuclear physics","score_opus":0.009174424291519595,"score_gpt":0.2409725413243075,"score_spread":0.2317981170327879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784014468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98843735,0.0003420859,0.008803751,0.00003590988,0.000010753677,0.000018881303,0.0005319721,0.00023173701,0.0015875886],"genre_scores_gemma":[0.9963814,0.00008352751,0.002310903,0.00001518905,0.0000028096758,0.000013473601,0.00030945337,0.00002458228,0.0008585862],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979144,0.000011962185,0.000007594192,0.000049551534,0.00011081656,0.000028643404],"domain_scores_gemma":[0.99953866,0.00009824479,0.000092633345,0.00003849575,0.00019981895,0.000032216652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017927018,0.00017215511,0.00016699955,0.0008829251,0.00016314845,0.00024015055,0.00037352185,0.00026049672,0.0011802702],"category_scores_gemma":[0.0006963452,0.00010808923,0.000120805045,0.00049549696,0.0002682288,0.00024666262,0.0001710875,0.00022962184,0.0001647604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000290457,0.00006966687,0.021331813,0.00015583381,0.00004029849,0.0003442957,0.00030474403,0.0033674634,0.9594732,0.00040702985,0.00036336362,0.0138518885],"study_design_scores_gemma":[0.0000080291275,0.0002726326,0.13289149,0.000013841384,0.000033692788,0.000494373,0.00021533924,0.033666123,0.83021957,0.00017129254,0.001980557,0.000033160275],"about_ca_topic_score_codex":0.0012858296,"about_ca_topic_score_gemma":0.0015975693,"teacher_disagreement_score":0.0012858296,"about_ca_system_score_codex":0.00051647064,"about_ca_system_score_gemma":0.000113174356,"threshold_uncertainty_score":0.0039483905},"labels":[],"label_agreement":null},{"id":"W2785232536","doi":"10.3390/s18010298","title":"Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Science Fund for Distinguished Young Scholars; National Key Research and Development Program of China; Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Autoregressive integrated moving average; Kalman filter; Autoregressive model; Autoregressive conditional heteroskedasticity; Computer science; Structural health monitoring; Deformation monitoring; Time series; Algorithm; Deformation (meteorology); Engineering; Artificial intelligence; Statistics; Econometrics; Mathematics; Machine learning; Geography; Meteorology; Structural engineering; Volatility (finance)","score_opus":0.07522417476631248,"score_gpt":0.3309959473673324,"score_spread":0.2557717726010199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785232536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32662073,0.0008275724,0.66617894,0.00040161188,0.00012384739,0.000053172615,0.00057038676,0.0015285523,0.0036952063],"genre_scores_gemma":[0.98566914,0.00035159744,0.011889229,0.00003162084,0.000029626493,0.000025283864,0.00043314687,0.00002173884,0.001548614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996828,0.000048422695,0.000023143151,0.00010917293,0.00009232037,0.000044201523],"domain_scores_gemma":[0.9997224,0.00011658458,0.000043220425,0.000026730657,0.000080587146,0.000010438705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057955814,0.00057329005,0.0005639846,0.0005881223,0.00025210067,0.0005514638,0.0006817491,0.00055448175,0.0007192954],"category_scores_gemma":[0.0013999842,0.00034796825,0.0007561153,0.0006203165,0.00023070528,0.0008278287,0.00032570152,0.00063367415,0.00024784345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054985918,0.00003379988,0.012407771,0.00005304501,0.00012092134,0.000102764854,0.00006229188,0.94158924,0.002234268,0.0022500602,0.0008987846,0.04019217],"study_design_scores_gemma":[0.000002788505,0.000007057997,0.0015282445,0.0000016498515,0.000009241753,0.000006152925,0.0000036036793,0.9977537,0.00017860546,0.00041142383,0.00009201471,0.000005565308],"about_ca_topic_score_codex":0.032218326,"about_ca_topic_score_gemma":0.020730415,"teacher_disagreement_score":0.032218326,"about_ca_system_score_codex":0.0004311171,"about_ca_system_score_gemma":0.0006077972,"threshold_uncertainty_score":0.06406158},"labels":[],"label_agreement":null},{"id":"W2787391877","doi":"10.3390/s18010268","title":"A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Wasting; Computer science; Myotonic dystrophy; Population; Human–computer interaction; Multimedia; Embedded system; Operating system; Medicine","score_opus":0.01879660456042717,"score_gpt":0.2375070556333404,"score_spread":0.21871045107291323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787391877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061427087,0.0011757562,0.8562519,0.00089640834,0.0010175686,0.00086214516,0.003170491,0.060728043,0.014470693],"genre_scores_gemma":[0.5415244,0.0007377988,0.4192829,0.0013501594,0.00042066904,0.0010042698,0.0070133647,0.00074075966,0.02792572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941623,0.000059472186,0.000049025195,0.00021912035,0.00018767935,0.00006852973],"domain_scores_gemma":[0.9994529,0.00007249065,0.000039526498,0.00013064094,0.0002393555,0.00006506584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046974627,0.00087613735,0.0009790512,0.0010259291,0.00032567795,0.0010630848,0.0013821588,0.0010523871,0.011237936],"category_scores_gemma":[0.001098291,0.00033789696,0.00043793075,0.0007665579,0.00015354146,0.00175236,0.0009599576,0.000800904,0.006052953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009980565,0.00082767085,0.0060463585,0.00055047654,0.00019310428,0.00048308977,0.00023927852,0.0052282927,0.2542665,0.002666507,0.046141483,0.6823593],"study_design_scores_gemma":[0.0006640218,0.0021183183,0.043733165,0.00028947822,0.00070912903,0.002614426,0.00061061204,0.44285485,0.2734562,0.008350654,0.22407448,0.00052471354],"about_ca_topic_score_codex":0.002878489,"about_ca_topic_score_gemma":0.0033354277,"teacher_disagreement_score":0.011237936,"about_ca_system_score_codex":0.0004099112,"about_ca_system_score_gemma":0.0005609858,"threshold_uncertainty_score":0.037594676},"labels":[],"label_agreement":null},{"id":"W2789448519","doi":"10.3390/s18040958","title":"A Highly Thermostable In2O3/ITO Thin Film Thermocouple Prepared via Screen Printing for High Temperature Measurements","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor Technologies Research","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Higher Education Discipline Innovation Project; International Joint Laboratory for MicroNano Manufacturing and Measurement Technologies","keywords":"Thermocouple; Materials science; Screen printing; Thermoelectric effect; Annealing (glass); Sintering; Seebeck coefficient; Grain size; Composite material; Thin film; Analytical Chemistry (journal); Optoelectronics; Nanotechnology; Thermal conductivity; Chemistry; Thermodynamics","score_opus":0.025696256129675153,"score_gpt":0.26707010833157047,"score_spread":0.2413738522018953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789448519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6625222,0.0011166065,0.32178146,0.00022739549,0.0004036865,0.00035718354,0.0026467203,0.003096331,0.007848545],"genre_scores_gemma":[0.80893856,0.0010407862,0.18022135,0.00008999411,0.000052534062,0.00047159923,0.0010657543,0.00041150567,0.0077078883],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955255,0.000046304576,0.00003549699,0.00012532833,0.00018916758,0.000051084768],"domain_scores_gemma":[0.9995734,0.0000870523,0.00007903897,0.000069220325,0.00015877029,0.000032623735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004309825,0.00065147836,0.0005511196,0.00061149563,0.000395987,0.00041153296,0.00061699894,0.00041079978,0.0022834637],"category_scores_gemma":[0.00047674417,0.00050034636,0.00021100414,0.0008312279,0.00029402834,0.00036246324,0.00021509171,0.0006906543,0.0007802849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015783166,0.000008803869,0.000080768776,0.000021891388,0.0000019813115,0.000024064731,0.00001752532,0.000038434067,0.99798846,0.000040765706,0.00006976523,0.0016916031],"study_design_scores_gemma":[0.0000018494461,0.000018186116,0.0005957192,0.0000016451996,0.0000058782334,0.000038539645,0.000009245519,0.00095898885,0.9972631,0.000018859222,0.0010841975,0.0000038144935],"about_ca_topic_score_codex":0.0004947525,"about_ca_topic_score_gemma":0.0017172652,"teacher_disagreement_score":0.0022834637,"about_ca_system_score_codex":0.00041543538,"about_ca_system_score_gemma":0.00025951653,"threshold_uncertainty_score":0.0076389313},"labels":[],"label_agreement":null},{"id":"W2790831645","doi":"10.3390/s18020377","title":"Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quality (philosophy); Quality assessment; Hyperspectral imaging; Iodine value; Biochemical engineering; Process engineering; Food science; Computer science; Biotechnology; Chemistry; Artificial intelligence; Engineering; Reliability engineering; Biology; Evaluation methods; Physics","score_opus":0.06325614674195124,"score_gpt":0.4354867959694505,"score_spread":0.37223064922749927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790831645","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004260178,0.9952866,0.0019363365,0.00022780614,0.00027526746,0.00001385934,0.000035870344,0.000023598992,0.0017745758],"genre_scores_gemma":[0.0022202295,0.99353796,0.0023588985,0.00014140774,0.0002362391,0.000018866956,0.00006142557,0.0000077060695,0.001417232],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948835,0.000058283284,0.000053004514,0.00010768546,0.00026071578,0.00003195915],"domain_scores_gemma":[0.9991498,0.0004192801,0.00011098819,0.000030111525,0.00025922296,0.00003065346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010312338,0.0013558264,0.0013177571,0.0038046062,0.0003840839,0.001185861,0.0011514532,0.0012735,0.0042132526],"category_scores_gemma":[0.0011436341,0.00046358554,0.00089278485,0.003397912,0.00063249335,0.0018348361,0.0006406106,0.0015377904,0.0021624607],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040902145,0.00015230155,0.0005079179,0.023232287,0.00013263256,0.00024578618,0.00011025573,0.0009109719,0.010548294,0.0052835555,0.014578755,0.94425625],"study_design_scores_gemma":[0.0000075744424,0.00019430346,0.001884292,0.0034530237,0.00021416023,0.0020913042,0.00014973881,0.0008301194,0.0077976957,0.0034744851,0.97982794,0.00007526059],"about_ca_topic_score_codex":0.0011379864,"about_ca_topic_score_gemma":0.0016030816,"teacher_disagreement_score":0.0042132526,"about_ca_system_score_codex":0.00047045137,"about_ca_system_score_gemma":0.0009958403,"threshold_uncertainty_score":0.01409471},"labels":[],"label_agreement":null},{"id":"W2791088620","doi":"10.3390/s18030907","title":"How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Office of Naval Research; Office of Naval Research Global; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Natural disaster; Computer science; Protocol (science); Data collection; Wireless sensor network; Internet of Things; Property (philosophy); Fault (geology); Task (project management); Real-time computing; Computer security; Data mining; Computer network; Engineering; Geography; Meteorology; Statistics","score_opus":0.056158227559635135,"score_gpt":0.3184596743380817,"score_spread":0.2623014467784466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791088620","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95531636,0.0007724668,0.036355354,0.0022626333,0.00007851497,0.00022201135,0.00022008792,0.00041130272,0.0043614022],"genre_scores_gemma":[0.9871662,0.00029314304,0.011621901,0.000056577996,0.000025324369,0.000028280569,0.00007515501,0.000017939197,0.000715407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912494,0.00042634035,0.00007201959,0.00008861789,0.00017193663,0.000116039184],"domain_scores_gemma":[0.995877,0.0028623394,0.00022670148,0.00030510614,0.0005006015,0.00022816683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019166407,0.0004915674,0.00041518515,0.0007538081,0.0008364003,0.0009972189,0.00077507034,0.0015049229,0.0006530676],"category_scores_gemma":[0.005026577,0.0001702775,0.00042391891,0.0008621148,0.00063336326,0.0015069527,0.00065569574,0.00071650033,0.00009918053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013714692,0.002005049,0.08601907,0.001314166,0.00026660305,0.02214686,0.0072945617,0.6288621,0.018018506,0.008555741,0.007908184,0.21623766],"study_design_scores_gemma":[0.00020987193,0.002005128,0.03947418,0.00015907624,0.00023370721,0.0030626073,0.012690825,0.89084053,0.025345711,0.008264561,0.017579382,0.00013430124],"about_ca_topic_score_codex":0.0048516397,"about_ca_topic_score_gemma":0.00539921,"teacher_disagreement_score":0.0048516397,"about_ca_system_score_codex":0.00062539167,"about_ca_system_score_gemma":0.00046207337,"threshold_uncertainty_score":0.010136306},"labels":[],"label_agreement":null},{"id":"W2791874237","doi":"10.3390/s18020381","title":"An Improved Method for Magnetic Nanocarrier Drug Delivery across the Cell Membrane","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Electrohydrodynamics and Fluid Dynamics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Nanocarriers; Drug delivery; Nanotechnology; Nanobiotechnology; Targeted drug delivery; Materials science; Magnetic nanoparticles; Biocompatibility; Nanomedicine; Nanoparticle","score_opus":0.0036383467446099407,"score_gpt":0.22848447205924363,"score_spread":0.2248461253146337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791874237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10102437,0.013918413,0.873541,0.001625281,0.0007379219,0.00042105588,0.00023418579,0.0011658808,0.0073318905],"genre_scores_gemma":[0.34141657,0.008612916,0.6358173,0.0006424653,0.0000988318,0.0005473797,0.00019365372,0.00009966832,0.012571178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995877,0.00007034895,0.000032242548,0.00009605398,0.00018410389,0.00002950917],"domain_scores_gemma":[0.9998958,0.000026071823,0.000015185429,0.000017416158,0.000037424314,0.000008070614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004499202,0.00039785923,0.00043240946,0.000557065,0.00045249867,0.00032696495,0.00049932464,0.00095335866,0.00097379653],"category_scores_gemma":[0.00035974898,0.00020295057,0.0004448468,0.00029709755,0.00029960804,0.00059979275,0.0003693619,0.0009586366,0.0005627118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003740193,0.000046093388,0.00008968543,0.000312847,0.00001365868,0.00011486947,0.00007332174,0.00069633726,0.9645144,0.0036830602,0.0005747391,0.029843654],"study_design_scores_gemma":[0.000038666367,0.00015979727,0.0003996759,0.00002603005,0.000029601897,0.0005596454,0.000022543614,0.014006135,0.9487544,0.00069177436,0.03527004,0.000041706287],"about_ca_topic_score_codex":0.0012282049,"about_ca_topic_score_gemma":0.0012630515,"teacher_disagreement_score":0.0012282049,"about_ca_system_score_codex":0.00048283246,"about_ca_system_score_gemma":0.00050303387,"threshold_uncertainty_score":0.0035032034},"labels":[],"label_agreement":null},{"id":"W2792143728","doi":"10.3390/s18030845","title":"Design and Implementation of an RTK-Based Vector Phase Locked Loop","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Ambiguity resolution; Computer science; Sensitivity (control systems); Phase-locked loop; Control theory (sociology); Delay-locked loop; Loop (graph theory); Scalar (mathematics); Kinematics; Tracking (education); Satellite system; Global Positioning System; Real-time computing; Electronic engineering; Engineering; Jitter; Physics; Telecommunications; Mathematics; Artificial intelligence","score_opus":0.018280957044709046,"score_gpt":0.2916830429558911,"score_spread":0.27340208591118204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792143728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011758911,0.00017430147,0.98092675,0.00010440315,0.00011835494,0.000120965036,0.000061331215,0.0023646778,0.004370305],"genre_scores_gemma":[0.51511866,0.00024076499,0.47311264,0.0002455331,0.0001289225,0.00030620475,0.00030278665,0.00015619585,0.010388307],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935085,0.000063892476,0.000040423805,0.00016591676,0.00032604075,0.00005287525],"domain_scores_gemma":[0.9995716,0.000053609336,0.00006892864,0.00005338465,0.0002295372,0.000023010518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036159056,0.000427446,0.00041301403,0.00043675452,0.0002961143,0.0008157157,0.0013062373,0.00055442785,0.0030038343],"category_scores_gemma":[0.00066574925,0.0002608208,0.00020217436,0.00023486516,0.00025471847,0.0006273127,0.00040958173,0.00047393766,0.0022095353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040787333,0.0002003615,0.0022263003,0.00035122104,0.000082881175,0.00033343362,0.00029909174,0.035523653,0.3727487,0.012563863,0.0037910517,0.5714716],"study_design_scores_gemma":[0.0002409929,0.0013463167,0.0016281198,0.000055038465,0.00007922002,0.0011025575,0.000051884293,0.61703104,0.3055436,0.0017137746,0.07112258,0.00008489379],"about_ca_topic_score_codex":0.00061808835,"about_ca_topic_score_gemma":0.00064634037,"teacher_disagreement_score":0.0030038343,"about_ca_system_score_codex":0.00043364722,"about_ca_system_score_gemma":0.00071041106,"threshold_uncertainty_score":0.010048807},"labels":[],"label_agreement":null},{"id":"W2792152971","doi":"10.3390/s18040973","title":"A Portable Wireless Communication Platform Based on a Multi-Material Fiber Sensor for Real-Time Breath Detection","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Wireless; Computer science; Embedded system; Wireless sensor network; Real-time computing; Computer network; Telecommunications","score_opus":0.012361060514910411,"score_gpt":0.22584474876729813,"score_spread":0.21348368825238773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792152971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42050588,0.0028309391,0.56488055,0.00039087565,0.00052079215,0.0005197983,0.00037915798,0.003013529,0.0069584595],"genre_scores_gemma":[0.6959628,0.0011418,0.29222867,0.00037299405,0.00021230207,0.00031216146,0.00030436198,0.00010003508,0.009364884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998379,0.000020880889,0.000006960498,0.00004524838,0.00007526111,0.00001382561],"domain_scores_gemma":[0.9998517,0.000031944743,0.00004336508,0.000020969861,0.000034077006,0.000017870687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013283607,0.00046726436,0.0002954015,0.0003914885,0.00018650084,0.0002152743,0.00070302846,0.0005244966,0.0022605741],"category_scores_gemma":[0.00022271897,0.00018986291,0.00021228069,0.0002506079,0.00016704049,0.00047329903,0.0003176529,0.00026583712,0.0006942605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024611986,0.00015816324,0.0028142976,0.00032031248,0.000031679196,0.00049180735,0.00008767242,0.0012697246,0.8411547,0.0006762842,0.00145697,0.1512922],"study_design_scores_gemma":[0.00014947273,0.005420752,0.03350644,0.00013802748,0.00024542803,0.008986439,0.00014827772,0.08605684,0.80825144,0.0009478947,0.055970103,0.00017892463],"about_ca_topic_score_codex":0.00020947045,"about_ca_topic_score_gemma":0.00046458555,"teacher_disagreement_score":0.0022605741,"about_ca_system_score_codex":0.00012829572,"about_ca_system_score_gemma":0.00016118391,"threshold_uncertainty_score":0.0075623393},"labels":[],"label_agreement":null},{"id":"W2792271554","doi":"10.3390/s18020626","title":"Three-Level De-Multiplexed Dual-Branch Complex Delta-Sigma Transmitter","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Calgary","funders":"CMC Microsystems","keywords":"Multiplexing; Delta-sigma modulation; Transmitter; Electronic engineering; Topology (electrical circuits); Amplifier; Transceiver; Engineering; Channel (broadcasting); Electrical engineering; CMOS","score_opus":0.04931335928692441,"score_gpt":0.2582193885179974,"score_spread":0.20890602923107301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792271554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3036352,0.0016961469,0.6738085,0.00044421654,0.000346722,0.00017976244,0.00028568538,0.0012165082,0.018387243],"genre_scores_gemma":[0.83683866,0.0005336176,0.15085977,0.00022607659,0.000080259524,0.00009083968,0.00018017936,0.000036252997,0.0111543145],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998167,0.000027287311,0.000011264779,0.0000425074,0.000081852915,0.000020327501],"domain_scores_gemma":[0.9998425,0.000026481766,0.000033378954,0.000022369108,0.000057101362,0.000018132754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017586962,0.00033993556,0.00026602577,0.000294843,0.0002082809,0.00063867506,0.000641779,0.00038318473,0.0020565062],"category_scores_gemma":[0.00017312533,0.00018217735,0.0002111287,0.00030444257,0.00022180814,0.0005535257,0.00034484826,0.0005401593,0.0007282575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002897789,0.00011031952,0.001456799,0.00021367996,0.00005410409,0.00035639384,0.00019232396,0.00606894,0.8616245,0.018860832,0.0014070971,0.109365195],"study_design_scores_gemma":[0.000079412406,0.0011702991,0.0017898185,0.00006706662,0.00008478913,0.0015838951,0.00006775675,0.16554904,0.7871377,0.003769599,0.038632657,0.00006798945],"about_ca_topic_score_codex":0.00017824818,"about_ca_topic_score_gemma":0.00043699206,"teacher_disagreement_score":0.0020565062,"about_ca_system_score_codex":0.00030773398,"about_ca_system_score_gemma":0.00022909592,"threshold_uncertainty_score":0.0068796873},"labels":[],"label_agreement":null},{"id":"W2792734452","doi":"10.3390/s18030770","title":"Forward Behavioral Modeling of a Three-Way Amplitude Modulator-Based Transmitter Using an Augmented Memory Polynomial","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Transmitter; Electronic engineering; Computer science; Bandwidth (computing); Amplitude; Behavioral modeling; Amplitude modulation; Telecommunications; Engineering; Frequency modulation; Channel (broadcasting)","score_opus":0.04823863217129722,"score_gpt":0.2804993760674084,"score_spread":0.23226074389611115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792734452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09592213,0.0007219994,0.88269854,0.00026378225,0.00007764938,0.000104474624,0.0002620632,0.00076752057,0.019181885],"genre_scores_gemma":[0.94643337,0.00074612815,0.041916177,0.00006163564,0.000024082463,0.0001486491,0.0001406536,0.00003949985,0.010489842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998851,0.00002479117,0.0000058342093,0.000022990049,0.000047589994,0.0000137173065],"domain_scores_gemma":[0.9999058,0.000026227974,0.000021221129,0.000015564507,0.000027694998,0.0000034563673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015255087,0.0004411726,0.00028584505,0.00022600386,0.0002530128,0.0005463741,0.00090947957,0.0006793031,0.001677312],"category_scores_gemma":[0.00021674421,0.0001754611,0.00057093246,0.00022633788,0.00027155122,0.00062810455,0.00023307094,0.0004952831,0.0005304865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001207718,0.0000695,0.00093565346,0.0001592978,0.00005199574,0.00031924722,0.00017106433,0.86810005,0.09059232,0.01857283,0.00051864324,0.020388573],"study_design_scores_gemma":[0.0000040344303,0.000044074946,0.00011237761,0.0000055921882,0.00000942905,0.000045849752,0.000007051621,0.9940433,0.0042331363,0.00053037726,0.00096005225,0.0000047456347],"about_ca_topic_score_codex":0.0023241683,"about_ca_topic_score_gemma":0.0025818704,"teacher_disagreement_score":0.0023241683,"about_ca_system_score_codex":0.00046862886,"about_ca_system_score_gemma":0.0004603469,"threshold_uncertainty_score":0.0056111217},"labels":[],"label_agreement":null},{"id":"W2792850704","doi":"10.3390/s18030819","title":"Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Segmentation; Outlier; Artificial intelligence; Computer science; Planar; Pattern recognition (psychology); Similarity (geometry); Laser scanning; Point (geometry); Computer vision; Mathematics; Laser; Image (mathematics); Geometry; Optics","score_opus":0.026020117135031006,"score_gpt":0.22446086238473875,"score_spread":0.19844074524970776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792850704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26506212,0.00031059326,0.7265698,0.000093491995,0.000038179165,0.00026857335,0.0014776031,0.00409479,0.0020847234],"genre_scores_gemma":[0.6043825,0.00027597233,0.3886033,0.000044171913,0.000025428917,0.0002163355,0.005059158,0.0002818817,0.0011111943],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988863,0.00008220203,0.00006558237,0.00025443867,0.00051494234,0.00019660243],"domain_scores_gemma":[0.9990865,0.00013255866,0.00017775767,0.00018706125,0.00038501737,0.000031026793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005203893,0.001046474,0.00075980055,0.0041343137,0.00052653305,0.0012264987,0.0010555348,0.0008888617,0.00059148984],"category_scores_gemma":[0.0013655936,0.0005212933,0.0010561539,0.0035513937,0.00062149705,0.0007515502,0.0011155445,0.00066173024,0.00076540094],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003569881,0.0002504688,0.02054886,0.000332329,0.00023285596,0.0005367389,0.00047403915,0.19043876,0.24406353,0.0013001766,0.0040714517,0.5373938],"study_design_scores_gemma":[0.000030653253,0.00014118312,0.07440741,0.000042673164,0.00008287799,0.0006005649,0.00058813085,0.7947454,0.123162016,0.0017297645,0.0043609333,0.000108412074],"about_ca_topic_score_codex":0.009960174,"about_ca_topic_score_gemma":0.016694438,"teacher_disagreement_score":0.009960174,"about_ca_system_score_codex":0.00061956287,"about_ca_system_score_gemma":0.0013492672,"threshold_uncertainty_score":0.019804418},"labels":[],"label_agreement":null},{"id":"W2793181757","doi":"10.3390/s18010288","title":"Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Consortium de Recherche et d’innovation en Aérospatiale au Québec; Pratt and Whitney Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Bombardier","keywords":"Thermography; Materials science; Fiber; Orientation (vector space); Aerospace; Nondestructive testing; Stiffness; Composite material; Infrared; Structural engineering; Computer science; Engineering; Optics; Aerospace engineering; Mathematics","score_opus":0.006680054496430517,"score_gpt":0.24815897440968188,"score_spread":0.24147891991325135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793181757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29106572,0.00048066868,0.7066549,0.0000616815,0.000025727888,0.000043225784,0.0000373031,0.0004010958,0.0012296401],"genre_scores_gemma":[0.8615427,0.00022929638,0.13724354,0.000016507905,0.000010790403,0.000038249902,0.00003426352,0.000017493518,0.00086710893],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997857,0.000059800663,0.000010012144,0.000044706438,0.00008691723,0.000012891105],"domain_scores_gemma":[0.99948114,0.00028359552,0.00008961844,0.000034219167,0.000097709744,0.000013744914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041465947,0.00038988978,0.00017857905,0.0006907462,0.000095740565,0.00022662876,0.000230974,0.00031740862,0.00047134756],"category_scores_gemma":[0.0010651554,0.00015081541,0.00017142764,0.00033860139,0.00023323277,0.00043048366,0.000174371,0.00024050205,0.00017795322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026003612,0.00017314749,0.008217303,0.00017624912,0.00004455028,0.00013095504,0.00013408497,0.15321739,0.40976757,0.0017366909,0.00030580178,0.42583627],"study_design_scores_gemma":[0.0000036605643,0.000079022466,0.0035411834,0.0000054114953,0.000011493914,0.00006560896,0.000019110877,0.93087006,0.0647191,0.00037001976,0.00030357708,0.000011742046],"about_ca_topic_score_codex":0.00068294664,"about_ca_topic_score_gemma":0.0012613697,"teacher_disagreement_score":0.0006907462,"about_ca_system_score_codex":0.00020061128,"about_ca_system_score_gemma":0.00021257681,"threshold_uncertainty_score":0.0021929145},"labels":[],"label_agreement":null},{"id":"W2793298960","doi":"10.3390/s18040964","title":"Green, Hydrothermal Synthesis of Fluorescent Carbon Nanodots from Gardenia, Enabling the Detection of Metronidazole in Pharmaceuticals and Rabbit Plasma","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada","funders":"Fundamental Research Funds of China West Normal University; China West Normal University; National Natural Science Foundation of China","keywords":"Detection limit; X-ray photoelectron spectroscopy; Fourier transform infrared spectroscopy; Fluorescence; Quenching (fluorescence); Materials science; Hydrothermal circulation; Nanodot; Carbon fibers; Analytical Chemistry (journal); Nuclear chemistry; Spectroscopy; Fluorescence spectroscopy; Photoluminescence; Chemistry; Nanotechnology; Chemical engineering; Chromatography; Optoelectronics; Optics","score_opus":0.016589119793183373,"score_gpt":0.25744606485765525,"score_spread":0.2408569450644719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793298960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9559081,0.00243805,0.03732433,0.00011369803,0.00004322946,0.00012489026,0.00071822916,0.00028112662,0.0030483545],"genre_scores_gemma":[0.95615786,0.0010151853,0.03997863,0.000037194823,0.000005886457,0.00007559156,0.0005892939,0.000030514444,0.0021098757],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999193,0.0000059742156,0.000006370761,0.000028614575,0.000027789745,0.000012056789],"domain_scores_gemma":[0.9999043,0.000017353223,0.00003662909,0.000009771041,0.000019805286,0.000012265524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010880121,0.00029600254,0.00011400253,0.00030923006,0.00010689256,0.000109991255,0.00017554041,0.000272097,0.00036331863],"category_scores_gemma":[0.0001766929,0.00012826844,0.00014629214,0.00018823502,0.00018829173,0.00018505329,0.000173058,0.00023646565,0.00012039014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009230163,0.0000031964944,0.000069518974,0.00003099628,0.0000015403452,0.000024118872,0.0000043965215,0.00008887831,0.99852765,0.00004547272,0.00001787777,0.0011770057],"study_design_scores_gemma":[0.0000051311226,0.00005557182,0.0014711965,0.0000040807568,0.000004690673,0.00013934296,0.0000057819093,0.0005732012,0.9960646,0.000029268826,0.0016424808,0.00000466423],"about_ca_topic_score_codex":0.0012337762,"about_ca_topic_score_gemma":0.004531627,"teacher_disagreement_score":0.0012337762,"about_ca_system_score_codex":0.00040794452,"about_ca_system_score_gemma":0.0001995696,"threshold_uncertainty_score":0.002959907},"labels":[],"label_agreement":null},{"id":"W2793416254","doi":"10.3390/s18030708","title":"Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Residue (chemistry); Mobile device; Grid; Computer science; Transect; Remote sensing; Environmental science; Mathematics; Geography; World Wide Web","score_opus":0.033599036834003,"score_gpt":0.2856052752796455,"score_spread":0.2520062384456425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793416254","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97340405,0.00020116857,0.021624308,0.00004135479,0.000022422197,0.00027181688,0.00068595796,0.0006969011,0.0030520968],"genre_scores_gemma":[0.9221354,0.00028170334,0.07435019,0.000063536885,0.000016145259,0.00018859735,0.00049688976,0.00002300401,0.0024444163],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996598,0.000054057142,0.000017424441,0.000108853535,0.00013291645,0.000027016238],"domain_scores_gemma":[0.9994754,0.00015015122,0.000078895675,0.000049329763,0.00021466817,0.00003156599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004151903,0.00052135915,0.00026463796,0.000974715,0.00022145988,0.00038851178,0.00035553367,0.00030839982,0.0011511483],"category_scores_gemma":[0.0008579927,0.00015864846,0.00020353252,0.0005637237,0.00019302756,0.00039776386,0.0003542097,0.00012678043,0.0005775674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095242297,0.00033868564,0.37851334,0.0007451187,0.00017469343,0.00068179844,0.001730181,0.0038235288,0.18155777,0.00027148414,0.0018698156,0.42934114],"study_design_scores_gemma":[0.00005740172,0.0017470428,0.8763176,0.00011150986,0.00023467088,0.0010047014,0.0014690398,0.046196483,0.06494385,0.00024637286,0.007563686,0.00010774366],"about_ca_topic_score_codex":0.014045834,"about_ca_topic_score_gemma":0.044151276,"teacher_disagreement_score":0.014045834,"about_ca_system_score_codex":0.0003274285,"about_ca_system_score_gemma":0.0003211731,"threshold_uncertainty_score":0.027928174},"labels":[],"label_agreement":null},{"id":"W2794120267","doi":"10.3390/s18020542","title":"Plane Wave SH0 Piezoceramic Transduction Optimized Using Geometrical Parameters","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Transducer; Acoustics; Multiphysics; Structural health monitoring; Laser Doppler vibrometer; Finite element method; Guided wave testing; Piezoelectricity; Ultrasonic sensor; Wavefront; Materials science; Electromagnetic acoustic transducer; Optics; Engineering; Physics; Structural engineering; Ultrasonic testing; Laser","score_opus":0.023552359471942248,"score_gpt":0.22059913185436214,"score_spread":0.19704677238241988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794120267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8492267,0.0005427427,0.14263986,0.00022836371,0.00007072738,0.00012768013,0.00030328557,0.000374422,0.006486167],"genre_scores_gemma":[0.9006956,0.00039175275,0.09726278,0.000039656894,0.000014533174,0.00007091531,0.0001469555,0.0000387395,0.0013391456],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982363,0.000014135927,0.000009471732,0.000038195987,0.00009711298,0.00001750415],"domain_scores_gemma":[0.9998234,0.000037708738,0.000075167845,0.000012256849,0.00004360321,0.0000078068815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002169938,0.0005281785,0.00019637035,0.00019908875,0.00007063516,0.00029199323,0.0004260929,0.00039644443,0.0006375915],"category_scores_gemma":[0.00034536346,0.00023014877,0.00019166416,0.00022603876,0.0002809959,0.0003160308,0.00021744613,0.00023146409,0.00028166163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001637048,0.000010515416,0.0002861841,0.000048045506,0.0000034461546,0.000024301555,0.000013214317,0.0028556879,0.99221545,0.0005673592,0.000085289445,0.0038742505],"study_design_scores_gemma":[0.000025538611,0.00055623025,0.00426881,0.00000940727,0.00003216484,0.00028826864,0.000052371553,0.069192596,0.920339,0.0003849734,0.0048186057,0.000032084692],"about_ca_topic_score_codex":0.0003163455,"about_ca_topic_score_gemma":0.00084919186,"teacher_disagreement_score":0.0006375915,"about_ca_system_score_codex":0.00036149923,"about_ca_system_score_gemma":0.00039563948,"threshold_uncertainty_score":0.0026229024},"labels":[],"label_agreement":null},{"id":"W2794219322","doi":"10.3390/s18030926","title":"Motor Planning Error: Toward Measuring Cognitive Frailty in Older Adults Using Wearables","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Frailty in Older Adults","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Cancer Institute; National Institute on Aging; National Institutes of Health","keywords":"Ankle; Cognition; Physical medicine and rehabilitation; Psychology; Body mass index; Montreal Cognitive Assessment; Cognitive decline; Physical therapy; Medicine; Cognitive impairment; Audiology; Dementia; Psychiatry; Surgery","score_opus":0.08500766050234215,"score_gpt":0.3335635682845439,"score_spread":0.24855590778220177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794219322","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95531374,0.0016766972,0.03786907,0.00014538509,0.00006708739,0.00038287212,0.0015751696,0.00032422305,0.0026457459],"genre_scores_gemma":[0.95944357,0.00085319404,0.037266172,0.00013202566,0.00006345786,0.00035500762,0.0006994489,0.000023427117,0.0011636355],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927574,0.00019255551,0.000108583874,0.00015938708,0.00022463103,0.000039049803],"domain_scores_gemma":[0.9984579,0.00035715997,0.0005650151,0.00010714851,0.00041135921,0.00010143823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00159633,0.0012359398,0.00059507485,0.001644654,0.00023564571,0.0010336614,0.0005553058,0.00079805974,0.0007643816],"category_scores_gemma":[0.005042887,0.0003258186,0.00034163115,0.0009969833,0.00025943562,0.0007150171,0.0009561053,0.00046876777,0.00038860957],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016556472,0.00041064367,0.8261978,0.00043172366,0.00027299946,0.00020273226,0.000558005,0.0025423695,0.01953032,0.00016718835,0.0009543905,0.14707607],"study_design_scores_gemma":[0.00008337063,0.001867093,0.9639029,0.00015790154,0.0001789375,0.0008314224,0.00047645706,0.022660056,0.0077132075,0.0005417231,0.0015258692,0.000061070976],"about_ca_topic_score_codex":0.002980582,"about_ca_topic_score_gemma":0.004832232,"teacher_disagreement_score":0.002980582,"about_ca_system_score_codex":0.00020329707,"about_ca_system_score_gemma":0.00026244242,"threshold_uncertainty_score":0.008442283},"labels":[],"label_agreement":null},{"id":"W2794457065","doi":"10.3390/s18041018","title":"Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Installation; Echo (communications protocol); Structural health monitoring; Scale (ratio); Artificial neural network; Computer science; Field (mathematics); Structural engineering; Engineering; Marine engineering; Real-time computing; Artificial intelligence; Mechanical engineering","score_opus":0.00874244804172544,"score_gpt":0.31167288014277766,"score_spread":0.30293043210105225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794457065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37209573,0.00012560643,0.6241795,0.00004410474,0.000027364347,0.00006756497,0.00015090346,0.00090648263,0.0024028781],"genre_scores_gemma":[0.7411307,0.00008348775,0.25624254,0.0000246023,0.000009997699,0.00005373201,0.0001706408,0.00003736282,0.0022469247],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998472,0.000031632408,0.0000050181166,0.000037887134,0.000067431574,0.000010849247],"domain_scores_gemma":[0.9997974,0.0000584227,0.00002659324,0.000030908388,0.00007476846,0.000011962018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001999309,0.0002748215,0.00015945468,0.0004688259,0.00012061662,0.0002036449,0.00036269275,0.00030094068,0.0011731096],"category_scores_gemma":[0.00039419418,0.0001543987,0.00013102705,0.00030541996,0.00012471376,0.00030868573,0.00026261187,0.00021033805,0.00042550682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020862464,0.0001723624,0.009186836,0.00011152542,0.000036893773,0.00012285323,0.0002097109,0.027155202,0.58297527,0.00054260495,0.0007129922,0.37856513],"study_design_scores_gemma":[0.00002238067,0.00023231846,0.033364143,0.000011271666,0.000036603218,0.00029278555,0.00016334066,0.83568484,0.12743858,0.0005039775,0.0022063055,0.000043462216],"about_ca_topic_score_codex":0.0009340459,"about_ca_topic_score_gemma":0.0030380646,"teacher_disagreement_score":0.0011731096,"about_ca_system_score_codex":0.00010388452,"about_ca_system_score_gemma":0.00019728865,"threshold_uncertainty_score":0.0039244294},"labels":[],"label_agreement":null},{"id":"W2794487322","doi":"10.3390/s18041062","title":"Mobile Sinks Assisted Geographic and Opportunistic Routing Based Interference Avoidance for Underwater Wireless Sensor Network","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"King Saud University","keywords":"Computer network; Forwarder; Network packet; Computer science; Geographic routing; Wireless sensor network; Energy consumption; Multipath routing; Routing protocol; Dynamic Source Routing; Engineering; Electrical engineering","score_opus":0.027153500642711773,"score_gpt":0.2400466391225497,"score_spread":0.21289313847983793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794487322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21668215,0.0009710072,0.7766663,0.00024285879,0.00009697333,0.000064134656,0.000059383627,0.0004413686,0.0047758105],"genre_scores_gemma":[0.95869607,0.00032510486,0.039438907,0.000034189485,0.000015106882,0.000036316294,0.000043711,0.000012084929,0.0013985233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988127,0.00003742591,0.0000072555235,0.000018521145,0.000036439098,0.000018986853],"domain_scores_gemma":[0.9998735,0.000038630766,0.000033561035,0.000017900824,0.000026403251,0.000009863652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019575327,0.00028664301,0.00022840976,0.0003868918,0.00030534048,0.00020723103,0.0005029931,0.00017438298,0.00027926257],"category_scores_gemma":[0.00031543328,0.00011748832,0.00021717594,0.0003005493,0.00020298676,0.00038267966,0.0004803936,0.000121289966,0.000061880906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029715034,0.00014091942,0.0049376106,0.0002312173,0.000119530625,0.00077063206,0.00039465562,0.6445392,0.13600557,0.017061612,0.0023382315,0.1931637],"study_design_scores_gemma":[0.000007907864,0.00013711567,0.000716632,0.000007138466,0.000024865845,0.00015799266,0.0000962644,0.983576,0.010523295,0.0023524342,0.0023883816,0.000011956756],"about_ca_topic_score_codex":0.0009426749,"about_ca_topic_score_gemma":0.0021125989,"teacher_disagreement_score":0.0009426749,"about_ca_system_score_codex":0.00020081643,"about_ca_system_score_gemma":0.000328573,"threshold_uncertainty_score":0.0018743277},"labels":[],"label_agreement":null},{"id":"W2794498418","doi":"10.3390/s18041004","title":"Evaluating Muscle Activation Models for Elbow Motion Estimation","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Universidad Pontificia Bolivariana; Ministero dello Sviluppo Economico","keywords":"Computer science; Wearable computer; Torque; Motion (physics); Simulation; Artificial intelligence","score_opus":0.04996835743390351,"score_gpt":0.3017954350538343,"score_spread":0.25182707761993084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794498418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4124026,0.0009499844,0.5801369,0.0002921932,0.000038667964,0.00016268599,0.0002621222,0.0009094238,0.004845371],"genre_scores_gemma":[0.9523116,0.00026617406,0.045667954,0.00003316093,0.000007815914,0.00010972018,0.00021542917,0.000037736892,0.0013503544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999703,0.000120671044,0.000027623362,0.000054282347,0.00006689284,0.00002748855],"domain_scores_gemma":[0.99883896,0.0008385681,0.00009951287,0.00006006665,0.00014438374,0.000018491606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010844154,0.000728023,0.00051669095,0.00057312864,0.0001844691,0.00052829477,0.0003819034,0.00070770807,0.0010694951],"category_scores_gemma":[0.0038963696,0.0003305234,0.0005350923,0.0003363308,0.00019555812,0.0005427281,0.00034749732,0.00038477982,0.00032799962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013470702,0.00006308217,0.0022053164,0.00012408254,0.00005957282,0.000035994075,0.000044720775,0.94564086,0.0058252206,0.0005069077,0.00020122422,0.04515826],"study_design_scores_gemma":[0.000006454037,0.00010209375,0.0013090551,0.000014645279,0.000012255742,0.000014484364,0.000017480546,0.9957975,0.0022751458,0.00021803775,0.0002258541,0.000006954673],"about_ca_topic_score_codex":0.00799694,"about_ca_topic_score_gemma":0.0073172133,"teacher_disagreement_score":0.00799694,"about_ca_system_score_codex":0.00042206788,"about_ca_system_score_gemma":0.0005824171,"threshold_uncertainty_score":0.01590079},"labels":[],"label_agreement":null},{"id":"W2794659127","doi":"10.3390/s18041038","title":"Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"FP7 Energy; European Commission","keywords":"Operating point; Turbine; Power (physics); Grid; Hydropower; Engineering; Automotive engineering; Range (aeronautics); Hydraulic machinery; Wind power; Renewable energy; Work (physics); Maximum power principle; Control engineering; Control theory (sociology); Computer science; Mechanical engineering; Control (management); Electrical engineering","score_opus":0.007490892259711704,"score_gpt":0.21371694310521036,"score_spread":0.20622605084549867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794659127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6896826,0.0003697984,0.30512968,0.00012134586,0.00004220957,0.00009375057,0.00006931349,0.00040921886,0.004082097],"genre_scores_gemma":[0.9952619,0.000034678218,0.004382471,0.000006979688,0.0000029349524,0.000018645529,0.000009127527,0.0000063731713,0.00027680455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998882,0.000019941235,0.000007401733,0.000026967473,0.000038231763,0.00001929603],"domain_scores_gemma":[0.99968207,0.00015429145,0.00008632883,0.000009901193,0.000052096642,0.000015376068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027278406,0.000402561,0.00033579674,0.00014646535,0.00015529494,0.00046721936,0.0004288126,0.00022216393,0.00060272426],"category_scores_gemma":[0.0004908578,0.00016029036,0.00010793762,0.00012957609,0.00031461308,0.00028064838,0.00029092454,0.00024676352,0.00007590324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083536364,0.00036840822,0.0012127549,0.00037516927,0.000061024868,0.00025179182,0.00022322065,0.5557274,0.35527185,0.001944983,0.0009620013,0.08276601],"study_design_scores_gemma":[0.0000230448,0.0003004885,0.0012382675,0.000005599587,0.000011251497,0.000016266376,0.000018738454,0.9735834,0.02416665,0.0002727611,0.00035403227,0.000009446408],"about_ca_topic_score_codex":0.0011430907,"about_ca_topic_score_gemma":0.0020669962,"teacher_disagreement_score":0.0011430907,"about_ca_system_score_codex":0.00028694983,"about_ca_system_score_gemma":0.0002325717,"threshold_uncertainty_score":0.002272904},"labels":[],"label_agreement":null},{"id":"W2794933426","doi":"10.3390/s18041029","title":"A Novel Model to Simulate Flexural Complements in Compliant Sensor Systems","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Spline (mechanical); Torsion (gastropod); Nonlinear system; Revolute joint; Kinematics; Flexural strength; Rigid body; Structural engineering; Computer science; Engineering; Control theory (sociology); Mechanical engineering; Classical mechanics; Control (management); Physics; Artificial intelligence","score_opus":0.03174874769474604,"score_gpt":0.2591759077495325,"score_spread":0.22742716005478644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794933426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014550276,0.00012085647,0.98029894,0.00008576026,0.000042980657,0.000024560979,0.00006619705,0.00021577282,0.00459473],"genre_scores_gemma":[0.7668505,0.0006118697,0.219479,0.000107961765,0.00005758515,0.00028707046,0.00024718375,0.00013030539,0.012228597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998016,0.00004336667,0.000008945148,0.000031144817,0.00009716485,0.000017801514],"domain_scores_gemma":[0.9998441,0.00005620524,0.000022181965,0.000029447066,0.00003626506,0.00001190198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002694188,0.0004057631,0.00041554903,0.00039844358,0.00026035667,0.0004808197,0.000954121,0.0011292262,0.0019282775],"category_scores_gemma":[0.00060747034,0.00027650408,0.0007379548,0.00040157425,0.00052339263,0.00086878607,0.00054805883,0.00067637506,0.00043669622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026952157,0.000026088572,0.00036009698,0.00005293842,0.000012251045,0.000127587,0.00006386031,0.938891,0.011837493,0.03459271,0.0003574385,0.013651508],"study_design_scores_gemma":[0.0000035882085,0.00001973082,0.00006465157,0.0000032054745,0.0000024982266,0.000031621334,0.000004911384,0.9954325,0.0006943338,0.0024774752,0.0012607552,0.000004713401],"about_ca_topic_score_codex":0.0016229368,"about_ca_topic_score_gemma":0.00109261,"teacher_disagreement_score":0.0019282775,"about_ca_system_score_codex":0.00021982046,"about_ca_system_score_gemma":0.00065267005,"threshold_uncertainty_score":0.0064507127},"labels":[],"label_agreement":null},{"id":"W2795558363","doi":"10.3390/s18041137","title":"Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Characterization (materials science); Visual servoing; Trajectory; Magnification; Computer science; Tracking (education); Cantilever; Artificial intelligence; Carbon nanotube; Computer vision; Nanotechnology; Nanorobotics; Robotics; Machine vision; Materials science; Robot","score_opus":0.007326391194699579,"score_gpt":0.274570797329808,"score_spread":0.2672444061351084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795558363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23832008,0.00056711707,0.7492153,0.00018334526,0.0001368862,0.00035590297,0.00026612214,0.0067008324,0.004254358],"genre_scores_gemma":[0.6996329,0.00017885832,0.296158,0.00013741506,0.000031193606,0.00023953934,0.0001386268,0.00006899719,0.0034144986],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963295,0.000030622225,0.000021592157,0.00010071588,0.00018524098,0.000028939583],"domain_scores_gemma":[0.9997104,0.000055097677,0.00005241872,0.00005141103,0.000101694575,0.000028974715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025442988,0.00037559058,0.00034492256,0.0003814539,0.00028679884,0.00025043805,0.0006943138,0.00045316629,0.0015715731],"category_scores_gemma":[0.0004776783,0.00022970681,0.00015278393,0.00017417675,0.0002456942,0.00036238812,0.00050747884,0.0003110126,0.00042509387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007606099,0.00003586869,0.00032942908,0.00007487199,0.0000042520214,0.00007151077,0.000063780906,0.0010678751,0.9636511,0.0005072979,0.00038811122,0.033729933],"study_design_scores_gemma":[0.00007406828,0.0006766481,0.005291484,0.000028036739,0.000026533815,0.0007751301,0.000040033585,0.108173884,0.87206894,0.00052277866,0.012246685,0.00007593751],"about_ca_topic_score_codex":0.0006203523,"about_ca_topic_score_gemma":0.0010158313,"teacher_disagreement_score":0.0015715731,"about_ca_system_score_codex":0.00022811136,"about_ca_system_score_gemma":0.00047332136,"threshold_uncertainty_score":0.0052574277},"labels":[],"label_agreement":null},{"id":"W2796142916","doi":"10.3390/s18041067","title":"Artifact Noise Removal Techniques on Seismocardiogram Using Two Tri-Axial Accelerometers","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Accelerometer; Acceleration; Noise (video); Computer science; Digital signal processing; Artifact (error); Signal processing; Data acquisition; Digital filter; Filter (signal processing); Electronic engineering; Computer vision; Engineering; Computer hardware; Physics","score_opus":0.030209358688319102,"score_gpt":0.2753834303683182,"score_spread":0.2451740716799991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796142916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.494608,0.0035591887,0.4975563,0.00022773234,0.00024606436,0.0001672672,0.00015199366,0.00095088495,0.0025325455],"genre_scores_gemma":[0.79168296,0.003151977,0.20139566,0.00016559738,0.00016167063,0.00011393331,0.00036160325,0.00009797921,0.0028686034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950814,0.00009799775,0.000042471103,0.0000728734,0.00024142751,0.00003712955],"domain_scores_gemma":[0.99943393,0.00018664154,0.00008116626,0.00008436439,0.00018459256,0.000029353916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039262316,0.000699001,0.0003996209,0.00080954446,0.00015876483,0.00037755296,0.0003152403,0.0005014048,0.0009899479],"category_scores_gemma":[0.0017749234,0.00017507706,0.00054840423,0.00056209636,0.00021311114,0.00048419397,0.0003308519,0.00022191602,0.00043610114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012449926,0.00018285352,0.0052767615,0.0007506981,0.00015837546,0.00058943254,0.00027081234,0.0030964366,0.5271247,0.00039778772,0.00045500105,0.46045217],"study_design_scores_gemma":[0.00023226788,0.0057843905,0.19004902,0.00024966948,0.0009600428,0.009917853,0.0007148428,0.0902745,0.6875059,0.0010194683,0.013130664,0.00016137747],"about_ca_topic_score_codex":0.0003484285,"about_ca_topic_score_gemma":0.0007365766,"teacher_disagreement_score":0.0009899479,"about_ca_system_score_codex":0.000092363516,"about_ca_system_score_gemma":0.00019309713,"threshold_uncertainty_score":0.0033116937},"labels":[],"label_agreement":null},{"id":"W2796241454","doi":"10.3390/s18041116","title":"Soil Water Measurement Using Actively Heated Fiber Optics at Field Scale","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Soil water; Soil science; Calibration; Transect; Hydrology (agriculture); Spatial variability; Remote sensing; Mean squared error; Optical fiber; Water content; Optics; Geotechnical engineering; Geology; Mathematics; Physics","score_opus":0.02419202183939431,"score_gpt":0.2318464225454437,"score_spread":0.20765440070604937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796241454","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97218394,0.00016383077,0.02652998,0.000018180173,0.000011797706,0.000034921562,0.00019313948,0.00013104352,0.0007330178],"genre_scores_gemma":[0.96857566,0.00020066669,0.03021657,0.000024652232,0.000009316987,0.00003686261,0.00009306814,0.000012101496,0.00083093764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987185,0.000014616014,0.0000026000569,0.00005267898,0.000043043296,0.000015212156],"domain_scores_gemma":[0.99985576,0.000050353916,0.000027504395,0.000012371172,0.000045465295,0.000008575883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017622969,0.00026788504,0.00018127303,0.00025280687,0.00017605604,0.00018139613,0.00030934566,0.00021215383,0.00061578956],"category_scores_gemma":[0.00024102772,0.00013127743,0.00012204768,0.00024485792,0.00023176553,0.00043433937,0.00016630339,0.00022462168,0.00009237937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009195896,0.00004196991,0.006883435,0.00004891156,0.000011719277,0.000016678403,0.00007005275,0.0007720202,0.9749926,0.00004538414,0.00005755435,0.016967809],"study_design_scores_gemma":[0.000031707357,0.0005160898,0.0709465,0.000010607111,0.00004104317,0.00013691421,0.00012794697,0.021199359,0.9047675,0.00013797502,0.002051685,0.000032659183],"about_ca_topic_score_codex":0.0042202123,"about_ca_topic_score_gemma":0.008540773,"teacher_disagreement_score":0.0042202123,"about_ca_system_score_codex":0.00029948296,"about_ca_system_score_gemma":0.0001609055,"threshold_uncertainty_score":0.008391321},"labels":[],"label_agreement":null},{"id":"W2797411148","doi":"10.3390/s18041179","title":"Study on Interference Suppression Algorithms for Electronic Noses: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Interference (communication); Electronic nose; Computer science; Electronic engineering; Adjacent-channel interference; Engineering; Telecommunications; Artificial intelligence; Channel (broadcasting)","score_opus":0.0667075922207361,"score_gpt":0.3645934561243251,"score_spread":0.29788586390358895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797411148","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000808904,0.98647517,0.009575137,0.00021739872,0.0002712841,0.000020852181,0.00003401392,0.00004721176,0.0025500907],"genre_scores_gemma":[0.00704328,0.9792351,0.011184863,0.00027276977,0.00038042202,0.000035188736,0.00012201858,0.000021617381,0.0017047699],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995628,0.000053697895,0.000057676498,0.00011868447,0.00018242847,0.000024672483],"domain_scores_gemma":[0.9989191,0.0006030408,0.000084772866,0.000033430886,0.0003345304,0.000025173782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009246445,0.0011435389,0.0012605956,0.0025893631,0.00029578983,0.0011160179,0.0011152272,0.001196114,0.0031293314],"category_scores_gemma":[0.001956963,0.0005649086,0.0010298633,0.0028283857,0.00047144393,0.0021631122,0.00055986195,0.001132644,0.0017700975],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005723863,0.00008852493,0.0004188219,0.01285891,0.00012822807,0.00012322968,0.00006984123,0.0020579805,0.0035754975,0.0044775424,0.0077050035,0.9684391],"study_design_scores_gemma":[0.000036412366,0.00065075484,0.0028576432,0.006866537,0.0007058281,0.0029831924,0.00022197457,0.010424948,0.012988104,0.00754607,0.9545416,0.00017692646],"about_ca_topic_score_codex":0.0012149336,"about_ca_topic_score_gemma":0.00083387335,"teacher_disagreement_score":0.0031293314,"about_ca_system_score_codex":0.00038562514,"about_ca_system_score_gemma":0.00097618985,"threshold_uncertainty_score":0.010468662},"labels":[],"label_agreement":null},{"id":"W2797484651","doi":"10.3390/s18041207","title":"Continuous-Wave Fiber Cavity Ringdown Pressure Sensing Based on Frequency-Shifted Interferometry","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Hubei University; Hubei University of Technology; National Natural Science Foundation of China","keywords":"Interferometry; Sensitivity (control systems); Optics; Materials science; Pressure sensor; Fiber optic sensor; Optical fiber; Pressure measurement; Fiber; Continuous wave; Physics; Electronic engineering; Laser; Engineering","score_opus":0.012910985866036333,"score_gpt":0.22384466120131305,"score_spread":0.2109336753352767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797484651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6394971,0.002892581,0.35278863,0.00031961984,0.00020708976,0.00015080432,0.00029380806,0.0010383184,0.0028120833],"genre_scores_gemma":[0.77599645,0.000865961,0.22170706,0.00007246289,0.00006507367,0.00009158621,0.000102248814,0.000023686984,0.0010754071],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994173,0.000055755423,0.000019746052,0.00016855959,0.00030046504,0.000038199294],"domain_scores_gemma":[0.99957675,0.00013652744,0.000101046746,0.00005376762,0.00010794927,0.000023959337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044833266,0.000579234,0.00039968206,0.00043554878,0.0002270975,0.00034696568,0.0010826626,0.00054433907,0.00052637246],"category_scores_gemma":[0.0007569419,0.00031413682,0.00022910617,0.00034910627,0.00053017185,0.001023624,0.0005625799,0.000530729,0.00017583709],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051425468,0.000024771278,0.0006018058,0.0000886521,0.0000062512136,0.000049293507,0.000047024845,0.00036915627,0.98114073,0.000518535,0.00010754325,0.016994765],"study_design_scores_gemma":[0.000012041855,0.00017672138,0.0018106463,0.0000052390187,0.000013131797,0.00023891141,0.000015170218,0.018282019,0.9776438,0.000117606236,0.0016434719,0.00004129777],"about_ca_topic_score_codex":0.00066548475,"about_ca_topic_score_gemma":0.0007238356,"teacher_disagreement_score":0.0010826626,"about_ca_system_score_codex":0.00037455952,"about_ca_system_score_gemma":0.00037426737,"threshold_uncertainty_score":0.0027176142},"labels":[],"label_agreement":null},{"id":"W2799980096","doi":"10.3390/s18051518","title":"Hypersensitivity and Applications of Cladding Modes of Optical Fibers Coated with Nanoscale Metal Layers","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cladding (metalworking); Materials science; Nanoscopic scale; Metal; Copper; Optical fiber; Composite material; Palladium; Optoelectronics; Nanotechnology; Optics; Metallurgy; Chemistry","score_opus":0.008395146985892657,"score_gpt":0.21617345814330888,"score_spread":0.2077783111574162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799980096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97992206,0.00062709046,0.015472276,0.00007427219,0.00004112824,0.000030219617,0.0000434366,0.000110149515,0.0036793728],"genre_scores_gemma":[0.99632925,0.00023144939,0.002910501,0.000018646204,0.000007841496,0.0000060803127,0.000019169018,0.0000048220304,0.00047238916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983644,0.000026941607,0.000006624614,0.00003660981,0.000069971036,0.000023312836],"domain_scores_gemma":[0.9993512,0.00036086646,0.00009439403,0.00007947001,0.000078543184,0.000035537258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003551905,0.00035688508,0.00009588857,0.0002442894,0.00015586594,0.00016933905,0.00030208536,0.00020955615,0.00078796706],"category_scores_gemma":[0.00063297973,0.00019175612,0.00013989142,0.00009072191,0.0005210072,0.00019114914,0.00015741643,0.00021145146,0.00012380331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082187835,0.00001516635,0.0009694175,0.000034170665,0.0000047431113,0.00010575725,0.000039566978,0.00088470837,0.99496824,0.000597718,0.000026711736,0.002271614],"study_design_scores_gemma":[0.000009218671,0.00019369196,0.004567568,0.00000873534,0.000009755381,0.00027461266,0.00002542477,0.011057581,0.982951,0.00033235233,0.00056326436,0.000006800015],"about_ca_topic_score_codex":0.0005920815,"about_ca_topic_score_gemma":0.0006217284,"teacher_disagreement_score":0.00078796706,"about_ca_system_score_codex":0.00042780495,"about_ca_system_score_gemma":0.000118709046,"threshold_uncertainty_score":0.003104031},"labels":[],"label_agreement":null},{"id":"W2800730691","doi":"10.3390/s18041244","title":"Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer vision; Computer science; Artificial intelligence; Sky; Kalman filter; Satellite; Remote sensing; Geography; Global Positioning System; Engineering; Telecommunications","score_opus":0.01305800141646397,"score_gpt":0.24981873407708272,"score_spread":0.23676073266061876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800730691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089784995,0.00022387788,0.9071342,0.000029540537,0.000035197747,0.000056053017,0.000039076713,0.0011000409,0.0015970758],"genre_scores_gemma":[0.6608028,0.00019681732,0.33597648,0.00006273483,0.00003516827,0.00005694818,0.0002199424,0.00010345287,0.0025457244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999479,0.000076734344,0.000016538153,0.00015402434,0.00021201768,0.000061750994],"domain_scores_gemma":[0.99977976,0.000028621882,0.000029207002,0.000047134545,0.00009615061,0.00001912297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037228988,0.00078622915,0.0005567818,0.0007073099,0.00037036985,0.00042287074,0.0006323496,0.00041535794,0.0007639301],"category_scores_gemma":[0.00059747003,0.0004842477,0.00047514314,0.00071750314,0.00032277757,0.00071509817,0.0009990333,0.00052276993,0.0005008071],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003109934,0.00011014728,0.008943299,0.00017212205,0.00024096125,0.00027300647,0.0003789803,0.13337734,0.1935461,0.0018142792,0.0012740403,0.6595587],"study_design_scores_gemma":[0.000043181517,0.00026233745,0.014216714,0.000023012488,0.0001371492,0.00026716973,0.00017113805,0.9267563,0.05170224,0.0012683471,0.005078665,0.0000737157],"about_ca_topic_score_codex":0.011050592,"about_ca_topic_score_gemma":0.017512895,"teacher_disagreement_score":0.011050592,"about_ca_system_score_codex":0.0003147151,"about_ca_system_score_gemma":0.0006391001,"threshold_uncertainty_score":0.021972537},"labels":[],"label_agreement":null},{"id":"W2801000756","doi":"10.3390/s18051359","title":"Performance Analysis of Satellite Missions for Multi-Temporal SAR Interferometry","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Programma Operativo Nazionale Ricerca e Competitività; Canadian Space Agency; Regione Puglia; Agenzia Spaziale Italiana; Ministero dell’Istruzione, dell’Università e della Ricerca; European Space Agency","keywords":"Remote sensing; Interferometry; Satellite; Terrain; GNSS augmentation; Synthetic aperture radar; Interferometric synthetic aperture radar; Visibility; Geology; Temporal resolution; Geodesy; Computer science; Meteorology; Geography; Aerospace engineering; Satellite navigation; Physics; Engineering; Optics","score_opus":0.025077433563689737,"score_gpt":0.2787848292804616,"score_spread":0.25370739571677187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801000756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95687455,0.00091110007,0.03515781,0.00020971235,0.0000481322,0.000052168834,0.0013789645,0.00044601993,0.0049216375],"genre_scores_gemma":[0.9925823,0.00018330105,0.005142129,0.000027065824,0.000021176871,0.000021574786,0.0016078006,0.000049709273,0.00036489347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890995,0.0002873552,0.00004568542,0.00016312578,0.0004735215,0.00012030114],"domain_scores_gemma":[0.9979358,0.00083481095,0.00043430235,0.00024269003,0.00045404246,0.000098388926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021459237,0.0006374685,0.00026257258,0.0009582384,0.00017036975,0.0005972739,0.00024734982,0.0003858776,0.00064888416],"category_scores_gemma":[0.0042208517,0.0001034835,0.00022753725,0.00092851627,0.00018849471,0.0005943482,0.00042250243,0.00020167645,0.00034764165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012306661,0.00025418337,0.18938014,0.00028663807,0.00036971242,0.00025945585,0.00022480002,0.5622969,0.045082565,0.003769704,0.0036321823,0.19321296],"study_design_scores_gemma":[0.000023296308,0.00076977746,0.14337458,0.0000189135,0.000058889796,0.00018278934,0.0001541797,0.84268755,0.0095546795,0.0010066597,0.002132955,0.000035699257],"about_ca_topic_score_codex":0.0025835054,"about_ca_topic_score_gemma":0.0015024451,"teacher_disagreement_score":0.0025835054,"about_ca_system_score_codex":0.0004680264,"about_ca_system_score_gemma":0.00034916418,"threshold_uncertainty_score":0.011348903},"labels":[],"label_agreement":null},{"id":"W2801327258","doi":"10.3390/s18051305","title":"Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Odometer; GNSS applications; Spoofing attack; Computer science; Inertial measurement unit; Inertial navigation system; Global Positioning System; Real-time computing; GLONASS; Artificial intelligence; Computer security; Telecommunications; Inertial frame of reference","score_opus":0.016308050111354423,"score_gpt":0.24025400043634293,"score_spread":0.2239459503249885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801327258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3918559,0.0010630315,0.6032955,0.00007482293,0.000095016745,0.0000686951,0.00011700619,0.001206134,0.0022238046],"genre_scores_gemma":[0.95842344,0.00027763884,0.040704623,0.000014197704,0.000017420924,0.000014392419,0.00011466286,0.000014024764,0.00041964054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995415,0.00008292829,0.00002449855,0.000071082926,0.00023091857,0.000049022925],"domain_scores_gemma":[0.99958247,0.00009343995,0.000119184515,0.00005669133,0.000129147,0.00001908861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031008237,0.0005537529,0.000340516,0.0011165757,0.00018931518,0.0003903857,0.00032295304,0.00033720137,0.00023307519],"category_scores_gemma":[0.0011577229,0.00018889236,0.00019032619,0.00065831776,0.00021981647,0.00038768232,0.00039696327,0.00027107034,0.00017425498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084346195,0.000171472,0.049051132,0.00036271987,0.00018067079,0.00044656065,0.00026760364,0.112636074,0.25249207,0.0029741668,0.0012725273,0.57930154],"study_design_scores_gemma":[0.000023875322,0.0006269848,0.03294366,0.00004215228,0.0001150638,0.0008973095,0.00018166931,0.8193572,0.14066096,0.0011238909,0.0039434056,0.00008381542],"about_ca_topic_score_codex":0.0015951648,"about_ca_topic_score_gemma":0.0022126534,"teacher_disagreement_score":0.0015951648,"about_ca_system_score_codex":0.0002452301,"about_ca_system_score_gemma":0.0003798974,"threshold_uncertainty_score":0.0031716824},"labels":[],"label_agreement":null},{"id":"W2802145197","doi":"10.3390/s18051440","title":"Fluorescent Nanobiosensors for Sensing Glucose","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Fluorescence; Nanomaterials; Nanotechnology; Optical sensing; Biosensor; Materials science; Biochemical engineering; Computer science; Engineering; Optoelectronics","score_opus":0.029935295729618006,"score_gpt":0.3380040129072298,"score_spread":0.3080687171776118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802145197","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010120135,0.9833702,0.0060986727,0.0004937678,0.0007216953,0.000030495123,0.000055913617,0.000067646186,0.008149611],"genre_scores_gemma":[0.009127347,0.97712404,0.00631901,0.00054182124,0.00031310305,0.00006733669,0.00012110932,0.0000121750945,0.0063740024],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968266,0.000041000912,0.000020003661,0.00006720453,0.00015546802,0.000033648685],"domain_scores_gemma":[0.9998758,0.00004464076,0.000016872933,0.0000065184468,0.000044405402,0.000011618658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046848346,0.0010471671,0.00072109397,0.0019656967,0.0003206435,0.00068533426,0.00072465447,0.0014212318,0.0027087722],"category_scores_gemma":[0.0004328216,0.00037986733,0.00055345555,0.0015242274,0.00043915524,0.0012641004,0.0007111795,0.0017762868,0.002334886],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058453243,0.00012477853,0.00022510462,0.0150047,0.00006570571,0.00046498483,0.00018921375,0.00086405926,0.11186944,0.023462404,0.026757693,0.82091355],"study_design_scores_gemma":[0.000005972864,0.000092275644,0.00036471023,0.0011437089,0.000044687255,0.0011288384,0.000055846976,0.00047903555,0.039762747,0.0033167114,0.953571,0.000034564975],"about_ca_topic_score_codex":0.00074546295,"about_ca_topic_score_gemma":0.0009870642,"teacher_disagreement_score":0.0027087722,"about_ca_system_score_codex":0.0006463825,"about_ca_system_score_gemma":0.00057411136,"threshold_uncertainty_score":0.009061694},"labels":[],"label_agreement":null},{"id":"W2802195418","doi":"10.3390/s18051447","title":"Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microwave; Microwave imaging; Materials science; Biomedical engineering; Optoelectronics; Computer science; Engineering; Telecommunications","score_opus":0.0132766404387637,"score_gpt":0.23185947513027225,"score_spread":0.21858283469150855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802195418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5180864,0.0022605062,0.47326407,0.00047011088,0.00028760277,0.00014344124,0.00025353566,0.0012351836,0.0039992286],"genre_scores_gemma":[0.79402715,0.0005469452,0.20160405,0.0002872945,0.00006315218,0.00011334173,0.00016394482,0.00006405585,0.0031301056],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995183,0.00007388182,0.00001997961,0.00010448221,0.00025067065,0.00003270398],"domain_scores_gemma":[0.9995536,0.00015938902,0.00006336908,0.000039807364,0.00014776921,0.00003598293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006658835,0.00041097097,0.00033645413,0.00029121788,0.0001517454,0.00054193335,0.000983865,0.00072880223,0.00090804877],"category_scores_gemma":[0.0007532021,0.0003106401,0.00018810209,0.00023845187,0.00038404597,0.00085992663,0.00037755456,0.00057563896,0.000359563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025070363,0.000010586273,0.00009276066,0.000021309228,0.000003209444,0.000010788821,0.0000131421275,0.00010273929,0.9967127,0.00018596905,0.000048833303,0.0027730006],"study_design_scores_gemma":[0.0000090776475,0.00015073808,0.0005519141,0.0000037606378,0.000013742283,0.0001256757,0.00001306345,0.006523621,0.99046236,0.00013590351,0.0019991512,0.000011008966],"about_ca_topic_score_codex":0.00021535004,"about_ca_topic_score_gemma":0.00040282917,"teacher_disagreement_score":0.000983865,"about_ca_system_score_codex":0.00041768074,"about_ca_system_score_gemma":0.00020466627,"threshold_uncertainty_score":0.0035216212},"labels":[],"label_agreement":null},{"id":"W2802364326","doi":"10.3390/s18041275","title":"Dual-Task Elderly Gait of Prospective Fallers and Non-Fallers: A Wearable-Sensor Based Analysis","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Gait; Physical medicine and rehabilitation; Prospective cohort study; Quartile; Gait analysis; Medicine; Accelerometer; Physical therapy; Confidence interval; Computer science; Surgery; Internal medicine","score_opus":0.017255179663812518,"score_gpt":0.32136291027566993,"score_spread":0.3041077306118574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802364326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989967,0.00006067639,0.0005759166,0.000005627998,0.0000027902222,0.000008751272,0.00021577459,0.0000049246055,0.00012884993],"genre_scores_gemma":[0.99855226,0.000058008878,0.00077360217,0.0000058742917,0.0000038624244,0.000012356104,0.00037339452,0.0000017947181,0.00021877169],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99986637,0.000024153187,0.00002437926,0.00002954539,0.0000399285,0.000015597414],"domain_scores_gemma":[0.99970883,0.00003823927,0.00010750847,0.000020823669,0.000082701656,0.000041928753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022681187,0.00029521988,0.00031769348,0.0009861233,0.00015139447,0.00031767774,0.0001269453,0.00022128977,0.00069760485],"category_scores_gemma":[0.00087316136,0.0001234911,0.00021036292,0.00057350897,0.000100976235,0.00018301985,0.0002874205,0.000106139516,0.00018564939],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009905564,0.00021281884,0.9513511,0.000107669024,0.00010809239,0.00022398715,0.00040821912,0.00030001675,0.01774895,0.000032225358,0.00015867352,0.028357666],"study_design_scores_gemma":[0.0000055729747,0.000377664,0.9973878,0.000005632599,0.000019726298,0.00035694634,0.00020051825,0.0008049501,0.0007002964,0.00002541138,0.00011121178,0.0000042632482],"about_ca_topic_score_codex":0.0017390085,"about_ca_topic_score_gemma":0.0036513973,"teacher_disagreement_score":0.0017390085,"about_ca_system_score_codex":0.000089413945,"about_ca_system_score_gemma":0.00009927676,"threshold_uncertainty_score":0.0034577847},"labels":[],"label_agreement":null},{"id":"W2802384909","doi":"10.3390/s18041279","title":"A Wearable Gait Phase Detection System Based on Force Myography Techniques","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Gait; Wearable computer; Gait analysis; Electrical impedance myography; Computer science; Artificial intelligence; STRIDE; Linear discriminant analysis; Wearable technology; Pattern recognition (psychology); Physical medicine and rehabilitation; Medicine","score_opus":0.007619642919757459,"score_gpt":0.22242930514627002,"score_spread":0.21480966222651257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802384909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28482145,0.00086023886,0.7062016,0.00024085832,0.00026272095,0.00067744835,0.0005864722,0.0035395427,0.0028096498],"genre_scores_gemma":[0.6504542,0.00042664068,0.34372348,0.00026354473,0.00010971813,0.0005728597,0.00041400595,0.00006825964,0.0039673625],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997329,0.000045451736,0.000021781292,0.00007440033,0.00010944214,0.000015950693],"domain_scores_gemma":[0.99965966,0.00009414713,0.000061496554,0.000036908546,0.00011989394,0.000027929782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039998582,0.00048555763,0.00054019433,0.0005878253,0.00013905317,0.00034009476,0.00048525733,0.00068719033,0.0020321037],"category_scores_gemma":[0.0007008928,0.00019637709,0.00019022459,0.00028692576,0.00016499667,0.00043855968,0.0002920921,0.00022532372,0.00082673336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053977594,0.00029387214,0.0045921006,0.00034900944,0.000058197093,0.0002598831,0.00010926909,0.0011961452,0.7658668,0.0003552306,0.001563099,0.2248167],"study_design_scores_gemma":[0.00046040144,0.0064205546,0.08638445,0.00019622693,0.00036171993,0.006876485,0.00015282605,0.17284715,0.70785415,0.00085038325,0.017385805,0.00020984725],"about_ca_topic_score_codex":0.0002896103,"about_ca_topic_score_gemma":0.000562991,"teacher_disagreement_score":0.0020321037,"about_ca_system_score_codex":0.000112326605,"about_ca_system_score_gemma":0.0001960927,"threshold_uncertainty_score":0.006798029},"labels":[],"label_agreement":null},{"id":"W2802615216","doi":"10.3390/s18051530","title":"Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Face recognition and analysis","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petroleum Technology Research Centre; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Guizhou Science and Technology Department; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Minimum bounding box; Convolutional neural network; Artificial intelligence; Bounding overwatch; Robot; Software deployment; Object detection; Machine learning; Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.030860118905306724,"score_gpt":0.27188179663382,"score_spread":0.2410216777285133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802615216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7469196,0.0012022087,0.24319607,0.00036815586,0.00016352913,0.00013030817,0.0005477177,0.003512563,0.0039597857],"genre_scores_gemma":[0.96394545,0.00022397676,0.03314518,0.000086355096,0.000017692531,0.000040176128,0.0005219253,0.000021209586,0.0019979991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978,0.000028419985,0.000010561708,0.00007699001,0.00004533543,0.000058669077],"domain_scores_gemma":[0.9997987,0.000048291342,0.000037811267,0.00002572337,0.00006791834,0.000021556643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030369204,0.0006877433,0.00042809884,0.0004749414,0.0002550755,0.00031567155,0.0007517189,0.00051740336,0.00077251834],"category_scores_gemma":[0.00084734533,0.00025782493,0.0003925779,0.00029864555,0.00021487195,0.00065517006,0.00046787164,0.00054760324,0.00023542318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009232948,0.000595445,0.02794767,0.00021632687,0.00021595604,0.0008483177,0.00031949638,0.29954702,0.03046903,0.00090658455,0.006158158,0.6318527],"study_design_scores_gemma":[0.000005495195,0.000059972142,0.005404944,0.000009171546,0.00002316522,0.000048256847,0.000044585326,0.9893085,0.0044941423,0.0002663105,0.00032582897,0.000009629498],"about_ca_topic_score_codex":0.020379337,"about_ca_topic_score_gemma":0.022239992,"teacher_disagreement_score":0.020379337,"about_ca_system_score_codex":0.00078583177,"about_ca_system_score_gemma":0.00050185213,"threshold_uncertainty_score":0.040521443},"labels":[],"label_agreement":null},{"id":"W2802787693","doi":"10.3390/s18041253","title":"A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Histogram; Hue; Segmentation; Vegetation (pathology); Artificial intelligence; Pattern recognition (psychology); Computer science; Environmental science; Computer vision; Remote sensing; Geography; Image (mathematics); Medicine","score_opus":0.012301873568277721,"score_gpt":0.22315202751459268,"score_spread":0.21085015394631496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802787693","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020671712,0.00034486727,0.9760149,0.000038736638,0.00004055247,0.000073489326,0.00009268832,0.0014076013,0.001315397],"genre_scores_gemma":[0.19202243,0.0004966445,0.8035093,0.00006591219,0.000041908042,0.000082677296,0.0003673322,0.00022177497,0.0031919975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996772,0.00002430207,0.000016578024,0.00010319286,0.00013384978,0.000045001252],"domain_scores_gemma":[0.9997731,0.000050617535,0.000032647058,0.000024805326,0.00009827458,0.000020541365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002801255,0.0005069789,0.0006088928,0.0020708032,0.00029124258,0.0008892608,0.0008715401,0.0004849191,0.0018061177],"category_scores_gemma":[0.00043703956,0.0003757958,0.0007534465,0.0012922474,0.00037451184,0.0010229679,0.00044974845,0.00047144125,0.0008705175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022312715,0.00012551418,0.0027117978,0.00032384007,0.00010585008,0.00022999887,0.00027321547,0.028518008,0.3189055,0.005009479,0.002267103,0.6413066],"study_design_scores_gemma":[0.000029829924,0.00016859396,0.007426046,0.00004032795,0.00007550898,0.00057632773,0.00017036394,0.834491,0.14340977,0.0036539643,0.009877749,0.0000805388],"about_ca_topic_score_codex":0.0041301986,"about_ca_topic_score_gemma":0.0053537376,"teacher_disagreement_score":0.0041301986,"about_ca_system_score_codex":0.0005605179,"about_ca_system_score_gemma":0.00062763895,"threshold_uncertainty_score":0.008212268},"labels":[],"label_agreement":null},{"id":"W2802854596","doi":"10.3390/s18051340","title":"Adaptive Monostatic System for Measuring Microwave Reflections from the Breast","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Keysight Technologies","keywords":"Microwave; Microwave imaging; Measure (data warehouse); Acoustics; Fidelity; Time domain; Reflection (computer programming); Optics; Sensitivity (control systems); Radar; Computer science; Physics; Electronic engineering; Telecommunications; Computer vision; Engineering","score_opus":0.03316293399947203,"score_gpt":0.2358379748473094,"score_spread":0.20267504084783736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802854596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20164548,0.00083904515,0.7876522,0.00024603718,0.0002115442,0.00023593409,0.0004462731,0.0030941444,0.0056293025],"genre_scores_gemma":[0.48999792,0.00046550136,0.50247,0.00038048063,0.00013502459,0.00030172642,0.00050852506,0.000090276204,0.005650518],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995524,0.000103299215,0.000014641139,0.00009633359,0.00020366706,0.000029626417],"domain_scores_gemma":[0.9996563,0.00006593846,0.000049412854,0.00007133313,0.00012604568,0.00003084405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041133215,0.00042277403,0.00029169527,0.00042337732,0.00015298306,0.00024353847,0.0005238403,0.00044648044,0.0021839226],"category_scores_gemma":[0.0005608861,0.00017085666,0.00014168116,0.00027034327,0.00014363615,0.00032234183,0.00035423823,0.00041759614,0.0009324241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021734851,0.000053737793,0.0016014389,0.00009458731,0.000022122891,0.0000730234,0.00007828211,0.0007974795,0.89666206,0.0010458056,0.0014604842,0.0978937],"study_design_scores_gemma":[0.00010114133,0.0024124742,0.021053351,0.000030378535,0.00014795315,0.003778024,0.00010017533,0.066840164,0.87511516,0.00074576127,0.029541697,0.0001337538],"about_ca_topic_score_codex":0.00032436935,"about_ca_topic_score_gemma":0.00064438506,"teacher_disagreement_score":0.0021839226,"about_ca_system_score_codex":0.00016189153,"about_ca_system_score_gemma":0.00033987954,"threshold_uncertainty_score":0.00730592},"labels":[],"label_agreement":null},{"id":"W2802873703","doi":"10.3390/s18051519","title":"Data Gathering and Energy Transfer Dilemma in UAV-Assisted Flying Access Network for IoT","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Centre National pour la Recherche Scientifique et Technique; Université du Québec à Montréal","keywords":"Base station; Network packet; Computer science; Wireless; Real-time computing; Computer network; Battery (electricity); Energy (signal processing); Efficient energy use; Wireless network; Telecommunications; Electrical engineering; Engineering; Power (physics)","score_opus":0.04739960008009474,"score_gpt":0.27359951874433824,"score_spread":0.2261999186642435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802873703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47863397,0.0016177339,0.4958658,0.0028023014,0.00015670921,0.00024207913,0.00021987995,0.00012857093,0.020332994],"genre_scores_gemma":[0.9883736,0.00032228263,0.009328488,0.00009882895,0.000030650048,0.000072482304,0.000035906127,0.000010102456,0.0017277935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943155,0.0002024587,0.000023564535,0.00011113274,0.00009178385,0.00013952561],"domain_scores_gemma":[0.9978497,0.0014379512,0.00026773222,0.000059350205,0.00017705523,0.00020823424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009768114,0.0006050354,0.00075012667,0.00061244017,0.0011909878,0.001147934,0.001266287,0.0015598944,0.0018811094],"category_scores_gemma":[0.003568067,0.00031957417,0.00049408135,0.00046734166,0.0011015424,0.0016142012,0.0012371453,0.00080755976,0.0001164718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042590528,0.00015293655,0.004079693,0.0003298377,0.00013983267,0.0024453695,0.0010429958,0.82154495,0.012392813,0.13455223,0.0040267142,0.018866783],"study_design_scores_gemma":[0.000021593904,0.000070973365,0.0003772987,0.000013534128,0.000012908716,0.00019512455,0.00019380578,0.9798418,0.00044175814,0.018310815,0.00050366257,0.000016759343],"about_ca_topic_score_codex":0.0030451743,"about_ca_topic_score_gemma":0.0020738,"teacher_disagreement_score":0.0030451743,"about_ca_system_score_codex":0.0011380852,"about_ca_system_score_gemma":0.00059711735,"threshold_uncertainty_score":0.008257449},"labels":[],"label_agreement":null},{"id":"W2803122122","doi":"10.3390/s18061678","title":"Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland; Alberta Innovates; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures","keywords":"Algorithm; Microwave imaging; Computer science; Iterative reconstruction; Image quality; Artificial intelligence; Reconstruction algorithm; Image processing; Computer vision; Image (mathematics); Microwave","score_opus":0.03317976394726615,"score_gpt":0.295453730584505,"score_spread":0.2622739666372389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803122122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45924106,0.00060466153,0.5365682,0.00026188514,0.000037042002,0.0005417859,0.00038132674,0.0011306804,0.0012332443],"genre_scores_gemma":[0.41320062,0.00035753305,0.58499867,0.000063993786,0.000009481786,0.00021818498,0.0004673149,0.00023048674,0.00045384426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844754,0.00066294573,0.00013927397,0.00015815753,0.00054018653,0.000051927065],"domain_scores_gemma":[0.99116254,0.005923597,0.0004488194,0.0007794088,0.0015957231,0.00008992865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045901127,0.0007002926,0.0005193516,0.0010266835,0.00027323488,0.00073179166,0.0007391069,0.0008246063,0.0012131758],"category_scores_gemma":[0.017341617,0.0002773183,0.00054537406,0.00093877636,0.00043093303,0.00050707726,0.000558858,0.00050144305,0.0002998578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028350851,0.00070834963,0.030023135,0.0007483383,0.00036866922,0.0005497563,0.00086385594,0.26010922,0.1543036,0.0027085808,0.0012265451,0.5455549],"study_design_scores_gemma":[0.0001849418,0.0012493326,0.016244229,0.00004298609,0.00013417169,0.002166726,0.0003201706,0.8039604,0.17203926,0.0008310255,0.0027223325,0.00010440257],"about_ca_topic_score_codex":0.0021304586,"about_ca_topic_score_gemma":0.0023477706,"teacher_disagreement_score":0.0045901127,"about_ca_system_score_codex":0.0005042922,"about_ca_system_score_gemma":0.00087609445,"threshold_uncertainty_score":0.024275124},"labels":[],"label_agreement":null},{"id":"W2803704245","doi":"10.3390/s18051570","title":"Remote Sensing of Wildland Fire-Induced Risk Assessment at the Community Level","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wildland–urban interface; Damages; Vegetation (pathology); Environmental science; Remote sensing; Environmental resource management; Geography","score_opus":0.028932741379097966,"score_gpt":0.271888503274563,"score_spread":0.24295576189546503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803704245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987882,0.000162727,0.008914195,0.000044447897,0.000008384248,0.00003485736,0.0005480544,0.00014232229,0.0022629732],"genre_scores_gemma":[0.9926519,0.00005822929,0.0064729173,0.0000093532835,0.000004035127,0.0000071015497,0.0003994858,0.000002573979,0.00039441357],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99985766,0.000019015906,0.0000065926747,0.000026198964,0.00006647731,0.00002399985],"domain_scores_gemma":[0.99984324,0.000021258267,0.00003731515,0.000014411291,0.00006049544,0.000023182565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032521904,0.00022982794,0.00012793034,0.001080781,0.00016721404,0.00036735204,0.00030942974,0.00018756531,0.00056940643],"category_scores_gemma":[0.00036671155,0.0000789859,0.0001551814,0.00061896583,0.000106458,0.00022152788,0.00024039565,0.0001384179,0.00009590961],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024140261,0.0003695304,0.6471416,0.00012805009,0.0001277443,0.0005847409,0.0005190417,0.09098013,0.054122105,0.0011509362,0.00168166,0.20295301],"study_design_scores_gemma":[0.000014819826,0.00011639345,0.69118476,0.000028450688,0.0000499128,0.00016799047,0.00077925046,0.29962304,0.0060378774,0.00046807626,0.0015044371,0.000025014004],"about_ca_topic_score_codex":0.070126206,"about_ca_topic_score_gemma":0.16433316,"teacher_disagreement_score":0.070126206,"about_ca_system_score_codex":0.00044903604,"about_ca_system_score_gemma":0.0004506308,"threshold_uncertainty_score":0.13943607},"labels":[],"label_agreement":null},{"id":"W2803819785","doi":"10.3390/s18051593","title":"SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Wearable computer; Participatory sensing; Interconnectivity; Computer science; Mobile device; Citizen science; Trustworthiness; Process (computing); Human–computer interaction; Computer security; Internet privacy; World Wide Web; Data science; Embedded system; Artificial intelligence","score_opus":0.026474105379510178,"score_gpt":0.28271283104825035,"score_spread":0.2562387256687402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803819785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0783761,0.00068505516,0.89448696,0.002035166,0.00041988914,0.0013055848,0.00040689445,0.0025897832,0.019694554],"genre_scores_gemma":[0.88857394,0.00027929235,0.101770274,0.0006334609,0.00015583032,0.0011239134,0.00023014135,0.000117338735,0.007115769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760216,0.0009083646,0.00011576061,0.00044359625,0.0005732323,0.00035691087],"domain_scores_gemma":[0.9971033,0.0011598992,0.00041299063,0.00034135167,0.00042733466,0.00055519934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024802797,0.0010821228,0.0010946519,0.00072861294,0.0011706693,0.0011694551,0.002512075,0.0014944781,0.0032166906],"category_scores_gemma":[0.008434729,0.000318776,0.0005747668,0.00044691973,0.0015613463,0.0017476444,0.0037068566,0.0010922598,0.00074995693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007744976,0.0012397498,0.014782942,0.00097435096,0.00016043226,0.0011234225,0.0024604048,0.48198956,0.028958648,0.1444705,0.024076583,0.29898885],"study_design_scores_gemma":[0.00007531983,0.00038251514,0.0020600902,0.000082340295,0.000038250473,0.0002861665,0.0004946506,0.94065183,0.0029933602,0.037588965,0.01527146,0.00007500973],"about_ca_topic_score_codex":0.003568384,"about_ca_topic_score_gemma":0.004829871,"teacher_disagreement_score":0.003568384,"about_ca_system_score_codex":0.001028133,"about_ca_system_score_gemma":0.002537332,"threshold_uncertainty_score":0.013117135},"labels":[],"label_agreement":null},{"id":"W2804278342","doi":"10.3390/s18051584","title":"Effects of Proof Mass Geometry on Piezoelectric Vibration Energy Harvesters","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Proof mass; Piezoelectricity; Energy harvesting; Vibration; Acoustics; Beam (structure); Resonance (particle physics); Power (physics); Range (aeronautics); Energy (signal processing); Materials science; Dimension (graph theory); Electrical engineering; Physics; Optics; Engineering; Mathematics; Atomic physics; Composite material","score_opus":0.005901040248552888,"score_gpt":0.19437507575314922,"score_spread":0.18847403550459632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804278342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98864114,0.0010067937,0.007674315,0.00009039322,0.00007702341,0.00004861713,0.00012685616,0.00015734864,0.0021775048],"genre_scores_gemma":[0.9937263,0.0005723117,0.0050367275,0.000027468117,0.000010925135,0.00002219594,0.000044827666,0.00005096312,0.00050837523],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959,0.00006624816,0.000036386016,0.000112440066,0.00013289679,0.00006218503],"domain_scores_gemma":[0.997693,0.0013760075,0.0004861386,0.00015049835,0.00022997925,0.00006440717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004293556,0.00071517687,0.00041984097,0.0003117512,0.00017773159,0.00057343184,0.00055054575,0.0005955018,0.00223729],"category_scores_gemma":[0.0024019193,0.00041221714,0.00028157572,0.00026584827,0.0005326162,0.00063013565,0.0004018879,0.00027813597,0.0004963107],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001865056,0.000027876195,0.00043684704,0.00016101079,0.00001636948,0.00024435006,0.00009270838,0.0027517,0.99109274,0.00020454428,0.00009581514,0.0046894993],"study_design_scores_gemma":[0.000033976794,0.0008741138,0.0066090496,0.000025828887,0.0000816213,0.0003408001,0.00014916423,0.0072117564,0.98291135,0.00011515256,0.0016057264,0.000041389107],"about_ca_topic_score_codex":0.0002165141,"about_ca_topic_score_gemma":0.00040267195,"teacher_disagreement_score":0.00223729,"about_ca_system_score_codex":0.00020491231,"about_ca_system_score_gemma":0.00015229115,"threshold_uncertainty_score":0.0074844956},"labels":[],"label_agreement":null},{"id":"W2804301333","doi":"10.3390/s18051615","title":"Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":333,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Wearable computer; Computer science; Feature extraction; Electromyography; Pattern recognition (psychology); Bandwidth (computing); Feature selection; Artificial intelligence; Wearable technology; Speech recognition; Physical medicine and rehabilitation; Medicine; Telecommunications; Embedded system","score_opus":0.006806696876876803,"score_gpt":0.22082687590698985,"score_spread":0.21402017903011306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804301333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.558574,0.002341617,0.43497905,0.00020797423,0.0001354984,0.00023243658,0.00054170424,0.0016797978,0.0013078505],"genre_scores_gemma":[0.8871788,0.00044774514,0.11015336,0.000056434194,0.000057194742,0.0002128919,0.0007850494,0.00004256721,0.0010659366],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999645,0.000058917038,0.000036393747,0.00009628904,0.00012101852,0.00004235367],"domain_scores_gemma":[0.9995733,0.00019188151,0.000056162644,0.00003318352,0.00012118735,0.000024290366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004819448,0.0006334571,0.00074688153,0.00077934976,0.00016741488,0.0003341247,0.0003727082,0.00038107045,0.0011125624],"category_scores_gemma":[0.001830507,0.00012626515,0.00045018186,0.0005597947,0.00013965378,0.0002975897,0.00032200583,0.0003047992,0.00037073792],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009439994,0.00044580552,0.009456321,0.00023809113,0.00013861163,0.0002404313,0.00008438661,0.010560063,0.17913,0.00020379352,0.0022750718,0.79628354],"study_design_scores_gemma":[0.00025916833,0.001789773,0.17142116,0.00010926633,0.00035333078,0.001244993,0.00026804418,0.65752256,0.1597587,0.00087340444,0.0062685106,0.00013105708],"about_ca_topic_score_codex":0.0009490634,"about_ca_topic_score_gemma":0.001477219,"teacher_disagreement_score":0.0011125624,"about_ca_system_score_codex":0.00012961587,"about_ca_system_score_gemma":0.00021205776,"threshold_uncertainty_score":0.0037218332},"labels":[],"label_agreement":null},{"id":"W2804365421","doi":"10.3390/s18072185","title":"Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Flexibility (engineering); Computer science; Mathematical optimization; Constraint (computer-aided design); Motion planning; SAFER; Range (aeronautics); Convexity; Planner; Optimization problem; Convex optimization; Simulation; Robot; Regular polygon; Engineering; Algorithm; Artificial intelligence; Mathematics","score_opus":0.047076309862448346,"score_gpt":0.28813234755868594,"score_spread":0.2410560376962376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804365421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020189402,0.00004934961,0.99686795,0.000050628187,0.000008642089,0.000019467001,0.000022279979,0.00009709452,0.0008656903],"genre_scores_gemma":[0.17515941,0.00028272523,0.8206827,0.00010511615,0.000044574568,0.0002503591,0.0002724842,0.00029807785,0.0029046277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995009,0.0001362064,0.000021501939,0.000118106145,0.00016302301,0.000060359835],"domain_scores_gemma":[0.9993393,0.00035998086,0.000072454626,0.00007505848,0.00011202938,0.000041226984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008186506,0.001309928,0.00086858706,0.0006200561,0.00051131396,0.0010462811,0.0012633085,0.0010426298,0.0024240897],"category_scores_gemma":[0.0022403307,0.00068484846,0.0011294294,0.00063232455,0.0009526327,0.0013423157,0.0015453751,0.0019106998,0.0005390995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012488272,0.000017031814,0.00014868162,0.000058144324,0.000013630235,0.000030447452,0.000042486834,0.9645089,0.0009939788,0.013406038,0.00079735403,0.019970724],"study_design_scores_gemma":[0.0000028892675,0.000012191343,0.00002515297,0.00000590466,0.0000025101244,0.000010991589,0.000009586184,0.9929541,0.00034321574,0.00574801,0.0008820379,0.0000034670452],"about_ca_topic_score_codex":0.005548549,"about_ca_topic_score_gemma":0.005490425,"teacher_disagreement_score":0.005548549,"about_ca_system_score_codex":0.0008163646,"about_ca_system_score_gemma":0.0022156348,"threshold_uncertainty_score":0.011032462},"labels":[],"label_agreement":null},{"id":"W2804368216","doi":"10.3390/s18051655","title":"Suspended Carbon Nanotubes for Humidity Sensing","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Carbon Nanotubes in Composites","field":"Materials Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Carbon nanotube; Materials science; Humidity; Microfabrication; Photoresist; Hysteresis; Relative humidity; Composite material; Nanotechnology; Layer (electronics); Fabrication; Meteorology","score_opus":0.02224809852946868,"score_gpt":0.27702029646461507,"score_spread":0.2547721979351464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804368216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7190712,0.01836272,0.24486195,0.00078304345,0.00095557934,0.0004059427,0.0016368768,0.0010667448,0.01285582],"genre_scores_gemma":[0.842197,0.0052760234,0.14371984,0.00018921762,0.0001109335,0.00016008064,0.0008232305,0.0000837453,0.0074398215],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997687,0.000026409147,0.000014182761,0.00005794958,0.000114072434,0.000018723675],"domain_scores_gemma":[0.9998338,0.000052277013,0.000027795451,0.000018848948,0.000051867915,0.000015461885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016728802,0.00041170107,0.00022013843,0.0002907005,0.0002231013,0.00021454522,0.00033343674,0.00050923054,0.0012122847],"category_scores_gemma":[0.0002346449,0.00018924387,0.00020747991,0.00022756742,0.000105863975,0.00031330742,0.00016869328,0.00046379238,0.00059167284],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062095564,0.0000051628986,0.00003993997,0.00003760958,0.0000026662688,0.000027677508,0.0000053619387,0.00006303732,0.99712676,0.00009587644,0.00006247504,0.0025272898],"study_design_scores_gemma":[0.0000021276746,0.00006448314,0.0003777288,0.000003944033,0.000005549003,0.00012439922,0.0000063212137,0.00153736,0.9952879,0.00006940863,0.0025159223,0.0000048153274],"about_ca_topic_score_codex":0.0003219745,"about_ca_topic_score_gemma":0.001415872,"teacher_disagreement_score":0.0012122847,"about_ca_system_score_codex":0.0001560975,"about_ca_system_score_gemma":0.0001276463,"threshold_uncertainty_score":0.0040555596},"labels":[],"label_agreement":null},{"id":"W2804404899","doi":"10.3390/s18051566","title":"A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Key Laboratory of Universal Wireless Communications of Ministry of Education; Newton Fund; National Natural Science Foundation of China; Beijing Union University; Beijing University of Posts and Telecommunications; Royal Academy of Engineering","keywords":"Computer science; Key (lock); Service (business); Web service; The Internet; World Wide Web; Computer security","score_opus":0.02945450596718529,"score_gpt":0.2920786044694548,"score_spread":0.2626240985022695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804404899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026079457,0.00036403045,0.96744084,0.00034043728,0.00005699849,0.0001496265,0.00021797765,0.00022625898,0.005124352],"genre_scores_gemma":[0.91879857,0.0006026561,0.07689741,0.00011225149,0.000104762184,0.00025053104,0.00017093797,0.00004372564,0.0030191268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900395,0.00027731084,0.000050098963,0.00021373181,0.0003261549,0.00012865859],"domain_scores_gemma":[0.9984906,0.00078939466,0.0002180396,0.000119821925,0.00031962816,0.000062487445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011761424,0.0009811322,0.0006151279,0.0014913013,0.0006152795,0.0015225426,0.001430192,0.0009278158,0.0012641426],"category_scores_gemma":[0.004077799,0.00034194006,0.0008840363,0.0010788948,0.0011405831,0.00123068,0.0015386158,0.00088866655,0.00020854955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121291894,0.00016144784,0.006731056,0.0004015845,0.00009299311,0.00070475903,0.0011296234,0.8367869,0.015938316,0.083092004,0.0023205352,0.052519474],"study_design_scores_gemma":[0.0000024340297,0.000021642198,0.0008304542,0.000010705607,0.000010312815,0.000053103726,0.00014999972,0.9874068,0.00067529426,0.010078095,0.0007408287,0.000020321753],"about_ca_topic_score_codex":0.014407644,"about_ca_topic_score_gemma":0.006716404,"teacher_disagreement_score":0.014407644,"about_ca_system_score_codex":0.0016592038,"about_ca_system_score_gemma":0.0011872771,"threshold_uncertainty_score":0.028647542},"labels":[],"label_agreement":null},{"id":"W2804867869","doi":"10.3390/s18061691","title":"A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"University of Pretoria; National Research Foundation; Stiftelsen för Miljöstrategisk Forskning","keywords":"Wireless sensor network; Denial-of-service attack; Jamming; Computer science; Network packet; Computer network; Overhead (engineering); EWMA chart; Key distribution in wireless sensor networks; Process (computing); Real-time computing; Computer security; Wireless; Wireless network; Telecommunications; Control chart","score_opus":0.015916853300201473,"score_gpt":0.25816629560690224,"score_spread":0.24224944230670076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804867869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018596679,0.00012749279,0.9803894,0.0000619327,0.00002298731,0.000028752997,0.00001553549,0.00032030235,0.00043690612],"genre_scores_gemma":[0.8313803,0.0004026141,0.16667946,0.00011860619,0.0001133379,0.00017122232,0.0001046121,0.000054945212,0.00097497174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990766,0.00023276435,0.00006584777,0.00013659585,0.0004370525,0.00005110682],"domain_scores_gemma":[0.9973623,0.0016549908,0.00030482077,0.00019158074,0.00043931627,0.000046969795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011662835,0.00062773615,0.0004427466,0.0013244041,0.00026592307,0.0004894751,0.0007332702,0.0005272688,0.00035465267],"category_scores_gemma":[0.0071696383,0.00028691534,0.000425212,0.00087171444,0.00057175336,0.00089988403,0.00049561955,0.00063696236,0.00014416937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114227594,0.00017769718,0.0058921985,0.00010122959,0.00012438954,0.00012856888,0.00008834539,0.79767543,0.026300931,0.015623613,0.00044413513,0.15332921],"study_design_scores_gemma":[0.000002026713,0.000054892407,0.00053174276,0.0000019673273,0.000007367708,0.000028916751,0.000005968487,0.99624145,0.0016694263,0.0012654094,0.00018481807,0.000005964458],"about_ca_topic_score_codex":0.0015950995,"about_ca_topic_score_gemma":0.001460536,"teacher_disagreement_score":0.0015950995,"about_ca_system_score_codex":0.00037124543,"about_ca_system_score_gemma":0.0005718918,"threshold_uncertainty_score":0.0061679482},"labels":[],"label_agreement":null},{"id":"W2805158007","doi":"10.3390/s18061887","title":"Multi-Focus Fusion Technique on Low-Cost Camera Images for Canola Phenotyping","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Artificial intelligence; Image fusion; Computer vision; Computer science; Focus (optics); Distortion (music); Canola; Filter (signal processing); Field (mathematics); Fusion; Similarity (geometry); Pixel; Image quality; Sensor fusion; Image (mathematics); Mathematics","score_opus":0.011814612949726258,"score_gpt":0.2666210517814442,"score_spread":0.25480643883171794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805158007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10587445,0.00077278656,0.88949454,0.00016083852,0.00005023693,0.00009636677,0.00018309153,0.0015790071,0.0017886354],"genre_scores_gemma":[0.3831959,0.0006650456,0.61412454,0.00008803455,0.00003132288,0.00008069195,0.0003196153,0.00014317628,0.0013515976],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966013,0.000040797247,0.000012521342,0.0000836013,0.00016523438,0.000037724876],"domain_scores_gemma":[0.9995498,0.000104229235,0.00006037816,0.00007210782,0.00018821137,0.000025196718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005627155,0.000701267,0.0005640801,0.0018308164,0.00031622004,0.0005421596,0.00056583254,0.00069787644,0.0014535071],"category_scores_gemma":[0.0011805271,0.00028991088,0.00068751036,0.0010313522,0.0002487091,0.001181879,0.000796067,0.0006202405,0.0004966498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040743122,0.00009010526,0.001918613,0.00031640046,0.00009981689,0.00022898181,0.0003363235,0.018021725,0.5571927,0.0015250465,0.0014909974,0.41837183],"study_design_scores_gemma":[0.000046886948,0.0003191181,0.017265733,0.000058121706,0.00026785902,0.0012631528,0.0003728311,0.4874998,0.47927982,0.00373336,0.009781324,0.00011196203],"about_ca_topic_score_codex":0.0020332683,"about_ca_topic_score_gemma":0.0035638243,"teacher_disagreement_score":0.0020332683,"about_ca_system_score_codex":0.0003960091,"about_ca_system_score_gemma":0.00048447945,"threshold_uncertainty_score":0.0048624873},"labels":[],"label_agreement":null},{"id":"W2805696881","doi":"10.3390/s18061872","title":"Satellite Launcher Navigation with One Versus Three IMUs: Sensor Positioning and Data Fusion Model Analysis","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Defence Research and Development Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Inertial measurement unit; Global Positioning System; Sensor fusion; Context (archaeology); Units of measurement; Inertial navigation system; Computer science; Filter (signal processing); Position (finance); Engineering; Computer vision; Orientation (vector space); Geography; Mathematics; Telecommunications","score_opus":0.04011042811310002,"score_gpt":0.2615783832741941,"score_spread":0.22146795516109408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805696881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4223037,0.00030115724,0.5740621,0.00018252945,0.000056892088,0.000052618736,0.0002555863,0.0007727652,0.002012685],"genre_scores_gemma":[0.95361114,0.00011010235,0.045527894,0.000019025041,0.000006478082,0.000035275563,0.00019123607,0.000044905133,0.00045398148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995732,0.000078036435,0.000027920523,0.0000853346,0.00017454871,0.00006095786],"domain_scores_gemma":[0.99945194,0.00021115599,0.00007087647,0.00009240209,0.0001573,0.000016379803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001021988,0.0007687752,0.00064100174,0.0004949612,0.00025678403,0.0005726243,0.0005539226,0.00062795606,0.00074160943],"category_scores_gemma":[0.002023375,0.00032558915,0.00094898115,0.0006575581,0.00030158175,0.0011711702,0.0006269054,0.0006847692,0.00020052522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003152098,0.00006842992,0.007361083,0.00010931505,0.00017630856,0.000077926605,0.000108076325,0.90284634,0.014039684,0.0017790347,0.0002862592,0.07283239],"study_design_scores_gemma":[0.000009043375,0.000066280496,0.0040474962,0.000008351535,0.000037034402,0.000030130499,0.000028160533,0.9876709,0.007117118,0.00064937025,0.00031934984,0.000016712529],"about_ca_topic_score_codex":0.013479634,"about_ca_topic_score_gemma":0.008603936,"teacher_disagreement_score":0.013479634,"about_ca_system_score_codex":0.000555915,"about_ca_system_score_gemma":0.0007873804,"threshold_uncertainty_score":0.026802301},"labels":[],"label_agreement":null},{"id":"W2805794463","doi":"10.3390/s19061409","title":"Construction of All-in-Focus Images Assisted by Depth Sensing","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"Focus (optics); Artificial intelligence; Computer vision; Computer science; Image fusion; Depth of field; RGB color model; Segmentation; Image (mathematics)","score_opus":0.010687419685730537,"score_gpt":0.24460421677741065,"score_spread":0.23391679709168012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805794463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03570318,0.0005011199,0.96005595,0.00010166673,0.00006639269,0.00009718544,0.0001511498,0.0011524478,0.0021707837],"genre_scores_gemma":[0.18979038,0.00065848633,0.8072835,0.00009893524,0.00003533864,0.000066523215,0.00033516568,0.00023969321,0.001491996],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995629,0.00003797644,0.000016635977,0.00009391635,0.00024065272,0.000047771246],"domain_scores_gemma":[0.9994566,0.00008928593,0.00007789231,0.00010183489,0.00024504776,0.000029324028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044305332,0.0011589719,0.0006914548,0.0016839098,0.00033214322,0.00087826204,0.0010145725,0.00081408577,0.0014966263],"category_scores_gemma":[0.0010549597,0.0005080652,0.00083826174,0.0009030754,0.0004120028,0.0015892294,0.0011398324,0.00086667197,0.0005721328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031994062,0.00009327902,0.001127857,0.0005902234,0.00010621969,0.0002934472,0.0003528461,0.033353955,0.5586262,0.004872595,0.00262016,0.3976432],"study_design_scores_gemma":[0.00003568712,0.0002473091,0.00378061,0.000046086214,0.00010851822,0.0009229689,0.00018894028,0.38425487,0.5957281,0.003944541,0.010644264,0.00009804262],"about_ca_topic_score_codex":0.0013104922,"about_ca_topic_score_gemma":0.0019118647,"teacher_disagreement_score":0.0016839098,"about_ca_system_score_codex":0.00041309925,"about_ca_system_score_gemma":0.0005495203,"threshold_uncertainty_score":0.005006671},"labels":[],"label_agreement":null},{"id":"W2808191774","doi":"10.3390/s18061910","title":"Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Defence Research and Development Canada","funders":"","keywords":"Sensor fusion; Kalman filter; Computer science; Residual; Context (archaeology); Acceleration; Monte Carlo method; Algorithm; Fault detection and isolation; Inertial frame of reference; Fusion; Fault (geology); Real-time computing; Artificial intelligence; Mathematics","score_opus":0.07180134914243036,"score_gpt":0.2685737268192406,"score_spread":0.19677237767681022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808191774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06606406,0.00092551095,0.9286812,0.00026824983,0.000103356906,0.00004532431,0.000057276287,0.0005650277,0.0032901359],"genre_scores_gemma":[0.85270756,0.00037861292,0.14454179,0.00009647303,0.00008034054,0.00008775747,0.000099714605,0.000018012253,0.0019898287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994466,0.0001196013,0.000044949873,0.00012441444,0.00019374516,0.000070812515],"domain_scores_gemma":[0.99930024,0.00013109483,0.00010380022,0.00015457887,0.00028067923,0.000029648785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082493795,0.0004490947,0.0005979514,0.0004527742,0.00044960805,0.00068770593,0.00097182655,0.00046529798,0.0011517605],"category_scores_gemma":[0.0013913852,0.00023580507,0.00037288698,0.00051532785,0.0003974775,0.0013522034,0.00092455343,0.0006299519,0.00038844917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042214125,0.0001421719,0.0025596337,0.0002173825,0.00011734749,0.00019918988,0.00027921092,0.50979924,0.04049307,0.04166321,0.002939064,0.40116832],"study_design_scores_gemma":[0.000027519962,0.00020586247,0.0006358777,0.000019722926,0.000032371252,0.00007506915,0.000045542598,0.97378796,0.010839819,0.010586013,0.0037251255,0.000019215659],"about_ca_topic_score_codex":0.0013844485,"about_ca_topic_score_gemma":0.00141799,"teacher_disagreement_score":0.0013844485,"about_ca_system_score_codex":0.0005702037,"about_ca_system_score_gemma":0.00057604676,"threshold_uncertainty_score":0.004362762},"labels":[],"label_agreement":null},{"id":"W2808526226","doi":"10.3390/s18061900","title":"Human Part Segmentation in Depth Images with Annotated Part Positions","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Random walker algorithm; Artificial intelligence; Segmentation; Cut; Markov random field; Computer science; Random forest; Graph; Pattern recognition (psychology); Image segmentation; Computer vision; Range segmentation; Conditional random field; Scale-space segmentation","score_opus":0.01997575774372173,"score_gpt":0.2900268334659139,"score_spread":0.27005107572219217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808526226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037767272,0.0011717711,0.94215995,0.00018185457,0.00014258201,0.00032692426,0.0028477258,0.011935913,0.0034660685],"genre_scores_gemma":[0.21206553,0.00069002085,0.7769747,0.00031344447,0.00007680206,0.00022675308,0.006050628,0.0011359917,0.002466104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990879,0.00009429936,0.000035285382,0.00035596098,0.0002833737,0.00014324393],"domain_scores_gemma":[0.9989436,0.00026306984,0.00016521534,0.0002937559,0.00026319508,0.00007124912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054751267,0.0018317088,0.00114351,0.0027696614,0.0004349484,0.0012354509,0.0014417445,0.0016583422,0.004464845],"category_scores_gemma":[0.0024238843,0.001020183,0.0012393816,0.0017461153,0.0006861289,0.001359516,0.0014012994,0.0010633726,0.0026849322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013936639,0.0002403393,0.0053998865,0.0008141443,0.00024166246,0.00039852675,0.00045163993,0.0637988,0.1786914,0.0032060246,0.026423017,0.718941],"study_design_scores_gemma":[0.0000884461,0.00029847742,0.018394547,0.0002372269,0.00015188893,0.0016214639,0.0003699828,0.8118982,0.12483603,0.012932118,0.029058486,0.00011315558],"about_ca_topic_score_codex":0.008129402,"about_ca_topic_score_gemma":0.022270426,"teacher_disagreement_score":0.008129402,"about_ca_system_score_codex":0.0007092614,"about_ca_system_score_gemma":0.0008684612,"threshold_uncertainty_score":0.016164124},"labels":[],"label_agreement":null},{"id":"W2808747593","doi":"10.3390/s18061967","title":"GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"GNSS applications; Multipath mitigation; Multipath propagation; Code (set theory); Computer science; Remote sensing; Real-time computing; Global Positioning System; Telecommunications; Geography; Programming language","score_opus":0.02032084508895616,"score_gpt":0.2402382699376835,"score_spread":0.21991742484872734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808747593","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076673694,0.00032263485,0.9203113,0.00007011548,0.00006610394,0.000059836693,0.000046829227,0.0011719416,0.0012775342],"genre_scores_gemma":[0.42870426,0.0003383282,0.5682233,0.00005073541,0.00007024655,0.000076754586,0.00023306436,0.00012074858,0.0021826003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907386,0.00013872192,0.000052305175,0.00024689137,0.00040639914,0.00008184617],"domain_scores_gemma":[0.9989298,0.00021533156,0.0002464721,0.00019118353,0.00037721524,0.00003996696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006049651,0.0017788131,0.0006469209,0.0012065863,0.00040277003,0.00060447486,0.0010829561,0.00044324106,0.00049696735],"category_scores_gemma":[0.002152156,0.0003846916,0.0007595023,0.0012379433,0.00034500496,0.00086197926,0.0014781175,0.00070931617,0.00047372762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027057572,0.00013043387,0.010089298,0.0002032761,0.0001607062,0.00013460353,0.00024084651,0.10830554,0.10601016,0.0016950187,0.000756894,0.7720026],"study_design_scores_gemma":[0.000036310474,0.00053553865,0.016015984,0.000055718898,0.000167648,0.0005302791,0.000122906,0.8804514,0.09421273,0.0017076524,0.0060830703,0.00008072065],"about_ca_topic_score_codex":0.0034657647,"about_ca_topic_score_gemma":0.0064413636,"teacher_disagreement_score":0.0034657647,"about_ca_system_score_codex":0.00028044937,"about_ca_system_score_gemma":0.00091300334,"threshold_uncertainty_score":0.006891191},"labels":[],"label_agreement":null},{"id":"W2809182693","doi":"10.3390/s18061964","title":"TM02 Quarter-Mode Substrate-Integrated Waveguide Resonator for Dual Detection of Chemicals","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Dual mode; Substrate (aquarium); Resonator; Quarter (Canadian coin); Dual (grammatical number); Optoelectronics; Materials science; Mode (computer interface); Waveguide; Computer science; Electronic engineering; Engineering; Biology; Art","score_opus":0.00924382318133332,"score_gpt":0.22910227363192306,"score_spread":0.21985845045058974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809182693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8874642,0.0010917889,0.10487735,0.00014583996,0.00018953503,0.00007143375,0.00030428084,0.0010069063,0.004848671],"genre_scores_gemma":[0.8678902,0.0004226889,0.12645324,0.00007773056,0.000033070824,0.0000757945,0.0002777571,0.00007095045,0.0046985103],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996424,0.000045331002,0.000020140313,0.00013065872,0.000119889126,0.00004155659],"domain_scores_gemma":[0.99977905,0.000050294766,0.000055111497,0.000040781055,0.00004932663,0.000025517844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032526243,0.0005319486,0.0004015503,0.00023625651,0.00013639443,0.00034855807,0.0010053612,0.00056770165,0.0014291846],"category_scores_gemma":[0.00031070854,0.00043252468,0.0003381899,0.00019982018,0.00018665972,0.00043827886,0.00031317634,0.00030822755,0.00080676714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038268212,0.000010672096,0.00011203622,0.000030776744,0.0000045103384,0.000023167831,0.000008650452,0.00007454919,0.9972372,0.00009748005,0.0000848391,0.0022777433],"study_design_scores_gemma":[0.00001179069,0.00020401388,0.00086386775,0.0000017305304,0.000011785655,0.00020105294,0.00000869178,0.005028347,0.99193,0.000015242581,0.0017143089,0.000009114355],"about_ca_topic_score_codex":0.00027182113,"about_ca_topic_score_gemma":0.00091278064,"teacher_disagreement_score":0.0014291846,"about_ca_system_score_codex":0.0003531321,"about_ca_system_score_gemma":0.00028474943,"threshold_uncertainty_score":0.0047810674},"labels":[],"label_agreement":null},{"id":"W2809931387","doi":"10.3390/s18072088","title":"Response Time to a Vibrotactile Stimulus Presented on the Foot at Rest and During Walking on Different Surfaces","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Ankle; Stimulus (psychology); Physical medicine and rehabilitation; Analysis of variance; Medicine; Simulation; Psychology; Computer science; Surgery","score_opus":0.028883155636869155,"score_gpt":0.27571992616385405,"score_spread":0.24683677052698488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809931387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978004,0.000111970214,0.0014304024,0.000006392181,0.000013961649,0.000020681457,0.00011666045,0.000024005556,0.00047555828],"genre_scores_gemma":[0.997701,0.00009393462,0.0011860381,0.000017422468,0.00001000137,0.00004983737,0.00019103012,0.000015919793,0.00073490886],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99973756,0.000052386462,0.000018586212,0.000088945795,0.00006990556,0.00003249023],"domain_scores_gemma":[0.9992556,0.00039338716,0.0001392113,0.00003585985,0.00009996843,0.000075900105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023219349,0.00035178973,0.00038295632,0.0002738352,0.00007238089,0.00020801,0.00016571186,0.00033484664,0.0017907387],"category_scores_gemma":[0.0023961698,0.000116433366,0.00016895021,0.00014425574,0.00013286369,0.00016931386,0.00023295965,0.00019230449,0.00027547],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045537883,0.00017254947,0.027630562,0.0003133748,0.00010318434,0.00025432353,0.00071409426,0.00031255028,0.92771614,0.000056346787,0.0001343208,0.038038768],"study_design_scores_gemma":[0.00008221001,0.0045608813,0.938663,0.000028303657,0.00010614616,0.0008083847,0.00052722776,0.0019121013,0.052559894,0.00012412868,0.00057801744,0.000049549035],"about_ca_topic_score_codex":0.00040884988,"about_ca_topic_score_gemma":0.00057685113,"teacher_disagreement_score":0.0017907387,"about_ca_system_score_codex":0.000063470114,"about_ca_system_score_gemma":0.00006902119,"threshold_uncertainty_score":0.0059906244},"labels":[],"label_agreement":null},{"id":"W2810801822","doi":"10.3390/s18072117","title":"Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Stigmergy; Event (particle physics); Computer science; Data science; Psychology; Human–computer interaction; Cognitive science; Communication; Artificial intelligence; Physics","score_opus":0.05025072984723933,"score_gpt":0.3580322272384279,"score_spread":0.3077814973911886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810801822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6526486,0.0008079453,0.34051532,0.00024453603,0.00008846973,0.00024560504,0.00034650875,0.00080231635,0.004300669],"genre_scores_gemma":[0.9434175,0.00015826184,0.055271804,0.00002735256,0.00002940463,0.00007084708,0.00023898443,0.000054074004,0.0007317727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929094,0.0001801635,0.00006439126,0.00023424476,0.00016308922,0.00006708242],"domain_scores_gemma":[0.9965714,0.0019264717,0.00067398365,0.00034328055,0.00027337697,0.00021147088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011847888,0.0005565046,0.00057432754,0.0040416038,0.00066226436,0.0019489446,0.00058353535,0.0007763019,0.0012474766],"category_scores_gemma":[0.01019766,0.00034887888,0.0006433976,0.0021559566,0.0010586441,0.0034463594,0.0015072038,0.00062901323,0.00041178468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023502272,0.0005275286,0.100903496,0.0010379658,0.0005130743,0.0006897528,0.006061507,0.24329878,0.072475746,0.049927846,0.0022520327,0.5199619],"study_design_scores_gemma":[0.000045117944,0.00034366615,0.0633957,0.00006308687,0.00012796975,0.0004498387,0.0011627428,0.8644791,0.02057744,0.044613276,0.00460493,0.00013714623],"about_ca_topic_score_codex":0.0018532454,"about_ca_topic_score_gemma":0.0024242303,"teacher_disagreement_score":0.0040416038,"about_ca_system_score_codex":0.0009190903,"about_ca_system_score_gemma":0.0004972459,"threshold_uncertainty_score":0.006668508},"labels":[],"label_agreement":null},{"id":"W2811107749","doi":"10.3390/s18072076","title":"Multifunction RF Systems for Naval Platforms","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Satellite Communication Systems","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Key (lock); Radar; Systems engineering; Electronic warfare; Electronics; Radio frequency; Antenna (radio); Engineering; Systems design; Computer science; Telecommunications; Electrical engineering; Embedded system; Operating system","score_opus":0.11207376965036724,"score_gpt":0.32534338471507124,"score_spread":0.213269615064704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811107749","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006082194,0.99008185,0.0014838929,0.00039103875,0.0004450418,0.000013555228,0.000024151166,0.00002743281,0.00692476],"genre_scores_gemma":[0.0056118486,0.9864086,0.0017522976,0.0003278756,0.0003737062,0.0000200329,0.0000565018,0.0000079663705,0.005441218],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968266,0.000035826903,0.000034504046,0.000053110005,0.00016918984,0.000024592091],"domain_scores_gemma":[0.9996251,0.00015146444,0.000059268303,0.000016853588,0.00012581811,0.00002150906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059782516,0.0007200516,0.00049536995,0.0022901741,0.0003437408,0.0008774051,0.0007100371,0.0010935565,0.0058502047],"category_scores_gemma":[0.0007963599,0.00027542585,0.0004768212,0.0016270207,0.00035316325,0.0015604338,0.0005912424,0.0013296682,0.0030372187],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003268236,0.000056655725,0.00019391239,0.010813266,0.000032051692,0.00019887091,0.00010726368,0.00082447863,0.008012763,0.011789957,0.0170424,0.9508957],"study_design_scores_gemma":[0.0000033072972,0.000068938374,0.00052044884,0.00166719,0.000027181162,0.0007662118,0.000053159492,0.00020341671,0.0018517222,0.0021427476,0.9926825,0.000013139138],"about_ca_topic_score_codex":0.0008681423,"about_ca_topic_score_gemma":0.0013263804,"teacher_disagreement_score":0.0058502047,"about_ca_system_score_codex":0.0004970204,"about_ca_system_score_gemma":0.0008337577,"threshold_uncertainty_score":0.019570827},"labels":[],"label_agreement":null},{"id":"W2853311888","doi":"10.3390/s18072209","title":"Assessment of Retrieved N2O, NO2, and HF Profiles from the Atmospheric Infrared Ultraspectral Sounder Based on Simulated Spectra","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Atmospheric Infrared Sounder; Environmental science; Spectral line; Standard deviation; Occultation; Remote sensing; Satellite; Range (aeronautics); Atmospheric sciences; Meteorology; Physics; Mathematics; Materials science; Troposphere; Geology; Statistics","score_opus":0.010585329008397305,"score_gpt":0.23144393674770053,"score_spread":0.22085860773930321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2853311888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98582435,0.00035827165,0.0101725785,0.00006888196,0.000052578587,0.000036432804,0.0009777831,0.0002811409,0.0022280954],"genre_scores_gemma":[0.9895862,0.00021551047,0.0073122843,0.00004194633,0.000019548737,0.000018914552,0.0022573902,0.00006211983,0.00048610888],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952114,0.000047943544,0.000032259562,0.00011387194,0.00022644347,0.00005836557],"domain_scores_gemma":[0.99972314,0.000040029303,0.00003302167,0.000041668573,0.00014229472,0.000019866917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009730055,0.0009099459,0.00039863525,0.00093472213,0.0003470226,0.00068632874,0.0005378751,0.0006715437,0.00046533698],"category_scores_gemma":[0.0011048449,0.00027471778,0.0006617971,0.00088828686,0.00033674666,0.0012019817,0.00047887277,0.00033210585,0.0002591056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017895717,0.00062871235,0.29223353,0.0006359651,0.0006443588,0.0015037359,0.00075032894,0.1246226,0.35602108,0.0012788613,0.0043656295,0.21552564],"study_design_scores_gemma":[0.00020133963,0.00027475297,0.38857323,0.00006250627,0.0002616431,0.00030415878,0.00050545385,0.5032311,0.10206197,0.0005996706,0.0037629367,0.00016130321],"about_ca_topic_score_codex":0.014284109,"about_ca_topic_score_gemma":0.011730208,"teacher_disagreement_score":0.014284109,"about_ca_system_score_codex":0.00046016727,"about_ca_system_score_gemma":0.00052579126,"threshold_uncertainty_score":0.028401911},"labels":[],"label_agreement":null},{"id":"W2883084370","doi":"10.3390/s18072369","title":"A Wearable Textile Thermograph","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Thermoregulation and physiological responses","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Defence Science and Technology Group; Defence Science and Technology Laboratory; Trent University; Nottingham Trent University","keywords":"Textile; Yarn; Thermistor; Wearable computer; Materials science; Composite material; Bending; Mechanical engineering; Computer science; Electrical engineering; Engineering; Embedded system","score_opus":0.024158351005838106,"score_gpt":0.3008826001750757,"score_spread":0.27672424916923755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883084370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37179637,0.014159704,0.5531146,0.0018340346,0.0043339254,0.0010095742,0.0032227011,0.01126941,0.0392596],"genre_scores_gemma":[0.7239384,0.004241893,0.21334484,0.0015738471,0.00053768547,0.000465379,0.0009588351,0.00057583046,0.05436321],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995975,0.00007961524,0.000021706224,0.000112579866,0.00015912762,0.000029606117],"domain_scores_gemma":[0.9995384,0.00013682825,0.00006681088,0.00011180299,0.00009179042,0.00005435981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003488526,0.00045576316,0.0005060196,0.00041229944,0.00027701943,0.0006305132,0.00061653566,0.00091038743,0.0059127673],"category_scores_gemma":[0.0006968211,0.000298386,0.00047078446,0.00042247627,0.00027601392,0.00077447237,0.00057815044,0.0005952649,0.0017915061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004684082,0.00009274053,0.0013579444,0.0007998055,0.000054743217,0.0006642386,0.0003073204,0.0011983971,0.88722485,0.001557044,0.006995882,0.099278525],"study_design_scores_gemma":[0.00011950728,0.0033681649,0.029419012,0.00033649142,0.0002961257,0.01248757,0.00026294874,0.017821543,0.7031853,0.0011814579,0.23123801,0.00028381502],"about_ca_topic_score_codex":0.0001887098,"about_ca_topic_score_gemma":0.00035044912,"teacher_disagreement_score":0.0059127673,"about_ca_system_score_codex":0.0002603579,"about_ca_system_score_gemma":0.00022478728,"threshold_uncertainty_score":0.019780219},"labels":[],"label_agreement":null},{"id":"W2883375722","doi":"10.3390/s18082413","title":"UAV Based Relay for Wireless Sensor Networks in 5G Systems","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Relay; Wireless sensor network; Computer science; Leverage (statistics); Wireless; Base station; Energy consumption; Power consumption; Computer network; Real-time computing; Power (physics); Engineering; Telecommunications; Electrical engineering","score_opus":0.007412677579361529,"score_gpt":0.20556348531410792,"score_spread":0.1981508077347464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883375722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05937213,0.01569555,0.89584476,0.0011616747,0.0005462931,0.00016316822,0.00024401865,0.0007070003,0.026265314],"genre_scores_gemma":[0.94909346,0.0052440763,0.04097894,0.00014251343,0.00009889528,0.00007738668,0.00011583021,0.000020695741,0.0042281365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997609,0.000095233125,0.000012719225,0.000048827806,0.000050789447,0.000031537147],"domain_scores_gemma":[0.9998703,0.000054326287,0.000020109836,0.000016162388,0.00003196459,0.0000070757087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023547346,0.00060923176,0.0004172611,0.0002631949,0.00040760363,0.0006064316,0.0005541579,0.0007261747,0.0025374289],"category_scores_gemma":[0.00052506407,0.00010722598,0.00031697706,0.00036838275,0.00026882224,0.00083022023,0.0003985488,0.00041321604,0.000474524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042240438,0.0000911822,0.0034452274,0.001220844,0.00013964405,0.002983961,0.00040273715,0.5310593,0.050577372,0.10992537,0.01516515,0.2845667],"study_design_scores_gemma":[0.000020199239,0.0005099541,0.0013366411,0.00010659108,0.0000782248,0.0011040407,0.00019422648,0.94163966,0.009322828,0.017600877,0.028052581,0.0000341761],"about_ca_topic_score_codex":0.0022505256,"about_ca_topic_score_gemma":0.0034420844,"teacher_disagreement_score":0.0025374289,"about_ca_system_score_codex":0.00042894835,"about_ca_system_score_gemma":0.00024772578,"threshold_uncertainty_score":0.008488536},"labels":[],"label_agreement":null},{"id":"W2883618064","doi":"10.3390/s18072370","title":"Ambient Refractive-Index Measurement with Simultaneous Temperature Monitoring Based on a Dual-Resonance Long-Period Grating Inside a Fiber Loop Mirror Structure","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Refractive index; Loop (graph theory); Materials science; Optics; Long-period fiber grating; Fiber Bragg grating; Grating; Temperature measurement; Resonance (particle physics); Optical fiber; Fiber optic sensor; Period (music); Graded-index fiber; Optoelectronics; Acoustics; Physics","score_opus":0.011465622027179156,"score_gpt":0.23070118774211631,"score_spread":0.21923556571493716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883618064","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9599715,0.00045534622,0.038320284,0.000053240114,0.00004768159,0.000045104563,0.00006236729,0.00022771904,0.0008166895],"genre_scores_gemma":[0.91393447,0.00020780772,0.08493009,0.000036585847,0.000023195267,0.000038853654,0.000069457805,0.000026243504,0.0007332387],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999503,0.00006598036,0.000022875693,0.00014407617,0.00022433294,0.00003968391],"domain_scores_gemma":[0.99951434,0.000085577805,0.00018188442,0.00006262846,0.00012460905,0.00003093924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041220235,0.0004917338,0.00035893914,0.00028814457,0.00018895179,0.0003103012,0.0007346201,0.00050035736,0.00030711],"category_scores_gemma":[0.00045289614,0.00027126528,0.0002803371,0.0001962414,0.00035905893,0.0005973567,0.00036218014,0.00033748292,0.00017301163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041198917,0.000018519175,0.00027328214,0.00002143555,0.0000036848307,0.000015505286,0.000015474796,0.00012462132,0.99780506,0.000039142516,0.000010976654,0.0016311833],"study_design_scores_gemma":[0.0000071013123,0.00016415768,0.0013809889,0.0000018277952,0.00001371091,0.00008791596,0.000007148366,0.0037834195,0.99421316,0.000018189416,0.00031264182,0.000009730895],"about_ca_topic_score_codex":0.0003304642,"about_ca_topic_score_gemma":0.00090827025,"teacher_disagreement_score":0.0007346201,"about_ca_system_score_codex":0.00034834314,"about_ca_system_score_gemma":0.0002750396,"threshold_uncertainty_score":0.0025274158},"labels":[],"label_agreement":null},{"id":"W2883760536","doi":"10.3390/s18082446","title":"Energy Harvesting Sources, Storage Devices and System Topologies for Environmental Wireless Sensor Networks: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":240,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Regional Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Energy harvesting; Network topology; Context (archaeology); Computer science; Wireless; Energy storage; Internet of Things; Computer network; Key distribution in wireless sensor networks; Energy source; Wireless network; Energy (signal processing); Renewable energy; Electrical engineering; Telecommunications; Engineering; Power (physics); Embedded system","score_opus":0.020266094414500037,"score_gpt":0.23904131918671737,"score_spread":0.21877522477221734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883760536","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005256738,0.9948395,0.0011787899,0.00018286363,0.00023432038,0.0000149289435,0.00005025543,0.000019768588,0.0029538698],"genre_scores_gemma":[0.0025278179,0.99454933,0.0011295805,0.000106520194,0.00017463938,0.000017514116,0.00007029592,0.0000052839328,0.00141899],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997851,0.000024542573,0.000032680906,0.00004940033,0.000092011265,0.000016220181],"domain_scores_gemma":[0.9995683,0.00025088168,0.00005477808,0.000014895247,0.00009833244,0.000012827849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004332747,0.00090963865,0.0011109896,0.0024544857,0.00035590908,0.0010361534,0.000972625,0.0010245689,0.0047421786],"category_scores_gemma":[0.00078104116,0.00044364246,0.0006610457,0.0027911563,0.00032434711,0.0018224283,0.00056877616,0.000890326,0.0017370981],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046975605,0.00010840777,0.0003511886,0.03584552,0.00008709546,0.00024646622,0.000114948256,0.001623408,0.0074667768,0.008909301,0.018518852,0.92668104],"study_design_scores_gemma":[0.0000065289205,0.00018312092,0.0007811994,0.004890803,0.00016672588,0.0015788459,0.000120762175,0.0007672547,0.003289543,0.004540249,0.98363227,0.000042715154],"about_ca_topic_score_codex":0.00062035816,"about_ca_topic_score_gemma":0.0009367848,"teacher_disagreement_score":0.0047421786,"about_ca_system_score_codex":0.00033842953,"about_ca_system_score_gemma":0.00070370716,"threshold_uncertainty_score":0.015864134},"labels":[],"label_agreement":null},{"id":"W2883854343","doi":"10.3390/s18082438","title":"An Ultra-Wideband Frequency System for Non-Destructive Root Imaging","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Synthetic aperture radar; Computer science; Data acquisition; Image processing; Electronic engineering; Signal processing; Artificial intelligence; Wideband; Computer vision; Remote sensing; Computer hardware; Digital signal processing; Engineering; Image (mathematics)","score_opus":0.005657182096778505,"score_gpt":0.2294412951065445,"score_spread":0.223784113009766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883854343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07317769,0.0008746682,0.91827416,0.00015701287,0.00011702372,0.00018205127,0.00027806638,0.002680068,0.0042592348],"genre_scores_gemma":[0.35338145,0.00069815613,0.63652384,0.0003620308,0.00008233451,0.00035980763,0.00046836995,0.00018796962,0.0079360865],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996228,0.000060700295,0.000016497337,0.000095420866,0.00017703728,0.000027526718],"domain_scores_gemma":[0.99948126,0.000115243914,0.000079340396,0.00010126852,0.00019140787,0.00003160684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004980498,0.0003424196,0.00034623613,0.0005304969,0.00021655727,0.00049506593,0.0006521639,0.0007107456,0.0027197835],"category_scores_gemma":[0.00054687663,0.00023786831,0.00022214974,0.00042210735,0.00021876207,0.00070849794,0.00046921306,0.0005359838,0.0013551577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113656584,0.000036863366,0.0010310623,0.00016926418,0.000019830863,0.00006354615,0.00012698428,0.00073809683,0.9085603,0.0012994047,0.0008940606,0.08694704],"study_design_scores_gemma":[0.000090176196,0.001450844,0.017652718,0.0001072684,0.00016573607,0.0021890944,0.00021143818,0.05552067,0.82812554,0.0015108348,0.09284049,0.00013522674],"about_ca_topic_score_codex":0.00027601287,"about_ca_topic_score_gemma":0.0007716123,"teacher_disagreement_score":0.0027197835,"about_ca_system_score_codex":0.0003181369,"about_ca_system_score_gemma":0.00025924278,"threshold_uncertainty_score":0.009098589},"labels":[],"label_agreement":null},{"id":"W2884076111","doi":"10.3390/s18072355","title":"Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Gesture; Imitation; Collision avoidance; Robot; Social robot; Computer science; Human–computer interaction; Human–robot interaction; Artificial intelligence; Collision; Psychology; Computer vision; Mobile robot; Robot control; Computer security; Social psychology","score_opus":0.015200126470724775,"score_gpt":0.23993987215579374,"score_spread":0.22473974568506896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884076111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12900522,0.00013714512,0.8669523,0.00011176051,0.000026800735,0.00007723799,0.000021198135,0.0013866975,0.0022816425],"genre_scores_gemma":[0.8567856,0.000062919076,0.14154424,0.000050815263,0.0000074410946,0.00008649278,0.000025194562,0.000048088634,0.0013891207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996195,0.00008900537,0.00002373602,0.00008858872,0.00014801012,0.000031232055],"domain_scores_gemma":[0.9993486,0.00025770473,0.00012421185,0.00012394406,0.000099734425,0.000045759567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049391866,0.00052769465,0.00055540795,0.00028121565,0.00031120717,0.00037492873,0.0009094211,0.0005816627,0.00092706736],"category_scores_gemma":[0.0016576201,0.00037720436,0.00049057417,0.0001840299,0.0008146057,0.00065134524,0.0013279781,0.00042356283,0.00020276624],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029250403,0.00021997688,0.00406128,0.00016415173,0.000098728444,0.00068010134,0.00075507455,0.3756237,0.29353192,0.005384564,0.0009047023,0.31828335],"study_design_scores_gemma":[0.000010704442,0.00012427077,0.0011783222,0.0000063457474,0.000010948253,0.00016768806,0.000034643217,0.9688268,0.027639017,0.0012227101,0.000758121,0.000020444744],"about_ca_topic_score_codex":0.0018451043,"about_ca_topic_score_gemma":0.0018471816,"teacher_disagreement_score":0.0018451043,"about_ca_system_score_codex":0.00039284016,"about_ca_system_score_gemma":0.00052400486,"threshold_uncertainty_score":0.003668785},"labels":[],"label_agreement":null},{"id":"W2884089857","doi":"10.3390/s18082416","title":"Design and Optimization of a Novel Three-Dimensional Force Sensor with Parallel Structure","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Jacobian matrix and determinant; Reconfigurability; Sensitivity (control systems); Stiffness; Measure (data warehouse); Kinematics; Computer science; Control theory (sociology); Engineering; Electronic engineering; Structural engineering; Physics; Mathematics; Artificial intelligence","score_opus":0.026064346348932513,"score_gpt":0.22494646839665083,"score_spread":0.19888212204771832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884089857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076202326,0.00033146967,0.9192284,0.00010850013,0.000054491873,0.00016982303,0.000057987712,0.00026996553,0.0035771355],"genre_scores_gemma":[0.5108776,0.00031765958,0.48599702,0.000052218624,0.000019180117,0.00031545965,0.00007585493,0.00002664115,0.0023183788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999716,0.0000235931,0.00001386816,0.00007699873,0.00014241331,0.000027153188],"domain_scores_gemma":[0.99972016,0.000047804147,0.00008868565,0.000022328008,0.00010240866,0.000018595196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042366702,0.00073026115,0.00054145214,0.0005139702,0.00025098308,0.0004397966,0.00079770456,0.00081520394,0.00072444667],"category_scores_gemma":[0.00040200612,0.00039509824,0.00041107362,0.0003768069,0.00031447905,0.00051876955,0.00038104228,0.00034355145,0.00018920563],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012744397,0.00023460819,0.001447068,0.00042915196,0.00008968642,0.00022908597,0.00008202045,0.5051439,0.38142926,0.0038274163,0.00076606526,0.10619431],"study_design_scores_gemma":[0.000027207365,0.00034876392,0.00088399916,0.000008411456,0.000028831655,0.00010630943,0.000018732404,0.95565665,0.040285278,0.00038810036,0.002226621,0.000021058275],"about_ca_topic_score_codex":0.0010354242,"about_ca_topic_score_gemma":0.0014985371,"teacher_disagreement_score":0.0010354242,"about_ca_system_score_codex":0.0005115048,"about_ca_system_score_gemma":0.00081620435,"threshold_uncertainty_score":0.0037112832},"labels":[],"label_agreement":null},{"id":"W2884705561","doi":"10.3390/s18082440","title":"A Privacy Preserving Scheme for Nearest Neighbor Query","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; k-nearest neighbors algorithm; Location-based service; Obfuscation; Data mining; Adaptability; Mobile device; Noise (video); Information privacy; Information retrieval; Computer network; Computer security; Machine learning; Artificial intelligence; World Wide Web","score_opus":0.014510829475756755,"score_gpt":0.23885217467122916,"score_spread":0.2243413451954724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884705561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009660093,0.0006760333,0.98648757,0.00030749937,0.00009744422,0.00013269157,0.00016218981,0.0005089489,0.0019675537],"genre_scores_gemma":[0.56756693,0.0011555941,0.4219728,0.00056025654,0.0003233391,0.00043394166,0.00083349337,0.000096586155,0.0070571243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930352,0.0017577399,0.00059498375,0.0011377686,0.0029340906,0.0005402566],"domain_scores_gemma":[0.99534297,0.001321438,0.00042637056,0.0018732086,0.0008898186,0.0001463528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026606522,0.00066341873,0.0016984643,0.0012634896,0.0019099204,0.0015345081,0.0028112999,0.0018640413,0.0018172493],"category_scores_gemma":[0.009709375,0.00036676467,0.001077151,0.0026945125,0.0013066708,0.0051489724,0.0043995907,0.002001801,0.0011386366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015929095,0.00035050744,0.001725504,0.00059320003,0.00019403879,0.0007537658,0.001481797,0.13662048,0.06530296,0.34244096,0.0133538265,0.43559006],"study_design_scores_gemma":[0.00019261501,0.00050742144,0.00044806593,0.000040061634,0.00008970484,0.001545215,0.00028873933,0.8583289,0.026901338,0.08658415,0.024916807,0.0001570253],"about_ca_topic_score_codex":0.0015008721,"about_ca_topic_score_gemma":0.0007997162,"teacher_disagreement_score":0.0028112999,"about_ca_system_score_codex":0.0012019926,"about_ca_system_score_gemma":0.001597367,"threshold_uncertainty_score":0.014070988},"labels":[],"label_agreement":null},{"id":"W2884889770","doi":"10.3390/s18072379","title":"Fast Feature-Preserving Approach to Carpal Bone Surface Denoising","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Laplacian matrix; Conjugate gradient method; Computer science; Fidelity; Graph; Regularization (linguistics); Kernel (algebra); Algorithm; Sparse matrix; Mathematical optimization; Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.012518146588990923,"score_gpt":0.21634874874463145,"score_spread":0.20383060215564053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884889770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030422998,0.00007034631,0.9964132,0.000042010746,0.000010568004,0.000008555293,0.000013540163,0.00011899917,0.0002805412],"genre_scores_gemma":[0.13008453,0.00046899533,0.866391,0.00010886994,0.00007797283,0.000070863774,0.00022287869,0.00021069136,0.002364197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996118,0.000056298068,0.0000145667145,0.00006698293,0.00022424545,0.00002613568],"domain_scores_gemma":[0.9995229,0.00014965756,0.000053165415,0.000104090046,0.00014708252,0.000023124801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061319454,0.0007331403,0.00075990695,0.0011530981,0.000267071,0.00076065323,0.0013540632,0.001133995,0.0013248319],"category_scores_gemma":[0.0016106303,0.00038817804,0.0009239999,0.0009756186,0.0007460055,0.00092964707,0.0010731022,0.0011188105,0.0006513828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001500779,0.00008109788,0.00066285516,0.0002659787,0.00008470664,0.00020417386,0.00019053742,0.46776116,0.12642504,0.027759345,0.002861011,0.37355408],"study_design_scores_gemma":[0.0000054025545,0.000035160207,0.000162194,0.0000063802477,0.000008734441,0.00013708787,0.000017200715,0.9808983,0.0104561355,0.006448357,0.0018133892,0.000011616209],"about_ca_topic_score_codex":0.0011463129,"about_ca_topic_score_gemma":0.0014951875,"teacher_disagreement_score":0.0013540632,"about_ca_system_score_codex":0.00038435636,"about_ca_system_score_gemma":0.0006423077,"threshold_uncertainty_score":0.0044320226},"labels":[],"label_agreement":null},{"id":"W2885020160","doi":"10.3390/s18072258","title":"Engineering Vehicles Detection Based on Modified Faster R-CNN for Power Grid Surveillance","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer science; Pooling; Object detection; Feature engineering; Key (lock); Artificial intelligence; Intrusion detection system; Grid; Feature (linguistics); Real-time computing; Task (project management); Convolutional neural network; Deep learning; Warning system; False alarm; Pattern recognition (psychology); Engineering; Computer security","score_opus":0.01407233236089022,"score_gpt":0.23780081091139044,"score_spread":0.2237284785505002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885020160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22758986,0.0010630531,0.75832534,0.000389833,0.00019870498,0.00012437167,0.00059627503,0.006074904,0.0056376653],"genre_scores_gemma":[0.8447008,0.00043937255,0.14812392,0.0002879561,0.00006762944,0.00008213756,0.0013431818,0.00012288071,0.0048321327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999747,0.000023149492,0.00001034579,0.00009727005,0.00006128839,0.000060934803],"domain_scores_gemma":[0.9997862,0.000035738605,0.0000364316,0.000051417577,0.00007514312,0.000015088509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003409534,0.0010464032,0.00051021925,0.0006204353,0.000176075,0.00043589622,0.0011800234,0.0005675258,0.0010365127],"category_scores_gemma":[0.0008074659,0.00034798004,0.0005312777,0.00045974724,0.00022982554,0.0010554977,0.0005400798,0.0006394513,0.00039474663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039020742,0.00026418586,0.0067105456,0.00013612019,0.00018676571,0.00039547237,0.00007563885,0.35725537,0.07940454,0.0028173537,0.0063554193,0.54600835],"study_design_scores_gemma":[0.0000050678636,0.000037761343,0.00088611647,0.0000039872007,0.000016262791,0.00004659112,0.0000057392363,0.9896773,0.008311328,0.00049669837,0.0005063255,0.0000067313827],"about_ca_topic_score_codex":0.009918099,"about_ca_topic_score_gemma":0.010359168,"teacher_disagreement_score":0.009918099,"about_ca_system_score_codex":0.0006958313,"about_ca_system_score_gemma":0.0005470398,"threshold_uncertainty_score":0.019720733},"labels":[],"label_agreement":null},{"id":"W2885310130","doi":"10.3390/s18082474","title":"Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Northern Ireland Community Relations Council","keywords":"Wearable computer; Loneliness; Random forest; Artificial intelligence; Sitting; Computer science; Machine learning; Classifier (UML); Human–computer interaction; Psychology; Medicine; Embedded system","score_opus":0.058981283372181975,"score_gpt":0.3097974550540443,"score_spread":0.2508161716818623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885310130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9165848,0.0006684933,0.08079724,0.00018022039,0.00008843842,0.00028389713,0.00022160764,0.00020373189,0.00097156357],"genre_scores_gemma":[0.9591533,0.00033529606,0.039395086,0.00012392622,0.00008789713,0.00020204305,0.00016812322,0.00001875145,0.0005155619],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988079,0.0005266392,0.00007542689,0.00024462986,0.00026395783,0.00008152264],"domain_scores_gemma":[0.99879265,0.0006038403,0.00011444541,0.0000637469,0.00033323796,0.0000919942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011710682,0.00052789313,0.000547293,0.0006837086,0.00019426149,0.0005905618,0.000554324,0.0010239239,0.00095331913],"category_scores_gemma":[0.0022386932,0.00026376036,0.00043465226,0.00037479543,0.0002770313,0.0008223848,0.00043779408,0.00023141997,0.00036296117],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038073107,0.0020851986,0.12619719,0.0012363003,0.00037311538,0.0011288367,0.0010524308,0.0061271186,0.60329527,0.00041149964,0.00082662905,0.25345922],"study_design_scores_gemma":[0.0005752527,0.027724205,0.47689652,0.00025970777,0.0009671753,0.008112847,0.0024761055,0.24983002,0.2278199,0.0007371001,0.0043439497,0.00025714256],"about_ca_topic_score_codex":0.0005448607,"about_ca_topic_score_gemma":0.00075584376,"teacher_disagreement_score":0.0011710682,"about_ca_system_score_codex":0.00012284923,"about_ca_system_score_gemma":0.00019561184,"threshold_uncertainty_score":0.00619328},"labels":[],"label_agreement":null},{"id":"W2885499182","doi":"10.3390/s18082565","title":"A 77-GHz Six-Port Sensor for Accurate Near-Field Displacement and Doppler Measurements","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electronic engineering; Radar; Transmitter; Electrical engineering; Printed circuit board; Engineering; Port (circuit theory); Computer science; Telecommunications","score_opus":0.038765926831207324,"score_gpt":0.2690260307346611,"score_spread":0.23026010390345375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885499182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15710448,0.0015766465,0.8334836,0.0004086734,0.00045686436,0.0002835648,0.0003797217,0.0019236706,0.0043828245],"genre_scores_gemma":[0.3759409,0.00090478454,0.6181283,0.00023871555,0.000101240796,0.0002077673,0.00040133778,0.00006177781,0.0040152096],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931467,0.0000840216,0.00003735184,0.000105471365,0.00042163368,0.00003689528],"domain_scores_gemma":[0.9994771,0.000089650996,0.00012914755,0.00007821967,0.00017741758,0.00004844409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005238056,0.00060165324,0.0006810065,0.00050563435,0.00017387557,0.0005601747,0.0013721433,0.0007560008,0.0012263801],"category_scores_gemma":[0.00062800007,0.00033866402,0.00031670643,0.00050811586,0.00026009468,0.0011111018,0.0005464449,0.00075888477,0.0009234212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007087981,0.000039736955,0.00072247704,0.00014799499,0.000012069952,0.00007208841,0.000029711753,0.0005360762,0.9654615,0.0013685621,0.0004400186,0.031098899],"study_design_scores_gemma":[0.00002392544,0.00054595666,0.0017986273,0.000014912451,0.000038778126,0.0012743653,0.00002987778,0.0210969,0.9614977,0.00036383752,0.013262897,0.00005218745],"about_ca_topic_score_codex":0.0000871815,"about_ca_topic_score_gemma":0.00020509957,"teacher_disagreement_score":0.0013721433,"about_ca_system_score_codex":0.00027884115,"about_ca_system_score_gemma":0.00034147975,"threshold_uncertainty_score":0.0041025877},"labels":[],"label_agreement":null},{"id":"W2886040064","doi":"10.3390/s18082454","title":"An Egg Volume Measurement System Based on the Microsoft Kinect","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saskatchewan Polytechnic; University of British Columbia; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Volume (thermodynamics); Point cloud; Scanner; Computer science; Software; Computer vision; Orientation (vector space); Computer graphics (images); Artificial intelligence; Mathematics; Geometry","score_opus":0.04412540516998062,"score_gpt":0.2034369054342265,"score_spread":0.15931150026424587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886040064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07827966,0.0008122612,0.907612,0.0001028973,0.00027126225,0.00056453364,0.0022863438,0.0051724943,0.0048984266],"genre_scores_gemma":[0.30820417,0.0008594277,0.6786381,0.00027676864,0.00007164271,0.0010462562,0.0024652563,0.0003557554,0.008082647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992105,0.00007730789,0.00004433846,0.00019640793,0.00044170255,0.000029753159],"domain_scores_gemma":[0.99954396,0.00009967718,0.00008120005,0.00006583788,0.00016488839,0.000044389522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057415623,0.000722204,0.00077541725,0.0012775894,0.00020773707,0.000534633,0.0011856548,0.00071905623,0.0037549678],"category_scores_gemma":[0.0008047455,0.00049536733,0.00044542746,0.00073294045,0.0002310174,0.0009281487,0.00092454674,0.0005555183,0.0011836591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005821327,0.00014787518,0.005490917,0.0010347407,0.000098740216,0.00029172248,0.00023914408,0.004525436,0.73867166,0.0016508127,0.0039106463,0.24335618],"study_design_scores_gemma":[0.00025026564,0.001360667,0.09172707,0.00025834888,0.00025066262,0.0041809194,0.00025084225,0.2262036,0.6241821,0.0020562229,0.04878524,0.00049407245],"about_ca_topic_score_codex":0.00095560215,"about_ca_topic_score_gemma":0.0017353228,"teacher_disagreement_score":0.0037549678,"about_ca_system_score_codex":0.00024852302,"about_ca_system_score_gemma":0.0005067584,"threshold_uncertainty_score":0.012561619},"labels":[],"label_agreement":null},{"id":"W2886153089","doi":"10.3390/s18082617","title":"Ultrasound Sensors for Diaphragm Motion Tracking: An Application in Non-Invasive Respiratory Monitoring","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Spirometer; Diaphragm (acoustics); Photoplethysmogram; Ultrasound; Biomedical engineering; Breathing; Acoustics; Transducer; Accelerometer; Computer science; Sensitivity (control systems); SIGNAL (programming language); Computer vision; Vibration; Medicine; Engineering; Electronic engineering; Surgery; Physics; Anatomy","score_opus":0.02196997527399682,"score_gpt":0.2671681434446581,"score_spread":0.24519816817066128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886153089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14643827,0.027806759,0.8176206,0.0007060482,0.0006299096,0.0003078369,0.00019192205,0.0016309454,0.0046677464],"genre_scores_gemma":[0.65244436,0.009149209,0.33112282,0.0007650821,0.00045263476,0.00018888807,0.00015836707,0.00007940332,0.0056390944],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938035,0.0001721363,0.000033833898,0.000102340186,0.0002903965,0.00002083643],"domain_scores_gemma":[0.9992902,0.00037205577,0.00007819244,0.0000511235,0.00017126296,0.000037107082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005800682,0.00038668147,0.00032813224,0.00041072676,0.00010960877,0.00040467607,0.0006299537,0.0010398177,0.0012977812],"category_scores_gemma":[0.0011878036,0.00020630463,0.0002140683,0.00031553704,0.00024110333,0.00045461397,0.00036073566,0.00035457037,0.0005510729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021530951,0.00009642415,0.0036438399,0.0007449197,0.000034673187,0.00023340664,0.00012722344,0.00094746216,0.750718,0.000809804,0.0007174643,0.2417115],"study_design_scores_gemma":[0.00007136944,0.004071056,0.043278985,0.0002753384,0.0002224149,0.006568603,0.00018149047,0.05371864,0.856195,0.00140276,0.03386774,0.0001466319],"about_ca_topic_score_codex":0.00015268575,"about_ca_topic_score_gemma":0.00023216693,"teacher_disagreement_score":0.0012977812,"about_ca_system_score_codex":0.00012793558,"about_ca_system_score_gemma":0.00015560711,"threshold_uncertainty_score":0.004341483},"labels":[],"label_agreement":null},{"id":"W2886213316","doi":"10.3390/s18082560","title":"Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer vision; Computer science; Artificial intelligence; Object detection; Detector; Vehicle tracking system; Tracking (education); Foreground detection; Pixel; Perspective (graphical); Image sensor; Background subtraction; Kalman filter; Pattern recognition (psychology)","score_opus":0.022360762357936312,"score_gpt":0.2694368697879695,"score_spread":0.24707610743003317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886213316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13460952,0.00040574963,0.85975444,0.0000585718,0.00010128681,0.00014164302,0.00021378548,0.0024313773,0.0022836505],"genre_scores_gemma":[0.6535771,0.0005608766,0.34177652,0.000072261006,0.00007390306,0.00009333771,0.0007465304,0.000084632404,0.0030148567],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996289,0.000027958637,0.00001842626,0.000134544,0.00013239392,0.000057696157],"domain_scores_gemma":[0.9996871,0.000055527616,0.000053934098,0.000043370426,0.00013203609,0.00002801028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022140794,0.00071275805,0.00066532654,0.0015941118,0.00029057416,0.00052086246,0.00096125685,0.00041577965,0.0006051905],"category_scores_gemma":[0.00083713623,0.00024221551,0.0003126167,0.0009580426,0.00020464834,0.0006420026,0.00046704346,0.00034208986,0.00034159634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002265749,0.00014521938,0.0063597066,0.00016179396,0.000058623486,0.0004917764,0.0001305169,0.04827275,0.088774286,0.0018796873,0.0026218796,0.8508772],"study_design_scores_gemma":[0.000010728663,0.000073666,0.0044858004,0.000014313228,0.00003881233,0.0003237501,0.000076842705,0.9446121,0.047694627,0.0006678789,0.0019819771,0.000019559087],"about_ca_topic_score_codex":0.008437788,"about_ca_topic_score_gemma":0.009318672,"teacher_disagreement_score":0.008437788,"about_ca_system_score_codex":0.00031514623,"about_ca_system_score_gemma":0.0004700679,"threshold_uncertainty_score":0.016777337},"labels":[],"label_agreement":null},{"id":"W2886428232","doi":"10.3390/s18082568","title":"Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Compressed sensing; Mathematical optimization; Wireless sensor network; Convex optimization; Computer science; Node (physics); Covariance matrix; Optimization problem; Algorithm; Sensor node; Semidefinite programming; Mathematics; Regular polygon; Engineering; Key distribution in wireless sensor networks","score_opus":0.012478161608082735,"score_gpt":0.23025736135881203,"score_spread":0.2177791997507293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886428232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020863017,0.00032643453,0.97707134,0.00021993244,0.000022999107,0.000034233326,0.0000325716,0.000110975765,0.0013183934],"genre_scores_gemma":[0.83096313,0.00077940535,0.16527312,0.00015418818,0.00006104076,0.00015463184,0.00015338563,0.000031776373,0.0024294015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933976,0.00023745395,0.00002500902,0.00009420428,0.00025478392,0.000048742302],"domain_scores_gemma":[0.99960667,0.0001937274,0.00007499469,0.000027366945,0.00008057166,0.000016792535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070408516,0.0004755353,0.00056917773,0.00030324332,0.00026761444,0.00031398502,0.0007840758,0.00049658096,0.0005138252],"category_scores_gemma":[0.0015772721,0.00028148043,0.00021573705,0.00055479154,0.00046172776,0.0007543388,0.0005836594,0.00044960916,0.00011013923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016713834,0.00005645566,0.0005816993,0.00011046242,0.000033346983,0.000114497576,0.000111134774,0.8716717,0.016952608,0.017865526,0.0017804301,0.09055503],"study_design_scores_gemma":[0.0000086314585,0.00002751679,0.000108970075,0.000003884967,0.0000049809432,0.000020533103,0.000006774567,0.99604356,0.0011381739,0.002341946,0.00028864352,0.00000648198],"about_ca_topic_score_codex":0.0034709265,"about_ca_topic_score_gemma":0.0039736284,"teacher_disagreement_score":0.0034709265,"about_ca_system_score_codex":0.00047230034,"about_ca_system_score_gemma":0.000760841,"threshold_uncertainty_score":0.006901443},"labels":[],"label_agreement":null},{"id":"W2887203096","doi":"10.3390/s18082668","title":"Biosensors for the Detection of Interaction between Legionella pneumophila Collagen-Like Protein and Glycosaminoglycans","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Legionella and Acanthamoeba research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Bacterial adhesin; Legionella pneumophila; Fucoidan; Legionella; Chemistry; Surface plasmon resonance; Dermatan sulfate; Microbiology; Biosensor; Biochemistry; Glycosaminoglycan; Glycan; Chondroitin sulfate; Biophysics; Glycoprotein; Biology; Materials science; Escherichia coli; Polysaccharide; Nanotechnology; Bacteria","score_opus":0.02293677184405297,"score_gpt":0.28579324358082614,"score_spread":0.2628564717367732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887203096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6955134,0.008938219,0.28510123,0.0014079983,0.0006291999,0.00033345402,0.0011815187,0.0023493564,0.0045456374],"genre_scores_gemma":[0.81886756,0.0044127908,0.16911843,0.00077323715,0.00007202694,0.00046643382,0.00064867915,0.000041781077,0.005598959],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991634,0.00020333496,0.000045815403,0.00016118749,0.00035079964,0.00007541058],"domain_scores_gemma":[0.9996207,0.0001629022,0.000067569235,0.000019486904,0.000086554915,0.000042889205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007655461,0.0008054026,0.0005558162,0.00068793574,0.00024718762,0.00042692924,0.0008415005,0.001617104,0.0008750153],"category_scores_gemma":[0.0010093965,0.0004107138,0.00031797437,0.0005258151,0.00025041538,0.0005326165,0.00041594042,0.0008832428,0.00069797656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021600603,0.000018402205,0.00012834264,0.000046136774,0.0000057434054,0.00003601424,0.000018173389,0.00006067185,0.9975872,0.00010507398,0.000077319775,0.0018953076],"study_design_scores_gemma":[0.000013886331,0.00018925442,0.0013836956,0.0000135091805,0.000017845576,0.00026641978,0.00006958465,0.007138442,0.98875415,0.00015219541,0.001985136,0.000015878193],"about_ca_topic_score_codex":0.000490228,"about_ca_topic_score_gemma":0.00090294896,"teacher_disagreement_score":0.001617104,"about_ca_system_score_codex":0.00051982515,"about_ca_system_score_gemma":0.00024339966,"threshold_uncertainty_score":0.0040486455},"labels":[],"label_agreement":null},{"id":"W2888102724","doi":"10.3390/s18092776","title":"Radar and Visual Odometry Integrated System Aided Navigation for UAVS in GNSS Denied Environment","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Visual odometry; Odometry; Radar; Computer science; Computer vision; Artificial intelligence; Remote sensing; Engineering; Aerospace engineering; Global Positioning System; Geography; Robot; Mobile robot; Telecommunications","score_opus":0.0067139786868167075,"score_gpt":0.2095864698490472,"score_spread":0.2028724911622305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888102724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16856503,0.0014374063,0.81801784,0.00017892111,0.00031684854,0.00008831428,0.00017946096,0.0037694667,0.007446709],"genre_scores_gemma":[0.84219885,0.00043146053,0.15028872,0.00014758848,0.000047984835,0.00006508863,0.00041742428,0.000043811902,0.006359072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997764,0.000031993368,0.000011282752,0.000052307274,0.00010331459,0.000024588395],"domain_scores_gemma":[0.99984026,0.000013526176,0.000027624865,0.000031705884,0.00007614722,0.000010636863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017949527,0.00039427518,0.0002862492,0.00034160918,0.00015122337,0.00034508095,0.0003923246,0.00035623345,0.0009784796],"category_scores_gemma":[0.00030489019,0.00013286356,0.00015150293,0.00025962517,0.00015378541,0.0004749346,0.0006177053,0.00026665832,0.00057538773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042509774,0.00014858658,0.006231795,0.00027936877,0.00013041384,0.00043119208,0.00030146763,0.037456546,0.3113182,0.003249922,0.005528127,0.6344994],"study_design_scores_gemma":[0.00012469919,0.00093024375,0.017888557,0.00009174708,0.00015631058,0.0009003145,0.00021568494,0.8214555,0.11492682,0.0016743494,0.041559596,0.000076209486],"about_ca_topic_score_codex":0.0025542863,"about_ca_topic_score_gemma":0.0038963342,"teacher_disagreement_score":0.0025542863,"about_ca_system_score_codex":0.00014533903,"about_ca_system_score_gemma":0.00036909562,"threshold_uncertainty_score":0.005078852},"labels":[],"label_agreement":null},{"id":"W2888415610","doi":"10.3390/s18082731","title":"Distributed Egocentric Betweenness Measure as a Vehicle Selection Mechanism in VANETs: A Performance Evaluation Study","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Betweenness centrality; Computer science; Node (physics); Centrality; Mechanism (biology); Network topology; Selection (genetic algorithm); Reduction (mathematics); Measure (data warehouse); Distributed computing; Artificial intelligence; Data mining; Topology (electrical circuits); Computer network; Engineering","score_opus":0.011533721679378602,"score_gpt":0.23256869197882798,"score_spread":0.22103497029944938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888415610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8482913,0.0054129823,0.13367458,0.00039663148,0.00034101924,0.00048193167,0.0004506789,0.0011719145,0.00977897],"genre_scores_gemma":[0.9838573,0.0004066572,0.0146946665,0.000021904301,0.000028678549,0.00007280234,0.00026468554,0.000025644893,0.0006276738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99517715,0.0021420647,0.00029567044,0.0005435884,0.001338241,0.0005033121],"domain_scores_gemma":[0.988058,0.0073840218,0.0009928519,0.00102757,0.0020263481,0.00051128067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006799396,0.0017685309,0.0015718784,0.0032711178,0.0006446673,0.0016365087,0.0015389441,0.0014052605,0.0008555019],"category_scores_gemma":[0.012546266,0.00024360023,0.0005999314,0.0025154506,0.000769286,0.0019061503,0.0014000428,0.0008390053,0.00022014472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016475546,0.00090521236,0.016489798,0.0006103597,0.0005927674,0.0002891263,0.00022816045,0.83689696,0.007972155,0.0054621994,0.002269304,0.12663639],"study_design_scores_gemma":[0.000047288675,0.0016164007,0.0046146913,0.000024419782,0.000112121335,0.00020430822,0.00018481346,0.987599,0.0041103596,0.0008310945,0.0006113552,0.000044133456],"about_ca_topic_score_codex":0.0065287906,"about_ca_topic_score_gemma":0.0035341626,"teacher_disagreement_score":0.006799396,"about_ca_system_score_codex":0.0019242868,"about_ca_system_score_gemma":0.00086850097,"threshold_uncertainty_score":0.035959065},"labels":[],"label_agreement":null},{"id":"W2888460178","doi":"10.3390/s18082725","title":"Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quaternion; Wearable computer; Orientation (vector space); Computer science; Artificial intelligence; Computer vision; Frame (networking); Units of measurement; Wireless sensor network; Mathematics; Embedded system","score_opus":0.06201171240997967,"score_gpt":0.3019740769788967,"score_spread":0.23996236456891706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888460178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10203289,0.0027307135,0.8803167,0.00026173444,0.0005474448,0.00028558783,0.0014738401,0.0066720643,0.005679075],"genre_scores_gemma":[0.7586492,0.0031456929,0.22128549,0.00027004417,0.0004159959,0.00032930396,0.007331435,0.0002650909,0.008307688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992131,0.00011642077,0.00007313731,0.0002826723,0.0002323913,0.000082244835],"domain_scores_gemma":[0.9992694,0.00013617772,0.00012434588,0.00017262153,0.00025723423,0.000040200925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005990138,0.001094261,0.0010241648,0.0012610699,0.00017703947,0.00082314137,0.00071593793,0.00036005914,0.0018986452],"category_scores_gemma":[0.0022743738,0.00018262972,0.0008590541,0.0012994163,0.00030640166,0.00080357346,0.0005851338,0.000688763,0.0023558214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027840273,0.0001472734,0.005597235,0.00017527002,0.0001281397,0.00010079856,0.00007712774,0.018124953,0.026274651,0.0012723681,0.006111192,0.94171256],"study_design_scores_gemma":[0.00008014073,0.0005843444,0.044079605,0.000136597,0.00019869307,0.0010457899,0.00022468521,0.8533558,0.068589106,0.0065314653,0.025063759,0.00010995313],"about_ca_topic_score_codex":0.0029293213,"about_ca_topic_score_gemma":0.0027763706,"teacher_disagreement_score":0.0029293213,"about_ca_system_score_codex":0.00029063123,"about_ca_system_score_gemma":0.00059485086,"threshold_uncertainty_score":0.00635159},"labels":[],"label_agreement":null},{"id":"W2888610341","doi":"10.3390/s18092779","title":"Comparison of L1 and L5 Bands GNSS Signals Acquisition","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Global Positioning System; GPS signals; Computer science; Narrowband; Galileo (satellite navigation); Binary offset carrier modulation; SIGNAL (programming language); Doppler effect; Satellite navigation; Code (set theory); Satellite system; Channel (broadcasting); Real-time computing; Electronic engineering; Telecommunications; Assisted GPS; Remote sensing; Geography; Engineering; Physics","score_opus":0.01818751833868145,"score_gpt":0.2793862858285421,"score_spread":0.26119876748986065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888610341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97181904,0.0004331328,0.014198879,0.00007918256,0.00003992736,0.000059910803,0.00077838317,0.00033658475,0.012254822],"genre_scores_gemma":[0.98593706,0.00028200474,0.009550201,0.00006345158,0.000020066602,0.00002917605,0.0018834871,0.00007361151,0.0021608449],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927,0.00010121815,0.000047949696,0.0001195666,0.00029431214,0.00016686761],"domain_scores_gemma":[0.99901676,0.00029284428,0.00009152622,0.000086337874,0.00045470556,0.000057756588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049204606,0.00050406874,0.00033058514,0.0013493835,0.00031238495,0.00078475755,0.0003499435,0.00063471834,0.002680458],"category_scores_gemma":[0.0018612443,0.00014318817,0.00041468223,0.0009774538,0.00020809726,0.00061545486,0.0005840403,0.00022887715,0.0008696843],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005335022,0.00036185372,0.1926215,0.0013288739,0.0004530014,0.0011034942,0.0011074297,0.08541824,0.23914039,0.0030732148,0.004643643,0.46541333],"study_design_scores_gemma":[0.0001114416,0.0013558129,0.72606826,0.00013349368,0.00028216743,0.0011067855,0.0010847476,0.092907235,0.16221724,0.0006695602,0.013910767,0.00015241095],"about_ca_topic_score_codex":0.00391453,"about_ca_topic_score_gemma":0.004808518,"teacher_disagreement_score":0.00391453,"about_ca_system_score_codex":0.00032774673,"about_ca_system_score_gemma":0.00031317337,"threshold_uncertainty_score":0.008966982},"labels":[],"label_agreement":null},{"id":"W2888615145","doi":"10.3390/s19020349","title":"A Novel Method for Extrinsic Calibration of Multiple RGB-D Cameras Using Descriptor-Based Patterns","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"RGB color model; Artificial intelligence; Computer vision; Calibration; Computer science; Set (abstract data type); Field (mathematics); Robot; Mathematics","score_opus":0.02960069441414571,"score_gpt":0.25227464267353694,"score_spread":0.22267394825939124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888615145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023489047,0.00006507134,0.996734,0.000022758753,0.00003881672,0.0000249144,0.000028579405,0.0003158624,0.00042110268],"genre_scores_gemma":[0.10822374,0.00026704773,0.8879476,0.00007603685,0.000062340514,0.00010843065,0.00029946284,0.00017131737,0.0028440019],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984938,0.00016817849,0.00007571397,0.0004228019,0.0007604899,0.00007899819],"domain_scores_gemma":[0.9989667,0.000111082634,0.00014336307,0.00033098692,0.00040239235,0.00004541443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064038654,0.0008243596,0.00087532256,0.0014450854,0.00037382895,0.0010673632,0.0015840273,0.00085981976,0.0021361],"category_scores_gemma":[0.002511492,0.00061932934,0.00072350795,0.0022910263,0.0005831737,0.0017044977,0.0016008004,0.0014690275,0.0017840152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015917493,0.000073790776,0.001032521,0.00020619562,0.00008544497,0.000100408855,0.0001371109,0.022435501,0.08146672,0.010380907,0.0034850908,0.8804371],"study_design_scores_gemma":[0.000070397364,0.00030190495,0.002907659,0.000056758407,0.000083468214,0.0014353073,0.00012265697,0.8061245,0.1473813,0.009602521,0.03180003,0.00011352378],"about_ca_topic_score_codex":0.0015948404,"about_ca_topic_score_gemma":0.0016791376,"teacher_disagreement_score":0.0021361,"about_ca_system_score_codex":0.0004739162,"about_ca_system_score_gemma":0.000978281,"threshold_uncertainty_score":0.007146001},"labels":[],"label_agreement":null},{"id":"W2888679949","doi":"10.3390/s18082726","title":"Direct Detection of Toxic Contaminants in Minimally Processed Food Products Using Dendritic Surface-Enhanced Raman Scattering Substrates","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Melamine detection and toxicity","field":"Agricultural and Biological Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detection limit; Melamine; Contamination; Raman scattering; Nanotechnology; Chemistry; Materials science; Chromatography; Raman spectroscopy; Optics","score_opus":0.02799407692152693,"score_gpt":0.2362954884300872,"score_spread":0.2083014115085603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888679949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8973898,0.0013630192,0.092690386,0.0003874899,0.00010111248,0.00009639262,0.00032060078,0.00042328465,0.007227868],"genre_scores_gemma":[0.8953901,0.001153812,0.09849795,0.00021081384,0.000036746587,0.00008487566,0.0002899813,0.000046260084,0.0042894855],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980396,0.000017539129,0.000012067096,0.00004476062,0.00010730738,0.00001450913],"domain_scores_gemma":[0.99983037,0.00004505479,0.00004144131,0.000018149418,0.00005065844,0.000014235171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012052108,0.00028806351,0.00019657418,0.00021116757,0.0001427439,0.00028740734,0.00042559986,0.00041252788,0.0006176791],"category_scores_gemma":[0.00026738414,0.00018496066,0.00021562865,0.00013897588,0.00022096338,0.00020135059,0.00026904658,0.0003432722,0.0004840968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008926018,0.000004742173,0.00008713333,0.000021953616,0.0000015542514,0.000027820019,0.0000054223724,0.000043962937,0.99864775,0.000054949585,0.000025572044,0.0010702665],"study_design_scores_gemma":[0.0000021025542,0.000045702385,0.00032908603,0.0000019211532,0.0000027304704,0.00008605978,0.000006950108,0.0010057834,0.9978599,0.00002213463,0.0006340832,0.0000035092016],"about_ca_topic_score_codex":0.0003268593,"about_ca_topic_score_gemma":0.0006286231,"teacher_disagreement_score":0.0006176791,"about_ca_system_score_codex":0.00022939513,"about_ca_system_score_gemma":0.00015080663,"threshold_uncertainty_score":0.0020663738},"labels":[],"label_agreement":null},{"id":"W2889095607","doi":"10.3390/s18113634","title":"Selective Uropathogenic E. coli Detection Using Crossed Surface-Relief Gratings","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Foundation for Innovation","keywords":"Detection limit; Surface plasmon resonance; Materials science; Antimicrobial; Nanotechnology; Microbiology; Optoelectronics; Nanoparticle; Chemistry; Biology; Chromatography","score_opus":0.01058933637382668,"score_gpt":0.22171675892526027,"score_spread":0.2111274225514336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889095607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9732809,0.0019032981,0.022180645,0.00019500981,0.00009018809,0.000041046675,0.00022102502,0.00042013358,0.001667698],"genre_scores_gemma":[0.96838623,0.0011299063,0.028784316,0.00016420352,0.000022427901,0.0000307337,0.00021788562,0.000021027752,0.0012433148],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970657,0.000032693424,0.00001694504,0.0000847416,0.000109293556,0.000049772992],"domain_scores_gemma":[0.9998585,0.000027132259,0.000051372084,0.000014957287,0.000032958214,0.000014973442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499314,0.00042701085,0.00023577563,0.0002814203,0.00007447245,0.00026757005,0.00046459862,0.0005394886,0.0003331244],"category_scores_gemma":[0.00025312137,0.00021657173,0.00028314357,0.00020255716,0.0002226593,0.00027300182,0.00034370937,0.00030750784,0.000271075],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000143865955,0.000009817301,0.000100116136,0.000035620003,0.0000041165335,0.000045901375,0.000014026083,0.00010992953,0.9978358,0.000051561372,0.000043078795,0.0017355965],"study_design_scores_gemma":[0.0000057422876,0.00013683453,0.0009655437,0.000003697627,0.0000064892347,0.00018272734,0.000016441149,0.0033097134,0.99443746,0.000028376833,0.0008957048,0.000011349873],"about_ca_topic_score_codex":0.00042356848,"about_ca_topic_score_gemma":0.0005549978,"teacher_disagreement_score":0.0005394886,"about_ca_system_score_codex":0.00021037959,"about_ca_system_score_gemma":0.00013769965,"threshold_uncertainty_score":0.0015263557},"labels":[],"label_agreement":null},{"id":"W2889166151","doi":"10.3390/s18092828","title":"Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates - Health Solutions","keywords":"Wearable computer; Machine learning; Artificial intelligence; Gait; Computer science; Outlier; Physical medicine and rehabilitation; Wearable technology; Medicine","score_opus":0.10588878231875426,"score_gpt":0.31798467822650067,"score_spread":0.2120958959077464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889166151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17695187,0.0021381981,0.8133344,0.000501362,0.00035739504,0.00052100397,0.002713855,0.001131027,0.0023509483],"genre_scores_gemma":[0.75952196,0.0013444992,0.23379827,0.00045362205,0.00020940951,0.0010026525,0.0021243815,0.00006824183,0.00147694],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991535,0.00021842837,0.00011656802,0.00024699798,0.00022903718,0.000035430814],"domain_scores_gemma":[0.99838674,0.0005299871,0.00048367877,0.00025189648,0.00031265657,0.000035023208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001103766,0.0009902654,0.0008583447,0.0013839831,0.00014985826,0.00065140263,0.0008134821,0.0008345418,0.0008120238],"category_scores_gemma":[0.0046937834,0.00029322153,0.0005165984,0.0021476275,0.00034297272,0.0007420651,0.0005110781,0.0006147036,0.00038952476],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008469287,0.0013389494,0.11715075,0.0018336595,0.0009282719,0.0003770839,0.00037992003,0.070126936,0.11257322,0.0033263022,0.0043308814,0.6867871],"study_design_scores_gemma":[0.00015528624,0.0030280272,0.1994923,0.0004269108,0.00047484157,0.0013377226,0.00050209684,0.705705,0.06742263,0.011037197,0.010232553,0.00018556301],"about_ca_topic_score_codex":0.0008887156,"about_ca_topic_score_gemma":0.0013974307,"teacher_disagreement_score":0.0013839831,"about_ca_system_score_codex":0.00019386385,"about_ca_system_score_gemma":0.0003038467,"threshold_uncertainty_score":0.0058373213},"labels":[],"label_agreement":null},{"id":"W2889204818","doi":"10.3390/s18092895","title":"An Integrated Dead Reckoning with Cooperative Positioning Solution to Assist GPS NLOS Using Vehicular Communications","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Bureau for International Education","keywords":"Unavailability; Global Positioning System; Computer science; Ranging; Real-time computing; Vehicular ad hoc network; Dead reckoning; Intelligent transportation system; Positioning system; Wireless ad hoc network; Wireless; Simulation; Computer network; Telecommunications; Engineering; Transport engineering","score_opus":0.019812610229472036,"score_gpt":0.2630426406629753,"score_spread":0.24323003043350325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889204818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12390612,0.00054540444,0.8687902,0.00010292087,0.00011681564,0.0001420793,0.00017475919,0.0019486696,0.0042730854],"genre_scores_gemma":[0.843461,0.00018869531,0.15173006,0.00006174517,0.000019453824,0.000119952456,0.00041435807,0.000041207633,0.003963569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995358,0.000064792206,0.000022989996,0.000106386615,0.00019123092,0.0000788129],"domain_scores_gemma":[0.99967706,0.00003661923,0.000026863683,0.00006717347,0.000173478,0.000018849803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000343779,0.0006636939,0.0005003094,0.00056219526,0.0003832162,0.0005148371,0.0012507483,0.0006545509,0.0007317014],"category_scores_gemma":[0.0006407678,0.00020205295,0.00034429049,0.0006363249,0.00025184962,0.0007385,0.0009872157,0.0004032768,0.00045530117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054658466,0.00023298028,0.0038924888,0.00037030957,0.00011855213,0.00072894193,0.0005947794,0.29434076,0.15254572,0.0063599483,0.0043299464,0.53593904],"study_design_scores_gemma":[0.00007680263,0.0011640452,0.0023932266,0.000027163262,0.00009606409,0.0004563754,0.0002190377,0.92348194,0.05501082,0.0016293203,0.015370249,0.000074867035],"about_ca_topic_score_codex":0.0044302978,"about_ca_topic_score_gemma":0.0041051623,"teacher_disagreement_score":0.0044302978,"about_ca_system_score_codex":0.00032145434,"about_ca_system_score_gemma":0.0006883289,"threshold_uncertainty_score":0.00880903},"labels":[],"label_agreement":null},{"id":"W2889488196","doi":"10.3390/s19153309","title":"Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St Joseph's Health Care; Western University","funders":"Ministero dello Sviluppo Economico; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Linear discriminant analysis; Elbow; Support vector machine; Electromyography; Wearable computer; Random forest; Feature selection; Physical medicine and rehabilitation; Artificial intelligence; Feature (linguistics); Computer science; Pattern recognition (psychology); Feature extraction; Receiver operating characteristic; Muscle fatigue; Medicine; Machine learning; Surgery","score_opus":0.018618644183899077,"score_gpt":0.23776870896861482,"score_spread":0.21915006478471574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889488196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22793636,0.00057972467,0.7661075,0.00057118555,0.0000768874,0.00020836835,0.00060951506,0.00093411835,0.0029763267],"genre_scores_gemma":[0.9541312,0.00029768195,0.042261615,0.00009862187,0.000025441028,0.00026881785,0.00044422995,0.000026873926,0.0024454386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989104,0.000019456738,0.0000097423845,0.000036854508,0.000023439648,0.000019411886],"domain_scores_gemma":[0.9997675,0.00008804071,0.00003759647,0.0000117158215,0.000080663056,0.000014528968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041420656,0.00041734581,0.00047306519,0.00042632347,0.00018109664,0.00055183866,0.0005466419,0.000725202,0.0010843731],"category_scores_gemma":[0.00094807014,0.00024599163,0.0006132433,0.00022299946,0.00015943949,0.00038164447,0.0003871944,0.0005446297,0.0004616166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024685843,0.00028263265,0.021401813,0.00012109829,0.00014691484,0.00030765095,0.00014092852,0.83681715,0.015891897,0.0012962328,0.0014100537,0.121936835],"study_design_scores_gemma":[0.000004284963,0.00005303993,0.0020510626,0.000008314108,0.000011127329,0.000039008504,0.000012052597,0.9968395,0.00051447813,0.00025118896,0.00021108784,0.0000049009327],"about_ca_topic_score_codex":0.008063452,"about_ca_topic_score_gemma":0.005808469,"teacher_disagreement_score":0.008063452,"about_ca_system_score_codex":0.00041724934,"about_ca_system_score_gemma":0.0005818229,"threshold_uncertainty_score":0.016033053},"labels":[],"label_agreement":null},{"id":"W2889734641","doi":"10.3390/s18103519","title":"Silicon Photonic Biosensors Using Label-Free Detection","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":355,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council","keywords":"Miniaturization; Microfabrication; Biosensor; Photonics; Lab-on-a-chip; Nanotechnology; Chip; CMOS; Materials science; Optofluidics; Silicon photonics; Microfluidics; Computer science; Optoelectronics; Telecommunications; Fabrication","score_opus":0.04261918735871912,"score_gpt":0.2888843297533967,"score_spread":0.24626514239467756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889734641","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033284393,0.9635969,0.015932642,0.000761263,0.001011064,0.00007036719,0.00012602571,0.00014084078,0.015032483],"genre_scores_gemma":[0.02315268,0.9504208,0.013513954,0.000955669,0.00047946893,0.00012818944,0.00026148625,0.000015474581,0.011072241],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99955565,0.00004869577,0.00003220596,0.000099324585,0.00022269506,0.00004143562],"domain_scores_gemma":[0.99984515,0.00005288809,0.000024726878,0.000008786834,0.000059064034,0.000009403156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054675806,0.0011759463,0.0007857988,0.0016560511,0.00022071894,0.00097470987,0.0008749728,0.0014073953,0.001564677],"category_scores_gemma":[0.0004367928,0.0004867254,0.00065153925,0.0011728208,0.00049192976,0.0013231888,0.000668978,0.0015248501,0.0017135041],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005168461,0.00014868859,0.0003633123,0.01668439,0.00011923423,0.000500407,0.00015041747,0.00093428354,0.19955732,0.020409033,0.014863916,0.74621725],"study_design_scores_gemma":[0.000013681267,0.00019830363,0.0005400104,0.0009943864,0.00009966732,0.001767933,0.000054497577,0.00115149,0.13209516,0.003314957,0.8597091,0.000060774175],"about_ca_topic_score_codex":0.00042894037,"about_ca_topic_score_gemma":0.0006016251,"teacher_disagreement_score":0.0016560511,"about_ca_system_score_codex":0.0005625785,"about_ca_system_score_gemma":0.00065110595,"threshold_uncertainty_score":0.0052343607},"labels":[],"label_agreement":null},{"id":"W2889824416","doi":"10.3390/s18092966","title":"A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Green IT and Sustainability","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Actigraphy; Computer science; Quantization (signal processing); Encoder; Artificial intelligence; Encoding (memory); SIGNAL (programming language); Real-time computing; Speech recognition; Computer vision; Medicine","score_opus":0.022881885002662233,"score_gpt":0.2850757014769684,"score_spread":0.2621938164743062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889824416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1566845,0.0013352406,0.82822466,0.00063370867,0.00043687818,0.0005254576,0.0010613772,0.004219335,0.0068788454],"genre_scores_gemma":[0.48324004,0.0009540999,0.5019918,0.0006969106,0.00016255071,0.00047221777,0.0007379054,0.00019684045,0.011547599],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996921,0.000050944866,0.000025161351,0.00008555288,0.00012725974,0.000019033529],"domain_scores_gemma":[0.99949193,0.00014777659,0.0000836129,0.00008294046,0.00016295144,0.000030648054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036014582,0.00043431408,0.00045449717,0.00050873106,0.00023062543,0.0006276709,0.0007494857,0.0006619753,0.0037336408],"category_scores_gemma":[0.0009931339,0.0001993393,0.00021659526,0.00067569246,0.0002955305,0.00072439795,0.00039131835,0.0005067045,0.0016359814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004833199,0.00017568104,0.0015165762,0.00047726466,0.000028686336,0.0002259111,0.000119857774,0.001005425,0.75989825,0.0013698355,0.0034350308,0.23126405],"study_design_scores_gemma":[0.000118359676,0.001482655,0.01552511,0.00009989008,0.00015182835,0.0031988535,0.000088853056,0.050068263,0.88850194,0.0007738148,0.039867636,0.00012291952],"about_ca_topic_score_codex":0.0003649921,"about_ca_topic_score_gemma":0.0008284359,"teacher_disagreement_score":0.0037336408,"about_ca_system_score_codex":0.0002408522,"about_ca_system_score_gemma":0.00031626408,"threshold_uncertainty_score":0.0124902725},"labels":[],"label_agreement":null},{"id":"W2890022364","doi":"10.3390/s18093017","title":"Computer-Aided Approach for Rapid Post-Event Visual Evaluation of a Building Façade","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Indiana Department of Transportation; U.S. Department of Transportation; Nvidia; National Science Foundation","keywords":"Orthophoto; Image stitching; Computer vision; Computer science; Artificial intelligence; Process (computing); Visual inspection; Facade; Event (particle physics); Engineering; Civil engineering","score_opus":0.05177989464155775,"score_gpt":0.2991751509424278,"score_spread":0.24739525630087006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890022364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08349608,0.00018360498,0.90702224,0.000068491085,0.000033085398,0.00018966697,0.00040523146,0.0056667016,0.0029348796],"genre_scores_gemma":[0.32071343,0.00012355993,0.67626154,0.00003588129,0.000012574035,0.0001001757,0.00056798174,0.00017898317,0.0020058255],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997342,0.00002911779,0.000010962054,0.000063196196,0.00012877988,0.000033652923],"domain_scores_gemma":[0.9996599,0.00007476402,0.000041035888,0.0000651979,0.00013361778,0.000025578487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002962431,0.0005741004,0.00034935566,0.0018467446,0.00019430892,0.0007088569,0.0005961626,0.00045512326,0.0040819165],"category_scores_gemma":[0.0007051387,0.00033792193,0.00036658763,0.0005172219,0.00019522762,0.00038533538,0.00068135874,0.00036982226,0.0013142782],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003487953,0.00016915609,0.00374037,0.00023683572,0.00006634274,0.00022921721,0.0002853864,0.031546135,0.38452825,0.0012546241,0.003457156,0.57413775],"study_design_scores_gemma":[0.00004228264,0.00023448402,0.020319123,0.000042449752,0.00005320666,0.00070370943,0.00022839913,0.8432648,0.1234897,0.0012093206,0.010345097,0.00006743434],"about_ca_topic_score_codex":0.0020406905,"about_ca_topic_score_gemma":0.006219783,"teacher_disagreement_score":0.0040819165,"about_ca_system_score_codex":0.0002591459,"about_ca_system_score_gemma":0.00053194806,"threshold_uncertainty_score":0.013655305},"labels":[],"label_agreement":null},{"id":"W2890071045","doi":"10.3390/s18093065","title":"Experimental Study of Wireless Monitoring of Human Respiratory Movements Using UWB Impulse Radar Systems","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Radar; Computer science; Impulse (physics); Impulse response; Impulse noise; Wireless; Electronic engineering; Artificial intelligence; Telecommunications; Engineering; Mathematics; Pixel","score_opus":0.04056555673556806,"score_gpt":0.299037766914163,"score_spread":0.25847221017859495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890071045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9676347,0.0002024497,0.030232713,0.00008344395,0.000075519376,0.000055961384,0.00013063483,0.0001954983,0.0013890663],"genre_scores_gemma":[0.99016047,0.00016181452,0.00861093,0.000035663044,0.000020482903,0.00005352212,0.00010919209,0.000010904333,0.0008368516],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995766,0.000115586336,0.000041563133,0.000090288355,0.00011942925,0.00005649767],"domain_scores_gemma":[0.99916494,0.00037890344,0.000099944016,0.00009955485,0.00020455361,0.000052174822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006742917,0.00034076316,0.00031391619,0.00043978172,0.00018777594,0.00026606154,0.00045534436,0.00056000415,0.0011498805],"category_scores_gemma":[0.0015140423,0.0001114631,0.00021281387,0.0002748383,0.00028864827,0.00042392538,0.00032224815,0.00025076038,0.00023296164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018775302,0.001031713,0.014451618,0.00078087504,0.00012320375,0.0009996162,0.0007336422,0.0151776625,0.8714728,0.0012566864,0.00090369454,0.09119091],"study_design_scores_gemma":[0.00032217405,0.017963158,0.07283065,0.00009950345,0.00023256538,0.0026301711,0.0010830265,0.1755737,0.7233476,0.0011097697,0.0046913386,0.00011630324],"about_ca_topic_score_codex":0.00019289159,"about_ca_topic_score_gemma":0.00017992296,"teacher_disagreement_score":0.0011498805,"about_ca_system_score_codex":0.00008486476,"about_ca_system_score_gemma":0.00010475234,"threshold_uncertainty_score":0.0038467646},"labels":[],"label_agreement":null},{"id":"W2890112281","doi":"10.3390/s18093127","title":"On the Use of Focused Incident Near-Field Beams in Microwave Imaging","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Inverse scattering problem; Microwave imaging; Scattering; Optics; Bessel function; Bessel beam; Microwave; Physics; Inverse problem; Computer science; Inversion (geology); Inverse; Mathematics; Telecommunications; Geology; Geometry","score_opus":0.017750015428889484,"score_gpt":0.2162297631820252,"score_spread":0.1984797477531357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890112281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08773575,0.0023456886,0.89898443,0.00021418654,0.000031700532,0.00009379462,0.000057964015,0.00011631946,0.010420194],"genre_scores_gemma":[0.46094656,0.0054943124,0.52948415,0.00025919237,0.00006979278,0.00014842484,0.00011293106,0.000051427825,0.003433199],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998254,0.00006223592,0.0000063489965,0.00002979601,0.0000608772,0.000015347827],"domain_scores_gemma":[0.9997141,0.00016712274,0.00002970582,0.000036871184,0.00004550301,0.000006751985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000494413,0.00055684283,0.0002360262,0.0003003004,0.0001434485,0.00045523993,0.0004330092,0.00056965294,0.00078319653],"category_scores_gemma":[0.0007457432,0.0001999008,0.00026382576,0.00032943775,0.0005876343,0.0006707761,0.0003741386,0.0002892168,0.00024265445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025914868,0.00015836812,0.0021852374,0.0005027335,0.000067011184,0.00063699187,0.000532688,0.13241419,0.62557405,0.102835394,0.0007220531,0.1341121],"study_design_scores_gemma":[0.000040163293,0.00051604764,0.003014091,0.00018859908,0.000073110685,0.0009917233,0.00017935187,0.6585246,0.30345696,0.022457076,0.01047081,0.0000874805],"about_ca_topic_score_codex":0.0005070176,"about_ca_topic_score_gemma":0.0006678333,"teacher_disagreement_score":0.00078319653,"about_ca_system_score_codex":0.0002647533,"about_ca_system_score_gemma":0.0001491078,"threshold_uncertainty_score":0.002620101},"labels":[],"label_agreement":null},{"id":"W2890551687","doi":"10.3390/s18093141","title":"Smartphone-Based Microfluidic Colorimetric Sensor for Gaseous Formaldehyde Determination with High Sensitivity and Selectivity","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Beijing University of Chemical Technology","keywords":"Formaldehyde; Reagent; Selectivity; Detection limit; Microfluidics; Parts-per notation; Acetaldehyde; Chemistry; Mixing (physics); Chromatography; Nanotechnology; Materials science; Organic chemistry; Ethanol; Catalysis","score_opus":0.006017935567310902,"score_gpt":0.1957654447987176,"score_spread":0.1897475092314067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890551687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5841558,0.016491866,0.3791454,0.0013303413,0.0015407569,0.00076310174,0.0020611177,0.0046155644,0.009896035],"genre_scores_gemma":[0.7882724,0.004075003,0.19955133,0.0007667894,0.00028775973,0.0004830641,0.00077797304,0.000050326642,0.005735407],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994831,0.000054162036,0.00003469472,0.00015028016,0.00022085941,0.000056881785],"domain_scores_gemma":[0.9997447,0.00006004222,0.00004485595,0.000021184896,0.000106790896,0.000022407974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040721736,0.0006860142,0.0005513681,0.00047041324,0.00021181931,0.00027486842,0.0006589972,0.00073609105,0.0010345235],"category_scores_gemma":[0.00051898643,0.00030748177,0.00041510243,0.00021965578,0.00020901598,0.00035465808,0.00053406326,0.00030850832,0.0005775842],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069320464,0.000029252868,0.00057594327,0.0002161618,0.000014543722,0.000096769465,0.000027988104,0.00020467937,0.98488,0.00018953356,0.00065211626,0.013043768],"study_design_scores_gemma":[0.00002787109,0.00039387489,0.0034306427,0.000017872011,0.00005907089,0.00078464113,0.00003803,0.009187296,0.9791652,0.00007553675,0.006768644,0.00005130739],"about_ca_topic_score_codex":0.00064172334,"about_ca_topic_score_gemma":0.0013654272,"teacher_disagreement_score":0.0010345235,"about_ca_system_score_codex":0.00036361208,"about_ca_system_score_gemma":0.0003405543,"threshold_uncertainty_score":0.0034608245},"labels":[],"label_agreement":null},{"id":"W2890866014","doi":"10.3390/s18092970","title":"A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Gait; Computer science; Artificial intelligence; Segmentation; Filter (signal processing); Computer vision; Physical medicine and rehabilitation; Medicine","score_opus":0.041564081599261754,"score_gpt":0.2483485702274265,"score_spread":0.20678448862816476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890866014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010333632,0.00013329834,0.98861885,0.000042046675,0.000036230085,0.000040486866,0.000030154903,0.0003565237,0.00040874496],"genre_scores_gemma":[0.2463242,0.00025653662,0.7503491,0.0001098957,0.00005662251,0.00013805268,0.00016905085,0.00006707361,0.0025294132],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959356,0.000061266655,0.0000298256,0.00012552449,0.00015114484,0.00003880277],"domain_scores_gemma":[0.9997063,0.00010893109,0.00004428493,0.000038922302,0.00008678245,0.000014739099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062227587,0.0006191399,0.0006752258,0.0012585752,0.00032512014,0.00053317705,0.00072392425,0.0009251213,0.0013313954],"category_scores_gemma":[0.0015247195,0.0003155178,0.0005251259,0.0010562284,0.00031660259,0.00071842776,0.00037446473,0.0004288232,0.0004853987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003706808,0.0002560869,0.0017183617,0.00016277283,0.00012316936,0.00015075922,0.00013949905,0.121145494,0.09388769,0.004784283,0.001327033,0.7759341],"study_design_scores_gemma":[0.000016236902,0.000189706,0.0030783545,0.000015303454,0.000025807607,0.00017641915,0.00003212954,0.9691584,0.022664037,0.002069585,0.0025489808,0.000024981882],"about_ca_topic_score_codex":0.0035138635,"about_ca_topic_score_gemma":0.004209277,"teacher_disagreement_score":0.0035138635,"about_ca_system_score_codex":0.00047768012,"about_ca_system_score_gemma":0.0006539311,"threshold_uncertainty_score":0.0069868565},"labels":[],"label_agreement":null},{"id":"W2890965859","doi":"10.3390/s18092952","title":"An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; China Scholarship Council","keywords":"Inertial measurement unit; Inertial navigation system; Gyroscope; Navigation system; Computer science; Reliability (semiconductor); Wind triangle; Computer vision; Artificial intelligence; Inertial frame of reference; Engineering; Simulation; Robot; Mobile robot; Aerospace engineering","score_opus":0.004866560607590487,"score_gpt":0.22063298531361566,"score_spread":0.21576642470602517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890965859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09201962,0.0017159049,0.88835156,0.00023491883,0.0005446474,0.00023229094,0.00033370557,0.005335543,0.011231769],"genre_scores_gemma":[0.7701355,0.0007091015,0.21466015,0.00025395607,0.00015284089,0.00027013844,0.00050516037,0.00005219949,0.013260899],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996778,0.0000256771,0.000011936645,0.00009343915,0.00016397299,0.000027200953],"domain_scores_gemma":[0.9998128,0.000017998042,0.000022118638,0.000017504979,0.00010711773,0.000022450871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018316405,0.00047974428,0.0004855488,0.00044225095,0.00038009754,0.0004355656,0.0007695258,0.0005295562,0.0011443038],"category_scores_gemma":[0.00034226145,0.00028932578,0.00024034915,0.00042372238,0.00018379316,0.0006872909,0.0006328328,0.00031840362,0.00058446993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036739098,0.00016491493,0.006204559,0.0004943562,0.000113468326,0.0003848688,0.00023828264,0.014383675,0.38892582,0.005253559,0.0083243605,0.57514477],"study_design_scores_gemma":[0.00039865967,0.0023927705,0.026125856,0.00015617367,0.00041132706,0.0024573726,0.0001896251,0.6352822,0.21104112,0.005204136,0.116092555,0.00024825745],"about_ca_topic_score_codex":0.003718614,"about_ca_topic_score_gemma":0.004725352,"teacher_disagreement_score":0.003718614,"about_ca_system_score_codex":0.00023026684,"about_ca_system_score_gemma":0.0007688884,"threshold_uncertainty_score":0.007393956},"labels":[],"label_agreement":null},{"id":"W2891594263","doi":"10.3390/s18093155","title":"Bandwidth Enhancement and Frequency Scanning Array Antenna Using Novel UWB Filter Integration Technique for OFDM UWB Radar Applications in Wireless Vital Signs Monitoring","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Polytechnique Montréal","funders":"Qatar National Research Fund; Jahrom University of Medical Sciences; Fonds National de la Recherche Luxembourg","keywords":"Coplanar waveguide; Microstrip antenna; Electronic engineering; Bandwidth (computing); Patch antenna; Antenna (radio); Engineering; Electrical engineering; Computer science; Telecommunications; Microwave","score_opus":0.02226263169971827,"score_gpt":0.25842301868772194,"score_spread":0.23616038698800368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891594263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31750914,0.0012030889,0.67301375,0.00018269719,0.00011259673,0.00003318097,0.0000418717,0.0009181879,0.006985486],"genre_scores_gemma":[0.79102224,0.00062555517,0.2047935,0.00010661688,0.000076519624,0.000031601572,0.00007219337,0.000055735232,0.0032160878],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998485,0.000025122941,0.000007114949,0.000042185024,0.00005648394,0.0000205745],"domain_scores_gemma":[0.9997749,0.000056560635,0.00005674893,0.000028698627,0.00007230387,0.000010754762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014044423,0.0003413658,0.00024170798,0.0003073402,0.00008456642,0.00020417938,0.0003171687,0.0003740341,0.00083929155],"category_scores_gemma":[0.00025542593,0.00015824402,0.00028976443,0.00025813683,0.00013319425,0.00037747575,0.00014557772,0.00019719084,0.0003466422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014058963,0.000025145971,0.0008357729,0.00006625846,0.000019360657,0.00010841496,0.00006317158,0.0011001482,0.9437708,0.0009739107,0.0003006786,0.052595634],"study_design_scores_gemma":[0.000018123643,0.0005569153,0.0032542797,0.000013487565,0.000071458504,0.0015557158,0.00004108451,0.036102343,0.94873774,0.00027894467,0.00934317,0.00002683722],"about_ca_topic_score_codex":0.00011253148,"about_ca_topic_score_gemma":0.00019218224,"teacher_disagreement_score":0.00083929155,"about_ca_system_score_codex":0.00015448367,"about_ca_system_score_gemma":0.00007203364,"threshold_uncertainty_score":0.0028077364},"labels":[],"label_agreement":null},{"id":"W2892421788","doi":"10.3390/s18103222","title":"Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Support vector machine; Genetic algorithm; Principal component analysis; Near-infrared spectroscopy; Pattern recognition (psychology); Computer science; Identification (biology); Artificial intelligence; Data mining; Algorithm; Machine learning; Biology","score_opus":0.019828658309922153,"score_gpt":0.30295132539008746,"score_spread":0.2831226670801653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892421788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037824243,0.00072147464,0.9590095,0.00015522054,0.000055163786,0.00008052212,0.000075976684,0.0012503052,0.0008274631],"genre_scores_gemma":[0.5843035,0.00044898147,0.41251248,0.00010510452,0.000044046137,0.00040264826,0.0004559158,0.000099055054,0.0016283356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999308,0.00020627675,0.000060595652,0.00015102529,0.00018857559,0.00008550239],"domain_scores_gemma":[0.9989392,0.00059441815,0.0000876937,0.00003876154,0.0003158622,0.000024104247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237279,0.001043371,0.001244691,0.0009940976,0.000372085,0.0006918863,0.00092727446,0.0009893719,0.00085290184],"category_scores_gemma":[0.0034493315,0.00037019633,0.0008171697,0.0011106975,0.00032392683,0.00059929106,0.0004516885,0.0013745152,0.00031257232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101894126,0.000083107036,0.0010706563,0.00009512598,0.00006176521,0.0000572754,0.000043995853,0.8283723,0.0034247313,0.00138321,0.0007907172,0.16451527],"study_design_scores_gemma":[0.0000037222078,0.000015889973,0.00010547141,0.000002395143,0.0000032617368,0.000004792879,0.000003850551,0.9989635,0.0004718116,0.00030955044,0.00011295182,0.0000028047145],"about_ca_topic_score_codex":0.008762734,"about_ca_topic_score_gemma":0.0040627643,"teacher_disagreement_score":0.008762734,"about_ca_system_score_codex":0.00069639715,"about_ca_system_score_gemma":0.0013312617,"threshold_uncertainty_score":0.01742351},"labels":[],"label_agreement":null},{"id":"W2895083861","doi":"10.3390/s18103329","title":"Gait Type Analysis Using Dynamic Bayesian Networks","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Gait analysis; Gait; Bayesian probability; Dynamic Bayesian network; Computer science; Type (biology); Bayesian network; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Biology","score_opus":0.009373543899570104,"score_gpt":0.23543287148051245,"score_spread":0.22605932758094235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895083861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024579043,0.00030781858,0.97230464,0.00014770198,0.000039523577,0.000081536265,0.00029858397,0.0004018718,0.001839291],"genre_scores_gemma":[0.76904076,0.0007525757,0.22485317,0.00014227524,0.00011467581,0.00024774225,0.0012321301,0.00009035194,0.0035262709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999292,0.00022945178,0.000049355578,0.00018011713,0.00018908129,0.00006004434],"domain_scores_gemma":[0.9989336,0.00056821655,0.0001809984,0.000058736474,0.00021793401,0.000040531395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012746837,0.00081085425,0.0008011058,0.003038537,0.0003758687,0.0009454452,0.0009032441,0.0006998589,0.0016787186],"category_scores_gemma":[0.004808842,0.00045315825,0.0008961068,0.0014345736,0.00035148836,0.0010474629,0.0004540122,0.00062493945,0.00060917286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024847517,0.00016336524,0.013744919,0.00010476495,0.00022492949,0.00016739413,0.000070170376,0.67050093,0.003840329,0.0082237,0.0017752852,0.30093578],"study_design_scores_gemma":[0.000004141204,0.00001617398,0.0014060107,0.000011233367,0.00001622928,0.00004247364,0.000010686037,0.9933548,0.00034660407,0.0044458583,0.0003362461,0.000009571194],"about_ca_topic_score_codex":0.009850623,"about_ca_topic_score_gemma":0.008855734,"teacher_disagreement_score":0.009850623,"about_ca_system_score_codex":0.00086501037,"about_ca_system_score_gemma":0.0005381977,"threshold_uncertainty_score":0.019586563},"labels":[],"label_agreement":null},{"id":"W2895089729","doi":"10.3390/s18103330","title":"Resonator Based Switching Technique between Ultra Wide Band (UWB) and Single/Dual Continuously Tunable-Notch Behaviors in UWB Radar for Wireless Vital Signs Monitoring","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Polytechnique Montréal","funders":"Qatar National Research Fund; Jahrom University of Medical Sciences; Fonds National de la Recherche Luxembourg","keywords":"Ground plane; Resonator; Antenna (radio); Capacitor; Electronic engineering; Wireless; Dielectric resonator antenna; Electrical engineering; Computer science; Engineering; Telecommunications","score_opus":0.012916570524130155,"score_gpt":0.23420411613736827,"score_spread":0.22128754561323813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895089729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80142635,0.0029907266,0.18941598,0.00030136184,0.00029691495,0.000073418545,0.000078613506,0.00042719158,0.004989324],"genre_scores_gemma":[0.9314199,0.0005162387,0.0658039,0.00009494951,0.000050917122,0.00002180043,0.00003420129,0.00003534246,0.002022717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996568,0.00006504288,0.000023435476,0.000106803396,0.0001158247,0.000032104334],"domain_scores_gemma":[0.99963653,0.00013360854,0.000092817696,0.000048534184,0.00006667262,0.000021837302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032725403,0.00026876174,0.00020552124,0.00018040792,0.00011661826,0.0003062162,0.0005981133,0.0004678034,0.00078785396],"category_scores_gemma":[0.00049334794,0.00021011576,0.00029653328,0.00012978246,0.00025369858,0.00056196627,0.00020753751,0.00029111907,0.00032281547],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004766474,0.0000140266175,0.00012282566,0.00003847274,0.0000064255287,0.000048605212,0.000031849944,0.00016426819,0.9927216,0.00025148076,0.000056389967,0.006496255],"study_design_scores_gemma":[0.000015552847,0.0003947293,0.0009212759,0.000004350005,0.0000333763,0.00047680095,0.000032490123,0.0070985714,0.98736936,0.000099317054,0.0035336532,0.000020473373],"about_ca_topic_score_codex":0.00009141229,"about_ca_topic_score_gemma":0.0002339709,"teacher_disagreement_score":0.00078785396,"about_ca_system_score_codex":0.00018602364,"about_ca_system_score_gemma":0.00010029355,"threshold_uncertainty_score":0.0026356578},"labels":[],"label_agreement":null},{"id":"W2895108942","doi":"10.3390/s18103302","title":"Testing of Automated Photochemical Reflectance Index Sensors as Proxy Measurements of Light Use Efficiency in an Aspen Forest","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Photochemical Reflectance Index; Irradiance; Photosynthetically active radiation; Eddy covariance; Environmental science; Remote sensing; Calibration; Primary production; Atmospheric sciences; Reflectivity; Canopy; Diurnal temperature variation; Leaf area index; Chemistry; Ecosystem; Normalized Difference Vegetation Index; Mathematics; Optics; Ecology; Photosynthesis; Statistics; Physics; Geography","score_opus":0.03129301363889057,"score_gpt":0.27739431427471256,"score_spread":0.246101300635822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895108942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982173,0.000022123519,0.0015654494,0.000003700467,0.0000021590852,0.000011511908,0.000049926923,0.000032170505,0.000095623516],"genre_scores_gemma":[0.9955319,0.000022953305,0.004153201,0.000011420801,0.0000027163023,0.000017323819,0.00013135117,0.000008501938,0.000120518635],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987739,0.00029525143,0.000065026405,0.00038371072,0.0004045431,0.00007754607],"domain_scores_gemma":[0.9978776,0.0010035395,0.0002782054,0.00024795858,0.0005045474,0.00008820408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015783611,0.00057640404,0.00032226532,0.00038711668,0.0003326439,0.00039175764,0.0009415384,0.00055623066,0.0002456942],"category_scores_gemma":[0.0022236195,0.0002843547,0.00032725467,0.00036488476,0.0003296602,0.00058791885,0.00031566853,0.00024705497,0.00015121803],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032737434,0.002045667,0.3481134,0.00019291301,0.00032212966,0.0002756985,0.0005637914,0.043293726,0.52764595,0.00014028137,0.00020281995,0.07392985],"study_design_scores_gemma":[0.00016203607,0.0074394387,0.46165818,0.000012828061,0.00020047682,0.0003260877,0.00030436445,0.28784072,0.24111362,0.00016534985,0.00070019474,0.00007670875],"about_ca_topic_score_codex":0.010253933,"about_ca_topic_score_gemma":0.0108982045,"teacher_disagreement_score":0.010253933,"about_ca_system_score_codex":0.000557087,"about_ca_system_score_gemma":0.00032867544,"threshold_uncertainty_score":0.020388484},"labels":[],"label_agreement":null},{"id":"W2895884130","doi":"10.3390/s19010013","title":"PZT/PZT and PZT/BiT Composite Piezo-Sensors in Aerospace SHM Applications: Photochemical Metal Organic + Infiltration Deposition and Characterization","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Yonsei University; National Research Foundation","keywords":"Materials science; Aerospace; Composite number; Characterization (materials science); Infiltration (HVAC); Optoelectronics; Deposition (geology); Metal; Composite material; Nanotechnology; Aerospace engineering; Engineering; Metallurgy","score_opus":0.005169680568367117,"score_gpt":0.20050676983956142,"score_spread":0.1953370892711943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895884130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98859566,0.0014267177,0.0074673076,0.000043958527,0.00002696825,0.000037599726,0.0004227733,0.00013244106,0.0018465705],"genre_scores_gemma":[0.98282605,0.0010304529,0.0127067035,0.000026991185,0.0000126551,0.00005906239,0.00040136673,0.000031537995,0.0029051434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998068,0.0000109777275,0.000008426328,0.000037432514,0.00011568476,0.000020574369],"domain_scores_gemma":[0.99992454,0.000012254045,0.00002449761,0.0000070198207,0.000022125478,0.000009400863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014555955,0.00030588562,0.00027070256,0.0002670672,0.00013675987,0.00027916196,0.0002514101,0.00029506345,0.00090269046],"category_scores_gemma":[0.00016974223,0.00022660477,0.00011507989,0.00035916676,0.00013207333,0.00020060898,0.00015760178,0.00023926835,0.00022413088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002041553,0.000004232628,0.00013726216,0.000030712734,0.0000015494796,0.00002179461,0.0000079049505,0.000037760594,0.9988532,0.000015016647,0.000021433545,0.00084882503],"study_design_scores_gemma":[0.00000312257,0.00008681101,0.004522105,0.0000026180874,0.0000072254415,0.00013481078,0.000022117043,0.00081768126,0.9934232,0.0000103544,0.0009664377,0.0000034484283],"about_ca_topic_score_codex":0.00038277966,"about_ca_topic_score_gemma":0.00096825033,"teacher_disagreement_score":0.00090269046,"about_ca_system_score_codex":0.00014983326,"about_ca_system_score_gemma":0.00011753258,"threshold_uncertainty_score":0.0030197501},"labels":[],"label_agreement":null},{"id":"W2896069565","doi":"10.3390/s18103404","title":"Improving the Surface-Enhanced Raman Scattering Performance of Silver Nanodendritic Substrates with Sprayed-On Graphene-Based Coatings","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Graphene; Materials science; Coating; Oxide; Nanotechnology; Raman scattering; Substrate (aquarium); Raman spectroscopy; Optics; Metallurgy","score_opus":0.011408331362494542,"score_gpt":0.21446927259550147,"score_spread":0.20306094123300694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896069565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947567,0.0006149814,0.003914551,0.000030291898,0.000019805664,0.000014899627,0.00003491757,0.000075595424,0.000538269],"genre_scores_gemma":[0.983872,0.00058687397,0.014634734,0.000028939323,0.000007284965,0.000012271562,0.0000877216,0.000020865296,0.0007493548],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998047,0.000024608044,0.000012869486,0.000041874864,0.00008578385,0.000030233734],"domain_scores_gemma":[0.99981946,0.000049107686,0.000041567404,0.00002225227,0.00005325051,0.000014318929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021837153,0.00050120195,0.00021604705,0.00025588978,0.00008626076,0.00024070665,0.00035005543,0.0003618975,0.00040415462],"category_scores_gemma":[0.00041823735,0.00022812419,0.00023734273,0.00015089971,0.00019193887,0.00023630542,0.00020557044,0.0002298604,0.00020065835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007171424,0.0000028574775,0.00004294111,0.000012559395,0.0000015262044,0.000010648331,0.000004004622,0.000029660334,0.99942064,0.0000059066238,0.0000042741985,0.0004577642],"study_design_scores_gemma":[0.0000016905698,0.000048940492,0.00068747794,0.000001177384,0.0000039777965,0.000032173833,0.000006042742,0.0005388311,0.998531,0.0000029223631,0.00014361177,0.0000021560552],"about_ca_topic_score_codex":0.00054803974,"about_ca_topic_score_gemma":0.0014525454,"teacher_disagreement_score":0.00054803974,"about_ca_system_score_codex":0.00022333651,"about_ca_system_score_gemma":0.000087164466,"threshold_uncertainty_score":0.0016204715},"labels":[],"label_agreement":null},{"id":"W2896218777","doi":"10.3390/s18124280","title":"Highly Accurate and Fully Automatic 3D Head Pose Estimation and Eye Gaze Estimation Using RGB-D Sensors and 3D Morphable Models","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Pose; Artificial intelligence; Computer science; Computer vision; RGB color model; Gaze; 3D pose estimation; Head (geology); Estimator; Articulated body pose estimation; Pattern recognition (psychology); Mathematics","score_opus":0.029044016276368316,"score_gpt":0.2890268859712641,"score_spread":0.25998286969489576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896218777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02793537,0.00059707405,0.96559614,0.000098357064,0.00006053758,0.000071343486,0.0005630099,0.0036880174,0.0013901604],"genre_scores_gemma":[0.3881976,0.0012201571,0.60324997,0.00021820933,0.00010042919,0.00018916382,0.0019045348,0.00044875298,0.0044712583],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946266,0.0000773586,0.00002510544,0.00014859275,0.00025015997,0.00003602652],"domain_scores_gemma":[0.99949574,0.00009906681,0.000071171984,0.00014693693,0.00016722508,0.000019954901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002969081,0.0010651803,0.00071725796,0.001237483,0.00022134061,0.0006113164,0.0007676149,0.0007381654,0.002607537],"category_scores_gemma":[0.0014925927,0.00063678966,0.00074461097,0.00070109323,0.00026246795,0.0008934686,0.0012067052,0.00063060306,0.0018212324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029717217,0.00008733863,0.0047472543,0.00027950254,0.00019795199,0.00023610661,0.0002479138,0.026755126,0.22912772,0.0017043491,0.00697373,0.72934586],"study_design_scores_gemma":[0.00005462009,0.00025256653,0.0322544,0.00011479995,0.00011401482,0.0022438413,0.00025479615,0.7703257,0.17282802,0.0053073117,0.016096303,0.00015368496],"about_ca_topic_score_codex":0.0031676136,"about_ca_topic_score_gemma":0.006497445,"teacher_disagreement_score":0.0031676136,"about_ca_system_score_codex":0.00026095784,"about_ca_system_score_gemma":0.00044562118,"threshold_uncertainty_score":0.008723021},"labels":[],"label_agreement":null},{"id":"W2897095722","doi":"10.3390/s18103380","title":"Self-Calibration of an Industrial Robot Using a Novel Affordable 3D Measuring Device","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Laser tracker; Calibration; Robot; Robot calibration; Industrial robot; Simulation; Computer science; Position (finance); Accuracy and precision; Engineering; Artificial intelligence; Computer vision; Robot kinematics; Mobile robot; Laser; Mathematics; Optics; Physics","score_opus":0.08794335055564695,"score_gpt":0.27099371152385693,"score_spread":0.18305036096820998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897095722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087302096,0.00034582734,0.90794134,0.00011968217,0.00013660287,0.00013121568,0.0000838975,0.001767454,0.002171899],"genre_scores_gemma":[0.49106818,0.00025549377,0.5059074,0.00011321551,0.000049570124,0.00020658504,0.00010648766,0.00007537206,0.0022176597],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981268,0.00022993557,0.00008291808,0.0003234072,0.001164473,0.00007238287],"domain_scores_gemma":[0.99890757,0.0002945184,0.00025074932,0.00030977203,0.00019552323,0.0000417959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011808082,0.00067310326,0.000679085,0.0007624919,0.00020955416,0.00064230646,0.0014590812,0.00083759666,0.001540975],"category_scores_gemma":[0.002155772,0.0003743111,0.000453758,0.00054710056,0.00051190914,0.00071043335,0.0010302529,0.0005181123,0.0005713949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022752416,0.0001487786,0.004795296,0.00054727815,0.000049122093,0.00035027406,0.0003885405,0.012165914,0.77101326,0.004105876,0.0013887457,0.20481947],"study_design_scores_gemma":[0.00016071409,0.0021952596,0.024318688,0.0001433031,0.00018108559,0.0034543246,0.00016304519,0.1957565,0.73195636,0.0013916906,0.040039226,0.00023982165],"about_ca_topic_score_codex":0.00034286908,"about_ca_topic_score_gemma":0.00038221062,"teacher_disagreement_score":0.001540975,"about_ca_system_score_codex":0.00033646007,"about_ca_system_score_gemma":0.0005230316,"threshold_uncertainty_score":0.0062448382},"labels":[],"label_agreement":null},{"id":"W2897160171","doi":"10.3390/s18103587","title":"Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Pooling; Convolutional neural network; Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Computer science; Kernel (algebra); Contextual image classification; Image (mathematics); Remote sensing; Artificial neural network; Computer vision; Mathematics; Geography","score_opus":0.020285484473557547,"score_gpt":0.2399135230176408,"score_spread":0.21962803854408325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897160171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27259722,0.0010862091,0.7161065,0.00029987388,0.000116201394,0.00017229836,0.00045094927,0.003943757,0.005226994],"genre_scores_gemma":[0.84420377,0.000412041,0.1499128,0.0001349453,0.000050886723,0.00012404432,0.0010682772,0.00006900375,0.0040242183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995461,0.00004134132,0.000023333801,0.000138396,0.00016528407,0.00008552782],"domain_scores_gemma":[0.9997131,0.00007270757,0.000048197122,0.000043589418,0.00010498555,0.000017359322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064862415,0.0009867793,0.00072434417,0.0009005505,0.00036157557,0.0005831245,0.0009262084,0.0006452334,0.00092162594],"category_scores_gemma":[0.000932409,0.00032548298,0.00067063904,0.0008914129,0.00036271766,0.0011701833,0.00064166647,0.0005697588,0.00034339147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065371743,0.0004337742,0.0051979586,0.00016457295,0.0002735314,0.0002474518,0.000101751866,0.25786415,0.10302523,0.0023325055,0.0045967004,0.6251087],"study_design_scores_gemma":[0.000005645851,0.000036887584,0.0017744964,0.0000040944324,0.000021419683,0.0000307335,0.000006828471,0.9852248,0.012169439,0.00033330623,0.00038173227,0.0000106015095],"about_ca_topic_score_codex":0.010524775,"about_ca_topic_score_gemma":0.009360099,"teacher_disagreement_score":0.010524775,"about_ca_system_score_codex":0.00069962983,"about_ca_system_score_gemma":0.000578294,"threshold_uncertainty_score":0.020927012},"labels":[],"label_agreement":null},{"id":"W2897194081","doi":"10.3390/s18103560","title":"Resource Management in Energy Harvesting Cooperative IoT Network under QoS Constraints","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Thompson Rivers University","funders":"","keywords":"Energy harvesting; Quality of service; Internet of Things; Resource (disambiguation); Computer science; Resource management (computing); Computer network; Energy (signal processing); Embedded system","score_opus":0.010794685273700248,"score_gpt":0.2122257082273022,"score_spread":0.20143102295360193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897194081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12009998,0.000858529,0.87215304,0.00048407674,0.000039340262,0.00005768967,0.000057861584,0.00011030377,0.006139122],"genre_scores_gemma":[0.98016673,0.0003340892,0.01790997,0.000050975592,0.000018288161,0.00006606711,0.000023636028,0.000011774674,0.0014184066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964464,0.00011108225,0.000014317845,0.00008189934,0.00007102291,0.00007699264],"domain_scores_gemma":[0.99946445,0.00032168563,0.00008532296,0.000024518702,0.000069879265,0.000034066892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007146877,0.00050145335,0.00059579365,0.00026369098,0.0005682636,0.0010011971,0.00083470525,0.00062574376,0.00064956915],"category_scores_gemma":[0.0012601478,0.00024987612,0.00023422246,0.00051354297,0.0005325637,0.0012843553,0.00083650154,0.00044371816,0.000089364934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006392852,0.00003440309,0.00044075656,0.000057084417,0.000015215175,0.00020740021,0.000107515036,0.9628438,0.006157258,0.016058287,0.00060803525,0.013406258],"study_design_scores_gemma":[0.0000032756627,0.000016961976,0.00005460047,0.000001934118,0.000002802646,0.000022175627,0.000023686882,0.9966961,0.0004273535,0.002615906,0.00013227593,0.000002916821],"about_ca_topic_score_codex":0.0021139123,"about_ca_topic_score_gemma":0.0020083208,"teacher_disagreement_score":0.0021139123,"about_ca_system_score_codex":0.00085081777,"about_ca_system_score_gemma":0.0006522316,"threshold_uncertainty_score":0.0061730742},"labels":[],"label_agreement":null},{"id":"W2898023787","doi":"10.3390/s18103370","title":"Toward High Throughput Core-CBCM CMOS Capacitive Sensors for Life Science Applications: A Novel Current-Mode for High Dynamic Range Circuitry","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Capacitive sensing; CMOS; Capacitance; Electrical engineering; Parasitic capacitance; Electronic engineering; Engineering; Computer science; Physics; Electrode","score_opus":0.04441784400491611,"score_gpt":0.3111609379380095,"score_spread":0.26674309393309337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898023787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0832272,0.0076149823,0.90018266,0.00092144666,0.00043216423,0.0003078078,0.00020416932,0.0017824847,0.0053270003],"genre_scores_gemma":[0.3679276,0.0021304137,0.6250278,0.0009463498,0.00026584137,0.00021153863,0.00016661301,0.00009680766,0.0032270742],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994103,0.000050316026,0.000028815031,0.00016186576,0.00031347835,0.000035235826],"domain_scores_gemma":[0.9994591,0.00015019474,0.00009689979,0.000036033023,0.00022224986,0.000035673187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006657099,0.0006452786,0.0005106362,0.0006258007,0.00029680057,0.0008650842,0.0018034342,0.0008527344,0.0009119261],"category_scores_gemma":[0.0010128056,0.00035181188,0.00031030682,0.0005211463,0.00042195607,0.0015795518,0.0006736369,0.0007424481,0.00046996033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007748687,0.000040274368,0.0002979244,0.00024064814,0.000010535896,0.00005984279,0.000060848273,0.0006779938,0.94876736,0.0031355936,0.00046045028,0.046171132],"study_design_scores_gemma":[0.000036823654,0.0005702987,0.0005918557,0.000032497504,0.000037253558,0.00059800193,0.000027932272,0.027436435,0.95251334,0.0011309767,0.016980108,0.00004444844],"about_ca_topic_score_codex":0.00027692883,"about_ca_topic_score_gemma":0.0006475676,"teacher_disagreement_score":0.0018034342,"about_ca_system_score_codex":0.00059322227,"about_ca_system_score_gemma":0.0005377639,"threshold_uncertainty_score":0.004304111},"labels":[],"label_agreement":null},{"id":"W2898455399","doi":"10.3390/s18113605","title":"Piezoelectric Polymer and Paper Substrates: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":321,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Piezoelectricity; Polymer; Materials science; Engineering; Computer science; Composite material","score_opus":0.02727759309194462,"score_gpt":0.27353139974919677,"score_spread":0.24625380665725216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898455399","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003006254,0.9964856,0.0002383333,0.00012008542,0.00034405116,0.000008801332,0.000032577525,0.000009780148,0.0024601668],"genre_scores_gemma":[0.0013702096,0.99556017,0.00038683423,0.00011000702,0.00020079128,0.000012578632,0.00006520927,0.0000030971978,0.0022910484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99973303,0.000022783495,0.000041176965,0.00005564415,0.00012156245,0.000025763957],"domain_scores_gemma":[0.99965084,0.0001333991,0.000060828737,0.000014386453,0.00011306911,0.00002752683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004007635,0.0010837322,0.0009916278,0.0033599476,0.0004234742,0.0011717986,0.00086011714,0.00095298997,0.006696466],"category_scores_gemma":[0.0006630038,0.0004727082,0.0004397467,0.0035918495,0.00027181074,0.0017427049,0.00063626486,0.0009836949,0.004130438],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003887937,0.00010862338,0.0002804036,0.02418551,0.000049905964,0.00037720572,0.000081201295,0.00036344133,0.006068472,0.003357517,0.030446148,0.93464273],"study_design_scores_gemma":[0.000003398842,0.00005478526,0.0003967261,0.0016970121,0.00004125006,0.0011467236,0.000046651945,0.00006729852,0.0015865958,0.0006267665,0.99431956,0.000013172283],"about_ca_topic_score_codex":0.0008438922,"about_ca_topic_score_gemma":0.0012825179,"teacher_disagreement_score":0.006696466,"about_ca_system_score_codex":0.00035217276,"about_ca_system_score_gemma":0.000860833,"threshold_uncertainty_score":0.022401929},"labels":[],"label_agreement":null},{"id":"W2898636633","doi":"10.3390/s18113659","title":"Monitoring Method of Total Seed Mass in a Vibrating Tray Using Artificial Neural Network","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Science Foundation of Anhui Province; Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Tray; Artificial neural network; Computer science; Engineering; Environmental science; Acoustics; Artificial intelligence; Mechanical engineering; Physics","score_opus":0.02373810195059105,"score_gpt":0.27203656624714984,"score_spread":0.24829846429655877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898636633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21428147,0.00033605102,0.7818926,0.000099859266,0.000050051804,0.0000711191,0.000095672185,0.0011993507,0.0019738933],"genre_scores_gemma":[0.8406256,0.00020908473,0.15706913,0.00003406129,0.00000897526,0.00009466777,0.000084217245,0.000023166835,0.0018510736],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997571,0.000030807296,0.000014425435,0.00008795184,0.000096441385,0.00001328505],"domain_scores_gemma":[0.99973756,0.000075950404,0.000049605696,0.000022134996,0.00010387468,0.000010860799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032682254,0.0005316282,0.00033327242,0.00048771556,0.0001737395,0.00026691623,0.00058016914,0.0004397115,0.00060336996],"category_scores_gemma":[0.00062968925,0.00024387633,0.00021313106,0.00042288486,0.00018543974,0.00044032934,0.00022041798,0.00030625038,0.00013025098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052961335,0.00015371232,0.00864514,0.00041079137,0.00008490922,0.00018261852,0.00023576358,0.18997446,0.40891626,0.0011981898,0.0008973998,0.38877106],"study_design_scores_gemma":[0.000015026783,0.00012288263,0.0047397586,0.0000091558995,0.00002551753,0.000051149444,0.000020912104,0.9434248,0.05081916,0.00022295567,0.00052035536,0.000028271203],"about_ca_topic_score_codex":0.0023451908,"about_ca_topic_score_gemma":0.002876344,"teacher_disagreement_score":0.0023451908,"about_ca_system_score_codex":0.00039064055,"about_ca_system_score_gemma":0.00028506995,"threshold_uncertainty_score":0.00466311},"labels":[],"label_agreement":null},{"id":"W2898671709","doi":"10.3390/s18113690","title":"New Approach of High Sensitivity Techniques Using Collective Detection Method with Multiple GNSS Receivers","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Computer science; Weighting; Sensitivity (control systems); Dilution of precision; GPS signals; Real-time computing; Satellite navigation; Global Positioning System; Satellite; SIGNAL (programming language); Satellite system; GNSS augmentation; Electronic engineering; Remote sensing; Assisted GPS; Engineering; Telecommunications; Geography","score_opus":0.014196372551910204,"score_gpt":0.2359615327202323,"score_spread":0.2217651601683221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898671709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024247069,0.00044003813,0.97269183,0.00007207381,0.000037885522,0.000027518046,0.00001672754,0.00034380064,0.0021231412],"genre_scores_gemma":[0.29050726,0.00044020667,0.7039329,0.00006490575,0.00007107879,0.00006762583,0.00007841203,0.00007024426,0.004767401],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992663,0.00014127974,0.00002148359,0.00018534248,0.00034124043,0.000044315784],"domain_scores_gemma":[0.9996024,0.000090600945,0.000060765196,0.00006943423,0.00015271736,0.000024083749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052970764,0.00083125057,0.000555651,0.0012824489,0.000375739,0.00069607474,0.0009054599,0.0005986311,0.0010766432],"category_scores_gemma":[0.0006195135,0.00037851481,0.0005726173,0.0009805476,0.0004972609,0.0010792942,0.0010958236,0.00061412295,0.0005312068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022344838,0.00009844306,0.0034284575,0.00031385128,0.00016193502,0.00017641732,0.00044726537,0.047892407,0.4029047,0.015835296,0.0015130879,0.5270047],"study_design_scores_gemma":[0.00006579143,0.0009520715,0.004477806,0.00006515741,0.00020803456,0.0011908308,0.00022224334,0.7156825,0.24169761,0.0075883367,0.027701592,0.00014803949],"about_ca_topic_score_codex":0.000670896,"about_ca_topic_score_gemma":0.001126068,"teacher_disagreement_score":0.0012824489,"about_ca_system_score_codex":0.0004742845,"about_ca_system_score_gemma":0.00042643482,"threshold_uncertainty_score":0.0036017299},"labels":[],"label_agreement":null},{"id":"W2899350054","doi":"10.3390/s18113680","title":"Characterization of a Time-Resolved Diffuse Optical Spectroscopy Prototype Using Low-Cost, Compact Single Photon Avalanche Detectors for Tissue Optics Applications","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Detector; CMOS; Single-photon avalanche diode; Avalanche photodiode; Diffuse optical imaging; Optics; Optoelectronics; Computer science; Materials science; Avalanche diode; Electronic engineering; Physics; Engineering","score_opus":0.024886961309529176,"score_gpt":0.32837252530914296,"score_spread":0.3034855639996138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899350054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88820887,0.00058102707,0.10587296,0.00031724272,0.00019923458,0.00059815083,0.0008835537,0.0011087515,0.0022302163],"genre_scores_gemma":[0.86867964,0.00045274713,0.12531531,0.00013413375,0.000025706098,0.00050091033,0.00037088743,0.000108548695,0.004412143],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995023,0.000038184877,0.000022755265,0.00013140301,0.0002597639,0.00004556388],"domain_scores_gemma":[0.9990727,0.00018155316,0.0001507263,0.00015001945,0.00036495845,0.00008000495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076573907,0.00040135052,0.00036205418,0.00035396783,0.00018783701,0.000521075,0.001361315,0.0006654502,0.0011024163],"category_scores_gemma":[0.0012384752,0.00027515448,0.0003497372,0.00029394482,0.0003538877,0.00069661264,0.00030032702,0.0003888842,0.00044644516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008836998,0.00008545498,0.0009888724,0.00014607006,0.000019273515,0.00013005092,0.00011426408,0.0012058971,0.98773396,0.0004902082,0.00053388893,0.008463559],"study_design_scores_gemma":[0.000032790358,0.0015856973,0.0044411286,0.000013881028,0.000038057875,0.00041659313,0.0000848089,0.011946794,0.9741693,0.000081695754,0.0071529234,0.000036319925],"about_ca_topic_score_codex":0.0006197663,"about_ca_topic_score_gemma":0.0008002255,"teacher_disagreement_score":0.001361315,"about_ca_system_score_codex":0.0007824607,"about_ca_system_score_gemma":0.00075891695,"threshold_uncertainty_score":0.0056771636},"labels":[],"label_agreement":null},{"id":"W2899433558","doi":"10.3390/s18113667","title":"Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"Hydro-Québec; Agence Nationale de la Recherche","keywords":"Physical layer; Computer science; Encoder; Orthogonal frequency-division multiplexing; Concatenation (mathematics); Convolutional code; Coding (social sciences); Noise (video); Electronic engineering; Code (set theory); Software-defined radio; Real-time computing; Wireless; Computer engineering; Decoding methods; Algorithm; Telecommunications; Engineering; Channel (broadcasting); Set (abstract data type)","score_opus":0.01278436446150526,"score_gpt":0.25822558369350634,"score_spread":0.24544121923200107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899433558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9027277,0.00011819293,0.09256954,0.00013084798,0.000057664853,0.00008252865,0.00022134879,0.0004330465,0.003659233],"genre_scores_gemma":[0.98975277,0.000049883634,0.009301821,0.00001563828,0.0000056247254,0.000025592477,0.000071459646,0.00002027743,0.00075692387],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993925,0.00011176897,0.000026735626,0.00006972652,0.000328217,0.00007103398],"domain_scores_gemma":[0.99799025,0.00087832427,0.0002806301,0.00030030278,0.00048769717,0.00006286134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007509724,0.00034065807,0.00018710019,0.00036840554,0.0003032725,0.00034173115,0.00060714385,0.00056701375,0.0013731943],"category_scores_gemma":[0.0023515455,0.00011226364,0.00016841684,0.0003222896,0.0005626909,0.00035918338,0.00031762896,0.00034156424,0.0002331503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091706694,0.0005717412,0.009888401,0.00052988547,0.000080311016,0.00090888044,0.00056413386,0.11227346,0.8212536,0.0064344346,0.00090844923,0.045669705],"study_design_scores_gemma":[0.00004860628,0.0015957127,0.0064006285,0.000031237872,0.0000537888,0.00029660092,0.00017505196,0.19693235,0.7915384,0.0008317316,0.0020541644,0.00004171006],"about_ca_topic_score_codex":0.0009700643,"about_ca_topic_score_gemma":0.00085327815,"teacher_disagreement_score":0.0013731943,"about_ca_system_score_codex":0.00049763615,"about_ca_system_score_gemma":0.00026150228,"threshold_uncertainty_score":0.0045937896},"labels":[],"label_agreement":null},{"id":"W2899556547","doi":"10.3390/s18113789","title":"Development of Nanocomposite-Based Strain Sensor with Piezoelectric and Piezoresistive Properties","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Piezoresistive effect; Nanocomposite; Piezoelectricity; Materials science; Strain (injury); Composite material; Piezoelectric sensor; Electrical engineering; Nanotechnology; Engineering","score_opus":0.013695522976927894,"score_gpt":0.19880387196624222,"score_spread":0.18510834898931433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899556547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8281165,0.0041357493,0.16231818,0.00030536443,0.00019427126,0.00009529814,0.00027485617,0.0008454527,0.003714299],"genre_scores_gemma":[0.907865,0.0012896244,0.08799769,0.000072132316,0.00002498896,0.00007485183,0.00018134393,0.000038911505,0.0024554408],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998103,0.00001574221,0.000010915161,0.0000546959,0.0000975671,0.000010740127],"domain_scores_gemma":[0.9998714,0.00003010498,0.000034784083,0.0000076111137,0.00004344118,0.000012747457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016459661,0.00038028674,0.00029028868,0.00033209223,0.00011034591,0.00022000603,0.00051255536,0.0006309087,0.0002860408],"category_scores_gemma":[0.0003153476,0.00022949115,0.00023163541,0.00025184648,0.0001560383,0.000634159,0.00027120893,0.0003768038,0.00014550709],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017756027,0.00003156471,0.00019384967,0.0000884492,0.0000060242405,0.00005447368,0.000009831589,0.00077175425,0.9936261,0.00015419401,0.000073208874,0.004972664],"study_design_scores_gemma":[0.0000066566136,0.00014513274,0.00080879097,0.000005448785,0.000015535798,0.00021111092,0.000012516123,0.030919258,0.966154,0.00005809466,0.0016466184,0.000016716445],"about_ca_topic_score_codex":0.00027073768,"about_ca_topic_score_gemma":0.0007166646,"teacher_disagreement_score":0.0006309087,"about_ca_system_score_codex":0.00018853949,"about_ca_system_score_gemma":0.00013645206,"threshold_uncertainty_score":0.0013679862},"labels":[],"label_agreement":null},{"id":"W2899895176","doi":"10.3390/s18113780","title":"A Video Based Fire Smoke Detection Using Robust AdaBoost","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; AdaBoost; Smoke; Computer vision; Computer science; Histogram; Fire detection; Pattern recognition (psychology); Classifier (UML); Histogram of oriented gradients; Luminance; Engineering; Image (mathematics)","score_opus":0.024556624884898265,"score_gpt":0.2167378825340213,"score_spread":0.19218125764912303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899895176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103747904,0.00082878134,0.88954926,0.00014277558,0.00029693404,0.00017338734,0.00009507133,0.0027289588,0.0024367755],"genre_scores_gemma":[0.68854505,0.0003883612,0.3040916,0.0002342302,0.000106014304,0.00017286293,0.00040416964,0.00011470414,0.0059430264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994351,0.00006118023,0.0000221443,0.00016119966,0.00021492766,0.00010535177],"domain_scores_gemma":[0.999676,0.000057063437,0.000035398123,0.000021479142,0.00017961058,0.000030491994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080112723,0.00096785737,0.0014299558,0.0012929881,0.00047224163,0.00064328173,0.0016356332,0.0011174264,0.0011686705],"category_scores_gemma":[0.0007442757,0.00042976794,0.0010214521,0.0006566397,0.0002856222,0.0006662056,0.00049607374,0.0009249517,0.0007208934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008307202,0.0009488195,0.0038746234,0.00020777376,0.00023453314,0.00018287264,0.00004967025,0.11817944,0.08637652,0.0009678996,0.0027667142,0.7853803],"study_design_scores_gemma":[0.00001699577,0.0001417531,0.0012320868,0.000008611649,0.000024335866,0.00006136443,0.000011071356,0.9824338,0.015259594,0.00022775399,0.00056820194,0.000014505192],"about_ca_topic_score_codex":0.005568604,"about_ca_topic_score_gemma":0.005244893,"teacher_disagreement_score":0.005568604,"about_ca_system_score_codex":0.0005680702,"about_ca_system_score_gemma":0.0008166887,"threshold_uncertainty_score":0.011072338},"labels":[],"label_agreement":null},{"id":"W2899975414","doi":"10.3390/s18113733","title":"A Simple Wireless Sensor Node System for Electricity Monitoring Applications: Design, Integration, and Testing with Different Piezoelectric Energy Harvesters","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Duty cycle; Microcontroller; Electricity; Electrical engineering; Engineering; Node (physics); Automotive engineering; Power (physics); Wireless; Alternating current; Upgrade; Computer science; Embedded system; Voltage; Telecommunications","score_opus":0.022959917460104437,"score_gpt":0.225412753711876,"score_spread":0.20245283625177157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899975414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54289126,0.0011829613,0.44763413,0.00042106374,0.00029102343,0.0009721155,0.0004013313,0.0021132736,0.0040927497],"genre_scores_gemma":[0.7712633,0.00067557214,0.21745916,0.00016841132,0.0000561176,0.0006596457,0.00026526957,0.00010788412,0.0093447175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950874,0.00005844238,0.000027909884,0.000113813745,0.00026757573,0.000023454471],"domain_scores_gemma":[0.9997273,0.0000503486,0.000053563715,0.00004470894,0.000099917044,0.000024190445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041658938,0.0004979007,0.00042698745,0.00030984567,0.00018241165,0.00029442948,0.0010070925,0.0005655629,0.0012622958],"category_scores_gemma":[0.00058231835,0.00024511648,0.00025625114,0.00024191847,0.00026470912,0.0007322789,0.0003198235,0.00032536447,0.00046819882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083233506,0.00008668503,0.0010361888,0.0002318624,0.00002175226,0.000116549934,0.000083621766,0.0012071561,0.9640756,0.00041861754,0.00037199963,0.032266717],"study_design_scores_gemma":[0.00006779953,0.0030722055,0.009187096,0.00002626243,0.00007899793,0.0009982318,0.00006065426,0.022843178,0.94888884,0.00039991408,0.01431965,0.000057230736],"about_ca_topic_score_codex":0.00018200684,"about_ca_topic_score_gemma":0.00030431114,"teacher_disagreement_score":0.0012622958,"about_ca_system_score_codex":0.00018347606,"about_ca_system_score_gemma":0.00026921876,"threshold_uncertainty_score":0.0042228103},"labels":[],"label_agreement":null},{"id":"W2900009200","doi":"10.3390/s18113812","title":"Wearable Hardware Design for the Internet of Medical Things (IoMT)","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wearable computer; Wearable technology; Biosignal; Context (archaeology); Computer science; Health care; Life expectancy; The Internet; Internet privacy; Human–computer interaction; Multimedia; Wireless; Embedded system; Medicine; World Wide Web; Telecommunications; Population","score_opus":0.047915013528311456,"score_gpt":0.2878585103399324,"score_spread":0.23994349681162097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900009200","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011294115,0.96750623,0.011131796,0.0008788956,0.0019004298,0.00009745216,0.000079352816,0.00009290618,0.01718354],"genre_scores_gemma":[0.008548738,0.9634628,0.013818634,0.00083580613,0.00065902696,0.00013046568,0.00014462322,0.00003007492,0.012369802],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994456,0.00007444139,0.000074585645,0.0000832217,0.00028957418,0.00003259369],"domain_scores_gemma":[0.9995059,0.00021644366,0.000073688236,0.000031081552,0.00015278274,0.000020178433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006493367,0.0008260486,0.0006144052,0.0018427899,0.0002996295,0.00092280726,0.0008180509,0.0014784603,0.005128636],"category_scores_gemma":[0.0011758596,0.0003236484,0.0008691947,0.00151395,0.00044710486,0.0016460763,0.0007061347,0.0014120868,0.0031854012],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003647399,0.000052899715,0.00023268464,0.017248463,0.00004535328,0.00025171976,0.00010932607,0.0004486474,0.009998746,0.013670419,0.014755889,0.9431493],"study_design_scores_gemma":[0.000008504714,0.0001682918,0.0008538611,0.0047609676,0.00008153682,0.0021783458,0.00007379217,0.00025739055,0.0043242616,0.003650197,0.98361766,0.00002516465],"about_ca_topic_score_codex":0.00041399157,"about_ca_topic_score_gemma":0.00049862027,"teacher_disagreement_score":0.005128636,"about_ca_system_score_codex":0.00039463467,"about_ca_system_score_gemma":0.0007611699,"threshold_uncertainty_score":0.017156959},"labels":[],"label_agreement":null},{"id":"W2900251987","doi":"10.3390/s18113779","title":"Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Load balancing (electrical power); Markov decision process; Network packet; Heterogeneous network; Scheme (mathematics); Distributed computing; Telecommunications link; Internet of Things; Cellular network; Efficient energy use; Process (computing); Computer network; Markov process; Wireless network; Wireless; Embedded system; Engineering; Telecommunications","score_opus":0.010393003199387702,"score_gpt":0.23833155779587417,"score_spread":0.22793855459648646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900251987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052097462,0.00012122656,0.9419355,0.00027178528,0.000104396255,0.000107658394,0.000053921445,0.00065128424,0.004656814],"genre_scores_gemma":[0.913056,0.00006765682,0.08344304,0.0001590088,0.000060031525,0.00011424273,0.00010804758,0.000026737785,0.0029650996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994091,0.0001380183,0.000043109005,0.000106469546,0.000191745,0.00011164086],"domain_scores_gemma":[0.999003,0.0003741983,0.00015173947,0.00013357136,0.0002910042,0.000046461573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008353947,0.0005120831,0.000646001,0.00053961156,0.0008251903,0.0005966378,0.0011908227,0.00069066026,0.0020731043],"category_scores_gemma":[0.0021834143,0.00015726042,0.00024195608,0.00068101886,0.00046787137,0.0008879047,0.00076352654,0.0006139556,0.0005094558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033226493,0.00027209497,0.001210851,0.00005946834,0.000033866592,0.0001698353,0.00012122681,0.7843926,0.01408283,0.017664813,0.0031596415,0.17850056],"study_design_scores_gemma":[0.0000062168588,0.000018514891,0.00006619468,0.0000013582774,0.0000019127508,0.000017005912,0.000004057252,0.99765223,0.00067924755,0.0012888968,0.00026134035,0.0000030990539],"about_ca_topic_score_codex":0.002218811,"about_ca_topic_score_gemma":0.003341282,"teacher_disagreement_score":0.002218811,"about_ca_system_score_codex":0.00081460766,"about_ca_system_score_gemma":0.000871839,"threshold_uncertainty_score":0.006935239},"labels":[],"label_agreement":null},{"id":"W2900473430","doi":"10.3390/s19030444","title":"Contactless In Situ Electrical Characterization Method of Printed Electronic Devices with Terahertz Spectroscopy","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Terahertz technology and applications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Terahertz radiation; Characterization (materials science); Inkwell; Materials science; Printed electronics; Optoelectronics; Electronics; 3D printing; Conductive ink; Electronic component; Terahertz spectroscopy and technology; Electronic engineering; Nanotechnology; Electrical engineering; Engineering; Sheet resistance","score_opus":0.003332797825638841,"score_gpt":0.22427150154842776,"score_spread":0.22093870372278893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900473430","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51560616,0.0028619524,0.47242662,0.0004490125,0.0004944612,0.00018462432,0.0005172017,0.0013178743,0.00614199],"genre_scores_gemma":[0.8562162,0.0013975743,0.136848,0.00018899326,0.000072526534,0.00014781478,0.00019955519,0.00012860633,0.004800668],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991441,0.000107963584,0.000055513603,0.00022884725,0.00041830103,0.000045240966],"domain_scores_gemma":[0.99919003,0.00025452868,0.00020166757,0.00020753153,0.00011610387,0.000030091207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029577574,0.00057098386,0.00040613575,0.0005568062,0.0002579459,0.00075299107,0.00078833505,0.0008102132,0.0016198063],"category_scores_gemma":[0.000981486,0.0003329431,0.00027139904,0.00048076833,0.00057138427,0.00078138465,0.00058345054,0.00083312474,0.00060019677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000122668525,0.000012225707,0.00009699748,0.000049885854,0.0000042874826,0.000047673013,0.000026285743,0.000114271454,0.99620605,0.00018008464,0.00005801437,0.0031919624],"study_design_scores_gemma":[0.0000016883569,0.000025624846,0.0002955818,0.0000021728317,0.000004806618,0.00009069889,0.000012832779,0.0014843959,0.9973476,0.0000725931,0.0006572491,0.0000048821626],"about_ca_topic_score_codex":0.00013258046,"about_ca_topic_score_gemma":0.00029528886,"teacher_disagreement_score":0.0016198063,"about_ca_system_score_codex":0.00023804141,"about_ca_system_score_gemma":0.00015206246,"threshold_uncertainty_score":0.005418837},"labels":[],"label_agreement":null},{"id":"W2900760155","doi":"10.3390/s18113845","title":"Road Surface Monitoring Using Smartphone Sensors: A Review","year":2018,"lang":"en","type":"review","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":197,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Road surface; Anomaly detection; Key (lock); Anomaly (physics); Smartphone application; Computer science; Real-time computing; Plan (archaeology); Remote sensing; Transport engineering; Computer security; Engineering; Artificial intelligence; Geography; Civil engineering; Multimedia","score_opus":0.04244868212734403,"score_gpt":0.3120904995979918,"score_spread":0.26964181747064775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900760155","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004538613,0.9961112,0.00059306255,0.00033811535,0.00032431228,0.000021713813,0.00008166201,0.00002018208,0.002055959],"genre_scores_gemma":[0.0028739807,0.99470526,0.000876558,0.000163179,0.00025623105,0.000022869599,0.00010551465,0.0000044509234,0.0009918949],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999469,0.000075307,0.00008224825,0.00011844697,0.00022012158,0.00003500057],"domain_scores_gemma":[0.99837273,0.0008075874,0.00015754775,0.00004159368,0.00057154533,0.000048974285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093188544,0.0011732577,0.0011550835,0.003376603,0.00036119987,0.0011919428,0.001410333,0.0015064296,0.004931736],"category_scores_gemma":[0.0022055106,0.00043719393,0.00108231,0.0027964276,0.00035998673,0.002084333,0.0007161188,0.0009129259,0.0021747001],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006456488,0.000092209106,0.000928265,0.044872347,0.00013190116,0.0002614806,0.00013819552,0.00061993615,0.0031409601,0.0027114805,0.025482068,0.9215567],"study_design_scores_gemma":[0.000008807132,0.0002184609,0.0028752247,0.010456524,0.00033351072,0.0017120126,0.0002318785,0.0005182539,0.0018523966,0.0014168496,0.9803124,0.00006363276],"about_ca_topic_score_codex":0.0023027752,"about_ca_topic_score_gemma":0.002836159,"teacher_disagreement_score":0.004931736,"about_ca_system_score_codex":0.0004508211,"about_ca_system_score_gemma":0.0013049403,"threshold_uncertainty_score":0.016498327},"labels":[],"label_agreement":null},{"id":"W2901016545","doi":"10.3390/s18124088","title":"Conformal and Disposable Antenna-Based Sensor for Non-Invasive Sweat Monitoring","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Telemetry; Antenna (radio); Conformal map; Remote sensing; Computer science; Engineering; Embedded system; Real-time computing; Electrical engineering; Telecommunications; Geology","score_opus":0.013767944918692652,"score_gpt":0.23473536693536692,"score_spread":0.22096742201667427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901016545","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7416154,0.0030157145,0.24872787,0.00041588172,0.0003474373,0.00022293071,0.00021418497,0.00064275134,0.004797722],"genre_scores_gemma":[0.8791318,0.0009001234,0.11635578,0.0002213425,0.00005877204,0.00007980696,0.00011708665,0.000041913612,0.003093366],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972314,0.00005157521,0.000012501453,0.00006731887,0.000128015,0.000017479171],"domain_scores_gemma":[0.99975425,0.000051212548,0.00006594888,0.00004070684,0.000062297826,0.000025591029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017615173,0.00035885858,0.00030946662,0.0001918439,0.00007199718,0.00032210376,0.0005893259,0.0004953891,0.0006416814],"category_scores_gemma":[0.00046750178,0.00015191609,0.00029589786,0.00014727736,0.00017036943,0.00043620027,0.00028938148,0.00025697972,0.0004772357],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046587964,0.000036542137,0.00060577446,0.00008590741,0.000012162796,0.00017131327,0.000021321937,0.0002694635,0.98859334,0.0001845289,0.00015650866,0.00981664],"study_design_scores_gemma":[0.00001671644,0.00084367866,0.0044557774,0.000008649686,0.0000390543,0.0013012391,0.000048350095,0.0074247336,0.981198,0.00009968453,0.0045409147,0.000023261171],"about_ca_topic_score_codex":0.00008771904,"about_ca_topic_score_gemma":0.00014664381,"teacher_disagreement_score":0.0006416814,"about_ca_system_score_codex":0.00015110042,"about_ca_system_score_gemma":0.00010057004,"threshold_uncertainty_score":0.0021466017},"labels":[],"label_agreement":null},{"id":"W2901342801","doi":"10.3390/s18114061","title":"A Tellurium Oxide Microcavity Resonator Sensor Integrated On-Chip with a Silicon Waveguide","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; CMC Microsystems","keywords":"Resonator; Silicon; Optoelectronics; Tellurium; Materials science; Silicon chip; Waveguide; Chip; Oxide; Electrical engineering; Engineering; Metallurgy","score_opus":0.008449273618777653,"score_gpt":0.20953340130510176,"score_spread":0.2010841276863241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901342801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93668836,0.0011050539,0.05715067,0.00021035245,0.00015663225,0.00010535989,0.00047838865,0.0011756038,0.0029295133],"genre_scores_gemma":[0.86408865,0.00048554226,0.12917848,0.00016432207,0.000054098855,0.00009293357,0.0004628073,0.00008768006,0.005385571],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940467,0.000043039472,0.00002198367,0.0002133867,0.00025680038,0.000060152794],"domain_scores_gemma":[0.99966764,0.00009520227,0.0000739298,0.000046980364,0.00008451192,0.0000317979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023330553,0.0004456579,0.0005468696,0.00026439156,0.0002202327,0.0004798723,0.0015840975,0.0006122698,0.001065466],"category_scores_gemma":[0.00041382687,0.00038461297,0.00039826607,0.00020809054,0.00023675962,0.00061516545,0.00039375684,0.00035703156,0.00061635714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054576507,0.000018457878,0.00028784535,0.00003437078,0.000013029092,0.000047909027,0.000017958024,0.00021202171,0.9959527,0.00010291189,0.00008441318,0.0031736633],"study_design_scores_gemma":[0.000012943661,0.00033888992,0.0018154811,0.0000039613246,0.000032197437,0.00022962342,0.000017121894,0.010418384,0.9853329,0.000028440001,0.001750495,0.000019545838],"about_ca_topic_score_codex":0.0015279927,"about_ca_topic_score_gemma":0.0032189633,"teacher_disagreement_score":0.0015840975,"about_ca_system_score_codex":0.0005212081,"about_ca_system_score_gemma":0.00044425312,"threshold_uncertainty_score":0.0037816763},"labels":[],"label_agreement":null},{"id":"W2901606653","doi":"10.3390/s18124105","title":"Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Magnetometer; Gradient descent; Descent (aeronautics); Inertial measurement unit; Inertial frame of reference; Computer science; Pedestrian; Accelerometer; Acceleration; Algorithm; Computer vision; Artificial intelligence; Engineering; Aerospace engineering; Physics; Magnetic field; Artificial neural network; Classical mechanics","score_opus":0.0489970585882894,"score_gpt":0.34948204704695873,"score_spread":0.30048498845866933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901606653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004642827,0.00022477767,0.9939061,0.00004428837,0.00009939864,0.000029963323,0.000016346585,0.0003574915,0.0006788037],"genre_scores_gemma":[0.23679401,0.00066519773,0.7559726,0.00013704869,0.00020409115,0.00018810043,0.00023180989,0.00012690207,0.0056803017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993206,0.0001105919,0.00004623559,0.00022479778,0.00023403556,0.00006374484],"domain_scores_gemma":[0.9995666,0.00007249385,0.00003734199,0.000041825147,0.00025300597,0.000028731807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054513244,0.0009740508,0.00108657,0.0007472499,0.00051026035,0.0006419962,0.0011830521,0.0007810542,0.0020638441],"category_scores_gemma":[0.0012972831,0.00042202466,0.0007681326,0.0009809597,0.00034867623,0.0010226896,0.0006576897,0.00086612627,0.0009589941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002375576,0.000112092435,0.00442289,0.00033018537,0.00014562572,0.0001966565,0.00022287184,0.2229485,0.028707795,0.014305218,0.005177843,0.72319275],"study_design_scores_gemma":[0.000034133864,0.00007366708,0.000976468,0.000012243004,0.000024906247,0.000081174876,0.000022948958,0.9898057,0.0033226216,0.0013302235,0.00429088,0.00002502119],"about_ca_topic_score_codex":0.011368953,"about_ca_topic_score_gemma":0.0056318673,"teacher_disagreement_score":0.011368953,"about_ca_system_score_codex":0.00041023426,"about_ca_system_score_gemma":0.0013686456,"threshold_uncertainty_score":0.022605598},"labels":[],"label_agreement":null},{"id":"W2902201063","doi":"10.3390/s18124153","title":"Quantifying Airborne Lidar Bathymetry Quality-Control Measures: A Case Study in Frio River, Texas","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Texas Department of Transportation; Texas Water Development Board; U.S. Department of Transportation","keywords":"Bathymetry; Lidar; Traverse; Environmental science; Remote sensing; Quality (philosophy); Water quality; Software; Geography; Computer science; Cartography","score_opus":0.04532570092912337,"score_gpt":0.3139766406613847,"score_spread":0.26865093973226134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902201063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958192,0.000045251272,0.0020239917,0.0001391651,0.0000046761247,0.000072646704,0.00030238729,0.000025675577,0.0015669698],"genre_scores_gemma":[0.9927422,0.00010143321,0.005612744,0.000036866288,0.000006639992,0.00005024949,0.00042470146,0.0000113725055,0.0010138395],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994536,0.000106060805,0.000035983205,0.0001125745,0.00016743729,0.00012438754],"domain_scores_gemma":[0.9981791,0.0005596305,0.00032229538,0.00013679016,0.00067699054,0.00012525402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007798909,0.00035420747,0.00021042692,0.00091603433,0.0013229342,0.00096835964,0.00073031895,0.000628223,0.0006287504],"category_scores_gemma":[0.0024914844,0.0001433219,0.0002711011,0.0016444613,0.00062475126,0.000681588,0.0006542659,0.00038570105,0.00010684578],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024090249,0.001032436,0.8603221,0.0002336562,0.00012084976,0.0063970448,0.007342583,0.03656464,0.013347095,0.002102168,0.0027177096,0.069578774],"study_design_scores_gemma":[0.000039878374,0.0005821182,0.8962476,0.000083217245,0.00008514407,0.000667071,0.030194955,0.057946663,0.0062608,0.00053027703,0.0072964854,0.00006571886],"about_ca_topic_score_codex":0.20204711,"about_ca_topic_score_gemma":0.3513234,"teacher_disagreement_score":0.20204711,"about_ca_system_score_codex":0.0024624553,"about_ca_system_score_gemma":0.0016323763,"threshold_uncertainty_score":0.4017421},"labels":[],"label_agreement":null},{"id":"W2902669059","doi":"10.3390/s18124185","title":"Evaluation of a Low Cost Hand Held Unit with GNSS Raw Data Capability and Comparison with an Android Smartphone","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Global Positioning System; Computer science; Chipset; Multipath propagation; Precise Point Positioning; GLONASS; Differential GPS; Android (operating system); Real-time computing; Remote sensing; Geodesy; Telecommunications; Geography; Chip","score_opus":0.06343492118538092,"score_gpt":0.29902451162264215,"score_spread":0.23558959043726124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902669059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9737626,0.0011352912,0.016656697,0.00015316758,0.0001982618,0.0008114524,0.0009913374,0.0013122695,0.0049789874],"genre_scores_gemma":[0.9602229,0.00069204246,0.027079485,0.00016777545,0.000050670682,0.00030402237,0.0012276956,0.00010547049,0.010149966],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906415,0.0001443151,0.000085223655,0.00014432501,0.00047013318,0.00009189065],"domain_scores_gemma":[0.99802303,0.00044813482,0.00013133358,0.0001955913,0.0010598461,0.0001420442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007604792,0.0008788698,0.0007326811,0.0010980164,0.00019728657,0.0006599259,0.0013303206,0.0005842352,0.0044664415],"category_scores_gemma":[0.0026179252,0.00020365714,0.00027460256,0.0006834366,0.00020513887,0.00067523576,0.00035981965,0.00018983014,0.0012935811],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010353312,0.0014519383,0.034699745,0.004561841,0.00039233395,0.0026233192,0.0010149521,0.0071385205,0.4269178,0.00078222656,0.0074304123,0.50263363],"study_design_scores_gemma":[0.0013996265,0.0999964,0.43374607,0.0005862589,0.0020964248,0.007466739,0.0035717923,0.10019091,0.3031458,0.0002719198,0.04705487,0.00047319892],"about_ca_topic_score_codex":0.0034384062,"about_ca_topic_score_gemma":0.0038021945,"teacher_disagreement_score":0.0044664415,"about_ca_system_score_codex":0.0003596492,"about_ca_system_score_gemma":0.0003495419,"threshold_uncertainty_score":0.014941752},"labels":[],"label_agreement":null},{"id":"W2903564331","doi":"10.3390/s18124258","title":"Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Wireless sensor network; Energy consumption; Computer science; Distributed computing; Efficient energy use; Computer network; Engineering; Artificial intelligence","score_opus":0.01730881306138787,"score_gpt":0.23716589536649113,"score_spread":0.21985708230510326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903564331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0665486,0.00068457966,0.9288135,0.0001672277,0.00006757802,0.00008690316,0.00002349335,0.00046845456,0.003139709],"genre_scores_gemma":[0.8210324,0.00041629857,0.17514132,0.00013563721,0.000037805734,0.000092212416,0.00008233901,0.00009328515,0.002968779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993611,0.00015610474,0.000033431934,0.00012496134,0.00024008633,0.00008436644],"domain_scores_gemma":[0.99921584,0.00026311117,0.00009032095,0.00013628826,0.00024447948,0.00005001825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077596796,0.0005521262,0.000597701,0.00090028974,0.00091066235,0.0005183025,0.0013597712,0.0006775141,0.0009738527],"category_scores_gemma":[0.0020611815,0.00020638785,0.0004678649,0.00084981194,0.00048639328,0.0014111142,0.0012258155,0.00056451646,0.00026010137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046332,0.00027753162,0.0024565628,0.00035920195,0.00012509886,0.000327726,0.0006918534,0.47303107,0.08767083,0.03692271,0.0038391883,0.3938349],"study_design_scores_gemma":[0.000029087912,0.0002647814,0.001263951,0.000021554688,0.0000617659,0.00039682587,0.0002004109,0.9499273,0.028044235,0.015026661,0.0047168867,0.000046635538],"about_ca_topic_score_codex":0.001655048,"about_ca_topic_score_gemma":0.0021713576,"teacher_disagreement_score":0.001655048,"about_ca_system_score_codex":0.0005730991,"about_ca_system_score_gemma":0.0006776232,"threshold_uncertainty_score":0.004158139},"labels":[],"label_agreement":null},{"id":"W2903735432","doi":"10.3390/s18124494","title":"A Software Defined Radio Evaluation Platform for WBAN Systems","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; Chongqing Research Program of Basic Research and Frontier Technology; National Natural Science Foundation of China; National Science Foundation","keywords":"Body area network; Bluetooth; Wireless; Personal area network; Computer science; Embedded system; Reliability (semiconductor); Computer network; Software; Telecommunications; Power (physics); Operating system","score_opus":0.024821541828865618,"score_gpt":0.24351259935246272,"score_spread":0.2186910575235971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903735432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045957245,0.0005467003,0.90093505,0.00023608938,0.0002984979,0.0025533985,0.0006528019,0.032883547,0.015936712],"genre_scores_gemma":[0.36432943,0.00065049465,0.606331,0.00030578705,0.00011955821,0.004116989,0.0037399412,0.004073556,0.016333265],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953028,0.00134137,0.0004861163,0.0003938905,0.0021304183,0.00034538427],"domain_scores_gemma":[0.99583286,0.0009805429,0.00042775716,0.000697502,0.0017788318,0.00028258382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057762824,0.0013520148,0.0006989514,0.00158004,0.00047296146,0.0015382268,0.0021682747,0.0007044278,0.006125013],"category_scores_gemma":[0.007192072,0.00041316936,0.00056846533,0.0005139469,0.00048719582,0.0017053782,0.0013924742,0.0015232437,0.002666337],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025982836,0.00245077,0.007607751,0.0017896998,0.000292401,0.0015501166,0.0016902647,0.06529718,0.21680032,0.0614016,0.0371843,0.6013374],"study_design_scores_gemma":[0.00069970184,0.0045055933,0.0064164335,0.00043184284,0.0002102765,0.0013782212,0.00023697497,0.5744035,0.19225359,0.010556316,0.2086137,0.00029386184],"about_ca_topic_score_codex":0.0011082065,"about_ca_topic_score_gemma":0.00063649775,"teacher_disagreement_score":0.006125013,"about_ca_system_score_codex":0.0007519693,"about_ca_system_score_gemma":0.0013936543,"threshold_uncertainty_score":0.030548275},"labels":[],"label_agreement":null},{"id":"W2904166664","doi":"10.3390/s18124300","title":"Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Cyber-physical system; Transmission (telecommunications); Beamforming; Wireless sensor network; Distributed computing; Efficient energy use; Computer network; Engineering; Telecommunications; Electrical engineering","score_opus":0.012412576616583907,"score_gpt":0.24575407349475825,"score_spread":0.23334149687817435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904166664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016694855,0.00020854097,0.9802695,0.00018049523,0.000031306674,0.00002823679,0.00001919218,0.000053155913,0.0025147516],"genre_scores_gemma":[0.9353116,0.00042930548,0.0602278,0.000098053264,0.00003626688,0.00015421296,0.00003780016,0.00002492208,0.0036801249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924254,0.00032038175,0.000029974153,0.00014389171,0.00015224135,0.00011096399],"domain_scores_gemma":[0.99908006,0.00056606473,0.00014834758,0.000036782356,0.00012165035,0.000047051944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010289517,0.00080963975,0.00078481965,0.000323258,0.0004845813,0.0008096961,0.000874476,0.00093764823,0.00125016],"category_scores_gemma":[0.002090215,0.00042598974,0.0007115547,0.0004802638,0.0010805288,0.0011900687,0.0011771853,0.0008224012,0.00017303854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003962928,0.000025783109,0.00042915903,0.000054005202,0.00003128975,0.00011122258,0.0000752795,0.9660442,0.0019451525,0.019897781,0.00036812352,0.01097848],"study_design_scores_gemma":[0.0000043333466,0.000018490187,0.000067366374,0.000002436608,0.000004811545,0.000010998454,0.000012413464,0.99673647,0.0001548897,0.0028478375,0.00013519579,0.000004651379],"about_ca_topic_score_codex":0.0046471013,"about_ca_topic_score_gemma":0.0026112974,"teacher_disagreement_score":0.0046471013,"about_ca_system_score_codex":0.00084126357,"about_ca_system_score_gemma":0.00082175655,"threshold_uncertainty_score":0.009240091},"labels":[],"label_agreement":null},{"id":"W2904454788","doi":"10.3390/s18124444","title":"IoT Device Security: Challenging “A Lightweight RFID Mutual Authentication Protocol Based on Physical Unclonable Function”","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Physical unclonable function; Mutual authentication; Computer science; Computer security; Radio-frequency identification; Reflection attack; Authentication (law); Protocol (science); Authentication protocol; Identification (biology); Internet of Things; Cryptographic protocol; Computer network; Challenge–response authentication; Cryptography","score_opus":0.01374498695890482,"score_gpt":0.2667277211643339,"score_spread":0.2529827342054291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904454788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01706812,0.00065526384,0.96719444,0.003188065,0.00027438474,0.00021555013,0.00005124043,0.0003611173,0.010991897],"genre_scores_gemma":[0.613572,0.001963633,0.3641944,0.0023365335,0.0004977315,0.0011033162,0.00019957803,0.00025866658,0.015874103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934685,0.002831764,0.0005055218,0.0007958706,0.002018208,0.0003800089],"domain_scores_gemma":[0.9911849,0.00413231,0.00074452796,0.0027421971,0.0010616882,0.0001343992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005531153,0.00084603066,0.0006317764,0.00069075444,0.001473571,0.0027064686,0.0017581099,0.003221536,0.003169201],"category_scores_gemma":[0.012883951,0.0006128346,0.0015012559,0.0006392846,0.005357299,0.010251315,0.0034946883,0.005447642,0.0012226112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009276671,0.000050572602,0.00032255054,0.00037998927,0.000037785845,0.00039175095,0.00079697534,0.00663026,0.012761332,0.95162827,0.0020469397,0.024860846],"study_design_scores_gemma":[0.00008336491,0.00037373888,0.00045166223,0.00054945855,0.00012835028,0.001858378,0.00046790307,0.20547229,0.05435142,0.64592284,0.090162694,0.00017788108],"about_ca_topic_score_codex":0.0004888267,"about_ca_topic_score_gemma":0.00019072331,"teacher_disagreement_score":0.005531153,"about_ca_system_score_codex":0.001433231,"about_ca_system_score_gemma":0.0015388421,"threshold_uncertainty_score":0.029251873},"labels":[],"label_agreement":null},{"id":"W2904570205","doi":"10.3390/s18124408","title":"Efficient Path Planning and Truthful Incentive Mechanism Design for Mobile Crowdsensing","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowdsensing; Incentive; Mechanism (biology); Path (computing); Mechanism design; Computer science; Motion planning; Computer security; Risk analysis (engineering); Business; Computer network; Artificial intelligence; Economics; Microeconomics; Robot","score_opus":0.02087792937710901,"score_gpt":0.25566169738287253,"score_spread":0.23478376800576353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904570205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008975886,0.00043114764,0.98724824,0.00038873404,0.00007971073,0.00018528421,0.00009803166,0.0002864417,0.0023064567],"genre_scores_gemma":[0.6858787,0.0007859719,0.3076873,0.00028323714,0.00010602771,0.0005826641,0.00023342333,0.00008985917,0.004352765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778384,0.0007702076,0.00012786432,0.0005147637,0.00045945126,0.00034382672],"domain_scores_gemma":[0.9952749,0.0030188342,0.0005280481,0.00036671964,0.00051050796,0.0003008486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034015568,0.0013814798,0.0019044798,0.0010529271,0.000949726,0.0019283473,0.0032295284,0.0024521227,0.0042284424],"category_scores_gemma":[0.009138915,0.0009886718,0.0011851263,0.0014833382,0.001383375,0.00235239,0.0019779198,0.0020755958,0.0004624272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022244043,0.00011079501,0.00063614,0.00034369485,0.000067456705,0.00024540874,0.00021147706,0.8495428,0.0028304912,0.08012163,0.002493921,0.063173756],"study_design_scores_gemma":[0.00004602607,0.0000768021,0.00008951928,0.000021526383,0.000019044239,0.00007910656,0.00004100176,0.9659223,0.0006360324,0.031211477,0.0018398844,0.000017223898],"about_ca_topic_score_codex":0.0035190631,"about_ca_topic_score_gemma":0.0024784766,"teacher_disagreement_score":0.0042284424,"about_ca_system_score_codex":0.002065994,"about_ca_system_score_gemma":0.004006621,"threshold_uncertainty_score":0.017989397},"labels":[],"label_agreement":null},{"id":"W2904773330","doi":"10.3390/s18124307","title":"An Edge Computing Based Smart Healthcare Framework for Resource Management","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":213,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gnowit (Canada)","funders":"","keywords":"Cloud computing; Computer science; Edge computing; Enhanced Data Rates for GSM Evolution; Resource (disambiguation); Key (lock); Service (business); Distributed computing; Petri net; Resource allocation; The Internet; Computer security; Computer network; World Wide Web; Operating system; Artificial intelligence; Business","score_opus":0.027976871564668052,"score_gpt":0.30559722543220963,"score_spread":0.2776203538675416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904773330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069836513,0.0005872552,0.9753727,0.00079970167,0.00016001333,0.00014445513,0.0002571841,0.001136338,0.014558686],"genre_scores_gemma":[0.34603631,0.0014109551,0.6334569,0.0006182118,0.00011325071,0.00034406994,0.00064594543,0.00017208995,0.01720228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968493,0.00008220642,0.00002344217,0.00005853485,0.00009269604,0.000058118334],"domain_scores_gemma":[0.99983454,0.00005203467,0.000016459433,0.000024094214,0.000042035404,0.000030842784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007139736,0.00044802466,0.0002997635,0.00047394395,0.0006316042,0.0014335499,0.0013172249,0.0007370211,0.0027935263],"category_scores_gemma":[0.00056510576,0.00021380477,0.0005486084,0.0004523245,0.00056799006,0.0014185997,0.0013127669,0.0008431748,0.0005286454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017253042,0.00012753239,0.0009405012,0.00020799255,0.000046228346,0.0005904962,0.00030451745,0.24347037,0.0069277547,0.64216125,0.011256213,0.09379457],"study_design_scores_gemma":[0.00003694278,0.00006464643,0.00034278547,0.000081113714,0.00004051733,0.00025762353,0.00010474236,0.80651385,0.004240927,0.11112164,0.07715889,0.00003623968],"about_ca_topic_score_codex":0.007021746,"about_ca_topic_score_gemma":0.01020325,"teacher_disagreement_score":0.007021746,"about_ca_system_score_codex":0.0009805729,"about_ca_system_score_gemma":0.0021844828,"threshold_uncertainty_score":0.013961792},"labels":[],"label_agreement":null},{"id":"W2905491325","doi":"10.3390/s18124305","title":"Implementation and Analysis of Tightly Coupled Global Navigation Satellite System Precise Point Positioning/Inertial Navigation System (GNSS PPP/INS) with Insufficient Satellites for Land Vehicle Navigation","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Mitacs; National Natural Science Foundation of China","keywords":"GNSS applications; Precise Point Positioning; Inertial navigation system; Satellite system; Computer science; Inertial measurement unit; GNSS augmentation; Satellite navigation; Satellite; Global Positioning System; Navigation system; Real-time computing; Air navigation; Kalman filter; BeiDou Navigation Satellite System; Remote sensing; Aerospace engineering; Engineering; Inertial frame of reference; Telecommunications; Geography; Artificial intelligence; Physics","score_opus":0.006695071562026413,"score_gpt":0.24326091762462296,"score_spread":0.23656584606259654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905491325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31097913,0.00046218556,0.6795915,0.00012853942,0.000095673364,0.00015849131,0.00011591569,0.0017431976,0.0067254114],"genre_scores_gemma":[0.8889399,0.00018918958,0.108854145,0.00006733332,0.000020689797,0.000064167296,0.0002733118,0.000096428084,0.0014948163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985378,0.00021374499,0.00008094428,0.00021527044,0.00080110616,0.00015113644],"domain_scores_gemma":[0.9993279,0.000076154225,0.00007019491,0.00012813353,0.00036083104,0.000036869034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009596523,0.0004939653,0.0003623449,0.00056037336,0.00034143907,0.00074557663,0.00071086246,0.00041523005,0.00065326865],"category_scores_gemma":[0.0015357031,0.00028000295,0.00043431966,0.0004200037,0.00032321518,0.00083563913,0.0007494182,0.00046746663,0.00031822518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048583036,0.00020664907,0.042727713,0.00045634792,0.000295331,0.0009066799,0.0007629726,0.29708797,0.19888109,0.009696718,0.0018693776,0.44662333],"study_design_scores_gemma":[0.00005647494,0.0009694396,0.031352907,0.00006321835,0.00027597928,0.0003355434,0.00027396402,0.84190536,0.110711776,0.0019881881,0.01198589,0.00008119178],"about_ca_topic_score_codex":0.0056944,"about_ca_topic_score_gemma":0.003953681,"teacher_disagreement_score":0.0056944,"about_ca_system_score_codex":0.00052955543,"about_ca_system_score_gemma":0.0009780197,"threshold_uncertainty_score":0.011322498},"labels":[],"label_agreement":null},{"id":"W2905530669","doi":"10.3390/s18124430","title":"Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Natural Science Foundation of China; Jiangxi Provincial Department of Science and Technology; National Science Foundation","keywords":"Transformer; Artificial neural network; Engineering; Reliability engineering; Dissolved gas analysis; Smart grid; Data mining; Data modeling; Machine learning; Computer science; Transformer oil; Voltage; Electrical engineering","score_opus":0.040959241556824434,"score_gpt":0.31934353576926927,"score_spread":0.2783842942124448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905530669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005643776,0.00026352349,0.9931385,0.000080965496,0.000015225142,0.000018825893,0.000037200585,0.00022614944,0.0005758229],"genre_scores_gemma":[0.75345963,0.0007069283,0.24266061,0.00010967372,0.000054554275,0.00024915612,0.00027469493,0.00011498773,0.0023697612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970645,0.0001031702,0.000024793244,0.00006781183,0.00007800063,0.000019649877],"domain_scores_gemma":[0.9993149,0.00041422082,0.000076008924,0.000034567947,0.00014770182,0.000012563169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008774666,0.00077995413,0.00060956716,0.0006688789,0.00029233363,0.000514686,0.00078692654,0.0006861682,0.0010018627],"category_scores_gemma":[0.002610156,0.00040569113,0.00068568316,0.0007451884,0.0003039058,0.00083581713,0.00040669675,0.0010502017,0.00022560729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008363351,0.000010645993,0.00025406104,0.000022981008,0.000012628488,0.000012777897,0.000013049927,0.9775368,0.0004509939,0.0016049917,0.0001407242,0.019932011],"study_design_scores_gemma":[4.4821724e-7,0.000001725006,0.000025023559,0.0000010666994,0.0000010119446,0.0000017397593,9.2782386e-7,0.9994517,0.00009185234,0.00034682298,0.00007684151,8.644294e-7],"about_ca_topic_score_codex":0.010527994,"about_ca_topic_score_gemma":0.0066948836,"teacher_disagreement_score":0.010527994,"about_ca_system_score_codex":0.0008339226,"about_ca_system_score_gemma":0.0008407492,"threshold_uncertainty_score":0.02093345},"labels":[],"label_agreement":null},{"id":"W2905946778","doi":"10.3390/s19010046","title":"Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Control theory (sociology); Inertial measurement unit; Sensor fusion; Robustness (evolution); Kalman filter; Attitude and heading reference system; Attitude control; Computer science; Parametric statistics; Engineering; Control engineering; Mathematics; Artificial intelligence; Control (management)","score_opus":0.03059733415306281,"score_gpt":0.2684951458647395,"score_spread":0.23789781171167668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905946778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0670913,0.00028063473,0.93034476,0.00009869674,0.00008403376,0.000030441164,0.00002512401,0.00025816943,0.0017867258],"genre_scores_gemma":[0.9686955,0.0001043925,0.03018748,0.00003721562,0.000018694174,0.000033296903,0.00003281512,0.000007475121,0.0008830647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997676,0.00003760924,0.000015399806,0.00007467288,0.00008019027,0.000024496008],"domain_scores_gemma":[0.9998047,0.000049009745,0.000056819234,0.000019734236,0.00005921934,0.00001045776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034529055,0.0004161869,0.00037351137,0.00015982351,0.00026981466,0.00038902264,0.00043495215,0.00031376927,0.0005113242],"category_scores_gemma":[0.0005278101,0.00014605785,0.0002281031,0.00020210342,0.00026908275,0.00036067478,0.0003586282,0.00039553447,0.000108994354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005974749,0.00015722432,0.0029236139,0.0003666986,0.00012651173,0.00037738352,0.00028836352,0.617692,0.15407519,0.0077212853,0.0014522378,0.21422203],"study_design_scores_gemma":[0.00001551376,0.00019091545,0.0006449661,0.000004788202,0.000013553143,0.000041288913,0.000011358816,0.9920806,0.0059908703,0.00038058776,0.00061896764,0.0000066230373],"about_ca_topic_score_codex":0.002397828,"about_ca_topic_score_gemma":0.0019940047,"teacher_disagreement_score":0.002397828,"about_ca_system_score_codex":0.00022487408,"about_ca_system_score_gemma":0.0002935838,"threshold_uncertainty_score":0.0047677755},"labels":[],"label_agreement":null},{"id":"W2906430486","doi":"10.3390/s19204510","title":"Amplitude Dependence of Resonance Frequency and its Consequences for Scanning Probe Microscopy","year":2019,"lang":"en","type":"preprint","venue":"Sensors","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Amplitude; Oscillation (cell signaling); Resonance (particle physics); Cantilever; Atomic physics; Optics; Molecular physics; Physics; Chemistry; Materials science","score_opus":0.02105732407294261,"score_gpt":0.3157912422471029,"score_spread":0.29473391817416034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906430486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24740553,0.0017208331,0.7349729,0.001677371,0.00028352253,0.00009839938,0.00018526088,0.0014268644,0.012229364],"genre_scores_gemma":[0.9276702,0.00089388376,0.06939716,0.00016146421,0.00006946536,0.000081888604,0.00009549866,0.000247588,0.001382809],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835485,0.00029174125,0.000065900545,0.00028740097,0.0009389838,0.000060976767],"domain_scores_gemma":[0.995291,0.003467895,0.00029390468,0.0005445608,0.00036388694,0.00003872413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013631313,0.00045030378,0.0003036599,0.0004795719,0.00028995043,0.00046015935,0.00079331925,0.0009603524,0.0015173734],"category_scores_gemma":[0.011272343,0.00026503895,0.00024554337,0.0003691713,0.0010354843,0.0008633684,0.0006023389,0.001053896,0.00039911474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016029223,0.000108126194,0.002136541,0.00033573213,0.00002607238,0.00075946195,0.00033734553,0.046868343,0.85895854,0.050450336,0.00080815004,0.039051067],"study_design_scores_gemma":[0.000026287393,0.00011882984,0.0040646624,0.000058892398,0.00002496938,0.00082416245,0.000056381414,0.66915023,0.28711727,0.035332646,0.0031457904,0.000079816324],"about_ca_topic_score_codex":0.00051169767,"about_ca_topic_score_gemma":0.00026026188,"teacher_disagreement_score":0.0015173734,"about_ca_system_score_codex":0.0003954794,"about_ca_system_score_gemma":0.00020969287,"threshold_uncertainty_score":0.007209003},"labels":[],"label_agreement":null},{"id":"W2906988669","doi":"10.3390/s19010095","title":"Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Clutter; Algorithm; Kurtosis; Radar; Wavelet packet decomposition; Wavelet; Noise reduction; Standard deviation; Artificial intelligence; Pattern recognition (psychology); Wavelet transform; Mathematics; Statistics; Telecommunications","score_opus":0.011276499893076455,"score_gpt":0.24389372567667528,"score_spread":0.23261722578359884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906988669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026401574,0.00019661171,0.9729276,0.00003196765,0.000025833368,0.000013068175,0.000011296486,0.00012023724,0.00027185696],"genre_scores_gemma":[0.22569053,0.0007428165,0.77155495,0.00005136037,0.00007720832,0.000055654044,0.00014147122,0.00008601383,0.0015999769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964905,0.00006647998,0.000025749361,0.00008281423,0.00015714976,0.000018714489],"domain_scores_gemma":[0.9996382,0.0001451408,0.00004838241,0.00004130922,0.00011191443,0.00001503751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060064544,0.000558124,0.00061339117,0.00049182965,0.00016207146,0.00036768682,0.0004290428,0.0004286812,0.00041185494],"category_scores_gemma":[0.0012768947,0.00020553987,0.00061752077,0.00056336704,0.00021743079,0.00064547604,0.00031875505,0.00040958484,0.0002467426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042981643,0.0001462503,0.0018945301,0.00022568565,0.00011955776,0.00022651668,0.00019388457,0.072719425,0.27496305,0.0044980827,0.00079840556,0.64378476],"study_design_scores_gemma":[0.00001973601,0.00023391508,0.0028856352,0.000011969993,0.00006880849,0.00043983877,0.00002642013,0.9294195,0.06365445,0.0007928685,0.002417181,0.000029725354],"about_ca_topic_score_codex":0.0004391883,"about_ca_topic_score_gemma":0.0004824784,"teacher_disagreement_score":0.00061339117,"about_ca_system_score_codex":0.00012446725,"about_ca_system_score_gemma":0.00029128703,"threshold_uncertainty_score":0.00317657},"labels":[],"label_agreement":null},{"id":"W2907498677","doi":"10.3390/s19040806","title":"Hollow Fiber Coupler Sensor","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; CMC Microsystems","keywords":"Materials science; Fiber; Optoelectronics; Fiber optic sensor; Composite material","score_opus":0.007284667009698699,"score_gpt":0.20686359467229573,"score_spread":0.19957892766259702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907498677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48658282,0.007932475,0.4874085,0.00077617995,0.0010183502,0.0004002487,0.001405869,0.004809716,0.009665895],"genre_scores_gemma":[0.7220875,0.0017185773,0.2655094,0.0005648059,0.00021590835,0.00018011384,0.00073517964,0.00013551977,0.008852994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99839956,0.00012235864,0.00006653615,0.00044733123,0.000888656,0.00007552704],"domain_scores_gemma":[0.99929607,0.000106236,0.00019292725,0.000053711065,0.00028848046,0.00006241863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050853466,0.0006257324,0.00066749,0.00063076685,0.00037360663,0.0005341412,0.001298627,0.0011638231,0.0012365968],"category_scores_gemma":[0.0006811812,0.00033200046,0.00037295558,0.00039819544,0.00038327096,0.0012533965,0.0007082519,0.0005285988,0.00095742487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012624981,0.000044320557,0.00047733643,0.00011074387,0.000017393546,0.00007233811,0.000019762716,0.00057607505,0.9857961,0.00043257,0.0004526879,0.011874554],"study_design_scores_gemma":[0.000018733439,0.00021377832,0.0014687291,0.0000081949365,0.000028790826,0.0005237727,0.000017765717,0.014625351,0.9786068,0.00015327081,0.0042913407,0.000043277643],"about_ca_topic_score_codex":0.0007305894,"about_ca_topic_score_gemma":0.0007457756,"teacher_disagreement_score":0.001298627,"about_ca_system_score_codex":0.00048983423,"about_ca_system_score_gemma":0.00036210698,"threshold_uncertainty_score":0.0041368008},"labels":[],"label_agreement":null},{"id":"W2907578683","doi":"10.3390/s19010105","title":"A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Orthogonal frequency-division multiplexing; Doppler effect; Compensation (psychology); Acoustics; Underwater acoustic communication; Underwater; Computer science; Underwater acoustics; Doppler frequency; Electronic engineering; Telecommunications; Engineering; Geology; Physics; Psychology; Channel (broadcasting)","score_opus":0.024028057173007177,"score_gpt":0.23615523999383575,"score_spread":0.21212718282082857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907578683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005650037,0.00013666537,0.9927678,0.00002293157,0.000104348495,0.000034384662,0.000017426164,0.0004217245,0.00084464793],"genre_scores_gemma":[0.11676941,0.00039113133,0.8782176,0.00006389052,0.00006812788,0.00010959319,0.00010687114,0.0001017481,0.00417162],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998265,0.000024257393,0.000010649974,0.0000426991,0.00007908183,0.000016869719],"domain_scores_gemma":[0.9998134,0.00006235262,0.000019576837,0.000024300882,0.00007416515,0.0000062868485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025944627,0.0004950296,0.0003005513,0.00038381867,0.00040888556,0.00032964008,0.00050186436,0.00060036726,0.0017916373],"category_scores_gemma":[0.0008653071,0.00015711263,0.00037288235,0.00039351126,0.00019655017,0.0003109124,0.00018888622,0.00063548126,0.00069708115],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018895349,0.000118803255,0.00092455704,0.00018616319,0.00006929574,0.00023210114,0.0001620375,0.08921009,0.19615054,0.014345187,0.0020821174,0.69633013],"study_design_scores_gemma":[0.00002250439,0.00015209257,0.00084595615,0.000021206868,0.000037245485,0.00022300902,0.000016049484,0.89682454,0.08005445,0.0010617191,0.020709628,0.000031580366],"about_ca_topic_score_codex":0.003935125,"about_ca_topic_score_gemma":0.0045863013,"teacher_disagreement_score":0.003935125,"about_ca_system_score_codex":0.00036913698,"about_ca_system_score_gemma":0.0005499589,"threshold_uncertainty_score":0.007824421},"labels":[],"label_agreement":null},{"id":"W2907765090","doi":"10.3390/s19010109","title":"Ultrasonic Tethering to Enable Side-by-Side Following for Powered Wheelchairs","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wheelchair; Conversation; Ultrasonic sensor; Distraction; Computer science; Human–computer interaction; Simulation; Trajectory; Engineering; Psychology; Acoustics; Communication","score_opus":0.013498964110694964,"score_gpt":0.2590613611135193,"score_spread":0.24556239700282434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907765090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4290199,0.0013185282,0.5526583,0.0003542672,0.00039259155,0.00038699404,0.00025479242,0.0063362727,0.00927835],"genre_scores_gemma":[0.8687295,0.000408362,0.117103375,0.000169902,0.00003597551,0.00018309134,0.00014390479,0.00014193854,0.013084025],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998344,0.000025760772,0.000012351597,0.000027931015,0.000077102355,0.000022316806],"domain_scores_gemma":[0.99975425,0.00005978065,0.000030179608,0.000031436703,0.00010545982,0.000018860466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021884906,0.00032966584,0.00028551195,0.00032763864,0.0002401542,0.00025455703,0.00061070995,0.00035970836,0.004122625],"category_scores_gemma":[0.00079417584,0.00020303922,0.00017497092,0.0001652426,0.00016425358,0.00045812465,0.00054696266,0.00017001014,0.0009779563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032420858,0.00012352584,0.0019900615,0.00026692706,0.00002457583,0.00047845746,0.0005415985,0.0013183166,0.7285184,0.0013503514,0.0038898485,0.2611737],"study_design_scores_gemma":[0.00021100687,0.00310603,0.025659088,0.00022644641,0.00033852615,0.0037145112,0.00075482647,0.11712829,0.7533096,0.0018553366,0.09347415,0.00022209345],"about_ca_topic_score_codex":0.0019581926,"about_ca_topic_score_gemma":0.0032965795,"teacher_disagreement_score":0.004122625,"about_ca_system_score_codex":0.00017683126,"about_ca_system_score_gemma":0.0002534643,"threshold_uncertainty_score":0.0137915015},"labels":[],"label_agreement":null},{"id":"W2908697872","doi":"10.3390/s19020417","title":"Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ; National Key Research and Development Program of China; Wuhan University; National Natural Science Foundation of China","keywords":"Global Positioning System; Kalman filter; Computer science; GPS/INS; Real-time computing; Precise Point Positioning; Remote sensing; Geodesy; Assisted GPS; Geography; Telecommunications; GNSS applications; Artificial intelligence","score_opus":0.01538437501462447,"score_gpt":0.1958902502619086,"score_spread":0.18050587524728412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908697872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013841228,0.00015921038,0.98016936,0.000073244,0.000077765704,0.00005416618,0.00011678758,0.0019301891,0.0035779967],"genre_scores_gemma":[0.8503494,0.00045614774,0.14029814,0.00015419465,0.00007948351,0.0002152187,0.00085920497,0.00016978988,0.0074182865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992274,0.000080015285,0.00005140305,0.00022508013,0.00033803773,0.000077945115],"domain_scores_gemma":[0.9997117,0.000047769972,0.000056822515,0.00004167104,0.00013361158,0.000008348016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005005646,0.0010136198,0.0009981875,0.0004981758,0.00041240087,0.00089959084,0.0010077702,0.0007214892,0.0018347437],"category_scores_gemma":[0.001203196,0.00048641488,0.000771119,0.00054227025,0.0003947609,0.0011324423,0.00093136844,0.00089878077,0.0011284928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011575802,0.00005061503,0.0033386385,0.0001336263,0.00008362407,0.00016049492,0.00009107136,0.86238956,0.010294043,0.0039320285,0.0021536257,0.11725697],"study_design_scores_gemma":[0.000010583664,0.00003927219,0.001199117,0.0000068180752,0.000019939373,0.00003456559,0.000011176552,0.9943198,0.002280785,0.00061447854,0.0014485171,0.000014871736],"about_ca_topic_score_codex":0.019501347,"about_ca_topic_score_gemma":0.010087253,"teacher_disagreement_score":0.019501347,"about_ca_system_score_codex":0.0005799123,"about_ca_system_score_gemma":0.0014744821,"threshold_uncertainty_score":0.038775682},"labels":[],"label_agreement":null},{"id":"W2908731036","doi":"10.3390/s19020268","title":"A Facile Electrochemical Sensor Labeled by Ferrocenoyl Cysteine Conjugate for the Detection of Nitrite in Pickle Juice","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources; Guangxi Normal University; National Natural Science Foundation of China","keywords":"Nitrite; Conjugate; Cysteine; Chemistry; Electrochemistry; Combinatorial chemistry; Electrochemical gas sensor; Amperometry; Computer science; Food science; Biochemistry; Electrode; Organic chemistry; Mathematics; Nitrate; Enzyme","score_opus":0.004820604205626145,"score_gpt":0.23796495694087721,"score_spread":0.23314435273525108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908731036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8228147,0.0072340625,0.16576856,0.00044388254,0.00032397837,0.00025795394,0.0007136149,0.001020636,0.0014226502],"genre_scores_gemma":[0.7133322,0.0035646423,0.27543622,0.00033219985,0.00007536135,0.00028792088,0.00084181613,0.000052054125,0.0060777175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995648,0.00005399368,0.000025042218,0.00015217284,0.00017332836,0.00003065064],"domain_scores_gemma":[0.9998324,0.000035459892,0.000034472112,0.00001327321,0.00006003351,0.00002431994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004360924,0.00084282266,0.00067232596,0.00044970817,0.00025420947,0.00022133531,0.0007948133,0.0011043551,0.0005115523],"category_scores_gemma":[0.0003745656,0.00036985698,0.00032448504,0.00027335342,0.00027847957,0.0005490491,0.00034852154,0.0006047823,0.00022411681],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008992229,0.00000480196,0.00004242873,0.000034772846,0.0000023576497,0.000019866686,0.000007962312,0.000022251139,0.9990338,0.000015756737,0.000016337108,0.0007905845],"study_design_scores_gemma":[0.000006753671,0.00014720717,0.00084105524,0.0000039348834,0.000013555559,0.00022251607,0.000011357722,0.0010028539,0.9965307,0.000017663942,0.0011900039,0.00001232152],"about_ca_topic_score_codex":0.00053397653,"about_ca_topic_score_gemma":0.0012218906,"teacher_disagreement_score":0.0011043551,"about_ca_system_score_codex":0.00028836963,"about_ca_system_score_gemma":0.00029249923,"threshold_uncertainty_score":0.0023063421},"labels":[],"label_agreement":null},{"id":"W2908737503","doi":"10.3390/s19020326","title":"A Decentralized Privacy-Preserving Healthcare Blockchain for IoT","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":867,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Blockchain; Internet of Things; Computer security; Internet privacy; Computer science; Health care; Information privacy; Business; Political science; Law","score_opus":0.014369527064096504,"score_gpt":0.26377519851708425,"score_spread":0.24940567145298775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908737503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042618528,0.00041631763,0.9388125,0.0012682183,0.00018014222,0.00033201234,0.00032607102,0.0005627703,0.015483367],"genre_scores_gemma":[0.9155442,0.00041949152,0.07315356,0.00017054674,0.00007623184,0.00032642845,0.00029591913,0.000047262158,0.009966317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870956,0.00046218844,0.000076270364,0.0002179795,0.0003688006,0.00016512211],"domain_scores_gemma":[0.99839276,0.0005860334,0.00015474028,0.00042849497,0.00023777275,0.00020022053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00145621,0.00039593264,0.0007254473,0.0003944658,0.0013721361,0.0015090305,0.0014652845,0.0012843822,0.005575585],"category_scores_gemma":[0.0031479942,0.00030814734,0.0005256595,0.0008200747,0.00130631,0.0026691698,0.002721476,0.0010317597,0.00081817876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007350486,0.00021979307,0.0012726702,0.00022172723,0.00006633509,0.0010238356,0.0005353259,0.5440931,0.011235942,0.3485482,0.0071175043,0.08493059],"study_design_scores_gemma":[0.0001387223,0.0001244442,0.00014475315,0.0000347228,0.000016503234,0.0001899568,0.000057810303,0.86697865,0.0023439112,0.11949209,0.010452901,0.00002555915],"about_ca_topic_score_codex":0.0028263477,"about_ca_topic_score_gemma":0.0036119046,"teacher_disagreement_score":0.005575585,"about_ca_system_score_codex":0.0013311064,"about_ca_system_score_gemma":0.0026772462,"threshold_uncertainty_score":0.01865226},"labels":[],"label_agreement":null},{"id":"W2908906402","doi":"10.3390/s19020424","title":"A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Southwest Jiaotong University; Simon Fraser University; Tsinghua University","keywords":"Trilateration; Bluetooth; Computer science; Real-time computing; Beacon; Kalman filter; Dead reckoning; Hybrid positioning system; Sensor fusion; Indoor positioning system; Embedded system; Positioning system; Wireless; Computer vision; Artificial intelligence; Global Positioning System; Engineering; Telecommunications; Accelerometer","score_opus":0.004258112508648164,"score_gpt":0.20312561856144268,"score_spread":0.19886750605279452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908906402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008834518,0.00026826517,0.989122,0.000033474564,0.000075111406,0.00002065877,0.000016976803,0.00075480784,0.00087420107],"genre_scores_gemma":[0.40779603,0.00078776944,0.58468974,0.00013460814,0.00012286287,0.00009934568,0.0001979206,0.00015177703,0.006019928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922407,0.00009391074,0.000056715664,0.00020427933,0.00035080675,0.00007024587],"domain_scores_gemma":[0.9993437,0.0001333248,0.000068853864,0.000118066964,0.0003064732,0.000029495835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058724626,0.0009294261,0.0007620368,0.0013478704,0.00046161513,0.00066604215,0.00096381083,0.00079383655,0.0014852054],"category_scores_gemma":[0.0016372522,0.00043344076,0.00090563507,0.0010036229,0.00031952417,0.0014139,0.0009849473,0.00056218717,0.0009314079],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024828894,0.00010177675,0.0023470127,0.00029129582,0.00012087927,0.00021819338,0.00037319004,0.08348869,0.111781746,0.0037690245,0.001941038,0.79531884],"study_design_scores_gemma":[0.000049308757,0.00045177262,0.0038036143,0.000037591017,0.00015238118,0.00064517366,0.00013836291,0.92872816,0.054510783,0.0015209822,0.0098545635,0.00010731391],"about_ca_topic_score_codex":0.0040095733,"about_ca_topic_score_gemma":0.0042758733,"teacher_disagreement_score":0.0040095733,"about_ca_system_score_codex":0.000301599,"about_ca_system_score_gemma":0.00054209295,"threshold_uncertainty_score":0.007972479},"labels":[],"label_agreement":null},{"id":"W2909708709","doi":"10.3390/s19020345","title":"Skull’s Photoacoustic Attenuation and Dispersion Modeling with Deterministic Ray-Tracing: Towards Real-Time Aberration Correction","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital","funders":"","keywords":"Attenuation; Ray tracing (physics); Acoustics; Reflection (computer programming); Dispersion (optics); Computer science; Optics; Refraction; Transmission (telecommunications); SIGNAL (programming language); Physics; Telecommunications","score_opus":0.005906223985934745,"score_gpt":0.19663042163159686,"score_spread":0.19072419764566212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909708709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007112584,0.00008350542,0.99187016,0.000051677165,0.000009456003,0.000013678089,0.000018692594,0.00029099098,0.000549282],"genre_scores_gemma":[0.3108171,0.0005762656,0.68516016,0.000052758573,0.000018297307,0.00008399473,0.00010933372,0.00028037082,0.0029016924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997224,0.00005429106,0.00001553612,0.00003669567,0.0001542234,0.000016876073],"domain_scores_gemma":[0.99935895,0.00023115905,0.00014313338,0.00011077432,0.00013498003,0.000021100599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049721776,0.00061396294,0.0002728946,0.0005480724,0.00024714472,0.0007197175,0.00091928255,0.0007229628,0.00091653154],"category_scores_gemma":[0.0020259763,0.00039208608,0.00064438384,0.00046148352,0.00051638304,0.0006188871,0.00069215766,0.00061385735,0.000306968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004780922,0.000028770804,0.001382909,0.000082646635,0.000031516898,0.00012095979,0.00021945163,0.90295595,0.02872431,0.019541418,0.0006218801,0.04624239],"study_design_scores_gemma":[0.0000030098358,0.00000830046,0.000117872485,0.0000040853347,0.0000041013395,0.000046286266,0.000007732637,0.9937018,0.0040111593,0.0010031197,0.00108547,0.0000070647243],"about_ca_topic_score_codex":0.010708548,"about_ca_topic_score_gemma":0.007151163,"teacher_disagreement_score":0.010708548,"about_ca_system_score_codex":0.0008482396,"about_ca_system_score_gemma":0.0014035619,"threshold_uncertainty_score":0.021292448},"labels":[],"label_agreement":null},{"id":"W2909910397","doi":"10.3390/s19020294","title":"Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Higher Education Discipline Innovation Project; Government of Jiangsu Province; China Postdoctoral Science Foundation; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Dead reckoning; Inertial measurement unit; Heading (navigation); Kalman filter; Computer science; Inertial navigation system; Outlier; Extended Kalman filter; Computer vision; Artificial intelligence; Control theory (sociology); Engineering; Global Positioning System; Inertial frame of reference","score_opus":0.014666809061953875,"score_gpt":0.20389052152059178,"score_spread":0.1892237124586379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909910397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014937561,0.0002770933,0.98278934,0.000043589713,0.00008478241,0.000020040934,0.000030038906,0.0008665147,0.00095121516],"genre_scores_gemma":[0.7511061,0.00077415264,0.24308729,0.00010539551,0.0001170996,0.00011434641,0.00029204265,0.000060082573,0.0043435185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999566,0.00006202712,0.00003493084,0.00013186948,0.000162257,0.00004292884],"domain_scores_gemma":[0.9997147,0.000048138765,0.000040069906,0.000039691633,0.00014680746,0.000010582936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037957338,0.0006667395,0.0006509297,0.00043472205,0.00037324635,0.00039425245,0.00065463857,0.0005211026,0.0010038146],"category_scores_gemma":[0.0007961896,0.0003193089,0.000533542,0.00043764865,0.00020397002,0.00071123644,0.00042379668,0.00047401423,0.00058898475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056441565,0.000114278155,0.004530932,0.0003924614,0.0001990389,0.0003509252,0.00033232,0.23973939,0.092255145,0.0040054466,0.0041607087,0.65335494],"study_design_scores_gemma":[0.00003671986,0.00022248126,0.0022288645,0.00001986622,0.00009488894,0.00019436459,0.00003563997,0.97289103,0.019829217,0.00049659226,0.0039018681,0.000048394835],"about_ca_topic_score_codex":0.005205305,"about_ca_topic_score_gemma":0.004481808,"teacher_disagreement_score":0.005205305,"about_ca_system_score_codex":0.00025814987,"about_ca_system_score_gemma":0.00054206094,"threshold_uncertainty_score":0.010349989},"labels":[],"label_agreement":null},{"id":"W2910779736","doi":"10.3390/s19020324","title":"Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Extended Kalman filter; Wireless; Computer science; Wireless sensor network; Artificial intelligence; Kalman filter; Artificial neural network; Real-time computing; Wireless network; Machine learning; Data mining; Telecommunications; Computer network","score_opus":0.004617575737699861,"score_gpt":0.1798695773789948,"score_spread":0.17525200164129495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910779736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051522385,0.00034475455,0.9465859,0.00008529632,0.00003124397,0.000021048992,0.000049897262,0.0004894894,0.0008700955],"genre_scores_gemma":[0.9535385,0.00024529034,0.045381773,0.000039187657,0.00003160387,0.000035212182,0.000085679465,0.000021275746,0.0006214377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992805,0.00014333372,0.000056688805,0.0002022197,0.00024674024,0.00007051954],"domain_scores_gemma":[0.9982503,0.00086646056,0.0002905704,0.00013702149,0.00041832318,0.000037355563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093561533,0.0007685858,0.00069113006,0.0009468668,0.00027633095,0.000669577,0.00064907223,0.0005494389,0.0004934782],"category_scores_gemma":[0.0042918297,0.00023393678,0.0004109328,0.0007015811,0.0003876893,0.0012302458,0.00071339007,0.00071496767,0.00018482545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012974719,0.000056928897,0.007937625,0.00008249452,0.0000549314,0.00011534827,0.00006769164,0.7920223,0.0047557103,0.0017718422,0.00052301335,0.19248246],"study_design_scores_gemma":[0.0000019426066,0.000017782664,0.00081134157,0.0000051334414,0.0000058154533,0.000022120312,0.0000055223254,0.9971269,0.0013952444,0.0005105809,0.00009181083,0.000005825594],"about_ca_topic_score_codex":0.0034582433,"about_ca_topic_score_gemma":0.0022712369,"teacher_disagreement_score":0.0034582433,"about_ca_system_score_codex":0.00048494292,"about_ca_system_score_gemma":0.00043342425,"threshold_uncertainty_score":0.0068762302},"labels":[],"label_agreement":null},{"id":"W2911016401","doi":"10.3390/s19020430","title":"Spatial and Temporal Variation of Drought Based on Satellite Derived Vegetation Condition Index in Nepal from 1982–2015","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Oceanic and Atmospheric Administration; National Natural Science Foundation of China; Third World Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences; Tribhuvan University","keywords":"Vegetation Index; Vegetation (pathology); Index (typography); Variation (astronomy); Satellite; Spatial variability; Enhanced vegetation index; Normalized Difference Vegetation Index; Remote sensing; Geography; Physical geography; Climatology; Environmental science; Statistics; Leaf area index; Ecology; Geology; Mathematics; Computer science; Engineering; Biology; Medicine","score_opus":0.004939220082512206,"score_gpt":0.2137455087756485,"score_spread":0.20880628869313628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911016401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971812,0.00017263641,0.00010499366,0.000053985368,0.0000040246027,0.000005626966,0.0016185258,0.000008991814,0.00085003517],"genre_scores_gemma":[0.9981889,0.0001166559,0.000102260004,0.000015507108,0.0000040639015,0.0000124643775,0.0013298563,0.0000013745005,0.00022890075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999064,0.000013013524,0.000011715885,0.00002978859,0.000016306527,0.000022806818],"domain_scores_gemma":[0.9997428,0.00004645432,0.000082152,0.000013205307,0.0000814207,0.00003403627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020626273,0.00012075415,0.0001161373,0.0006857815,0.00021041348,0.00032721442,0.00021500005,0.00014096088,0.00056297064],"category_scores_gemma":[0.000592346,0.00008560007,0.00014750363,0.0007976734,0.00017478666,0.00030258976,0.00041148567,0.00019112545,0.0000736983],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004535478,0.000020668695,0.98398745,0.00007671197,0.00008475684,0.00051463395,0.0012087676,0.000518377,0.0013654883,0.00013221084,0.0009326276,0.011113004],"study_design_scores_gemma":[0.0000010471776,0.0000088832185,0.9981261,0.000012466137,0.00001085977,0.00011391967,0.00040821385,0.0005527813,0.000103115024,0.000017784449,0.0006411371,0.0000037041996],"about_ca_topic_score_codex":0.027451687,"about_ca_topic_score_gemma":0.05785491,"teacher_disagreement_score":0.027451687,"about_ca_system_score_codex":0.00035883632,"about_ca_system_score_gemma":0.00026929475,"threshold_uncertainty_score":0.054583788},"labels":[],"label_agreement":null},{"id":"W2911029596","doi":"10.3390/s19020258","title":"Label-Free Capacitive Biosensor for Detection of Cryptosporidium","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Parasitic Infections and Diagnostics","field":"Immunology and Microbiology","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Cryptosporidium; Biosensor; Detection limit; Cryptosporidium parvum; Monoclonal antibody; Chemistry; Materials science; Chromatography; Biology; Nanotechnology; Microbiology; Antibody","score_opus":0.00963331743630928,"score_gpt":0.23695308334785914,"score_spread":0.22731976591154987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911029596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44505623,0.01837187,0.52485585,0.0012706259,0.0015838807,0.00039436214,0.0011545406,0.0020674926,0.0052450723],"genre_scores_gemma":[0.7021216,0.00925981,0.27554104,0.0011545811,0.0002510373,0.00039010483,0.0011543918,0.00009670269,0.010030759],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988655,0.00016924803,0.000056017372,0.0002731091,0.00055501435,0.000081233506],"domain_scores_gemma":[0.99966204,0.00009902236,0.000055102104,0.00002716146,0.00012830464,0.00002836024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006539405,0.0010184719,0.00058817444,0.0005524768,0.00031948034,0.00046903928,0.0014447647,0.001903639,0.00095696875],"category_scores_gemma":[0.0007765179,0.0004937045,0.00050812477,0.00051981496,0.0003229192,0.0009424696,0.0006284482,0.0011335922,0.00067384075],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019554178,0.000015471534,0.000075054755,0.00011180374,0.0000060638326,0.000027819908,0.000011005878,0.0000650136,0.99624664,0.000075811826,0.00008293586,0.0032627492],"study_design_scores_gemma":[0.00001043457,0.0001727956,0.0007032745,0.000011277255,0.00002440896,0.0002799768,0.000024158915,0.003168726,0.9924077,0.00005646468,0.0031136698,0.00002717742],"about_ca_topic_score_codex":0.00051354076,"about_ca_topic_score_gemma":0.0009231986,"teacher_disagreement_score":0.001903639,"about_ca_system_score_codex":0.0004702198,"about_ca_system_score_gemma":0.00035286322,"threshold_uncertainty_score":0.0034583807},"labels":[],"label_agreement":null},{"id":"W2911055096","doi":"10.3390/s19020274","title":"An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Clutter; Fingerprint (computing); Computer science; Noise (video); Interference (communication); Singular value decomposition; Artificial intelligence; Real-time computing; Computer vision; Radar; Telecommunications; Image (mathematics)","score_opus":0.0075880015956413405,"score_gpt":0.2494676934972329,"score_spread":0.24187969190159156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911055096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031285323,0.00022731959,0.96669275,0.000045815865,0.00004237974,0.00002520715,0.000048338017,0.0010532439,0.0005796194],"genre_scores_gemma":[0.30550957,0.00030397825,0.6913066,0.000085733715,0.000058062968,0.00006804265,0.00028276135,0.00006265275,0.0023226396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961424,0.000034576125,0.000027722723,0.00010838731,0.0001761536,0.000038896786],"domain_scores_gemma":[0.99957186,0.000088474415,0.00006606595,0.000058729514,0.0001956346,0.000019306986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033778985,0.00047909303,0.0007210768,0.0013320132,0.00025693342,0.0005883387,0.00069592055,0.00044119847,0.0006965535],"category_scores_gemma":[0.0010019977,0.00021955067,0.0004988142,0.00082866603,0.00019586117,0.00084969285,0.00039445376,0.00041078648,0.00066987507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017692946,0.0001004328,0.003447959,0.000062953615,0.00006475147,0.00012356462,0.000050948733,0.029145325,0.100412846,0.0013806878,0.0015063506,0.8635273],"study_design_scores_gemma":[0.00003341127,0.00017568152,0.0048016743,0.00001029731,0.000047967682,0.0005746921,0.000029333465,0.93891644,0.05100308,0.0007685032,0.0035968423,0.000042091968],"about_ca_topic_score_codex":0.0021664114,"about_ca_topic_score_gemma":0.0017997519,"teacher_disagreement_score":0.0021664114,"about_ca_system_score_codex":0.00024886496,"about_ca_system_score_gemma":0.0004364715,"threshold_uncertainty_score":0.004307568},"labels":[],"label_agreement":null},{"id":"W2911498096","doi":"10.3390/s19030587","title":"Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ; Natural Sciences and Engineering Research Council of Canada; Centre National d’Etudes Spatiales","keywords":"GNSS applications; Precise Point Positioning; Zenith; Initialization; Computer science; Convergence (economics); Troposphere; Atmosphere (unit); Environmental science; Geodesy; Remote sensing; Meteorology; Global Positioning System; Telecommunications; Geography","score_opus":0.007050173721861325,"score_gpt":0.2066722225256182,"score_spread":0.1996220488037569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911498096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33744678,0.0017810316,0.6254637,0.0017383562,0.00027238193,0.00010499637,0.0005051422,0.0008957776,0.03179174],"genre_scores_gemma":[0.8762049,0.0005763587,0.120852165,0.0001791386,0.000083898856,0.00004054001,0.00028386083,0.00018737343,0.0015917228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997886,0.00074601505,0.00010157678,0.00040592812,0.00056323555,0.00029713678],"domain_scores_gemma":[0.9955426,0.0017937246,0.000500503,0.0010907914,0.00093499856,0.00013748222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033111887,0.00063368626,0.00061537244,0.00050511723,0.00063209113,0.0016413704,0.00093295064,0.0010220342,0.0017737547],"category_scores_gemma":[0.011856824,0.0002519781,0.00041080583,0.0012172775,0.0010465712,0.0025049662,0.0017526755,0.0010708398,0.00060060166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007007384,0.00014936892,0.018777123,0.0005739088,0.00023000342,0.0017888214,0.0006596999,0.71530694,0.026213724,0.06260541,0.003927612,0.16906667],"study_design_scores_gemma":[0.00007515182,0.00042491747,0.016032731,0.00016343733,0.00008236725,0.0011982634,0.00062122685,0.90834135,0.022034174,0.032857534,0.018078258,0.00009057478],"about_ca_topic_score_codex":0.0053438465,"about_ca_topic_score_gemma":0.0048589227,"teacher_disagreement_score":0.0053438465,"about_ca_system_score_codex":0.0005232208,"about_ca_system_score_gemma":0.000778013,"threshold_uncertainty_score":0.017511427},"labels":[],"label_agreement":null},{"id":"W2911702732","doi":"10.3390/s19030631","title":"Long-Range Surface Plasmon-Polariton Waveguide Biosensors for Human Cardiac Troponin I Detection","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Plasmonic and Surface Plasmon Research","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biosensor; Troponin I; Analyte; Materials science; Surface plasmon polariton; Detection limit; Refractive index; Surface plasmon resonance; Surface plasmon; Optoelectronics; Biomedical engineering; Nanotechnology; Plasmon; Analytical Chemistry (journal); Chemistry; Myocardial infarction; Chromatography; Medicine; Cardiology; Nanoparticle","score_opus":0.012080705624179615,"score_gpt":0.24199778688399534,"score_spread":0.22991708125981572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911702732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34086838,0.014605461,0.6364252,0.0011219181,0.0005240602,0.00031776645,0.00033224616,0.001267299,0.0045376695],"genre_scores_gemma":[0.5140663,0.0073537915,0.4696961,0.00042682805,0.00013427537,0.00048037508,0.0002982549,0.000079294216,0.00746475],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961936,0.000110938425,0.000021510969,0.000064622356,0.00014903431,0.000034447054],"domain_scores_gemma":[0.99977785,0.00011015704,0.000034519377,0.000015134038,0.00004483332,0.00001745624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080502464,0.0007882704,0.00038321395,0.0003568854,0.00018807984,0.00036875394,0.00066205167,0.0009368892,0.0008779333],"category_scores_gemma":[0.0006066273,0.00039005463,0.00034492207,0.0003400087,0.00045295616,0.00060166273,0.00041864358,0.00089450524,0.00073900336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035366433,0.000019843652,0.00008098437,0.00010275414,0.0000047281055,0.00004862806,0.000029245854,0.00035833128,0.9928227,0.0006752548,0.0001583388,0.005663869],"study_design_scores_gemma":[0.000016019309,0.0003697056,0.00044318594,0.00001564343,0.000013130708,0.00029061423,0.000037999056,0.013611927,0.98136073,0.0006319114,0.003188551,0.000020533664],"about_ca_topic_score_codex":0.00015177202,"about_ca_topic_score_gemma":0.00037292216,"teacher_disagreement_score":0.0009368892,"about_ca_system_score_codex":0.00033952703,"about_ca_system_score_gemma":0.00024633078,"threshold_uncertainty_score":0.004257381},"labels":[],"label_agreement":null},{"id":"W2911752852","doi":"10.3390/s19030686","title":"Development and Validation of Ambulosono: A Wearable Sensor for Bio-Feedback Rehabilitation Training","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Alberta; University of Calgary","funders":"University of Calgary","keywords":"Wearable computer; Cadence; Reliability (semiconductor); Gait; Computer science; Wearable technology; Rehabilitation; Simulation; System of measurement; Embedded system; Physical medicine and rehabilitation; Medicine; Physical therapy","score_opus":0.015859307660213248,"score_gpt":0.22071564648232633,"score_spread":0.20485633882211307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911752852","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74464893,0.0031056087,0.24642798,0.00026582624,0.00037649233,0.0010768366,0.0008609966,0.00084797636,0.0023893921],"genre_scores_gemma":[0.85081524,0.00096514344,0.14449032,0.00027482907,0.000078491015,0.00073390914,0.00061309297,0.00004365717,0.0019852745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988502,0.0003098464,0.000094124916,0.00019070489,0.00052171055,0.000033485692],"domain_scores_gemma":[0.99902856,0.00022638196,0.00012153159,0.00011106134,0.00045117064,0.00006144736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017264179,0.00065001595,0.0005370114,0.0006355452,0.00018565681,0.0004430628,0.0006373303,0.0006890676,0.0008729658],"category_scores_gemma":[0.0022576924,0.0001797203,0.0002716434,0.00037107596,0.00030874694,0.00047193922,0.00050450733,0.00027349783,0.00024372547],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096719246,0.0005186511,0.03726127,0.0009085598,0.00017562474,0.00023731112,0.00034292485,0.0019722213,0.75969595,0.00052308,0.0012255894,0.19617163],"study_design_scores_gemma":[0.0003989315,0.018102901,0.29368785,0.00035702108,0.0006845453,0.0042993156,0.00067547895,0.05578687,0.59888035,0.0007533655,0.026171327,0.00020201657],"about_ca_topic_score_codex":0.00053490186,"about_ca_topic_score_gemma":0.0011348503,"teacher_disagreement_score":0.0017264179,"about_ca_system_score_codex":0.00018212976,"about_ca_system_score_gemma":0.00039625098,"threshold_uncertainty_score":0.009130299},"labels":[],"label_agreement":null},{"id":"W2912120649","doi":"10.3390/s19040817","title":"Emerging Point-of-care Technologies for Food Safety Analysis","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":178,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Vancouver Hospital and Health Sciences Centre; University of British Columbia","funders":"","keywords":"Food safety; Risk analysis (engineering); Point-of-care testing; Food packaging; Computer science; Business; Medicine; Engineering","score_opus":0.02178942505327633,"score_gpt":0.27659401200305156,"score_spread":0.25480458694977526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912120649","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024786996,0.99466646,0.001707314,0.0003339026,0.00037963057,0.000013813833,0.000029454872,0.000026155567,0.002595331],"genre_scores_gemma":[0.0012300485,0.9952493,0.001603361,0.00027228025,0.00022765667,0.00001740968,0.00004180589,0.0000038577264,0.0013542876],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915814,0.00011748081,0.00006895666,0.00015517026,0.00043031585,0.00006987377],"domain_scores_gemma":[0.9991359,0.0004352254,0.000116902185,0.000032389085,0.00023680292,0.00004283657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012780725,0.0014010392,0.0012999629,0.0035356362,0.000371796,0.0014051072,0.0014663481,0.0023132314,0.0038990316],"category_scores_gemma":[0.0012164076,0.0005422438,0.0008941282,0.0031718612,0.000768665,0.0026295672,0.0010249863,0.0025231878,0.003133358],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034482655,0.00009559571,0.00025348962,0.023451852,0.000066947745,0.00027417493,0.00009268786,0.0006288298,0.012699521,0.01577686,0.021410748,0.9252149],"study_design_scores_gemma":[0.0000044937137,0.00011136563,0.00039976306,0.0021904968,0.000059940132,0.0010454762,0.00007228201,0.00029095422,0.0038695587,0.0034677442,0.988458,0.000029937743],"about_ca_topic_score_codex":0.0008523648,"about_ca_topic_score_gemma":0.0011072851,"teacher_disagreement_score":0.0038990316,"about_ca_system_score_codex":0.000830536,"about_ca_system_score_gemma":0.0011330427,"threshold_uncertainty_score":0.013043582},"labels":[],"label_agreement":null},{"id":"W2912318445","doi":"10.3390/s19030676","title":"Management Platforms and Protocols for Internet of Things: A Survey","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; Instituto Nacional de Telecomunicações; Fundação para a Ciência e a Tecnologia; Fundo para o Desenvolvimento Tecnológico das Telecomunicações; Conselho Nacional de Desenvolvimento Científico e Tecnológico; King Saud University","keywords":"Interoperability; Computer science; Scalability; Software deployment; Standardization; Context (archaeology); Network management; Communications protocol; Key (lock); Data science; Computer security; Software engineering; World Wide Web; Computer network; Database","score_opus":0.026002058744086197,"score_gpt":0.2734080953544166,"score_spread":0.24740603661033042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912318445","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012018784,0.78340864,0.14233904,0.004068175,0.0031798568,0.0007831923,0.0008982109,0.0012229609,0.0520811],"genre_scores_gemma":[0.03255176,0.8229373,0.12540895,0.001998769,0.0017474486,0.0008299593,0.0022672769,0.0003573292,0.0119012315],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978204,0.00030335985,0.00047851264,0.0002765734,0.00098278,0.00013838505],"domain_scores_gemma":[0.9976585,0.0012275848,0.00023527071,0.00019827337,0.000587659,0.00009281283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021896514,0.0014640355,0.001152201,0.0050171916,0.00088399125,0.0034428271,0.0017105859,0.0022518323,0.003195548],"category_scores_gemma":[0.0036535366,0.0009920913,0.00088834524,0.0069717667,0.00076839834,0.006756049,0.0012399495,0.0027418362,0.0026848528],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012195436,0.00025096064,0.0020550587,0.009079022,0.0000844862,0.00056700286,0.00053326867,0.0046467013,0.0063053365,0.061579082,0.03142793,0.8833492],"study_design_scores_gemma":[0.000013981174,0.00017144695,0.001675977,0.0036818734,0.00010792563,0.0026129242,0.0005140444,0.006342903,0.0026635577,0.025653724,0.95645744,0.000104305764],"about_ca_topic_score_codex":0.0012131539,"about_ca_topic_score_gemma":0.00076459354,"teacher_disagreement_score":0.0050171916,"about_ca_system_score_codex":0.0007993931,"about_ca_system_score_gemma":0.0019244116,"threshold_uncertainty_score":0.01158011},"labels":[],"label_agreement":null},{"id":"W2912934805","doi":"10.3390/s19030601","title":"Quality Assessment of Single-Channel EEG for Wearable Devices","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Theratechnologies (Canada)","funders":"","keywords":"Electroencephalography; Wearable computer; Computer science; Channel (broadcasting); Noise (video); Quality (philosophy); Block (permutation group theory); Pattern recognition (psychology); Artificial intelligence; Embedded system; Telecommunications; Mathematics; Medicine","score_opus":0.08341303173223322,"score_gpt":0.3451334679876862,"score_spread":0.26172043625545294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912934805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3755549,0.0036329224,0.61645293,0.0001842629,0.00018028566,0.00018569054,0.0007723446,0.0013970165,0.0016397102],"genre_scores_gemma":[0.89351285,0.0015669946,0.10271012,0.00006501751,0.0001064074,0.000073820076,0.0008615578,0.00012959534,0.00097352546],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990163,0.0002009512,0.00010132707,0.00017400764,0.00046508058,0.000042296488],"domain_scores_gemma":[0.9976877,0.0006458375,0.0005127695,0.00027352246,0.0007948969,0.00008528547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010209588,0.00059381366,0.0005648599,0.0014012574,0.00015837596,0.00090107013,0.00046037653,0.00065421505,0.00090709364],"category_scores_gemma":[0.005137081,0.00012638356,0.0003404159,0.0008889079,0.0002817594,0.0006263816,0.0004488888,0.00027390337,0.00048492345],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014544043,0.00014336247,0.02251636,0.0011967941,0.00023953922,0.0006010053,0.0003015121,0.013699856,0.3625795,0.0005633366,0.0023374252,0.59436697],"study_design_scores_gemma":[0.00012520907,0.0016818428,0.2242962,0.00025969616,0.00036680317,0.0052813417,0.000480945,0.46504653,0.29187745,0.0032317906,0.007218452,0.00013368025],"about_ca_topic_score_codex":0.00061738305,"about_ca_topic_score_gemma":0.00077696313,"teacher_disagreement_score":0.0014012574,"about_ca_system_score_codex":0.00019607382,"about_ca_system_score_gemma":0.00015894115,"threshold_uncertainty_score":0.005399406},"labels":[],"label_agreement":null},{"id":"W2913860895","doi":"10.3390/s19030680","title":"Low-Hysteresis and Fast Response Time Humidity Sensors Using Suspended Functionalized Carbon Nanotubes","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hysteresis; Carbon nanotube; Materials science; Humidity; Chemical engineering; Composite material; Relative humidity; Response time; Nanotechnology; Meteorology","score_opus":0.01013825896070357,"score_gpt":0.20101976377408706,"score_spread":0.1908815048133835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913860895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9349909,0.0014764336,0.060206093,0.00023600543,0.00016630333,0.00006428395,0.00042606366,0.0004926573,0.0019413008],"genre_scores_gemma":[0.9599329,0.00042305732,0.036479305,0.0000815289,0.000042562162,0.00003952019,0.0002899863,0.000039098974,0.002672125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997125,0.000030384716,0.00001504009,0.00006871452,0.00014493552,0.000028422757],"domain_scores_gemma":[0.99973375,0.00006640109,0.000044397697,0.00002407084,0.00010837474,0.000022932398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018308652,0.0002495614,0.0002055238,0.00015465007,0.00011385076,0.00019711208,0.0003102879,0.00051975227,0.00042244504],"category_scores_gemma":[0.00043962747,0.00020160667,0.00015164429,0.00016493424,0.00016702949,0.00040380054,0.00013520056,0.00038699494,0.00026211533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015597021,0.000005284525,0.00006113806,0.000012136563,0.0000017027374,0.00002418983,0.000004901763,0.000081478436,0.9985576,0.000033849174,0.00003099127,0.0011712029],"study_design_scores_gemma":[0.0000035622004,0.000077677316,0.00072091864,0.0000014366124,0.0000023551174,0.00008294066,0.000004713724,0.0022475359,0.99635565,0.000028924369,0.00046849146,0.000005719065],"about_ca_topic_score_codex":0.00034379194,"about_ca_topic_score_gemma":0.0009656864,"teacher_disagreement_score":0.00051975227,"about_ca_system_score_codex":0.00017843081,"about_ca_system_score_gemma":0.000110850764,"threshold_uncertainty_score":0.0014132261},"labels":[],"label_agreement":null},{"id":"W2913977709","doi":"10.3390/s19040750","title":"Deep Attention Models for Human Tracking Using RGBD","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Camouflage; Artificial intelligence; Computer science; Computer vision; RGB color model; Object (grammar); Feature (linguistics); Tracking (education); Active appearance model; Video tracking; Layer (electronics); Modular design; Eye tracking; Property (philosophy); Pattern recognition (psychology); Image (mathematics)","score_opus":0.07473545932203214,"score_gpt":0.3354438198992686,"score_spread":0.26070836057723645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913977709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039391704,0.0014526531,0.9518909,0.00043670912,0.00014543335,0.0000387489,0.00030093684,0.0029243766,0.0034185045],"genre_scores_gemma":[0.8977961,0.00083646964,0.09146192,0.00035796946,0.00009363125,0.00010191908,0.00048285682,0.00012987231,0.008739319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979466,0.00002819028,0.000008270816,0.00008261204,0.000045923636,0.00004044068],"domain_scores_gemma":[0.999764,0.000084047366,0.000031972842,0.000029637804,0.000071206574,0.000019212093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312321,0.00095519435,0.00054543075,0.0006162258,0.00028476462,0.00070097926,0.0016095166,0.001025758,0.0027809595],"category_scores_gemma":[0.0013172238,0.00049333804,0.0007822679,0.00071363855,0.00043431018,0.0009753918,0.0009582384,0.0013380131,0.00086662365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018589433,0.00008544043,0.0011802925,0.00006398729,0.000060679373,0.00006212848,0.00007856862,0.8296397,0.007285606,0.0049737114,0.002506584,0.15387747],"study_design_scores_gemma":[0.0000017912441,0.000008515821,0.00016864135,0.000002614605,0.0000046887053,0.0000052140485,0.0000013220623,0.99794906,0.00046367457,0.0012076467,0.0001845174,0.0000023194707],"about_ca_topic_score_codex":0.031430397,"about_ca_topic_score_gemma":0.027853375,"teacher_disagreement_score":0.031430397,"about_ca_system_score_codex":0.0017803445,"about_ca_system_score_gemma":0.00071447185,"threshold_uncertainty_score":0.062494874},"labels":[],"label_agreement":null},{"id":"W2914403949","doi":"10.3390/s19040801","title":"Waste Coffee Ground Biochar: A Material for Humidity Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Coffee research and impacts","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Relative humidity; Humidity; Materials science; Biochar; Thermogravimetric analysis; Polyvinyl alcohol; Composite material; Chemistry; Pyrolysis; Organic chemistry; Meteorology","score_opus":0.032925583870287274,"score_gpt":0.31356259276630744,"score_spread":0.28063700889602017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914403949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9021038,0.02707412,0.05967769,0.000576469,0.00031646917,0.00010749188,0.0011378004,0.0007425156,0.008263683],"genre_scores_gemma":[0.9731372,0.0036717036,0.018716712,0.00011154113,0.000042573334,0.00002654539,0.00035544686,0.0000638773,0.0038744025],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978286,0.00003487513,0.000012614348,0.000049620812,0.00009247148,0.000027585764],"domain_scores_gemma":[0.99993527,0.000011156778,0.00001594136,0.000009764171,0.00001600569,0.000011865381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016254411,0.0004691851,0.00027575353,0.0004996822,0.0001601454,0.00044666542,0.00032123373,0.00049724616,0.0010555539],"category_scores_gemma":[0.00018679306,0.0002404768,0.0002127094,0.00037585132,0.00015065774,0.00033278315,0.00023988416,0.00030478527,0.0008268672],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036215468,0.0000099858735,0.00016846697,0.00012654673,0.000006526189,0.00007138478,0.000006713776,0.000121757876,0.99344736,0.00007094398,0.00006009711,0.0058739465],"study_design_scores_gemma":[0.0000037211219,0.00010313404,0.0013413368,0.000011383766,0.000013368222,0.00024268916,0.000018397952,0.0009941064,0.99334514,0.000053943088,0.0038659575,0.0000068585264],"about_ca_topic_score_codex":0.00032723596,"about_ca_topic_score_gemma":0.0007631181,"teacher_disagreement_score":0.0010555539,"about_ca_system_score_codex":0.00021934268,"about_ca_system_score_gemma":0.00010348084,"threshold_uncertainty_score":0.0035312176},"labels":[],"label_agreement":null},{"id":"W2914578823","doi":"10.3390/s19030717","title":"A Simplified SSY Estimate Method to Determine EPFM Constraint Parameter for Sensor Design","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Constraint (computer-aided design); Computer science; Engineering; Mechanical engineering","score_opus":0.053421987570192064,"score_gpt":0.35215638804134225,"score_spread":0.2987344004711502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914578823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016114216,0.00004892406,0.997638,0.00001418115,0.0000071873014,0.000020443362,0.00002025622,0.00016497375,0.00047459023],"genre_scores_gemma":[0.11723931,0.00031947548,0.8786298,0.00006778887,0.000038387596,0.00033351328,0.00024669897,0.00015473862,0.0029703425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992105,0.00011817896,0.000054324773,0.00016610337,0.00042255333,0.000028429811],"domain_scores_gemma":[0.9991203,0.0002755338,0.000110397574,0.00012682081,0.00034767253,0.000019230125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069547497,0.0010763048,0.00067402766,0.0011744122,0.00033949365,0.0009006127,0.0011474731,0.0009195755,0.004338648],"category_scores_gemma":[0.0024529276,0.00055973895,0.0006195076,0.0006492436,0.00040410087,0.0014419759,0.0009971077,0.00095550565,0.0013275101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017244555,0.000102645456,0.002037695,0.0006014713,0.00006205743,0.00020127118,0.00023165043,0.2794577,0.14593771,0.025743412,0.0037998096,0.54165214],"study_design_scores_gemma":[0.000016599748,0.00007262762,0.0006775856,0.000022535021,0.000019929805,0.00013539955,0.000030832758,0.9701872,0.019558694,0.0030758101,0.0061646374,0.000038147806],"about_ca_topic_score_codex":0.0026442898,"about_ca_topic_score_gemma":0.0024345708,"teacher_disagreement_score":0.004338648,"about_ca_system_score_codex":0.00048504065,"about_ca_system_score_gemma":0.0011222359,"threshold_uncertainty_score":0.014514208},"labels":[],"label_agreement":null},{"id":"W2914763012","doi":"10.3390/s19030735","title":"3D SSY Estimate of EPFM Constraint Parameter under Biaxial Loading for Sensor Structure Design","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Constraint (computer-aided design); Structural engineering; Materials science; Strain hardening exponent; Stress (linguistics); Fracture (geology); Range (aeronautics); Sensitivity (control systems); Engineering design process; Enhanced Data Rates for GSM Evolution; Fracture mechanics; Process (computing); Computer science; Mechanical engineering; Engineering; Composite material; Electronic engineering","score_opus":0.01956561638849764,"score_gpt":0.23892854313943082,"score_spread":0.2193629267509332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914763012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099561125,0.00012519881,0.8966957,0.00007197878,0.000016562477,0.00005397417,0.00013056726,0.0005383987,0.0028066053],"genre_scores_gemma":[0.642675,0.00013671683,0.3558028,0.000028582126,0.0000060042603,0.000115801224,0.000204744,0.0000713658,0.0009589787],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996526,0.000040478724,0.00001698389,0.00006462901,0.00020756446,0.000017618715],"domain_scores_gemma":[0.9994236,0.00016059338,0.00009292235,0.00008852307,0.00021905846,0.00001524752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041432798,0.0006231231,0.00033535063,0.00049515965,0.00024300431,0.0005935364,0.00065874483,0.00071879895,0.0015418291],"category_scores_gemma":[0.0012466694,0.00033198748,0.00026797163,0.00024736396,0.00028704287,0.00092588185,0.0006159493,0.0004886008,0.00032207734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024321533,0.00011261333,0.008972592,0.00042175403,0.00003556784,0.00033708673,0.0003235406,0.26954842,0.5277548,0.008714799,0.0015463504,0.18198937],"study_design_scores_gemma":[0.000007944611,0.000102059916,0.002925918,0.000016731505,0.000009717356,0.00010566087,0.000053354506,0.9225765,0.071772136,0.00084466534,0.0015565536,0.00002880539],"about_ca_topic_score_codex":0.0014789595,"about_ca_topic_score_gemma":0.0025305913,"teacher_disagreement_score":0.0015418291,"about_ca_system_score_codex":0.00032560216,"about_ca_system_score_gemma":0.0008323546,"threshold_uncertainty_score":0.005157888},"labels":[],"label_agreement":null},{"id":"W2915318211","doi":"10.3390/s19050984","title":"Design and Accuracy of an Instrumented Insole Using Pressure Sensors for Step Count","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Transport Systems and Technology","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"","keywords":"Pressure sensor; Count data; Computer science; Reliability engineering; Engineering; Statistics; Mathematics; Mechanical engineering","score_opus":0.014390453197319386,"score_gpt":0.22266531619310162,"score_spread":0.20827486299578224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915318211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4083485,0.0006616052,0.5870077,0.00014680657,0.0002034721,0.00046874004,0.00027872346,0.0014121932,0.0014722206],"genre_scores_gemma":[0.7449073,0.00027465363,0.25288147,0.00011518295,0.00006774751,0.00033725932,0.00018380802,0.00007942377,0.001153137],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803513,0.00040259873,0.00016076812,0.0003854145,0.0009583534,0.000057756817],"domain_scores_gemma":[0.9977914,0.0007133028,0.000225946,0.00031417338,0.0008617673,0.00009330517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012467543,0.00065849535,0.0006490249,0.0005872054,0.0001881426,0.00070870767,0.0011106948,0.00078162673,0.0010862346],"category_scores_gemma":[0.0035600825,0.0004505161,0.000321733,0.00034321263,0.0002753235,0.00044439733,0.0004421989,0.00031111788,0.000550705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068327755,0.0003957103,0.027218923,0.0005943755,0.00017160394,0.0004051565,0.00025962206,0.0034993072,0.8353474,0.0003212215,0.00063898455,0.13046438],"study_design_scores_gemma":[0.00036315122,0.011680627,0.21956922,0.0001514443,0.00075917784,0.005130331,0.00023046431,0.15619442,0.5950032,0.00041590928,0.010291051,0.00021109299],"about_ca_topic_score_codex":0.00027005115,"about_ca_topic_score_gemma":0.000390257,"teacher_disagreement_score":0.0012467543,"about_ca_system_score_codex":0.0001331422,"about_ca_system_score_gemma":0.00027080817,"threshold_uncertainty_score":0.0065935254},"labels":[],"label_agreement":null},{"id":"W2915923901","doi":"10.3390/s19040891","title":"Measurement of Human Gait Symmetry using Body Surface Normals Extracted from Depth Maps","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Symmetry (geometry); Gait analysis; Treadmill; Artificial intelligence; Computer science; Embedding; Mathematics; Pattern recognition (psychology); Computer vision; Physical medicine and rehabilitation; Geometry; Physical therapy; Medicine","score_opus":0.02518018591854954,"score_gpt":0.235702225163805,"score_spread":0.21052203924525548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915923901","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45964462,0.0008127673,0.5326768,0.00008674309,0.000087119566,0.00024135059,0.002175382,0.0014089043,0.0028663033],"genre_scores_gemma":[0.87103164,0.00054328894,0.1263584,0.000028497376,0.00003767132,0.00008186814,0.0012775348,0.000061185325,0.00057993137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996482,0.00006234542,0.000024068828,0.00007617305,0.00015670333,0.00003244775],"domain_scores_gemma":[0.9995478,0.000100825884,0.00009840116,0.00005328257,0.00016093979,0.00003863321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028055956,0.000644914,0.0005087696,0.002014856,0.000118937925,0.00043882293,0.00029721737,0.0003291229,0.001005458],"category_scores_gemma":[0.0017533905,0.000199261,0.0002651451,0.0008759809,0.00020863766,0.00062646606,0.0005101361,0.00020081412,0.0003433879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011814018,0.00019106614,0.038222384,0.00069270283,0.0002206483,0.00038373197,0.000290509,0.018369414,0.2930103,0.0013789204,0.002315114,0.6437439],"study_design_scores_gemma":[0.000111593545,0.0009266764,0.2871545,0.00015347333,0.00021738955,0.0045839744,0.0005388351,0.56537986,0.13071246,0.004563017,0.0054513575,0.00020682407],"about_ca_topic_score_codex":0.001342904,"about_ca_topic_score_gemma":0.002225332,"teacher_disagreement_score":0.002014856,"about_ca_system_score_codex":0.0001502834,"about_ca_system_score_gemma":0.00028600043,"threshold_uncertainty_score":0.0033636093},"labels":[],"label_agreement":null},{"id":"W2917588446","doi":"10.3390/s19040862","title":"A Short Review on the Role of the Metal-Graphene Hybrid Nanostructure in Promoting the Localized Surface Plasmon Resonance Sensor Performance","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Plasmonic and Surface Plasmon Research","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"OZ Optics (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Taibah University","keywords":"Surface plasmon resonance; Graphene; Materials science; Plasmon; Nanotechnology; Figure of merit; Optoelectronics; Surface plasmon; Resonance (particle physics); Nanoparticle; Physics","score_opus":0.025054862748868773,"score_gpt":0.26116587888090614,"score_spread":0.23611101613203736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917588446","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043829903,0.99599093,0.00036426022,0.00020261669,0.00038736864,0.0000083793875,0.000048086687,0.000015404337,0.0025446834],"genre_scores_gemma":[0.0014699824,0.9961869,0.0004608373,0.00015292593,0.00017739042,0.000011236349,0.000049991908,0.0000025351742,0.0014881443],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998604,0.000018647943,0.000020716647,0.000033818946,0.000052327687,0.000014025741],"domain_scores_gemma":[0.99981076,0.00008718664,0.000032050597,0.000005781371,0.000049771723,0.000014380455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027118306,0.0008744523,0.0006899698,0.002009685,0.00029621625,0.0006559992,0.00047620665,0.0007744388,0.0042905295],"category_scores_gemma":[0.00045183644,0.00032703072,0.0004866521,0.0022373057,0.00017948383,0.0010313336,0.0003983868,0.00080756017,0.0021536446],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007743913,0.00012290574,0.0002215688,0.05256663,0.00009782635,0.0003923786,0.00009307596,0.0007833989,0.017736005,0.00594998,0.042222682,0.87973607],"study_design_scores_gemma":[0.000005897896,0.00014931071,0.0007180125,0.002817844,0.00010384366,0.00089561194,0.00003884609,0.00014669534,0.0032695562,0.0011491114,0.9906821,0.000023082588],"about_ca_topic_score_codex":0.00082702015,"about_ca_topic_score_gemma":0.001358652,"teacher_disagreement_score":0.0042905295,"about_ca_system_score_codex":0.00037592012,"about_ca_system_score_gemma":0.00070605054,"threshold_uncertainty_score":0.014353216},"labels":[],"label_agreement":null},{"id":"W2917651750","doi":"10.3390/s19040937","title":"Big Data-Driven Cellular Information Detection and Coverage Identification","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Beijing Union University; Beijing University of Posts and Telecommunications; Key Laboratory of Universal Wireless Communications of Ministry of Education; Ministry of Science and Technology","keywords":"Computer science; Identification (biology); Base station; Cellular network; Big data; Granularity; Data mining; Service provider; Service (business); Core network; Computer network; Real-time computing","score_opus":0.02313299144115182,"score_gpt":0.2596501107689988,"score_spread":0.23651711932784697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917651750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065134674,0.0002957761,0.93114936,0.00025688973,0.000111707646,0.00009284827,0.00029987658,0.00085298263,0.0018057806],"genre_scores_gemma":[0.8504502,0.00020045429,0.14647554,0.00012552211,0.00010784213,0.0001634295,0.0006658325,0.00003800337,0.0017731672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964964,0.000054341497,0.000018238647,0.000107420645,0.00012392146,0.000046396377],"domain_scores_gemma":[0.9990815,0.00033548035,0.00012794345,0.00013318524,0.00025766066,0.00006436395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031985,0.0006564329,0.0005920025,0.0010628201,0.00039306114,0.0008420082,0.0011032515,0.0005876463,0.0005235114],"category_scores_gemma":[0.0016863139,0.00025819542,0.00042227897,0.0009665678,0.00032081385,0.0008872189,0.0009251376,0.00055446726,0.0002404502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032048716,0.0002538016,0.023266587,0.00027520297,0.0001497481,0.0005424383,0.00031643632,0.5171718,0.02689546,0.012268825,0.0061192852,0.41241997],"study_design_scores_gemma":[0.0000037200666,0.000018855088,0.0013225315,0.0000037954792,0.000006414668,0.000065603745,0.00003352485,0.9930521,0.0033735924,0.0014912459,0.0006218863,0.00000679666],"about_ca_topic_score_codex":0.0034771997,"about_ca_topic_score_gemma":0.0039866716,"teacher_disagreement_score":0.0034771997,"about_ca_system_score_codex":0.0005695704,"about_ca_system_score_gemma":0.0005637925,"threshold_uncertainty_score":0.0069139004},"labels":[],"label_agreement":null},{"id":"W2917893531","doi":"10.3390/s19040860","title":"A Highlight-Generation Method for Rendering Translucent Objects","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Standard illuminant; Rendering (computer graphics); Bidirectional reflectance distribution function; Computer science; Scattering; Artificial intelligence; Computer vision; Optics; Computer graphics (images); Reflectivity; Physics","score_opus":0.030012128127669076,"score_gpt":0.309631710958086,"score_spread":0.27961958283041694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917893531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014653356,0.00016375116,0.9820842,0.00005694473,0.00007071481,0.000041303985,0.00006742154,0.0012453339,0.0016168882],"genre_scores_gemma":[0.1996258,0.00060462754,0.79273975,0.00009834015,0.00009641353,0.00013249848,0.00023066968,0.00086434133,0.005607522],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998841,0.000014900549,0.000003845769,0.00002114199,0.000061014052,0.0000149959915],"domain_scores_gemma":[0.99980193,0.00006322254,0.000023018147,0.000039485287,0.000050826126,0.000021635553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021085834,0.0008852767,0.00041632814,0.00059244817,0.00029324586,0.0006521757,0.0006316569,0.00052623975,0.003960539],"category_scores_gemma":[0.00065381307,0.000338789,0.00067334133,0.00041648198,0.00029872102,0.0006361879,0.0006521953,0.00085295184,0.0007358999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004014208,0.000095942785,0.00045859304,0.00032697213,0.00007578055,0.00058701896,0.0005023793,0.06720361,0.53952754,0.016220862,0.0070795445,0.36752042],"study_design_scores_gemma":[0.00008249152,0.0001378575,0.00061297405,0.000033329332,0.000061225604,0.0008704083,0.00007863225,0.74137986,0.22504033,0.0041805482,0.027438782,0.000083482686],"about_ca_topic_score_codex":0.0010762535,"about_ca_topic_score_gemma":0.0017629629,"teacher_disagreement_score":0.003960539,"about_ca_system_score_codex":0.00038093716,"about_ca_system_score_gemma":0.00032433443,"threshold_uncertainty_score":0.013249338},"labels":[],"label_agreement":null},{"id":"W2919637296","doi":"10.3390/s19051161","title":"Erratum: Luan, E.X.; Shoman, H.; Ratner, D.M.; Cheung, K.C.; Chrostowski, L. Silicon Photonic Biosensors Using Label-Free Detection. Sensors 2018, 18, 3519","year":2019,"lang":"en","type":"erratum","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Biosensor; Silicon; Detection limit; Analytical Chemistry (journal); Nanotechnology; Materials science; Chemistry; Physics; Chromatography; Optoelectronics","score_opus":0.02047581258391389,"score_gpt":0.2430813425152061,"score_spread":0.2226055299312922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919637296","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029257263,0.0024915342,0.0020382104,0.040280376,0.94665945,0.000036318776,0.0012403419,0.00054947793,0.006411825],"genre_scores_gemma":[0.016681178,0.03198761,0.01901222,0.12482206,0.13643691,0.00032176127,0.010400894,0.0043007312,0.6560366],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99421185,0.00052546064,0.0008343738,0.000592016,0.0034781112,0.00035819842],"domain_scores_gemma":[0.9734196,0.0040622223,0.001413021,0.0011046493,0.018950995,0.001049569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035454486,0.0028221484,0.00205229,0.0048046745,0.004482591,0.0039272513,0.002775362,0.0054589817,0.043790568],"category_scores_gemma":[0.03340278,0.0016029341,0.0013967868,0.0035404793,0.0025939962,0.0036235203,0.0023690984,0.0086167045,0.048525956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033488366,0.0000121084695,0.0000771331,0.0001733857,0.000007118935,0.00018054589,0.00003681104,0.00004730036,0.00022545563,0.0010473683,0.98853856,0.009620638],"study_design_scores_gemma":[0.0000092074815,0.000015822283,0.00019291944,0.00016033382,0.000019382556,0.00031756182,0.00006516824,0.00010359801,0.00091115094,0.00067165925,0.9975056,0.00002763819],"about_ca_topic_score_codex":0.015126409,"about_ca_topic_score_gemma":0.02441959,"teacher_disagreement_score":0.043790568,"about_ca_system_score_codex":0.004430012,"about_ca_system_score_gemma":0.006366936,"threshold_uncertainty_score":0.14649409},"labels":[],"label_agreement":null},{"id":"W2920160035","doi":"10.3390/s19051061","title":"Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Division of Administrative Services; Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; Wireless sensor network; Channel (broadcasting); Computer science; Channel state information; Wireless; Node (physics); Ranging; Signal-to-noise ratio (imaging); Wireless network; Electronic engineering; Real-time computing; Computer network; Telecommunications; Engineering","score_opus":0.006596363708176575,"score_gpt":0.2011831345151606,"score_spread":0.19458677080698403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920160035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021645892,0.00013255666,0.99706393,0.000038426522,0.00001280102,0.000008366332,0.000008126842,0.000068179936,0.00050297024],"genre_scores_gemma":[0.43359017,0.0009231015,0.56113255,0.00023862587,0.00014485653,0.00022618512,0.00013498456,0.000083909625,0.0035255267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915636,0.00024337601,0.000032426437,0.00016470723,0.0003251294,0.00007804773],"domain_scores_gemma":[0.99925846,0.00031581492,0.00012530865,0.000111051515,0.0001591275,0.000030212606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008900876,0.0013188248,0.000775683,0.0003849515,0.00036075304,0.0005955543,0.0012412972,0.00092155166,0.0009878046],"category_scores_gemma":[0.0018843677,0.00038644965,0.0006312654,0.000732982,0.0006559094,0.0010447811,0.0012598105,0.0008907443,0.00053932745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013616118,0.00006639866,0.000328941,0.00019440457,0.000116602205,0.00009021076,0.00009810382,0.77813065,0.039543618,0.02165662,0.001715854,0.15792239],"study_design_scores_gemma":[0.000017960903,0.0001251642,0.000108550055,0.000011659639,0.000020249701,0.00006207533,0.000016820573,0.98819005,0.0050901677,0.00486815,0.0014737591,0.000015351128],"about_ca_topic_score_codex":0.00096728496,"about_ca_topic_score_gemma":0.0011563937,"teacher_disagreement_score":0.0013188248,"about_ca_system_score_codex":0.00039701472,"about_ca_system_score_gemma":0.00068137626,"threshold_uncertainty_score":0.0047073364},"labels":[],"label_agreement":null},{"id":"W2920478483","doi":"10.3390/s19051123","title":"A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"European Commission","keywords":"RANSAC; Scale-invariant feature transform; Artificial intelligence; Remote sensing; Computer science; Ground truth; Mean squared error; Satellite; Computer vision; Point cloud; Digital elevation model; Feature extraction; Ground sample distance; Scale (ratio); Pattern recognition (psychology); Mathematics; Geography; Image (mathematics); Pixel; Engineering; Cartography","score_opus":0.01718140037537988,"score_gpt":0.2383179257866002,"score_spread":0.2211365254112203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920478483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03314226,0.000112344496,0.95619285,0.000048281734,0.000054367352,0.00021644006,0.0012876461,0.0046267086,0.004319117],"genre_scores_gemma":[0.19928384,0.00015363189,0.7932926,0.000034867724,0.000026205902,0.00015908598,0.003097156,0.00015429423,0.0037983356],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998809,0.000008590455,0.000006529717,0.000028899098,0.000060754777,0.000014289442],"domain_scores_gemma":[0.9999137,0.0000066610596,0.000009126809,0.000018087308,0.000047757163,0.000004519572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015769513,0.00056828005,0.00039311644,0.0016888822,0.00024652696,0.0004285149,0.00049338344,0.00032668328,0.00313341],"category_scores_gemma":[0.00025923166,0.00026758818,0.0005148128,0.0011051886,0.0001261644,0.00048449193,0.0004543824,0.00023593799,0.0016915085],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010309089,0.00013196998,0.0027979761,0.00029581686,0.00010158315,0.0001897816,0.00010188712,0.07337021,0.121296406,0.0034248026,0.008904496,0.78928196],"study_design_scores_gemma":[0.00006286963,0.00019055001,0.020832926,0.00005036545,0.00010051264,0.00065303757,0.00020078165,0.86502343,0.082939684,0.0036074393,0.026257718,0.000080746766],"about_ca_topic_score_codex":0.002590569,"about_ca_topic_score_gemma":0.0049994155,"teacher_disagreement_score":0.00313341,"about_ca_system_score_codex":0.00027386917,"about_ca_system_score_gemma":0.00046030173,"threshold_uncertainty_score":0.010482311},"labels":[],"label_agreement":null},{"id":"W2920853192","doi":"10.3390/s19051242","title":"Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer network; Computer science; Forwarder; Network packet; Relay; Routing protocol; Vehicular ad hoc network; Intersection (aeronautics); Packet forwarding; Wireless ad hoc network; Leverage (statistics); Link-state routing protocol; Wireless; Transport engineering; Engineering; Telecommunications","score_opus":0.00537541047107121,"score_gpt":0.18703352811128418,"score_spread":0.18165811764021297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920853192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10082626,0.0014370515,0.8941304,0.00013025843,0.00007286509,0.00006688199,0.000057261557,0.00053240784,0.00274665],"genre_scores_gemma":[0.9350483,0.0009492885,0.062342666,0.000026989856,0.000029560324,0.000057058947,0.00012173528,0.000023386057,0.0014010582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995254,0.00015096222,0.00002315518,0.00006551009,0.00017790208,0.00005704587],"domain_scores_gemma":[0.9994616,0.00024523956,0.0001158181,0.000045112538,0.000115538,0.000016637352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088383886,0.00032212096,0.00041673356,0.0007206848,0.0004247437,0.00045018984,0.001192213,0.0004040703,0.00029656748],"category_scores_gemma":[0.0014926164,0.00025047746,0.00024471484,0.0008716588,0.00046332928,0.0007350456,0.000539557,0.00039064462,0.00012674126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010036714,0.0000489945,0.0017606913,0.0001132648,0.000037937432,0.00018168407,0.00016005467,0.9008514,0.013198765,0.013144813,0.000719541,0.06968242],"study_design_scores_gemma":[0.000007648522,0.00011193907,0.00029993555,0.0000073112496,0.000018256062,0.00007405109,0.000043228607,0.9922287,0.0034135305,0.0024286404,0.0013518342,0.0000149089765],"about_ca_topic_score_codex":0.0032202625,"about_ca_topic_score_gemma":0.0025273424,"teacher_disagreement_score":0.0032202625,"about_ca_system_score_codex":0.00049898063,"about_ca_system_score_gemma":0.0006092303,"threshold_uncertainty_score":0.006403029},"labels":[],"label_agreement":null},{"id":"W2920970828","doi":"10.3390/s19061415","title":"The Life of a New York City Noise Sensor Network","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Noise Effects and Management","field":"Health Professions","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Center for Urban Science and Progress; National Science Foundation","keywords":"Noise (video); Wireless sensor network; Telecommunications; Computer science; Engineering; Computer network; Artificial intelligence","score_opus":0.048324477294699346,"score_gpt":0.34770486043442184,"score_spread":0.2993803831397225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920970828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5498057,0.004454458,0.26100776,0.0236759,0.0015976188,0.0011747645,0.0055319774,0.007917525,0.14483435],"genre_scores_gemma":[0.84843713,0.0025917455,0.06517127,0.00073512207,0.00014925691,0.00047507996,0.00413576,0.00012966788,0.078174986],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99972886,0.000052429084,0.000011437742,0.000048139125,0.00012047546,0.000038602455],"domain_scores_gemma":[0.99936825,0.00010362935,0.000039326773,0.00008281703,0.00031624688,0.00008965952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056796,0.0003813421,0.00024575042,0.00031977438,0.00084087776,0.0010347649,0.0005045509,0.00045284998,0.0062984917],"category_scores_gemma":[0.0014291165,0.00018569312,0.00015869325,0.00036386642,0.00024240656,0.0014343058,0.00071232347,0.00043150215,0.0011100332],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053820875,0.0003633767,0.062200837,0.00040079287,0.00018339357,0.00096473296,0.00089576904,0.2209833,0.030401804,0.027998123,0.20828144,0.44678822],"study_design_scores_gemma":[0.000053852564,0.0005927916,0.02274342,0.00013104838,0.00010001995,0.00051581324,0.0011103989,0.76535857,0.0075320676,0.00515724,0.19662046,0.00008429081],"about_ca_topic_score_codex":0.059286386,"about_ca_topic_score_gemma":0.1006336,"teacher_disagreement_score":0.059286386,"about_ca_system_score_codex":0.0010850995,"about_ca_system_score_gemma":0.0013939057,"threshold_uncertainty_score":0.11788261},"labels":[],"label_agreement":null},{"id":"W2921073965","doi":"10.3390/s19051191","title":"A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pairwise comparison; Image registration; Computer science; Point (geometry); Set (abstract data type); Point set registration; Artificial intelligence; Data set; Data mining; Computer vision; Mathematics; Image (mathematics)","score_opus":0.05631782226594738,"score_gpt":0.2976563125164667,"score_spread":0.2413384902505193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921073965","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033306246,0.974861,0.020278113,0.00047961637,0.0005418247,0.000030134068,0.00014881986,0.00013678482,0.0031906695],"genre_scores_gemma":[0.0034251106,0.97572845,0.017844342,0.0004069693,0.00069053663,0.000062738625,0.00035092732,0.000062856365,0.0014280811],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989237,0.00020300955,0.00018785862,0.00026689324,0.0003729305,0.000045518536],"domain_scores_gemma":[0.9973738,0.0016393985,0.0001955269,0.00014910595,0.00058473955,0.000057319263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018721848,0.0014791264,0.0021143015,0.006190622,0.00049000845,0.0018050048,0.0020146365,0.0018469671,0.005809862],"category_scores_gemma":[0.0054763495,0.0009523247,0.0013518797,0.008495434,0.001140293,0.0030919663,0.0013446196,0.0014067052,0.004722258],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004169865,0.00003188268,0.0002632989,0.017163409,0.000106545944,0.000116791394,0.00009458037,0.0016125091,0.0014422926,0.0069756727,0.02063333,0.95151806],"study_design_scores_gemma":[0.000016143145,0.00017471616,0.0014743145,0.007549178,0.00032494334,0.00239782,0.00018200155,0.0028351753,0.0031569828,0.013173686,0.96857417,0.00014081222],"about_ca_topic_score_codex":0.0020916378,"about_ca_topic_score_gemma":0.0017618563,"teacher_disagreement_score":0.006190622,"about_ca_system_score_codex":0.0007636779,"about_ca_system_score_gemma":0.0018935106,"threshold_uncertainty_score":0.019435942},"labels":[],"label_agreement":null},{"id":"W2921366156","doi":"10.3390/s19061313","title":"Towards Void Hole Alleviation by Exploiting the Energy Efficient Path and by Providing the Interference-Free Proactive Routing Protocols in IoT Enabled Underwater WSNs","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"King Saud University","keywords":"Computer network; Computer science; Link-state routing protocol; Routing protocol; Equal-cost multi-path routing; Dynamic Source Routing; Static routing; Routing Information Protocol; Multipath routing; Policy-based routing; Distributed computing; Network packet","score_opus":0.014737411449748649,"score_gpt":0.21551553723255015,"score_spread":0.2007781257828015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921366156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053255428,0.0012683839,0.9419132,0.00016551564,0.00004679691,0.000028493738,0.000023401213,0.00042353093,0.0028752817],"genre_scores_gemma":[0.83452886,0.0018144429,0.16021745,0.00012195507,0.00002600635,0.00006980878,0.000089074856,0.00007094221,0.0030614887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.00005365333,0.000012962269,0.000035534384,0.000091551476,0.00002871242],"domain_scores_gemma":[0.99973017,0.00009012233,0.00007094977,0.0000409014,0.000054046406,0.000013915999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003618275,0.00054741924,0.00030634995,0.00052956806,0.000310025,0.0005588905,0.0008369053,0.00037589503,0.00048168068],"category_scores_gemma":[0.0005981914,0.00017629373,0.00040027476,0.0004857363,0.00044771685,0.0011256293,0.00086242164,0.00042331376,0.00011834985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014725368,0.0001333307,0.0016143706,0.00056994613,0.000097740995,0.00056697364,0.00044450164,0.5509374,0.1704786,0.056516573,0.0021439171,0.21634944],"study_design_scores_gemma":[0.000012423658,0.000185919,0.00059788337,0.00003151441,0.000041867195,0.00031527865,0.00020793523,0.9377903,0.040085547,0.014970016,0.0057240413,0.000037381986],"about_ca_topic_score_codex":0.0011291739,"about_ca_topic_score_gemma":0.0010032204,"teacher_disagreement_score":0.0011291739,"about_ca_system_score_codex":0.00023840232,"about_ca_system_score_gemma":0.00053628354,"threshold_uncertainty_score":0.0022451282},"labels":[],"label_agreement":null},{"id":"W2921519987","doi":"10.3390/s19061345","title":"AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Global Positioning System; Sensor fusion; Deep learning; Computer science; GNSS applications; Big data; Artificial intelligence; Data science; Data integration; Machine learning; Data mining; Telecommunications","score_opus":0.004565904510005094,"score_gpt":0.20851128886189285,"score_spread":0.20394538435188775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921519987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032007396,0.00037976497,0.9593447,0.00043921132,0.00012827168,0.0001003282,0.00026377206,0.0047788266,0.0025577385],"genre_scores_gemma":[0.75147134,0.00021457234,0.24494587,0.0003527983,0.00008906274,0.00014892333,0.0006030555,0.00009228069,0.0020820738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939346,0.00008534758,0.000046023037,0.00018548753,0.00023293166,0.000056774745],"domain_scores_gemma":[0.9991629,0.00028807667,0.00008545249,0.00012314937,0.0003008986,0.00003954998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010230211,0.0008882639,0.0006332681,0.00075217674,0.0006003916,0.0006206382,0.0013831866,0.00093230593,0.002298489],"category_scores_gemma":[0.002418309,0.00033098317,0.0005481374,0.00080646353,0.0005970832,0.0018696471,0.0012941541,0.0015940252,0.0007112439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040184194,0.0006723329,0.0036534362,0.00018892906,0.00021349228,0.00030744428,0.0003526877,0.46267352,0.041522257,0.012407244,0.00812272,0.46948406],"study_design_scores_gemma":[0.0000047901667,0.000025637166,0.00023573948,0.000003994068,0.000009950201,0.000016214217,0.000009591492,0.99017805,0.005493279,0.0031874357,0.0008283011,0.0000069487987],"about_ca_topic_score_codex":0.0058099832,"about_ca_topic_score_gemma":0.0061899885,"teacher_disagreement_score":0.0058099832,"about_ca_system_score_codex":0.0010231521,"about_ca_system_score_gemma":0.0009277434,"threshold_uncertainty_score":0.011552334},"labels":[],"label_agreement":null},{"id":"W2921832736","doi":"10.3390/s19061285","title":"Advanced Micro- and Nano-Gas Sensor Technology: A Review","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":602,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Miniaturization; Fabrication; Nano-; Nanotechnology; Machining; Computer science; Materials science; Engineering; Mechanical engineering","score_opus":0.018778032720841012,"score_gpt":0.26187879114123735,"score_spread":0.24310075842039636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921832736","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025130366,0.9972671,0.00039376804,0.00023549893,0.00027617792,0.0000061423398,0.000027171822,0.000014793223,0.0015280872],"genre_scores_gemma":[0.0010052432,0.9974533,0.00045899997,0.000119982826,0.00017157661,0.000007237539,0.000032661726,0.0000018041209,0.0007492172],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998036,0.000021544005,0.000022842934,0.000043252057,0.000088832814,0.000019934985],"domain_scores_gemma":[0.9996991,0.00013646722,0.000047309473,0.000009485321,0.0000840674,0.00002357462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003922498,0.0009794389,0.0009855805,0.0021273557,0.0003179171,0.0008613889,0.0007473261,0.0010328311,0.003736375],"category_scores_gemma":[0.00048413026,0.00036085604,0.00047626634,0.0029689611,0.00033239982,0.001564946,0.00052810775,0.0011785581,0.0023108732],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040021852,0.00011282445,0.00028845595,0.028329125,0.00007425207,0.00026880126,0.00007537852,0.00080286094,0.008116279,0.006125572,0.03169839,0.9240681],"study_design_scores_gemma":[0.0000040323266,0.000090932524,0.0005801154,0.0018979711,0.000069972106,0.0010424824,0.000049918086,0.00019029227,0.0016851631,0.001846052,0.99252313,0.000019923778],"about_ca_topic_score_codex":0.0008343148,"about_ca_topic_score_gemma":0.0013482948,"teacher_disagreement_score":0.003736375,"about_ca_system_score_codex":0.00038260838,"about_ca_system_score_gemma":0.00096373464,"threshold_uncertainty_score":0.012499452},"labels":[],"label_agreement":null},{"id":"W2923319240","doi":"10.3390/s19061449","title":"Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; China Scholarship Council","keywords":"Computer science; Quality of service; Computer network; Polling; Software-defined networking; OpenFlow; Differentiated services; Distributed computing; Queueing theory; Middleware (distributed applications); Message queue; Network packet; Packet loss","score_opus":0.024300184737334498,"score_gpt":0.24348440387769438,"score_spread":0.21918421914035988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923319240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041019384,0.0002533324,0.9566709,0.00019588615,0.00006062578,0.000035861398,0.000016759688,0.0008684615,0.0008787803],"genre_scores_gemma":[0.69379985,0.00021560158,0.30421242,0.00019146124,0.000059020767,0.00008043414,0.000088606925,0.000054752225,0.0012978268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995696,0.000115560615,0.000029202698,0.00009285583,0.0001358446,0.00005693789],"domain_scores_gemma":[0.99936956,0.0002932599,0.00007144153,0.00006567363,0.00016355219,0.000036415724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013780652,0.00039975042,0.0005289357,0.0004266725,0.0004024983,0.00066377164,0.00080771674,0.0006405984,0.0004076265],"category_scores_gemma":[0.0023470393,0.00023945229,0.0003746172,0.00044574717,0.0003811591,0.0009160483,0.00052342785,0.000842332,0.00015230596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014226847,0.00025986283,0.0035448822,0.000079373494,0.00007162077,0.00008173018,0.000089987596,0.7117575,0.014400547,0.0095772585,0.001612689,0.25838232],"study_design_scores_gemma":[0.0000035062221,0.000011997358,0.00009129098,0.0000010985559,0.0000026389607,0.0000048559123,0.0000025288712,0.9974644,0.001111052,0.0011142353,0.00019012853,0.0000022452814],"about_ca_topic_score_codex":0.0022778583,"about_ca_topic_score_gemma":0.0018011008,"teacher_disagreement_score":0.0022778583,"about_ca_system_score_codex":0.00076704234,"about_ca_system_score_gemma":0.0009129851,"threshold_uncertainty_score":0.007287979},"labels":[],"label_agreement":null},{"id":"W2924881791","doi":"10.3390/s19061466","title":"How to Efficiently Determine the Range Precision of 3D Terrestrial Laser Scanners","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Laser; Laser scanning; Intensity (physics); Perpendicular; Range (aeronautics); Measure (data warehouse); Optics; Scanner; Computer science; Orientation (vector space); Object (grammar); Plane (geometry); Artificial intelligence; Computer vision; Materials science; Physics; Mathematics; Geometry","score_opus":0.010414652001428557,"score_gpt":0.22581414369543854,"score_spread":0.21539949169401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924881791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045104172,0.00090110966,0.9486738,0.00025568745,0.000052469837,0.00004510389,0.00039164,0.002585407,0.0019906235],"genre_scores_gemma":[0.2781815,0.0006695725,0.71873134,0.00010467295,0.000046623198,0.00009262055,0.0008637504,0.00055437424,0.0007555462],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99741423,0.00039235008,0.00015395303,0.0005755519,0.0013119584,0.00015187847],"domain_scores_gemma":[0.9965911,0.0011761837,0.0003106467,0.0007559272,0.0010987751,0.00006736458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015658024,0.0006739602,0.0007552782,0.0016736232,0.0004469853,0.0013865795,0.0011561265,0.0009220497,0.0021332419],"category_scores_gemma":[0.008420793,0.00067701587,0.00078243046,0.001423216,0.0005489842,0.0019949216,0.00140631,0.0007704044,0.0019345621],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028275014,0.00008366887,0.019127417,0.00076363113,0.00015821152,0.00022051715,0.00044027856,0.075194344,0.23763125,0.007850602,0.0037010876,0.6545462],"study_design_scores_gemma":[0.000061113955,0.00024679146,0.03829622,0.00023528218,0.00012972468,0.0014496056,0.0005500387,0.654283,0.26200223,0.019740177,0.02269423,0.00031163997],"about_ca_topic_score_codex":0.0026424068,"about_ca_topic_score_gemma":0.002717327,"teacher_disagreement_score":0.0026424068,"about_ca_system_score_codex":0.00042223532,"about_ca_system_score_gemma":0.00084245246,"threshold_uncertainty_score":0.008280814},"labels":[],"label_agreement":null},{"id":"W2924951916","doi":"10.3390/s19071483","title":"Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Accelerometer; Gait; Wearable computer; Event (particle physics); Gait analysis; Computer science; Step detection; Artificial intelligence; Simulation; Computer vision; Physical medicine and rehabilitation; Embedded system; Medicine","score_opus":0.010203824283012461,"score_gpt":0.21810997396635273,"score_spread":0.20790614968334026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924951916","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8477407,0.0003898249,0.14758931,0.00004404072,0.00008841535,0.00021879192,0.001331569,0.0008693298,0.0017280206],"genre_scores_gemma":[0.95170397,0.0002053374,0.04631585,0.00003400943,0.000032559845,0.00011039641,0.00078411633,0.00004006446,0.0007737087],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99964416,0.000066558074,0.00002914367,0.00009347763,0.00014131117,0.00002531249],"domain_scores_gemma":[0.9995492,0.00012285613,0.000098562,0.00003569315,0.00017450225,0.000019176501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003107607,0.0005053135,0.0004623126,0.0010118396,0.000102620914,0.0004384479,0.00028806296,0.00041172933,0.00076076377],"category_scores_gemma":[0.0013054115,0.00015587798,0.00017260975,0.0005900602,0.000121046745,0.0003177771,0.00022788985,0.00015616094,0.0003953175],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020542128,0.00062039454,0.1255795,0.00083625125,0.00030314497,0.00037116426,0.00034088996,0.017298818,0.32556713,0.00041844533,0.0023694609,0.5242406],"study_design_scores_gemma":[0.000095983036,0.0011798412,0.69206965,0.00008940002,0.00015366099,0.001158769,0.00019255621,0.2248785,0.07731112,0.0006418522,0.0021251445,0.00010342475],"about_ca_topic_score_codex":0.0009782858,"about_ca_topic_score_gemma":0.0027120833,"teacher_disagreement_score":0.0010118396,"about_ca_system_score_codex":0.00010940296,"about_ca_system_score_gemma":0.00017010923,"threshold_uncertainty_score":0.0025449991},"labels":[],"label_agreement":null},{"id":"W2926054342","doi":"10.3390/s19071618","title":"Steering Angle Assisted Vehicular Navigation Using Portable Devices in GNSS-Denied Environments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Heading (navigation); Inertial measurement unit; Accelerometer; Steering wheel; GNSS applications; Gyroscope; Extended Kalman filter; Kalman filter; Computer science; Acceleration; Simulation; Road surface; Engineering; Automotive engineering; Global Positioning System; Artificial intelligence; Aerospace engineering","score_opus":0.008920192243685754,"score_gpt":0.20435048675113449,"score_spread":0.19543029450744873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2926054342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6472459,0.00064232125,0.34371436,0.00008811823,0.00012726156,0.00012935087,0.000373317,0.0020770088,0.0056024953],"genre_scores_gemma":[0.9512481,0.0003161282,0.0452487,0.000026392008,0.000018566936,0.000054393302,0.00027322504,0.000024035491,0.002790439],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997564,0.000051599265,0.000008319998,0.000052834443,0.00009903445,0.00003180725],"domain_scores_gemma":[0.99983263,0.000029320068,0.000025350242,0.000038231097,0.00006167825,0.000012763652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017911197,0.00068853755,0.00042027724,0.00041814847,0.00016131168,0.00033525584,0.0004911017,0.00031502158,0.00072907173],"category_scores_gemma":[0.0004769303,0.00016387843,0.00019208202,0.00045270234,0.00016836643,0.0002922272,0.00043897776,0.00019653913,0.00040310863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016627271,0.00019068223,0.016509201,0.0005565074,0.00014574551,0.0011299871,0.00046920378,0.09657556,0.25098398,0.0010228946,0.0023511637,0.62840235],"study_design_scores_gemma":[0.00036566998,0.0029303737,0.0676089,0.00014415894,0.00027736768,0.0017529192,0.0008340277,0.66704,0.23620579,0.00150714,0.02112048,0.00021321652],"about_ca_topic_score_codex":0.0047092484,"about_ca_topic_score_gemma":0.0047845743,"teacher_disagreement_score":0.0047092484,"about_ca_system_score_codex":0.00017186675,"about_ca_system_score_gemma":0.00025469952,"threshold_uncertainty_score":0.009363651},"labels":[],"label_agreement":null},{"id":"W2926531959","doi":"10.3390/s19071534","title":"An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Kinesthetic learning; Convolutional neural network; Object (grammar); Cognitive neuroscience of visual object recognition; Haptic technology; Pattern recognition (psychology); Process (computing); Set (abstract data type); Tactile sensor; Robot","score_opus":0.056182331759014846,"score_gpt":0.35382280237887354,"score_spread":0.2976404706198587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2926531959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071253605,0.0010695386,0.92198473,0.00052044634,0.00013157412,0.00005812178,0.00016370136,0.0018935051,0.0029248777],"genre_scores_gemma":[0.79538804,0.00052445714,0.19920924,0.0002978512,0.000072388575,0.00007123293,0.00031772762,0.000061015216,0.0040580956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998016,0.000024402963,0.000011758197,0.00006896422,0.000057651007,0.00003554941],"domain_scores_gemma":[0.99970883,0.00009239764,0.000033695676,0.000055767003,0.00008378592,0.000025452546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039859154,0.00057081954,0.00040039423,0.00039669147,0.00019602577,0.00050728105,0.00094695436,0.0007912408,0.0014550823],"category_scores_gemma":[0.0012576137,0.0002558066,0.00049236417,0.0004864392,0.000338721,0.0006932298,0.0007880447,0.00071216904,0.00039148942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002167225,0.00020292377,0.0020087627,0.00015692103,0.00008912784,0.00022505055,0.00009485167,0.22801805,0.06386754,0.0054041576,0.0028280597,0.69688773],"study_design_scores_gemma":[0.0000042453753,0.000055923123,0.0005028842,0.000008276366,0.000008435033,0.00003455653,0.000009673137,0.9870724,0.008577018,0.0028915666,0.0008289796,0.000006132013],"about_ca_topic_score_codex":0.004144783,"about_ca_topic_score_gemma":0.003937445,"teacher_disagreement_score":0.004144783,"about_ca_system_score_codex":0.0005893818,"about_ca_system_score_gemma":0.00062237907,"threshold_uncertainty_score":0.008241296},"labels":[],"label_agreement":null},{"id":"W2929680053","doi":"10.3390/s19071604","title":"Sensitive Electrochemical Detection of Caffeic Acid in Wine Based on Fluorine-Doped Graphene Oxide","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Cyclic voltammetry; Detection limit; Ascorbic acid; Electrochemical gas sensor; Electrochemistry; X-ray photoelectron spectroscopy; Materials science; Nuclear chemistry; Oxide; Differential pulse voltammetry; Chemistry; Electrode; Inorganic chemistry; Analytical Chemistry (journal); Nanotechnology; Chemical engineering; Chromatography","score_opus":0.002908605728806496,"score_gpt":0.1764417357373449,"score_spread":0.1735331300085384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929680053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9061431,0.020021306,0.07064466,0.000257391,0.000389965,0.00013195875,0.00047515638,0.0004931898,0.0014432498],"genre_scores_gemma":[0.91692996,0.0048394715,0.07597254,0.00019613247,0.000086157626,0.000043777178,0.00037129055,0.000020269299,0.0015403337],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967754,0.00004076525,0.000020767804,0.000116763935,0.00010439626,0.00003981401],"domain_scores_gemma":[0.99987626,0.00002539265,0.000025921272,0.000009102832,0.000044154545,0.00001929753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026951102,0.00088740874,0.00052995206,0.0006116737,0.00020757888,0.0003649479,0.0007250265,0.0011533173,0.00025143765],"category_scores_gemma":[0.0002950806,0.0002785786,0.0005165455,0.00033163285,0.00030813177,0.0005888944,0.0003334364,0.00045628636,0.00018812863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021863743,0.000012419058,0.00022621942,0.00008238026,0.000013195507,0.00009949834,0.000014539302,0.0000526092,0.9967625,0.000026061207,0.000017609074,0.0026711032],"study_design_scores_gemma":[0.000005157056,0.00022329873,0.0016785918,0.000007029439,0.000034327255,0.00048972626,0.000018924631,0.0014999757,0.99462587,0.00003073675,0.0013627517,0.000023613797],"about_ca_topic_score_codex":0.00071324356,"about_ca_topic_score_gemma":0.0013845745,"teacher_disagreement_score":0.0011533173,"about_ca_system_score_codex":0.00025959496,"about_ca_system_score_gemma":0.00012970576,"threshold_uncertainty_score":0.0018835068},"labels":[],"label_agreement":null},{"id":"W2929958646","doi":"10.3390/s19071606","title":"Fast Volumetric Feedback under Microscope by Temporally Coded Exposure Camera","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Genetics; Japan Science and Technology Agency","keywords":"Lens (geology); Focus (optics); Computer science; Artificial intelligence; Optics; Microscope; Computer vision; Tracking (education); Brightness; Focal length; Camera lens; High-speed camera; Physics","score_opus":0.005997164075535386,"score_gpt":0.2166015250980347,"score_spread":0.21060436102249933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929958646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55808866,0.0011731476,0.43426964,0.00028101262,0.00020750529,0.00018354738,0.00016868171,0.001792255,0.003835535],"genre_scores_gemma":[0.8043845,0.0005357734,0.19097358,0.00017652976,0.00006214828,0.00015491479,0.00009661121,0.000092025184,0.0035238038],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960226,0.000033420747,0.0000105339195,0.000086312626,0.00021240511,0.000055176213],"domain_scores_gemma":[0.99957556,0.00011764002,0.00007760062,0.000040831237,0.00014128922,0.000047034846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004096026,0.0005093793,0.0003161426,0.00027631567,0.00017608592,0.00034771903,0.0007506898,0.00046109463,0.0009954429],"category_scores_gemma":[0.00074896705,0.00022950191,0.00017290823,0.00018468998,0.000365116,0.00076505926,0.00081503345,0.00044955383,0.00019600932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005725536,0.00001899119,0.00016261113,0.000056395766,0.0000030381352,0.00006739385,0.000057921206,0.0011171661,0.98620194,0.0005443423,0.00019643139,0.0115164155],"study_design_scores_gemma":[0.000043780605,0.00036864838,0.0011173998,0.000014019275,0.000013322325,0.00024409965,0.000034432614,0.059267465,0.9349444,0.0003201871,0.003582217,0.00005006496],"about_ca_topic_score_codex":0.0014580021,"about_ca_topic_score_gemma":0.0012439922,"teacher_disagreement_score":0.0014580021,"about_ca_system_score_codex":0.00051667547,"about_ca_system_score_gemma":0.00046565075,"threshold_uncertainty_score":0.0037488341},"labels":[],"label_agreement":null},{"id":"W2931543172","doi":"10.3390/s19071563","title":"Portable System for Monitoring and Controlling Driver Behavior and the Use of a Mobile Phone While Driving","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Qatar National Library","keywords":"Mobile phone; Phone; Computer science; Smart phone; Engineering; Embedded system; Automotive engineering; Telecommunications","score_opus":0.0319881380731019,"score_gpt":0.31221523886184466,"score_spread":0.28022710078874274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931543172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25323144,0.0028270427,0.632943,0.0005594452,0.0009940069,0.00492731,0.008699156,0.04985072,0.045967862],"genre_scores_gemma":[0.75449574,0.0013627517,0.18134141,0.00088328373,0.00041020097,0.0031199185,0.0046007656,0.00038688292,0.053399004],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994217,0.00006692371,0.000039834467,0.00018049579,0.00023821105,0.0000529597],"domain_scores_gemma":[0.99933404,0.00013619887,0.00008814885,0.000082684455,0.00030131172,0.000057664674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033514175,0.00089702173,0.00071356987,0.0013666521,0.00034048955,0.0005256151,0.0010323928,0.00070846186,0.010158259],"category_scores_gemma":[0.00074055995,0.00030334797,0.00040889432,0.00059994,0.00015856953,0.00045525946,0.00054853753,0.0003737021,0.004164025],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012483882,0.00073066907,0.043160513,0.0015720046,0.00025648336,0.0015447347,0.0008486101,0.0021200085,0.3570714,0.0017039548,0.038318627,0.55142474],"study_design_scores_gemma":[0.0007890996,0.009184884,0.28263322,0.00078074203,0.0015753496,0.013454623,0.00090933277,0.09443432,0.31612957,0.0017687218,0.27772534,0.00061478966],"about_ca_topic_score_codex":0.001274029,"about_ca_topic_score_gemma":0.0012145141,"teacher_disagreement_score":0.010158259,"about_ca_system_score_codex":0.00021047588,"about_ca_system_score_gemma":0.00037530778,"threshold_uncertainty_score":0.033982813},"labels":[],"label_agreement":null},{"id":"W2933649475","doi":"10.3390/s19071652","title":"Tapered Fiber-Optic Mach-Zehnder Interferometer for Ultra-High Sensitivity Measurement of Refractive Index","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Victoria","funders":"Korea Carbon Capture and Sequestration R and D Center; Ministry of Trade, Industry and Energy","keywords":"Tapering; Interferometry; Cladding (metalworking); Mach–Zehnder interferometer; Materials science; Refractive index; Optics; Optical fiber; Microfiber; Sensitivity (control systems); Fiber optic sensor; Fusion splicing; Single-mode optical fiber; Photonic-crystal fiber; Optoelectronics; Physics; Electronic engineering; Composite material","score_opus":0.014608879947943909,"score_gpt":0.22319111156431157,"score_spread":0.20858223161636766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2933649475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63397145,0.004126859,0.35281295,0.00026999353,0.0003290955,0.00020804848,0.0005793387,0.0028654193,0.0048367903],"genre_scores_gemma":[0.6784168,0.0012363655,0.3175066,0.00012203548,0.000060060553,0.00014630884,0.00032066647,0.000040115585,0.0021509659],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992679,0.0000753501,0.000025888188,0.0001428758,0.00044816028,0.000039967294],"domain_scores_gemma":[0.99968064,0.00008009396,0.000082087456,0.0000412583,0.00009118624,0.000024698653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004280519,0.00047381705,0.0004226104,0.0006312907,0.0002208551,0.00018977458,0.0008438234,0.00044683475,0.00071589835],"category_scores_gemma":[0.00050891243,0.0003471904,0.00029361402,0.0004729962,0.0003282821,0.00050036545,0.00042569824,0.0006079267,0.00032230065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033826615,0.000017248858,0.00041753546,0.000059491504,0.0000066701355,0.00004118876,0.000025401032,0.00019438582,0.9924084,0.00016535855,0.00013744966,0.0064930064],"study_design_scores_gemma":[0.000013242692,0.00019043316,0.0029114676,0.000006594302,0.00002033419,0.0003806795,0.000022475098,0.013395223,0.98001,0.00010528369,0.0029120385,0.00003225171],"about_ca_topic_score_codex":0.0006847098,"about_ca_topic_score_gemma":0.001964281,"teacher_disagreement_score":0.0008438234,"about_ca_system_score_codex":0.00047663355,"about_ca_system_score_gemma":0.00035549013,"threshold_uncertainty_score":0.003458202},"labels":[],"label_agreement":null},{"id":"W2934279294","doi":"10.3390/s19071554","title":"Ag@Au Core–Shell Porous Nanocages with Outstanding SERS Activity for Highly Sensitive SERS Immunoassay","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Nanocages; Chloroauric acid; Bimetallic strip; Detection limit; Porosity; Immunoassay; Materials science; Analyte; Surface-enhanced Raman spectroscopy; Nanotechnology; Shell (structure); Linear range; Raman spectroscopy; Chemistry; Chemical engineering; Nanoparticle; Raman scattering; Chromatography; Metal; Colloidal gold; Catalysis; Organic chemistry; Optics","score_opus":0.016856957179914298,"score_gpt":0.23659300747975756,"score_spread":0.21973605029984328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2934279294","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91013914,0.002812856,0.08240763,0.00026552804,0.00011396751,0.00010156122,0.00031716283,0.0013971124,0.002445092],"genre_scores_gemma":[0.95893246,0.0006382772,0.03877466,0.00006732238,0.000021734862,0.000044505614,0.00018676814,0.000044703665,0.0012895595],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997354,0.000023178873,0.000020699837,0.000066487446,0.00010350669,0.00005063542],"domain_scores_gemma":[0.9998085,0.000037621576,0.00005273626,0.000021860125,0.000050502396,0.000028841583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031994854,0.00040257003,0.00032188394,0.0003369957,0.0001275661,0.00031025178,0.0003145957,0.0005205364,0.00032713177],"category_scores_gemma":[0.00046694552,0.00023115311,0.00026233034,0.00024563892,0.00033520535,0.00034400274,0.00028294252,0.00035987812,0.00019487148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017109893,0.0000070697615,0.000045788707,0.000023628681,0.000003582856,0.0000360624,0.0000065262943,0.00015595362,0.9980976,0.00008326876,0.000031688614,0.001491731],"study_design_scores_gemma":[0.0000064658407,0.00006925086,0.0005476835,0.0000017555095,0.0000072783423,0.00012342099,0.0000051762595,0.0023762002,0.9959941,0.000052670064,0.0008096088,0.0000065313793],"about_ca_topic_score_codex":0.0006460962,"about_ca_topic_score_gemma":0.0014618705,"teacher_disagreement_score":0.0006460962,"about_ca_system_score_codex":0.00034879957,"about_ca_system_score_gemma":0.00023608562,"threshold_uncertainty_score":0.0025307536},"labels":[],"label_agreement":null},{"id":"W2935004960","doi":"10.3390/s19071555","title":"Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":304,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Reliability (semiconductor); Context (archaeology); Inertial measurement unit; Computer science; Wearable computer; Criterion validity; Motion capture; Joint (building); Validity; Reliability engineering; Data extraction; Task (project management); Wearable technology; Construct validity; Data mining; Motion (physics); Artificial intelligence; Engineering; MEDLINE; Psychometrics; Statistics; Mathematics; Systems engineering","score_opus":0.10628997671363613,"score_gpt":0.36069280115479224,"score_spread":0.25440282444115614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935004960","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000538826,0.9984268,0.00017218835,0.00014272261,0.000074430565,0.00018292194,0.0002627197,0.000006549793,0.00019287104],"genre_scores_gemma":[0.008480374,0.98993903,0.00059280836,0.00026089922,0.00005650225,0.00037406766,0.0002159402,0.000006309839,0.000074051335],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9888615,0.0030314352,0.00457942,0.00084598374,0.0024681815,0.00021337639],"domain_scores_gemma":[0.94418174,0.043013543,0.0072631906,0.0007371032,0.004534458,0.00026989475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0119881425,0.001888378,0.008861663,0.012823508,0.00067422265,0.003063421,0.0022607893,0.0020361624,0.004657786],"category_scores_gemma":[0.0666379,0.0012058021,0.008058336,0.011023995,0.0012094975,0.002416536,0.001728607,0.001099186,0.0004432837],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009842476,0.000013680369,0.00065905834,0.94925016,0.0056596263,0.00004073005,0.00012940021,0.00007030156,0.000085700514,0.00017133575,0.0010442627,0.042777367],"study_design_scores_gemma":[0.00012538314,0.00013169147,0.0043978407,0.9225184,0.056466043,0.0002831995,0.00022365867,0.0000876612,0.00018477965,0.0002805758,0.015260483,0.000040356645],"about_ca_topic_score_codex":0.009467259,"about_ca_topic_score_gemma":0.020728257,"teacher_disagreement_score":0.012823508,"about_ca_system_score_codex":0.0033818912,"about_ca_system_score_gemma":0.012249909,"threshold_uncertainty_score":0.06340015},"labels":[],"label_agreement":null},{"id":"W2935974895","doi":"10.3390/s19081887","title":"A Practical Neighbor Discovery Framework for Wireless Sensor Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Neighbor Discovery Protocol; Latency (audio); Computer science; Spear; Wireless sensor network; Service discovery; Boosting (machine learning); Computer network; Wireless; Telecommunications; World Wide Web; Machine learning; The Internet","score_opus":0.015803830027085464,"score_gpt":0.27350537638539385,"score_spread":0.2577015463583084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935974895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000497962,0.0002749357,0.99650466,0.00011339249,0.00005040358,0.00007964353,0.000052528372,0.0012882494,0.0011382218],"genre_scores_gemma":[0.03709746,0.00088103686,0.95737916,0.00014082991,0.00009292855,0.00041143168,0.0004747184,0.00020830308,0.0033140276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824405,0.00043846687,0.00014658688,0.0002238656,0.0008414915,0.00010561993],"domain_scores_gemma":[0.99919814,0.000300598,0.00006699097,0.00016297564,0.00021436109,0.000056953435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031832522,0.0008658576,0.00088164187,0.0011231118,0.0011964644,0.0013165196,0.003199153,0.0011465875,0.0028897598],"category_scores_gemma":[0.0035274439,0.00055403373,0.0010551492,0.0012991719,0.0007851982,0.0026402136,0.0023839534,0.001713063,0.001473929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001552557,0.00013856006,0.0005709874,0.000655388,0.0001002537,0.0003255067,0.00046697663,0.20465812,0.008836569,0.36364582,0.021026189,0.39942044],"study_design_scores_gemma":[0.00006127634,0.00012904189,0.00013863134,0.00006426128,0.000051682582,0.00045921482,0.00009628159,0.7953576,0.003365452,0.09558839,0.10463175,0.00005647469],"about_ca_topic_score_codex":0.0033707572,"about_ca_topic_score_gemma":0.005620852,"teacher_disagreement_score":0.0033707572,"about_ca_system_score_codex":0.0007972498,"about_ca_system_score_gemma":0.0017841775,"threshold_uncertainty_score":0.016834795},"labels":[],"label_agreement":null},{"id":"W2936291384","doi":"10.3390/s19081800","title":"A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Node (physics); Energy consumption; Process (computing); Sensor node; Agile software development; Embedded system; Field (mathematics); Wireless; Energy (signal processing); Computer network; Real-time computing; Reliability engineering; Key distribution in wireless sensor networks; Distributed computing; Wireless network; Engineering; Electrical engineering; Telecommunications","score_opus":0.04112634935420239,"score_gpt":0.24104407447214138,"score_spread":0.19991772511793898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936291384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045469718,0.00023463476,0.9965467,0.000078995115,0.000045097244,0.00016238668,0.000040975836,0.00032669082,0.002109845],"genre_scores_gemma":[0.027692428,0.00088217616,0.96686393,0.00012590933,0.000059473256,0.0009551799,0.00021183175,0.00016423677,0.0030448493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862504,0.0003198752,0.00012111766,0.00017528133,0.00067017815,0.000088507855],"domain_scores_gemma":[0.99894565,0.00033454117,0.00011874655,0.00017432105,0.00039596832,0.00003082548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020575107,0.0015657941,0.0007408409,0.00086562056,0.0007354152,0.002369706,0.0025698864,0.0012684036,0.0047933855],"category_scores_gemma":[0.003199527,0.00092055916,0.001557376,0.00069026183,0.0011606098,0.0014099281,0.0012884573,0.0022422087,0.0024189223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000512291,0.0001208139,0.0005800029,0.002108292,0.00013175952,0.0005820631,0.0005155414,0.48085475,0.03115564,0.303477,0.0055817417,0.17484117],"study_design_scores_gemma":[0.000052026193,0.00030381442,0.0002460851,0.00049894536,0.00011369603,0.00037923368,0.00010732531,0.77689326,0.013984058,0.092145905,0.11522138,0.000054345124],"about_ca_topic_score_codex":0.0015677821,"about_ca_topic_score_gemma":0.0020311514,"teacher_disagreement_score":0.0047933855,"about_ca_system_score_codex":0.00090057455,"about_ca_system_score_gemma":0.0021837875,"threshold_uncertainty_score":0.016035438},"labels":[],"label_agreement":null},{"id":"W2937305999","doi":"10.3390/s19081778","title":"Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chongqing Municipal Education Commission","keywords":"Sensor fusion; Support vector machine; Adaptive neuro fuzzy inference system; Computer science; Curing (chemistry); Fusion; Artificial intelligence; Schedule; Fuzzy logic; Fuzzy control system; Materials science; Composite material","score_opus":0.07071946790902632,"score_gpt":0.2610265860194193,"score_spread":0.19030711811039297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937305999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10495313,0.00054987665,0.89138675,0.0001067872,0.00007852352,0.00008683162,0.000065684115,0.00067487085,0.0020975044],"genre_scores_gemma":[0.9476488,0.00019769349,0.0509543,0.00003406294,0.000019002997,0.00007552663,0.00007685278,0.00001692862,0.0009767992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996679,0.000045732737,0.00003756656,0.00009812995,0.00011782705,0.000032818298],"domain_scores_gemma":[0.999726,0.000083035324,0.00007369519,0.000018582647,0.000086422486,0.0000122658985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007042103,0.0007986325,0.00073429325,0.00055493833,0.00028882528,0.0005304504,0.0005145241,0.00051756465,0.0005607741],"category_scores_gemma":[0.00088766793,0.0003075395,0.00074749964,0.00036518177,0.00023991229,0.000869454,0.0003678516,0.0005351594,0.00012876083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036637514,0.00020050711,0.0029591674,0.00030200867,0.00017007635,0.0001589781,0.0002482246,0.72415143,0.060235836,0.0018943764,0.0005323457,0.20878068],"study_design_scores_gemma":[0.00000441252,0.00005559978,0.0007137465,0.000005805743,0.000016301596,0.000013520608,0.000014784919,0.9941802,0.004475065,0.00031415513,0.00019841405,0.000008015318],"about_ca_topic_score_codex":0.0033566898,"about_ca_topic_score_gemma":0.0029359304,"teacher_disagreement_score":0.0033566898,"about_ca_system_score_codex":0.0003726146,"about_ca_system_score_gemma":0.00037013617,"threshold_uncertainty_score":0.0066742897},"labels":[],"label_agreement":null},{"id":"W2937336022","doi":"10.3390/s19071686","title":"Non-Enzymatic Impedimetric Sensor Based on 3-Aminophenylboronic Acid Functionalized Screen-Printed Carbon Electrode for Highly Sensitive Glucose Detection","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electrode; Biosensor; Chemistry; Nanotechnology; Materials science","score_opus":0.004294365755627997,"score_gpt":0.190357726308283,"score_spread":0.186063360552655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937336022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.637842,0.011004463,0.34253842,0.00048088277,0.0007210982,0.0002898514,0.0008181414,0.0029732515,0.00333181],"genre_scores_gemma":[0.7556625,0.0036029618,0.23439555,0.00043593178,0.00007799605,0.00028106562,0.00082014286,0.00007401418,0.0046498594],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990901,0.00014296814,0.000058395897,0.00024514395,0.00038091003,0.00008260595],"domain_scores_gemma":[0.99968064,0.00012040246,0.00005179181,0.000024752606,0.00008449126,0.000037886097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041647063,0.0009849403,0.00072314264,0.00060432823,0.00018238713,0.0004830863,0.0013494928,0.0012649604,0.0009181122],"category_scores_gemma":[0.0004642604,0.0004493889,0.0003839076,0.00058582297,0.00032495375,0.00052343553,0.0003903664,0.0008725155,0.0007340015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025247251,0.000011966059,0.000033385317,0.000051598687,0.0000057945035,0.00004311515,0.000004852999,0.000037147845,0.99831414,0.000035073637,0.00002866815,0.0014089947],"study_design_scores_gemma":[0.0000067333344,0.00013253881,0.0006032252,0.0000037333355,0.000015808397,0.00024882544,0.000007690626,0.0019331403,0.99632144,0.000038420363,0.00067545974,0.000012976651],"about_ca_topic_score_codex":0.0003013429,"about_ca_topic_score_gemma":0.00059167395,"teacher_disagreement_score":0.0013494928,"about_ca_system_score_codex":0.00031198162,"about_ca_system_score_gemma":0.00021050098,"threshold_uncertainty_score":0.0030714273},"labels":[],"label_agreement":null},{"id":"W2937377430","doi":"10.3390/s19081885","title":"Validity of Wearable Sensors at the Shoulder Joint: Combining Wireless Electromyography Sensors and Inertial Measurement Units to Perform Physical Workplace Assessments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Wearable computer; Inertial measurement unit; Electromyography; Accelerometer; Units of measurement; Wireless; Joint (building); Inertial frame of reference; Computer science; Engineering; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Telecommunications; Embedded system; Physics; Structural engineering","score_opus":0.03156207964331245,"score_gpt":0.24930048938401494,"score_spread":0.2177384097407025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937377430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9587584,0.0022423288,0.033782132,0.00026197534,0.00017767842,0.0002552267,0.0003404363,0.000071289774,0.0041103796],"genre_scores_gemma":[0.9884603,0.00034292118,0.0103458185,0.00009933334,0.000056918278,0.00009618678,0.00019343145,0.00001928885,0.00038575308],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9887518,0.0060497723,0.0010111779,0.001132928,0.0028404305,0.00021384672],"domain_scores_gemma":[0.96906596,0.018472612,0.0048362645,0.0022326591,0.0050995857,0.00029287682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0132608535,0.0006494945,0.00033849827,0.0008268294,0.00027049618,0.0011368699,0.0006459564,0.0007334457,0.0009662971],"category_scores_gemma":[0.044887036,0.0003082017,0.0006735984,0.0007622532,0.001062068,0.000764696,0.0012056241,0.00037580833,0.0004543326],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014053109,0.00026650776,0.8659952,0.00064390927,0.00086848077,0.000071927694,0.0008259211,0.0018440455,0.018087476,0.00019618505,0.00038408203,0.10941107],"study_design_scores_gemma":[0.00007997478,0.0024125397,0.97508395,0.00030669486,0.00037266128,0.00039499404,0.0008417002,0.008960211,0.009303214,0.0004785189,0.0017234929,0.000042180538],"about_ca_topic_score_codex":0.0011248646,"about_ca_topic_score_gemma":0.002469942,"teacher_disagreement_score":0.0132608535,"about_ca_system_score_codex":0.00024181519,"about_ca_system_score_gemma":0.00047029334,"threshold_uncertainty_score":0.070130944},"labels":[],"label_agreement":null},{"id":"W2938320955","doi":"10.3390/s19081785","title":"A GaN-Based Wireless Monitoring System for High-Temperature Applications","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GaN-based semiconductor devices and materials","field":"Physics and Astronomy","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Research Council Canada; Airbus; Safran; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Wireless; Computer science; Engineering; Telecommunications","score_opus":0.008485965035347503,"score_gpt":0.2335208369443985,"score_spread":0.225034871909051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938320955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32993263,0.001605992,0.6514247,0.000413019,0.0003173986,0.00025333258,0.0008387874,0.0055969213,0.009617277],"genre_scores_gemma":[0.91292614,0.0004449352,0.08018905,0.00024447456,0.00008662579,0.000094955445,0.00034358504,0.000057120185,0.0056131],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983954,0.000026091646,0.000006473447,0.000052109466,0.0000562503,0.00001951924],"domain_scores_gemma":[0.99989104,0.00001639322,0.000023355546,0.00001924874,0.00003853798,0.000011452041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014708297,0.00036040944,0.00029730296,0.00016662688,0.00015292587,0.00030522668,0.00091610773,0.00030761957,0.0016251936],"category_scores_gemma":[0.00014286971,0.00012225687,0.00018633464,0.00019975427,0.00010691682,0.00042886572,0.000244737,0.00023725472,0.0007236548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017659953,0.000067834495,0.0022141093,0.00020917402,0.000052877138,0.00020698426,0.00007336491,0.0028211349,0.9166462,0.0011559897,0.0026447673,0.073730975],"study_design_scores_gemma":[0.00007448147,0.0017122882,0.016718851,0.000045612094,0.00020234541,0.0025116159,0.00007071671,0.14230111,0.79958653,0.0006821162,0.036003735,0.000090673806],"about_ca_topic_score_codex":0.00046565896,"about_ca_topic_score_gemma":0.0007863029,"teacher_disagreement_score":0.0016251936,"about_ca_system_score_codex":0.0002628516,"about_ca_system_score_gemma":0.0002449674,"threshold_uncertainty_score":0.005436778},"labels":[],"label_agreement":null},{"id":"W2939699006","doi":"10.3390/s19071678","title":"Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Component (thermodynamics); Scale (ratio); Computer science; Environmental science; Geography; Physics; Cartography","score_opus":0.013148581437981345,"score_gpt":0.21890889969915273,"score_spread":0.2057603182611714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2939699006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027397668,0.0056310426,0.9597587,0.00037577373,0.00010903154,0.000061952596,0.000072991046,0.00057978026,0.006013016],"genre_scores_gemma":[0.7824137,0.015971214,0.19468954,0.00024381233,0.0003089637,0.000111623085,0.00046221455,0.00011275816,0.0056861523],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996258,0.00008848754,0.000011740713,0.00007145133,0.00017431087,0.000028093604],"domain_scores_gemma":[0.9994796,0.0002007953,0.00004584957,0.00006967902,0.00019157483,0.000012541487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003857334,0.0004940649,0.00036531285,0.0006025473,0.00029094671,0.00056003884,0.00039299595,0.000351168,0.0008244008],"category_scores_gemma":[0.0013535293,0.0001717267,0.0003633217,0.0013519247,0.00032847997,0.00097597553,0.00032364746,0.00041192464,0.00036281568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010851142,0.00010199744,0.005538391,0.0007899755,0.00014823339,0.0005805058,0.00018342824,0.1401206,0.038621534,0.019643912,0.0052589877,0.78890395],"study_design_scores_gemma":[0.000008535932,0.00024831077,0.017722704,0.00009766741,0.000117866264,0.0011342111,0.00023249505,0.9130943,0.026913965,0.009022958,0.031324774,0.00008231122],"about_ca_topic_score_codex":0.0017502968,"about_ca_topic_score_gemma":0.0014994214,"teacher_disagreement_score":0.0017502968,"about_ca_system_score_codex":0.00027349524,"about_ca_system_score_gemma":0.00042678538,"threshold_uncertainty_score":0.003480196},"labels":[],"label_agreement":null},{"id":"W2940765313","doi":"10.3390/s19091980","title":"Capillary Sensor with Disposable Optrode for Diesel Fuel Quality Testing","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Politechnika Warszawska","keywords":"Diesel fuel; Optode; Automotive engineering; Fuel injection; Process engineering; Engineering; Fuel tank; Computer science; Mechanical engineering; Chemistry","score_opus":0.018079646382980485,"score_gpt":0.23787380739990535,"score_spread":0.21979416101692487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940765313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26283124,0.019518321,0.69058126,0.0011449091,0.0022085742,0.00097572414,0.0020110891,0.005422497,0.015306408],"genre_scores_gemma":[0.52244985,0.0040650186,0.4551711,0.0013342488,0.00017485936,0.00051045505,0.0009685026,0.00016374589,0.015162124],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984681,0.000096462645,0.00005280729,0.0003248918,0.0010039059,0.00005378277],"domain_scores_gemma":[0.99938846,0.00015403991,0.00008091927,0.000075128635,0.00025282847,0.000048605598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042556436,0.0008066369,0.0005013057,0.00079996814,0.0003617366,0.00069053785,0.0016346999,0.0014211005,0.002223609],"category_scores_gemma":[0.0010467177,0.00033817813,0.00047212502,0.0008613392,0.0004263212,0.00092827785,0.00043607267,0.0007065123,0.0008459839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009527586,0.00006552605,0.0008236648,0.00026128613,0.000024593372,0.00017353465,0.000033179385,0.00023314322,0.9606692,0.0010519197,0.0011843689,0.035384253],"study_design_scores_gemma":[0.000019588844,0.00039570458,0.0029641357,0.000022383369,0.000057798945,0.0015634801,0.000043188254,0.008329834,0.97262144,0.00035451894,0.013569968,0.00005790885],"about_ca_topic_score_codex":0.0012704191,"about_ca_topic_score_gemma":0.0034111314,"teacher_disagreement_score":0.002223609,"about_ca_system_score_codex":0.0008219058,"about_ca_system_score_gemma":0.000749244,"threshold_uncertainty_score":0.0074387193},"labels":[],"label_agreement":null},{"id":"W2941409423","doi":"10.3390/s19091982","title":"Miniaturized Multi-Port Microstrip Patch Antenna Using Metamaterial for Passive UHF RFID-Tag Sensor Applications","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"RFID technology advancements","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Ultra high frequency; Radio-frequency identification; Wireless sensor network; Sensor node; Antenna (radio); Electro-optical sensor; Microstrip antenna; Microstrip; Electrical engineering; Computer science; Patch antenna; Node (physics); Electronic engineering; Wireless; Telecommunications; Engineering; Key distribution in wireless sensor networks; Acoustics; Computer network; Wireless network; Physics","score_opus":0.013829362281551016,"score_gpt":0.2538757034205664,"score_spread":0.2400463411390154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941409423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38247213,0.0033769181,0.6037847,0.0004559529,0.00070416403,0.000092686794,0.00027552588,0.0020668264,0.006771098],"genre_scores_gemma":[0.8419302,0.0007874832,0.15331867,0.00020840592,0.00007225638,0.000046291294,0.00016991631,0.000065678345,0.0034011342],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975795,0.000045215646,0.00001640358,0.00006996928,0.000090602174,0.000019886756],"domain_scores_gemma":[0.99971896,0.0000490888,0.00008296887,0.000068107445,0.00006593661,0.000014965672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017183776,0.00046195867,0.00038171033,0.00021058126,0.00007626252,0.00037821996,0.00084323395,0.00079799234,0.00062543224],"category_scores_gemma":[0.0002656737,0.00022855744,0.00052901136,0.0002469212,0.00018466744,0.0007060792,0.0002961403,0.0002983806,0.000777612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102788945,0.000029693892,0.0005918323,0.00017695501,0.00003436166,0.00017479053,0.000019184128,0.0015697145,0.97875136,0.0006309534,0.0004212482,0.017497102],"study_design_scores_gemma":[0.000023659994,0.0005944487,0.002373772,0.000012554481,0.00008399299,0.0014181647,0.0000312477,0.028951876,0.9537581,0.0003041269,0.012414698,0.00003328534],"about_ca_topic_score_codex":0.000042935484,"about_ca_topic_score_gemma":0.0000907142,"teacher_disagreement_score":0.00084323395,"about_ca_system_score_codex":0.0002072484,"about_ca_system_score_gemma":0.0000935995,"threshold_uncertainty_score":0.0020923018},"labels":[],"label_agreement":null},{"id":"W2943135107","doi":"10.3390/s19092072","title":"Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Universidad de Guanajuato","keywords":"Parkinson's disease; Histogram; Artificial intelligence; Rating scale; Classifier (UML); Pattern recognition (psychology); Computer science; Disease; Mathematics; Medicine; Statistics; Pathology","score_opus":0.021715818001608225,"score_gpt":0.26742658576361744,"score_spread":0.24571076776200923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943135107","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84108007,0.0020722325,0.15028991,0.00027026673,0.00016186023,0.00017824401,0.0019385874,0.0007079855,0.0033009537],"genre_scores_gemma":[0.9697852,0.0003570143,0.02789994,0.0000349026,0.00006843407,0.000062965584,0.0009384708,0.00002126742,0.0008318238],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99978393,0.000033462828,0.000021010015,0.000049329745,0.0000755761,0.000036786303],"domain_scores_gemma":[0.99969435,0.000118163945,0.000043787673,0.000020883586,0.00009352512,0.000029402758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035561688,0.00041365548,0.00041670838,0.0015079395,0.00014827285,0.00044165205,0.00019384203,0.00037942562,0.0011934959],"category_scores_gemma":[0.0010766535,0.00007843235,0.00047556727,0.00062707334,0.00012532533,0.0003039852,0.00027609657,0.00021865737,0.00034389962],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044578672,0.00053120597,0.07794906,0.00041302163,0.00028029637,0.00052811456,0.00015166333,0.01766537,0.17444137,0.00084832247,0.0043137614,0.71841985],"study_design_scores_gemma":[0.00017320107,0.0020802722,0.370946,0.000090683956,0.00048754216,0.0021891694,0.0004557233,0.53523296,0.08202331,0.001428097,0.0047927727,0.000100253426],"about_ca_topic_score_codex":0.0016515636,"about_ca_topic_score_gemma":0.0022476697,"teacher_disagreement_score":0.0016515636,"about_ca_system_score_codex":0.00017783685,"about_ca_system_score_gemma":0.00022512874,"threshold_uncertainty_score":0.003992617},"labels":[],"label_agreement":null},{"id":"W2943952292","doi":"10.3390/s19092164","title":"Smartphone Sensors for Health Monitoring and Diagnosis","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":440,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Health care; Life expectancy; Business; Population; Continuous monitoring; Risk analysis (engineering); Internet privacy; Medicine; Computer science; Environmental health; Economic growth","score_opus":0.188111324571153,"score_gpt":0.5254837506810075,"score_spread":0.3373724261098545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943952292","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040386198,0.99057055,0.0016478125,0.00056722376,0.0006532386,0.00003912724,0.0001773775,0.00006248039,0.005878371],"genre_scores_gemma":[0.0061018094,0.9853962,0.0022270877,0.0005724379,0.00059729203,0.000047688598,0.00027315549,0.000007950881,0.004776288],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922657,0.00016161607,0.000090689355,0.00014318309,0.00033028872,0.000047714246],"domain_scores_gemma":[0.999252,0.00038857656,0.00008201025,0.000029444844,0.00022127316,0.000026662588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008511256,0.0011422412,0.0011210173,0.0027465562,0.00027117296,0.0010777875,0.0010290968,0.0017993125,0.008855269],"category_scores_gemma":[0.0015841315,0.00031254103,0.0009080267,0.002005448,0.0004530041,0.0014053495,0.0010443834,0.0013023204,0.004919566],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006608821,0.00005788234,0.00039178846,0.0205754,0.000110645575,0.0002051669,0.00009197165,0.0004007755,0.004787494,0.0047022975,0.03392197,0.9346885],"study_design_scores_gemma":[0.000013766052,0.00015116003,0.0012946897,0.0053889668,0.0001359214,0.0013141147,0.00009545788,0.0003951353,0.002301386,0.0024179514,0.98645604,0.00003545157],"about_ca_topic_score_codex":0.0012493713,"about_ca_topic_score_gemma":0.001473155,"teacher_disagreement_score":0.008855269,"about_ca_system_score_codex":0.00047057067,"about_ca_system_score_gemma":0.0008587517,"threshold_uncertainty_score":0.029623866},"labels":[],"label_agreement":null},{"id":"W2944028835","doi":"10.3390/s19092177","title":"Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bioelectrical impedance analysis; Calibration; Simulation; Electrical impedance; Computer science; Accuracy and precision; Wearable computer; Biomedical engineering; Statistics; Engineering; Mathematics; Electrical engineering; Body mass index; Embedded system; Medicine","score_opus":0.04816739098566163,"score_gpt":0.30744773218610644,"score_spread":0.2592803412004448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944028835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1421251,0.0005761075,0.84844655,0.00018660133,0.0002500705,0.00021691721,0.0007073907,0.004686225,0.0028050637],"genre_scores_gemma":[0.76844114,0.00052393205,0.2220349,0.00044369634,0.00017000615,0.00051915296,0.0010708424,0.00011552356,0.0066807517],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968755,0.000042023854,0.00002863038,0.00011646384,0.00010829937,0.000017072114],"domain_scores_gemma":[0.99979395,0.000038955313,0.00003318423,0.00003797175,0.000078722216,0.000017268947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031292863,0.00067948765,0.0006978023,0.000506741,0.00016554697,0.0003892712,0.0007358375,0.00055819895,0.0031946704],"category_scores_gemma":[0.0006500432,0.00024551785,0.00026953928,0.00045372755,0.00011984985,0.000489358,0.000602907,0.00033738272,0.0012554808],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012350014,0.00049834437,0.023053173,0.0005936079,0.00026481893,0.0006370385,0.00019695537,0.011970918,0.37182695,0.00068607065,0.005616419,0.58342075],"study_design_scores_gemma":[0.00039008446,0.0018783531,0.09742291,0.0001754421,0.0004064066,0.002868177,0.0002000223,0.7084435,0.1704274,0.0018807783,0.015705682,0.00020128411],"about_ca_topic_score_codex":0.00086763455,"about_ca_topic_score_gemma":0.001457662,"teacher_disagreement_score":0.0031946704,"about_ca_system_score_codex":0.0001443738,"about_ca_system_score_gemma":0.00020028617,"threshold_uncertainty_score":0.010687172},"labels":[],"label_agreement":null},{"id":"W2944068553","doi":"10.3390/s19092144","title":"Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; Ministério da Ciência, Tecnologia, Inovações e Comunicações; SENSHIN Medical Research Foundation; Conselho Nacional de Desenvolvimento Científico e Tecnológico; King Saud University; Fundação para a Ciência e a Tecnologia; Instituto Nacional de Telecomunicações","keywords":"Computer network; Computer science; Routing protocol; Policy-based routing; Link-state routing protocol; IPv6; Dynamic Source Routing; Static routing; Routing domain; Interior gateway protocol; 6LoWPAN; Distributed computing; Wireless Routing Protocol; Routing (electronic design automation); The Internet; World Wide Web","score_opus":0.04132885037994975,"score_gpt":0.33748465576033027,"score_spread":0.2961558053803805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944068553","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062449686,0.21495275,0.68354,0.008990062,0.005953276,0.0006693924,0.00038006302,0.001089429,0.07818012],"genre_scores_gemma":[0.1223733,0.3497132,0.44843236,0.007231587,0.007223384,0.0020669482,0.0023735745,0.000765945,0.059819717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972058,0.0009260617,0.00040681427,0.00032115926,0.0009815189,0.00015858663],"domain_scores_gemma":[0.99813604,0.000810902,0.00018872753,0.00025355627,0.00056478934,0.000045886994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024799057,0.0009798234,0.0008592866,0.0021480825,0.001152431,0.0036990352,0.0018247848,0.0021818753,0.0031259835],"category_scores_gemma":[0.0034390108,0.00067404495,0.00079462666,0.004078866,0.0012783762,0.0047255536,0.0018385639,0.00376633,0.0026365046],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068555164,0.00011120811,0.00046364608,0.0019999924,0.00006692201,0.0007366759,0.00086160394,0.008618767,0.008727364,0.5712572,0.0433096,0.3637785],"study_design_scores_gemma":[0.000015453063,0.00013178705,0.0005705446,0.0011120832,0.00006178685,0.0012222409,0.00036603012,0.019170187,0.0048541613,0.16835515,0.80404246,0.00009800328],"about_ca_topic_score_codex":0.000782455,"about_ca_topic_score_gemma":0.0008356495,"teacher_disagreement_score":0.0036990352,"about_ca_system_score_codex":0.0011749291,"about_ca_system_score_gemma":0.0012042875,"threshold_uncertainty_score":0.013115168},"labels":[],"label_agreement":null},{"id":"W2944439235","doi":"10.3390/s19092175","title":"Brain Inspired Dynamic System for the Quality of Service Control over the Long-Haul Nonlinear Fiber-Optic Link","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Network Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Coral Reef Conservation Program; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bit error rate; Computer science; Optical fiber; Electronic engineering; Channel (broadcasting); Nonlinear system; Engineering; Telecommunications","score_opus":0.009547608555438474,"score_gpt":0.24337835393789867,"score_spread":0.23383074538246018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944439235","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20164365,0.0012672477,0.7754658,0.0005104697,0.00025229005,0.00013228328,0.00006917291,0.001087518,0.01957161],"genre_scores_gemma":[0.97553045,0.00017186967,0.022011848,0.000062087536,0.000017566252,0.000046848298,0.000019961213,0.000008938791,0.0021303212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999249,0.000011211036,0.0000037895913,0.000017414108,0.000028001183,0.000014599597],"domain_scores_gemma":[0.9999404,0.000015188062,0.000009768255,0.000005410128,0.000021972035,0.0000073232745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013613833,0.0002558727,0.0002274507,0.00014994315,0.00032414217,0.00042367086,0.0004969986,0.00031776255,0.0010526864],"category_scores_gemma":[0.00023128053,0.00006924084,0.0002739138,0.00013664374,0.0002922102,0.0003269216,0.00033414,0.00038734538,0.00010538554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031884483,0.00026902137,0.0021506078,0.000283679,0.00019394455,0.00064717146,0.0004209059,0.4567188,0.3222208,0.052537132,0.002639343,0.16159971],"study_design_scores_gemma":[0.00001643321,0.00017714537,0.000611065,0.0000071892573,0.00002544808,0.00007875306,0.000020591075,0.9830784,0.010778741,0.0031396418,0.002048317,0.00001819931],"about_ca_topic_score_codex":0.0022938007,"about_ca_topic_score_gemma":0.0024467001,"teacher_disagreement_score":0.0022938007,"about_ca_system_score_codex":0.00047087984,"about_ca_system_score_gemma":0.0004058455,"threshold_uncertainty_score":0.0045609474},"labels":[],"label_agreement":null},{"id":"W2945402292","doi":"10.3390/s19102285","title":"Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministério da Educação; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Orientation (vector space); Artificial intelligence; Computer vision; Computer science; Frame of reference; Reference frame; Pattern recognition (psychology); Frame (networking); Mathematics; Geometry; Physics","score_opus":0.10065616920309275,"score_gpt":0.41256657857964313,"score_spread":0.31191040937655035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945402292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17606689,0.00032251017,0.8224556,0.000041731873,0.000030588784,0.000029048482,0.000026707534,0.00042605665,0.0006009307],"genre_scores_gemma":[0.76579565,0.00021963195,0.23307128,0.000037549413,0.000016271284,0.0000341921,0.000069033325,0.000027756152,0.00072863296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996376,0.00006400266,0.000024706826,0.000108138025,0.00013777286,0.000027852348],"domain_scores_gemma":[0.99915755,0.00029055084,0.00020880705,0.000105526204,0.00021699083,0.00002057619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064369483,0.00052048004,0.00049279147,0.00056824356,0.00018299193,0.00057397323,0.00034661577,0.00041815857,0.00032991046],"category_scores_gemma":[0.0020980153,0.00023481052,0.00028664246,0.00051188475,0.0003605225,0.00066502637,0.0003845891,0.0004231391,0.00015934739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032489607,0.00014913021,0.0069930293,0.00018534242,0.00011127913,0.00015472327,0.00020996801,0.16012643,0.24652642,0.0011422139,0.00036593553,0.5837106],"study_design_scores_gemma":[0.000016229525,0.00021891086,0.009950265,0.000022139699,0.000031792268,0.00012210723,0.000073245996,0.91406494,0.073615074,0.0010740984,0.00077306066,0.000038092843],"about_ca_topic_score_codex":0.002591942,"about_ca_topic_score_gemma":0.0032542972,"teacher_disagreement_score":0.002591942,"about_ca_system_score_codex":0.00030171173,"about_ca_system_score_gemma":0.0004666298,"threshold_uncertainty_score":0.005153775},"labels":[],"label_agreement":null},{"id":"W2946952390","doi":"10.3390/s19112497","title":"Validity of the Walked Distance Estimated by Wearable Devices in Stroke Individuals","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Fondation de l'Avenir pour la Recherche Médicale Appliquée","keywords":"Pedometer; Activity monitor; Wearable computer; Stroke (engine); Physical therapy; Physical medicine and rehabilitation; Sedentary behavior; Physical activity; Ankle; Medicine; Psychology; Statistics; Computer science; Mathematics; Engineering; Surgery","score_opus":0.018169362299776757,"score_gpt":0.28114369823228824,"score_spread":0.26297433593251146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946952390","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942162,0.00078687485,0.003639132,0.00005020795,0.000033537337,0.000026844074,0.00023996907,0.000016077845,0.0009910944],"genre_scores_gemma":[0.99841106,0.00012440339,0.0011029138,0.000016128462,0.000009390304,0.000017407985,0.00016864605,0.000004105204,0.00014593304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99550927,0.002240688,0.0007033308,0.0005495498,0.00086734135,0.00012975196],"domain_scores_gemma":[0.98820275,0.006243221,0.0027984655,0.0007627202,0.0018507157,0.0001421784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005149732,0.00029790305,0.00035947608,0.0007331528,0.00020141147,0.0006215809,0.0003881514,0.0003514197,0.00059008633],"category_scores_gemma":[0.018115215,0.00012527347,0.0004123228,0.00058450643,0.00042741036,0.00046071477,0.0005583283,0.00024119645,0.00023779258],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055999303,0.00006763988,0.96918696,0.00018231306,0.00032174904,0.000056306988,0.0007161126,0.0005913828,0.0013853123,0.00008634697,0.00015399707,0.026691863],"study_design_scores_gemma":[0.000010096338,0.0004657953,0.9935381,0.00009488101,0.00012107734,0.0003050129,0.00042894177,0.002450678,0.0017969452,0.00016692936,0.00060551555,0.000016079712],"about_ca_topic_score_codex":0.001327473,"about_ca_topic_score_gemma":0.0018507545,"teacher_disagreement_score":0.005149732,"about_ca_system_score_codex":0.00018523034,"about_ca_system_score_gemma":0.00023259234,"threshold_uncertainty_score":0.027234733},"labels":[],"label_agreement":null},{"id":"W2947142702","doi":"10.3390/s19102416","title":"Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Physical medicine and rehabilitation; Parkinson's disease; Falling (accident); Medicine; Computer science; Artificial intelligence; Simulation; Disease","score_opus":0.013153420163602516,"score_gpt":0.28592670937870696,"score_spread":0.27277328921510446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947142702","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7008727,0.0029359516,0.23312956,0.00027707944,0.0004194213,0.0013675108,0.009576194,0.039803676,0.0116179725],"genre_scores_gemma":[0.94072497,0.00036863,0.05222048,0.00020543477,0.00003282668,0.00039276155,0.0037679053,0.00008881437,0.0021982344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984574,0.000016565598,0.000017933357,0.000042951015,0.000056264114,0.00002054366],"domain_scores_gemma":[0.9998982,0.000016679434,0.000016462773,0.000008502043,0.00004181038,0.000018304454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022674935,0.00064864184,0.00059503404,0.00073334744,0.00019083264,0.00024954756,0.00051556586,0.00048427237,0.0013753584],"category_scores_gemma":[0.00046414844,0.00015619966,0.0002681053,0.00020722173,0.000091846894,0.00030914872,0.0005481513,0.00021902671,0.00037226046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00433602,0.0010868651,0.06753937,0.00136485,0.00036508753,0.0021767113,0.0006341805,0.022555038,0.172909,0.00070253876,0.05360368,0.67272663],"study_design_scores_gemma":[0.0006382215,0.0023304832,0.2714021,0.00022613541,0.00035332548,0.003066428,0.00033109973,0.6484273,0.052969124,0.001468269,0.01843237,0.00035518495],"about_ca_topic_score_codex":0.0039232615,"about_ca_topic_score_gemma":0.0052907886,"teacher_disagreement_score":0.0039232615,"about_ca_system_score_codex":0.0002105385,"about_ca_system_score_gemma":0.0003076187,"threshold_uncertainty_score":0.007800877},"labels":[],"label_agreement":null},{"id":"W2947292774","doi":"10.3390/s19112516","title":"New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Wearable computer; Gait; Computer science; Wearable technology; Physical medicine and rehabilitation; Gait analysis; Simulation; Medicine; Embedded system","score_opus":0.05872626031084552,"score_gpt":0.29274713303921995,"score_spread":0.23402087272837443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947292774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6106674,0.011729724,0.3569093,0.008181773,0.0012592353,0.00059245364,0.0014281641,0.0012474898,0.007984403],"genre_scores_gemma":[0.70158446,0.004331856,0.28907257,0.0014409744,0.00077346805,0.000600937,0.00065372494,0.00026356563,0.0012785601],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99165875,0.0038585372,0.0010333527,0.0009616793,0.0023289546,0.00015881486],"domain_scores_gemma":[0.9581094,0.026880955,0.0038011717,0.0039346907,0.006533239,0.00074051804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012901591,0.0008231932,0.001362912,0.0009638292,0.00064394146,0.0023217208,0.0013236086,0.0009904249,0.0010205169],"category_scores_gemma":[0.046919927,0.00062345277,0.00057201996,0.0014918561,0.0012214099,0.0023404162,0.00082340697,0.0009192974,0.00060715224],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015246507,0.000527246,0.4646503,0.001720062,0.00038590774,0.0005728411,0.002050181,0.0050728717,0.09442928,0.0018483222,0.003855967,0.42336243],"study_design_scores_gemma":[0.000078874196,0.003965736,0.899884,0.0011859525,0.00053648185,0.0032661986,0.0043277303,0.033072736,0.031332698,0.0068293987,0.0151379835,0.00038217442],"about_ca_topic_score_codex":0.0021386528,"about_ca_topic_score_gemma":0.008938626,"teacher_disagreement_score":0.012901591,"about_ca_system_score_codex":0.00042176142,"about_ca_system_score_gemma":0.00079681264,"threshold_uncertainty_score":0.06823093},"labels":[],"label_agreement":null},{"id":"W2947501215","doi":"10.3390/s19112430","title":"Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Internet of Things; Big data; Smart city; Characterization (materials science); Computer science; Engineering; Data science; Computer security; Nanotechnology; Data mining; Materials science","score_opus":0.024424186733318413,"score_gpt":0.20385763624911005,"score_spread":0.17943344951579163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947501215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26706487,0.001979371,0.7134702,0.0047862763,0.00021084394,0.00044040396,0.0017577175,0.0005145506,0.009775855],"genre_scores_gemma":[0.88519686,0.00147092,0.110257745,0.00017552069,0.00008411189,0.00021118761,0.0016328373,0.000068164874,0.00090262335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977983,0.0006488584,0.00021298339,0.00028466617,0.0008329711,0.00022211536],"domain_scores_gemma":[0.9938128,0.0031199434,0.00083335035,0.0007400995,0.0012180483,0.0002757904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030367074,0.00051054737,0.00069832226,0.001526446,0.0007925882,0.0033579217,0.0010662082,0.0008876523,0.00045597285],"category_scores_gemma":[0.009608046,0.0004907374,0.00048515975,0.0031183567,0.0013909724,0.005149099,0.0016639988,0.0012733277,0.00013210319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027846685,0.00032982018,0.07265642,0.0007592167,0.00018508117,0.0016231238,0.0010708628,0.48241553,0.015501465,0.29793397,0.007298375,0.11994763],"study_design_scores_gemma":[0.0000063250395,0.00003147686,0.006301787,0.000032827724,0.000018876797,0.00018692842,0.0006174842,0.9235662,0.0048431656,0.05981266,0.0045589106,0.000023294331],"about_ca_topic_score_codex":0.0039765416,"about_ca_topic_score_gemma":0.0048285644,"teacher_disagreement_score":0.0039765416,"about_ca_system_score_codex":0.0016921994,"about_ca_system_score_gemma":0.0023405442,"threshold_uncertainty_score":0.016059816},"labels":[],"label_agreement":null},{"id":"W2947842327","doi":"10.3390/s19112432","title":"An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Isometric exercise; SIGNAL (programming language); Sampling (signal processing); Wrist; Electrical impedance myography; Computer science; Forearm; Acoustics; Physics; Computer vision; Medicine; Anatomy; Physical therapy","score_opus":0.021499134372090854,"score_gpt":0.2215158607257832,"score_spread":0.20001672635369233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947842327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84733194,0.0017153791,0.14861733,0.00040641645,0.00013840816,0.00014276466,0.00013676513,0.00018114981,0.0013298909],"genre_scores_gemma":[0.9227809,0.000618221,0.07563905,0.00010115721,0.000096778174,0.00015712905,0.00013188708,0.00004653891,0.00042837212],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994037,0.00016512805,0.0000771643,0.00014075112,0.00017121217,0.000041971318],"domain_scores_gemma":[0.996024,0.0024580085,0.00037331504,0.00028707364,0.00077631435,0.00008135033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001034794,0.0003412321,0.00044371517,0.00045877407,0.00029360846,0.00038260652,0.0003623551,0.0007255165,0.0012010324],"category_scores_gemma":[0.008495416,0.00019454121,0.00016307457,0.00033057886,0.00027375613,0.0006323541,0.00016755941,0.000296888,0.00033530442],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027253488,0.00021789069,0.019885609,0.0007143904,0.000030199286,0.00045332874,0.00061903073,0.0012344302,0.8310657,0.00058810075,0.0003076229,0.14215825],"study_design_scores_gemma":[0.00022725487,0.006377485,0.27583778,0.0003621058,0.00033561728,0.005188675,0.00085845287,0.029624026,0.673016,0.0012923988,0.006773823,0.00010639141],"about_ca_topic_score_codex":0.00046435554,"about_ca_topic_score_gemma":0.00052170735,"teacher_disagreement_score":0.0012010324,"about_ca_system_score_codex":0.00014101104,"about_ca_system_score_gemma":0.00025712186,"threshold_uncertainty_score":0.0054726005},"labels":[],"label_agreement":null},{"id":"W2947863890","doi":"10.3390/s19112466","title":"ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Assisted living; Authentication (law); Computer science; Activity recognition; Computer security; Embedded system; Artificial intelligence; Medicine; Gerontology","score_opus":0.020902879288154156,"score_gpt":0.2469108195432807,"score_spread":0.22600794025512655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947863890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18542348,0.0008187541,0.8057232,0.000467593,0.00019725773,0.00022801958,0.00026869474,0.0036214218,0.0032516608],"genre_scores_gemma":[0.9516204,0.0001391204,0.04660859,0.000077585246,0.000032053795,0.00006719395,0.0002103139,0.000013223794,0.0012315814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991172,0.00032734146,0.00004959747,0.00019345626,0.00020799153,0.00010447525],"domain_scores_gemma":[0.99951696,0.0001163039,0.0000858184,0.00011890595,0.00010532463,0.000056653946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008235976,0.0005572314,0.0008656296,0.000893564,0.00060062285,0.0008306836,0.0009049036,0.0006514477,0.0006075983],"category_scores_gemma":[0.001208426,0.00013399834,0.00040887445,0.0007193067,0.0005512665,0.0014758362,0.001801913,0.0005051668,0.00035864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016577757,0.0008472417,0.030426906,0.0003763569,0.00030089435,0.001232311,0.0007546613,0.14591785,0.055178434,0.022673326,0.008215108,0.73241913],"study_design_scores_gemma":[0.00004311961,0.00028775164,0.008329591,0.000020066445,0.000055207493,0.00044564743,0.00017652368,0.96393204,0.016417226,0.0059251958,0.0042964593,0.00007120505],"about_ca_topic_score_codex":0.003206466,"about_ca_topic_score_gemma":0.002654555,"teacher_disagreement_score":0.003206466,"about_ca_system_score_codex":0.00063781976,"about_ca_system_score_gemma":0.0006504321,"threshold_uncertainty_score":0.006375611},"labels":[],"label_agreement":null},{"id":"W2947928278","doi":"10.3390/s19112438","title":"Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Children's Physical and Motor Development","field":"Psychology","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centres Intégré Universitaires de Santé et de Services Sociaux; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"","keywords":"Criterion validity; Computer science; Identification (biology); Artificial intelligence; Physical medicine and rehabilitation; Statistics; Mathematics; Medicine; Construct validity; Psychometrics","score_opus":0.08380980439222449,"score_gpt":0.34989229895078705,"score_spread":0.2660824945585626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947928278","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056989165,0.99859184,0.00015949826,0.000094165116,0.00006529635,0.00015631234,0.0001805476,0.0000057296925,0.00017670631],"genre_scores_gemma":[0.007889025,0.9905682,0.0007288536,0.00017798098,0.000045141365,0.00032172917,0.00019430666,0.0000047380686,0.000069962065],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99010473,0.002657563,0.003965252,0.00077160954,0.0023047673,0.00019612374],"domain_scores_gemma":[0.9505218,0.038640182,0.0058073425,0.0006532403,0.0041236226,0.00025380673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010889315,0.001809222,0.0078266645,0.011865699,0.0006634191,0.0031940949,0.002122653,0.001988939,0.0036193528],"category_scores_gemma":[0.061891794,0.0012632646,0.007099719,0.010208408,0.0011695052,0.002450137,0.0016642666,0.0010721812,0.00040245315],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001075785,0.000020514766,0.0008016616,0.92298466,0.004115607,0.000050657825,0.00016192858,0.00007735888,0.00010903662,0.00023073216,0.0010731991,0.0702671],"study_design_scores_gemma":[0.00013673272,0.00017885101,0.005637418,0.9313974,0.04211779,0.0003714435,0.0003447978,0.00011051115,0.0002746697,0.00030973673,0.019073855,0.000046853966],"about_ca_topic_score_codex":0.010339378,"about_ca_topic_score_gemma":0.024327977,"teacher_disagreement_score":0.011865699,"about_ca_system_score_codex":0.0035451506,"about_ca_system_score_gemma":0.012173273,"threshold_uncertainty_score":0.057588935},"labels":[],"label_agreement":null},{"id":"W2947946094","doi":"10.3390/s19112443","title":"An Improved Spatiotemporal Fusion Approach Based on Multiple Endmember Spectral Mixture Analysis","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario; Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Central South University; National Natural Science Foundation of China","keywords":"Endmember; Pixel; Fusion; Remote sensing; Sensor fusion; Image resolution; Land cover; Image fusion; Pattern recognition (psychology); Computer science; Multispectral image; Artificial intelligence; Image (mathematics); Geography; Land use; Engineering","score_opus":0.004657217370046493,"score_gpt":0.21518372651342993,"score_spread":0.21052650914338344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947946094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008672653,0.00023797754,0.9899109,0.000058836595,0.00003111941,0.00002025925,0.00006216001,0.0004318366,0.0005742086],"genre_scores_gemma":[0.22113131,0.00057301176,0.77434695,0.0001281608,0.00008295273,0.000099013065,0.0006572689,0.00019281183,0.0027885924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926287,0.0001071576,0.000061713676,0.00020152346,0.00030671683,0.00005999348],"domain_scores_gemma":[0.999608,0.00006597887,0.000049213468,0.00007498042,0.00018014591,0.000021694264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093452306,0.00090052694,0.00086834206,0.001627439,0.0005186541,0.00073194865,0.0012339825,0.0007051381,0.0012186244],"category_scores_gemma":[0.0011778595,0.0004928119,0.0020565258,0.0013317524,0.0002996866,0.002156455,0.0013808944,0.0008542463,0.0006227292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026227525,0.00013912261,0.0035986938,0.00022779327,0.00035696043,0.0002327522,0.00027089124,0.1687637,0.08408081,0.011158259,0.002798048,0.7281108],"study_design_scores_gemma":[0.000009272089,0.000040589417,0.0010634467,0.000008118524,0.000062155814,0.00012795489,0.000029272203,0.98413336,0.00980159,0.0022556216,0.0024407269,0.000027895529],"about_ca_topic_score_codex":0.0047255694,"about_ca_topic_score_gemma":0.004105357,"teacher_disagreement_score":0.0047255694,"about_ca_system_score_codex":0.0004416491,"about_ca_system_score_gemma":0.0006947877,"threshold_uncertainty_score":0.009396136},"labels":[],"label_agreement":null},{"id":"W2948471521","doi":"10.3390/s19112629","title":"Monitoring Methods of Human Body Joints: State-of-the-Art and Research Challenges","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":183,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL; McMaster University","keywords":"Joint (building); Continuous monitoring; Population; Risk analysis (engineering); Computer science; Engineering; Medicine; Physical medicine and rehabilitation; Operations management; Environmental health; Civil engineering","score_opus":0.37819852086805045,"score_gpt":0.4958291659095268,"score_spread":0.11763064504147636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948471521","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036269077,0.99458635,0.0028550795,0.00037702004,0.0003043837,0.000015120447,0.00003050877,0.00002059625,0.0014481532],"genre_scores_gemma":[0.004114606,0.9902919,0.003615205,0.0003006679,0.00051737315,0.00003312039,0.00008670258,0.00000916116,0.0010311994],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984022,0.0002437261,0.00016417657,0.00037002633,0.00074334996,0.00007649927],"domain_scores_gemma":[0.99663925,0.0020490508,0.000226147,0.00013356487,0.0008869446,0.00006514214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023667994,0.0012240823,0.0019200131,0.0028153986,0.00039156424,0.0017021828,0.0022285688,0.0020832901,0.0025768934],"category_scores_gemma":[0.0032232897,0.0006218529,0.0010341854,0.003249755,0.0012197683,0.0032282022,0.0009126807,0.0017664707,0.0018317509],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006157204,0.00007804172,0.0006512129,0.017881276,0.000082686725,0.0000981697,0.0001293277,0.0007513242,0.005278327,0.0055504064,0.009037304,0.96040046],"study_design_scores_gemma":[0.000016774973,0.00039940994,0.004873839,0.009412839,0.00041846905,0.002683914,0.0005561146,0.0031836557,0.009948294,0.009542678,0.9588062,0.00015789266],"about_ca_topic_score_codex":0.0017355442,"about_ca_topic_score_gemma":0.0013230894,"teacher_disagreement_score":0.0028153986,"about_ca_system_score_codex":0.0006594446,"about_ca_system_score_gemma":0.0014090247,"threshold_uncertainty_score":0.012516975},"labels":[],"label_agreement":null},{"id":"W2948725440","doi":"10.3390/s19112593","title":"Precise Point Positioning Using World’s First Dual-Frequency GPS/GALILEO Smartphone","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Resources Canada; Government of Ontario","keywords":"GNSS applications; Precise Point Positioning; Global Positioning System; Geodetic datum; Real-time computing; Computer science; Galileo (satellite navigation); Ephemeris; Software; Remote sensing; Geodesy; Geography; Engineering; Telecommunications; Satellite","score_opus":0.009967064554672025,"score_gpt":0.20686534920876457,"score_spread":0.19689828465409254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948725440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8014422,0.0009907561,0.15228753,0.0003794218,0.00037901461,0.00035334783,0.005393003,0.008077017,0.03069774],"genre_scores_gemma":[0.91523397,0.00044630657,0.07256415,0.00011032344,0.000048943355,0.00014243934,0.0039151064,0.00013612618,0.007402641],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945897,0.000036236957,0.000027433554,0.000107321925,0.00031327998,0.00005683242],"domain_scores_gemma":[0.99965537,0.000029811177,0.000031979154,0.00007983563,0.00017503767,0.000028006345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030058052,0.00040379743,0.00039610686,0.00067225646,0.0002463397,0.0004778887,0.00040545512,0.00040626663,0.003310943],"category_scores_gemma":[0.0008604488,0.00013989142,0.00026247493,0.0006363907,0.00016215724,0.00046696066,0.0007866942,0.00032676957,0.0016167753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010280336,0.0001938808,0.07112651,0.00091612904,0.00017583405,0.0012410259,0.0009982047,0.014900959,0.3553492,0.0031028474,0.025017854,0.52594954],"study_design_scores_gemma":[0.0002564644,0.0030639055,0.3258959,0.0002617757,0.00035312065,0.0030124444,0.0014386624,0.15117508,0.37308455,0.0019006307,0.13923173,0.00032583627],"about_ca_topic_score_codex":0.003471644,"about_ca_topic_score_gemma":0.004681727,"teacher_disagreement_score":0.003471644,"about_ca_system_score_codex":0.00022038119,"about_ca_system_score_gemma":0.00038619337,"threshold_uncertainty_score":0.011076212},"labels":[],"label_agreement":null},{"id":"W2949282949","doi":"10.3390/s19122796","title":"Estimation of Ankle Joint Power during Walking Using Two Inertial Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GF Strong Rehabilitation Centre; Vancouver Coastal Health Research Institute; University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Ankle; Inverse dynamics; Gait; Inertial measurement unit; Joint (building); Simulation; Treadmill; Ground reaction force; Gait analysis; Mean squared error; Engineering; Computer science; Kinematics; Mathematics; Artificial intelligence; Physical medicine and rehabilitation; Statistics; Structural engineering; Physical therapy; Medicine; Surgery","score_opus":0.00963585160803927,"score_gpt":0.22131055453322274,"score_spread":0.21167470292518348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949282949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72828245,0.0003464382,0.2699762,0.0000446687,0.000049363178,0.00008150224,0.00019914395,0.0002973023,0.0007228522],"genre_scores_gemma":[0.9640734,0.00009194681,0.03535355,0.000018302388,0.000018719666,0.00006743062,0.00010703566,0.0000060726,0.00026348565],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997633,0.000047366466,0.000019630072,0.00007697631,0.000071776296,0.000020887295],"domain_scores_gemma":[0.9997373,0.0000726146,0.000057784357,0.000025840052,0.000090648704,0.000015753913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022508517,0.000613388,0.00041342585,0.0006026958,0.00013919725,0.00026130688,0.00030251045,0.00046781072,0.0005319113],"category_scores_gemma":[0.0010094749,0.00019155892,0.00022635386,0.00045193228,0.00013095586,0.00027437278,0.00028852472,0.00016946407,0.0001689771],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011003476,0.00051211147,0.11195797,0.0006569445,0.00031200043,0.00045947463,0.00064154615,0.025216477,0.37688708,0.0003105192,0.00075729063,0.4811882],"study_design_scores_gemma":[0.00015092497,0.0021433772,0.4284467,0.000109307846,0.00029421662,0.0014669496,0.00036097452,0.4530817,0.11142481,0.00060095673,0.0018001163,0.000119903234],"about_ca_topic_score_codex":0.0010549055,"about_ca_topic_score_gemma":0.0014597594,"teacher_disagreement_score":0.0010549055,"about_ca_system_score_codex":0.00008034227,"about_ca_system_score_gemma":0.00014450878,"threshold_uncertainty_score":0.0020974874},"labels":[],"label_agreement":null},{"id":"W2949544190","doi":"10.3390/s19122811","title":"A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Computer science; Gesture; Wearable computer; Inertial measurement unit; Gesture recognition; Wireless; Artificial intelligence; Modalities; Wearable technology; Human–computer interaction; Computer hardware; Speech recognition; Computer vision; Embedded system","score_opus":0.011202387882835462,"score_gpt":0.21099125839076996,"score_spread":0.1997888705079345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949544190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2434979,0.0015756614,0.66048706,0.0003939446,0.00062451017,0.0009465862,0.04326696,0.025273127,0.023934374],"genre_scores_gemma":[0.5028123,0.001566717,0.38703823,0.00049676665,0.00013629427,0.0019633602,0.06811637,0.0008844724,0.036985446],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998079,0.00002337736,0.000013514792,0.00005216664,0.000086139255,0.000016971195],"domain_scores_gemma":[0.9997824,0.000054625572,0.000030051817,0.00006241269,0.0000562215,0.0000142726985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020190966,0.0005614207,0.00039927228,0.00069351454,0.00017513048,0.0003787565,0.0005193192,0.00046280955,0.008906184],"category_scores_gemma":[0.0005722258,0.00019733416,0.000369049,0.0006813685,0.00013138462,0.0003557322,0.00047371094,0.00030399536,0.0051058535],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007391951,0.00023671496,0.007138611,0.0011509098,0.000094715906,0.0003941105,0.00010003479,0.007573784,0.19197716,0.0009852753,0.05919854,0.73041093],"study_design_scores_gemma":[0.00024342694,0.0018027145,0.16424273,0.00029195886,0.00021896622,0.005739892,0.00025754198,0.15850872,0.32077456,0.0025856711,0.34508055,0.00025321572],"about_ca_topic_score_codex":0.001495216,"about_ca_topic_score_gemma":0.0053127017,"teacher_disagreement_score":0.008906184,"about_ca_system_score_codex":0.00016947811,"about_ca_system_score_gemma":0.00028166216,"threshold_uncertainty_score":0.029794157},"labels":[],"label_agreement":null},{"id":"W2950674901","doi":"10.3390/s19122680","title":"A Sub-mW 18-MHz MEMS Oscillator Based on a 98-dBΩ Adjustable Bandwidth Transimpedance Amplifier and a Lamé-Mode Resonator","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; CMC Microsystems","keywords":"Transimpedance amplifier; Phase noise; Resonator; dBc; Bandwidth (computing); CMOS; Capacitive sensing; Amplifier; Electrical engineering; Capacitance; Optoelectronics; Materials science; Physics; Differential amplifier; Engineering; Telecommunications","score_opus":0.008028017950501351,"score_gpt":0.2184850130090454,"score_spread":0.21045699505854407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950674901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7467304,0.0016858899,0.24096555,0.0004578192,0.00027447875,0.00019754547,0.000530795,0.0024251859,0.006732379],"genre_scores_gemma":[0.8800218,0.000253975,0.11493222,0.00014661119,0.000099419165,0.000079307276,0.00019170584,0.000066177505,0.004208765],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977106,0.000018305376,0.000013041279,0.00009964041,0.00006835593,0.000029580746],"domain_scores_gemma":[0.99983776,0.00003681882,0.000036988662,0.000021240236,0.00004481514,0.000022396793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020982946,0.00034304726,0.00036746624,0.00030572063,0.00018492204,0.00030973018,0.0009870421,0.00037034546,0.0014603372],"category_scores_gemma":[0.00029429083,0.00023219902,0.00022494612,0.0002298847,0.00017602532,0.00053068175,0.00027704952,0.00035763485,0.0005777648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111298,0.000024742078,0.0007538269,0.000059716644,0.000019683483,0.00010866889,0.00004896428,0.00021146973,0.982434,0.00033964228,0.0002236705,0.015664343],"study_design_scores_gemma":[0.00009433413,0.0016792516,0.008684431,0.00002243664,0.000116229094,0.0028889142,0.000076206874,0.024048034,0.94506854,0.00025018866,0.017015483,0.000055915003],"about_ca_topic_score_codex":0.00023511182,"about_ca_topic_score_gemma":0.00072910544,"teacher_disagreement_score":0.0014603372,"about_ca_system_score_codex":0.00023994569,"about_ca_system_score_gemma":0.0002253478,"threshold_uncertainty_score":0.004885316},"labels":[],"label_agreement":null},{"id":"W2951101326","doi":"10.3390/s19122795","title":"An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Ministry of Higher Education and Scientific Research","keywords":"Extended Kalman filter; Simultaneous localization and mapping; Computer vision; Artificial intelligence; Ellipsoid; Computer science; Landmark; Monocular vision; Feature (linguistics); Monocular; Kalman filter; Robot; Mobile robot; Geography","score_opus":0.0066739403153307526,"score_gpt":0.21419653846175,"score_spread":0.20752259814641924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951101326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006531144,0.00008460993,0.9924656,0.00002750581,0.000029371431,0.000016834132,0.00001823498,0.000321161,0.00050560496],"genre_scores_gemma":[0.3568487,0.00022243787,0.64105296,0.000065743516,0.00003325218,0.00009205973,0.00015020421,0.00005613412,0.0014785083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996414,0.00007163748,0.000019852472,0.00008315041,0.00015264952,0.00003122104],"domain_scores_gemma":[0.99972576,0.000051066436,0.00004098212,0.000073208554,0.00009164274,0.000017311531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003668243,0.00053412846,0.0005151056,0.000421907,0.00032347965,0.00044406322,0.00094026094,0.00047098778,0.00077416137],"category_scores_gemma":[0.0009821507,0.0003282045,0.0004646584,0.00055457157,0.00035725813,0.0008431543,0.0011335707,0.0006748237,0.00035536574],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002400709,0.00007823369,0.0012652634,0.00022225597,0.00008127177,0.00021658483,0.00028678903,0.33178756,0.07837046,0.013926861,0.0031435466,0.5703811],"study_design_scores_gemma":[0.000020621257,0.00009813217,0.0006505388,0.000010376815,0.000013553887,0.00012898148,0.000032648437,0.9866643,0.0064236964,0.0020371834,0.0038977112,0.000022163082],"about_ca_topic_score_codex":0.0023615945,"about_ca_topic_score_gemma":0.002572868,"teacher_disagreement_score":0.0023615945,"about_ca_system_score_codex":0.0002253796,"about_ca_system_score_gemma":0.0007513926,"threshold_uncertainty_score":0.0046957135},"labels":[],"label_agreement":null},{"id":"W2951112720","doi":"10.3390/s19122805","title":"Temperature Effects on Electromechanical Response of Deposited Piezoelectric Sensors Used in Structural Health Monitoring of Aerospace Structures","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Korea Institute of Energy Technology Evaluation and Planning; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Materials science; Structural health monitoring; Piezoelectricity; Electrical impedance; Acoustics; Wafer; Capacitive sensing; Transducer; SIGNAL (programming language); Electrical engineering; Optoelectronics; Composite material; Engineering; Computer science","score_opus":0.004294061686580099,"score_gpt":0.2213373670341661,"score_spread":0.217043305347586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951112720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959942,0.00032025093,0.0028487635,0.000043116856,0.000025901281,0.000011189598,0.00012466441,0.000046071757,0.00058581796],"genre_scores_gemma":[0.9975242,0.00017869797,0.0014266528,0.000022741418,0.000004871863,0.000012132469,0.000078967256,0.000011864925,0.00073982467],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972624,0.000030019079,0.00002167867,0.00006742689,0.00011708464,0.000037606536],"domain_scores_gemma":[0.9996118,0.00013473172,0.00008506294,0.000045505294,0.000101317484,0.000021635358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020012028,0.00031481532,0.00017865487,0.0001603078,0.00011600748,0.00020102045,0.00030286366,0.000374043,0.001190292],"category_scores_gemma":[0.0007021948,0.00023807888,0.00013401802,0.00019620304,0.00023873897,0.00027503434,0.00018313332,0.00034378373,0.00025693883],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054039938,0.000009132625,0.00035896388,0.000020360207,0.0000036538356,0.000047819096,0.000037201404,0.00017417541,0.99825007,0.000013827305,0.000018879995,0.0010117454],"study_design_scores_gemma":[0.0000028241504,0.00021563665,0.008765289,0.0000036291076,0.000008383848,0.00008035598,0.000052865405,0.0016314578,0.98892844,0.00001565049,0.00028958917,0.000005878636],"about_ca_topic_score_codex":0.00033549065,"about_ca_topic_score_gemma":0.000696445,"teacher_disagreement_score":0.001190292,"about_ca_system_score_codex":0.00013371014,"about_ca_system_score_gemma":0.00007070106,"threshold_uncertainty_score":0.003981948},"labels":[],"label_agreement":null},{"id":"W2952429811","doi":"10.3390/s19122804","title":"Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Correctness; Overhead (engineering); Context (archaeology); Data flow diagram; Wireless sensor network; Real-time computing; Big data; Distributed computing; Internet of Things; Data mining; Embedded system; Database; Computer network","score_opus":0.029838047239415615,"score_gpt":0.2707657416357896,"score_spread":0.240927694396374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952429811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01101742,0.00074984896,0.9838244,0.0003676251,0.00013284938,0.00007977804,0.00004319558,0.0014906591,0.0022942587],"genre_scores_gemma":[0.6934146,0.0010211277,0.3012709,0.0005794096,0.00013123128,0.00022909373,0.0002147333,0.00012040369,0.0030184202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947304,0.00009742208,0.000028529723,0.00013351586,0.00023254623,0.000035045836],"domain_scores_gemma":[0.9994673,0.00017826435,0.00008466793,0.000121547986,0.00012541411,0.000022671726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006326706,0.0005915369,0.00047134914,0.0007021557,0.00040812968,0.0006409987,0.0018025341,0.00054824713,0.0011512296],"category_scores_gemma":[0.0013896789,0.0002418085,0.0005521754,0.00095315883,0.0004576262,0.0020649817,0.00072889356,0.0010681612,0.0003693968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004366536,0.0004050379,0.0049799117,0.0006795702,0.0001518798,0.00063422427,0.00033072184,0.14624918,0.10788798,0.06526263,0.013581256,0.65940094],"study_design_scores_gemma":[0.000029580855,0.00023600327,0.00080430653,0.000034000237,0.000054454806,0.0005360412,0.000053739714,0.9304716,0.021948328,0.030347683,0.015451,0.000033201857],"about_ca_topic_score_codex":0.00086523045,"about_ca_topic_score_gemma":0.0011334907,"teacher_disagreement_score":0.0018025341,"about_ca_system_score_codex":0.00080847566,"about_ca_system_score_gemma":0.0004944222,"threshold_uncertainty_score":0.0058659315},"labels":[],"label_agreement":null},{"id":"W2952546791","doi":"10.3390/s19122718","title":"Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; European Space Agency","keywords":"Moisture; Environmental science; Remote sensing; Water content; Soil science; Geotechnical engineering; Materials science; Engineering; Geology; Composite material","score_opus":0.03637760027394528,"score_gpt":0.2582109972441445,"score_spread":0.22183339697019921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952546791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97355956,0.00022501971,0.013764497,0.00007126762,0.000049795726,0.00013003385,0.007876605,0.0018279578,0.0024952265],"genre_scores_gemma":[0.9522441,0.00013427075,0.03206167,0.000050009414,0.000025253328,0.00013121555,0.014348853,0.0002027743,0.00080186327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99910057,0.00014429435,0.000050707422,0.00023797355,0.0003874971,0.00007906844],"domain_scores_gemma":[0.99848586,0.00026334557,0.00015839591,0.00028535147,0.0006990342,0.00010799942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023382073,0.001233345,0.00061884074,0.0016917984,0.00025239406,0.001002352,0.0010766817,0.00072674546,0.000959469],"category_scores_gemma":[0.0024971673,0.00031288477,0.0007369519,0.0022357672,0.00024856164,0.0011608708,0.0007246031,0.00030326875,0.0005037289],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021421756,0.0012453449,0.30230504,0.00055912486,0.0012831284,0.00069567416,0.000420951,0.34749135,0.0651687,0.00084572355,0.0063805273,0.2714622],"study_design_scores_gemma":[0.0004391351,0.0003629648,0.31979248,0.000048402228,0.00027327068,0.0001284885,0.00024645182,0.6441773,0.029330391,0.0003000381,0.0048048487,0.00009619875],"about_ca_topic_score_codex":0.022587009,"about_ca_topic_score_gemma":0.02581836,"teacher_disagreement_score":0.022587009,"about_ca_system_score_codex":0.0007308003,"about_ca_system_score_gemma":0.00063699426,"threshold_uncertainty_score":0.044911087},"labels":[],"label_agreement":null},{"id":"W2954038435","doi":"10.3390/s19132954","title":"A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Scalability; Cloud computing; Resource allocation; Resource (disambiguation); Distributed computing; Latency (audio); Computer network; Database; Telecommunications","score_opus":0.035405226839509286,"score_gpt":0.23658276490731078,"score_spread":0.2011775380678015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954038435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0133313555,0.00056967523,0.9791403,0.00036217683,0.00009223679,0.000071434035,0.00009001681,0.00020198849,0.006140827],"genre_scores_gemma":[0.8825248,0.0011407175,0.10549818,0.00021820208,0.00010457676,0.00027019368,0.00018703513,0.000108706525,0.009947637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995921,0.000074523196,0.000016573214,0.00008735491,0.00013466844,0.00009483868],"domain_scores_gemma":[0.9995993,0.00018036748,0.000042227955,0.000022386412,0.00012610714,0.000029764791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059645926,0.0009685978,0.0009510693,0.00061307644,0.00060867233,0.0015603519,0.002018864,0.0011786973,0.0034079512],"category_scores_gemma":[0.0013878623,0.00041332358,0.00068117696,0.0009868112,0.00061945536,0.0019104577,0.0006699897,0.0011297141,0.0003774585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026982494,0.000026163505,0.00027711721,0.000039816776,0.000014180979,0.00005261778,0.000023661769,0.9714696,0.00089403184,0.01509325,0.0011866157,0.010895983],"study_design_scores_gemma":[0.000001384479,0.000004193707,0.000052746167,0.0000020436428,0.000003503151,0.000010036777,0.000003359188,0.99854773,0.00007715971,0.0011220275,0.00017297047,0.0000028296372],"about_ca_topic_score_codex":0.015083357,"about_ca_topic_score_gemma":0.0131941205,"teacher_disagreement_score":0.015083357,"about_ca_system_score_codex":0.0024830278,"about_ca_system_score_gemma":0.0015474373,"threshold_uncertainty_score":0.02999109},"labels":[],"label_agreement":null},{"id":"W2954788441","doi":"10.3390/s19132993","title":"Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Chinese University of Hong Kong","keywords":"Odometry; Mobile robot; Artificial intelligence; Mobile robot navigation; Simultaneous localization and mapping; Computer vision; Robot; Computer science; Modular design; Robot control","score_opus":0.004719171267021721,"score_gpt":0.17595524805328694,"score_spread":0.1712360767862652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954788441","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07020048,0.0005187296,0.9255778,0.000070927665,0.000043500633,0.000027909817,0.00006598997,0.0020336383,0.0014610003],"genre_scores_gemma":[0.7684374,0.0006607336,0.2285788,0.00006165163,0.000028694692,0.00007206528,0.000254499,0.00012272639,0.0017835521],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997799,0.000027999096,0.000010212906,0.000054854085,0.00009182257,0.000035303154],"domain_scores_gemma":[0.99978405,0.000037940637,0.00005340012,0.000056405694,0.000051791634,0.000016389418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018240279,0.00060091,0.0005091,0.00035846123,0.00035551572,0.0005248542,0.00047541293,0.00043436894,0.0005213671],"category_scores_gemma":[0.0006291359,0.00024701306,0.00027014076,0.00036602683,0.00043334204,0.0008136588,0.00085689256,0.00032571892,0.00057276053],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018122305,0.000051075494,0.0056109987,0.00030046405,0.000100440055,0.0010658177,0.00035245589,0.34796187,0.2102517,0.0048830095,0.0027648835,0.426476],"study_design_scores_gemma":[0.000024490828,0.0002129435,0.00742841,0.00004964236,0.00005317844,0.0008248972,0.000337189,0.9268138,0.044635903,0.007433221,0.012124117,0.00006214824],"about_ca_topic_score_codex":0.002228235,"about_ca_topic_score_gemma":0.003042337,"teacher_disagreement_score":0.002228235,"about_ca_system_score_codex":0.00015664473,"about_ca_system_score_gemma":0.00050070253,"threshold_uncertainty_score":0.004430592},"labels":[],"label_agreement":null},{"id":"W2955163944","doi":"10.3390/s19132913","title":"Locating Underground Pipe Using Wideband Chaotic Ground Penetrating Radar","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Natural Science Foundation of Shanxi Province; National Natural Science Foundation of China","keywords":"Ground-penetrating radar; Chaotic; Radar; Wideband; SIGNAL (programming language); Geology; Remote sensing; Bandwidth (computing); Acoustics; Range (aeronautics); Engineering; Electronic engineering; Computer science; Physics; Telecommunications; Aerospace engineering","score_opus":0.02026841765653987,"score_gpt":0.2529081607911081,"score_spread":0.23263974313456826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955163944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82837033,0.00019545342,0.16925356,0.00011729448,0.00003783711,0.00004623264,0.00005883594,0.0004746455,0.0014458363],"genre_scores_gemma":[0.97695404,0.00008979724,0.022413308,0.000023192939,0.0000074798168,0.00001852002,0.000029026476,0.000006264486,0.0004583854],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998481,0.000032146818,0.0000059614295,0.000036812668,0.000057062378,0.00001986315],"domain_scores_gemma":[0.9997863,0.00003804696,0.00006422234,0.000037667876,0.000057054498,0.000016608717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017360819,0.0002635618,0.0002616312,0.0001833519,0.00012240186,0.0001794566,0.00035412918,0.00041444172,0.00036749672],"category_scores_gemma":[0.00035840966,0.00014632854,0.000101942656,0.00016027523,0.00031751726,0.0005208168,0.0004750313,0.00016960487,0.00010471019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031624033,0.0000403271,0.0038074104,0.000155749,0.000016692456,0.00030582797,0.00013479684,0.006837912,0.9519249,0.00073940423,0.00020200235,0.035518732],"study_design_scores_gemma":[0.00011564398,0.0018854237,0.014083076,0.000031759515,0.000083900035,0.0012341677,0.00020649613,0.16632639,0.81260574,0.000754952,0.0026104692,0.000061962375],"about_ca_topic_score_codex":0.00020819412,"about_ca_topic_score_gemma":0.00018845285,"teacher_disagreement_score":0.00041444172,"about_ca_system_score_codex":0.00012864302,"about_ca_system_score_gemma":0.0001563451,"threshold_uncertainty_score":0.0012294054},"labels":[],"label_agreement":null},{"id":"W2955779970","doi":"10.3390/s19132966","title":"Two-Dimensional Graphene Family Material: Assembly, Biocompatibility and Sensors Applications","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Graphene research and applications","field":"Materials Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Graphene; Biocompatibility; Nanotechnology; Materials science; Aptamer; Fabrication; Biosensor; Covalent bond; Chemistry","score_opus":0.06236245868833812,"score_gpt":0.3591035873950805,"score_spread":0.2967411287067424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955779970","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008659764,0.9956418,0.00041666906,0.00016474794,0.00021784092,0.000009669042,0.00003498816,0.000013569675,0.0026348087],"genre_scores_gemma":[0.004371885,0.9922714,0.0006558725,0.00015322074,0.000117653886,0.000015407435,0.00007175691,0.000002494943,0.002340375],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999167,0.000008062751,0.000007691367,0.000015405922,0.00004079647,0.000011388513],"domain_scores_gemma":[0.9999449,0.000020608555,0.000011834215,0.0000030066408,0.000013581536,0.000006012023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020160204,0.0006143051,0.00055224856,0.0015779276,0.0001661099,0.00043548507,0.000477137,0.0007124942,0.0018295445],"category_scores_gemma":[0.0001910542,0.0003077915,0.00031396557,0.0014405698,0.0002020618,0.0006295381,0.00038992602,0.0007874918,0.0010429493],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044326778,0.00008951352,0.00012286719,0.022765934,0.000075967255,0.00035507852,0.000066125926,0.0007697651,0.030783089,0.007866605,0.020664651,0.91639614],"study_design_scores_gemma":[0.000009157541,0.00012854738,0.00072235643,0.0014370158,0.00006400079,0.0012894452,0.00003181334,0.00024188182,0.008874897,0.0017233717,0.98545307,0.000024379995],"about_ca_topic_score_codex":0.0006858717,"about_ca_topic_score_gemma":0.0013113575,"teacher_disagreement_score":0.0018295445,"about_ca_system_score_codex":0.00042469206,"about_ca_system_score_gemma":0.00038631266,"threshold_uncertainty_score":0.0061203837},"labels":[],"label_agreement":null},{"id":"W2956195643","doi":"10.3390/s19143126","title":"A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Science Foundation","keywords":"Efficient energy use; Computer science; Energy consumption; Telecommunications link; Baseband; Telecommunications; Spectral efficiency; Quality of service; Base station; Software deployment; Key (lock); Cellular network; Computer network; Bandwidth (computing); Engineering; Channel (broadcasting); Computer security; Electrical engineering","score_opus":0.056365493054177235,"score_gpt":0.3354451980058635,"score_spread":0.2790797049516862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956195643","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043293755,0.9908647,0.0009907747,0.000775373,0.00048620428,0.000009890042,0.000063162246,0.00002063366,0.0063563366],"genre_scores_gemma":[0.0021642775,0.99519473,0.00058467156,0.00029494314,0.00047039866,0.000008132745,0.000070265385,0.0000054585935,0.0012071134],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967515,0.00005402787,0.000040164196,0.00006950534,0.00013317456,0.000027888827],"domain_scores_gemma":[0.99897075,0.000650116,0.00009149147,0.000029791585,0.00021919968,0.000038652797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006761589,0.00086692296,0.00089102314,0.002635523,0.00032466173,0.0011567043,0.0007001631,0.0010705583,0.0060771243],"category_scores_gemma":[0.0014133623,0.0003550255,0.00048794923,0.004793827,0.00041319893,0.0020298015,0.00059134443,0.0011775172,0.0021623096],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054544445,0.0000788769,0.0005299696,0.01852462,0.000063308034,0.0001611483,0.00010298119,0.0015663952,0.0015647303,0.021766681,0.044037055,0.9115498],"study_design_scores_gemma":[0.0000035032404,0.0001016318,0.0009610379,0.0037604792,0.0000686277,0.00041332128,0.00011915845,0.00043274366,0.0004600476,0.005172104,0.98848045,0.00002693216],"about_ca_topic_score_codex":0.00093854155,"about_ca_topic_score_gemma":0.0012929469,"teacher_disagreement_score":0.0060771243,"about_ca_system_score_codex":0.0005774846,"about_ca_system_score_gemma":0.0009428235,"threshold_uncertainty_score":0.020329952},"labels":[],"label_agreement":null},{"id":"W2956303394","doi":"10.3390/s19143165","title":"A Blockchain Framework for Securing Connected and Autonomous Vehicles","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gnowit (Canada)","funders":"","keywords":"Computer security; Computer science; Context (archaeology); Authentication (law); Key (lock); Compromise; Vehicular ad hoc network; Service (business); The Internet; Adversary; Computer network; Wireless; Telecommunications; Wireless ad hoc network","score_opus":0.008675220321673546,"score_gpt":0.23369124342992292,"score_spread":0.22501602310824936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956303394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044911597,0.0010223101,0.937601,0.0006212219,0.0001556367,0.0005188019,0.00024164685,0.0011267343,0.0138010755],"genre_scores_gemma":[0.855255,0.001075803,0.13176948,0.000101753074,0.00007344714,0.0005006253,0.00036942321,0.0000634301,0.0107911825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907863,0.00028628894,0.000064048916,0.00011839235,0.00031263824,0.00013991425],"domain_scores_gemma":[0.9990472,0.00035681194,0.0001004232,0.00017816004,0.00019483629,0.00012251954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013131084,0.00041989362,0.00057053566,0.0005808455,0.00124925,0.0014617085,0.0011112341,0.0011552701,0.005300688],"category_scores_gemma":[0.0021258774,0.00027268523,0.00039731557,0.0006469465,0.0011044424,0.002378178,0.0018214944,0.0009185497,0.0008857572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045942984,0.00019221296,0.0015669747,0.00033727803,0.00007190369,0.001245257,0.0006279419,0.5335649,0.012471145,0.32340315,0.004410342,0.1216495],"study_design_scores_gemma":[0.000116269555,0.00018922801,0.00020918761,0.00005719657,0.00002312811,0.00021996535,0.00008608326,0.8854708,0.0041264873,0.08510709,0.02436285,0.000031697327],"about_ca_topic_score_codex":0.00613151,"about_ca_topic_score_gemma":0.005489569,"teacher_disagreement_score":0.00613151,"about_ca_system_score_codex":0.0010357309,"about_ca_system_score_gemma":0.0024575652,"threshold_uncertainty_score":0.01773262},"labels":[],"label_agreement":null},{"id":"W2957104866","doi":"10.3390/s19143140","title":"Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Kinematics; Orientation (vector space); STRIDE; Motion capture; Computer science; Computer vision; Inertial measurement unit; Step detection; Simulation; Dead reckoning; Artificial intelligence; Geodesy; Mathematics; Motion (physics); Physics; Global Positioning System; Geometry; Geology; Telecommunications","score_opus":0.011695895905981784,"score_gpt":0.22756781377758628,"score_spread":0.2158719178716045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957104866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25173616,0.00038513224,0.7443863,0.000052993295,0.00008417096,0.0000441152,0.00016233076,0.0009985599,0.0021502492],"genre_scores_gemma":[0.8814248,0.00026707823,0.11688863,0.000021322463,0.000018944671,0.000027609478,0.0001714502,0.000025646368,0.00115451],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999045,0.000013869639,0.000005491062,0.0000264514,0.00004092305,0.000008787606],"domain_scores_gemma":[0.9998871,0.000019657382,0.000027185797,0.0000156979,0.000041791383,0.000008539873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012555055,0.0005861684,0.00030021966,0.00044398048,0.00012980845,0.00026815513,0.00027963452,0.0001939833,0.00079287036],"category_scores_gemma":[0.00044311793,0.00018040724,0.0002392859,0.00036610183,0.00009026902,0.00028586277,0.00024547923,0.00013645596,0.0003381138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005046447,0.00013621326,0.026349347,0.0004006223,0.00011348486,0.00033404655,0.00019638723,0.07863904,0.24533084,0.0011529821,0.0015033104,0.6453391],"study_design_scores_gemma":[0.000043225096,0.00069645245,0.06255588,0.00008463701,0.00017571398,0.00094424404,0.00018652818,0.8108349,0.117715195,0.0012909092,0.005384619,0.000087784494],"about_ca_topic_score_codex":0.002197942,"about_ca_topic_score_gemma":0.00639785,"teacher_disagreement_score":0.002197942,"about_ca_system_score_codex":0.00011684578,"about_ca_system_score_gemma":0.00024886685,"threshold_uncertainty_score":0.004370272},"labels":[],"label_agreement":null},{"id":"W2960547351","doi":"10.3390/s19143066","title":"A Fiber Bragg Grating Sensing Structure for the Design, Simulation and Stress Strain Monitoring of Human Puncture Surgery","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Jiangsu Postdoctoral Research Foundation; Government of Jiangsu Province; Natural Science Foundation of Jiangxi Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Puncturing; Fiber Bragg grating; Calibration; Torque; Sensitivity (control systems); Optics; Materials science; Wavelength; Linearity; Strain gauge; Grating; Acoustics; Physics; Engineering; Electronic engineering; Composite material","score_opus":0.024361177324570802,"score_gpt":0.26100140146064466,"score_spread":0.23664022413607386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960547351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12462062,0.0008636218,0.86926943,0.00024738393,0.00011298357,0.00011339604,0.00011424563,0.0013041806,0.003354227],"genre_scores_gemma":[0.6939125,0.0005329874,0.30259478,0.00008665959,0.000026776646,0.00014932336,0.00014283323,0.000055082714,0.0024990246],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996991,0.000048088186,0.000011213166,0.00004512494,0.00018124976,0.00001518111],"domain_scores_gemma":[0.99979323,0.00003711606,0.000032425793,0.0000329271,0.00009177591,0.000012501424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044843354,0.0004041431,0.0002415769,0.00024590766,0.0001357664,0.00016104104,0.0005910734,0.00041608713,0.00052513217],"category_scores_gemma":[0.00043711314,0.00021497873,0.00032885597,0.00021831928,0.00019238758,0.00039516905,0.00015367974,0.00017490018,0.00017449225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022012436,0.00011851387,0.0046835635,0.00036943075,0.000049437484,0.00022187385,0.0002134943,0.10513741,0.73999345,0.0045422716,0.0017538171,0.14269668],"study_design_scores_gemma":[0.000034151693,0.00060109905,0.00490215,0.000023470995,0.000043582935,0.00025544295,0.00003324848,0.80707157,0.1783149,0.0007648004,0.007907935,0.00004771367],"about_ca_topic_score_codex":0.0018593707,"about_ca_topic_score_gemma":0.001944718,"teacher_disagreement_score":0.0018593707,"about_ca_system_score_codex":0.00030899135,"about_ca_system_score_gemma":0.00056512514,"threshold_uncertainty_score":0.0036970973},"labels":[],"label_agreement":null},{"id":"W2962704257","doi":"10.3390/s19143187","title":"Erratum: Wang, Y. et al., A Privacy Preserving Scheme for Nearest Neighbor Query. Sensors 2018, 18, 2440","year":2019,"lang":"en","type":"erratum","venue":"Sensors","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Scheme (mathematics); k-nearest neighbors algorithm; Computer science; Data mining; Information retrieval; Mathematics; Artificial intelligence","score_opus":0.04512608719206292,"score_gpt":0.30519647124599697,"score_spread":0.26007038405393407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962704257","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043751873,0.0025525365,0.004336456,0.09705149,0.88541645,0.000060866943,0.002470674,0.0005125904,0.0071614464],"genre_scores_gemma":[0.032902285,0.026321791,0.026683927,0.21900007,0.20675702,0.0005602416,0.02154039,0.0019011379,0.4643331],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.995086,0.0007287365,0.0007075909,0.00048381698,0.002664093,0.00032976086],"domain_scores_gemma":[0.97813797,0.004148614,0.0009246006,0.0011485028,0.014924638,0.00071579055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033040112,0.0018258726,0.0014088261,0.0024532755,0.003768495,0.0027154956,0.0026019178,0.00674177,0.034609534],"category_scores_gemma":[0.04488938,0.0007493086,0.0009876738,0.002821613,0.002162641,0.003674881,0.002716433,0.006948219,0.026724907],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003355573,0.0000066773173,0.000051613442,0.00008884523,0.000005224792,0.00011795712,0.000029077033,0.000051664265,0.00010977232,0.0011533465,0.9927031,0.0056492155],"study_design_scores_gemma":[0.000026277869,0.000036650563,0.00034417558,0.00023576623,0.000022710174,0.00035453224,0.00012512783,0.00052555086,0.00077985827,0.0017279873,0.9957766,0.000044900025],"about_ca_topic_score_codex":0.009715213,"about_ca_topic_score_gemma":0.014007992,"teacher_disagreement_score":0.034609534,"about_ca_system_score_codex":0.0039049587,"about_ca_system_score_gemma":0.0037984864,"threshold_uncertainty_score":0.11578041},"labels":[],"label_agreement":null},{"id":"W2963026069","doi":"10.3390/s19153247","title":"Design of the Squared Daisy: A Multi-Mode Energy Harvester, with Reduced Variability and a Non-Linear Frequency Response","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"CMC Microsystems","keywords":"Energy harvesting; Bandwidth (computing); Microelectromechanical systems; Vibration; Frequency response; Electronic engineering; Energy (signal processing); Computer science; Power (physics); Electrical engineering; Engineering; Acoustics; Telecommunications; Materials science; Physics","score_opus":0.01605435316954134,"score_gpt":0.22762585731231896,"score_spread":0.21157150414277762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963026069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63033277,0.0019132613,0.35058287,0.00093655876,0.00034661207,0.00038582835,0.0005363597,0.0010658198,0.013899864],"genre_scores_gemma":[0.86073166,0.0005714201,0.12792079,0.00014739577,0.000041822914,0.00015576441,0.00018006751,0.00008089734,0.01017007],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998671,0.000009673059,0.0000060845864,0.000055274108,0.00004887511,0.000013003223],"domain_scores_gemma":[0.9998714,0.000016954315,0.0000386903,0.000014396956,0.00004292087,0.000015613448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020019567,0.00030231266,0.0002759518,0.00015803926,0.00015262418,0.00044450662,0.00077795883,0.0004986944,0.00096514507],"category_scores_gemma":[0.00020289485,0.00020507336,0.00020952678,0.00017400377,0.0002648424,0.000525885,0.0002801661,0.0003486634,0.00046591426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006600279,0.000025300018,0.00018270103,0.00016655392,0.000013919238,0.00007145429,0.000045927773,0.0013907742,0.98628104,0.0012167118,0.00032494156,0.0102148],"study_design_scores_gemma":[0.000055206317,0.0015129115,0.0035774172,0.000021284379,0.00005109778,0.00082040206,0.00006240019,0.04588727,0.9162566,0.0005332032,0.031168455,0.000053839078],"about_ca_topic_score_codex":0.00020202385,"about_ca_topic_score_gemma":0.0004934904,"teacher_disagreement_score":0.00096514507,"about_ca_system_score_codex":0.00034281285,"about_ca_system_score_gemma":0.0002383987,"threshold_uncertainty_score":0.003228724},"labels":[],"label_agreement":null},{"id":"W2963563124","doi":"10.3390/s19143213","title":"Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Fundação de Amparo à Pesquisa do Estado do Amazonas","keywords":"Activity recognition; Software portability; Accelerometer; Context (archaeology); Computer science; Inertial measurement unit; Perspective (graphical); Variety (cybernetics); Human–computer interaction; Ubiquitous computing; Data science; Artificial intelligence","score_opus":0.11253697159660356,"score_gpt":0.33036939376693997,"score_spread":0.2178324221703364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963563124","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0110659655,0.84137803,0.11625959,0.0014811768,0.0012238129,0.0002779261,0.00049632747,0.0006472976,0.027169928],"genre_scores_gemma":[0.09289874,0.7970984,0.09161053,0.0010196185,0.0027813518,0.0003032615,0.0013875196,0.00009056319,0.0128099425],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999526,0.00007456419,0.00007246706,0.00013763523,0.0001546728,0.00003470594],"domain_scores_gemma":[0.9995115,0.00019271432,0.000038783488,0.000026111882,0.00020667519,0.000024255394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057262793,0.0008269083,0.0004901752,0.0025918433,0.00023185967,0.0013745597,0.0005967063,0.0011027561,0.0020737092],"category_scores_gemma":[0.00081320346,0.0005426849,0.0005676651,0.002294247,0.0003557722,0.0023464086,0.0006718532,0.00078403583,0.0016493975],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013296663,0.00011436921,0.0044555496,0.008253646,0.00012222318,0.0004540826,0.000497985,0.00293147,0.012368415,0.015702337,0.015597946,0.9393691],"study_design_scores_gemma":[0.000018907682,0.0008822137,0.017192682,0.004203958,0.00044227132,0.0037448283,0.0009500038,0.026321461,0.01314185,0.010709354,0.92216533,0.00022709796],"about_ca_topic_score_codex":0.0018928384,"about_ca_topic_score_gemma":0.0013106383,"teacher_disagreement_score":0.0025918433,"about_ca_system_score_codex":0.00042985418,"about_ca_system_score_gemma":0.00070592976,"threshold_uncertainty_score":0.006937206},"labels":[],"label_agreement":null},{"id":"W2964478143","doi":"10.3390/s19153376","title":"A New Asynchronous RTK Method to Mitigate Base Station Observation Outages","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"GNSS applications; Asynchronous communication; Computer science; Ephemeris; Precise Point Positioning; Real Time Kinematic; Real-time computing; Global Positioning System; Base station; Satellite; Residual; Kinematics; Remote sensing; Simulation; Geodesy; Algorithm; Telecommunications; Engineering; Geography; Aerospace engineering","score_opus":0.013172582532622866,"score_gpt":0.24607584538296481,"score_spread":0.23290326285034194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964478143","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009193627,0.00024390176,0.9883786,0.00005206233,0.00009899688,0.000040301496,0.000050411283,0.0006974757,0.001244561],"genre_scores_gemma":[0.31326878,0.0006203411,0.67520446,0.0001729039,0.00030823835,0.00020330593,0.000484123,0.00042009618,0.009317673],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989862,0.00012812964,0.00006770496,0.00030348182,0.00042734333,0.000087154796],"domain_scores_gemma":[0.99908924,0.00014676516,0.00017098177,0.00018362114,0.00035535617,0.00005394664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000645493,0.0009987963,0.000897847,0.00084289454,0.00062010164,0.00082262227,0.0017463637,0.0006874943,0.0030778402],"category_scores_gemma":[0.0023725352,0.00035719617,0.0007623745,0.00078826287,0.0004125147,0.0016699646,0.0010569033,0.0009821446,0.0018011575],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006914061,0.00018254548,0.001932912,0.0003343533,0.00010183555,0.00031089218,0.00035068524,0.08676744,0.10523904,0.010182395,0.005513537,0.78839284],"study_design_scores_gemma":[0.00006193087,0.00019883929,0.0011679165,0.00002189341,0.0000677443,0.00036997,0.00006345475,0.9630329,0.022148581,0.0031592909,0.009652008,0.000055426495],"about_ca_topic_score_codex":0.0020536887,"about_ca_topic_score_gemma":0.0019614887,"teacher_disagreement_score":0.0030778402,"about_ca_system_score_codex":0.00036402987,"about_ca_system_score_gemma":0.0009957952,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2964630260","doi":"10.3390/s19153303","title":"Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hamilton Health Sciences; McMaster University; St. Joseph’s Healthcare Hamilton; University of Guelph","funders":"NIH Office of the Director; National Institutes of Health","keywords":"Spinal cord injury; Electromyography; Spinal cord; Neurophysiology; Lesion; Medicine; Biomedical engineering; Anatomy; Physical medicine and rehabilitation; Surgery","score_opus":0.02182236189869782,"score_gpt":0.2738853058319368,"score_spread":0.252062943933239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964630260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9812444,0.0017066208,0.014583691,0.00017870475,0.00003525734,0.00016385893,0.00040661843,0.000068940724,0.0016119967],"genre_scores_gemma":[0.9725576,0.003223092,0.016236924,0.0001587109,0.000025338177,0.0006465085,0.0007621841,0.000021912143,0.0063677034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997814,0.000036225632,0.000014595025,0.00006049228,0.00007628148,0.000031077372],"domain_scores_gemma":[0.999846,0.000024864643,0.000052262094,0.00002248545,0.000034376517,0.000020169404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003355604,0.00050618366,0.0004404236,0.00045969247,0.00030125357,0.00032084534,0.00035876877,0.0004322252,0.0018560851],"category_scores_gemma":[0.0004738024,0.00014453386,0.00028875947,0.00031434835,0.00042133607,0.00033215998,0.0002143655,0.00044352107,0.00033674005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050881837,0.00026342753,0.0024254478,0.00023432635,0.000041140047,0.00047044927,0.00017764325,0.00042444374,0.9822489,0.00018475093,0.00013599897,0.012884761],"study_design_scores_gemma":[0.00012593405,0.02456371,0.23315212,0.00020598428,0.00041785339,0.010132981,0.0014336238,0.016954385,0.6964809,0.0015304744,0.014935993,0.000066021945],"about_ca_topic_score_codex":0.0028842785,"about_ca_topic_score_gemma":0.0067505054,"teacher_disagreement_score":0.0028842785,"about_ca_system_score_codex":0.00028397999,"about_ca_system_score_gemma":0.00039102917,"threshold_uncertainty_score":0.0062092543},"labels":[],"label_agreement":null},{"id":"W2965481567","doi":"10.3390/s19153428","title":"A Node Density Control Learning Method for the Internet of Things","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"AI and Multimedia in Education","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Node (physics); Wireless sensor network; Computer science; Flexibility (engineering); Key distribution in wireless sensor networks; Wireless network; Wireless; Topology control; Sensor node; Real-time computing; Computer network; Engineering; Telecommunications","score_opus":0.01131857584814981,"score_gpt":0.2767491375473509,"score_spread":0.2654305616992011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965481567","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019470687,0.0000878512,0.99713707,0.000044115463,0.00002222805,0.000019821178,0.0000065124195,0.00011897109,0.00061636954],"genre_scores_gemma":[0.4400757,0.00059029466,0.55162275,0.00017917492,0.000112359856,0.00027630624,0.000097586984,0.00012108017,0.00692473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961394,0.00008632344,0.000016871667,0.000087586384,0.00017190106,0.000023286964],"domain_scores_gemma":[0.9994338,0.00025979042,0.000049169335,0.000060123002,0.00017331837,0.00002370343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007142019,0.00044333193,0.00045320232,0.0005971188,0.0004195899,0.00043991485,0.00090248324,0.000576049,0.0016377495],"category_scores_gemma":[0.0025946987,0.0002272465,0.00051384873,0.0005087691,0.0005119189,0.00093113416,0.0006752702,0.00091785984,0.0003343758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006885823,0.00008531643,0.00090597366,0.00011672433,0.000055803095,0.00009017375,0.00013750694,0.57111734,0.009232084,0.038783703,0.0023508843,0.37705564],"study_design_scores_gemma":[0.0000035407365,0.00001808006,0.0001068565,0.0000037190987,0.00000429252,0.00003149402,0.0000054645266,0.9942984,0.0011336404,0.0032907391,0.0010971134,0.0000066631364],"about_ca_topic_score_codex":0.003544093,"about_ca_topic_score_gemma":0.0029117172,"teacher_disagreement_score":0.003544093,"about_ca_system_score_codex":0.00063171407,"about_ca_system_score_gemma":0.0005684193,"threshold_uncertainty_score":0.007046938},"labels":[],"label_agreement":null},{"id":"W2965796510","doi":"10.3390/s19153357","title":"Monitoring of Carbon Dioxide Using Hollow-Core Photonic Crystal Fiber Mach–Zehnder Interferometer","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mach–Zehnder interferometer; Interferometry; Photonic-crystal fiber; Core (optical fiber); Materials science; Optical fiber; Optics; Photonics; Optoelectronics; Physics; Composite material","score_opus":0.01984137126888679,"score_gpt":0.24239847893345942,"score_spread":0.22255710766457262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965796510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8899501,0.0010927698,0.10519538,0.00023827927,0.000077237644,0.00010599131,0.0004135951,0.0007777587,0.002148874],"genre_scores_gemma":[0.93628633,0.0003630841,0.06258296,0.000046099176,0.000014960105,0.000058386133,0.00012815125,0.000013875887,0.00050621165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994832,0.00007286089,0.000017253542,0.00013121191,0.0002592939,0.000036167407],"domain_scores_gemma":[0.99973685,0.00007821918,0.00006787044,0.000019600146,0.000083338964,0.000014248602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034872323,0.00033847275,0.00029586966,0.000364197,0.00025712434,0.00021228757,0.00058832247,0.00048412773,0.00024465605],"category_scores_gemma":[0.00040046178,0.00014993288,0.0001633466,0.00038543183,0.0003441866,0.0005297286,0.0002928899,0.00030319302,0.000088363064],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045124405,0.00003206514,0.0008794902,0.000039905943,0.0000046284,0.000026768288,0.000017617747,0.00033720673,0.9950466,0.0001295285,0.000082714636,0.0033583909],"study_design_scores_gemma":[0.000009705748,0.00014854384,0.0027027116,0.0000022169806,0.000007821208,0.00011949341,0.000020343588,0.017924957,0.97839874,0.000067018664,0.0005817938,0.000016637206],"about_ca_topic_score_codex":0.0011124685,"about_ca_topic_score_gemma":0.0019022742,"teacher_disagreement_score":0.0011124685,"about_ca_system_score_codex":0.00049269537,"about_ca_system_score_gemma":0.00043886853,"threshold_uncertainty_score":0.003574729},"labels":[],"label_agreement":null},{"id":"W2966003450","doi":"10.3390/s19153335","title":"Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Research Council; Alberta Water Research Institute","keywords":"Smoke; Overfitting; Vineyard; Contamination; Environmental science; Wine; Computer science; Artificial intelligence; Remote sensing; Horticulture; Chemistry; Engineering; Waste management; Food science; Geography; Biology; Ecology","score_opus":0.0402683769055851,"score_gpt":0.24870958610374375,"score_spread":0.20844120919815864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966003450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16567335,0.0006472749,0.82985634,0.0002221916,0.000049100763,0.000075558826,0.00012171879,0.00075695437,0.0025976002],"genre_scores_gemma":[0.893204,0.00055177277,0.100797705,0.00009122672,0.000030540083,0.00009591382,0.00023038455,0.000036647223,0.004961775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998393,0.000028300581,0.00000718539,0.00005906273,0.000052298594,0.000013818538],"domain_scores_gemma":[0.99984086,0.00007444122,0.00002754173,0.000014458612,0.00003442549,0.000008250258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036950686,0.0008106063,0.00036860106,0.00046166262,0.0001572045,0.00055151427,0.00047722113,0.0005621192,0.00044470222],"category_scores_gemma":[0.00043436003,0.00026583893,0.000669464,0.00023081923,0.00015857044,0.00043669227,0.00027483725,0.00048582244,0.00022005301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012031085,0.000410821,0.018299472,0.00025626225,0.00020213808,0.00025133207,0.0001619401,0.6977923,0.07526574,0.0020080917,0.0010042738,0.20422727],"study_design_scores_gemma":[0.000001529679,0.00004298571,0.0021812692,0.000004067176,0.000011705098,0.00002496871,0.000013146196,0.9933796,0.0036759668,0.0003668119,0.00029142408,0.0000065478744],"about_ca_topic_score_codex":0.004629916,"about_ca_topic_score_gemma":0.005560103,"teacher_disagreement_score":0.004629916,"about_ca_system_score_codex":0.00033683333,"about_ca_system_score_gemma":0.0003324426,"threshold_uncertainty_score":0.009205937},"labels":[],"label_agreement":null},{"id":"W2966065488","doi":"10.3390/s19153362","title":"Automated Vulnerability Discovery and Exploitation in the Internet of Things","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Exploit; Vulnerability (computing); Fuzz testing; Vulnerability management; Scheduling (production processes); Computer security; Software; Vulnerability assessment; Distributed computing; The Internet; Task (project management); World Wide Web; Engineering; Systems engineering","score_opus":0.010477774143883617,"score_gpt":0.26149662633436654,"score_spread":0.2510188521904829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966065488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.600743,0.0028020577,0.370649,0.0017517688,0.0001438729,0.00063283846,0.0025804767,0.017108502,0.0035884997],"genre_scores_gemma":[0.800354,0.00051783334,0.1938332,0.00024985537,0.000032374865,0.00016579969,0.003857038,0.00020683832,0.0007830221],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840456,0.00037782118,0.000097899916,0.00036437143,0.00062379095,0.00013146865],"domain_scores_gemma":[0.99811506,0.0009592838,0.0002828754,0.00041375443,0.00016529884,0.00006384098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011599693,0.0010649018,0.0005785294,0.0025207696,0.0007117429,0.0006816461,0.0010001604,0.0011869882,0.00019042839],"category_scores_gemma":[0.0038276385,0.00030894304,0.0011498623,0.0010796125,0.00085597363,0.0017690524,0.0015351585,0.0009346628,0.00012129261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008193418,0.00060029345,0.05358516,0.0006897859,0.00053287786,0.002656681,0.00083725754,0.36038747,0.068411216,0.018535538,0.020358523,0.47258592],"study_design_scores_gemma":[0.000027827702,0.00009867706,0.0063121794,0.00004234393,0.000053044776,0.0009164389,0.00017834944,0.95559233,0.016131764,0.016018825,0.0045994776,0.000028776316],"about_ca_topic_score_codex":0.0035617845,"about_ca_topic_score_gemma":0.0052328734,"teacher_disagreement_score":0.0035617845,"about_ca_system_score_codex":0.0006029805,"about_ca_system_score_gemma":0.0010184214,"threshold_uncertainty_score":0.0070821047},"labels":[],"label_agreement":null},{"id":"W2966148766","doi":"10.3390/s19153442","title":"Iterative Trajectory Optimization for Physical-Layer Secure Buffer-Aided UAV Mobile Relaying","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Major Science and Technology Projects of China; National Science and Technology Major Project; National Natural Science Foundation of China; National Science Foundation","keywords":"Buffer (optical fiber); Physical layer; Trajectory; Computer science; Layer (electronics); Trajectory optimization; Computer network; Real-time computing; Embedded system; Simulation; Wireless; Materials science; Nanotechnology; Telecommunications; Physics","score_opus":0.006758016013695646,"score_gpt":0.2215456953297181,"score_spread":0.21478767931602247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966148766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0187482,0.0002783314,0.9786066,0.00013606029,0.000020385825,0.000025170828,0.00003497424,0.00013611311,0.0020140803],"genre_scores_gemma":[0.86277163,0.00047905176,0.13155708,0.00006617521,0.00002055186,0.0001746127,0.00015280007,0.000091265465,0.004686785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996331,0.00011556675,0.000016329534,0.00007019472,0.00009211773,0.00007270299],"domain_scores_gemma":[0.99925226,0.00047476942,0.00008576303,0.000041341737,0.00011600727,0.000029799212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008101314,0.0011548387,0.0009229621,0.00036068357,0.00040702856,0.00093867566,0.00066608307,0.000981797,0.0021484708],"category_scores_gemma":[0.0018319974,0.0004981613,0.00067733397,0.0004394329,0.00086946436,0.00087831187,0.0010677975,0.0010819074,0.00039655782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000429801,0.000008998668,0.00019096502,0.00003340794,0.000010601637,0.00005958035,0.000049016566,0.9842956,0.0015548883,0.006280439,0.00024281131,0.007230692],"study_design_scores_gemma":[0.0000037467905,0.000014092757,0.000020332021,0.0000024087478,0.0000022470335,0.00000832628,0.000008042802,0.9984314,0.0003026576,0.0010789363,0.00012544218,0.0000023405419],"about_ca_topic_score_codex":0.0054156156,"about_ca_topic_score_gemma":0.0034807408,"teacher_disagreement_score":0.0054156156,"about_ca_system_score_codex":0.0011885132,"about_ca_system_score_gemma":0.0013280021,"threshold_uncertainty_score":0.010768175},"labels":[],"label_agreement":null},{"id":"W2966219220","doi":"10.3390/s19153389","title":"Source Separation Using Sensor’s Frequency Response: Theory and Practice on Carbon Nanotubes Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); MW Canada (Canada); Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Carbon nanotube; Separation (statistics); Frequency response; Materials science; Nanotechnology; Optoelectronics; Computer science; Electrical engineering; Engineering","score_opus":0.0137759980218922,"score_gpt":0.29163023751981076,"score_spread":0.27785423949791854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966219220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025631792,0.01108787,0.97905296,0.0005391234,0.0002205856,0.000045511555,0.000028652965,0.00016393655,0.0062980917],"genre_scores_gemma":[0.24914865,0.03731454,0.700927,0.0011564048,0.0013627149,0.00041911614,0.00012738517,0.00025413456,0.009290104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838734,0.00034705224,0.00008337621,0.00039695416,0.0007326161,0.000052577998],"domain_scores_gemma":[0.9989699,0.0006444297,0.00006513315,0.00013129681,0.00017201835,0.000017296397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012974482,0.0008378816,0.0010267226,0.0012809876,0.00044933663,0.001345588,0.0012571333,0.002879646,0.0012056993],"category_scores_gemma":[0.0022512923,0.0007149506,0.00093509664,0.0013447502,0.0029144208,0.002678688,0.0010166425,0.0025682037,0.0010811734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104011386,0.00015253731,0.0008373957,0.0019706257,0.00010216113,0.0005821141,0.0006866366,0.045878466,0.11648985,0.58300894,0.0032633017,0.24692391],"study_design_scores_gemma":[0.000037892707,0.00029759252,0.0010387743,0.0006816526,0.00007185014,0.0019222421,0.00019528174,0.44081417,0.09469432,0.37047443,0.08950535,0.00026648762],"about_ca_topic_score_codex":0.00047001973,"about_ca_topic_score_gemma":0.00025684328,"teacher_disagreement_score":0.002879646,"about_ca_system_score_codex":0.00081479864,"about_ca_system_score_gemma":0.00039942513,"threshold_uncertainty_score":0.006861627},"labels":[],"label_agreement":null},{"id":"W2966522418","doi":"10.3390/s19153358","title":"Ground Level Deployment of Wireless Sensor Networks: Experiments, Evaluation and Engineering Insight","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Software deployment; Wireless sensor network; Payload (computing); Wireless; Network packet; Key distribution in wireless sensor networks; Computer science; Wireless network; Computer network; Field (mathematics); Real-time computing; Engineering; Telecommunications","score_opus":0.023488869201661235,"score_gpt":0.24333480908872324,"score_spread":0.219845939887062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966522418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9431716,0.00079438643,0.051154032,0.00032459726,0.000110963934,0.00033070613,0.00072223286,0.00061563705,0.0027757476],"genre_scores_gemma":[0.9833192,0.0004494944,0.015084995,0.000066901695,0.000020280211,0.00012490827,0.00039802326,0.000032862637,0.00050337263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99763024,0.0010666993,0.00016362262,0.0002569669,0.0006019883,0.00028037134],"domain_scores_gemma":[0.99433976,0.003558302,0.00057140895,0.00075119245,0.0006416349,0.00013769706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020434035,0.00076067646,0.00049367297,0.0006793028,0.00028075944,0.00043782452,0.0007733196,0.00074343506,0.0007225574],"category_scores_gemma":[0.0056301453,0.00018916848,0.00023624142,0.00085216056,0.0007509642,0.0010800543,0.00059150986,0.0004879694,0.0001772261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097322045,0.0028231505,0.01879417,0.0014724158,0.00017152895,0.00065919716,0.00039347465,0.727852,0.15204202,0.0034876734,0.0036488455,0.08768221],"study_design_scores_gemma":[0.00022533222,0.010414805,0.021353697,0.00015594246,0.00014939797,0.0008258029,0.000880133,0.7896731,0.16722213,0.0036836953,0.0053224447,0.00009350459],"about_ca_topic_score_codex":0.0013494072,"about_ca_topic_score_gemma":0.0016239849,"teacher_disagreement_score":0.0020434035,"about_ca_system_score_codex":0.0006988055,"about_ca_system_score_gemma":0.00027668287,"threshold_uncertainty_score":0.01080662},"labels":[],"label_agreement":null},{"id":"W2966886210","doi":"10.3390/s19163491","title":"Practical Considerations for Accuracy Evaluation in Sensor-Based Machine Learning and Deep Learning","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Deep learning; Artificial intelligence; Machine learning; Computer science","score_opus":0.024728870907169548,"score_gpt":0.29930160709550996,"score_spread":0.27457273618834044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966886210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06413795,0.010473309,0.8877028,0.0091561945,0.0018164075,0.0007154897,0.0022170546,0.0049448917,0.018835854],"genre_scores_gemma":[0.5365897,0.0023102644,0.44980457,0.0025947741,0.00044063898,0.0009813122,0.003672151,0.0010822112,0.0025242884],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9554242,0.021144703,0.0034657526,0.003076274,0.015885042,0.0010041852],"domain_scores_gemma":[0.88889164,0.069247745,0.0034966234,0.017109731,0.020490013,0.00076432293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032273456,0.0019928538,0.0016533848,0.0020839504,0.0012964829,0.0043765837,0.003701987,0.0031379068,0.003552381],"category_scores_gemma":[0.17406677,0.00072190666,0.0012108363,0.002912886,0.002223483,0.0064552515,0.0037131102,0.0037068604,0.0012650422],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028103094,0.00071097945,0.023374308,0.0023011726,0.00068202266,0.0006443601,0.00074663793,0.38770163,0.020633515,0.07139235,0.039945077,0.44905755],"study_design_scores_gemma":[0.0002634402,0.001755492,0.015530149,0.0012884071,0.00022537574,0.0008022019,0.00092299684,0.7872366,0.063381255,0.08047191,0.047877576,0.00024456912],"about_ca_topic_score_codex":0.0062734922,"about_ca_topic_score_gemma":0.0049513914,"teacher_disagreement_score":0.032273456,"about_ca_system_score_codex":0.0023882044,"about_ca_system_score_gemma":0.0021132787,"threshold_uncertainty_score":0.1706804},"labels":[],"label_agreement":null},{"id":"W2967051820","doi":"10.3390/s19163472","title":"Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supine position; Beat (acoustics); Artifact (error); Cardiac cycle; Heart rate; Computer science; Electrocardiography; Real-time computing; Heart beat; Simulation; Medicine; Biomedical engineering; Cardiology; Artificial intelligence; Internal medicine; Acoustics; Blood pressure; Physics","score_opus":0.004274221336641698,"score_gpt":0.18176361578743025,"score_spread":0.17748939445078854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967051820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41788912,0.0014366136,0.57033867,0.00021463246,0.00019122203,0.00038048942,0.0006411073,0.0047687623,0.0041393475],"genre_scores_gemma":[0.7429025,0.00076271733,0.252354,0.00023544542,0.0002920382,0.00024992874,0.0005176271,0.00022987819,0.0024557924],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943453,0.00012713418,0.000039295006,0.00012419112,0.0002451667,0.00002965639],"domain_scores_gemma":[0.99957174,0.00016138215,0.000066221495,0.000047701513,0.00011484695,0.000038058355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006060676,0.00062367413,0.0007453251,0.0008703653,0.0001085671,0.0005449841,0.00049862475,0.00052501675,0.0022898787],"category_scores_gemma":[0.001339801,0.00023958496,0.00023840711,0.00043331334,0.00018442847,0.00041642762,0.00056480954,0.00028262465,0.0007595295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010110652,0.0002486945,0.014723477,0.0004521457,0.00014050971,0.0003527956,0.0001961846,0.0021864558,0.6216421,0.0004289456,0.001980004,0.3566376],"study_design_scores_gemma":[0.0007565368,0.0035747618,0.2627528,0.00023319879,0.00067471294,0.00849282,0.00035744923,0.23867634,0.4636826,0.0024183434,0.0180847,0.00029571474],"about_ca_topic_score_codex":0.00045094636,"about_ca_topic_score_gemma":0.0011623806,"teacher_disagreement_score":0.0022898787,"about_ca_system_score_codex":0.000101327045,"about_ca_system_score_gemma":0.00018486034,"threshold_uncertainty_score":0.0076604486},"labels":[],"label_agreement":null},{"id":"W2967445991","doi":"10.3390/s19163558","title":"Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Cluster analysis; Intelligent transportation system; Traffic congestion; Distributed computing; Vehicular ad hoc network; Computer network; Transport engineering; Wireless ad hoc network; Artificial intelligence; Engineering; Telecommunications; Wireless","score_opus":0.003414820012727465,"score_gpt":0.21064131791998703,"score_spread":0.20722649790725955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967445991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14508069,0.0015438552,0.8348511,0.00093838404,0.00021750334,0.00024811938,0.00023062517,0.0010712417,0.015818516],"genre_scores_gemma":[0.9574101,0.00048826577,0.04019577,0.00006888485,0.000050201183,0.00006454368,0.00016934305,0.000027877366,0.0015250072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947566,0.00018304479,0.000025687557,0.000100382946,0.00012637663,0.00008883222],"domain_scores_gemma":[0.9994037,0.00021333584,0.00009217769,0.00007644204,0.00015414967,0.00006007551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006276258,0.0006890101,0.000518449,0.0016559031,0.00070585444,0.00082119805,0.00095480535,0.000548913,0.0007212459],"category_scores_gemma":[0.0016976666,0.00021974208,0.0005528537,0.0011846601,0.00042311326,0.0014015496,0.0014502289,0.00035818506,0.00023063995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015849675,0.00022545177,0.007985299,0.0002542531,0.00019758489,0.00037328544,0.0004992263,0.6940667,0.011961676,0.046390183,0.004497168,0.23339078],"study_design_scores_gemma":[0.000013646206,0.00009333532,0.0020538687,0.000016415177,0.000068124005,0.00009787159,0.0002724204,0.9704738,0.0025079916,0.017187668,0.0071802456,0.000034590037],"about_ca_topic_score_codex":0.0075486335,"about_ca_topic_score_gemma":0.009962835,"teacher_disagreement_score":0.0075486335,"about_ca_system_score_codex":0.0010033002,"about_ca_system_score_gemma":0.00073807925,"threshold_uncertainty_score":0.015009403},"labels":[],"label_agreement":null},{"id":"W2967880813","doi":"10.3390/s19163508","title":"Real-Time Ozone Sensor Based on Selective Oxidation of Methylene Blue in Mesoporous Silica Films","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ozone; Detection limit; Relative humidity; Mesoporous silica; Materials science; Linear range; Methylene blue; Mesoporous material; Environmental science; Atmosphere (unit); Response time; Analytical Chemistry (journal); Environmental chemistry; Chemistry; Computer science; Meteorology; Chromatography; Organic chemistry; Catalysis","score_opus":0.010082278500387901,"score_gpt":0.23934459441442446,"score_spread":0.22926231591403656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967880813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89197195,0.0037917548,0.100234605,0.00017409815,0.00016925036,0.00009799645,0.00050710794,0.0011723601,0.0018809219],"genre_scores_gemma":[0.94431233,0.0014696451,0.05069937,0.00009405211,0.000026435931,0.00006721166,0.00027870364,0.000027555976,0.003024666],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998287,0.000016723476,0.0000090682015,0.00005782966,0.00006945333,0.000018118466],"domain_scores_gemma":[0.9998796,0.00004313098,0.000026735903,0.000009758555,0.000028887955,0.000011901109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019696141,0.00034291076,0.00022698016,0.00025301738,0.00010605119,0.00017244293,0.00053156615,0.0004981516,0.0004217572],"category_scores_gemma":[0.00023514143,0.00026744787,0.00023238506,0.0001614725,0.00017411697,0.0003538646,0.00019379787,0.00025989115,0.0002109492],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018402368,0.000005107025,0.00009545734,0.000025343537,0.0000030890092,0.000013319493,0.0000052950213,0.000050912717,0.9984907,0.000023956962,0.000021698306,0.0012466292],"study_design_scores_gemma":[0.00000580163,0.00014469495,0.0015863362,0.0000027821982,0.000011499844,0.00011972946,0.000008552742,0.0032996128,0.99387866,0.000017772605,0.0009154741,0.000009103409],"about_ca_topic_score_codex":0.0007083235,"about_ca_topic_score_gemma":0.0011853679,"teacher_disagreement_score":0.0007083235,"about_ca_system_score_codex":0.00026116666,"about_ca_system_score_gemma":0.00012654685,"threshold_uncertainty_score":0.0018949509},"labels":[],"label_agreement":null},{"id":"W2968317634","doi":"10.3390/s19163524","title":"Fatigue Performance of Type I Fibre Bragg Grating Strain Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Office of Naval Research; Monash University","keywords":"Materials science; Fiber Bragg grating; Coating; Grating; Reflection (computer programming); Aerospace; Optical fiber; Optics; Computer science; Optoelectronics; Composite material; Engineering; Telecommunications","score_opus":0.013419972042276664,"score_gpt":0.2279184822184401,"score_spread":0.21449851017616345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968317634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99620914,0.00060816656,0.0025942128,0.000014841848,0.000015210544,0.0000058748897,0.00005283998,0.00005126186,0.00044846573],"genre_scores_gemma":[0.9946114,0.00043840357,0.0033256465,0.000024315304,0.0000061168466,0.000008737154,0.000121780744,0.000017432305,0.0014461064],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995908,0.000043877357,0.000032529544,0.00009876116,0.00019158536,0.00004241342],"domain_scores_gemma":[0.9993868,0.00014636989,0.0001454429,0.00005678227,0.00023871668,0.000025858893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002789821,0.00034159925,0.00020980307,0.00020502624,0.00012932686,0.00023470332,0.00029076813,0.0005884638,0.00048332516],"category_scores_gemma":[0.0006702333,0.00015668057,0.0002044724,0.00014286385,0.00023773704,0.00032928985,0.00015903848,0.00023784289,0.00019272613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040476338,0.000006998598,0.00048006326,0.00001953485,0.000003218681,0.000015010434,0.000030911142,0.00014534626,0.9971487,0.00000687117,0.000014702205,0.0020881407],"study_design_scores_gemma":[0.0000018221896,0.00044446858,0.0050644027,0.0000037369225,0.00000872996,0.000075019736,0.000032659656,0.001426182,0.99264634,0.000004995393,0.00028466503,0.0000070107603],"about_ca_topic_score_codex":0.000664175,"about_ca_topic_score_gemma":0.0013546344,"teacher_disagreement_score":0.000664175,"about_ca_system_score_codex":0.0001936333,"about_ca_system_score_gemma":0.000072244286,"threshold_uncertainty_score":0.0016168952},"labels":[],"label_agreement":null},{"id":"W2968628173","doi":"10.3390/s19163594","title":"An Edge-Fog-Cloud Architecture of Streaming Analytics for Internet of Things Applications","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Cloud computing; Internet of Things; Computer science; Architecture; Analytics; Enhanced Data Rates for GSM Evolution; Data stream mining; The Internet; Edge computing; Smart city; Data science; Fog computing; Big data; Business intelligence; Computer security; World Wide Web; Telecommunications; Database; Data mining; Operating system; Geography","score_opus":0.012384971263009158,"score_gpt":0.2682587392492548,"score_spread":0.25587376798624567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968628173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058158297,0.0009942984,0.91430503,0.0018741229,0.0005745986,0.00048732915,0.0004940696,0.004010547,0.019101633],"genre_scores_gemma":[0.68099046,0.0009646714,0.3076725,0.0010383808,0.00020835422,0.00019188631,0.0009427909,0.00023779813,0.007753105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966216,0.000042349668,0.000026198239,0.000077159464,0.000117672076,0.0000744995],"domain_scores_gemma":[0.9995797,0.00005836209,0.00001707504,0.000093366114,0.00018613866,0.00006538831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054806983,0.00046495703,0.000404129,0.0005926464,0.0010101382,0.0018307667,0.0018910851,0.000625482,0.001487094],"category_scores_gemma":[0.00085767306,0.00030632748,0.0005037529,0.0008982106,0.00066467596,0.001906435,0.0014848802,0.0009867544,0.00054641475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015377147,0.0012128426,0.016383663,0.0005551803,0.0005516122,0.0020259244,0.00096995913,0.12233054,0.079938225,0.28917646,0.09200372,0.3933141],"study_design_scores_gemma":[0.000037986443,0.0001155785,0.0017425007,0.000043851498,0.00007111552,0.00028357582,0.00013125455,0.9145316,0.013051456,0.0416844,0.028256843,0.000049903392],"about_ca_topic_score_codex":0.008271453,"about_ca_topic_score_gemma":0.014709209,"teacher_disagreement_score":0.008271453,"about_ca_system_score_codex":0.0008275215,"about_ca_system_score_gemma":0.0015377143,"threshold_uncertainty_score":0.01644665},"labels":[],"label_agreement":null},{"id":"W2968774000","doi":"10.3390/s19163474","title":"Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rehabilitation; Hemiparesis; Physical medicine and rehabilitation; Protocol (science); Task (project management); Training (meteorology); Computer science; Set (abstract data type); Work (physics); Robotic arm; Simulation; Stroke (engine); Physical therapy; Artificial intelligence; Medicine; Engineering; Surgery","score_opus":0.030553604000796365,"score_gpt":0.2911751589743189,"score_spread":0.2606215549735225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968774000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6617065,0.0026817734,0.32330298,0.00028036494,0.00018098924,0.0005739793,0.0025482571,0.0024733993,0.0062518334],"genre_scores_gemma":[0.91293883,0.000966688,0.07986843,0.00015004863,0.000019690073,0.0005657955,0.0004915712,0.00007233698,0.0049265977],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998235,0.000020301217,0.000014890072,0.00004634302,0.000077502045,0.00001753974],"domain_scores_gemma":[0.99993587,0.000016758928,0.000013259817,0.0000057733882,0.00001846681,0.000009831904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020252954,0.0004224666,0.00037464467,0.00036580127,0.00010198272,0.00019415372,0.00035204957,0.0003914135,0.0033893066],"category_scores_gemma":[0.00034537306,0.000181374,0.0002880822,0.0002512045,0.00011358346,0.00039028766,0.00037312115,0.00015875924,0.00043284948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024598977,0.00069037185,0.006948664,0.00153922,0.000090456604,0.00038179438,0.00029506104,0.007979959,0.49426183,0.00043322134,0.0027151625,0.4822044],"study_design_scores_gemma":[0.0006612453,0.006147992,0.26986814,0.0005078223,0.00045033448,0.0031083245,0.00061112165,0.28406963,0.41449174,0.0018800029,0.017764462,0.00043927575],"about_ca_topic_score_codex":0.0014458081,"about_ca_topic_score_gemma":0.0032567172,"teacher_disagreement_score":0.0033893066,"about_ca_system_score_codex":0.00011470565,"about_ca_system_score_gemma":0.00028786666,"threshold_uncertainty_score":0.011338353},"labels":[],"label_agreement":null},{"id":"W2969158765","doi":"10.3390/s19163599","title":"Stretching Method-Based Operational Modal Analysis of An Old Masonry Lighthouse","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Community College","funders":"Stavros Niarchos Foundation","keywords":"Masonry; Structural health monitoring; Modal; Vibration; Natural frequency; Structural engineering; Modal analysis; Instrumentation (computer programming); Computer science; Natural (archaeology); Interval (graph theory); Simple (philosophy); Operational Modal Analysis; Acoustics; Geology; Engineering; Materials science; Physics; Mathematics","score_opus":0.013691267432615575,"score_gpt":0.3023581953690593,"score_spread":0.2886669279364437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969158765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94079036,0.00005017399,0.057274945,0.000026085314,0.0000069974653,0.00002215135,0.00008650293,0.00009947638,0.0016434764],"genre_scores_gemma":[0.9939574,0.000022406935,0.0054039513,0.0000035950625,0.0000026893952,0.0000056884455,0.000054175816,0.0000065424047,0.0005435451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999193,0.00001110212,0.000002562042,0.000018126126,0.00003570841,0.000013193601],"domain_scores_gemma":[0.99990666,0.000036225196,0.000017289483,0.0000143621655,0.000017098879,0.000008474251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016619291,0.00023469773,0.00014434641,0.0005621438,0.0001349943,0.00021432585,0.00034548156,0.0003064931,0.0012207488],"category_scores_gemma":[0.00026873304,0.00009376414,0.00021968092,0.0002841398,0.00024973394,0.00025383843,0.0002139918,0.00017819127,0.00012555259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000324023,0.0002624625,0.029323451,0.00017100632,0.00006006552,0.0007141421,0.00062475266,0.56116164,0.29556513,0.0027416805,0.00045706745,0.10859462],"study_design_scores_gemma":[0.000004490785,0.00008159389,0.022654932,0.000006167173,0.000008941338,0.000072999734,0.00011584064,0.965198,0.011077699,0.0003335782,0.00043002638,0.000015703885],"about_ca_topic_score_codex":0.002643651,"about_ca_topic_score_gemma":0.002644759,"teacher_disagreement_score":0.002643651,"about_ca_system_score_codex":0.00019172594,"about_ca_system_score_gemma":0.0001102049,"threshold_uncertainty_score":0.0052565336},"labels":[],"label_agreement":null},{"id":"W2969398771","doi":"10.3390/s19173637","title":"A Wide-Range, Wireless Wearable Inertial Motion Sensing System for Capturing Fast Athletic Biomechanics in Overhead Pitching","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"MIT Media Lab; Centre for Interdisciplinary Research in Music Media and Technology; McGill University; Major League Baseball","keywords":"Wearable computer; Accelerometer; Motion capture; Computer science; Sports biomechanics; Wireless; Inertial measurement unit; Simulation; Motion analysis; Engineering; Motion (physics); Artificial intelligence; Embedded system; Telecommunications","score_opus":0.015405029405087754,"score_gpt":0.2600262558153951,"score_spread":0.24462122641030737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969398771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59401786,0.0015372459,0.38213754,0.00073511805,0.0006414089,0.0009984532,0.00138262,0.004665604,0.013884112],"genre_scores_gemma":[0.87175137,0.00077925186,0.11352358,0.00040262454,0.00015936697,0.00033755365,0.00065720605,0.00008744198,0.012301591],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996742,0.000039162882,0.000021529553,0.000096013464,0.00013762951,0.00003137851],"domain_scores_gemma":[0.999814,0.000031568732,0.000032513944,0.000019516974,0.0000646167,0.000037825834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029340334,0.00050200906,0.00046172267,0.00053877715,0.000386006,0.0004462922,0.00042427672,0.0004334222,0.0028009005],"category_scores_gemma":[0.0006005398,0.00018614535,0.00015797038,0.00034366283,0.00025972346,0.00048267786,0.00061892223,0.00029415675,0.0008727795],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000594869,0.00026822925,0.014994067,0.00053365383,0.000061404346,0.0012137464,0.00073752145,0.0019872936,0.6366601,0.0010600106,0.00726188,0.33462727],"study_design_scores_gemma":[0.0004454183,0.012648127,0.22000977,0.00043419906,0.00049485825,0.011244479,0.001667438,0.09132511,0.54613996,0.0017221442,0.11340168,0.000466838],"about_ca_topic_score_codex":0.0008906009,"about_ca_topic_score_gemma":0.0022275257,"teacher_disagreement_score":0.0028009005,"about_ca_system_score_codex":0.00015976485,"about_ca_system_score_gemma":0.0002899131,"threshold_uncertainty_score":0.009369969},"labels":[],"label_agreement":null},{"id":"W2969547819","doi":"10.3390/s19163617","title":"Photoacoustic Imaging with Capacitive Micromachined Ultrasound Transducers: Principles and Developments","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Capacitive micromachined ultrasonic transducers; Photoacoustic imaging in biomedicine; Capacitive sensing; Transducer; Ultrasound; Acoustics; Materials science; Ultrasonic imaging; Ultrasonic sensor; Engineering; Electrical engineering; Optics; Physics","score_opus":0.02310001265584256,"score_gpt":0.24334856489529397,"score_spread":0.22024855223945142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969547819","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01929693,0.18188806,0.7617565,0.0026557054,0.0010032927,0.00030079816,0.0001362666,0.0008303244,0.032132134],"genre_scores_gemma":[0.1996586,0.1008325,0.6766472,0.0014632165,0.0011424209,0.00042257502,0.000162179,0.0001268198,0.019544538],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990158,0.00007015452,0.000037834623,0.00020958616,0.00062051916,0.000046132882],"domain_scores_gemma":[0.99923146,0.00024332607,0.00009701391,0.00006770777,0.00029729435,0.00006325399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009773611,0.0006946818,0.00046217823,0.0009147012,0.00024417482,0.0014963463,0.001839898,0.0019115192,0.0013274957],"category_scores_gemma":[0.0011483162,0.00095877255,0.00037684923,0.0009888507,0.0010586531,0.0022870686,0.00073263387,0.0017514436,0.0009762653],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007600817,0.0001349815,0.000779988,0.0019824777,0.000035653607,0.00041797163,0.0005565007,0.004646982,0.6267773,0.054279983,0.0035801467,0.30673206],"study_design_scores_gemma":[0.00004066994,0.00086603867,0.002198069,0.0005358292,0.000056917084,0.0038622576,0.0002602233,0.0615873,0.5258021,0.016119959,0.38834906,0.00032156514],"about_ca_topic_score_codex":0.00065655616,"about_ca_topic_score_gemma":0.0008119777,"teacher_disagreement_score":0.0019115192,"about_ca_system_score_codex":0.0009066171,"about_ca_system_score_gemma":0.0005848567,"threshold_uncertainty_score":0.0065779686},"labels":[],"label_agreement":null},{"id":"W2969565266","doi":"10.3390/s19173632","title":"Agreement Analysis between Vive and Vicon Systems to Monitor Lumbar Postural Changes","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Motion capture; Kinematics; BitTorrent tracker; Computer science; Trunk; Tracking (education); Motion analysis; Tracking system; Computer vision; Artificial intelligence; Match moving; Virtual reality; Lumbar; Rotation (mathematics); Orientation (vector space); Simulation; Motion (physics); Medicine; Mathematics; Psychology; Anatomy; Physics; Eye tracking","score_opus":0.009399007194721604,"score_gpt":0.2727383738697318,"score_spread":0.26333936667501023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969565266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6504124,0.0024055527,0.33232576,0.00021811872,0.0003580317,0.00032282266,0.0014252618,0.0013748542,0.011157155],"genre_scores_gemma":[0.94941634,0.00033839644,0.046712074,0.00012831972,0.0000366845,0.00015871912,0.00070952385,0.00018480101,0.0023150456],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99472153,0.0018169404,0.00044640177,0.0011335514,0.0016529623,0.00022846441],"domain_scores_gemma":[0.9867346,0.005847251,0.001082579,0.0010705616,0.0050959745,0.00016907773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006028783,0.0005870789,0.00047460676,0.0014723508,0.00023719954,0.0010948553,0.00070077885,0.00075277616,0.0027928865],"category_scores_gemma":[0.022454362,0.00035943327,0.00043569185,0.0008380289,0.00039117623,0.0009208552,0.0013248894,0.00037495323,0.00089984154],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00755284,0.00023180986,0.25997192,0.0016600044,0.0010954728,0.00035024286,0.0042161043,0.013653652,0.23397031,0.0040701637,0.004509163,0.46871838],"study_design_scores_gemma":[0.0002611777,0.0029888507,0.7095239,0.0003199717,0.0006083861,0.0026949241,0.0029411959,0.1724374,0.09133517,0.0026545327,0.013915649,0.00031886564],"about_ca_topic_score_codex":0.0015859334,"about_ca_topic_score_gemma":0.002644398,"teacher_disagreement_score":0.006028783,"about_ca_system_score_codex":0.0003665139,"about_ca_system_score_gemma":0.00031240188,"threshold_uncertainty_score":0.031883657},"labels":[],"label_agreement":null},{"id":"W2969608721","doi":"10.3390/s19173699","title":"SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"RANSAC; Computer science; Artificial intelligence; Feature (linguistics); Simultaneous localization and mapping; Computer vision; Extended Kalman filter; Tracking (education); Kalman filter; Object (grammar); Video tracking; Algorithm; Robot; Mobile robot; Image (mathematics)","score_opus":0.0076174099912578,"score_gpt":0.20604023191514673,"score_spread":0.19842282192388894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969608721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004132223,0.00014673338,0.994511,0.00005498861,0.000021837672,0.0000122014135,0.00002148415,0.0006282095,0.000471358],"genre_scores_gemma":[0.30578375,0.00040415346,0.68955964,0.00014196284,0.000065533764,0.00012962118,0.00029578482,0.00017890554,0.0034406765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995653,0.00007933285,0.000025582092,0.00010903847,0.0001535709,0.00006711083],"domain_scores_gemma":[0.9996238,0.000082179125,0.000052787498,0.00006533839,0.00015492456,0.000020972082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079038285,0.00080022734,0.00092397124,0.00081862713,0.00052133255,0.000788326,0.0016997827,0.0011332284,0.0013336253],"category_scores_gemma":[0.0013697507,0.0005545502,0.000701545,0.0012201698,0.0004683897,0.0011645384,0.0012486153,0.0015262448,0.0006744691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082502,0.00008778976,0.0008196578,0.000075716176,0.00009849978,0.00008306498,0.00011315123,0.49251616,0.0077240258,0.0074628945,0.0024193192,0.48851722],"study_design_scores_gemma":[0.0000026738815,0.00001274396,0.00011186651,0.000004099355,0.000003835886,0.00000923862,0.000007978481,0.9971102,0.000739755,0.0013740553,0.0006192221,0.000004352548],"about_ca_topic_score_codex":0.010638008,"about_ca_topic_score_gemma":0.010400605,"teacher_disagreement_score":0.010638008,"about_ca_system_score_codex":0.0005973835,"about_ca_system_score_gemma":0.0012242473,"threshold_uncertainty_score":0.021152139},"labels":[],"label_agreement":null},{"id":"W2969818521","doi":"10.3390/s19173672","title":"A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Beijing Institute of Technology Research Fund Program for Young Scholars; Beijing Institute of Technology; National Natural Science Foundation of China","keywords":"Cruise control; Planner; Reinforcement learning; Smoothness; PID controller; Computer science; Controller (irrigation); Motion (physics); Control (management); Artificial intelligence; Driving simulator; Motion control; Control engineering; Engineering; Robot; Human–computer interaction; Simulation","score_opus":0.011253834689783826,"score_gpt":0.22827022086417673,"score_spread":0.2170163861743929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969818521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070643276,0.00011291701,0.91661286,0.00015213524,0.00007990189,0.0002043092,0.00007498611,0.007910601,0.0042091124],"genre_scores_gemma":[0.89605147,0.000066924644,0.09954147,0.00012926785,0.000026605403,0.00024187364,0.00015691253,0.00005865004,0.0037268184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986744,0.000017273545,0.000009993805,0.000051969975,0.000036161367,0.00001713824],"domain_scores_gemma":[0.9998336,0.00002537409,0.000028967124,0.000024785284,0.00005721565,0.00003003671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022749061,0.0003392858,0.00024384518,0.00017947218,0.0002414491,0.0002119581,0.00057896134,0.00032909279,0.0019598075],"category_scores_gemma":[0.00045368233,0.00016938572,0.00020045295,0.00009673691,0.00019445532,0.0003107737,0.0004573142,0.000373912,0.00048930757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003316735,0.0008291755,0.004776547,0.00022541077,0.00007017113,0.0003151935,0.00042344202,0.18603472,0.16701712,0.0049435706,0.0067644324,0.6282685],"study_design_scores_gemma":[0.000081689956,0.00046679363,0.002953785,0.000011332558,0.00003015302,0.00015143592,0.000033796812,0.97130054,0.016621865,0.0014127466,0.0069051604,0.000030844214],"about_ca_topic_score_codex":0.0031209355,"about_ca_topic_score_gemma":0.0027224207,"teacher_disagreement_score":0.0031209355,"about_ca_system_score_codex":0.00027674146,"about_ca_system_score_gemma":0.00069241365,"threshold_uncertainty_score":0.006556213},"labels":[],"label_agreement":null},{"id":"W2972932574","doi":"10.3390/s19183943","title":"A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Remote sensing; Lidar; Multispectral image; Point cloud; Bidirectional reflectance distribution function; Calibration; Computer science; Pixel; Environmental science; Laser scanning; Canopy; Artificial intelligence; Computer vision; Optics; Laser; Mathematics; Geography; Reflectivity; Physics","score_opus":0.009018323452684399,"score_gpt":0.23240134841691681,"score_spread":0.2233830249642324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972932574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67789036,0.00028305128,0.3064374,0.00015000819,0.000067021625,0.00025644404,0.0015104089,0.0058268476,0.0075784363],"genre_scores_gemma":[0.8107613,0.00007911555,0.18608043,0.00008736664,0.000013807283,0.00009447694,0.0007117056,0.000058079182,0.002113778],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968636,0.000027981745,0.000008442816,0.00007273579,0.0001767435,0.000027694228],"domain_scores_gemma":[0.99981207,0.000028156432,0.000024314642,0.000044509536,0.00007179225,0.000019193549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036162764,0.00028977878,0.00031107428,0.00066768314,0.0003098634,0.00025633886,0.0003812076,0.00032345593,0.0022753207],"category_scores_gemma":[0.0002790366,0.00015734171,0.0003169357,0.0004905856,0.00019096115,0.0003936052,0.0005096491,0.00022079975,0.00059297244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025625987,0.00013047621,0.0215135,0.00016198015,0.00003070276,0.00019410462,0.00026370067,0.008236855,0.684448,0.00087274006,0.0029251773,0.28096646],"study_design_scores_gemma":[0.00015740079,0.0017316482,0.1801286,0.00008329921,0.00013831061,0.0025540914,0.0004184429,0.34911126,0.4149173,0.0019404151,0.048596684,0.0002225937],"about_ca_topic_score_codex":0.002277232,"about_ca_topic_score_gemma":0.00480714,"teacher_disagreement_score":0.002277232,"about_ca_system_score_codex":0.00041376078,"about_ca_system_score_gemma":0.00062480784,"threshold_uncertainty_score":0.007611692},"labels":[],"label_agreement":null},{"id":"W2973515967","doi":"10.3390/s19184050","title":"Enhancement of Multimodal Microwave-Ultrasound Breast Imaging Using a Deep-Learning Technique","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society; University of Calgary","keywords":"Microwave imaging; Ultrasound; Breast imaging; Microwave; Deep learning; Ultrasound imaging; Computer science; Biomedical engineering; Artificial intelligence; Medicine; Medical physics; Mammography; Radiology; Telecommunications; Breast cancer","score_opus":0.003780055885661633,"score_gpt":0.2042239613605951,"score_spread":0.20044390547493346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973515967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105382204,0.00065067393,0.8896209,0.0003788184,0.00004857646,0.000047281566,0.0001978472,0.001724639,0.0019489963],"genre_scores_gemma":[0.54997444,0.00045987434,0.44509527,0.00027184698,0.00003835791,0.00006239078,0.0004984993,0.00013405013,0.0034652178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986506,0.000028645407,0.0000056642743,0.000022990991,0.000055317134,0.00002231098],"domain_scores_gemma":[0.99979573,0.00007867222,0.000027012491,0.000026666643,0.000057972426,0.0000139442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000605996,0.0005917126,0.00029636684,0.0004410452,0.0001135106,0.00033578873,0.0004850481,0.0005540517,0.0014676703],"category_scores_gemma":[0.0011553817,0.00022117945,0.00037308075,0.0002896904,0.0002012222,0.0005163783,0.00070618483,0.0004930548,0.00043724728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037885658,0.00023763164,0.0033711467,0.0002459221,0.00012796058,0.00030700394,0.00008218911,0.22741114,0.2663428,0.0028016237,0.0028154457,0.49587828],"study_design_scores_gemma":[0.000006598717,0.000076119766,0.00093972555,0.000009939384,0.000022037673,0.00017429453,0.0000074512436,0.9457769,0.05061862,0.00088934007,0.00147006,0.000008841351],"about_ca_topic_score_codex":0.0012903037,"about_ca_topic_score_gemma":0.0027047186,"teacher_disagreement_score":0.0014676703,"about_ca_system_score_codex":0.00035852703,"about_ca_system_score_gemma":0.0003418853,"threshold_uncertainty_score":0.004909873},"labels":[],"label_agreement":null},{"id":"W2973636681","doi":"10.3390/s19184037","title":"Reducing the Cost of Implementing Filters in LoRa Devices","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cisco Systems (Canada); University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems Canada; University of Saskatchewan; Cisco Systems","keywords":"Chirp; Quantization (signal processing); Computer science; Demodulation; Electronic engineering; Lookup table; Waveform; Multiplier (economics); Computer hardware; Real-time computing; Algorithm; Telecommunications; Engineering; Radar","score_opus":0.012153694299725517,"score_gpt":0.2486628985299494,"score_spread":0.2365092042302239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973636681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33147094,0.0033491529,0.60168856,0.002136356,0.0005313671,0.000712338,0.00043962715,0.010222939,0.049448747],"genre_scores_gemma":[0.67276573,0.0006296838,0.30356467,0.0005001751,0.00014099789,0.00020024386,0.00022968867,0.00035375505,0.021615058],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953485,0.000057102632,0.00003388295,0.00007011523,0.00023390095,0.000070263166],"domain_scores_gemma":[0.9986777,0.00047152484,0.00024206506,0.0002479142,0.00031225776,0.000048555565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003258158,0.00063126965,0.00046108582,0.0009081444,0.0006190564,0.0011843053,0.0016930398,0.0006794627,0.012390411],"category_scores_gemma":[0.0016775269,0.00037986392,0.00031701245,0.0005422064,0.0002982043,0.0021577238,0.00068201264,0.0006924724,0.0036934584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091237604,0.00027331265,0.0036209393,0.00082845404,0.000107687476,0.0005559055,0.00031871878,0.013146034,0.51606387,0.016577233,0.00707547,0.4405199],"study_design_scores_gemma":[0.00018966972,0.0020688116,0.0074623604,0.00017563209,0.00021696647,0.0026008876,0.00027266023,0.0936495,0.7479493,0.0045874957,0.14066406,0.0001627777],"about_ca_topic_score_codex":0.0006638061,"about_ca_topic_score_gemma":0.0014249356,"teacher_disagreement_score":0.012390411,"about_ca_system_score_codex":0.0010438642,"about_ca_system_score_gemma":0.0006529736,"threshold_uncertainty_score":0.041450024},"labels":[],"label_agreement":null},{"id":"W2973663004","doi":"10.3390/s19184053","title":"Detection of Hydraulic Phenomena in Francis Turbines with Different Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"FP7 Energy; Seventh Framework Programme; BC Hydro; Generalitat de Catalunya; European Commission","keywords":"Hydraulic machinery; SIGNAL (programming language); Hydropower; Engineering; Flexibility (engineering); Strain gauge; Turbine; Francis turbine; Automotive engineering; Accelerometer; Control engineering; Grid; Mechanical engineering; Marine engineering; Computer science; Electrical engineering","score_opus":0.0047175815341964,"score_gpt":0.18045442324556374,"score_spread":0.17573684171136733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973663004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95420533,0.00045540143,0.043774102,0.000082540995,0.0000745552,0.00004421106,0.00013731253,0.00024969806,0.0009768517],"genre_scores_gemma":[0.9901038,0.00010303542,0.009067883,0.00003565899,0.000012210655,0.000027245384,0.00004642133,0.0000092926275,0.0005944475],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956995,0.000086361295,0.000020095955,0.000098428776,0.00017567504,0.000049519018],"domain_scores_gemma":[0.9995715,0.00018465231,0.0000574859,0.000023714858,0.00014060528,0.0000220739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003630475,0.00041345521,0.0004949801,0.00053575577,0.00022512015,0.00029056586,0.00056544866,0.0007337305,0.0008216043],"category_scores_gemma":[0.0008361325,0.00020798143,0.00018260202,0.00044655605,0.00039783042,0.0004828159,0.0003223981,0.00024828597,0.00017697077],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003163429,0.000056165598,0.0039224722,0.0001317514,0.000025668007,0.00022958557,0.00024445224,0.0008528008,0.97441,0.00013689074,0.00024415192,0.019429728],"study_design_scores_gemma":[0.00003280097,0.0018688969,0.042946275,0.000027770313,0.00010624211,0.0007154447,0.0005495534,0.018670868,0.9329668,0.0001162219,0.0019348365,0.00006423117],"about_ca_topic_score_codex":0.0006375788,"about_ca_topic_score_gemma":0.0013250704,"teacher_disagreement_score":0.0008216043,"about_ca_system_score_codex":0.00021724717,"about_ca_system_score_gemma":0.00010141521,"threshold_uncertainty_score":0.0027484894},"labels":[],"label_agreement":null},{"id":"W2973847306","doi":"10.3390/s19183997","title":"Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada","keywords":"Robustness (evolution); Computer science; Wearable computer; Wearable technology; Computational complexity theory; Filter (signal processing); Artificial intelligence; Matching (statistics); Noise (video); Algorithm; Pattern recognition (psychology); Computer vision; Real-time computing; Mathematics; Embedded system; Statistics","score_opus":0.01924021344822323,"score_gpt":0.25013329396848,"score_spread":0.23089308052025675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973847306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006062304,0.00023611635,0.9921876,0.00003459171,0.000046443587,0.00004500046,0.00003828676,0.0008317932,0.0005177296],"genre_scores_gemma":[0.143194,0.00043105453,0.85347635,0.00012277885,0.000089843976,0.00012737622,0.0002744696,0.00017625524,0.002107819],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990845,0.000117042066,0.000071887946,0.00025138422,0.00040205312,0.000073107534],"domain_scores_gemma":[0.99909914,0.0003472261,0.00009812408,0.00012297028,0.00029434208,0.00003824508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075956236,0.0008317497,0.001053052,0.0013108286,0.00034402552,0.0011012165,0.0016293288,0.0010224603,0.0020159255],"category_scores_gemma":[0.003142304,0.00033089195,0.0007189243,0.0016940259,0.00031419643,0.0013713725,0.0006989651,0.0006851125,0.0016966002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005936594,0.00012600984,0.00163894,0.00025136233,0.0001118506,0.00031325838,0.00009450165,0.014089572,0.16756919,0.0044930684,0.002509324,0.8082093],"study_design_scores_gemma":[0.000071525355,0.0005096808,0.0041453973,0.00002797948,0.00012615327,0.0021668137,0.00005152824,0.8038516,0.17850408,0.0026976708,0.0077484585,0.000099074205],"about_ca_topic_score_codex":0.001582193,"about_ca_topic_score_gemma":0.0014487373,"teacher_disagreement_score":0.0020159255,"about_ca_system_score_codex":0.00031462178,"about_ca_system_score_gemma":0.00069255027,"threshold_uncertainty_score":0.006743908},"labels":[],"label_agreement":null},{"id":"W2976562623","doi":"10.3390/s19194224","title":"A Novel, Portable and Fast Moisture Content Measuring Method for Grains Based on an Ultra-Wideband (UWB) Radar Module and the Mode Matching Method","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Key Research and Development Program of China; Keysight Technologies","keywords":"Permittivity; Radar; Reflection coefficient; Materials science; Moisture; Correlation coefficient; Water content; Acoustics; Reflection (computer programming); Wideband; Electronic engineering; Coaxial; Waveguide; Optics; Remote sensing; Optoelectronics; Dielectric; Computer science; Engineering; Composite material; Electrical engineering; Physics; Telecommunications; Geology","score_opus":0.02915796837644317,"score_gpt":0.2561826760140962,"score_spread":0.22702470763765306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976562623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16792339,0.0008088533,0.8279965,0.0001224263,0.00016747757,0.00015359491,0.0001630752,0.0013079345,0.0013567494],"genre_scores_gemma":[0.45741618,0.0006403194,0.53930026,0.0001686962,0.000060325125,0.00017201672,0.00015785111,0.000060099854,0.0020242888],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999539,0.00006490789,0.000024510222,0.00015592258,0.00019190018,0.000023688815],"domain_scores_gemma":[0.99971277,0.00005886543,0.00007334687,0.000043667627,0.00009706583,0.0000143217985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040856507,0.0006446719,0.00055872166,0.000580662,0.0001543441,0.00032853696,0.0009144538,0.0006553399,0.0005929538],"category_scores_gemma":[0.0005357085,0.0003747311,0.00037164558,0.0004910195,0.0002055918,0.0010474629,0.00041555456,0.00041572517,0.00038386515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095129806,0.00005866403,0.0011935655,0.00015745309,0.000027274438,0.00006984081,0.000045598274,0.0005282787,0.95323443,0.000504172,0.00031262322,0.043772954],"study_design_scores_gemma":[0.000048426296,0.0004799378,0.004275655,0.000012169047,0.00009293509,0.0011093282,0.00004796557,0.039172545,0.94938797,0.00028867635,0.005018055,0.00006638359],"about_ca_topic_score_codex":0.00029149314,"about_ca_topic_score_gemma":0.0004930118,"teacher_disagreement_score":0.0009144538,"about_ca_system_score_codex":0.00024749088,"about_ca_system_score_gemma":0.00022820795,"threshold_uncertainty_score":0.0021606684},"labels":[],"label_agreement":null},{"id":"W2977286736","doi":"10.3390/s19194323","title":"Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Winter Sports Injuries and Performance","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Motion capture; Context (archaeology); Artificial intelligence; Monocular; Kinematics; Computer science; Computer vision; Orientation (vector space); Ankle; Data set; Geodesy; Mathematics; Motion (physics); Geology; Medicine; Physics; Anatomy; Geometry","score_opus":0.28673572684940896,"score_gpt":0.37105001871055904,"score_spread":0.08431429186115008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977286736","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5303352,0.0054443004,0.4492829,0.0009805658,0.00008952192,0.00020827545,0.0006554778,0.00045728404,0.012546572],"genre_scores_gemma":[0.7998016,0.0013125978,0.19668418,0.00013795074,0.000040709427,0.00010089747,0.00040887843,0.00004988912,0.0014633538],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997577,0.0010181563,0.000089309455,0.00064084434,0.0005479368,0.00012670961],"domain_scores_gemma":[0.9955514,0.0015869578,0.00045456222,0.0007522168,0.0015421951,0.00011263884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034620499,0.00042616238,0.0002991026,0.0011453753,0.0003830916,0.001312982,0.0007677086,0.00070344534,0.0014177266],"category_scores_gemma":[0.009471118,0.00027426396,0.00025335074,0.0011309324,0.00069202646,0.0011374457,0.00073973014,0.00036669965,0.0004949695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047656178,0.00013981825,0.07855647,0.0012042638,0.0002681101,0.00021696405,0.0030327432,0.025741953,0.05216471,0.007051067,0.0015259745,0.8296213],"study_design_scores_gemma":[0.00009234296,0.0026952673,0.48555133,0.0017629176,0.00046710437,0.0023189054,0.0099491505,0.33840418,0.07691211,0.015267903,0.06629892,0.00027984078],"about_ca_topic_score_codex":0.008228363,"about_ca_topic_score_gemma":0.012297636,"teacher_disagreement_score":0.008228363,"about_ca_system_score_codex":0.000607878,"about_ca_system_score_gemma":0.0007649434,"threshold_uncertainty_score":0.018309295},"labels":[],"label_agreement":null},{"id":"W2978159033","doi":"10.3390/s19194288","title":"Application-Based Production and Testing of a Core–Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Core (optical fiber); Electronics; Motion sensors; Strain (injury); Wearable technology; Acoustics; Biomedical engineering; Materials science; Electrical engineering; Engineering; Computer science; Physics; Composite material; Embedded system; Medicine; Anatomy; Artificial intelligence","score_opus":0.04725326141904278,"score_gpt":0.26227876302973663,"score_spread":0.21502550161069384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978159033","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.920595,0.00020362908,0.07780703,0.000099552366,0.000052108935,0.00039093144,0.00012969325,0.00016307253,0.00055892277],"genre_scores_gemma":[0.86315536,0.0002320767,0.1351879,0.000057552956,0.000019357281,0.00023893284,0.000121668454,0.000038277813,0.00094891543],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994455,0.00014417454,0.000037557485,0.000107210515,0.00023280032,0.00003279604],"domain_scores_gemma":[0.9990306,0.00027793963,0.00015229762,0.00014505922,0.00032528854,0.00006884923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014129942,0.0005078263,0.0002613403,0.00026123266,0.0002114982,0.00024791787,0.00052069017,0.00054175983,0.00048065628],"category_scores_gemma":[0.0020788398,0.00018299838,0.00025392266,0.00022793918,0.00032803247,0.00036385952,0.00030243114,0.00025752964,0.00020368258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093086026,0.0001374268,0.0021409236,0.00010223636,0.000009017006,0.00014230356,0.00014233777,0.00076877535,0.9833534,0.000067586356,0.00007212179,0.012970858],"study_design_scores_gemma":[0.00004202122,0.0046536545,0.01588492,0.000021100317,0.000033316228,0.00057814544,0.00017042599,0.012444667,0.96408325,0.00007573853,0.001990833,0.000021874064],"about_ca_topic_score_codex":0.00054031017,"about_ca_topic_score_gemma":0.0011006823,"teacher_disagreement_score":0.0014129942,"about_ca_system_score_codex":0.00013751583,"about_ca_system_score_gemma":0.00030302425,"threshold_uncertainty_score":0.007472694},"labels":[],"label_agreement":null},{"id":"W2978697615","doi":"10.3390/s19194270","title":"Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre; Queen's University","funders":"","keywords":"Computer science; Wearable computer; Machine learning; Artificial intelligence; Wearable technology; Human–computer interaction; Selection (genetic algorithm); Feature (linguistics); Embedded system","score_opus":0.08976507466249946,"score_gpt":0.3522517976971067,"score_spread":0.26248672303460724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978697615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29763138,0.00017798808,0.6984062,0.00028762707,0.000031344105,0.00013742842,0.0001351563,0.00059760886,0.002595298],"genre_scores_gemma":[0.9064004,0.00014464631,0.09235635,0.000065113694,0.000019050709,0.00010724934,0.00011062268,0.000021368422,0.0007751761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970466,0.00010075308,0.000014434256,0.00008636091,0.000058143265,0.000035583846],"domain_scores_gemma":[0.99958736,0.0002051634,0.000059021837,0.00004302296,0.000060144397,0.000045280118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045477084,0.00047001638,0.00039206495,0.00067636603,0.00015394695,0.00067517685,0.00040892686,0.0005263642,0.0009861453],"category_scores_gemma":[0.0020177893,0.00015027022,0.00039274932,0.00037036208,0.00033345548,0.0005309236,0.000783519,0.000399519,0.00031230718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089545094,0.0007229831,0.047989417,0.0002781647,0.00016103867,0.00031605194,0.0010593004,0.13847873,0.23380163,0.0032211973,0.0017714853,0.57130456],"study_design_scores_gemma":[0.000021445561,0.0004791093,0.033353083,0.00004838592,0.00003677354,0.00033347748,0.00032343745,0.9272276,0.03306501,0.0035680435,0.0014949772,0.00004861872],"about_ca_topic_score_codex":0.0008224444,"about_ca_topic_score_gemma":0.0009763039,"teacher_disagreement_score":0.0009861453,"about_ca_system_score_codex":0.00021991061,"about_ca_system_score_gemma":0.0002640719,"threshold_uncertainty_score":0.0032990575},"labels":[],"label_agreement":null},{"id":"W2978810585","doi":"10.3390/s19194317","title":"Pressure Signal Enhancement of Slowly Increasing Leaks Using Digital Compensator Based on Acoustic Sensor","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Leak; Pipeline transport; SIGNAL (programming language); Pipeline (software); Acoustics; Engineering; Digital signal; Compensation (psychology); Computer science; Control theory (sociology); Electronic engineering; Digital signal processing; Physics; Mechanical engineering; Artificial intelligence","score_opus":0.007855727732628357,"score_gpt":0.1899708691984571,"score_spread":0.18211514146582874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978810585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29723474,0.0011359403,0.6965341,0.0003058055,0.00017199328,0.00009614268,0.000056818008,0.0014665525,0.0029979276],"genre_scores_gemma":[0.8786304,0.0006280063,0.11810821,0.0001631232,0.000063229374,0.000050574334,0.000050437928,0.000055662396,0.002250367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966514,0.000044621564,0.000015531554,0.00008128195,0.00016938771,0.00002416777],"domain_scores_gemma":[0.99960774,0.0001321812,0.000071696995,0.00003303446,0.00013876324,0.000016674812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033986138,0.0005131231,0.00030562445,0.0005017861,0.00017913041,0.00028694244,0.0004909746,0.00048103443,0.000769539],"category_scores_gemma":[0.0007652784,0.00021885177,0.00025433893,0.0003555586,0.0003520098,0.0010379624,0.00040653086,0.00030448727,0.0001538036],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021042967,0.00005007685,0.0011004885,0.00022429321,0.00001647257,0.00008366403,0.000121648045,0.0036996864,0.88494194,0.0005732282,0.0004274229,0.10855061],"study_design_scores_gemma":[0.00006541574,0.0008808903,0.0051099868,0.00002770778,0.000081346094,0.00044496235,0.00008592179,0.15295802,0.8319858,0.0004230554,0.007850691,0.000086185064],"about_ca_topic_score_codex":0.00036801258,"about_ca_topic_score_gemma":0.00051673554,"teacher_disagreement_score":0.000769539,"about_ca_system_score_codex":0.0002163158,"about_ca_system_score_gemma":0.00022986103,"threshold_uncertainty_score":0.0025743246},"labels":[],"label_agreement":null},{"id":"W2979185377","doi":"10.3390/s19194326","title":"Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Multilateration; Microphone array; Nonlinear system; Computer science; Optimization problem; Robustness (evolution); Microphone; Particle swarm optimization; Parametric statistics; Algorithm; Nonlinear programming; Acoustics; Mathematical optimization; Mathematics; Loudspeaker; Physics","score_opus":0.011862359105226606,"score_gpt":0.24589777688046593,"score_spread":0.23403541777523934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979185377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004266815,0.00012824492,0.994156,0.000043127588,0.000018547458,0.000013103491,0.0000101531905,0.000095970056,0.001268045],"genre_scores_gemma":[0.42662647,0.0009562389,0.5678471,0.00010316975,0.000056492474,0.00025246176,0.00014017522,0.000112176334,0.003905745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996425,0.00010450217,0.000021357788,0.000092051145,0.00011365747,0.000025899275],"domain_scores_gemma":[0.9997445,0.00010751006,0.00003261345,0.000018952325,0.000087502885,0.000008884725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045263895,0.0007754232,0.00050645246,0.00036745938,0.00025027693,0.0005594936,0.0005027357,0.0005685198,0.0013561066],"category_scores_gemma":[0.0012328776,0.00032585123,0.00073007366,0.00044233442,0.00039580642,0.00069680053,0.00057877426,0.0006244156,0.0003267188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005661065,0.00002219922,0.0006572674,0.00020108842,0.000047902504,0.00008881593,0.00012626937,0.88654315,0.014770307,0.008888007,0.0009291135,0.087669306],"study_design_scores_gemma":[0.000007993006,0.000029446623,0.00012718326,0.000006156956,0.000012315655,0.00003008654,0.00001784329,0.9957183,0.0017851085,0.0013494414,0.00090861064,0.000007491411],"about_ca_topic_score_codex":0.0025417046,"about_ca_topic_score_gemma":0.0021939373,"teacher_disagreement_score":0.0025417046,"about_ca_system_score_codex":0.00028587066,"about_ca_system_score_gemma":0.0008320529,"threshold_uncertainty_score":0.005053878},"labels":[],"label_agreement":null},{"id":"W2980967249","doi":"10.3390/s19204557","title":"A Review of Force Myography Research and Development","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Electrical impedance myography; Human–computer interaction; Mechatronics; Data science; Artificial intelligence; Medicine","score_opus":0.12459429586490477,"score_gpt":0.3611297637010407,"score_spread":0.23653546783613594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980967249","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022393769,0.9960926,0.000547767,0.00037483967,0.0003227293,0.000012711011,0.0000883026,0.00001923573,0.0023178726],"genre_scores_gemma":[0.0010588935,0.99651414,0.0006955246,0.00020693056,0.00028024687,0.000015210469,0.00010896813,0.000004927156,0.0011152155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995265,0.000064310145,0.000096364165,0.00010057809,0.00018302703,0.00002926389],"domain_scores_gemma":[0.99859387,0.0008295755,0.00015154663,0.00003885182,0.0003312609,0.00005487153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094639556,0.0010818817,0.0013319592,0.004709635,0.0003369422,0.0012945598,0.0010612609,0.0013223428,0.010591909],"category_scores_gemma":[0.0021249652,0.00046667096,0.0008172765,0.0044186343,0.00042319173,0.0018214725,0.00064145797,0.0009863117,0.004689526],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006552697,0.00007952763,0.00039190034,0.0367897,0.00008919456,0.00016051822,0.000078081786,0.00031374246,0.0019962199,0.0031235889,0.023999006,0.93291306],"study_design_scores_gemma":[0.000008268208,0.00012787056,0.0014460154,0.008765665,0.00015827191,0.0010152584,0.00009165563,0.00013171702,0.000758855,0.001611709,0.9858549,0.00002985076],"about_ca_topic_score_codex":0.0013940299,"about_ca_topic_score_gemma":0.0019486906,"teacher_disagreement_score":0.010591909,"about_ca_system_score_codex":0.0005507192,"about_ca_system_score_gemma":0.001949507,"threshold_uncertainty_score":0.03543341},"labels":[],"label_agreement":null},{"id":"W2981416860","doi":"10.3390/s19214618","title":"Screen-Printed Voltammetric Biosensors for the Determination of Copper in Wine","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation","keywords":"Wine; Copper; Biosensor; Nanotechnology; Chemistry; Chromatography; Materials science; Metallurgy; Food science","score_opus":0.007314265121320653,"score_gpt":0.2129858834725956,"score_spread":0.20567161835127493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981416860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47514695,0.014341159,0.4996669,0.0006972103,0.001284121,0.00038442283,0.0009402798,0.002936415,0.004602519],"genre_scores_gemma":[0.7205547,0.0071373796,0.26194134,0.00034095635,0.00013425347,0.0003776869,0.0007677757,0.00007855552,0.008667361],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99862015,0.00029344048,0.000102482685,0.00025137886,0.00067402155,0.000058556765],"domain_scores_gemma":[0.9995807,0.0001568647,0.00005757509,0.0000532894,0.00012600406,0.000025645217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062674587,0.000726466,0.0007510662,0.0007899924,0.00019699015,0.0005290169,0.001279062,0.0011474694,0.0007267514],"category_scores_gemma":[0.0011466771,0.0005113042,0.00065878534,0.0006595989,0.00027859933,0.00044886614,0.00038569333,0.00070332806,0.00076168793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036199963,0.00002160659,0.00014164549,0.000112691116,0.00001651519,0.00010503666,0.000019930545,0.00019007172,0.99220043,0.000077715464,0.00008679631,0.0069912975],"study_design_scores_gemma":[0.0000116183,0.00026965316,0.0017429995,0.000015296893,0.000042038326,0.0004840107,0.000030475052,0.005985299,0.9888205,0.0001152009,0.0024635114,0.000019399424],"about_ca_topic_score_codex":0.0003357873,"about_ca_topic_score_gemma":0.0006969679,"teacher_disagreement_score":0.001279062,"about_ca_system_score_codex":0.00031456028,"about_ca_system_score_gemma":0.0001755051,"threshold_uncertainty_score":0.0033145547},"labels":[],"label_agreement":null},{"id":"W2981507930","doi":"10.3390/s19214664","title":"Augmenting Deep Learning Performance in an Evidential Multiple Classifier System","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"Qatar National Research Fund; Agence Nationale de la Recherche","keywords":"Artificial intelligence; Machine learning; Computer science; Classifier (UML); Exploit; Leverage (statistics); Ensemble learning; Artificial neural network; Crowds","score_opus":0.01188563110383821,"score_gpt":0.23161418725759758,"score_spread":0.21972855615375936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981507930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14611009,0.00035322038,0.8505699,0.00053015485,0.000047345307,0.00003052264,0.000053585038,0.00056554755,0.0017396366],"genre_scores_gemma":[0.92325103,0.00007663287,0.07555834,0.000087925655,0.000037393605,0.000026688243,0.00005035834,0.00002522954,0.0008863136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981876,0.0006773887,0.00009985492,0.00037881592,0.00044451273,0.00021172859],"domain_scores_gemma":[0.9927672,0.004133814,0.00066579663,0.001145598,0.0010862795,0.00020120325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00621869,0.0008513137,0.0012994814,0.0005971472,0.0005758807,0.0015147661,0.0017968863,0.0019562738,0.0010989086],"category_scores_gemma":[0.014092033,0.0004548023,0.00049097557,0.0005283817,0.0009522539,0.0030293984,0.002470005,0.0022626463,0.00040891927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004310893,0.00023037718,0.004248872,0.000094818664,0.00011636908,0.00012508033,0.00022588941,0.7896066,0.010809096,0.016060527,0.00080408686,0.17724723],"study_design_scores_gemma":[0.000002694025,0.000033074954,0.00015993677,0.0000037280895,0.000006411004,0.000010976552,0.0000053220815,0.9943362,0.0018335406,0.003497515,0.000105706196,0.0000049263936],"about_ca_topic_score_codex":0.0017855921,"about_ca_topic_score_gemma":0.0022256973,"teacher_disagreement_score":0.00621869,"about_ca_system_score_codex":0.001000097,"about_ca_system_score_gemma":0.0009606818,"threshold_uncertainty_score":0.032887936},"labels":[],"label_agreement":null},{"id":"W2981787497","doi":"10.3390/s19214619","title":"A Magnetically Tunable Check Valve Applied to a Lab-on-Chip Nitrite Sensor","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Dartmouth College","keywords":"Materials science; Fabrication; Check valve; Magnet; Microfluidics; Chip; Ball valve; Leakage (economics); Pressure sensor; Volumetric flow rate; Lab-on-a-chip; Optoelectronics; Mechanical engineering; Nanotechnology; Electrical engineering; Engineering","score_opus":0.004892133047028524,"score_gpt":0.18812741731745272,"score_spread":0.1832352842704242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981787497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80690587,0.0035864715,0.18074517,0.00056104374,0.0009839929,0.00040582183,0.0006280524,0.0027011721,0.0034822742],"genre_scores_gemma":[0.8977419,0.0007218509,0.0963501,0.0002898272,0.000094145624,0.00020947141,0.00023138529,0.00008613431,0.0042752307],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994911,0.000050049304,0.00003089736,0.00014102369,0.00023308153,0.00005387865],"domain_scores_gemma":[0.99943405,0.00017398316,0.00015014672,0.00006918084,0.00010335645,0.000069261514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050716597,0.00043624203,0.0005563465,0.00037284824,0.00029533278,0.00062182883,0.0011458107,0.00081441476,0.0006813941],"category_scores_gemma":[0.00080899935,0.00039061336,0.00028605442,0.00017073288,0.00041947336,0.00039111864,0.00043601135,0.0004330771,0.00037415215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032730288,0.000013054519,0.00013998064,0.00004265595,0.0000037350421,0.000062205196,0.000017160331,0.00009715604,0.99724376,0.00008605882,0.000060216233,0.002201433],"study_design_scores_gemma":[0.0000138731775,0.0001989157,0.00089822436,0.000004955832,0.000011211327,0.0001738823,0.000010625092,0.0026973572,0.99317354,0.000024397741,0.0027752563,0.000017675866],"about_ca_topic_score_codex":0.0002932856,"about_ca_topic_score_gemma":0.00046790903,"teacher_disagreement_score":0.0011458107,"about_ca_system_score_codex":0.00038391145,"about_ca_system_score_gemma":0.0004321643,"threshold_uncertainty_score":0.0027855039},"labels":[],"label_agreement":null},{"id":"W2982174979","doi":"10.3390/s19214641","title":"Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Biometrics; Spoofing attack; Word error rate; Password; Robustness (evolution); Artificial intelligence; Authentication (law); Pattern recognition (psychology); Replay attack; Computer security; Data mining; Machine learning","score_opus":0.011855100752669158,"score_gpt":0.24344283899908498,"score_spread":0.23158773824641582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982174979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1919379,0.0035581111,0.79576707,0.00042781213,0.00023127646,0.00007354365,0.0003725033,0.0018326201,0.005799108],"genre_scores_gemma":[0.9395825,0.0008505667,0.05739741,0.00010731933,0.000055785436,0.00003532087,0.00018262859,0.0000230968,0.0017652479],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980754,0.00002709491,0.000013118195,0.0000510883,0.000078318844,0.000022846336],"domain_scores_gemma":[0.9999026,0.00001960437,0.00001762497,0.000016291016,0.000036964437,0.000006866244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031847318,0.0004544768,0.00029641457,0.00053463987,0.00011960485,0.0003909328,0.00027616028,0.00042861028,0.0011890439],"category_scores_gemma":[0.000568545,0.00012661515,0.0003324933,0.0004603074,0.00018240504,0.00060282566,0.00041200616,0.00033662884,0.0005568696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043945533,0.00019062108,0.0045050955,0.00022782481,0.00015563605,0.00027227993,0.0000993207,0.060385525,0.14477403,0.0025546302,0.0016116167,0.7847839],"study_design_scores_gemma":[0.000012838662,0.00037322825,0.016225437,0.000045988592,0.00012727304,0.00052985,0.000097759126,0.90214336,0.069336265,0.00624882,0.0047968724,0.00006231158],"about_ca_topic_score_codex":0.0011415129,"about_ca_topic_score_gemma":0.0016884168,"teacher_disagreement_score":0.0011890439,"about_ca_system_score_codex":0.0002439912,"about_ca_system_score_gemma":0.0002780641,"threshold_uncertainty_score":0.0039777756},"labels":[],"label_agreement":null},{"id":"W2982262567","doi":"10.3390/s19214685","title":"Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; York University","keywords":"Point cloud; Lidar; Computer science; Ranging; Graph; Data mining; Point (geometry); Pattern recognition (psychology); Artificial intelligence; Contextual image classification; Image (mathematics); Remote sensing; Theoretical computer science; Mathematics; Geography","score_opus":0.024404826154883933,"score_gpt":0.2659991494869276,"score_spread":0.24159432333204367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982262567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15769616,0.00021810795,0.83937657,0.00017214182,0.00004947775,0.00016140891,0.00034291903,0.0012467807,0.0007364683],"genre_scores_gemma":[0.6015346,0.00014570184,0.39518207,0.000064371176,0.000031957556,0.00012366982,0.0018250163,0.00011713424,0.0009754622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990319,0.0001957233,0.00005732548,0.00024185251,0.00034336885,0.00012995602],"domain_scores_gemma":[0.9982653,0.0006474402,0.00016529851,0.00012423846,0.000703148,0.00009458878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001100374,0.0009252618,0.00093608524,0.002386208,0.0005060517,0.0013020769,0.0016874205,0.0012035478,0.0006980098],"category_scores_gemma":[0.0025783055,0.0004079245,0.0009501549,0.0017531342,0.00052008335,0.0010705277,0.0007877951,0.0009415698,0.00026237412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005691117,0.00029384342,0.0060396777,0.00015919624,0.00014523888,0.00022835463,0.00018233291,0.6247512,0.016600104,0.002906532,0.0031683138,0.34495613],"study_design_scores_gemma":[0.0000042004626,0.000025662212,0.00089259446,0.0000025085888,0.0000086523005,0.000023679815,0.000027924958,0.99603385,0.0020195385,0.0007869512,0.00016732284,0.000007130054],"about_ca_topic_score_codex":0.015554524,"about_ca_topic_score_gemma":0.014712687,"teacher_disagreement_score":0.015554524,"about_ca_system_score_codex":0.0009765732,"about_ca_system_score_gemma":0.0008196005,"threshold_uncertainty_score":0.030927956},"labels":[],"label_agreement":null},{"id":"W2982462992","doi":"10.3390/s19214734","title":"Particle Imaging Velocimetry Gyroscope","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Gyroscope; Inertial measurement unit; Velocimetry; Particle image velocimetry; Rate integrating gyroscope; Physics; Inertial frame of reference; Noise (video); Accelerometer; Instability; SIGNAL (programming language); Computer science; Acoustics; Engineering; Optics; Aerospace engineering; Artificial intelligence; Turbulence; Vibrating structure gyroscope; Mechanics; Classical mechanics","score_opus":0.0029961519613682062,"score_gpt":0.18990199574274003,"score_spread":0.18690584378137182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982462992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027481167,0.0057757236,0.9193345,0.0004515263,0.0015961671,0.00035789798,0.004268441,0.01068615,0.03004845],"genre_scores_gemma":[0.39799696,0.006044926,0.5517417,0.000521216,0.00076522556,0.0006115829,0.005972923,0.00060038234,0.035744976],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991622,0.000081932296,0.00003722205,0.00019578991,0.00047900717,0.000043742082],"domain_scores_gemma":[0.99956495,0.00008957127,0.00007435364,0.000064740976,0.000183669,0.000022728324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004440069,0.00073142396,0.0008781713,0.0012222019,0.00036215392,0.0009234899,0.0007234611,0.00076576497,0.0031469543],"category_scores_gemma":[0.0014289859,0.00025676764,0.0002327434,0.0009463338,0.00030715627,0.0006392505,0.0007943575,0.00069104903,0.0031892161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005263583,0.000096553056,0.008818656,0.0008243187,0.00010188567,0.00020468059,0.00029562312,0.012967109,0.24453448,0.03639515,0.04079516,0.6544401],"study_design_scores_gemma":[0.00011650364,0.00057124963,0.014147515,0.00018199318,0.00012854423,0.0009181903,0.000111833986,0.13107473,0.21595682,0.007567998,0.62905675,0.00016783131],"about_ca_topic_score_codex":0.0020715639,"about_ca_topic_score_gemma":0.0014179852,"teacher_disagreement_score":0.0031469543,"about_ca_system_score_codex":0.00040626703,"about_ca_system_score_gemma":0.000773797,"threshold_uncertainty_score":0.010527611},"labels":[],"label_agreement":null},{"id":"W2983609705","doi":"10.3390/s19214766","title":"Intelligent Sensing Using Multiple Sensors for Material Characterization","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Higher Education and Scientific Research; King Saud University; CMC Microsystems","keywords":"Microwave; Microstrip; Resonator; Computer science; Wideband; Planar; Artificial neural network; Electronic engineering; Modulation (music); Acoustics; Field (mathematics); Materials science; Engineering; Optoelectronics; Artificial intelligence; Physics; Telecommunications; Mathematics","score_opus":0.025448640319214007,"score_gpt":0.22810487956551026,"score_spread":0.20265623924629625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983609705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05509235,0.0011688665,0.9395186,0.00020990666,0.00014555959,0.00007512255,0.000046744004,0.0008240781,0.0029188176],"genre_scores_gemma":[0.53676784,0.0005423851,0.45983323,0.00018893612,0.00012617657,0.00012677847,0.00008291593,0.00006717273,0.0022645756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988726,0.000152223,0.000054446053,0.00034683695,0.0005288123,0.000045105695],"domain_scores_gemma":[0.9993414,0.0002430138,0.00010585328,0.00015186674,0.00013229832,0.000025401685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000650908,0.00069992023,0.00082361524,0.0009085226,0.0002795143,0.0006806231,0.0011135043,0.0008686789,0.00080820103],"category_scores_gemma":[0.001203361,0.00038660702,0.0004966266,0.00039827934,0.0007054308,0.0016242919,0.00091747957,0.00075322005,0.0003799014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029908898,0.00013044087,0.0020178612,0.00025997186,0.000096404576,0.00016692012,0.00014765558,0.016881812,0.74430937,0.009858582,0.00074783195,0.22508396],"study_design_scores_gemma":[0.00003824654,0.00061822234,0.002701224,0.00004826192,0.00011650257,0.00081456214,0.00007709035,0.4486794,0.5250242,0.007959445,0.013801497,0.000121443096],"about_ca_topic_score_codex":0.00017781276,"about_ca_topic_score_gemma":0.00035761422,"teacher_disagreement_score":0.0011135043,"about_ca_system_score_codex":0.00036813505,"about_ca_system_score_gemma":0.00016975177,"threshold_uncertainty_score":0.0034424067},"labels":[],"label_agreement":null},{"id":"W2983735760","doi":"10.3390/s19225026","title":"A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sliding window protocol; Accelerometer; Gyroscope; Computer science; Inertial measurement unit; Artificial intelligence; Activity recognition; Inertial frame of reference; Window (computing); Pattern recognition (psychology); Engineering; Operating system","score_opus":0.09615887368840895,"score_gpt":0.34251273637233604,"score_spread":0.2463538626839271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983735760","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5791692,0.008853052,0.4016211,0.0002324985,0.00060131116,0.00040080032,0.0012915236,0.0028402074,0.004990282],"genre_scores_gemma":[0.89497244,0.0011241307,0.10081869,0.000048999103,0.000101816004,0.00022907705,0.001480213,0.00015972873,0.0010649422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99573886,0.0010366191,0.00045096324,0.00075175887,0.0017107162,0.00031113735],"domain_scores_gemma":[0.9816743,0.013560617,0.0010430616,0.001255351,0.002125947,0.00034077454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056922724,0.0012806065,0.0011354056,0.0023550338,0.0003222589,0.0013243657,0.0008109014,0.0009056178,0.0017227223],"category_scores_gemma":[0.029325671,0.000250879,0.0006454785,0.0019007816,0.0005684716,0.0019589104,0.00092174317,0.00053593173,0.0004977726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00538045,0.00048082782,0.02607215,0.0017307359,0.00068093016,0.00040257833,0.00071601506,0.1543577,0.06163129,0.00272559,0.0032603364,0.74256134],"study_design_scores_gemma":[0.00011728649,0.0027522428,0.09837368,0.00025441888,0.00036984947,0.00068780483,0.00063776283,0.8430816,0.046637252,0.0028066519,0.0041384026,0.00014302372],"about_ca_topic_score_codex":0.0026409922,"about_ca_topic_score_gemma":0.0016921324,"teacher_disagreement_score":0.0056922724,"about_ca_system_score_codex":0.00036435275,"about_ca_system_score_gemma":0.00063408015,"threshold_uncertainty_score":0.030103981},"labels":[],"label_agreement":null},{"id":"W2983790479","doi":"10.3390/s19225019","title":"Sensors for Positron Emission Tomography Applications","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Silicon photomultiplier; Avalanche photodiode; Photomultiplier; Detector; Positron emission tomography; APDS; Computer science; Photodetector; Medical physics; Optoelectronics; Materials science; Scintillator; Electronic engineering; Physics; Engineering; Nuclear medicine; Telecommunications; Medicine","score_opus":0.03145493505259585,"score_gpt":0.3212191633967621,"score_spread":0.28976422834416626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983790479","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016653683,0.31732357,0.25894567,0.01252767,0.021416366,0.0023270585,0.008396471,0.012101706,0.35030773],"genre_scores_gemma":[0.19469868,0.21948119,0.2953939,0.015359755,0.00567755,0.0036541817,0.01635136,0.0016603194,0.24772309],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982774,0.00020362485,0.00013289503,0.00035211412,0.0008632357,0.00017070978],"domain_scores_gemma":[0.9991347,0.00011033337,0.00011885378,0.0000859933,0.00048984453,0.000060186117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010798522,0.0015656875,0.0010244469,0.0012042841,0.0006246078,0.0019394296,0.0019663882,0.0027793664,0.036367472],"category_scores_gemma":[0.0021698538,0.0006925971,0.0008178709,0.0013411574,0.00044842652,0.0025672805,0.0016049907,0.0025991362,0.034138013],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032558164,0.00016675329,0.0010871756,0.0065686717,0.00013462956,0.0008831737,0.00022848445,0.0014608516,0.2526612,0.04751453,0.22456406,0.46440476],"study_design_scores_gemma":[0.000028192077,0.00014188168,0.00034250974,0.0002263454,0.000047934114,0.0010057358,0.000041636828,0.0011971273,0.06431558,0.002940917,0.92966604,0.000046079855],"about_ca_topic_score_codex":0.00047101406,"about_ca_topic_score_gemma":0.00052463886,"teacher_disagreement_score":0.036367472,"about_ca_system_score_codex":0.0008719945,"about_ca_system_score_gemma":0.00072991673,"threshold_uncertainty_score":0.121661305},"labels":[],"label_agreement":null},{"id":"W2984700113","doi":"10.3390/s19214753","title":"Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Memorial University of Newfoundland","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft","keywords":"EMI; Electromagnetic induction; Electromagnetic coil; Electromagnetic interference; Calibration; Inversion (geology); Acoustics; Dipole; Electrical resistivity and conductivity; Magnetic dipole; Materials science; Remote sensing; Electronic engineering; Geology; Electrical engineering; Physics; Engineering","score_opus":0.05309820839435454,"score_gpt":0.27926812608056184,"score_spread":0.2261699176862073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984700113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084406585,0.00007398383,0.9117038,0.000072755734,0.00004031718,0.00008423313,0.00047567603,0.0020522003,0.0010903733],"genre_scores_gemma":[0.53236735,0.00015072859,0.46451208,0.000057649093,0.000020139087,0.0001627615,0.0013524264,0.00029737822,0.0010795499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999668,0.00006600787,0.000027806616,0.000071120405,0.00013182558,0.000035280827],"domain_scores_gemma":[0.99936527,0.00016791995,0.00007059349,0.0001556937,0.00022453272,0.000015934778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077621307,0.00082583574,0.00036385914,0.0011556365,0.00022812508,0.00081762654,0.0006710178,0.00044399887,0.0014829604],"category_scores_gemma":[0.0026070336,0.00033582284,0.00046015234,0.0010132765,0.00031540712,0.0011624763,0.0009830531,0.0006860534,0.00087571965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031721554,0.0001983587,0.014339031,0.0005027887,0.000119840566,0.00025292533,0.0004932962,0.18241654,0.31004402,0.004154935,0.0022334328,0.4849276],"study_design_scores_gemma":[0.00003543367,0.0001211103,0.011232048,0.00004121097,0.000042249176,0.00019458156,0.00015789496,0.7805188,0.19723865,0.0042684,0.006075098,0.000074650685],"about_ca_topic_score_codex":0.00125226,"about_ca_topic_score_gemma":0.0015976226,"teacher_disagreement_score":0.0014829604,"about_ca_system_score_codex":0.00029865155,"about_ca_system_score_gemma":0.0005728282,"threshold_uncertainty_score":0.004961014},"labels":[],"label_agreement":null},{"id":"W2984925010","doi":"10.3390/s19224891","title":"Development of Land-Use/Land-Cover Maps Using Landsat-8 and MODIS Data, and Their Integration for Hydro-Ecological Applications","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Calgary","funders":"Alberta Biodiversity Monitoring Institute; U.S. Geological Survey; Alberta Environment and Parks; National Aeronautics and Space Administration","keywords":"Watershed; Land cover; Environmental science; Remote sensing; Land use; Moderate-resolution imaging spectroradiometer; Vegetation (pathology); Deciduous; Thematic Mapper; Geospatial analysis; Hydrology (agriculture); Cartography; Geography; Satellite imagery; Computer science; Ecology; Geology; Satellite","score_opus":0.026537931521464216,"score_gpt":0.23873597652512643,"score_spread":0.2121980450036622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984925010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63689804,0.002307576,0.25259876,0.0011318327,0.00018621577,0.001867517,0.057647604,0.0069725737,0.040389813],"genre_scores_gemma":[0.61021584,0.0013254507,0.35761058,0.00008415349,0.000031607557,0.0005880275,0.023665933,0.00031755163,0.006160807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995975,0.00006990996,0.000038750703,0.000040107498,0.00022793023,0.000025846419],"domain_scores_gemma":[0.99908495,0.000113099115,0.00013087086,0.00008158765,0.0005439755,0.00004551928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009764071,0.00036841875,0.00013754428,0.0029279385,0.0003271745,0.0010063574,0.00042416304,0.00014229909,0.0015977161],"category_scores_gemma":[0.0018733641,0.00019475204,0.00022958331,0.0031350562,0.00018456957,0.00058331573,0.00037518152,0.00025067863,0.0005179975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011366094,0.00021529252,0.10985891,0.00044086477,0.000118841286,0.00039939614,0.0008759745,0.02010098,0.021031726,0.003829204,0.018569976,0.8244452],"study_design_scores_gemma":[0.00004271439,0.000111052395,0.69025534,0.00039984527,0.00013701872,0.00029689164,0.0038935002,0.15387547,0.024247447,0.004578274,0.1220227,0.00013991345],"about_ca_topic_score_codex":0.0761977,"about_ca_topic_score_gemma":0.15674397,"teacher_disagreement_score":0.0761977,"about_ca_system_score_codex":0.0009047445,"about_ca_system_score_gemma":0.0018520101,"threshold_uncertainty_score":0.15150833},"labels":[],"label_agreement":null},{"id":"W2985135687","doi":"10.3390/s19225029","title":"Proposal of the Tactile Glove Device","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Tactile sensor; Glovebox; Tactile display; Computer science; Human–computer interaction; Computer graphics (images); Artificial intelligence; Engineering; Mechanical engineering; Robot","score_opus":0.021760345350153486,"score_gpt":0.2698782183137631,"score_spread":0.2481178729636096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985135687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02136997,0.003555446,0.91742176,0.0012250249,0.0014040219,0.00089849415,0.00038907156,0.0030171701,0.050718967],"genre_scores_gemma":[0.2672078,0.003979375,0.64917463,0.0014080226,0.00036060935,0.0014043273,0.0006652862,0.00025171635,0.07554818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990722,0.00011451161,0.000052150826,0.00018984349,0.0004711437,0.00010012448],"domain_scores_gemma":[0.9995932,0.00006317456,0.000027366576,0.000050059098,0.0001834118,0.00008276507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008183526,0.00071264734,0.00062584545,0.0011186107,0.0005923386,0.0019310897,0.002496185,0.0021013292,0.010379303],"category_scores_gemma":[0.00084503123,0.0005696543,0.0006852268,0.0004728907,0.00071768946,0.0021653282,0.0018383677,0.00109788,0.0035644714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006639717,0.00027265694,0.0027060544,0.0014370176,0.00008424082,0.0017674988,0.00085350755,0.0059623653,0.33169973,0.15098746,0.016226156,0.48733938],"study_design_scores_gemma":[0.00024798926,0.0040847873,0.004899719,0.00076193636,0.00020419361,0.010218194,0.0008212241,0.07380023,0.20439617,0.026854161,0.67321277,0.00049863895],"about_ca_topic_score_codex":0.00028738758,"about_ca_topic_score_gemma":0.000207894,"teacher_disagreement_score":0.010379303,"about_ca_system_score_codex":0.00039321106,"about_ca_system_score_gemma":0.00092050084,"threshold_uncertainty_score":0.03472227},"labels":[],"label_agreement":null},{"id":"W2985307424","doi":"10.3390/s19224900","title":"Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Thermopile; Noise (video); Kalman filter; Extended Kalman filter; Computer science; Artificial intelligence; Physics; Optics; Infrared","score_opus":0.022425027039027264,"score_gpt":0.22753299172179547,"score_spread":0.2051079646827682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985307424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046083987,0.0005668699,0.9489757,0.00019295068,0.00014843504,0.000061322135,0.00006638305,0.0013871967,0.0025171814],"genre_scores_gemma":[0.83279455,0.00046859853,0.15761104,0.00023792847,0.00007227036,0.00022926724,0.00025775662,0.00004388702,0.0082847085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955255,0.000047471873,0.00003640267,0.00018241719,0.0001322261,0.000048808037],"domain_scores_gemma":[0.9996871,0.00007735339,0.000034138007,0.000022040856,0.00016799197,0.000011420402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057987747,0.0006654381,0.0004381066,0.00039595415,0.00048917864,0.00050104194,0.0007112073,0.00073320454,0.0016581811],"category_scores_gemma":[0.0011243096,0.00031491555,0.0006020373,0.00033661744,0.00029270092,0.000684336,0.00040088693,0.00077356363,0.00039176326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005132662,0.00021118055,0.007247922,0.00026681265,0.00020875827,0.0001903876,0.00023013636,0.44445848,0.047449853,0.003828538,0.0030731487,0.49232155],"study_design_scores_gemma":[0.000011757431,0.000084174986,0.0016586146,0.000008556348,0.000036815578,0.000026863074,0.000012811581,0.98985845,0.0068020774,0.0004969778,0.0009837609,0.000019067675],"about_ca_topic_score_codex":0.024835257,"about_ca_topic_score_gemma":0.020762388,"teacher_disagreement_score":0.024835257,"about_ca_system_score_codex":0.00083313824,"about_ca_system_score_gemma":0.001046307,"threshold_uncertainty_score":0.049381375},"labels":[],"label_agreement":null},{"id":"W2985661740","doi":"10.3390/s19225020","title":"Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data","year":2019,"lang":"en","type":"letter","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Government of Canada","keywords":"Deforestation (computer science); Amazon rainforest; Forest cover; Tree canopy; Cover (algebra); Canopy; Amazon basin; Reducing emissions from deforestation and forest degradation; Amazonian; Remote sensing; Forestry; Environmental science; Agroforestry; Geography; Ecology; Climate change; Computer science; Biology","score_opus":0.028141076062766723,"score_gpt":0.23281077668573308,"score_spread":0.20466970062296636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985661740","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15274718,0.0060060285,0.050891854,0.63751745,0.020390943,0.0006254517,0.0045361505,0.00096596667,0.12631898],"genre_scores_gemma":[0.680764,0.003145301,0.09752314,0.14491494,0.009890812,0.0004937842,0.0034323144,0.00053034106,0.059305258],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821436,0.0004834115,0.00032956578,0.00019391121,0.00061299896,0.000165791],"domain_scores_gemma":[0.98983663,0.0041461545,0.0007123302,0.00060017494,0.004319239,0.0003854765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036359557,0.00019196337,0.00021859235,0.0006384149,0.00077983184,0.0011651214,0.00045002034,0.002262572,0.0033916978],"category_scores_gemma":[0.018320154,0.00011984459,0.00023848418,0.00058797095,0.00043994735,0.00063371734,0.0004098391,0.0013358829,0.0031970402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028930788,0.00016667358,0.10995825,0.00033204784,0.000025131068,0.0016334255,0.0008742792,0.0007574371,0.015835365,0.009253376,0.5225143,0.33836055],"study_design_scores_gemma":[0.00009337386,0.00023114686,0.119523734,0.0006566236,0.00004500532,0.0037580517,0.0024052493,0.014332197,0.01760254,0.0143681485,0.8268516,0.00013232253],"about_ca_topic_score_codex":0.008169004,"about_ca_topic_score_gemma":0.02400245,"teacher_disagreement_score":0.008169004,"about_ca_system_score_codex":0.0010977759,"about_ca_system_score_gemma":0.00094922935,"threshold_uncertainty_score":0.019228995},"labels":[],"label_agreement":null},{"id":"W2987707710","doi":"10.3390/s19224896","title":"Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Trusted Positioning (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"GNSS applications; Precise Point Positioning; Real-time computing; Satellite system; Inertial navigation system; Computer science; Global Positioning System; Navigation system; Metre; Simulation; Telecommunications; Inertial frame of reference","score_opus":0.007113062002300298,"score_gpt":0.2210510081336407,"score_spread":0.2139379461313404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987707710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16850506,0.0005409247,0.8124445,0.00020566203,0.00022186489,0.00015746552,0.00035199884,0.0076906458,0.009881843],"genre_scores_gemma":[0.8377489,0.0002490502,0.15514643,0.00008081113,0.000050678653,0.00008120606,0.00077901746,0.00014875452,0.0057152444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962604,0.00003873112,0.00001196582,0.000055212426,0.00023384734,0.00003429941],"domain_scores_gemma":[0.9997491,0.000020606443,0.000027565162,0.000045897028,0.00014458147,0.000012295262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024105255,0.00037905664,0.00030860247,0.00039211372,0.00023711067,0.00042707598,0.0006672512,0.00040187582,0.0020657089],"category_scores_gemma":[0.00048126146,0.00017221256,0.00017511212,0.00038198117,0.00020190504,0.0006630528,0.0005520102,0.00038915416,0.0013725627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034759782,0.00013645712,0.011955903,0.00032213682,0.000059398568,0.0006703821,0.00028416872,0.03234793,0.2908502,0.0039345515,0.0076457597,0.6514456],"study_design_scores_gemma":[0.00011283449,0.0012777589,0.032010745,0.00007632222,0.00016511042,0.0015819462,0.00032942332,0.5524888,0.30913725,0.0032339247,0.09945679,0.0001290215],"about_ca_topic_score_codex":0.0024975392,"about_ca_topic_score_gemma":0.0024104374,"teacher_disagreement_score":0.0024975392,"about_ca_system_score_codex":0.00028359526,"about_ca_system_score_gemma":0.00051769224,"threshold_uncertainty_score":0.006910503},"labels":[],"label_agreement":null},{"id":"W2988718239","doi":"10.3390/s19224901","title":"Cadmium-Sensitive Measurement Using a Nano-Copper-Enhanced Carbon Fiber Electrode","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Copper; Cadmium; Electrode; Materials science; Electrochemistry; Nano-; Fiber; Carbon fibers; Sensitivity (control systems); Nanosensor; Analytical Chemistry (journal); Nanotechnology; Chemistry; Composite material; Environmental chemistry; Metallurgy; Electronic engineering; Composite number","score_opus":0.01319360291388609,"score_gpt":0.22979785193242688,"score_spread":0.2166042490185408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988718239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76407844,0.00805057,0.21651642,0.00097465795,0.0009790616,0.00034758367,0.0009496642,0.0017208502,0.006382838],"genre_scores_gemma":[0.83736736,0.002710264,0.15463716,0.00034962446,0.00012593216,0.00014623775,0.00026111052,0.000036950758,0.0043652845],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985372,0.00012715069,0.000068036265,0.00049585867,0.0006990254,0.00007274709],"domain_scores_gemma":[0.9994723,0.00012601409,0.000091250484,0.000049208058,0.00022368898,0.000037617425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006383592,0.0008432263,0.00059602055,0.0009918148,0.0005080468,0.00048564278,0.0015971632,0.0015353675,0.0006786418],"category_scores_gemma":[0.0007641052,0.00034732014,0.00034567577,0.0008700549,0.00037941386,0.0009855367,0.00044323958,0.0005238448,0.0003114114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005906796,0.000046780373,0.00080069964,0.00017658215,0.000023877086,0.00015726915,0.00004191147,0.00015737432,0.986429,0.0001824721,0.00020710526,0.011717937],"study_design_scores_gemma":[0.000008060511,0.0001111648,0.0014602224,0.000006588178,0.000019468665,0.00032560114,0.000029237972,0.004644099,0.99141896,0.000059060072,0.0018952956,0.000022288707],"about_ca_topic_score_codex":0.0015279322,"about_ca_topic_score_gemma":0.0032789179,"teacher_disagreement_score":0.0015971632,"about_ca_system_score_codex":0.00077727006,"about_ca_system_score_gemma":0.0003734836,"threshold_uncertainty_score":0.005639553},"labels":[],"label_agreement":null},{"id":"W2989703840","doi":"10.3390/s19235204","title":"Tag Localization with Asynchronous Inertial-Based Shifting and Trilateration","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Trilateration; Computer science; Scalability; Real-time computing; Crowdsourcing; GNSS applications; Global Positioning System; Leverage (statistics); Asynchronous communication; Dead reckoning; Computer network; Artificial intelligence; Engineering; Telecommunications; Node (physics)","score_opus":0.0027281074095130914,"score_gpt":0.1632904839487941,"score_spread":0.16056237653928102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989703840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024101978,0.00028367236,0.97208136,0.00011409413,0.00009307279,0.00004099941,0.000025820105,0.0006651255,0.0025938794],"genre_scores_gemma":[0.77338165,0.00045626643,0.22078042,0.00020924797,0.00014652092,0.0001279743,0.0001415011,0.00007447118,0.0046819644],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925834,0.00012207375,0.000036298432,0.00021298465,0.0003019522,0.00006827248],"domain_scores_gemma":[0.9993856,0.00012654637,0.00013597397,0.00015842165,0.00016126131,0.000032130385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005109753,0.00073062937,0.0005323848,0.00077445235,0.00061802124,0.00057217316,0.0014397174,0.0007157353,0.0007596981],"category_scores_gemma":[0.0014426187,0.0002909437,0.0005587256,0.0011484622,0.0007525632,0.0013767843,0.0015650883,0.0006038564,0.0007123588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073781295,0.00014989871,0.0037968687,0.00035272242,0.0001053899,0.0008206424,0.0011580348,0.2136094,0.1692601,0.014100536,0.0038349559,0.5920737],"study_design_scores_gemma":[0.00008651603,0.0005014397,0.0017315554,0.000028900751,0.000060977727,0.0008751741,0.00018936113,0.9269345,0.051320113,0.0072552445,0.010934963,0.00008125151],"about_ca_topic_score_codex":0.0016388241,"about_ca_topic_score_gemma":0.0016331862,"teacher_disagreement_score":0.0016388241,"about_ca_system_score_codex":0.00043408043,"about_ca_system_score_gemma":0.0004990496,"threshold_uncertainty_score":0.003258586},"labels":[],"label_agreement":null},{"id":"W2989804940","doi":"10.3390/s19235280","title":"Low-Input Estimation of Site-Specific Lime Demand Based on Apparent Soil Electrical Conductivity and In Situ Determined Topsoil pH","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Topsoil; In situ; Lime; Soil science; Environmental science; Electrical resistivity and conductivity; Estimation; Conductivity; Geotechnical engineering; Geology; Materials science; Chemistry; Engineering; Soil water; Metallurgy; Electrical engineering","score_opus":0.010153188588251311,"score_gpt":0.2186788497645191,"score_spread":0.20852566117626778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989804940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9701386,0.00017417235,0.026652789,0.000019824643,0.000010368877,0.00010427231,0.0009342021,0.000110762645,0.0018548925],"genre_scores_gemma":[0.97910553,0.00014992867,0.019219011,0.000022646811,0.0000065886875,0.00010898902,0.00053034374,0.00002278683,0.0008341378],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999585,0.000060273847,0.00002912455,0.00018774254,0.00010714738,0.000030860414],"domain_scores_gemma":[0.9994436,0.00019066774,0.00012952051,0.0000596507,0.00013434072,0.000042125095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041048936,0.00069052866,0.00058038125,0.00065542955,0.00020938479,0.0008556548,0.00067669555,0.0005136482,0.0009898456],"category_scores_gemma":[0.00086987956,0.0002905207,0.0002981187,0.0009321683,0.00024135543,0.00076501194,0.00040429473,0.00050174905,0.00041495904],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054639473,0.00021598715,0.15990368,0.0004597098,0.00012356867,0.00016889222,0.00027621808,0.0016275754,0.8134262,0.00011011658,0.0001414691,0.02300016],"study_design_scores_gemma":[0.000033058688,0.00058231974,0.637988,0.000027167078,0.00017442906,0.0002614508,0.00036803688,0.023382854,0.33535337,0.00033977104,0.0014470321,0.00004254628],"about_ca_topic_score_codex":0.0032651315,"about_ca_topic_score_gemma":0.011113037,"teacher_disagreement_score":0.0032651315,"about_ca_system_score_codex":0.00036061203,"about_ca_system_score_gemma":0.00025813017,"threshold_uncertainty_score":0.006492257},"labels":[],"label_agreement":null},{"id":"W2989989812","doi":"10.3390/s19235218","title":"EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Higher Education Commission Mauritius","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Support vector machine; Spectrogram; Feature extraction; Feature selection; Data set; Speech recognition","score_opus":0.03779442715701796,"score_gpt":0.2788942198067308,"score_spread":0.24109979264971282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989989812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060059045,0.00073532906,0.9369369,0.00023959516,0.00006859627,0.00006886561,0.00020189332,0.0009212008,0.00076855294],"genre_scores_gemma":[0.81136227,0.00046494912,0.18474749,0.00019154666,0.00009274109,0.0002006917,0.00083235983,0.00007578343,0.002032147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999742,0.000047765177,0.000018166724,0.000066139546,0.00007169233,0.000054245647],"domain_scores_gemma":[0.9998253,0.000046760993,0.00002393761,0.000016432337,0.00007473319,0.000012892404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003891629,0.00075266825,0.00077127194,0.00068453496,0.00020018918,0.0004477636,0.00060950156,0.00047722564,0.00080349843],"category_scores_gemma":[0.00081084843,0.00021723642,0.0007881119,0.0005969222,0.00016210623,0.00069271895,0.0005195457,0.00057613105,0.00027674515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034967618,0.0003151095,0.002656548,0.00009224561,0.0001336599,0.0001835186,0.00006821591,0.09092649,0.04256736,0.0016749565,0.004633977,0.8563981],"study_design_scores_gemma":[0.000010474389,0.00007489796,0.0018682468,0.000008024484,0.000023333405,0.000058800302,0.00002217225,0.9905918,0.0053578154,0.0013083406,0.00066479144,0.000011178313],"about_ca_topic_score_codex":0.0024580983,"about_ca_topic_score_gemma":0.0023461718,"teacher_disagreement_score":0.0024580983,"about_ca_system_score_codex":0.00033032967,"about_ca_system_score_gemma":0.00036517813,"threshold_uncertainty_score":0.004887581},"labels":[],"label_agreement":null},{"id":"W2990398679","doi":"10.3390/s19235262","title":"Automated Method of Extracting Urban Roads Based on Region Growing from Mobile Laser Scanning Data","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Gaussian curvature; Curvature; Laser scanning; Computer science; Focus (optics); Extraction (chemistry); Tangent; Region growing; Artificial intelligence; Computer vision; Plane (geometry); Point (geometry); Geography; Image processing; Mathematics; Image (mathematics); Laser; Geometry; Optics","score_opus":0.022100988705386217,"score_gpt":0.287474243036226,"score_spread":0.26537325433083975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990398679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04581552,0.00023420069,0.9493868,0.000055799424,0.0000359653,0.00018665734,0.00039128674,0.0025728303,0.0013209871],"genre_scores_gemma":[0.18098837,0.00025687038,0.81569093,0.00003054043,0.000033917288,0.00019373662,0.0011297524,0.00028359695,0.0013922602],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993298,0.00006705255,0.00004187422,0.00023117258,0.00025836923,0.00007163216],"domain_scores_gemma":[0.9991627,0.00021963983,0.000114258866,0.00013445687,0.00034090062,0.000028071245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004643126,0.00073685497,0.000852243,0.003095131,0.0006320215,0.0007474572,0.0012666627,0.00065220334,0.0011353012],"category_scores_gemma":[0.0011481412,0.00054553687,0.0010706129,0.0018565633,0.0003574944,0.0011239594,0.00067385135,0.00051332347,0.001189472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016348358,0.00015250295,0.0057788477,0.00035719573,0.00014246622,0.00069800334,0.0003509507,0.077830374,0.18536681,0.0032078174,0.0042430568,0.7217085],"study_design_scores_gemma":[0.000020882186,0.00008434869,0.0064640325,0.000023808618,0.000067305875,0.0007584591,0.0001533862,0.88083136,0.10171255,0.0018793009,0.007933831,0.000070666705],"about_ca_topic_score_codex":0.0037062948,"about_ca_topic_score_gemma":0.0060682343,"teacher_disagreement_score":0.0037062948,"about_ca_system_score_codex":0.00027601785,"about_ca_system_score_gemma":0.0010258054,"threshold_uncertainty_score":0.0073694587},"labels":[],"label_agreement":null},{"id":"W2990468808","doi":"10.3390/s19235178","title":"A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Odometry; Covariance; Covariance intersection; Computer science; Extended Kalman filter; Inertial measurement unit; Kalman filter; Encoder; Artificial intelligence; Algorithm; Sensor fusion; Computer vision; Control theory (sociology); Mathematics; Robot; Mobile robot; Statistics","score_opus":0.020694551295146333,"score_gpt":0.23862491833033983,"score_spread":0.2179303670351935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990468808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034889828,0.00022625594,0.99520314,0.000033026976,0.00006465129,0.000020790041,0.00006815733,0.000632004,0.0002629189],"genre_scores_gemma":[0.19999737,0.0006282154,0.7945884,0.00012593492,0.00017231578,0.0001592193,0.0012788258,0.00026176276,0.0027879043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989165,0.00011772513,0.00007472866,0.00038976752,0.0003931513,0.000108012726],"domain_scores_gemma":[0.99907684,0.00020853984,0.000104693245,0.00016119673,0.00041502182,0.000033685297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083200895,0.0011113436,0.0014168398,0.0014281629,0.0005632881,0.0008093034,0.001261208,0.00080794346,0.0010210099],"category_scores_gemma":[0.0036449027,0.00047275054,0.0009998302,0.0018166708,0.0004464328,0.0013941971,0.001442063,0.0012953467,0.00095115585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000178286,0.00012690811,0.0027019782,0.00015722885,0.00014221644,0.00013740883,0.00013160311,0.1169143,0.017595375,0.0062594605,0.0041907085,0.8514645],"study_design_scores_gemma":[0.000029883124,0.00007449229,0.0023330215,0.000025868423,0.000035885576,0.00023016073,0.000043198434,0.97817296,0.0095654605,0.0036676636,0.0057840273,0.000037337028],"about_ca_topic_score_codex":0.008970411,"about_ca_topic_score_gemma":0.008859791,"teacher_disagreement_score":0.008970411,"about_ca_system_score_codex":0.0004760292,"about_ca_system_score_gemma":0.0014331486,"threshold_uncertainty_score":0.017836392},"labels":[],"label_agreement":null},{"id":"W2990472833","doi":"10.3390/s19235131","title":"Strip Adjustment of Airborne LiDAR Data in Urban Scenes Using Planar Features by the Minimum Hausdorff Distance","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Hausdorff distance; Lidar; STRIPS; Computer science; Similarity (geometry); Transformation (genetics); Artificial intelligence; Matching (statistics); Boundary (topology); Planar; Ranging; Euclidean distance; Computer vision; Rectangle; Algorithm; Pattern recognition (psychology); Mathematics; Remote sensing; Image (mathematics); Geometry; Geology","score_opus":0.015827535022039492,"score_gpt":0.24712691157147182,"score_spread":0.23129937654943233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990472833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20989147,0.00047713815,0.7861561,0.0000795907,0.00004840776,0.00010339878,0.00034914684,0.001363193,0.0015314992],"genre_scores_gemma":[0.56018096,0.00040973997,0.43729904,0.000032631182,0.000031975054,0.00007448695,0.00107039,0.00014066999,0.00076005777],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993394,0.00007866106,0.000049747934,0.00018433832,0.00028920063,0.00005851384],"domain_scores_gemma":[0.99946374,0.000104572406,0.000110286164,0.00009440377,0.00020749714,0.000019405366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044264298,0.0005362539,0.00049284875,0.0018713723,0.00027510308,0.0007518586,0.0006791542,0.00032842893,0.0005717687],"category_scores_gemma":[0.0013513234,0.00033018106,0.0005655427,0.0019374682,0.0002742313,0.0009557237,0.00060024404,0.0003873749,0.0002975437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003579259,0.00011509554,0.010630831,0.00027228164,0.00014794819,0.00028216848,0.00037786487,0.055044595,0.1294726,0.0020278154,0.0021669217,0.79910403],"study_design_scores_gemma":[0.00003693241,0.00022012935,0.03603668,0.00002287389,0.00010076324,0.00064644904,0.00040729428,0.86024547,0.09316227,0.0023027495,0.006700553,0.00011797033],"about_ca_topic_score_codex":0.0020457765,"about_ca_topic_score_gemma":0.0023974082,"teacher_disagreement_score":0.0020457765,"about_ca_system_score_codex":0.0003000128,"about_ca_system_score_gemma":0.0004273236,"threshold_uncertainty_score":0.0040676594},"labels":[],"label_agreement":null},{"id":"W2990605185","doi":"10.3390/s19235179","title":"Pilot Study of the EncephaLog Smartphone Application for Gait Analysis","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montfort Hospital","funders":"","keywords":"Accelerometer; Gait; Physical medicine and rehabilitation; Wearable computer; Gait analysis; Limiting; Motion (physics); Population; Smartphone application; Computer science; Simulation; Medicine; Engineering; Artificial intelligence; Embedded system","score_opus":0.029560110268184208,"score_gpt":0.3493276928816927,"score_spread":0.31976758261350846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990605185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907997,0.000104764316,0.003673434,0.00022689794,0.00010812948,0.0026848905,0.00077597244,0.00027952957,0.0013466959],"genre_scores_gemma":[0.96010125,0.00030311683,0.02555589,0.0006664695,0.00013357717,0.0050798184,0.0018561244,0.00009592352,0.006207809],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992279,0.00030533152,0.00006710711,0.00011976196,0.00016331564,0.000116618736],"domain_scores_gemma":[0.9977946,0.0006942403,0.000079858044,0.00019067964,0.0008662794,0.00037438585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023259448,0.00072608225,0.00053537346,0.00048510078,0.00032847465,0.0005915003,0.0006745906,0.000791055,0.0053159106],"category_scores_gemma":[0.0048646093,0.00024028224,0.0003995011,0.00015014962,0.0003021513,0.0006810562,0.00078355597,0.00053992047,0.0017589569],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.021271225,0.09028062,0.2244574,0.004938951,0.00088401965,0.010100257,0.016323091,0.0023719755,0.1994475,0.0018799593,0.039101135,0.38894388],"study_design_scores_gemma":[0.0046603987,0.18790247,0.6619084,0.00077826687,0.0010152058,0.005667563,0.01000452,0.024970459,0.048676025,0.00094644685,0.053161588,0.00030867461],"about_ca_topic_score_codex":0.0013694873,"about_ca_topic_score_gemma":0.0023556366,"teacher_disagreement_score":0.0053159106,"about_ca_system_score_codex":0.0002194908,"about_ca_system_score_gemma":0.0005958867,"threshold_uncertainty_score":0.017783463},"labels":[],"label_agreement":null},{"id":"W2990845518","doi":"10.3390/s19235141","title":"Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Wearable computer; Computer science; Gait; Machine learning; Artificial intelligence; Wearable technology; Feature selection; Feature extraction; Parkinson's disease; Generalization; Disease; Physical medicine and rehabilitation; Medicine; Embedded system","score_opus":0.032864552216028096,"score_gpt":0.2605691067963282,"score_spread":0.22770455458030012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990845518","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023969817,0.99907935,0.00018260979,0.00008316883,0.00006561807,0.000010951384,0.00003876953,0.000005682127,0.00029415946],"genre_scores_gemma":[0.0020637393,0.99723405,0.00035832814,0.00008383123,0.000065540924,0.0000146353195,0.00004539688,0.000001562429,0.00013297598],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952245,0.00007828374,0.00014447495,0.000096284035,0.00013565812,0.000022775874],"domain_scores_gemma":[0.99814487,0.0012622232,0.0002467804,0.000030884006,0.00029080108,0.000024405736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010492766,0.0009656515,0.0020145185,0.0035812128,0.00020442891,0.0010539562,0.000896824,0.0012147792,0.0021950286],"category_scores_gemma":[0.0030186982,0.00035951706,0.0018540747,0.0029725274,0.00029578138,0.0010499201,0.00044331796,0.0006195059,0.0006538679],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014793985,0.000104229344,0.0009433699,0.19462642,0.00091824617,0.00018037611,0.00008874999,0.0005849996,0.0012366481,0.0011242564,0.006957546,0.79308724],"study_design_scores_gemma":[0.00010474538,0.0011860748,0.01472846,0.19263583,0.011200445,0.0053938488,0.0005254549,0.0019235946,0.0047733947,0.0040181484,0.7633009,0.00020908806],"about_ca_topic_score_codex":0.0017473408,"about_ca_topic_score_gemma":0.0019155625,"teacher_disagreement_score":0.0035812128,"about_ca_system_score_codex":0.00039531806,"about_ca_system_score_gemma":0.001394249,"threshold_uncertainty_score":0.0073431134},"labels":[],"label_agreement":null},{"id":"W2990932957","doi":"10.3390/s19235215","title":"Cellulose Nanopaper Cross-Linked Amino Graphene/Polyaniline Sensors to Detect CO2 Gas at Room Temperature","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Polyaniline; Graphene; Materials science; Nanocomposite; Cellulose; Bacterial cellulose; Thermogravimetric analysis; Nanomaterials; Chemical engineering; In situ polymerization; Scanning electron microscope; Polymerization; Gravimetric analysis; Monomer; Polymer; Nanotechnology; Chemistry; Composite material; Organic chemistry","score_opus":0.007096504952446732,"score_gpt":0.2179490534784448,"score_spread":0.21085254852599805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990932957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9520928,0.0034779385,0.039090242,0.00015534772,0.00014923594,0.00008340643,0.00050271786,0.0007982973,0.0036500418],"genre_scores_gemma":[0.95521694,0.00062892196,0.0404522,0.000106593834,0.000029813682,0.00007869944,0.00034068918,0.000028476832,0.0031177052],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996307,0.000038596932,0.00001627663,0.0001096975,0.00015909679,0.0000456113],"domain_scores_gemma":[0.9998066,0.00004212669,0.00005718236,0.00001888928,0.000043122942,0.00003206811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016241119,0.00047960636,0.00027346448,0.0002866862,0.00016728291,0.00017535256,0.00054246903,0.0005246426,0.00085046276],"category_scores_gemma":[0.0002347145,0.00022471143,0.00018204172,0.00026391383,0.00015091468,0.0003114283,0.00020204214,0.0004453759,0.00031578925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020556312,0.000012619382,0.000048925802,0.000026108297,0.0000030301364,0.000016752709,0.0000036350334,0.00006517592,0.9988626,0.000015141121,0.000020273796,0.00090519217],"study_design_scores_gemma":[0.0000020046327,0.00004007194,0.0007766215,9.008537e-7,0.0000036752042,0.00006244824,0.000002489387,0.0013527565,0.99739885,0.0000057896327,0.00035093134,0.0000034106556],"about_ca_topic_score_codex":0.0006917185,"about_ca_topic_score_gemma":0.002087698,"teacher_disagreement_score":0.00085046276,"about_ca_system_score_codex":0.00037812119,"about_ca_system_score_gemma":0.0001410468,"threshold_uncertainty_score":0.0028451085},"labels":[],"label_agreement":null},{"id":"W2991157920","doi":"10.3390/s19235231","title":"Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Global Positioning System; Computer science; Kalman filter; Vehicular ad hoc network; Position (finance); Wireless ad hoc network; Sensor fusion; Real-time computing; Trajectory; Track (disk drive); Data mining; Artificial intelligence; Wireless; Telecommunications","score_opus":0.004155638147319215,"score_gpt":0.18984169048475436,"score_spread":0.18568605233743515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991157920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08972707,0.0012612377,0.9061102,0.000105738975,0.00007197999,0.00003902717,0.000029689343,0.001017572,0.0016374136],"genre_scores_gemma":[0.88497907,0.00038309302,0.11285758,0.00006869288,0.000040473104,0.000038701557,0.000089020155,0.000038405287,0.0015048981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882144,0.00025648487,0.00006045431,0.0002909205,0.00046735455,0.00010335961],"domain_scores_gemma":[0.9985851,0.00046264325,0.00023066439,0.00021278129,0.00045614745,0.000052627147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090516836,0.00082037476,0.0006572022,0.0012637181,0.0004491071,0.0005082691,0.0013442975,0.00046619415,0.00033162022],"category_scores_gemma":[0.003184773,0.00027154293,0.00040213834,0.0012153654,0.0004201098,0.0011652885,0.0013942304,0.00040713334,0.00021309083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028565535,0.000120257195,0.0047792085,0.0001634866,0.00013275509,0.00018433967,0.00040645336,0.5122492,0.03375278,0.0043639233,0.0013637405,0.44219816],"study_design_scores_gemma":[0.00003705144,0.0003333844,0.0021050088,0.000016337877,0.000080696496,0.00020516803,0.0001328508,0.97141796,0.019207442,0.0022729144,0.0041461014,0.000045165038],"about_ca_topic_score_codex":0.00405203,"about_ca_topic_score_gemma":0.003533713,"teacher_disagreement_score":0.00405203,"about_ca_system_score_codex":0.00056285097,"about_ca_system_score_gemma":0.0005602821,"threshold_uncertainty_score":0.008056939},"labels":[],"label_agreement":null},{"id":"W2991562289","doi":"10.3390/s19235134","title":"Metal Cation Detection in Drinking Water","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Global Water Futures; Canada First Research Excellence Fund; U.S. Environmental Protection Agency","keywords":"Cadmium; Environmental chemistry; Water quality; Mercury (programming language); Chemistry; Barium; Absorbance; Quartz crystal microbalance; Inorganic chemistry; Chromatography; Adsorption","score_opus":0.02606023498982318,"score_gpt":0.28962601228956864,"score_spread":0.26356577729974545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991562289","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014861668,0.9917532,0.0013661674,0.00017400247,0.00037260196,0.000013274456,0.0000533057,0.000033738033,0.0047475593],"genre_scores_gemma":[0.010000836,0.9829736,0.0013661083,0.00024974742,0.0001951404,0.000024509198,0.00010267253,0.000006376641,0.0050809914],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997304,0.00003082328,0.00001963849,0.000070738584,0.0001207334,0.000027647946],"domain_scores_gemma":[0.9998853,0.000040529674,0.000020297515,0.000004408306,0.00004240164,0.000007024099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003366357,0.00096547016,0.0009026659,0.0020517586,0.00020417335,0.0007421839,0.00061693747,0.0009716483,0.0022609334],"category_scores_gemma":[0.0003712587,0.00039499582,0.0004537472,0.0015427544,0.000310588,0.0008798015,0.0005518427,0.00085424713,0.0021120713],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009149568,0.000086337954,0.0005817886,0.025428789,0.00012498107,0.00039042046,0.0000956128,0.00073039095,0.05231463,0.005819745,0.017849706,0.89648604],"study_design_scores_gemma":[0.000012748241,0.00019575845,0.0015431832,0.0014896566,0.0001333178,0.00205959,0.000090402755,0.00054893753,0.0334196,0.0020693496,0.958397,0.000040523446],"about_ca_topic_score_codex":0.001242583,"about_ca_topic_score_gemma":0.0016728897,"teacher_disagreement_score":0.0022609334,"about_ca_system_score_codex":0.00044768697,"about_ca_system_score_gemma":0.000521557,"threshold_uncertainty_score":0.0075635314},"labels":[],"label_agreement":null},{"id":"W2991970018","doi":"10.3390/s19245383","title":"Dry Coupling of Ultrasonic Transducer Components for High Temperature Applications","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Transducer; Ultrasonic sensor; Materials science; Clamping; Acoustics; Piezoelectricity; Bandwidth (computing); Composite material; Engineering; Mechanical engineering","score_opus":0.0088754209544222,"score_gpt":0.2141958681156141,"score_spread":0.2053204471611919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991970018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.851274,0.0024743401,0.14207508,0.00017245852,0.00020414533,0.00017527543,0.00016408405,0.000804477,0.0026561734],"genre_scores_gemma":[0.920814,0.000956819,0.072460674,0.0001450363,0.000062309715,0.00018281402,0.00026704514,0.00033975355,0.004771571],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947184,0.00005168888,0.00005004791,0.00012635108,0.00023524024,0.00006494128],"domain_scores_gemma":[0.99909544,0.0002592845,0.00021929185,0.00017003386,0.00020339522,0.000052562027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005585486,0.0006609128,0.0004335974,0.00035658153,0.00019665872,0.00050613924,0.000485166,0.00059323414,0.0015995823],"category_scores_gemma":[0.0009798659,0.00042715925,0.00031110097,0.0002572592,0.0004688994,0.0006176373,0.0006028584,0.0008126809,0.00069280475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014632219,0.000004843349,0.000054435062,0.0000298561,0.0000029288278,0.000014235258,0.000013044035,0.000053088246,0.99887484,0.000026202131,0.000017194317,0.00089464587],"study_design_scores_gemma":[0.000003879368,0.00011451096,0.0006754887,0.0000038501253,0.000010670943,0.00007220601,0.000013951581,0.00071294006,0.99759644,0.00001873227,0.0007720635,0.0000050633466],"about_ca_topic_score_codex":0.00036761898,"about_ca_topic_score_gemma":0.001105633,"teacher_disagreement_score":0.0015995823,"about_ca_system_score_codex":0.00031426293,"about_ca_system_score_gemma":0.00029423306,"threshold_uncertainty_score":0.005351126},"labels":[],"label_agreement":null},{"id":"W2992942736","doi":"10.3390/s19235325","title":"Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Sagittal plane; Kinematics; Mean squared error; Gait; Gait analysis; Wearable computer; Transverse plane; Computer science; Convolutional neural network; Coronal plane; Simulation; Motion capture; Artificial intelligence; Artificial neural network; Physical medicine and rehabilitation; Mathematics; Engineering; Medicine; Motion (physics); Structural engineering; Statistics; Physics","score_opus":0.012738435325240789,"score_gpt":0.22669211529822875,"score_spread":0.21395367997298795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992942736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6304913,0.00095064385,0.364761,0.00015889868,0.000092134236,0.00006934358,0.00040079997,0.0007408209,0.0023350879],"genre_scores_gemma":[0.9425773,0.00039992973,0.055420592,0.00007862221,0.000020757907,0.000048538088,0.00016853357,0.000022084245,0.0012636271],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983525,0.00002869092,0.000010118519,0.000058297064,0.00005169791,0.000015887617],"domain_scores_gemma":[0.9998456,0.0000385727,0.000042808577,0.000016348886,0.00004643478,0.0000101590595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023497155,0.00044658565,0.00027614526,0.00030449338,0.00010797819,0.000294521,0.00031294295,0.00033774122,0.0006458548],"category_scores_gemma":[0.0005059885,0.00021159707,0.00017825124,0.00027687976,0.0001400369,0.0003205008,0.00030009664,0.00019309108,0.00018330329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010375804,0.0005046331,0.045564834,0.00058275496,0.00023061456,0.00033718042,0.00024774313,0.042237937,0.47795644,0.0005763965,0.0012286811,0.4294951],"study_design_scores_gemma":[0.00005802493,0.0014580003,0.12560424,0.00012724215,0.0002332767,0.0009030692,0.00019522198,0.686902,0.1798419,0.0013848585,0.003210865,0.00008134027],"about_ca_topic_score_codex":0.0013529429,"about_ca_topic_score_gemma":0.0054329177,"teacher_disagreement_score":0.0013529429,"about_ca_system_score_codex":0.0001707788,"about_ca_system_score_gemma":0.00017201595,"threshold_uncertainty_score":0.002690196},"labels":[],"label_agreement":null},{"id":"W2994562317","doi":"10.3390/s19245421","title":"Individual Tree Crown Delineation Using Multispectral LiDAR Data","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Esri (Canada); York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Multispectral image; Crown (dentistry); Remote sensing; Tree (set theory); Computer science; Environmental science; Geography; Mathematics; Medicine","score_opus":0.039451023470188064,"score_gpt":0.2809741329250443,"score_spread":0.24152310945485625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994562317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75232816,0.00066159805,0.2418839,0.000046699122,0.000037606336,0.00012179458,0.0006549155,0.0010732033,0.0031921458],"genre_scores_gemma":[0.7987365,0.00021379492,0.19962679,0.000027655076,0.000009511693,0.00003237621,0.0006993526,0.0000649009,0.0005890953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997267,0.000027242992,0.000016461361,0.00007574328,0.000108797096,0.00004507272],"domain_scores_gemma":[0.9995535,0.000086857195,0.000055737924,0.00005991046,0.00021248225,0.000031432846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044721656,0.00035621374,0.0004035183,0.0022330638,0.00039456887,0.00078298274,0.00045614238,0.00042368314,0.0005756875],"category_scores_gemma":[0.0007500949,0.00017278593,0.00038828535,0.001324374,0.00013761317,0.0007904292,0.00044920362,0.00027771358,0.00028382463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024841027,0.00013192938,0.07963277,0.0004989035,0.00012514312,0.0005267939,0.001119579,0.039142992,0.41989762,0.0017655989,0.0011712755,0.45573893],"study_design_scores_gemma":[0.000025094358,0.0001378882,0.20354997,0.000119630036,0.00017018233,0.0006873428,0.0012357797,0.60366404,0.17895694,0.0016052923,0.009726406,0.00012147905],"about_ca_topic_score_codex":0.009062987,"about_ca_topic_score_gemma":0.03355707,"teacher_disagreement_score":0.009062987,"about_ca_system_score_codex":0.0003580572,"about_ca_system_score_gemma":0.000532662,"threshold_uncertainty_score":0.018020451},"labels":[],"label_agreement":null},{"id":"W2994790184","doi":"10.3390/s19245434","title":"Dual-Level Capacitive Micromachined Uncooled Thermal Detector","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"CMC Microsystems","keywords":"Capacitance; Capacitive sensing; Bimorph; Detector; Sensitivity (control systems); Parasitic capacitance; Electrical engineering; Surface micromachining; Electronic circuit; Electrode; Materials science; Optoelectronics; Electronic engineering; Engineering; Fabrication; Voltage; Physics","score_opus":0.010347598245265691,"score_gpt":0.22062664496727535,"score_spread":0.21027904672200967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994790184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36377963,0.0024371035,0.6156387,0.0005534679,0.00069742795,0.00026432984,0.0008300974,0.0043210355,0.01147824],"genre_scores_gemma":[0.6083294,0.00034790006,0.38225046,0.0003658212,0.000112848844,0.00012488024,0.00033604252,0.000149781,0.0079829125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902034,0.000052947264,0.000039100367,0.00033927316,0.0004600998,0.000088281005],"domain_scores_gemma":[0.9993475,0.00009801135,0.00015208681,0.00013070778,0.00020220994,0.00006950244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477225,0.00047190048,0.00055199256,0.0004229205,0.00022503754,0.00079018174,0.0025170194,0.0009252191,0.0020687298],"category_scores_gemma":[0.00073844125,0.0005036696,0.00037359685,0.00031247697,0.00029450527,0.0011579694,0.0008452212,0.00069104676,0.0011541256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006669562,0.000026175883,0.0003994428,0.00012214518,0.000017885315,0.00008368529,0.000036655718,0.0008232355,0.9795577,0.0009203509,0.0005845711,0.0173615],"study_design_scores_gemma":[0.000020155423,0.0003969648,0.0012257769,0.000009461334,0.00002653259,0.0005941982,0.000018195506,0.025440028,0.96167904,0.0001976691,0.010334403,0.000057541834],"about_ca_topic_score_codex":0.00036892202,"about_ca_topic_score_gemma":0.0010016804,"teacher_disagreement_score":0.0025170194,"about_ca_system_score_codex":0.00075954135,"about_ca_system_score_gemma":0.0005285678,"threshold_uncertainty_score":0.006920576},"labels":[],"label_agreement":null},{"id":"W2995335241","doi":"10.3390/s20010078","title":"LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Node (physics); Computer science; Ranging; Wireless ad hoc network; Computer network; Transceiver; Range (aeronautics); Positioning system; Ultra-wideband; Network topology; Real-time computing; Topology (electrical circuits); Distributed computing; Wireless; Engineering; Telecommunications; Electrical engineering","score_opus":0.0037162927433570052,"score_gpt":0.18877460018069284,"score_spread":0.18505830743733584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995335241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064415656,0.00074403256,0.916469,0.00010248979,0.0001607088,0.00020811234,0.00024738593,0.013793475,0.0038592638],"genre_scores_gemma":[0.6662137,0.0004406207,0.32272905,0.00027117468,0.00012632557,0.00037513257,0.0009883342,0.0002703792,0.008585376],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929535,0.00009645394,0.000045944675,0.00018647652,0.00029661242,0.00007908966],"domain_scores_gemma":[0.99921954,0.00012469926,0.0001265758,0.00022890537,0.00021748683,0.00008267316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004382536,0.00071192574,0.001062359,0.0010005407,0.0004460349,0.0005174643,0.0019643267,0.0008186412,0.0018077897],"category_scores_gemma":[0.0009850848,0.000326405,0.0003175539,0.0004893109,0.00034220863,0.0013757809,0.0016615283,0.000604474,0.0011823091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010000264,0.00024763393,0.0064166184,0.00058588985,0.00015198906,0.0009115597,0.00063920434,0.018917276,0.21060556,0.0046542953,0.011660416,0.7442095],"study_design_scores_gemma":[0.0006158442,0.0035466228,0.019293094,0.0001807542,0.0004445762,0.006391433,0.00058773335,0.62851286,0.2229502,0.0043362645,0.11258401,0.0005566276],"about_ca_topic_score_codex":0.0012582468,"about_ca_topic_score_gemma":0.0014934877,"teacher_disagreement_score":0.0019643267,"about_ca_system_score_codex":0.00031377675,"about_ca_system_score_gemma":0.00048359038,"threshold_uncertainty_score":0.0060476065},"labels":[],"label_agreement":null},{"id":"W2995365776","doi":"10.3390/s20010014","title":"A Systematic Study on Transit Time and Its Impact on Accuracy of Concentration Measured by Microfluidic Devices","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Global Water Futures","keywords":"Microfluidics; Amplitude; Gating; Rise time; Noise (video); SIGNAL (programming language); Particle (ecology); Scattering; Particle size; Light scattering; Materials science; Optics; Physics; Chemistry; Nanotechnology; Computer science","score_opus":0.009978023760331462,"score_gpt":0.23216654303613707,"score_spread":0.2221885192758056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995365776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7061516,0.024284737,0.26444116,0.00046178655,0.00050187303,0.00066186744,0.0006896471,0.0006588069,0.0021485242],"genre_scores_gemma":[0.8621714,0.008137445,0.12706444,0.00020694763,0.00010431357,0.0004375864,0.0006138212,0.0002504795,0.0010135776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99614406,0.0011168658,0.0005976139,0.0008412667,0.0011257145,0.00017442799],"domain_scores_gemma":[0.9899087,0.0055830446,0.0013751129,0.0010899514,0.0019296214,0.0001135529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006445381,0.0012398951,0.000823478,0.00079311937,0.00049410824,0.00080976944,0.0006711233,0.00071317254,0.00042725494],"category_scores_gemma":[0.013047309,0.0004259264,0.00046715388,0.0009127489,0.0006858302,0.0010931841,0.0005546776,0.00076146657,0.00022890136],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013130509,0.00006581983,0.0032968319,0.0004237354,0.000048010468,0.00007785759,0.00011438924,0.0006325798,0.9748058,0.0003392462,0.00010238425,0.019962123],"study_design_scores_gemma":[0.00000691655,0.00061888905,0.008019453,0.00005078654,0.000121322286,0.0001892227,0.000047900383,0.0049210982,0.9838266,0.00012866032,0.0020336977,0.0000355505],"about_ca_topic_score_codex":0.00085403025,"about_ca_topic_score_gemma":0.00090416113,"teacher_disagreement_score":0.006445381,"about_ca_system_score_codex":0.0005426304,"about_ca_system_score_gemma":0.0010418667,"threshold_uncertainty_score":0.034086823},"labels":[],"label_agreement":null},{"id":"W2995485006","doi":"10.3390/s19245519","title":"Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Compensation (psychology); Reduction (mathematics); Residual; Linear regression; Pressure sensor; Regression; Artificial neural network; Calibration; Biomedical engineering; Matrix (chemical analysis); Interface (matter); Polynomial regression; Computer science; Acoustics; Artificial intelligence; Engineering; Mathematics; Algorithm; Materials science; Statistics; Machine learning; Mechanical engineering","score_opus":0.023206392235123684,"score_gpt":0.2895830302574745,"score_spread":0.26637663802235084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995485006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05643691,0.00080240355,0.94066936,0.00011532983,0.000051037274,0.00006183017,0.000047853082,0.0009982702,0.00081692426],"genre_scores_gemma":[0.5512466,0.00094531284,0.44500962,0.00006803643,0.000041197916,0.00012451828,0.00014671922,0.00017539822,0.0022424953],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989814,0.0002592077,0.00006567481,0.00023033356,0.00041033584,0.000053071988],"domain_scores_gemma":[0.9968777,0.0017101666,0.0003685762,0.00016281843,0.00085260166,0.000028014394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023243465,0.0010030227,0.000527057,0.00082404906,0.00023835029,0.00038377568,0.00070544955,0.0005938335,0.0010155434],"category_scores_gemma":[0.00656838,0.00027058285,0.0005942559,0.00075962854,0.00025795007,0.0006911179,0.00027534892,0.0006937982,0.00045637527],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004049854,0.00026181457,0.0046686563,0.00051158224,0.00013645596,0.00017349496,0.0002586744,0.15696523,0.12296346,0.001619699,0.00089065195,0.7111452],"study_design_scores_gemma":[0.000014365086,0.0003517151,0.0047511733,0.000037684233,0.00005570591,0.00017828644,0.000050433668,0.93889827,0.053456873,0.00043526618,0.0017391139,0.000031204047],"about_ca_topic_score_codex":0.0027894594,"about_ca_topic_score_gemma":0.0028982067,"teacher_disagreement_score":0.0027894594,"about_ca_system_score_codex":0.00032362773,"about_ca_system_score_gemma":0.00042692985,"threshold_uncertainty_score":0.012292445},"labels":[],"label_agreement":null},{"id":"W2995957663","doi":"10.3390/s19245532","title":"Direction of Arrival Estimation of GPS Narrowband Jammers Using High-Resolution Techniques","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Jamming; Global Positioning System; Narrowband; Computer science; Direction of arrival; GPS signals; Assisted GPS; Telecommunications; Antenna (radio); Physics","score_opus":0.0072515333243267244,"score_gpt":0.21519474768885696,"score_spread":0.20794321436453023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995957663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27458993,0.00034461136,0.72104156,0.00008695951,0.00005602734,0.000039256738,0.00009380864,0.0009479551,0.002799957],"genre_scores_gemma":[0.78729266,0.0003417844,0.21058324,0.000033498756,0.00003586113,0.000028425984,0.0001570908,0.000040810322,0.0014866592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997732,0.000053656055,0.000009489897,0.00003396801,0.00010798109,0.000021713106],"domain_scores_gemma":[0.99954754,0.00010762643,0.00011024978,0.00004328108,0.00017172971,0.000019567806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024005002,0.00040932526,0.00024218755,0.0009084191,0.00016356792,0.00033534822,0.00023336467,0.00032803745,0.00065413467],"category_scores_gemma":[0.0012710026,0.0001712014,0.00021484328,0.0005730947,0.0001162604,0.00045500632,0.00034973604,0.00032522398,0.000390473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064702175,0.00016773738,0.010823978,0.00034338824,0.000086738226,0.00021089343,0.0003626363,0.09730077,0.36019266,0.0023561688,0.0014650888,0.5260429],"study_design_scores_gemma":[0.00008138542,0.00060460763,0.024170738,0.000042697546,0.000092473645,0.0008265047,0.00021786308,0.8200342,0.14808977,0.0010860501,0.004674116,0.00007960559],"about_ca_topic_score_codex":0.0007647492,"about_ca_topic_score_gemma":0.0010942202,"teacher_disagreement_score":0.0009084191,"about_ca_system_score_codex":0.00014241665,"about_ca_system_score_gemma":0.00033408808,"threshold_uncertainty_score":0.0021882653},"labels":[],"label_agreement":null},{"id":"W2996550105","doi":"10.3390/s19245474","title":"SDN Controller Placement in IoT Networks: An Optimized Submodularity-Based Approach","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Software-defined networking; Controller (irrigation); Distributed computing; Heuristic; Forwarding plane; Internet of Things; Latency (audio); Software; Computer network; Embedded system; Artificial intelligence","score_opus":0.011035274257331012,"score_gpt":0.21913507973917012,"score_spread":0.20809980548183912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996550105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009040855,0.00026920618,0.9884305,0.00017782704,0.00003680867,0.000057983816,0.000046067475,0.0001670626,0.0017736915],"genre_scores_gemma":[0.5194736,0.00056729506,0.47511953,0.00029977155,0.00008679333,0.00025466297,0.0003018239,0.0001615476,0.003734902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993242,0.00024632755,0.000028416107,0.00015013624,0.00012877828,0.00012208016],"domain_scores_gemma":[0.9990677,0.00052360626,0.00011108995,0.00006399058,0.00014237974,0.000091214926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014051173,0.0014532966,0.0014300892,0.0007306865,0.0006578997,0.0010232627,0.0014727504,0.0009983273,0.0026609723],"category_scores_gemma":[0.002031804,0.000656544,0.00082207884,0.0009552553,0.0007077334,0.0010970791,0.0014668222,0.0010554574,0.00031084483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000444647,0.000044901655,0.00024613945,0.00005185804,0.000024576006,0.000050891595,0.000030399891,0.96442765,0.0010340502,0.006428511,0.0010577885,0.026558641],"study_design_scores_gemma":[0.000007022357,0.000024397697,0.00003260028,0.0000049169284,0.0000042117645,0.000015057456,0.000013452798,0.9955207,0.00025105628,0.003761632,0.00036172447,0.0000032397506],"about_ca_topic_score_codex":0.003512802,"about_ca_topic_score_gemma":0.0039712735,"teacher_disagreement_score":0.003512802,"about_ca_system_score_codex":0.0013410157,"about_ca_system_score_gemma":0.0017101421,"threshold_uncertainty_score":0.009729803},"labels":[],"label_agreement":null},{"id":"W2996629586","doi":"10.3390/s19245505","title":"Surface Wave Enhanced Sensing in the Terahertz Spectral Range: Modalities, Materials, and Perspectives","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Terahertz radiation; Surface plasmon polariton; Surface wave; Polariton; Context (archaeology); Optics; Optoelectronics; Semiconductor; Surface phonon; Materials science; Surface plasmon; Dielectric; Plasmon; Phonon; Physics; Condensed matter physics","score_opus":0.04211435769513501,"score_gpt":0.2683345920534704,"score_spread":0.22622023435833538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996629586","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024851228,0.99777585,0.00034064244,0.00018700339,0.00016624338,0.000003364241,0.000008481053,0.0000054666107,0.0012644881],"genre_scores_gemma":[0.0015326033,0.99688196,0.000465069,0.00019295885,0.00015313497,0.000008234492,0.000013783711,0.0000014939371,0.0007508274],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99982613,0.000026082962,0.000016949087,0.000036778314,0.00006891556,0.000025276087],"domain_scores_gemma":[0.9997631,0.00012074195,0.000030148116,0.000008810024,0.0000609663,0.000016160193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052580406,0.0008204217,0.000981803,0.0025422473,0.00028485124,0.0009157577,0.0006588874,0.0012659228,0.002036805],"category_scores_gemma":[0.00042321536,0.00040953798,0.00047237327,0.0019939495,0.00061079545,0.0017165584,0.0006077801,0.0015396681,0.0012102622],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054504235,0.0001314188,0.0002864183,0.023803454,0.00008209623,0.00046057478,0.00016092004,0.0009904938,0.021238703,0.025597515,0.017186105,0.91000783],"study_design_scores_gemma":[0.000005990871,0.0001357119,0.0007000319,0.0021712102,0.000051370367,0.0018941517,0.0001135059,0.000294974,0.00489997,0.004804181,0.9848952,0.000033650336],"about_ca_topic_score_codex":0.00063050084,"about_ca_topic_score_gemma":0.0010094426,"teacher_disagreement_score":0.0025422473,"about_ca_system_score_codex":0.0005227597,"about_ca_system_score_gemma":0.0005596115,"threshold_uncertainty_score":0.006813824},"labels":[],"label_agreement":null},{"id":"W2997051287","doi":"10.3390/s20010261","title":"Ergodic Capacity Analysis of Full Duplex Relaying in the Presence of Co-Channel Interference in V2V Communications","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Ergodic theory; Relay; Interference (communication); Fading; Nakagami distribution; Upper and lower bounds; Moment-generating function; Topology (electrical circuits); Wireless; Monte Carlo method; Co-channel interference; Signal-to-noise ratio (imaging); Context (archaeology); Computer science; Channel (broadcasting); Telecommunications; Mathematics; Physics; Statistics; Probability density function; Mathematical analysis; Combinatorics","score_opus":0.06742955263490433,"score_gpt":0.27464536797438877,"score_spread":0.20721581533948444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997051287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1422684,0.00665787,0.8204362,0.00045653715,0.000135047,0.000074998934,0.0004820085,0.00043971936,0.029049248],"genre_scores_gemma":[0.9886883,0.0017563883,0.00728301,0.000064695894,0.000057191417,0.00005154687,0.00014750965,0.000042401054,0.001909025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988153,0.0003735709,0.00004327033,0.00010606567,0.00036489734,0.0002969824],"domain_scores_gemma":[0.9957853,0.0028865496,0.0003271337,0.00023679876,0.00068121374,0.00008286971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014930285,0.0010441139,0.00078976736,0.0013605859,0.0005228711,0.0012543489,0.001205821,0.0007203594,0.0016868092],"category_scores_gemma":[0.0066513005,0.00033285614,0.00057933666,0.0013662123,0.0013270015,0.0014283811,0.0011705769,0.00070576434,0.0004273963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006139441,0.000020414305,0.00077910884,0.00017077127,0.000059383896,0.00034578828,0.00016984955,0.93823826,0.0036824387,0.04656917,0.0009295275,0.008973982],"study_design_scores_gemma":[0.0000013084243,0.000021956845,0.00038971214,0.000020853291,0.000020475674,0.0001664901,0.000043553275,0.9893392,0.0012616261,0.008144952,0.00057253684,0.000017395038],"about_ca_topic_score_codex":0.0069734734,"about_ca_topic_score_gemma":0.0040348475,"teacher_disagreement_score":0.0069734734,"about_ca_system_score_codex":0.0020861917,"about_ca_system_score_gemma":0.0012470714,"threshold_uncertainty_score":0.01513648},"labels":[],"label_agreement":null},{"id":"W2997287449","doi":"10.3390/s20010321","title":"Distributed Optical Fiber-Based Approach for Soil–Structure Interaction","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Agence Nationale de la Recherche","keywords":"Reflectometry; Cantilever; Optical fiber; Fiber optic sensor; Optical time-domain reflectometer; Structural health monitoring; Geotechnical engineering; Structural engineering; Time domain; Environmental science; Computer science; Engineering; Fiber optic splitter; Telecommunications","score_opus":0.016835570004398347,"score_gpt":0.23141087960743875,"score_spread":0.21457530960304042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997287449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49796507,0.00288799,0.48558292,0.00030159164,0.00023600699,0.0001842677,0.00027913984,0.0010650494,0.011498026],"genre_scores_gemma":[0.82013893,0.0010778881,0.17183644,0.0001025726,0.000056719644,0.00008879205,0.000117735624,0.000042443353,0.0065385294],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975616,0.000027591761,0.0000044368403,0.000065995504,0.00012358508,0.000022269203],"domain_scores_gemma":[0.999897,0.000020271907,0.000023818924,0.000016840495,0.000036039393,0.0000060310635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017857073,0.00049853354,0.00020719861,0.00031644083,0.00016398972,0.00030792004,0.0006877718,0.000528498,0.0015401411],"category_scores_gemma":[0.00016637398,0.00015043207,0.00023347953,0.00029192783,0.00022494077,0.0005178909,0.00031635666,0.00022941132,0.00051434344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005841033,0.000074962016,0.00037268203,0.00007799456,0.000012987991,0.00006797307,0.000034597935,0.0037913097,0.9627409,0.0010601986,0.00020899437,0.031498965],"study_design_scores_gemma":[0.000028652465,0.00056468765,0.0031257486,0.000020628328,0.000047732272,0.00036121107,0.000104748295,0.20938213,0.77454686,0.0010429008,0.010708232,0.000066555025],"about_ca_topic_score_codex":0.00096373423,"about_ca_topic_score_gemma":0.002730237,"teacher_disagreement_score":0.0015401411,"about_ca_system_score_codex":0.00046228574,"about_ca_system_score_gemma":0.00019036014,"threshold_uncertainty_score":0.0051523447},"labels":[],"label_agreement":null},{"id":"W2997479167","doi":"10.3390/s20010267","title":"Distributed Fiber Optic Sensing for Real-Time Monitoring of Gas in Riser during Offshore Drilling","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Schlumberger (Canada)","funders":"Gulf Research Program; National Academy of Sciences","keywords":"Submarine pipeline; Petroleum engineering; Drilling; Optical fiber; Offshore drilling; Real-time data; Marine engineering; Measurement while drilling; Geology; Environmental science; Engineering; Computer science; Mechanical engineering; Geotechnical engineering; Telecommunications; Operating system","score_opus":0.013280856803983504,"score_gpt":0.22801390933973414,"score_spread":0.21473305253575065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997479167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8848259,0.0009224168,0.10966294,0.00019055197,0.00010175806,0.000081687656,0.00037139485,0.00078118924,0.0030621104],"genre_scores_gemma":[0.9691066,0.00024647557,0.029965412,0.00004268973,0.000015109708,0.000027118636,0.000069534224,0.000014340116,0.00051275874],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997842,0.000024075636,0.000006607207,0.00006645956,0.000098997356,0.000019742472],"domain_scores_gemma":[0.99983704,0.000043464555,0.000040385657,0.000014344083,0.000053630374,0.00001104529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023046827,0.0003792128,0.00020070502,0.00043957907,0.00023281331,0.00026051086,0.0004209101,0.00033679206,0.00040359542],"category_scores_gemma":[0.00036626487,0.000116934396,0.00012760382,0.00034292057,0.00021510934,0.00063098775,0.0003097036,0.0002814909,0.000110299414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004890595,0.00015542582,0.022960393,0.00030751343,0.000026085969,0.00020969033,0.0003553848,0.016059792,0.8096012,0.0006590207,0.000945257,0.14823134],"study_design_scores_gemma":[0.00007258928,0.0009454123,0.051522374,0.00007242705,0.000096754375,0.00042223185,0.0007701813,0.37054425,0.5676079,0.0016487119,0.006170927,0.00012618185],"about_ca_topic_score_codex":0.0017683619,"about_ca_topic_score_gemma":0.005568924,"teacher_disagreement_score":0.0017683619,"about_ca_system_score_codex":0.00027951004,"about_ca_system_score_gemma":0.00030951822,"threshold_uncertainty_score":0.0035161376},"labels":[],"label_agreement":null},{"id":"W2997700007","doi":"10.3390/s20010183","title":"A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":389,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Softmax function; Computer science; Discriminative model; Speech recognition; Spectrogram; Convolutional neural network; Pooling; Speaker recognition; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.04472940740871452,"score_gpt":0.3154214440366235,"score_spread":0.27069203662790897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997700007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09309864,0.0014370718,0.89068,0.00036326688,0.00056540244,0.00012321296,0.00083259586,0.0049718274,0.007927954],"genre_scores_gemma":[0.6958563,0.0010052009,0.27729705,0.00043979124,0.0001450304,0.00015636276,0.002886639,0.00014243796,0.022071144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998604,0.000009722984,0.000007462987,0.000042299213,0.000049596278,0.000030487876],"domain_scores_gemma":[0.9998945,0.000016000491,0.000007307347,0.000014137279,0.00006015232,0.000007843512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002021302,0.00073020754,0.0003101211,0.00029154847,0.00017346721,0.0002937406,0.0008295481,0.00041999147,0.0028584334],"category_scores_gemma":[0.00043299288,0.00020425649,0.00046306927,0.00027400366,0.00015933659,0.00046707928,0.0004222689,0.0006017089,0.0011491703],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004916369,0.00017967678,0.002112702,0.00015797073,0.0001324539,0.0003030102,0.00006145356,0.087085135,0.20629607,0.002658077,0.008839082,0.69168276],"study_design_scores_gemma":[0.000009195919,0.000094139,0.0013774405,0.00000989101,0.0000395771,0.00009598466,0.000010761295,0.95532995,0.038936574,0.0004127802,0.003670979,0.0000127510875],"about_ca_topic_score_codex":0.009055256,"about_ca_topic_score_gemma":0.012033354,"teacher_disagreement_score":0.009055256,"about_ca_system_score_codex":0.00043519435,"about_ca_system_score_gemma":0.0005329839,"threshold_uncertainty_score":0.018005073},"labels":[],"label_agreement":null},{"id":"W2998060566","doi":"10.3390/s20010138","title":"Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Anhui Provincial Department of Education; Chuzhou University","keywords":"Point cloud; RGB color model; Iterative closest point; Computer science; Artificial intelligence; Computer vision; Point (geometry); Algorithm; Transformation (genetics); Similarity (geometry); Rigid transformation; Filter (signal processing); Mathematics; Image (mathematics); Geometry","score_opus":0.020795141664789917,"score_gpt":0.2450731104112503,"score_spread":0.2242779687464604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998060566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00125261,0.00006847267,0.9969921,0.000025663197,0.00004180237,0.000061322746,0.000059029324,0.0010484384,0.0004505516],"genre_scores_gemma":[0.0331091,0.00020563738,0.9629148,0.000040236606,0.000042267533,0.0003396439,0.00083935825,0.0004763485,0.0020325093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99597037,0.000474797,0.0002502427,0.00093323155,0.0021115365,0.0002597674],"domain_scores_gemma":[0.9987061,0.00015467846,0.00009643586,0.00029131112,0.0007030333,0.00004848217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014111921,0.0018360042,0.0020580962,0.0041484297,0.0014477645,0.0021365485,0.0033370894,0.001414678,0.006291374],"category_scores_gemma":[0.003123656,0.0012422368,0.002906412,0.0049392367,0.0009693524,0.002562764,0.0035681042,0.0025283126,0.0045562973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026028728,0.000099338184,0.0018054427,0.00036992235,0.00019397312,0.00021883858,0.0003221413,0.086579435,0.039495714,0.016195133,0.012409378,0.8420503],"study_design_scores_gemma":[0.00008683162,0.00015400267,0.0021208695,0.000032667147,0.0000794305,0.000630545,0.00012943601,0.9202334,0.04143742,0.008810189,0.026132846,0.00015223955],"about_ca_topic_score_codex":0.007180906,"about_ca_topic_score_gemma":0.0043580537,"teacher_disagreement_score":0.007180906,"about_ca_system_score_codex":0.0009386543,"about_ca_system_score_gemma":0.0028692847,"threshold_uncertainty_score":0.021046698},"labels":[],"label_agreement":null},{"id":"W2998471726","doi":"10.3390/s20010229","title":"Allumo: Preprocessing and Calibration Software for Wearable Accelerometers Used in Posture Tracking","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Canada First Research Excellence Fund","keywords":"Accelerometer; Calibration; Software; Orientation (vector space); Computer science; Wearable computer; Software portability; Preprocessor; Inertial measurement unit; Usability; Motion capture; Visualization; Simulation; Artificial intelligence; Embedded system; Human–computer interaction; Motion (physics)","score_opus":0.012043565939716934,"score_gpt":0.22518244424030337,"score_spread":0.21313887830058645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998471726","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01237137,0.00042750212,0.6139144,0.00015054067,0.00029751903,0.0007049024,0.005715436,0.36163834,0.0047801435],"genre_scores_gemma":[0.17366362,0.00075076846,0.6910738,0.00076757185,0.00023879303,0.004229901,0.021208342,0.08261499,0.02545225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99919003,0.000092226204,0.0001113849,0.00022475062,0.00030620786,0.00007533518],"domain_scores_gemma":[0.99843913,0.00056152255,0.00022697562,0.00027735526,0.00040276727,0.00009219725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010223604,0.0027888624,0.0011028791,0.0016915874,0.0004117486,0.0008131291,0.001655286,0.0008701635,0.03722262],"category_scores_gemma":[0.004902648,0.0009944958,0.001051396,0.0009229013,0.0004196401,0.0010255362,0.0018485606,0.000981949,0.012717325],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015306969,0.00038041917,0.0073249154,0.0021865247,0.00031041104,0.00085124077,0.0010711382,0.0048480756,0.08001047,0.0025325653,0.15466423,0.7442893],"study_design_scores_gemma":[0.0009202622,0.0008449396,0.06243898,0.00085628673,0.00038645035,0.0050981273,0.0003715514,0.16169702,0.28486684,0.007983879,0.4738144,0.0007213009],"about_ca_topic_score_codex":0.0011999204,"about_ca_topic_score_gemma":0.001751241,"teacher_disagreement_score":0.03722262,"about_ca_system_score_codex":0.00033148366,"about_ca_system_score_gemma":0.00069456594,"threshold_uncertainty_score":0.12452209},"labels":[],"label_agreement":null},{"id":"W2999010100","doi":"10.3390/s20020567","title":"Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy Wireless Sensor Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Wireless sensor network; Smart grid; Real-time computing; Sensor fusion; Distributed computing; Wireless; Grid; Data aggregator; Data mining; Computer network; Engineering; Artificial intelligence; Telecommunications","score_opus":0.044560026946403104,"score_gpt":0.2735282659728782,"score_spread":0.22896823902647512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999010100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13941059,0.0009128498,0.85684294,0.00036918218,0.000045071432,0.000056243513,0.000041683503,0.00029127498,0.0020301526],"genre_scores_gemma":[0.97119755,0.00028796628,0.028008236,0.00003638657,0.000019550207,0.00004680513,0.00004968704,0.000017139871,0.00033670457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981881,0.000641295,0.000077201024,0.00026670485,0.0006715633,0.00015510063],"domain_scores_gemma":[0.9926317,0.0053882375,0.0004736063,0.0003995501,0.001018552,0.000088456036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032347052,0.0006780283,0.0007348617,0.00060312275,0.0005485634,0.0008713071,0.0007216532,0.0006691215,0.000493138],"category_scores_gemma":[0.012186054,0.00023642981,0.0004103379,0.00068854535,0.0007914896,0.0016103205,0.0010381853,0.00062287133,0.00009980899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018979699,0.000043072945,0.0017925987,0.000065309076,0.000044505374,0.000035556623,0.00006507563,0.9587134,0.0028913564,0.0054644984,0.00019508142,0.03049978],"study_design_scores_gemma":[0.0000026971295,0.000033912067,0.00028015903,0.0000022756424,0.0000052672485,0.000009982964,0.000011546916,0.99800676,0.00090790493,0.00068686146,0.00004939421,0.00000329984],"about_ca_topic_score_codex":0.003792603,"about_ca_topic_score_gemma":0.0025866923,"teacher_disagreement_score":0.003792603,"about_ca_system_score_codex":0.0014980735,"about_ca_system_score_gemma":0.0013948021,"threshold_uncertainty_score":0.01710695},"labels":[],"label_agreement":null},{"id":"W2999286278","doi":"10.3390/s20020438","title":"Defending Against Randomly Located Eavesdroppers by Establishing a Protecting Region","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Eavesdropping; Jamming; Computer network; Computer science; Transmission (telecommunications); Artificial noise; Wireless sensor network; Secrecy; Beamforming; Wireless; Computer security; Wireless network; Channel (broadcasting); Transmitter; Telecommunications","score_opus":0.020042669053734103,"score_gpt":0.20942603928269068,"score_spread":0.18938337022895657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999286278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07511726,0.0006562809,0.9211186,0.00015473597,0.000040553667,0.00003759195,0.000024474617,0.0002040142,0.0026465089],"genre_scores_gemma":[0.94742286,0.0007572398,0.0510252,0.00006992629,0.000048412694,0.00004818984,0.00002152135,0.00002355458,0.0005831616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814045,0.00078827725,0.000093648094,0.00027349812,0.0005156836,0.0001884009],"domain_scores_gemma":[0.9946232,0.0032077886,0.0009117869,0.00068366964,0.00043002292,0.00014358512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015847816,0.0013449179,0.0009188627,0.0007446745,0.00069629867,0.0010314982,0.0009649364,0.0010629877,0.000482138],"category_scores_gemma":[0.005456653,0.00030782228,0.00063409767,0.0005579537,0.0017968734,0.002465423,0.0020084411,0.00083728624,0.0002693926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046820022,0.000110510584,0.0038367745,0.00033911833,0.00024895574,0.0012980198,0.0005763277,0.68270475,0.15560935,0.07458142,0.0008632619,0.07936333],"study_design_scores_gemma":[0.000017838047,0.00059709756,0.00092004554,0.000040169998,0.00010969561,0.0010466605,0.00023797677,0.92153895,0.055876516,0.017720612,0.0018473298,0.000047110752],"about_ca_topic_score_codex":0.0003796279,"about_ca_topic_score_gemma":0.00024701565,"teacher_disagreement_score":0.0015847816,"about_ca_system_score_codex":0.00044998454,"about_ca_system_score_gemma":0.00070302375,"threshold_uncertainty_score":0.0083812475},"labels":[],"label_agreement":null},{"id":"W2999456656","doi":"10.3390/s20020465","title":"GNSS-ISE: Instruction Set Extension for GNSS Baseband Processing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Politechnika Warszawska; Consortium canadien en neurodégénérescence associée au vieillissement","keywords":"GNSS applications; Baseband; Computer science; Embedded system; Computer hardware; Global Positioning System; Telecommunications","score_opus":0.0295739955975468,"score_gpt":0.2375945781284117,"score_spread":0.2080205825308649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999456656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058813144,0.0020257444,0.8859041,0.0002631102,0.00042112407,0.00043567474,0.0009441912,0.02327441,0.027918467],"genre_scores_gemma":[0.46703383,0.001953073,0.4808926,0.00051239197,0.00026951067,0.00067740725,0.007878586,0.0029920468,0.03779045],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948573,0.000060882638,0.000056596175,0.000060622035,0.0002826885,0.000053453725],"domain_scores_gemma":[0.9992736,0.0001390489,0.00005431432,0.00017224943,0.00033252998,0.000028302862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004339075,0.0008378816,0.00036920456,0.0008653945,0.00025325752,0.0006407846,0.0014109785,0.0004845527,0.007190553],"category_scores_gemma":[0.0011526534,0.0002509972,0.00039616483,0.0005319389,0.0003167916,0.0008178105,0.0005112523,0.0011262113,0.0033935148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014029952,0.00033247445,0.0042375736,0.00089625636,0.00013328828,0.0005961106,0.00044627552,0.040069494,0.27319872,0.032542214,0.028922467,0.6172221],"study_design_scores_gemma":[0.00021055674,0.001540591,0.0032918009,0.00023209311,0.00017595023,0.001540311,0.00007108333,0.19086581,0.52385193,0.005471042,0.27262184,0.0001269302],"about_ca_topic_score_codex":0.00048857706,"about_ca_topic_score_gemma":0.00067257776,"teacher_disagreement_score":0.007190553,"about_ca_system_score_codex":0.00043216944,"about_ca_system_score_gemma":0.0005390833,"threshold_uncertainty_score":0.024054825},"labels":[],"label_agreement":null},{"id":"W2999734205","doi":"10.3390/s20020543","title":"Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer vision; Artificial intelligence; Computer science; Eye tracking; BitTorrent tracker; Robustness (evolution); Gaze; Convolutional neural network; Feature extraction; Mobile device; Feature (linguistics); Tracking system; Kalman filter","score_opus":0.019978474437803356,"score_gpt":0.26585590937766346,"score_spread":0.2458774349398601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999734205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20558135,0.00092301774,0.7799098,0.0002364244,0.00015240879,0.00014531054,0.001149085,0.006833752,0.005068867],"genre_scores_gemma":[0.74513024,0.0004960229,0.2446177,0.00024248958,0.000042380623,0.00013722118,0.0010790834,0.00016988048,0.008084995],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986756,0.000007852387,0.000005688524,0.000049629183,0.000051874908,0.000017324342],"domain_scores_gemma":[0.99989986,0.000017054208,0.000013215307,0.00001872581,0.000044959237,0.0000062308177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013763914,0.0005254621,0.0003656327,0.00039212007,0.000114376766,0.00033007594,0.00049149274,0.00037260255,0.0018263654],"category_scores_gemma":[0.00037634524,0.00025968457,0.0004795518,0.00026028618,0.000078557285,0.0003304695,0.00041262462,0.00027692303,0.0006798028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044977022,0.00014483661,0.007854417,0.00022462445,0.00023655329,0.00043722772,0.00013121523,0.068991676,0.2685834,0.00082220574,0.0054967795,0.6466274],"study_design_scores_gemma":[0.000018260627,0.00014062028,0.010326592,0.000026101121,0.000054935772,0.00036656816,0.000020344021,0.9359108,0.04925925,0.00038813805,0.0034529008,0.00003545591],"about_ca_topic_score_codex":0.010501243,"about_ca_topic_score_gemma":0.018262213,"teacher_disagreement_score":0.010501243,"about_ca_system_score_codex":0.00041224327,"about_ca_system_score_gemma":0.0003286203,"threshold_uncertainty_score":0.020880222},"labels":[],"label_agreement":null},{"id":"W2999922739","doi":"10.3390/s20020456","title":"Sensors for Ultrasonic Nondestructive Testing (NDT) in Harsh Environments","year":2020,"lang":"en","type":"editorial","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nondestructive testing; Piping; Petrochemical; Ultrasonic sensor; Ultrasonic testing; Transducer; Nuclear power; Engineering; Criticality; Nuclear power plant; Process engineering; Mechanical engineering; Manufacturing engineering; Nuclear engineering; Systems engineering; Electrical engineering; Acoustics; Waste management","score_opus":0.010694191800177472,"score_gpt":0.21301110508011023,"score_spread":0.20231691327993276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999922739","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000061105486,0.029022682,0.0005091709,0.016969828,0.9481549,0.00002253988,0.000034282886,0.000086070446,0.0051394766],"genre_scores_gemma":[0.00087134563,0.03813648,0.0006306532,0.015098013,0.8978542,0.000034610337,0.00008331269,0.00009406666,0.04719727],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99646145,0.0004339052,0.00036840283,0.0003824842,0.0022185026,0.00013537276],"domain_scores_gemma":[0.9895343,0.0036601387,0.00054729544,0.00037884724,0.0045580235,0.0013212664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005521166,0.0030265672,0.0024115602,0.0032150564,0.0014572275,0.0054833037,0.002561709,0.0091689555,0.012678032],"category_scores_gemma":[0.009343272,0.0011301107,0.0012246146,0.0012852754,0.002111814,0.0041270107,0.0010319719,0.010499703,0.017221289],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049227205,0.000026205706,0.000036849437,0.00045572274,0.000012859074,0.000103579754,0.000016416512,0.00003888036,0.0004558131,0.00066492235,0.96734804,0.030791493],"study_design_scores_gemma":[0.000012220893,0.000024104964,0.00012455673,0.0001562661,0.000011009503,0.00020066209,0.000015153653,0.00007836106,0.00017505264,0.0004949105,0.9986993,0.000008519607],"about_ca_topic_score_codex":0.00057469524,"about_ca_topic_score_gemma":0.0023660036,"teacher_disagreement_score":0.012678032,"about_ca_system_score_codex":0.0018765382,"about_ca_system_score_gemma":0.0013301477,"threshold_uncertainty_score":0.04241222},"labels":[],"label_agreement":null},{"id":"W2999955388","doi":"10.3390/s22145222","title":"Personalized Activity Recognition with Deep Triplet Embeddings","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Activity recognition; Pattern recognition (psychology); Cross entropy; Machine learning; Categorical variable; Deep learning; Feature (linguistics); Generalization; Mathematics","score_opus":0.02599975266163782,"score_gpt":0.23598395365014316,"score_spread":0.20998420098850534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999955388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1541328,0.00063603197,0.8248548,0.0002887164,0.00022699787,0.00015991658,0.003925549,0.0124257365,0.0033495221],"genre_scores_gemma":[0.8371418,0.0002775731,0.14848173,0.00026688876,0.00009333897,0.00021569699,0.008347206,0.00021749656,0.0049582473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946564,0.0000804914,0.000032228403,0.00026484096,0.00009210844,0.000064795386],"domain_scores_gemma":[0.9995215,0.00009319406,0.00007348314,0.00018001493,0.00009025497,0.000041607163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046056067,0.0010624618,0.0009163923,0.00072721625,0.00015264748,0.0005389769,0.0008423549,0.00053498626,0.0019039044],"category_scores_gemma":[0.001858656,0.00024324049,0.00056169793,0.0009131487,0.00021247123,0.0012348372,0.00096039136,0.0010173139,0.001176353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054163445,0.00071673037,0.014568011,0.00012310513,0.00016989431,0.00016688701,0.00011613972,0.09290711,0.018971698,0.0022984461,0.016499117,0.8529213],"study_design_scores_gemma":[0.00002723756,0.00021817694,0.0084658535,0.00001929834,0.0000296753,0.00021797583,0.000057947807,0.96976054,0.010736215,0.0069266893,0.0035110437,0.000029387473],"about_ca_topic_score_codex":0.003263281,"about_ca_topic_score_gemma":0.0068138246,"teacher_disagreement_score":0.003263281,"about_ca_system_score_codex":0.00044167883,"about_ca_system_score_gemma":0.00047861287,"threshold_uncertainty_score":0.0064886212},"labels":[],"label_agreement":null},{"id":"W3000659401","doi":"10.3390/s20020443","title":"Voltammetry at Hexamethyl-P-Terphenyl Poly(Benzimidazolium) (HMT-PMBI)-Coated Glassy Carbon Electrodes: Charge Transport Properties and Detection of Uric and Ascorbic Acid","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ascorbic acid; Chemistry; Cyclic voltammetry; Detection limit; Glassy carbon; Redox; Chronoamperometry; Inorganic chemistry; Voltammetry; Electrochemistry; Nuclear chemistry; Electrode; Chromatography; Physical chemistry","score_opus":0.01067293440416372,"score_gpt":0.1660303860808481,"score_spread":0.15535745167668438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000659401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9715447,0.0033602605,0.021838633,0.0002650914,0.00008405227,0.00005799421,0.00042290948,0.00042839255,0.0019979954],"genre_scores_gemma":[0.96155185,0.0019805753,0.032317862,0.00018216738,0.000036640417,0.000088521985,0.00048307798,0.00003121802,0.003328156],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995939,0.000062768224,0.000027914339,0.00008632738,0.00017330101,0.000055850218],"domain_scores_gemma":[0.9997241,0.000119421566,0.000048974074,0.000022654507,0.000056948535,0.000027915445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027139904,0.00032909494,0.00022431667,0.00034185563,0.0001894199,0.00030426384,0.0006051268,0.00079198764,0.0011319871],"category_scores_gemma":[0.00051425246,0.00022625006,0.00017038909,0.0004204894,0.00019843965,0.00023433057,0.00018877405,0.0005630825,0.0004167185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028275677,0.0000095263695,0.00019772717,0.00003064052,0.0000053179524,0.00006103418,0.000023353534,0.00005428979,0.9979254,0.000027111277,0.00002832577,0.0016088511],"study_design_scores_gemma":[0.0000068204117,0.00009805254,0.0040833335,0.00000579822,0.000009567685,0.00025416672,0.000021409056,0.0022298805,0.9926601,0.000034040586,0.0005909768,0.0000058507176],"about_ca_topic_score_codex":0.0010832641,"about_ca_topic_score_gemma":0.0012040439,"teacher_disagreement_score":0.0011319871,"about_ca_system_score_codex":0.00028622575,"about_ca_system_score_gemma":0.00013238452,"threshold_uncertainty_score":0.003786862},"labels":[],"label_agreement":null},{"id":"W3000896649","doi":"10.3390/s20030631","title":"A Multi-Sensor Cane Can Detect Changes in Gait Caused by Simulated Gait Abnormalities and Walking Terrains","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Gait; Cane; Physical medicine and rehabilitation; Terrain; Inertial measurement unit; Population; Gait analysis; Computer science; Simulation; Medicine; Artificial intelligence; Cartography; Geography; Biology","score_opus":0.04046622501638886,"score_gpt":0.32563527239028917,"score_spread":0.2851690473739003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000896649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75437,0.0017610027,0.23070253,0.00041786974,0.00044279074,0.00051220594,0.0018591136,0.0017997354,0.008134679],"genre_scores_gemma":[0.9472861,0.00040686075,0.049834438,0.00022039891,0.00003957082,0.00017600614,0.00030451617,0.000027192324,0.0017048928],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995695,0.00011060521,0.00003112449,0.00008575557,0.00016208836,0.000041009254],"domain_scores_gemma":[0.9993073,0.00027444106,0.00006925153,0.00007014688,0.00022636718,0.000052620624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004889728,0.0007923069,0.00059253426,0.00096272974,0.00014770348,0.0003601381,0.00055244676,0.00094074436,0.0018916918],"category_scores_gemma":[0.0016011099,0.00019679546,0.0002774939,0.0005772477,0.00019233274,0.00047976893,0.0005741748,0.00019534976,0.0003340954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044472646,0.00080064917,0.07087731,0.0023542487,0.0004924722,0.0015326667,0.00053947343,0.015262856,0.55764896,0.0008299571,0.004510241,0.3407039],"study_design_scores_gemma":[0.0005197072,0.0090962285,0.40639287,0.000552628,0.0010799968,0.009553668,0.0013129904,0.26210776,0.2828471,0.0025119013,0.023622695,0.0004024728],"about_ca_topic_score_codex":0.00066326605,"about_ca_topic_score_gemma":0.0016440519,"teacher_disagreement_score":0.0018916918,"about_ca_system_score_codex":0.000087597,"about_ca_system_score_gemma":0.00015254212,"threshold_uncertainty_score":0.006328404},"labels":[],"label_agreement":null},{"id":"W3002058140","doi":"10.3390/s20030590","title":"Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Filter (signal processing); Inertial navigation system; Fault (geology); Fault detection and isolation; Kalman filter; Real-time computing; GNSS augmentation; Inertial measurement unit; Computer science; Fault coverage; Satellite system; Global Positioning System; Navigation system; Engineering; Fault indicator; Artificial intelligence; Inertial frame of reference; Telecommunications","score_opus":0.014109386821506238,"score_gpt":0.20331912176773523,"score_spread":0.189209734946229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002058140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04086953,0.00010724611,0.9578942,0.00007527708,0.000028457578,0.000032437554,0.000013993258,0.00030815275,0.0006706744],"genre_scores_gemma":[0.96002924,0.00004775227,0.038931847,0.000049022125,0.000025837622,0.00003748512,0.000032760116,0.000012126318,0.0008338252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991455,0.00013536573,0.00005552941,0.00023369258,0.00028267503,0.00014726668],"domain_scores_gemma":[0.9993098,0.0002812856,0.0001491377,0.000080024096,0.00013358524,0.000046164725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007234321,0.00075627316,0.0008459525,0.00042932548,0.000727017,0.0005759496,0.00088917045,0.00060967734,0.00085506396],"category_scores_gemma":[0.0020578033,0.00021631707,0.0004308476,0.0002221131,0.00071238726,0.00080406445,0.0014214524,0.00061704003,0.00012316465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068787806,0.00016193373,0.0067485166,0.00016422581,0.000101777565,0.00040023404,0.00033901082,0.7047615,0.03471565,0.014976022,0.0008925938,0.2360506],"study_design_scores_gemma":[0.000015859456,0.00007810989,0.0006528552,0.0000042652555,0.0000144990945,0.000047766844,0.000012245202,0.9933209,0.0037421235,0.0017441794,0.00035799007,0.000009169921],"about_ca_topic_score_codex":0.005695863,"about_ca_topic_score_gemma":0.004324021,"teacher_disagreement_score":0.005695863,"about_ca_system_score_codex":0.00056117325,"about_ca_system_score_gemma":0.000962855,"threshold_uncertainty_score":0.011325419},"labels":[],"label_agreement":null},{"id":"W3003308521","doi":"10.3390/s20030787","title":"SoilCam: A Fully Automated Minirhizotron using Multispectral Imaging for Root Activity Monitoring","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Multispectral image; Computer science; Artificial intelligence; Computer vision; Image quality; Pixel; Image sensor; Image processing; Digital imaging; Remote sensing; Digital image; Image (mathematics); Geology","score_opus":0.024419197174791966,"score_gpt":0.2740767738480183,"score_spread":0.24965757667322636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003308521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14520283,0.0018740399,0.8039308,0.00020045349,0.00020937281,0.0005818037,0.0039751125,0.036529303,0.0074962853],"genre_scores_gemma":[0.22681908,0.00071751705,0.75948393,0.00032039164,0.00008952055,0.00052734866,0.002713474,0.00079342327,0.008535247],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996846,0.000021107153,0.000008931068,0.00008852389,0.00017360525,0.000023206358],"domain_scores_gemma":[0.99977034,0.000054104537,0.000036956975,0.00004035686,0.00007111209,0.000027161372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027151447,0.0006935698,0.00052375474,0.0009059604,0.00023460553,0.0004927003,0.0010704054,0.0007163829,0.003779959],"category_scores_gemma":[0.00030410173,0.00046710513,0.00030037857,0.0005584938,0.0001860804,0.0007465588,0.00058931694,0.00038081966,0.0011027697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016873702,0.0000904441,0.002144146,0.00033637724,0.000045531826,0.00010179139,0.00006649871,0.001069382,0.8126982,0.00040633875,0.006808136,0.17606442],"study_design_scores_gemma":[0.0001431673,0.0008494277,0.04297744,0.0000918408,0.00013621908,0.0018494276,0.00010610179,0.12671056,0.72784656,0.00086050836,0.09811912,0.00030959517],"about_ca_topic_score_codex":0.0011966577,"about_ca_topic_score_gemma":0.0035239612,"teacher_disagreement_score":0.003779959,"about_ca_system_score_codex":0.00033317713,"about_ca_system_score_gemma":0.00045023146,"threshold_uncertainty_score":0.012645185},"labels":[],"label_agreement":null},{"id":"W3003836971","doi":"10.3390/s20030813","title":"Immunosensor Based on Long-Period Fiber Gratings for Detection of Viruses Causing Gastroenteritis","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Viral gastroenteritis research and epidemiology","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Narodowe Centrum Badań i Rozwoju; Narodowe Centrum Nauki","keywords":"Period (music); Fiber; Optical fiber; Virology; Materials science; Medicine; Optics; Composite material; Physics; Acoustics","score_opus":0.04413826996366132,"score_gpt":0.3178606385283419,"score_spread":0.2737223685646806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003836971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8997951,0.009092579,0.08773264,0.00025811943,0.00024796676,0.00011286795,0.00025552887,0.0004997545,0.0020054234],"genre_scores_gemma":[0.92060703,0.0030688988,0.074386,0.00016407006,0.000059040947,0.000043641186,0.00015872231,0.000021561471,0.0014911173],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973017,0.000046744284,0.000012685291,0.00006400145,0.000112619186,0.000033777284],"domain_scores_gemma":[0.99984634,0.00004460566,0.00004017276,0.000010440866,0.000041106763,0.000017253786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003723916,0.000497131,0.00026766802,0.00035304745,0.00012130731,0.00018504569,0.00039250846,0.0005168713,0.00027553304],"category_scores_gemma":[0.0002862694,0.00019331748,0.00024509148,0.0002472713,0.00021096069,0.0003320299,0.00021472778,0.00028080255,0.00013299665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003092329,0.000011778039,0.00026036333,0.00004476414,0.00000435343,0.000035025303,0.000010725785,0.00012590288,0.9964813,0.000038380782,0.000029504365,0.002927013],"study_design_scores_gemma":[0.0000048629277,0.00025050933,0.0027569814,0.000006579146,0.000016280621,0.00020414684,0.00002352957,0.0044509284,0.99129933,0.000036302168,0.00093936967,0.000011057712],"about_ca_topic_score_codex":0.001050485,"about_ca_topic_score_gemma":0.0017389016,"teacher_disagreement_score":0.001050485,"about_ca_system_score_codex":0.00031085045,"about_ca_system_score_gemma":0.00020223334,"threshold_uncertainty_score":0.0022553802},"labels":[],"label_agreement":null},{"id":"W3004729186","doi":"10.3390/s20030904","title":"Multi-Channel Neural Recording Implants: A Review","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Interfacing; Computer science; Amplifier; Brain implant; Artificial neural network; Neural Prosthesis; Neural engineering; Electronic engineering; Converters; Artificial intelligence; Computer hardware; Engineering; Electrical engineering; CMOS; Biomedical engineering","score_opus":0.14023415258169017,"score_gpt":0.3662595460855244,"score_spread":0.22602539350383424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004729186","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023536492,0.9973339,0.00043042738,0.00014049993,0.00020336024,0.000008035739,0.000025475982,0.000013679175,0.001609209],"genre_scores_gemma":[0.0010611658,0.9971547,0.0005760772,0.00013534322,0.00017096249,0.000011797852,0.000043136108,0.0000032725384,0.00084360567],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99986815,0.000014744761,0.000025297068,0.00003134033,0.000048769605,0.000011701279],"domain_scores_gemma":[0.99965286,0.00018647638,0.000045548415,0.000010301735,0.00008470998,0.000020055899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000408898,0.00087528926,0.0008468017,0.0023561788,0.0002207147,0.0007786137,0.00091869844,0.0010072542,0.0045755925],"category_scores_gemma":[0.0005939385,0.00031238998,0.0004928417,0.0018214388,0.00029068693,0.0013264101,0.0004794994,0.00090345286,0.0023151878],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049473292,0.000067129404,0.00015049895,0.019431775,0.000048205082,0.00017663723,0.000048133326,0.00037444543,0.0020898108,0.0027868105,0.0149151,0.95986205],"study_design_scores_gemma":[0.000011670211,0.00019090454,0.00090154994,0.005978052,0.0001523242,0.002425382,0.000071803486,0.00026631434,0.0013329224,0.002093267,0.9865491,0.000026758102],"about_ca_topic_score_codex":0.0006882695,"about_ca_topic_score_gemma":0.0010324273,"teacher_disagreement_score":0.0045755925,"about_ca_system_score_codex":0.00029042183,"about_ca_system_score_gemma":0.00077806227,"threshold_uncertainty_score":0.01530683},"labels":[],"label_agreement":null},{"id":"W3004827807","doi":"10.3390/s20030905","title":"Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Wearable computer; Trunk; Inductive sensor; Bending; Simulation; Engineering; Computer science; Accelerometer; Embedded system; Electrical engineering; Structural engineering","score_opus":0.06088026689410546,"score_gpt":0.3339701690783716,"score_spread":0.27308990218426615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004827807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30400404,0.003952425,0.67217064,0.00051369844,0.0007661585,0.00048239328,0.00077106734,0.0035279288,0.013811616],"genre_scores_gemma":[0.8921128,0.0013447568,0.094635315,0.00045455436,0.00013925217,0.00029214585,0.00032090535,0.00004987979,0.010650475],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999645,0.00007626584,0.000025887834,0.00008855551,0.00014199202,0.0000222451],"domain_scores_gemma":[0.99981993,0.00004103776,0.00005146277,0.000026284062,0.000048868795,0.000012400695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021949751,0.00051627966,0.0003972381,0.00040989713,0.00014364836,0.0003284048,0.0006499893,0.000556446,0.0019717482],"category_scores_gemma":[0.0003158317,0.00019711583,0.00033567045,0.00029960694,0.00013508908,0.00046169056,0.0003431542,0.00018378528,0.000755388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040261817,0.00020446336,0.004473335,0.00076664664,0.00008419745,0.0007072105,0.00017854714,0.0019438602,0.87807214,0.0006937379,0.0022547592,0.1102185],"study_design_scores_gemma":[0.00022382823,0.0066781635,0.048035093,0.00033045615,0.00052324316,0.0077976906,0.00029860658,0.10395267,0.7672569,0.0011105706,0.06355319,0.00023954615],"about_ca_topic_score_codex":0.00015377002,"about_ca_topic_score_gemma":0.0002450228,"teacher_disagreement_score":0.0019717482,"about_ca_system_score_codex":0.00014936338,"about_ca_system_score_gemma":0.000113745395,"threshold_uncertainty_score":0.006596148},"labels":[],"label_agreement":null},{"id":"W3004829098","doi":"10.3390/s20030928","title":"Hy-Bridge: A Hybrid Blockchain for Privacy-Preserving and Trustful Energy Transactions in Internet-of-Things Platforms","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Blockchain; Bridge (graph theory); Computer security; Internet privacy; Internet of Things; The Internet; Computer science; World Wide Web; Biology","score_opus":0.017886873560372878,"score_gpt":0.23075533541373938,"score_spread":0.2128684618533665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004829098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11248444,0.00058904925,0.87106466,0.0006038503,0.00018165972,0.00050067005,0.00030844126,0.002093227,0.01217402],"genre_scores_gemma":[0.911941,0.0003356451,0.08113834,0.00011922614,0.000039633476,0.000329182,0.00035789458,0.00006999618,0.0056690453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999134,0.00023654333,0.000052843516,0.00012203923,0.00033533946,0.00011923333],"domain_scores_gemma":[0.999292,0.00020656914,0.00006658287,0.00020511198,0.00013479374,0.0000948956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010406303,0.0003560862,0.0005016651,0.0003936217,0.0008622125,0.00097580545,0.0012593427,0.0008085338,0.0035066027],"category_scores_gemma":[0.0016992505,0.00022193062,0.00037206532,0.0006002686,0.0006773094,0.0024030188,0.00178391,0.0007589409,0.0005616005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071324286,0.00039593686,0.003293642,0.00040561857,0.00012253749,0.0010616384,0.000513495,0.6002189,0.037923206,0.15224086,0.008728116,0.19438277],"study_design_scores_gemma":[0.00010261344,0.00017872565,0.00035738194,0.000021659665,0.000017484264,0.00014725298,0.000039641032,0.9513764,0.0067093875,0.030025661,0.010995443,0.000028356146],"about_ca_topic_score_codex":0.0031066388,"about_ca_topic_score_gemma":0.0031490824,"teacher_disagreement_score":0.0035066027,"about_ca_system_score_codex":0.0006267495,"about_ca_system_score_gemma":0.0016197258,"threshold_uncertainty_score":0.0117307305},"labels":[],"label_agreement":null},{"id":"W3005522532","doi":"10.3390/s20030874","title":"Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; U.S. Department of Veterans Affairs","keywords":"Overfitting; Computer science; Convolutional neural network; Artificial intelligence; Normalization (sociology); Initialization; Pattern recognition (psychology); Deep learning; Dropout (neural networks); Kernel (algebra); Artificial neural network; Machine learning; Mathematics","score_opus":0.025853043420453563,"score_gpt":0.2569353230166354,"score_spread":0.23108227959618186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005522532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07145053,0.0011691375,0.9175849,0.00035357644,0.00016443866,0.00009643996,0.00061867636,0.0056935376,0.0028687941],"genre_scores_gemma":[0.7391087,0.00063500006,0.25056225,0.00039593957,0.00005914774,0.00021585079,0.0024725844,0.00022555073,0.0063249944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995807,0.00003421037,0.000032827644,0.00014182374,0.00012902218,0.00008149971],"domain_scores_gemma":[0.9996295,0.000070950475,0.00004257619,0.00007019855,0.0001665211,0.000020366888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069468707,0.001310476,0.00062246184,0.00062557764,0.00032645353,0.00057622854,0.0014504201,0.00078079966,0.0016457833],"category_scores_gemma":[0.0014603512,0.0005034733,0.0007915534,0.0007215071,0.00033512068,0.0011965209,0.0009354616,0.0012555514,0.0007371798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004117754,0.00024536112,0.005612017,0.0001997357,0.00019301899,0.00025297597,0.000091768736,0.42640704,0.073162906,0.00341099,0.0071554235,0.48285693],"study_design_scores_gemma":[0.000006107098,0.000025318748,0.00062906736,0.0000051090747,0.000014114251,0.000022343183,0.00000545663,0.9888342,0.009137696,0.00062600203,0.0006858539,0.000008740075],"about_ca_topic_score_codex":0.016823763,"about_ca_topic_score_gemma":0.0164154,"teacher_disagreement_score":0.016823763,"about_ca_system_score_codex":0.0012110169,"about_ca_system_score_gemma":0.0013385568,"threshold_uncertainty_score":0.033451676},"labels":[],"label_agreement":null},{"id":"W3005529980","doi":"10.3390/s20030886","title":"Electroplating of Multiple Materials in Parallel Using Patterned Gels with Applications in Electrochemical Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Nanomaterials and Printing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; California HIV/AIDS Research Program","keywords":"Electroplating; Materials science; Electrolyte; Fabrication; Substrate (aquarium); Nanotechnology; Coating; Electrode; Microfluidics; Microelectrode; Agarose; Electrochemistry; Electrical conductor; Screen printing; Layer (electronics); Composite material; Chemistry","score_opus":0.014353079804437627,"score_gpt":0.20980979586276602,"score_spread":0.1954567160583284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005529980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71069396,0.0112493355,0.266381,0.00063317694,0.0005561594,0.0002994509,0.00032619087,0.0018089582,0.008051665],"genre_scores_gemma":[0.68732345,0.004285343,0.3001834,0.00037532806,0.00010422558,0.00016444639,0.00020504817,0.00019964144,0.007159205],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996007,0.000033811513,0.000037060603,0.00014052306,0.0001490601,0.00003882255],"domain_scores_gemma":[0.9996804,0.000099950375,0.000116957744,0.00005420497,0.000029233748,0.000019371535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026786895,0.000789863,0.00036956038,0.00063667796,0.00020894506,0.0005057156,0.00067176967,0.0005364584,0.0007341264],"category_scores_gemma":[0.00034661312,0.0005003411,0.00037657702,0.0004082653,0.00046607884,0.00060102367,0.00058519316,0.00057086884,0.00044578325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010521226,0.00000981984,0.000045951183,0.000064440115,0.0000046932287,0.000061894556,0.000014330988,0.000090412126,0.9952709,0.00012330408,0.000048984293,0.0042546648],"study_design_scores_gemma":[0.0000033714914,0.000052443986,0.00015645524,0.000005675819,0.00000779586,0.00015701172,0.000006620086,0.000654722,0.997232,0.000048067694,0.001670765,0.000005026095],"about_ca_topic_score_codex":0.0002586108,"about_ca_topic_score_gemma":0.00081671594,"teacher_disagreement_score":0.000789863,"about_ca_system_score_codex":0.00032852945,"about_ca_system_score_gemma":0.00021424024,"threshold_uncertainty_score":0.0024558902},"labels":[],"label_agreement":null},{"id":"W3005621308","doi":"10.3390/s20040993","title":"Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Conditional random field; Computer science; Artificial intelligence; Segmentation; Computer vision; Aerial image; Field (mathematics); Terrain; Image segmentation; Object (grammar); Scale (ratio); Image (mathematics); Pattern recognition (psychology); Geography; Cartography; Mathematics","score_opus":0.01797600338495906,"score_gpt":0.2700800010348486,"score_spread":0.2521039976498895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005621308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052449994,0.00044101052,0.9440388,0.00021615071,0.000047337868,0.000054955322,0.00026437285,0.0013585143,0.0011289249],"genre_scores_gemma":[0.8104683,0.00027363253,0.1855611,0.0002148507,0.00008612033,0.00008061167,0.0012053895,0.00015840949,0.0019516513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958044,0.00010044251,0.000018791805,0.00014411508,0.00009456428,0.0000615679],"domain_scores_gemma":[0.9992756,0.00037370386,0.00008336791,0.00007716281,0.00015371025,0.00003652289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010678957,0.0006729047,0.00093267014,0.0012709566,0.00047657354,0.00043603376,0.0011333898,0.0009237063,0.0015072181],"category_scores_gemma":[0.0019321193,0.00030163955,0.0009554491,0.0010072796,0.00056980527,0.0011348871,0.00065968063,0.0008470233,0.00029536543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029744522,0.00010689469,0.0017646635,0.00008055717,0.00007006541,0.00011539557,0.000077964025,0.81800735,0.008619386,0.007033747,0.0026715875,0.16115497],"study_design_scores_gemma":[0.0000021998876,0.000007862872,0.00021561289,0.0000016085056,0.000005269165,0.000011329048,0.000003488689,0.99765223,0.00071051385,0.0012533682,0.00013299458,0.0000034717323],"about_ca_topic_score_codex":0.016121939,"about_ca_topic_score_gemma":0.01409255,"teacher_disagreement_score":0.016121939,"about_ca_system_score_codex":0.0010294497,"about_ca_system_score_gemma":0.0009712198,"threshold_uncertainty_score":0.032056153},"labels":[],"label_agreement":null},{"id":"W3005819879","doi":"10.3390/s20041027","title":"Rapid High-Resolution Mosaic Acquisition for Photoacoustic Remote Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Centre for Bioengineering and Biotechnology, University of Waterloo; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Mitacs; illumiSonics","keywords":"Raster scan; Optics; Field of view; Resolution (logic); Image resolution; Materials science; Frame rate; Photoacoustic imaging in biomedicine; Computer science; Computer vision; Biomedical engineering; Artificial intelligence; Physics; Engineering","score_opus":0.01378173360466984,"score_gpt":0.2075306465857883,"score_spread":0.19374891298111846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005819879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3029118,0.001457759,0.6866749,0.00019880965,0.00008821233,0.00037738096,0.00048037982,0.0022445426,0.0055662896],"genre_scores_gemma":[0.36774972,0.0005502911,0.6289904,0.000060820505,0.000024050676,0.00028199016,0.0004247728,0.00016963799,0.0017482495],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999843,0.000018454357,0.000006878287,0.0000400212,0.000074778145,0.000016861408],"domain_scores_gemma":[0.9997949,0.000053720607,0.00003541314,0.000047903744,0.0000489044,0.000019223775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034137917,0.00030322772,0.0002013239,0.00048359708,0.00020959113,0.00024879805,0.00033435252,0.0003196537,0.0025185535],"category_scores_gemma":[0.00045143886,0.0002725662,0.00018344737,0.0003383216,0.00021812537,0.00040173662,0.00043192273,0.00041797786,0.00052691234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028745562,0.00001387032,0.00022370068,0.00003713699,0.0000042541096,0.00002965977,0.000025294237,0.00029930775,0.9840725,0.00045060107,0.00025148728,0.014563488],"study_design_scores_gemma":[0.000020795282,0.00021680817,0.0065992484,0.00002150507,0.000015136523,0.0007856579,0.000053221855,0.040082064,0.94212455,0.00084462395,0.009198522,0.000037855007],"about_ca_topic_score_codex":0.00079449796,"about_ca_topic_score_gemma":0.00219619,"teacher_disagreement_score":0.0025185535,"about_ca_system_score_codex":0.00029697918,"about_ca_system_score_gemma":0.0004037982,"threshold_uncertainty_score":0.008425415},"labels":[],"label_agreement":null},{"id":"W3005843662","doi":"10.3390/s20040984","title":"Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Environment and Protected Areas; University of Calgary","funders":"U.S. Forest Service; Government of Alberta; National Aeronautics and Space Administration","keywords":"Remote sensing; Computer science; Moderate-resolution imaging spectroradiometer; Environmental science; Satellite; Meteorology; Data processing; Database; Geography; Engineering","score_opus":0.010072915056132907,"score_gpt":0.21073740177811784,"score_spread":0.20066448672198492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005843662","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70509666,0.00054843334,0.28483742,0.00019182237,0.0001956811,0.0002009388,0.0012854742,0.0036739842,0.003969661],"genre_scores_gemma":[0.84249413,0.00027040424,0.15437831,0.000033768556,0.000044072414,0.00006902884,0.001298627,0.000072317875,0.0013393005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998222,0.000018338496,0.0000193946,0.00006237233,0.00005002398,0.000027681355],"domain_scores_gemma":[0.9998085,0.00003649932,0.00002179707,0.000020222613,0.00009139449,0.000021549744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390349,0.0005895453,0.00036211478,0.0009928397,0.0003376664,0.0005144572,0.0003653604,0.0004210781,0.00075505656],"category_scores_gemma":[0.0007140954,0.0001567006,0.00047778053,0.00072353176,0.000113780974,0.00057275046,0.00020674472,0.0002845806,0.0002838306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072137703,0.000614579,0.05608992,0.00023832473,0.00013717225,0.0010362895,0.0004476561,0.31711122,0.12275417,0.0012271493,0.0046784366,0.4949437],"study_design_scores_gemma":[0.000017364886,0.00009619501,0.024326514,0.000010262641,0.00003491283,0.00013045358,0.00014432846,0.94454724,0.02856141,0.0003842817,0.0017142975,0.000032778935],"about_ca_topic_score_codex":0.008948199,"about_ca_topic_score_gemma":0.00799559,"teacher_disagreement_score":0.008948199,"about_ca_system_score_codex":0.00023890885,"about_ca_system_score_gemma":0.00058033725,"threshold_uncertainty_score":0.017792225},"labels":[],"label_agreement":null},{"id":"W3005980554","doi":"10.3390/s20041087","title":"eXnet: An Efficient Approach for Emotion Recognition in the Wild","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Innovation, Science and Economic Development Canada","keywords":"Overfitting; Computer science; Benchmark (surveying); Convolutional neural network; Artificial intelligence; Deep learning; Generalization; Facial expression; Feature (linguistics); Key (lock); Machine learning; Pattern recognition (psychology); Feature extraction; Artificial neural network","score_opus":0.10071631323483636,"score_gpt":0.31820111574373167,"score_spread":0.2174848025088953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005980554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039608076,0.0017887529,0.87993973,0.0009357564,0.0010822145,0.0003607728,0.0065804822,0.058852687,0.010851472],"genre_scores_gemma":[0.26798606,0.0017586597,0.66468495,0.0011428092,0.00028779823,0.0007466019,0.03172666,0.0025927396,0.02907368],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960166,0.000044297187,0.00003289176,0.00014202474,0.00011747558,0.00006170218],"domain_scores_gemma":[0.99977154,0.00004292088,0.000019202565,0.00006203745,0.000089546644,0.000014782942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006403347,0.002345403,0.00070032256,0.0011198134,0.00048361043,0.0011543059,0.0025781915,0.0009286171,0.008468355],"category_scores_gemma":[0.0012823879,0.0006898919,0.0007963292,0.0007478901,0.0004037788,0.0028841728,0.0014378403,0.0015470631,0.0039920146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005936508,0.00032759944,0.0022253084,0.00037879733,0.00027213886,0.00047913915,0.00014248052,0.067748114,0.033722743,0.009993157,0.120698445,0.76341844],"study_design_scores_gemma":[0.000053738506,0.00010889621,0.00096073584,0.00003623456,0.000046094694,0.00018985498,0.00007382755,0.941494,0.02019795,0.010066807,0.026736114,0.000035689096],"about_ca_topic_score_codex":0.009409202,"about_ca_topic_score_gemma":0.015602465,"teacher_disagreement_score":0.009409202,"about_ca_system_score_codex":0.0008992472,"about_ca_system_score_gemma":0.0007164754,"threshold_uncertainty_score":0.028329492},"labels":[],"label_agreement":null},{"id":"W3006488808","doi":"10.3390/s20041057","title":"Detection System for U-Shaped Bellows Convolution Pitches Based on a Laser Line Scanner","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China; Government of Jiangsu Province","keywords":"Bellows; Convolution (computer science); Expansion joint; Laser scanning; Scanner; Sample (material); Computer science; Line (geometry); Laser; Engineering; Mechanical engineering; Artificial intelligence; Structural engineering; Mathematics; Optics","score_opus":0.026411813459069646,"score_gpt":0.2154899225173082,"score_spread":0.18907810905823855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006488808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10416888,0.00042570892,0.8856851,0.00024122297,0.00012601068,0.00016293407,0.00024888446,0.006404879,0.0025363553],"genre_scores_gemma":[0.39402172,0.0002976196,0.6007466,0.0002565908,0.00008966987,0.00023475815,0.00039062643,0.00016170605,0.0038006457],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992613,0.00008866946,0.00003615965,0.00019327892,0.00036223972,0.00005831068],"domain_scores_gemma":[0.99904996,0.00017518124,0.00009339988,0.00011529057,0.00049848674,0.00006760424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065724575,0.00052425347,0.000616754,0.0016062029,0.00036407565,0.0005739497,0.0012914514,0.0011050528,0.002664239],"category_scores_gemma":[0.00080912764,0.00046541155,0.0004118098,0.00093349925,0.00032135565,0.0012613425,0.0007825934,0.00071136525,0.0011521747],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004371927,0.00015539226,0.006748828,0.00020809195,0.00005302867,0.00029042698,0.0003180085,0.00322024,0.60751486,0.001559455,0.005815241,0.37367916],"study_design_scores_gemma":[0.0001561058,0.0006982808,0.016987087,0.000064607455,0.00012710037,0.0021600088,0.00024313522,0.4643454,0.49916598,0.0010489597,0.014779766,0.00022361122],"about_ca_topic_score_codex":0.0009951683,"about_ca_topic_score_gemma":0.0013976336,"teacher_disagreement_score":0.002664239,"about_ca_system_score_codex":0.0004984646,"about_ca_system_score_gemma":0.00075432623,"threshold_uncertainty_score":0.008912742},"labels":[],"label_agreement":null},{"id":"W3006713196","doi":"10.3390/s20041133","title":"Biometric Identification from Human Aesthetic Preferences","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Identification (biology); Classifier (UML); Artificial intelligence; Dimensionality reduction; Domain (mathematical analysis); Curse of dimensionality; Genetic programming; Machine learning; Feature (linguistics); Human–computer interaction","score_opus":0.05352842604327545,"score_gpt":0.28650493620481826,"score_spread":0.2329765101615428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006713196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7257516,0.0016408984,0.2501663,0.0002847105,0.00016764384,0.0002406481,0.0017129016,0.0014246505,0.018610615],"genre_scores_gemma":[0.9667408,0.00027615912,0.029260248,0.00006360886,0.000045610057,0.00005984241,0.0007245612,0.000034045934,0.002795166],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995814,0.000077712386,0.00001836691,0.0001145039,0.000167376,0.000040649607],"domain_scores_gemma":[0.99958235,0.00008158912,0.000073669704,0.00006405811,0.00016962829,0.000028630695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032178118,0.0003494654,0.0004933089,0.0011051759,0.0001451677,0.00040810418,0.00020630717,0.00030745653,0.002993314],"category_scores_gemma":[0.0016558951,0.00010900579,0.00031149806,0.00075553416,0.00020719366,0.00041632997,0.0004996003,0.0002147417,0.0013418525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089391664,0.00021338527,0.035247613,0.00038065368,0.00009365053,0.0002733386,0.00027293328,0.009221014,0.17274857,0.0016501178,0.0063320636,0.7726727],"study_design_scores_gemma":[0.000059712536,0.0009096777,0.45848608,0.0000875948,0.00011964001,0.0039231796,0.0007264721,0.44979203,0.06732823,0.00727053,0.011176638,0.000120290984],"about_ca_topic_score_codex":0.00077519525,"about_ca_topic_score_gemma":0.0013438936,"teacher_disagreement_score":0.002993314,"about_ca_system_score_codex":0.00026968683,"about_ca_system_score_gemma":0.000095748415,"threshold_uncertainty_score":0.01001364},"labels":[],"label_agreement":null},{"id":"W3006918185","doi":"10.3390/s20041190","title":"Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bridge (graph theory); Robustness (evolution); Filter (signal processing); Vibration; Computer science; Acceleration; Suspension (topology); Engineering; Acoustics; Computer vision","score_opus":0.04422643497326423,"score_gpt":0.2877312572762514,"score_spread":0.24350482230298715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006918185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.692857,0.00085198187,0.30130085,0.00026173482,0.0001508874,0.00021372677,0.00022039535,0.00080162904,0.0033418324],"genre_scores_gemma":[0.90614396,0.0004759673,0.09087151,0.00007396901,0.000034512545,0.00011579018,0.00018802093,0.000023685878,0.002072574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968505,0.000042541422,0.000015721635,0.000056651534,0.00017242393,0.000027656977],"domain_scores_gemma":[0.99967635,0.00008613762,0.000029684932,0.000050419356,0.00014199824,0.000015357575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039478313,0.0003237413,0.00025836262,0.0003953607,0.00019738855,0.00033827775,0.00033162592,0.00055025093,0.0012048716],"category_scores_gemma":[0.0008747398,0.00010500326,0.0002492729,0.0001865437,0.00021948495,0.0003332019,0.00031772532,0.00022753204,0.00040880236],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030652102,0.0002011915,0.009797524,0.0005216172,0.00004359033,0.00041293137,0.00072421826,0.0028871482,0.7547828,0.0009436732,0.0010829879,0.22829577],"study_design_scores_gemma":[0.00006809994,0.0031721739,0.05786383,0.00012844338,0.00011698249,0.0022742525,0.0012161228,0.12359223,0.79387176,0.0009174284,0.01667978,0.00009885791],"about_ca_topic_score_codex":0.0011597676,"about_ca_topic_score_gemma":0.0017944939,"teacher_disagreement_score":0.0012048716,"about_ca_system_score_codex":0.000102235004,"about_ca_system_score_gemma":0.00020408712,"threshold_uncertainty_score":0.0040307045},"labels":[],"label_agreement":null},{"id":"W3006979508","doi":"10.3390/s20041227","title":"Seismic Assessment of Footbridges under Spatial Variation of Earthquake Ground Motion (SVEGM): Experimental Testing and Finite Element Analyses","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Engineering and Vibration Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Structural engineering; Finite element method; Kinematics; Modal analysis; Modal; Modal testing; Engineering; Geology; Physics; Materials science","score_opus":0.041920580288403245,"score_gpt":0.28603868006767796,"score_spread":0.24411809977927473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006979508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99691594,0.000018522871,0.0027523665,0.000006782052,0.0000029358125,0.000008144496,0.000035994708,0.0000150475025,0.00024420978],"genre_scores_gemma":[0.9975139,0.000031218,0.0019584617,0.0000046956975,0.0000019956428,0.000010378314,0.000096499214,0.0000027735018,0.0003800785],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970335,0.000042935724,0.00001865846,0.00005430535,0.0001388113,0.000041887357],"domain_scores_gemma":[0.99967194,0.000090678135,0.00005432939,0.000052585434,0.000090871006,0.000039578736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062654406,0.00045795087,0.00027177396,0.0005493169,0.00022605513,0.00021833093,0.00038924636,0.00048343852,0.000928745],"category_scores_gemma":[0.00095570507,0.00016335251,0.00021894695,0.00029358492,0.00043887945,0.00022137356,0.00049110403,0.00019538405,0.00012625106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004900719,0.00041295105,0.03384756,0.00017998581,0.000045185618,0.0007544789,0.0010940464,0.064969674,0.8673047,0.00043595862,0.0001590244,0.030306408],"study_design_scores_gemma":[0.000065939326,0.006466571,0.29588142,0.000054459928,0.00011717014,0.00068846723,0.002215596,0.2977428,0.39378873,0.00036756456,0.0025506704,0.00006058861],"about_ca_topic_score_codex":0.001367467,"about_ca_topic_score_gemma":0.003743215,"teacher_disagreement_score":0.001367467,"about_ca_system_score_codex":0.000222414,"about_ca_system_score_gemma":0.00020426234,"threshold_uncertainty_score":0.0033135414},"labels":[],"label_agreement":null},{"id":"W3007149813","doi":"10.3390/s20051252","title":"Graphene-Oxide-Based Electrochemical Sensors for the Sensitive Detection of Pharmaceutical Drug Naproxen","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Electrochemical gas sensor; Differential pulse voltammetry; Naproxen; Oxide; Nanomaterials; Cyclic voltammetry; Materials science; Electrochemistry; Inorganic chemistry; Nanotechnology; Chemistry; Electrode","score_opus":0.01101695321346444,"score_gpt":0.22284184623821524,"score_spread":0.2118248930247508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007149813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60896707,0.14009519,0.23492429,0.0014441794,0.0009488644,0.0006499923,0.001691016,0.001592716,0.009686741],"genre_scores_gemma":[0.7766603,0.03451499,0.18106775,0.00084611936,0.00013299988,0.00019707932,0.00070773496,0.000038806556,0.005834133],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995283,0.00007929489,0.00002948075,0.00007631368,0.00024848318,0.000038229377],"domain_scores_gemma":[0.9998852,0.000033820754,0.000026077789,0.000009130727,0.00003333231,0.000012306391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027984308,0.00088263326,0.00039704423,0.00079394924,0.00019287909,0.00033531847,0.0008057603,0.0010791925,0.00054619],"category_scores_gemma":[0.0003553788,0.0003414983,0.0004536382,0.00039397727,0.00025991446,0.0006567624,0.0003851266,0.00061914907,0.00038368642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040823383,0.000024338658,0.00015882927,0.000315758,0.000023845792,0.000107911386,0.000014253537,0.00020939008,0.9885882,0.00016490734,0.00014284135,0.010209],"study_design_scores_gemma":[0.000005987915,0.00018665592,0.00091212423,0.000019171912,0.000048670518,0.00044525662,0.000024771924,0.0029558942,0.9904889,0.000085892316,0.00479953,0.000027090618],"about_ca_topic_score_codex":0.0006476439,"about_ca_topic_score_gemma":0.0024326793,"teacher_disagreement_score":0.0010791925,"about_ca_system_score_codex":0.00035420444,"about_ca_system_score_gemma":0.00019498204,"threshold_uncertainty_score":0.0025699139},"labels":[],"label_agreement":null},{"id":"W3007391921","doi":"10.3390/s20041162","title":"Correction: Osman, M., et al. A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization. Sensors 2019, 19, 5178","year":2020,"lang":"en","type":"erratum","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariance; Computer science; Estimation; Artificial intelligence; Engineering; Statistics; Mathematics; Systems engineering","score_opus":0.03502639717011449,"score_gpt":0.2853357776315134,"score_spread":0.25030938046139894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007391921","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013087658,0.0010699086,0.0016293168,0.022355044,0.96644473,0.000029037388,0.0043109977,0.00083431153,0.0031958423],"genre_scores_gemma":[0.020929735,0.013735894,0.024242051,0.0699857,0.32234266,0.00046626548,0.029987553,0.0066877087,0.5116224],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938263,0.00064511906,0.0009813193,0.00076144235,0.0033536532,0.00043210492],"domain_scores_gemma":[0.93187135,0.005720963,0.0023234831,0.0036564134,0.054669127,0.0017585693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004639848,0.0032372938,0.0021614404,0.006319516,0.0035786596,0.005004744,0.0041363207,0.0047960365,0.0948815],"category_scores_gemma":[0.07261276,0.0013702549,0.0020115422,0.004783528,0.0020731369,0.0030554584,0.0031730223,0.008766383,0.080197565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017540826,0.000003332556,0.000049702718,0.00010746431,0.0000058949386,0.000055981378,0.000017337607,0.00004022263,0.000039972812,0.00038040336,0.99389017,0.0053920625],"study_design_scores_gemma":[0.000019826273,0.000012743715,0.00049828965,0.00024521473,0.000024060615,0.0002891469,0.00006640581,0.0002409422,0.00035627227,0.0007374934,0.99747807,0.000031626507],"about_ca_topic_score_codex":0.034853343,"about_ca_topic_score_gemma":0.038596798,"teacher_disagreement_score":0.0948815,"about_ca_system_score_codex":0.003773737,"about_ca_system_score_gemma":0.007537001,"threshold_uncertainty_score":0.31741023},"labels":[],"label_agreement":null},{"id":"W3007670733","doi":"10.3390/s20051261","title":"Designing a Streaming Algorithm for Outlier Detection in Data Mining—An Incrementa Approach","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data stream mining; Sliding window protocol; Anomaly detection; Streaming data; Outlier; Data mining; Context (archaeology); Streaming algorithm; Concept drift; Scalability; Big data; Algorithm; Database; Artificial intelligence; Window (computing)","score_opus":0.07941839438265871,"score_gpt":0.2957602859902442,"score_spread":0.21634189160758546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007670733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028620379,0.00010565311,0.9960496,0.00010177464,0.000028445074,0.000049383554,0.000025497755,0.00056802726,0.0002095456],"genre_scores_gemma":[0.07136811,0.00020327947,0.9270345,0.000102512204,0.00008361761,0.00018509848,0.0002119905,0.00012020696,0.0006907456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977508,0.00042584803,0.0003232561,0.0006292008,0.0007214129,0.00014948015],"domain_scores_gemma":[0.9955776,0.0014330772,0.00033176894,0.000732943,0.0017384626,0.00018620778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002897945,0.0011603019,0.0014924662,0.0026066343,0.00097393803,0.001488515,0.0028105166,0.001255329,0.0014090261],"category_scores_gemma":[0.00947878,0.0006675877,0.0012602822,0.0023383496,0.0008788585,0.0030715275,0.0018606625,0.0015737212,0.00085145084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043101766,0.00022890589,0.0058130873,0.00031859605,0.0001758703,0.00035215277,0.00034406452,0.2436751,0.024295578,0.03377332,0.005123573,0.6854687],"study_design_scores_gemma":[0.00001907442,0.0000884111,0.00025591557,0.000012292536,0.000013893182,0.00013562298,0.00003147078,0.9842433,0.0044136234,0.007770864,0.003000862,0.000014673145],"about_ca_topic_score_codex":0.002877062,"about_ca_topic_score_gemma":0.0021611708,"teacher_disagreement_score":0.002897945,"about_ca_system_score_codex":0.00086584326,"about_ca_system_score_gemma":0.001771947,"threshold_uncertainty_score":0.015326023},"labels":[],"label_agreement":null},{"id":"W3008286244","doi":"10.3390/s20041184","title":"Multifunctional Ultrahigh Sensitive Microwave Planar Sensor to Monitor Mechanical Motion: Rotation, Displacement, and Stretch","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Planar; Resonator; Sensitivity (control systems); Detector; Rotation (mathematics); Materials science; Tracking (education); Microwave; Displacement (psychology); Acoustics; Dynamic range; Optoelectronics; Physics; Electronic engineering; Optics; Engineering; Computer science; Telecommunications","score_opus":0.01235938772334301,"score_gpt":0.22209554400636441,"score_spread":0.2097361562830214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008286244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77308583,0.002034518,0.21986544,0.00018182932,0.00013175172,0.00003967408,0.00018809656,0.0009321815,0.0035405974],"genre_scores_gemma":[0.92755777,0.00045174925,0.069557235,0.000095895106,0.000035791647,0.00002521999,0.00011829095,0.000022600412,0.0021353702],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997868,0.00002126025,0.0000068605273,0.000070942086,0.00009368741,0.00002043204],"domain_scores_gemma":[0.99985504,0.000029032628,0.000054865275,0.000016893515,0.000032054344,0.000012168243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001453526,0.00039345233,0.00033490124,0.00018890532,0.000083126455,0.00025181167,0.0004218583,0.0004240745,0.00062845956],"category_scores_gemma":[0.00019455122,0.00019244428,0.00021034127,0.00016274059,0.0001938217,0.0003906616,0.00023499977,0.0002469942,0.0002687456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002320459,0.0000061896753,0.00018666522,0.000027662876,0.00000529003,0.000018249968,0.0000064583733,0.00024234148,0.9957455,0.000091417605,0.000047086705,0.0035998889],"study_design_scores_gemma":[0.0000055362643,0.00037052442,0.002430041,0.0000038986973,0.0000254382,0.00036429713,0.00002816904,0.009543516,0.9851129,0.000059911355,0.002038532,0.000017172306],"about_ca_topic_score_codex":0.00011275276,"about_ca_topic_score_gemma":0.00031078735,"teacher_disagreement_score":0.00062845956,"about_ca_system_score_codex":0.00017302351,"about_ca_system_score_gemma":0.00011123311,"threshold_uncertainty_score":0.0021024346},"labels":[],"label_agreement":null},{"id":"W3009228778","doi":"10.3390/s20051350","title":"Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trilateration; Bluetooth Low Energy; Computer science; Received signal strength indication; Wireless; Bluetooth; Real-time computing; Signal strength; Artificial intelligence; Simulation; Engineering; Telecommunications","score_opus":0.07147044553420495,"score_gpt":0.277422457597863,"score_spread":0.20595201206365804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009228778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10493391,0.00033285815,0.8909842,0.00012333665,0.00006046514,0.000031587235,0.000030969048,0.0010017584,0.0025008852],"genre_scores_gemma":[0.8253556,0.00029891895,0.17155612,0.00014932662,0.00003080677,0.000047184836,0.00011605704,0.0000602328,0.0023857285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969363,0.000060629383,0.000014904133,0.0000735134,0.000114318274,0.000043067685],"domain_scores_gemma":[0.9996462,0.00011878133,0.000073662544,0.0000433462,0.00010067198,0.000017359782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036849445,0.0006375463,0.00055160257,0.0008492157,0.0003017756,0.0005279082,0.00069769553,0.00068779837,0.0006558257],"category_scores_gemma":[0.0011693103,0.00021831969,0.0004184469,0.0005353114,0.00038257154,0.0006508159,0.0006142831,0.00048466542,0.0003947658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039768033,0.00023460509,0.007750047,0.0001745698,0.000101846745,0.0002844261,0.0002812556,0.27785945,0.07265363,0.0017993673,0.0012822369,0.6371809],"study_design_scores_gemma":[0.00000980748,0.00019198188,0.004345014,0.0000142550825,0.000026296588,0.00020723476,0.00006819319,0.9773128,0.015841097,0.0009043695,0.0010547349,0.000024230265],"about_ca_topic_score_codex":0.0017784322,"about_ca_topic_score_gemma":0.0027184002,"teacher_disagreement_score":0.0017784322,"about_ca_system_score_codex":0.00019671986,"about_ca_system_score_gemma":0.00027357636,"threshold_uncertainty_score":0.0035361648},"labels":[],"label_agreement":null},{"id":"W3009235338","doi":"10.3390/s20051412","title":"Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Track (disk drive); Track geometry; Association (psychology); Computer science; Maximization; Probabilistic logic; Expectation–maximization algorithm; Tracking (education); Gaussian; Constraint (computer-aided design); Computer vision; Artificial intelligence; Algorithm; Geometry; Mathematics; Mathematical optimization; Maximum likelihood; Statistics","score_opus":0.03538670272402877,"score_gpt":0.29538072164245577,"score_spread":0.259994018918427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009235338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021565307,0.000256483,0.9770453,0.00006722534,0.0000437135,0.000024197843,0.000047865098,0.0003896097,0.0005602616],"genre_scores_gemma":[0.65214723,0.00044764578,0.34409612,0.00012111815,0.00010348341,0.000093960865,0.0006121525,0.00015372351,0.0022245606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988681,0.00021467198,0.000051276576,0.00039384465,0.00034101564,0.00013101629],"domain_scores_gemma":[0.99781775,0.000691974,0.0004562192,0.00050621526,0.00040059705,0.00012725953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014662552,0.0007942724,0.0012466434,0.0012883702,0.0008820939,0.0011291907,0.0020538336,0.001149236,0.00092009833],"category_scores_gemma":[0.0059520793,0.0005699334,0.00076544733,0.0023340043,0.0007013171,0.002333912,0.0022580212,0.0011175994,0.0007717101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003593445,0.0001106354,0.008418946,0.00011179294,0.00015113616,0.00017490303,0.00029343125,0.39301357,0.01087476,0.015099979,0.002834575,0.568557],"study_design_scores_gemma":[0.000017006407,0.00007783038,0.0012955053,0.0000088090355,0.000032660195,0.00015982159,0.00006327294,0.9822662,0.004379607,0.009255968,0.0024222669,0.00002100121],"about_ca_topic_score_codex":0.0039426587,"about_ca_topic_score_gemma":0.0051592467,"teacher_disagreement_score":0.0039426587,"about_ca_system_score_codex":0.00062184694,"about_ca_system_score_gemma":0.001510141,"threshold_uncertainty_score":0.007839441},"labels":[],"label_agreement":null},{"id":"W3009634742","doi":"10.3390/s20051461","title":"A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ambulatory ECG; Ambulatory; Metric (unit); Medicine; Anomaly detection; Medical emergency; Computer science; Artificial intelligence; Engineering; Internal medicine; Operations management","score_opus":0.0873217101206354,"score_gpt":0.3673160432000446,"score_spread":0.2799943330794092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009634742","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046551097,0.99600345,0.0011775759,0.00029496252,0.00023455294,0.000016305674,0.00008676581,0.000032189895,0.0016887119],"genre_scores_gemma":[0.0023727838,0.9950223,0.0012949939,0.0002524326,0.00023883546,0.000015334841,0.00016707544,0.0000075865714,0.0006287078],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9993593,0.00010092895,0.0000982712,0.00015664237,0.0002555901,0.000029349067],"domain_scores_gemma":[0.9981206,0.0010904053,0.00014123191,0.00005244608,0.000545335,0.00004999412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013486203,0.00097069616,0.0012793142,0.0031846506,0.00021798398,0.0009959115,0.0011821379,0.00096733106,0.0032995734],"category_scores_gemma":[0.0032987688,0.0003802942,0.0010283074,0.0034410148,0.0002927721,0.0015887279,0.0004894443,0.0008483519,0.002192467],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059987102,0.000052994805,0.00084394123,0.015176514,0.00015702112,0.00009926562,0.000043152897,0.0002490839,0.0013323629,0.0010343892,0.012941778,0.96800953],"study_design_scores_gemma":[0.000030860985,0.0005063745,0.0070149647,0.013633457,0.00096917356,0.003323577,0.00022866488,0.0010228502,0.003939597,0.0025396685,0.96668464,0.000106130035],"about_ca_topic_score_codex":0.0012775175,"about_ca_topic_score_gemma":0.0013505741,"teacher_disagreement_score":0.0032995734,"about_ca_system_score_codex":0.00033544414,"about_ca_system_score_gemma":0.0009956686,"threshold_uncertainty_score":0.011038125},"labels":[],"label_agreement":null},{"id":"W3010189188","doi":"10.3390/s20051449","title":"A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"","keywords":"Canola; Multispectral image; Greenhouse; Environmental science; Agronomy; Reflectivity; Correlation coefficient; Partial least squares regression; Remote sensing; Mathematics; Statistics; Biology","score_opus":0.045256405495904435,"score_gpt":0.24234112995161344,"score_spread":0.19708472445570901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010189188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4263997,0.002392051,0.56440246,0.00028031127,0.00018543458,0.00025399597,0.0006359841,0.0017939671,0.0036560649],"genre_scores_gemma":[0.75211865,0.0008357,0.24183638,0.00021269589,0.00005491859,0.00018292565,0.000433713,0.000034238346,0.0042907214],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999793,0.00002178869,0.0000069563107,0.00006300913,0.00010338401,0.000011980426],"domain_scores_gemma":[0.9998896,0.000021670017,0.000024765335,0.00001640893,0.000041514064,0.000006076588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024556209,0.0004632324,0.00031367264,0.00033433342,0.00017797238,0.00023148439,0.00063347974,0.00052187504,0.0006130236],"category_scores_gemma":[0.00023488913,0.00019427686,0.00023893527,0.00038803855,0.00014273448,0.00053354836,0.00027034458,0.0002770099,0.00027852046],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098520235,0.00008476234,0.004364271,0.00025679872,0.000028999306,0.00009153089,0.000044446206,0.002126328,0.8716599,0.00027076885,0.0008686241,0.120105036],"study_design_scores_gemma":[0.00007572001,0.0011826047,0.053570524,0.00004994603,0.00016437429,0.0013881593,0.00016690519,0.14037089,0.78700864,0.0006291485,0.0152672455,0.00012578879],"about_ca_topic_score_codex":0.0008907923,"about_ca_topic_score_gemma":0.0022482707,"teacher_disagreement_score":0.0008907923,"about_ca_system_score_codex":0.00024743026,"about_ca_system_score_gemma":0.00023087823,"threshold_uncertainty_score":0.002050817},"labels":[],"label_agreement":null},{"id":"W3010195458","doi":"10.3390/s20061613","title":"Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Electromyography; Computer science; Physical medicine and rehabilitation; Robustness (evolution); Simulation; Medicine","score_opus":0.022513290810125142,"score_gpt":0.27685369572325264,"score_spread":0.2543404049131275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010195458","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021391943,0.998923,0.00018361353,0.0002763298,0.00010205451,0.000009544554,0.000054634027,0.000003932963,0.0002329615],"genre_scores_gemma":[0.0034678439,0.9953323,0.0004065626,0.00036569408,0.00015295643,0.00003067568,0.000114240065,0.000004431406,0.00012522635],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99635243,0.0008875734,0.0012567291,0.00062343205,0.00076426676,0.0001154436],"domain_scores_gemma":[0.9730822,0.021224985,0.002658957,0.000499805,0.0023449664,0.00018909418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006108692,0.0010984124,0.0035224927,0.0073658912,0.00036196393,0.0024694158,0.0012462004,0.001443457,0.004389868],"category_scores_gemma":[0.01995789,0.0005341086,0.0027124523,0.008399503,0.0011205177,0.0024365333,0.0010403666,0.0011641834,0.0006875464],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017910457,0.000042917425,0.0019641658,0.36215302,0.0019378671,0.000103038416,0.00023147756,0.00023086622,0.0005042426,0.0026336627,0.0061186557,0.62390095],"study_design_scores_gemma":[0.00009297728,0.00045101153,0.01874997,0.44975376,0.022272574,0.0019813294,0.0009969381,0.00038339233,0.0013554414,0.005379326,0.4984425,0.0001408018],"about_ca_topic_score_codex":0.004332016,"about_ca_topic_score_gemma":0.008095947,"teacher_disagreement_score":0.0073658912,"about_ca_system_score_codex":0.0015970727,"about_ca_system_score_gemma":0.007155817,"threshold_uncertainty_score":0.032306194},"labels":[],"label_agreement":null},{"id":"W3010360782","doi":"10.3390/s20051320","title":"Asynchronous RTK Method for Detecting the Stability of the Reference Station in GNSS Deformation Monitoring","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; China Scholarship Council; Chang'an University; National Natural Science Foundation of China","keywords":"GNSS applications; Reference frame; Deformation monitoring; Computer science; Displacement (psychology); Global Positioning System; Real-time computing; Real Time Kinematic; Geodesy; Total station; Asynchronous communication; Satellite; Kinematics; Frame of reference; Remote sensing; Landslide; Frame (networking); Deformation (meteorology); Telecommunications; Geology; Engineering; Geography; Meteorology; Aerospace engineering","score_opus":0.04068389567866196,"score_gpt":0.27298361184911674,"score_spread":0.23229971617045478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010360782","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026554238,0.00034454564,0.9708891,0.000035511828,0.000104675004,0.000039933533,0.000097457356,0.00047658454,0.0014578737],"genre_scores_gemma":[0.48330474,0.0007955897,0.511146,0.00008088447,0.00018877468,0.00013025745,0.00040864557,0.00017519666,0.0037699079],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932945,0.00010371767,0.00004082936,0.0002340432,0.00024371131,0.000048224407],"domain_scores_gemma":[0.9995332,0.00010147235,0.00010630256,0.0000755704,0.00016182623,0.000021745314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048062604,0.00077955954,0.00044795248,0.0012419826,0.00030436768,0.0005821635,0.0008067168,0.0005437256,0.0011739379],"category_scores_gemma":[0.0015831332,0.00022226266,0.0003941525,0.0010748483,0.00034211157,0.0008383228,0.0004927661,0.0006125575,0.0008584289],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006294273,0.00013226419,0.007794128,0.00042390992,0.00008891627,0.00042603846,0.00033634124,0.0798003,0.17777441,0.008068064,0.00273304,0.7217932],"study_design_scores_gemma":[0.000044857974,0.00023630299,0.01023846,0.000031225998,0.00010821144,0.00090906903,0.00011985901,0.9126529,0.064005405,0.003461262,0.008096871,0.000095530326],"about_ca_topic_score_codex":0.0011399286,"about_ca_topic_score_gemma":0.0019827038,"teacher_disagreement_score":0.0012419826,"about_ca_system_score_codex":0.00024720223,"about_ca_system_score_gemma":0.00041488622,"threshold_uncertainty_score":0.003927231},"labels":[],"label_agreement":null},{"id":"W3010431109","doi":"10.3390/s20051392","title":"Semantically Guided Large Deformation Estimation with Deep Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Artificial intelligence; Segmentation; Deep learning; Parameterized complexity; Inference; Regularization (linguistics); Computer vision; Face (sociological concept); Network architecture; Pattern recognition (psychology); Algorithm","score_opus":0.013925458248526052,"score_gpt":0.2571882668844139,"score_spread":0.2432628086358878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010431109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012962599,0.00020060655,0.98408246,0.00016841677,0.000029706807,0.000023465174,0.0000901268,0.0016592459,0.00078335823],"genre_scores_gemma":[0.6098101,0.0004668422,0.3795539,0.00043614625,0.00011480233,0.00016981675,0.0011508251,0.0006026839,0.0076948795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962914,0.000080344806,0.000016683121,0.0001301368,0.00008980223,0.000053860043],"domain_scores_gemma":[0.99949896,0.00018687382,0.00008620261,0.0001138958,0.00007860808,0.000035437926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008006755,0.0014486898,0.00102124,0.00075674645,0.0003991952,0.0007993779,0.0015909729,0.0014424551,0.0020541335],"category_scores_gemma":[0.002197367,0.0007509252,0.0009221372,0.00084255397,0.00088469277,0.0018046114,0.0016374914,0.001912846,0.00091198174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014993756,0.000058178397,0.00057155424,0.000054798424,0.00006547438,0.00006327877,0.00004018394,0.79219496,0.010009225,0.009407488,0.002326886,0.18505792],"study_design_scores_gemma":[0.0000027910373,0.000010567726,0.00005394742,0.0000029626972,0.0000036362294,0.000010104289,0.0000027445556,0.99422,0.0013636012,0.004053126,0.000272994,0.0000035113644],"about_ca_topic_score_codex":0.0058085765,"about_ca_topic_score_gemma":0.010551764,"teacher_disagreement_score":0.0058085765,"about_ca_system_score_codex":0.0012333038,"about_ca_system_score_gemma":0.0011034511,"threshold_uncertainty_score":0.011549532},"labels":[],"label_agreement":null},{"id":"W3010601184","doi":"10.3390/s20051328","title":"Does the Presence of Cognitive Impairment Exacerbate the Risk of Falls in People with Peripheral Neuropathy? An Application of Body-Worn Inertial Sensors to Measure Gait Variability","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; National Cancer Institute; National Heart, Lung, and Blood Institute; Foundation for the National Institutes of Health","keywords":"STRIDE; Gait; Physical medicine and rehabilitation; Cognition; Fear of falling; Cognitive impairment; Montreal Cognitive Assessment; Gait analysis; Medicine; Task (project management); Preferred walking speed; Risk factor; Poison control; Physical therapy; Psychology; Injury prevention; Internal medicine; Medical emergency; Psychiatry","score_opus":0.013046001235112058,"score_gpt":0.29009366356654837,"score_spread":0.2770476623314363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010601184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99828476,0.00070858956,0.00046418014,0.000044124386,0.000010225758,0.000011382738,0.000073912845,0.000005699769,0.0003970095],"genre_scores_gemma":[0.9986951,0.00032510856,0.00077543134,0.000034398894,0.000020029482,0.000010455313,0.00007166556,9.191944e-7,0.00006706884],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99963355,0.00011119146,0.000049837992,0.000066385444,0.00010960015,0.000029458895],"domain_scores_gemma":[0.9990839,0.00023898437,0.00042591628,0.000041746862,0.00012854229,0.00008094993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000715575,0.0004214863,0.0004939912,0.00091198477,0.00020586171,0.00051803945,0.00020717057,0.00048669218,0.00040411187],"category_scores_gemma":[0.0025808797,0.00016373192,0.0004459547,0.00069154386,0.00019538535,0.0003164876,0.0004115535,0.00028000053,0.00008216929],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054286857,0.00011520934,0.98014235,0.00006857625,0.00017003469,0.00013981712,0.00014801102,0.0001771237,0.0025938475,0.000010391024,0.000048987742,0.015842827],"study_design_scores_gemma":[0.000005764092,0.00034832492,0.9983981,0.000011407691,0.00005875013,0.00019894046,0.00010924756,0.0005688128,0.00020370056,0.000027825165,0.00006281223,0.0000062469353],"about_ca_topic_score_codex":0.0021209165,"about_ca_topic_score_gemma":0.004381113,"teacher_disagreement_score":0.0021209165,"about_ca_system_score_codex":0.00013162437,"about_ca_system_score_gemma":0.00015781623,"threshold_uncertainty_score":0.004217148},"labels":[],"label_agreement":null},{"id":"W3010923682","doi":"10.3390/s20061642","title":"Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Convolutional neural network; Electromyography; Artificial intelligence; Biosignal; Gesture; Deep learning; Pattern recognition (psychology); Prosthetic hand; Gesture recognition; Speech recognition; Artificial neural network; Computer vision; Physical medicine and rehabilitation; Filter (signal processing); Medicine","score_opus":0.06589332529143185,"score_gpt":0.2560663453417574,"score_spread":0.19017302005032552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010923682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9522251,0.003698145,0.036222216,0.000281734,0.0002307002,0.000093507624,0.00044849937,0.001532682,0.0052674245],"genre_scores_gemma":[0.9868602,0.0005006546,0.010010539,0.000047224647,0.000013582278,0.000041755527,0.00058137794,0.000030667274,0.0019140417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996567,0.000053614716,0.000031053318,0.00008685066,0.00008661485,0.0000851169],"domain_scores_gemma":[0.9993799,0.00024871915,0.000042648982,0.000043677323,0.0002404577,0.000044640412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011151009,0.0011086572,0.0005610532,0.00041059384,0.00021692077,0.00044416712,0.00078424136,0.0007668278,0.0012850973],"category_scores_gemma":[0.0026898575,0.00025221438,0.00041407646,0.00030327163,0.00019950302,0.00042922975,0.00035997815,0.0005336822,0.00038070913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039136014,0.000690022,0.019771475,0.0006671716,0.0005051841,0.00040825488,0.00013213785,0.4266265,0.06867007,0.0006598725,0.0032740303,0.47468168],"study_design_scores_gemma":[0.00002670257,0.00055250264,0.0076834387,0.000029202783,0.00008574086,0.00007024162,0.000024784978,0.96978575,0.021266593,0.00010601415,0.00035097706,0.000017931086],"about_ca_topic_score_codex":0.022347897,"about_ca_topic_score_gemma":0.015548845,"teacher_disagreement_score":0.022347897,"about_ca_system_score_codex":0.00078477815,"about_ca_system_score_gemma":0.0007158775,"threshold_uncertainty_score":0.04443562},"labels":[],"label_agreement":null},{"id":"W3010936339","doi":"10.3390/s20051539","title":"Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Radiomics; Prostate cancer; Medicine; Diffusion MRI; Effective diffusion coefficient; Prostatectomy; Medical physics; Computer science; Artificial intelligence; Cancer; Magnetic resonance imaging; Radiology; Internal medicine","score_opus":0.032881133664240295,"score_gpt":0.28734145837520064,"score_spread":0.25446032471096036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010936339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13791935,0.0031773224,0.85274166,0.00036433808,0.00013792093,0.00023874479,0.00013294943,0.0013033166,0.0039843717],"genre_scores_gemma":[0.7572737,0.001163738,0.23750201,0.0007191495,0.000073318064,0.00017894678,0.00019934688,0.000105577914,0.0027843027],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958307,0.000074890435,0.000028986944,0.00013338306,0.00014368579,0.00003596889],"domain_scores_gemma":[0.99940574,0.00021053957,0.00015249728,0.00006642648,0.00012724235,0.000037533577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006414742,0.00072983536,0.00042784837,0.00043550646,0.00014451388,0.0006437865,0.00085841626,0.0007232729,0.00079005904],"category_scores_gemma":[0.0016432077,0.00027402272,0.00042119788,0.00021156679,0.00045075815,0.0008205496,0.000732547,0.0005681907,0.00043848736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037750122,0.000105224026,0.0016691945,0.00039311682,0.00005606952,0.00034918316,0.00010461238,0.02663975,0.84534264,0.0028051555,0.00077908166,0.12137857],"study_design_scores_gemma":[0.000053984917,0.0011488826,0.0021164252,0.00003415766,0.00010815437,0.0012868016,0.00008273627,0.3162315,0.6667063,0.0030406977,0.009095112,0.00009531536],"about_ca_topic_score_codex":0.00031770166,"about_ca_topic_score_gemma":0.00054335105,"teacher_disagreement_score":0.00085841626,"about_ca_system_score_codex":0.00028532173,"about_ca_system_score_gemma":0.00029944527,"threshold_uncertainty_score":0.0033925176},"labels":[],"label_agreement":null},{"id":"W3011123160","doi":"10.3390/s20061610","title":"Automated Indoor Image Localization to Support a Post-Event Building Assessment","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Computer science; Point cloud; Computer vision; Context (archaeology); Documentation; Artificial intelligence; Event (particle physics); Global Positioning System; Structure from motion; Point (geometry); Motion (physics); Geography","score_opus":0.02079877555348998,"score_gpt":0.27635316082656003,"score_spread":0.25555438527307006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011123160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03220233,0.0001576125,0.94609064,0.00011052219,0.00007749858,0.00022305346,0.00097213814,0.016037755,0.00412841],"genre_scores_gemma":[0.26242733,0.00019516185,0.7308116,0.00007082436,0.000049391158,0.00023450865,0.0023578568,0.0006064773,0.0032468275],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999548,0.000047155987,0.000015809232,0.000112736205,0.00020021551,0.00007603187],"domain_scores_gemma":[0.99922633,0.00008894236,0.00009382582,0.0001922229,0.00032792808,0.00007070616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043660586,0.0008732891,0.00064146286,0.0024178755,0.00039645593,0.00087418157,0.0013085647,0.0006245468,0.0066048754],"category_scores_gemma":[0.001325107,0.00040346038,0.00051456067,0.0012554575,0.00023446465,0.0008873195,0.0017184673,0.00062761916,0.0049270415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003562944,0.00035929162,0.010936661,0.000387661,0.0000760381,0.00048173926,0.0006426925,0.04964881,0.13836654,0.0026661167,0.017021924,0.77905625],"study_design_scores_gemma":[0.00007475281,0.0003497271,0.027752858,0.00012795402,0.00008567346,0.0004869872,0.000770845,0.8200539,0.10516185,0.003911021,0.041112933,0.00011155165],"about_ca_topic_score_codex":0.0036839445,"about_ca_topic_score_gemma":0.008136572,"teacher_disagreement_score":0.0066048754,"about_ca_system_score_codex":0.0004038829,"about_ca_system_score_gemma":0.00077021023,"threshold_uncertainty_score":0.022095501},"labels":[],"label_agreement":null},{"id":"W3011410192","doi":"10.3390/s20061700","title":"Large-Scale ALS Data Semantic Classification Integrating Location-Context-Semantics Cues by Higher-Order CRF","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Division of Graduate Education; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; China Postdoctoral Science Foundation; Lomonosov Moscow State University; Graduate Research and Innovation Projects of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Semantics (computer science); Artificial intelligence; Context (archaeology); Probabilistic logic; Conditional random field; Pairwise comparison; Pattern recognition (psychology); Spatial contextual awareness; Smoothing; Data mining; Computer vision","score_opus":0.035114755040315755,"score_gpt":0.27106084908158673,"score_spread":0.23594609404127098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011410192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022215083,0.00027406638,0.9710241,0.0002476838,0.0000622802,0.00012172599,0.0013668131,0.0037403298,0.0009479347],"genre_scores_gemma":[0.5380057,0.00033343828,0.44979233,0.00029182414,0.00018626652,0.00034648296,0.008564555,0.0003741504,0.0021052156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861455,0.00020600569,0.00008944541,0.0005017509,0.00040343648,0.00018482738],"domain_scores_gemma":[0.9989096,0.00035874575,0.000112559916,0.00023640115,0.0003241022,0.000058555477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012025096,0.0009728553,0.0014528629,0.0026209198,0.000999365,0.0009786973,0.0023733624,0.0012155372,0.0014584644],"category_scores_gemma":[0.002623217,0.00044170153,0.0021402356,0.0029077912,0.0007911202,0.0022883639,0.0013202237,0.0016652311,0.0009553951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035376838,0.00035921347,0.006712989,0.00033956143,0.00019370354,0.0004578989,0.00029759103,0.5250732,0.01688254,0.013273519,0.01579871,0.42025733],"study_design_scores_gemma":[0.000010004286,0.00002222776,0.00086873415,0.000010894154,0.000018640305,0.000059370937,0.000034669556,0.98843646,0.0020851889,0.0066074976,0.0018249853,0.000021326729],"about_ca_topic_score_codex":0.022173997,"about_ca_topic_score_gemma":0.025244555,"teacher_disagreement_score":0.022173997,"about_ca_system_score_codex":0.0012418329,"about_ca_system_score_gemma":0.0020976688,"threshold_uncertainty_score":0.044089854},"labels":[],"label_agreement":null},{"id":"W3011648299","doi":"10.3390/s20061660","title":"Ionization Gas Sensor Using Suspended Carbon Nanotube Beams","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Photoresist; Carbon nanotube; Ionization; Materials science; Argon; Helium; Optoelectronics; Analytical Chemistry (journal); Nanotechnology; Atomic physics; Layer (electronics); Chemistry; Ion","score_opus":0.01935822824622445,"score_gpt":0.203841191384621,"score_spread":0.18448296313839657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011648299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8183253,0.006572418,0.16641822,0.0005814009,0.0006031354,0.0002711341,0.0008272345,0.0013685253,0.005032661],"genre_scores_gemma":[0.8705744,0.002498233,0.121715344,0.00034016263,0.00011196393,0.00015678708,0.00045794275,0.000052014413,0.004093091],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996741,0.000030266796,0.000014395873,0.0000901987,0.00016188223,0.000029218847],"domain_scores_gemma":[0.9997627,0.00005792293,0.000044647037,0.000012950287,0.000100861995,0.00002090251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022624512,0.00057481526,0.00036056407,0.00027682193,0.00021607877,0.00023057683,0.000594822,0.0008740399,0.0005477583],"category_scores_gemma":[0.00030347757,0.00027914066,0.00025364826,0.0003020698,0.00021903694,0.0006266097,0.00035375293,0.00056785694,0.00035974092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012388728,0.0000050309727,0.00006704523,0.000031210646,0.0000031382904,0.000038376715,0.0000065932527,0.000078615514,0.9983998,0.000043313918,0.000048691716,0.0012658719],"study_design_scores_gemma":[0.000002426315,0.000075351345,0.00037600257,0.0000026511002,0.000005205618,0.00009837851,0.000005052541,0.002025605,0.9965996,0.000023031916,0.0007802111,0.000006498136],"about_ca_topic_score_codex":0.00040816606,"about_ca_topic_score_gemma":0.0007126096,"teacher_disagreement_score":0.0008740399,"about_ca_system_score_codex":0.00031543782,"about_ca_system_score_gemma":0.00026986687,"threshold_uncertainty_score":0.0022886992},"labels":[],"label_agreement":null},{"id":"W3011978934","doi":"10.3390/s20051540","title":"Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nelson Mandela African Institution of Science and Technology; International Development Research Centre; University of Ghana; Deutscher Akademischer Austauschdienst; National Institute of Advanced Industrial Science and Technology","keywords":"Wireless sensor network; Energy consumption; Cluster analysis; Computer science; Scheduling (production processes); Computer network; Real-time computing; Engineering; Electrical engineering; Artificial intelligence","score_opus":0.018787527185101138,"score_gpt":0.2206524578156057,"score_spread":0.20186493063050456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011978934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22512919,0.0016164972,0.764652,0.0005574727,0.00011697371,0.00012033413,0.000121543664,0.0012699065,0.0064160614],"genre_scores_gemma":[0.96893793,0.0001843507,0.029946476,0.00006711885,0.000013397385,0.000042933578,0.000058729263,0.000037268695,0.00071189494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996829,0.00007003092,0.000018313782,0.00008109238,0.00007832595,0.00006938621],"domain_scores_gemma":[0.99936944,0.00019492423,0.000109552915,0.000065697626,0.00019117247,0.00006916351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006515771,0.00047328143,0.000478269,0.0004660006,0.00054595276,0.0004206614,0.00091952516,0.00029432116,0.0006332838],"category_scores_gemma":[0.0016131748,0.00015738433,0.0002778595,0.00036840906,0.00033614153,0.00095094054,0.00063672505,0.00030391043,0.00012076336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021923687,0.000119378914,0.0027033815,0.00010892725,0.00004042203,0.00006732159,0.00012893692,0.87265736,0.014589743,0.003815523,0.0014971338,0.10405271],"study_design_scores_gemma":[0.000011649607,0.000084901665,0.0004252101,0.0000049511436,0.0000106933585,0.000029894349,0.000028319278,0.99559516,0.0021797617,0.0011539839,0.00046819504,0.000007324088],"about_ca_topic_score_codex":0.0039715944,"about_ca_topic_score_gemma":0.006467092,"teacher_disagreement_score":0.0039715944,"about_ca_system_score_codex":0.00095542596,"about_ca_system_score_gemma":0.0011828955,"threshold_uncertainty_score":0.00789696},"labels":[],"label_agreement":null},{"id":"W3012029195","doi":"10.3390/s20061628","title":"Biofeedback Systems for Gait Rehabilitation of Individuals with Lower-Limb Amputation: A Systematic Review","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Council of Ontario Universities","keywords":"Biofeedback; Rehabilitation; Physical medicine and rehabilitation; Gait; Amputation; Usability; Psychology; Physical therapy; Medicine; Wearable computer; Computer science; Human–computer interaction; Surgery","score_opus":0.01905377242734991,"score_gpt":0.2653145684786145,"score_spread":0.2462607960512646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012029195","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000428314,0.99895954,0.00009264124,0.00008349228,0.00005207622,0.00010783191,0.000090101224,0.0000060039565,0.00017985143],"genre_scores_gemma":[0.0040786946,0.9950368,0.00034201072,0.00016803393,0.000029752277,0.000160381,0.00007981273,0.0000025900883,0.000101950296],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9980215,0.0005182179,0.0007506427,0.00015599275,0.00047582132,0.0000779729],"domain_scores_gemma":[0.99340737,0.0047908467,0.0009826523,0.00008569089,0.0006537506,0.00007967587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028052535,0.0013346011,0.0047306544,0.0055222632,0.0005624316,0.0018912606,0.0013815798,0.0016672433,0.0054326085],"category_scores_gemma":[0.012428595,0.00061076606,0.0055962238,0.004960249,0.0004746975,0.0015106068,0.0009894557,0.00084562163,0.00048030386],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015353692,0.000031384785,0.00018946554,0.90195704,0.002794629,0.000064713226,0.000095446594,0.00006930213,0.00020570331,0.00016131463,0.0012248534,0.0930526],"study_design_scores_gemma":[0.00024382742,0.00038597928,0.0030424052,0.9170664,0.04208548,0.0006368021,0.00023664928,0.000116310315,0.00038904848,0.00034000693,0.035415474,0.00004160101],"about_ca_topic_score_codex":0.0053446605,"about_ca_topic_score_gemma":0.0147399455,"teacher_disagreement_score":0.0055222632,"about_ca_system_score_codex":0.0018141944,"about_ca_system_score_gemma":0.005934922,"threshold_uncertainty_score":0.018173873},"labels":[],"label_agreement":null},{"id":"W3012192396","doi":"10.3390/s20061601","title":"Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":149,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Jaccard index; Segmentation; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Test set; Lesion; Medicine; Pathology","score_opus":0.03864351926853788,"score_gpt":0.2799265061714448,"score_spread":0.24128298690290695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012192396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2869049,0.0029223077,0.6921838,0.0005519277,0.00019891636,0.00037259422,0.0014299296,0.009521647,0.0059140036],"genre_scores_gemma":[0.79249334,0.0011021944,0.19777276,0.0002817564,0.00008589095,0.000093685005,0.0025045325,0.0002415826,0.0054242755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998079,0.000019867464,0.000012359695,0.00007820699,0.00004948963,0.0000321581],"domain_scores_gemma":[0.9998061,0.000044258948,0.000034631743,0.000027015967,0.00006933873,0.000018650897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028230873,0.0008495795,0.0004801262,0.0013260671,0.00019872369,0.00058937113,0.00065348943,0.0007277632,0.0010209477],"category_scores_gemma":[0.0006457717,0.00035566115,0.0006162648,0.00042887637,0.00024157875,0.0005052887,0.00043363665,0.00062563136,0.0006657756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062778994,0.0003319932,0.006952714,0.00025475543,0.00015711485,0.00070292276,0.00011565769,0.19889602,0.11121056,0.0010212545,0.0077010924,0.67202806],"study_design_scores_gemma":[0.000007867884,0.00005229295,0.0025354896,0.00001910792,0.00002056701,0.00019443665,0.000020319298,0.97447205,0.02077932,0.0006479439,0.0012395148,0.0000111430645],"about_ca_topic_score_codex":0.0073435395,"about_ca_topic_score_gemma":0.012245035,"teacher_disagreement_score":0.0073435395,"about_ca_system_score_codex":0.00071863044,"about_ca_system_score_gemma":0.00051846675,"threshold_uncertainty_score":0.014601588},"labels":[],"label_agreement":null},{"id":"W3013149579","doi":"10.3390/s20071824","title":"Comparison between Linear and Branched Polyethylenimine and Reduced Graphene Oxide Coatings as a Capture Layer for Micro Resonant CO2 Gas Concentration Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Polyethylenimine; Graphene; Oxide; Materials science; Layer (electronics); Layer by layer; Nanotechnology; Composite material; Chemistry","score_opus":0.02596770724525891,"score_gpt":0.2551348777964102,"score_spread":0.2291671705511513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013149579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98812485,0.0036799533,0.006620045,0.00006218868,0.000049136026,0.000038941216,0.00009287176,0.000068914414,0.0012631004],"genre_scores_gemma":[0.9825745,0.0014798869,0.01370056,0.000056394314,0.000015391784,0.00003755056,0.0001294407,0.00002311361,0.0019831539],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994536,0.00008637925,0.000035816985,0.00011587959,0.00022910444,0.00007915169],"domain_scores_gemma":[0.99954754,0.0001609204,0.000072398856,0.00004763635,0.00013350634,0.000038007707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049669197,0.0004993741,0.0002312706,0.0003297438,0.00013443847,0.0003722498,0.00056888844,0.00061380555,0.0005962808],"category_scores_gemma":[0.00076974434,0.0003132289,0.00027302993,0.00022558641,0.00022402171,0.00040048317,0.00024128937,0.00023041728,0.00019536243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086289176,0.000010315998,0.00010528702,0.000044082874,0.0000062783924,0.000025465373,0.0000105389845,0.000050996918,0.9982438,0.000019850013,0.000011509481,0.0013855454],"study_design_scores_gemma":[0.0000038498088,0.0001795493,0.0011408909,0.0000036071265,0.000012843703,0.00006481806,0.000009635906,0.0008995814,0.99728703,0.000005443503,0.0003883213,0.000004447529],"about_ca_topic_score_codex":0.0008354859,"about_ca_topic_score_gemma":0.0021756825,"teacher_disagreement_score":0.0008354859,"about_ca_system_score_codex":0.00027213024,"about_ca_system_score_gemma":0.00015686714,"threshold_uncertainty_score":0.0026267767},"labels":[],"label_agreement":null},{"id":"W3013274927","doi":"10.3390/s20071832","title":"Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Orthophoto; Computer science; Artificial intelligence; Pixel; Dijkstra's algorithm; Artificial neural network; Computer vision; Binary image; Shortest path problem; Image processing; Image (mathematics); Graph","score_opus":0.016601508594314564,"score_gpt":0.22793081495481876,"score_spread":0.2113293063605042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013274927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14964952,0.00031062835,0.8428096,0.00013792372,0.00006244143,0.00008916685,0.0003489805,0.002661123,0.003930605],"genre_scores_gemma":[0.73738635,0.00023531009,0.25658768,0.00008129525,0.000026726002,0.00010997065,0.00090253877,0.0001216975,0.004548439],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998796,0.00000825327,0.0000043339724,0.000043951855,0.00003900058,0.000024919389],"domain_scores_gemma":[0.99989676,0.0000133165395,0.0000124864655,0.000012517273,0.00005596558,0.000008864402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018976246,0.0004933813,0.00034224434,0.0007676526,0.00029746883,0.00040258365,0.0005947236,0.00034886907,0.0016044604],"category_scores_gemma":[0.0004582087,0.00032943522,0.00033302468,0.0006096421,0.00018262884,0.00061819615,0.000451136,0.00038448977,0.00035202893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022444152,0.00013355445,0.0057452037,0.00013624119,0.00008182103,0.00013784571,0.00012812036,0.2048466,0.052092113,0.0016976452,0.004205741,0.7305707],"study_design_scores_gemma":[0.000008764886,0.000021883574,0.0033615397,0.0000053343174,0.000014672417,0.00004264206,0.000030015226,0.98485166,0.010158999,0.00065505377,0.00083957944,0.000009837939],"about_ca_topic_score_codex":0.009373302,"about_ca_topic_score_gemma":0.015592661,"teacher_disagreement_score":0.009373302,"about_ca_system_score_codex":0.0004671984,"about_ca_system_score_gemma":0.00061963167,"threshold_uncertainty_score":0.018637478},"labels":[],"label_agreement":null},{"id":"W3013748060","doi":"10.3390/s20071888","title":"Pilot Site Deployment of an IoT Solution for Older Adults’ Early Behavior Change Detection","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Horizon 2020 Framework Programme","keywords":"Software deployment; Gerontology; Population; Population ageing; Isolation (microbiology); Internet of Things; Psychology; Sedentary behavior; Physical activity; Medicine; Computer security; Computer science; Environmental health; Physical medicine and rehabilitation","score_opus":0.07084083939770626,"score_gpt":0.27133707747723884,"score_spread":0.2004962380795326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013748060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90263426,0.00054332125,0.08327796,0.0009121595,0.00060041726,0.0012636284,0.0015041104,0.003964595,0.00529951],"genre_scores_gemma":[0.95548993,0.000253778,0.03809157,0.00039568802,0.000058554368,0.0006013608,0.00062064093,0.00006584387,0.004422589],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963975,0.00011054127,0.0000212236,0.0000661825,0.00009668366,0.000065712],"domain_scores_gemma":[0.9993556,0.00015370276,0.000054444856,0.000072307266,0.00022658159,0.00013729911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007639265,0.000674652,0.0003851413,0.00045413472,0.00017336287,0.00038789673,0.00073756033,0.0008235611,0.0033025932],"category_scores_gemma":[0.0009704664,0.0001719697,0.00035249238,0.00014691435,0.0002074101,0.00042295965,0.0004524767,0.00043990917,0.0008395779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004220656,0.0042388765,0.050439976,0.0019625635,0.0003488328,0.0061951443,0.0014967535,0.0073120873,0.5632777,0.0020321193,0.026186088,0.33228925],"study_design_scores_gemma":[0.0015180494,0.044406444,0.19504862,0.0003757276,0.0007439194,0.008493328,0.0028423294,0.1368692,0.5183952,0.0015666386,0.08946816,0.000272383],"about_ca_topic_score_codex":0.0007615605,"about_ca_topic_score_gemma":0.0007156555,"teacher_disagreement_score":0.0033025932,"about_ca_system_score_codex":0.00015725054,"about_ca_system_score_gemma":0.00029327467,"threshold_uncertainty_score":0.011048317},"labels":[],"label_agreement":null},{"id":"W3014183306","doi":"10.3390/s20072055","title":"Game Theory in Mobile CrowdSensing: A Comprehensive Survey","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Stackelberg competition; Bayesian game; Game theory; Data collection; Data science; Sequential game","score_opus":0.06022065507536665,"score_gpt":0.31948347354512496,"score_spread":0.2592628184697583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014183306","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008456745,0.43466818,0.44669124,0.0071647475,0.0014185173,0.00036822673,0.00037049624,0.0001796186,0.100682266],"genre_scores_gemma":[0.25452858,0.6077256,0.1141144,0.0026884328,0.0044318973,0.00071618526,0.0005593968,0.00017890464,0.015056586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980021,0.00087174505,0.00014476458,0.00023885678,0.00059900165,0.00014345867],"domain_scores_gemma":[0.9966834,0.0026830432,0.00013306641,0.00011071085,0.0003221699,0.00006750485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025524478,0.0020486983,0.0024647499,0.0030450933,0.0010668433,0.0040690615,0.0025067304,0.0030905395,0.0038711098],"category_scores_gemma":[0.005211229,0.0009706467,0.0023061354,0.0040273736,0.0026323786,0.004070161,0.0019548954,0.002233376,0.0009739951],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058607642,0.00022679724,0.0017032942,0.0047845137,0.00022550234,0.00042033612,0.000564993,0.09762472,0.0005467808,0.68721503,0.015431698,0.19119777],"study_design_scores_gemma":[0.000038069258,0.00014857684,0.0012139743,0.0019681668,0.00010449261,0.00057929894,0.00063458446,0.16029672,0.00037534256,0.65764993,0.17685664,0.00013427754],"about_ca_topic_score_codex":0.005867778,"about_ca_topic_score_gemma":0.0029281934,"teacher_disagreement_score":0.005867778,"about_ca_system_score_codex":0.0027285356,"about_ca_system_score_gemma":0.0023685542,"threshold_uncertainty_score":0.019797027},"labels":[],"label_agreement":null},{"id":"W3014616410","doi":"10.3390/s20072010","title":"Mass Sensors Based on Capacitive and Piezoelectric Micromachined Ultrasonic Transducers—CMUT and PMUT","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor; Agricultural Adaptation Council; CMC Microsystems","keywords":"Capacitive micromachined ultrasonic transducers; Capacitive sensing; Microelectromechanical systems; Ultrasonic sensor; Piezoelectricity; Transducer; PMUT; Surface micromachining; Acoustics; Electronic circuit; Bulk micromachining; Materials science; Electronic engineering; Optoelectronics; Electrical engineering; Fabrication; Engineering; Physics","score_opus":0.013411282387800804,"score_gpt":0.2406072982653713,"score_spread":0.2271960158775705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014616410","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004615976,0.9806528,0.007398314,0.0003146002,0.0003614116,0.000035813544,0.000050111008,0.00006783563,0.006503262],"genre_scores_gemma":[0.030268718,0.94887435,0.011829619,0.00039746732,0.00027620042,0.00007849596,0.00011460549,0.0000072944545,0.008153257],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99987113,0.0000107545775,0.00000792871,0.000031446078,0.000067020505,0.000011645228],"domain_scores_gemma":[0.99993515,0.000019105397,0.0000148499685,0.000003116486,0.000023199384,0.000004472538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001775048,0.0006672439,0.0004958372,0.0013134567,0.00012517293,0.0004219474,0.0006070224,0.00071110466,0.0008790235],"category_scores_gemma":[0.0001961413,0.00025857848,0.00022095263,0.0011463217,0.00023053003,0.0008078049,0.00030958847,0.00054123875,0.00079107797],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049849972,0.00009686996,0.00045272347,0.008266772,0.000057955946,0.00032854642,0.000047982376,0.0008082087,0.12083308,0.01335917,0.0044194334,0.85127944],"study_design_scores_gemma":[0.000014215556,0.00047996474,0.0023187257,0.0010845151,0.00012694015,0.003357565,0.00011875215,0.0020274213,0.121107124,0.0033067279,0.8659953,0.000062748],"about_ca_topic_score_codex":0.00028795176,"about_ca_topic_score_gemma":0.0005899635,"teacher_disagreement_score":0.0013134567,"about_ca_system_score_codex":0.00026665351,"about_ca_system_score_gemma":0.00038348587,"threshold_uncertainty_score":0.0029405951},"labels":[],"label_agreement":null},{"id":"W3015059946","doi":"10.3390/s20072008","title":"Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Saab; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Constant false alarm rate; Artificial intelligence; Image (mathematics); Computer science; Computer vision; Remote sensing; False alarm; Synthetic aperture radar; Wavelength; Mean-shift; Pattern recognition (psychology); Geography; Physics; Optics","score_opus":0.02205347935894183,"score_gpt":0.2139314350740968,"score_spread":0.19187795571515495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015059946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30129114,0.00018625951,0.6941755,0.000051551022,0.000024974277,0.000052367326,0.00031394564,0.002637856,0.0012664384],"genre_scores_gemma":[0.7198222,0.00013915496,0.27890417,0.000015709356,0.000013386008,0.000034884157,0.00047116546,0.000129643,0.0004696588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977523,0.00003703912,0.000009853253,0.000049206774,0.00009702414,0.000031611733],"domain_scores_gemma":[0.9996307,0.000093300296,0.00007209082,0.00006535979,0.00012307373,0.00001540566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056608254,0.0005260562,0.0003821746,0.000853852,0.00016286867,0.0005083963,0.00042507704,0.00022684861,0.00046651353],"category_scores_gemma":[0.0011880101,0.00023016917,0.0004597788,0.0005395617,0.00019833997,0.0006657042,0.00030688755,0.0003579456,0.0003152989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059813383,0.00014672891,0.02908938,0.00013953957,0.00019055034,0.00011743833,0.00012724515,0.2858676,0.0953,0.0015400284,0.0011521257,0.5857312],"study_design_scores_gemma":[0.0000065623594,0.000073142706,0.014973233,0.00000872397,0.000039140352,0.000081748396,0.000026016241,0.95483744,0.028874576,0.00045820256,0.00059580884,0.000025370546],"about_ca_topic_score_codex":0.0024081306,"about_ca_topic_score_gemma":0.0033278405,"teacher_disagreement_score":0.0024081306,"about_ca_system_score_codex":0.0003004433,"about_ca_system_score_gemma":0.00039706944,"threshold_uncertainty_score":0.0047882795},"labels":[],"label_agreement":null},{"id":"W3015182559","doi":"10.3390/s20072057","title":"A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Global Positioning System; Map matching; Trajectory; Computer science; Matching (statistics); Sampling (signal processing); Assisted GPS; Computer vision; Mathematics; Statistics; Telecommunications","score_opus":0.03374187162097791,"score_gpt":0.254841267221003,"score_spread":0.22109939560002506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015182559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016115999,0.00036125557,0.97968274,0.00010856235,0.00010719906,0.00015381968,0.0005114786,0.0019150062,0.0010438806],"genre_scores_gemma":[0.28887713,0.00045060163,0.70330584,0.0001013946,0.00014343536,0.00029595595,0.0028427069,0.00023047841,0.0037524414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977235,0.00025420234,0.00014589401,0.0009451747,0.0007253018,0.00020588645],"domain_scores_gemma":[0.9981008,0.00033355958,0.00025365542,0.0005574981,0.0006247637,0.00012970547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011699933,0.0012678016,0.0017931481,0.0032239857,0.001168113,0.0012585975,0.003316909,0.0013210406,0.0020045245],"category_scores_gemma":[0.0053787357,0.00047540726,0.0014984272,0.00576538,0.00043708205,0.0027235097,0.002800221,0.0012569304,0.0017144148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052872446,0.00039586113,0.00867106,0.0003787744,0.00034667287,0.00048422147,0.0005354137,0.12549667,0.018840918,0.009809938,0.011402241,0.8231095],"study_design_scores_gemma":[0.000042219745,0.00017336561,0.0026036035,0.000023689772,0.0000818174,0.00047057396,0.00033169158,0.9675744,0.010333265,0.00746762,0.010843079,0.000054613178],"about_ca_topic_score_codex":0.012819296,"about_ca_topic_score_gemma":0.011420826,"teacher_disagreement_score":0.012819296,"about_ca_system_score_codex":0.0007343755,"about_ca_system_score_gemma":0.0023711966,"threshold_uncertainty_score":0.02548933},"labels":[],"label_agreement":null},{"id":"W3015293944","doi":"10.3390/s20072088","title":"A New Capacitance Sensor for Measuring the Void Fraction of Two-Phase Flow Through Tube Bundles","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Heat Transfer and Boiling Studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Safety Commission; McMaster University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Safety Commission","keywords":"Bundle; Capacitance; Capacitance probe; Materials science; Acoustics; Capacitive sensing; Vibration; Two-phase flow; Porosity; Flow measurement; Electronic engineering; Mechanics; Engineering; Flow (mathematics); Voltage; Electrical engineering; Composite material; Capacitor; Physics","score_opus":0.04400956817759913,"score_gpt":0.27168891583115085,"score_spread":0.22767934765355172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015293944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3610033,0.0016429292,0.6300671,0.00057882885,0.00037947562,0.00032202087,0.0007290592,0.0021711881,0.0031060698],"genre_scores_gemma":[0.77155346,0.0005861454,0.2254846,0.00025143314,0.000067031535,0.00015433105,0.00022049923,0.00006585086,0.0016167031],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919945,0.0000764943,0.000024195617,0.00016673526,0.00049185264,0.00004134326],"domain_scores_gemma":[0.9991726,0.0002946196,0.0001386338,0.000058994367,0.00027372665,0.000061337705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005087112,0.00041232517,0.0003941478,0.00075895205,0.0003078449,0.00039619883,0.00089194265,0.00094767235,0.000823516],"category_scores_gemma":[0.0013819113,0.00028613134,0.00018202142,0.00075753976,0.00043935393,0.0011960674,0.0003740999,0.0004968797,0.00020217107],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047799724,0.00002919465,0.00047300526,0.00011153182,0.0000048484785,0.00005515878,0.000045646673,0.0011190973,0.98465747,0.0003774854,0.00029646273,0.012782425],"study_design_scores_gemma":[0.000022204895,0.0003087268,0.0026253355,0.000013323935,0.000017853761,0.00040928883,0.000036892525,0.05905894,0.93316704,0.0001929654,0.00408269,0.00006467742],"about_ca_topic_score_codex":0.0009968418,"about_ca_topic_score_gemma":0.0018965645,"teacher_disagreement_score":0.0009968418,"about_ca_system_score_codex":0.0006841561,"about_ca_system_score_gemma":0.00059127173,"threshold_uncertainty_score":0.0049639344},"labels":[],"label_agreement":null},{"id":"W3015549492","doi":"10.3390/s20072104","title":"Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Diagonal; Computer science; Workspace; Perpendicular; Human arm; Support vector machine; Artificial intelligence; Simulation; Mathematics; Geometry; Robot","score_opus":0.012213134153548601,"score_gpt":0.22626122270172772,"score_spread":0.2140480885481791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015549492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72084135,0.00053953176,0.27701,0.00010592012,0.000022318483,0.00011388674,0.000102463426,0.0002690972,0.0009954175],"genre_scores_gemma":[0.93532485,0.00021759128,0.06325981,0.00003240235,0.000017498203,0.00005695006,0.00006119261,0.000014002274,0.0010157772],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997484,0.00008175371,0.000013902299,0.00005712224,0.000075514916,0.00002321913],"domain_scores_gemma":[0.9994823,0.0002742134,0.00005571939,0.00004519684,0.0001111104,0.0000313735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004642199,0.0004937198,0.00027025773,0.00023980139,0.0001052304,0.00030008345,0.00026161352,0.00058749196,0.00073917315],"category_scores_gemma":[0.0015749473,0.00015047312,0.00021567657,0.00014312264,0.00018001118,0.0002976856,0.00032221279,0.00019102689,0.00020740814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010187596,0.00028294465,0.027654817,0.0005315805,0.00012643126,0.00047340858,0.000828619,0.013649051,0.67337096,0.0003488007,0.00036442408,0.28135026],"study_design_scores_gemma":[0.00012905979,0.004786848,0.24174286,0.00009198109,0.00023620931,0.002279638,0.0007215546,0.532054,0.21387185,0.000520708,0.0034502223,0.000115073126],"about_ca_topic_score_codex":0.0015055606,"about_ca_topic_score_gemma":0.002757662,"teacher_disagreement_score":0.0015055606,"about_ca_system_score_codex":0.00006777012,"about_ca_system_score_gemma":0.00018425376,"threshold_uncertainty_score":0.0029935837},"labels":[],"label_agreement":null},{"id":"W3015595933","doi":"10.3390/s20072108","title":"Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Blood pressure; Measure (data warehouse); Computer science; Data mining; Medicine; Internal medicine","score_opus":0.14973303256800433,"score_gpt":0.2959996345688779,"score_spread":0.14626660200087357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015595933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027821854,0.0002375516,0.9714807,0.00004471255,0.000014808019,0.00000916747,0.000024650499,0.00015409545,0.00021245083],"genre_scores_gemma":[0.7365338,0.0005308532,0.26144472,0.00009147866,0.00008565706,0.00008749674,0.0002668374,0.0000690261,0.00089001463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987857,0.0004094696,0.000085084685,0.0003048619,0.000312453,0.000102425896],"domain_scores_gemma":[0.99665534,0.0021059506,0.00030821643,0.00031909606,0.0005592112,0.000052137962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020551602,0.0008355157,0.0011258856,0.00078191166,0.00036085895,0.0007890182,0.00091216515,0.0008773425,0.00045787342],"category_scores_gemma":[0.006875415,0.00040358465,0.00085481704,0.00072214473,0.00040802328,0.0012476054,0.0008728639,0.0010499445,0.00017024942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007278662,0.00005602079,0.0043191803,0.00006734378,0.00015870067,0.000103455975,0.00013102441,0.85141987,0.011346954,0.004421115,0.0005248007,0.12737884],"study_design_scores_gemma":[0.0000015936581,0.000014951351,0.00062891375,0.0000046298132,0.000010294401,0.000019776346,0.0000039849187,0.99711585,0.0012604735,0.00080411264,0.00012638052,0.000009016452],"about_ca_topic_score_codex":0.0044399914,"about_ca_topic_score_gemma":0.0038771862,"teacher_disagreement_score":0.0044399914,"about_ca_system_score_codex":0.0004344731,"about_ca_system_score_gemma":0.0006462276,"threshold_uncertainty_score":0.010868847},"labels":[],"label_agreement":null},{"id":"W3016234469","doi":"10.3390/s20082191","title":"Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Engineering and Physical Sciences Research Council; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Computer science; Distributed computing; Quality of service; Exploit; Edge computing; Resource allocation; Workload; Context (archaeology); Enhanced Data Rates for GSM Evolution; Computer network; Artificial intelligence; Computer security","score_opus":0.009637984337508069,"score_gpt":0.2469100027481358,"score_spread":0.23727201841062773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016234469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073967734,0.0068313587,0.9639593,0.0041627903,0.0003889172,0.00005142061,0.000080389626,0.00021420454,0.016914817],"genre_scores_gemma":[0.49131823,0.013523626,0.47821665,0.0019062404,0.0010650955,0.00028270963,0.00016932155,0.00021817179,0.013300007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993062,0.00024460908,0.000027244623,0.00014825839,0.00018987672,0.00008370419],"domain_scores_gemma":[0.9991737,0.00044475542,0.000053564694,0.00009597382,0.00011979235,0.000112134265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017901814,0.0007821408,0.00094343635,0.00092209893,0.00063998357,0.003062779,0.0026885825,0.0016373923,0.0019665097],"category_scores_gemma":[0.002638509,0.00050303416,0.000563548,0.0009177427,0.0022260468,0.0042168233,0.0022431188,0.0024422226,0.00037884535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009832097,0.000066444685,0.0003591329,0.00021145682,0.000034195844,0.00009121284,0.00008551713,0.23109874,0.0009548614,0.6912464,0.003957136,0.07179663],"study_design_scores_gemma":[0.0000137531415,0.000043432283,0.000093828945,0.00006763339,0.000012873612,0.000076391734,0.000045406996,0.6967459,0.0005579463,0.28438154,0.017942194,0.000019158224],"about_ca_topic_score_codex":0.0018301115,"about_ca_topic_score_gemma":0.0016765919,"teacher_disagreement_score":0.003062779,"about_ca_system_score_codex":0.0020218368,"about_ca_system_score_gemma":0.0011940582,"threshold_uncertainty_score":0.014669478},"labels":[],"label_agreement":null},{"id":"W3016294790","doi":"10.3390/s20082324","title":"End-to-End QoS “Smart Queue” Management Algorithms and Traffic Prioritization Mechanisms for Narrow-Band Internet of Things Services in 4G/5G Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"EnodeB; Computer network; Computer science; Quality of service; End-to-end principle; Scheduling (production processes); Telecommunications link; Queue; User equipment; LTE Advanced; Weighted fair queueing; Base station; Engineering","score_opus":0.008367256138598895,"score_gpt":0.21332087665452576,"score_spread":0.20495362051592686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016294790","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02400988,0.00076342723,0.97231174,0.0002210327,0.0001950787,0.00011460673,0.00004170684,0.0007609293,0.0015815947],"genre_scores_gemma":[0.74470127,0.0006299661,0.25165075,0.0003285083,0.0002084753,0.00013056955,0.00014844087,0.00008584088,0.0021162569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991627,0.00017647799,0.0000800027,0.00014330656,0.00030264456,0.00013489767],"domain_scores_gemma":[0.9987098,0.00037578226,0.0001787896,0.00012303566,0.00052671606,0.00008583205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021827111,0.0007803678,0.00055564067,0.0008458134,0.0009601667,0.0015511187,0.001612183,0.0006163641,0.0009189122],"category_scores_gemma":[0.0023344017,0.00030825136,0.00038016585,0.0005833474,0.0005152969,0.0016093502,0.0006463927,0.0012751118,0.00020513673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082237285,0.0006961997,0.0046611307,0.00034073848,0.0002082372,0.0002261038,0.0005054726,0.36517617,0.09067425,0.08136261,0.007840553,0.44748607],"study_design_scores_gemma":[0.000034352703,0.00014060488,0.00049850787,0.000012183661,0.00003527652,0.0000570441,0.000035111087,0.98195696,0.009019625,0.005482959,0.0027017812,0.00002565039],"about_ca_topic_score_codex":0.005272281,"about_ca_topic_score_gemma":0.00769343,"teacher_disagreement_score":0.005272281,"about_ca_system_score_codex":0.0019482855,"about_ca_system_score_gemma":0.0016197877,"threshold_uncertainty_score":0.014135838},"labels":[],"label_agreement":null},{"id":"W3016459259","doi":"10.3390/s20082276","title":"Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Laurentian University","funders":"","keywords":"Metropolitan area; Traffic congestion; Genetic algorithm; Computer science; Control (management); Transport engineering; Engineering; Geography; Artificial intelligence; Machine learning","score_opus":0.038814252314847704,"score_gpt":0.2883093116506178,"score_spread":0.2494950593357701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016459259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4227191,0.0010020398,0.5681896,0.00048324786,0.00010742088,0.00017544784,0.00013824258,0.0006926726,0.00649215],"genre_scores_gemma":[0.9219186,0.0002173755,0.076086044,0.00011062239,0.000011455894,0.00010411416,0.00013392296,0.000029238145,0.0013886701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996313,0.000102040525,0.000017188002,0.00009079897,0.00007196036,0.00008671191],"domain_scores_gemma":[0.99945706,0.00028369288,0.00007934529,0.000026813343,0.00010259019,0.000050464474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076339016,0.00086963584,0.0012115274,0.0009766119,0.0005548542,0.0011763318,0.0009550392,0.0012477499,0.0010895847],"category_scores_gemma":[0.0015074076,0.0005218822,0.0009495658,0.0007709603,0.0006966348,0.0007201487,0.0007404347,0.0006329441,0.0001371081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029892502,0.000029214527,0.0010906276,0.000016974005,0.000024034514,0.00004466948,0.000026896749,0.9866763,0.0006903597,0.00081360113,0.00014301937,0.010414481],"study_design_scores_gemma":[0.000014798705,0.000034102537,0.00030580006,0.0000046467308,0.000013731657,0.000012066377,0.000019356985,0.99844474,0.00022928478,0.0007073422,0.00020765062,0.00000645051],"about_ca_topic_score_codex":0.020547006,"about_ca_topic_score_gemma":0.01209533,"teacher_disagreement_score":0.020547006,"about_ca_system_score_codex":0.00093331066,"about_ca_system_score_gemma":0.0017080659,"threshold_uncertainty_score":0.04085481},"labels":[],"label_agreement":null},{"id":"W3016505560","doi":"10.3390/s20082292","title":"In-Situ LED-Based Observation of Snow Surface and Depth Transects","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Government of Alberta; University of Lethbridge","keywords":"Snowpack; Snow; Transect; Remote sensing; Environmental science; SIGNAL (programming language); Lidar; Albedo (alchemy); Measured depth; Geology; Snowmelt; Repeatability; Meteorology; Geography; Geomorphology; Chemistry; Computer science","score_opus":0.037567886829211,"score_gpt":0.22251500695806123,"score_spread":0.18494712012885023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016505560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96651053,0.00006964143,0.025780419,0.000018353312,0.000016325315,0.000048020993,0.0012504316,0.00040187646,0.005904284],"genre_scores_gemma":[0.95709014,0.00009533168,0.04031771,0.000018834215,0.000010460739,0.000043944903,0.00065266626,0.00003419692,0.0017366721],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998814,0.000018065002,0.0000032157377,0.000031419568,0.000050899485,0.000015004792],"domain_scores_gemma":[0.999876,0.000028338187,0.000012451141,0.000015509162,0.00005430225,0.000013402248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018664937,0.0001567302,0.00017393893,0.00039388373,0.00014132076,0.00024611855,0.0002883185,0.00014241027,0.0009580723],"category_scores_gemma":[0.00025722437,0.00010284485,0.00014147177,0.00041059972,0.000096405856,0.00024557824,0.00022661427,0.00012453871,0.00026138913],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042241413,0.00024404873,0.09359334,0.00024477596,0.00006382617,0.00011343542,0.00060640235,0.010207514,0.81004506,0.0002996655,0.0006592559,0.08350025],"study_design_scores_gemma":[0.000130649,0.00076284615,0.41445065,0.00003944007,0.0001563725,0.00034574905,0.0004893527,0.12479097,0.4439445,0.0004600196,0.014352688,0.00007665523],"about_ca_topic_score_codex":0.005084681,"about_ca_topic_score_gemma":0.023047302,"teacher_disagreement_score":0.005084681,"about_ca_system_score_codex":0.0002603651,"about_ca_system_score_gemma":0.00026672086,"threshold_uncertainty_score":0.010110199},"labels":[],"label_agreement":null},{"id":"W3016609882","doi":"10.3390/s20082330","title":"Comparison of Multi-Frequency and Multi-Coil Electromagnetic Induction (EMI) for Mapping Properties in Shallow Podsolic Soils","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Soil water; Silt; Water content; Soil science; Soil texture; Environmental science; Electromagnetic induction; Conductivity; Bulk density; EMI; Cation-exchange capacity; Electromagnetic interference; Geology; Geotechnical engineering; Electromagnetic coil; Chemistry; Physics; Engineering; Electronic engineering","score_opus":0.07074361529044303,"score_gpt":0.27660185475759763,"score_spread":0.2058582394671546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016609882","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9859175,0.00042004688,0.0128833605,0.000027671587,0.000007836268,0.000023466146,0.00011776783,0.0000548755,0.00054749654],"genre_scores_gemma":[0.98500174,0.0002859647,0.014182751,0.000025617885,0.000006346485,0.000017640763,0.00014353846,0.000009483256,0.0003267327],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963224,0.00006863632,0.000017114464,0.000108996486,0.00012306288,0.00004987692],"domain_scores_gemma":[0.999003,0.00041540668,0.00017283786,0.00007068199,0.0002753234,0.000062743085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008355492,0.00045442765,0.0003478008,0.00095899927,0.00011106704,0.0005024662,0.0003644693,0.00039987985,0.00029796272],"category_scores_gemma":[0.001806939,0.00022372973,0.00017461341,0.00067041564,0.00021641215,0.00056365493,0.00033704966,0.00023332509,0.00010628242],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091538584,0.00021792181,0.22767267,0.00026084017,0.00018499758,0.000107946435,0.00026896782,0.0052045914,0.66938984,0.00014600417,0.00014795404,0.09548298],"study_design_scores_gemma":[0.00004337886,0.0009756735,0.787488,0.000024781531,0.00018963449,0.0004026825,0.0004201347,0.062899426,0.14632234,0.00017446057,0.0009909337,0.00006855686],"about_ca_topic_score_codex":0.0068637747,"about_ca_topic_score_gemma":0.025720734,"teacher_disagreement_score":0.0068637747,"about_ca_system_score_codex":0.00040567934,"about_ca_system_score_gemma":0.00027916033,"threshold_uncertainty_score":0.013647616},"labels":[],"label_agreement":null},{"id":"W3017318545","doi":"10.3390/s20082300","title":"A New Construction of High Performance LDPC Matrices for Mobile Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Low-density parity-check code; Computer science; Additive white Gaussian noise; Computation; Algorithm; Decoding methods; Data flow diagram; Channel (broadcasting); Theoretical computer science; Computer engineering; Computer network","score_opus":0.01245650318579186,"score_gpt":0.23333440455426369,"score_spread":0.22087790136847182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017318545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01706608,0.00023385657,0.9772051,0.00015963032,0.00006220099,0.00006461591,0.00007288551,0.00027563053,0.0048598866],"genre_scores_gemma":[0.23774844,0.000456771,0.757499,0.0001072804,0.000045216795,0.00016910702,0.00018142188,0.00006605951,0.003726656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997601,0.00006462437,0.00001100412,0.000036566402,0.00010909769,0.00001865468],"domain_scores_gemma":[0.9995803,0.00013153325,0.00004814671,0.00010319871,0.00011119296,0.000025634374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024357517,0.0004687233,0.00022517424,0.000577631,0.00044453077,0.00040049443,0.00039094492,0.000422156,0.0014106904],"category_scores_gemma":[0.0012754824,0.00022922459,0.00033290754,0.00045448812,0.00052158977,0.00045598717,0.0005991574,0.0007503365,0.00062722276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013436883,0.00011048526,0.0012274302,0.0004369623,0.000055391694,0.00052836427,0.0003974447,0.1888058,0.15027674,0.39057913,0.005027638,0.2624202],"study_design_scores_gemma":[0.000049946735,0.00016288013,0.00040714422,0.00006267299,0.000024831716,0.0006774547,0.000046508965,0.84586173,0.06732196,0.058556333,0.02677129,0.000057188787],"about_ca_topic_score_codex":0.0007979323,"about_ca_topic_score_gemma":0.0014548344,"teacher_disagreement_score":0.0014106904,"about_ca_system_score_codex":0.00036191146,"about_ca_system_score_gemma":0.00093722495,"threshold_uncertainty_score":0.0047192574},"labels":[],"label_agreement":null},{"id":"W3017368764","doi":"10.3390/s20082268","title":"Wireless Motion Sensors—Useful in Assessing the Effectiveness of Physiotherapeutic Methods Used in Patients with Knee Osteoarthritis—Preliminary Report","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Proprioception; WOMAC; Osteoarthritis; Rehabilitation; Physical therapy; Medicine; Visual analogue scale; Knee Joint; Physical medicine and rehabilitation; Knee pain; Surgery","score_opus":0.0175941536595495,"score_gpt":0.30320045816856106,"score_spread":0.2856063045090116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017368764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97910655,0.013317408,0.004796471,0.00014199986,0.00008578331,0.00023381873,0.00034852632,0.000042248408,0.001927224],"genre_scores_gemma":[0.991443,0.002614687,0.0047295867,0.00008448229,0.000087749635,0.00015989642,0.00020860253,0.000003609853,0.0006684601],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99917454,0.00039299604,0.000090254965,0.000064330554,0.00023920581,0.000038604197],"domain_scores_gemma":[0.9991666,0.0003459441,0.00024323976,0.000036491685,0.00016734883,0.00004042121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011061777,0.00039863941,0.00037331876,0.00049262075,0.0001237102,0.00028346552,0.0002160353,0.00041212438,0.0012277594],"category_scores_gemma":[0.002183134,0.00015528013,0.00033306787,0.00037906299,0.00018157004,0.00027808396,0.00020945707,0.00024027651,0.00027457427],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013197,0.0031161678,0.4442223,0.0018108371,0.0007184609,0.00069491955,0.0006550616,0.00049089413,0.15209866,0.00012315805,0.0012633913,0.38160914],"study_design_scores_gemma":[0.00070788414,0.051671218,0.9030923,0.00019512577,0.0008814425,0.0034646953,0.0006792895,0.0023030376,0.03255678,0.0001052755,0.0042807194,0.000062109415],"about_ca_topic_score_codex":0.00024856947,"about_ca_topic_score_gemma":0.00049106445,"teacher_disagreement_score":0.0012277594,"about_ca_system_score_codex":0.0000745047,"about_ca_system_score_gemma":0.000095754796,"threshold_uncertainty_score":0.0058500767},"labels":[],"label_agreement":null},{"id":"W3017826578","doi":"10.3390/s20092477","title":"A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Monocular; Convolutional neural network; Robot; Orientation (vector space); Monocular vision; Mathematics","score_opus":0.011498346506665867,"score_gpt":0.19655960687085478,"score_spread":0.1850612603641889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017826578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04362099,0.00050778204,0.94433105,0.00017423401,0.00016185337,0.00013576995,0.0005314942,0.0052467883,0.0052901306],"genre_scores_gemma":[0.43721431,0.00038005493,0.55400234,0.00028548692,0.00008490866,0.00021710417,0.0007617695,0.0001472606,0.0069066994],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996985,0.000042025476,0.000007981588,0.00011596372,0.000092417664,0.00004319228],"domain_scores_gemma":[0.99980086,0.00002076573,0.000032514166,0.000041380063,0.00007143686,0.000033129945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020662727,0.0007117972,0.000536808,0.0007827571,0.00034288576,0.0004260925,0.00089365,0.00060475344,0.0037340382],"category_scores_gemma":[0.000463073,0.0003308115,0.00044493057,0.00039362247,0.0002586897,0.000688341,0.0012673917,0.00048250996,0.0013192156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038028322,0.00015576267,0.0063839,0.00028902543,0.00011470112,0.00038163693,0.0002963878,0.011267394,0.17140976,0.0037764066,0.011753441,0.7937914],"study_design_scores_gemma":[0.00012462954,0.0007395569,0.02375623,0.000130747,0.00015921857,0.0021445244,0.00060391997,0.7016253,0.19009371,0.005721879,0.07472544,0.00017480037],"about_ca_topic_score_codex":0.003812177,"about_ca_topic_score_gemma":0.010293803,"teacher_disagreement_score":0.003812177,"about_ca_system_score_codex":0.00045640182,"about_ca_system_score_gemma":0.00069050095,"threshold_uncertainty_score":0.012491643},"labels":[],"label_agreement":null},{"id":"W3018073244","doi":"10.3390/s20082417","title":"Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Covert channel; Covert; Computer science; Communication source; Computer network; Network packet; Channel (broadcasting); Computer security; Transmission (telecommunications); Key (lock); Real-time computing; Telecommunications","score_opus":0.02664982035012817,"score_gpt":0.2748025574096239,"score_spread":0.24815273705949573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018073244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81888634,0.00068660255,0.17221299,0.000236587,0.00013716705,0.00010739057,0.00043763383,0.0009079042,0.0063873073],"genre_scores_gemma":[0.99211603,0.00017771372,0.0070114643,0.00002495272,0.000017011847,0.000017163235,0.00011424902,0.000033390814,0.00048795642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903214,0.00015982277,0.000047164787,0.0001723008,0.00044473822,0.0001438536],"domain_scores_gemma":[0.99410087,0.0028436682,0.0014475373,0.0008353092,0.00063788693,0.00013462064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067114545,0.0005345614,0.0004582087,0.0016332034,0.00039176326,0.00091802346,0.0004133039,0.00060509454,0.0014882853],"category_scores_gemma":[0.0059794113,0.00014751994,0.0002715564,0.0012176811,0.00069468014,0.0016423059,0.000497014,0.0005799281,0.00027700383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019508713,0.0004993787,0.11388513,0.00063498504,0.00034861657,0.0028913498,0.00066237577,0.3236348,0.25672618,0.048427027,0.0041563553,0.24618286],"study_design_scores_gemma":[0.000016513757,0.00043038343,0.026528599,0.000044101405,0.00008511119,0.0027248247,0.00034375573,0.79913723,0.14697091,0.01946456,0.004186806,0.00006720392],"about_ca_topic_score_codex":0.00049315445,"about_ca_topic_score_gemma":0.00050171895,"teacher_disagreement_score":0.0016332034,"about_ca_system_score_codex":0.00061227387,"about_ca_system_score_gemma":0.00039055976,"threshold_uncertainty_score":0.0049788356},"labels":[],"label_agreement":null},{"id":"W3018270146","doi":"10.3390/s20082390","title":"Review of Microwaves Techniques for Breast Cancer Detection","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":223,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"King Fahd University of Petroleum and Minerals","keywords":"Microwave imaging; Mammography; Breast cancer; Microwave; Medical physics; Cancer detection; Medicine; Magnetic resonance imaging; Computer science; Cancer; Radiology; Telecommunications","score_opus":0.01889565202285362,"score_gpt":0.29541587795540813,"score_spread":0.27652022593255454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018270146","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018245337,0.99708754,0.00037669396,0.0001934018,0.00025637803,0.00000874174,0.000027952314,0.00001412246,0.0018526156],"genre_scores_gemma":[0.0007510431,0.997554,0.00042002788,0.00013151046,0.00020493906,0.000008155408,0.000039373936,0.0000028841098,0.0008880452],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996973,0.00004809255,0.000046002187,0.00005647454,0.00012864526,0.000023497185],"domain_scores_gemma":[0.9995426,0.0002414964,0.00006718557,0.000017498262,0.00010828367,0.00002289895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005751421,0.0010090638,0.001020478,0.0035701662,0.00033048086,0.00079526374,0.0008602946,0.000914527,0.007189495],"category_scores_gemma":[0.00084744545,0.0004398471,0.000748372,0.002806573,0.00036454105,0.0013191,0.0005386613,0.0011659069,0.003526594],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054614615,0.000084256586,0.00019292098,0.031693563,0.00009242451,0.00019909602,0.000076290264,0.00047136948,0.004660623,0.0034119543,0.028791962,0.9302709],"study_design_scores_gemma":[0.0000057499183,0.000096037824,0.0006606736,0.0031752056,0.00009427141,0.0010364867,0.000038712602,0.000098221964,0.0011926915,0.0011736463,0.9924085,0.000019730927],"about_ca_topic_score_codex":0.00090443215,"about_ca_topic_score_gemma":0.0011632944,"teacher_disagreement_score":0.007189495,"about_ca_system_score_codex":0.0004130219,"about_ca_system_score_gemma":0.0008727749,"threshold_uncertainty_score":0.02405119},"labels":[],"label_agreement":null},{"id":"W3019075546","doi":"10.3390/s20082359","title":"Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Autoencoder; Hyperparameter; Artificial intelligence; Deep learning; Computer science; Machine learning; Convolutional neural network; Activities of daily living; Independence (probability theory); Psychology","score_opus":0.07250050099421257,"score_gpt":0.26080822313372737,"score_spread":0.1883077221395148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019075546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.879206,0.0008827765,0.11521998,0.00042407375,0.000067676054,0.000051713305,0.001987823,0.00083765096,0.0013222714],"genre_scores_gemma":[0.98944086,0.0001805515,0.008774418,0.00004196725,0.000015574928,0.000020684613,0.00086019246,0.000007460414,0.0006583799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984014,0.000028172855,0.000016526717,0.000056146302,0.000027452208,0.00003149997],"domain_scores_gemma":[0.9996393,0.00013560658,0.000073025454,0.000031734868,0.000085192485,0.00003509257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046293574,0.0007268459,0.0003976723,0.000777416,0.00011778468,0.0003174843,0.000416149,0.000462321,0.0005896386],"category_scores_gemma":[0.0012372644,0.00018159566,0.00048083093,0.00041653778,0.00012920656,0.00038689404,0.0003566133,0.00051400694,0.0002672473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007027296,0.00079570874,0.18355641,0.00013962638,0.0002909119,0.00056299224,0.00017086447,0.5933992,0.005743285,0.0007006412,0.0036059434,0.21033168],"study_design_scores_gemma":[0.0000030445885,0.000033032105,0.007929078,0.000007605597,0.000012306641,0.000028989078,0.00001810856,0.99103665,0.0005085161,0.00031652552,0.000102888276,0.0000032729147],"about_ca_topic_score_codex":0.015487345,"about_ca_topic_score_gemma":0.022346472,"teacher_disagreement_score":0.015487345,"about_ca_system_score_codex":0.00048224904,"about_ca_system_score_gemma":0.00036950453,"threshold_uncertainty_score":0.030794382},"labels":[],"label_agreement":null},{"id":"W3019334934","doi":"10.3390/s20082431","title":"Combined Long-Period Fiber Grating and Microcavity In-Line Mach–Zehnder Interferometer for Refractive Index Measurements with Limited Cross-Sensitivity","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Narodowe Centrum Badań i Rozwoju; Narodowe Centrum Nauki","keywords":"Materials science; Mach–Zehnder interferometer; Interferometry; Refractive index; Optics; Sensitivity (control systems); Grating; Femtosecond; Optical fiber; Optoelectronics; Laser; Physics; Electronic engineering","score_opus":0.03446349235058926,"score_gpt":0.26376068285258053,"score_spread":0.22929719050199127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019334934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92717147,0.001706013,0.06920783,0.00010881222,0.00009358237,0.000068556496,0.00013508105,0.00043797662,0.0010706085],"genre_scores_gemma":[0.9084762,0.0003796973,0.089975804,0.00005802857,0.00003273328,0.00006856602,0.000080067584,0.000028196304,0.00090082234],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989292,0.0001426809,0.00004171437,0.00027299108,0.00053425826,0.0000792562],"domain_scores_gemma":[0.9993711,0.00020378592,0.00016959323,0.000107294174,0.000112404836,0.00003576911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052174815,0.00058074447,0.00042246256,0.00037510446,0.0001741902,0.00025371672,0.0007385742,0.0005961501,0.0004269797],"category_scores_gemma":[0.0004903154,0.00040948257,0.0002673798,0.00034901014,0.00034773294,0.00048558423,0.0004816908,0.0004335435,0.00021097215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060505987,0.000017041484,0.00059123454,0.000035144712,0.000011642248,0.000022742888,0.000026268559,0.00014122734,0.9960467,0.0000648705,0.000025332212,0.002957219],"study_design_scores_gemma":[0.000005813415,0.00013733203,0.0021947387,0.0000025936397,0.000019236511,0.00013079094,0.000013958963,0.0063149272,0.99054015,0.00003170258,0.0005957406,0.00001304279],"about_ca_topic_score_codex":0.00062241644,"about_ca_topic_score_gemma":0.0025383197,"teacher_disagreement_score":0.0007385742,"about_ca_system_score_codex":0.00046519336,"about_ca_system_score_gemma":0.00026921593,"threshold_uncertainty_score":0.0033752918},"labels":[],"label_agreement":null},{"id":"W3019448210","doi":"10.3390/s20082421","title":"Perception of a Haptic Stimulus Presented Under the Foot Under Workload","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stimulus (psychology); Workload; Perception; Haptic technology; Cognition; Computer science; Psychology; Audiology; Cognitive psychology; Simulation; Medicine; Neuroscience","score_opus":0.07041997038740985,"score_gpt":0.30403775986050235,"score_spread":0.2336177894730925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019448210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985025,0.00005293032,0.00097397494,0.000015287576,0.000016243383,0.000009480313,0.000020177775,0.000005997513,0.00040351835],"genre_scores_gemma":[0.9990983,0.00004967734,0.00038061067,0.000027409013,0.000015719032,0.000011124829,0.000031538075,0.00000361223,0.0003820639],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997348,0.000044033,0.00001700678,0.000041723375,0.00009929692,0.00006313177],"domain_scores_gemma":[0.9990864,0.00042021502,0.00015308155,0.000044660082,0.00013237911,0.00016338038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023025126,0.0002918761,0.0002898169,0.00027038506,0.00013400473,0.00043613373,0.00015395472,0.00036993684,0.0026420776],"category_scores_gemma":[0.002685623,0.00011872048,0.00015535596,0.00009997721,0.00034584964,0.00025452426,0.0005688139,0.00032171246,0.00013622832],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012721784,0.000113201044,0.00461606,0.000105485095,0.000021965125,0.00028116835,0.00042247272,0.00013977331,0.9867078,0.000049716895,0.00005524866,0.006214929],"study_design_scores_gemma":[0.00007512983,0.0067848037,0.7913732,0.00007473386,0.00008584884,0.0014143684,0.0019756055,0.003002446,0.1938874,0.00054942496,0.0007125619,0.0000644749],"about_ca_topic_score_codex":0.0005408752,"about_ca_topic_score_gemma":0.0003654578,"teacher_disagreement_score":0.0026420776,"about_ca_system_score_codex":0.00008887212,"about_ca_system_score_gemma":0.00011781355,"threshold_uncertainty_score":0.008838594},"labels":[],"label_agreement":null},{"id":"W3021432755","doi":"10.3390/s20092507","title":"Straight Long-Range Surface Plasmon Polariton Waveguide Sensor Operating at λ0 = 850 nm","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China; University of Ottawa","keywords":"Refractometer; Surface plasmon polariton; Materials science; Waveguide; Surface plasmon; Refractive index; Optics; Optoelectronics; Wavelength; Plasmon; Physics","score_opus":0.015183589689754825,"score_gpt":0.2141959756853209,"score_spread":0.19901238599556606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021432755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.867327,0.00070296426,0.12714441,0.00014041418,0.000081816914,0.00008436078,0.00032481144,0.0011071459,0.003087133],"genre_scores_gemma":[0.88621694,0.00034809473,0.110738955,0.000073862386,0.000018550196,0.000078856036,0.00022680896,0.000025546296,0.0022723353],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999777,0.000023471866,0.000008636745,0.000053070202,0.00011730742,0.000020628157],"domain_scores_gemma":[0.99982077,0.000044243392,0.00005185586,0.0000138175055,0.00005330525,0.000016078127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018106712,0.0005645168,0.00035637373,0.00025168638,0.00014238093,0.0002760234,0.0004939307,0.00042603258,0.00043291078],"category_scores_gemma":[0.0002048918,0.00027821286,0.0001599563,0.00016696117,0.00028332582,0.00039378647,0.0002516161,0.00027206354,0.0003512055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033787343,0.000012748771,0.00026528625,0.000029496807,0.000004292167,0.00003960283,0.000007517056,0.0003171599,0.9972345,0.00010481829,0.00003971713,0.0019110688],"study_design_scores_gemma":[0.00001720515,0.000339612,0.0020153474,0.0000051934767,0.00001503806,0.00023166185,0.000030530595,0.023537617,0.9729533,0.00017332098,0.0006608103,0.00002028024],"about_ca_topic_score_codex":0.00038654328,"about_ca_topic_score_gemma":0.00089932303,"teacher_disagreement_score":0.0005645168,"about_ca_system_score_codex":0.0002661308,"about_ca_system_score_gemma":0.00031791558,"threshold_uncertainty_score":0.0019309521},"labels":[],"label_agreement":null},{"id":"W3021796719","doi":"10.3390/s20092650","title":"Development of Willow Tree Yield-Mapping Technology","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Willow; Tree (set theory); Yield (engineering); Computer science; Engineering; Mathematics; Materials science; Biology; Botany; Combinatorics","score_opus":0.023229908044722462,"score_gpt":0.2171238804623948,"score_spread":0.19389397241767234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021796719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2788394,0.0018426564,0.70715487,0.0003423695,0.00013932308,0.00022572953,0.00065019773,0.0028346586,0.007970687],"genre_scores_gemma":[0.5659555,0.0013760779,0.4269571,0.00015442073,0.00009525669,0.00013162171,0.00091214955,0.00009251342,0.0043254094],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996804,0.00003270068,0.000011724705,0.0000758987,0.00017187823,0.00002747878],"domain_scores_gemma":[0.99973124,0.000035606452,0.000025537196,0.00003144839,0.00015546905,0.000020634669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051261665,0.0003995424,0.00029015946,0.0006565978,0.00018271228,0.0005147333,0.00059495185,0.00041502764,0.0011002006],"category_scores_gemma":[0.00046535788,0.00017394025,0.00021462911,0.00048464208,0.000119008255,0.001035041,0.0004123893,0.000309991,0.00066363945],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011746808,0.00010529299,0.018666038,0.00026796196,0.000040872437,0.00018874276,0.00019847852,0.003823186,0.41615355,0.0019112512,0.0020689275,0.5564582],"study_design_scores_gemma":[0.00004130104,0.0014220502,0.13570118,0.00013989721,0.00020718556,0.0013640502,0.00042314653,0.17851774,0.5988586,0.0036526339,0.079531096,0.00014104797],"about_ca_topic_score_codex":0.0013805657,"about_ca_topic_score_gemma":0.0026194134,"teacher_disagreement_score":0.0013805657,"about_ca_system_score_codex":0.0002571074,"about_ca_system_score_gemma":0.00040483678,"threshold_uncertainty_score":0.0036805868},"labels":[],"label_agreement":null},{"id":"W3021799457","doi":"10.3390/s20092646","title":"A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Brock University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Trunk; Physical medicine and rehabilitation; Biomechanics; Accelerometer; Lift (data mining); Physical therapy; Medicine; Computer science; Anatomy; Physics","score_opus":0.043789773611146214,"score_gpt":0.27998928707606374,"score_spread":0.23619951346491752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021799457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6717716,0.0018113396,0.3183908,0.00017242559,0.0002047579,0.001540727,0.0011803214,0.0007767584,0.0041512223],"genre_scores_gemma":[0.8123297,0.0010310467,0.18114232,0.00023167061,0.00013433548,0.0019439247,0.000696183,0.000062288396,0.002428605],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993855,0.00017151008,0.00006080928,0.00015355088,0.00019334164,0.00003529791],"domain_scores_gemma":[0.99936956,0.00016157416,0.00014865347,0.00007607675,0.00021545759,0.000028555294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010245792,0.0007946728,0.0006356588,0.0009776545,0.00018205632,0.00044654644,0.0003217867,0.000513742,0.0012214768],"category_scores_gemma":[0.002093209,0.0002404211,0.00042219323,0.00065168267,0.00017900851,0.00030187372,0.00038839199,0.00028003685,0.00029809825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012465625,0.0009318558,0.15540361,0.0015462011,0.00049819174,0.00014010527,0.00068125786,0.0030226484,0.44335058,0.00038114935,0.0014246824,0.39137322],"study_design_scores_gemma":[0.00009215016,0.0060514063,0.8668059,0.000121635545,0.00041514996,0.00075914996,0.000610164,0.01710507,0.102313355,0.00050025,0.0051467796,0.0000788757],"about_ca_topic_score_codex":0.00057824684,"about_ca_topic_score_gemma":0.0027846182,"teacher_disagreement_score":0.0012214768,"about_ca_system_score_codex":0.00013225745,"about_ca_system_score_gemma":0.0002108137,"threshold_uncertainty_score":0.005418539},"labels":[],"label_agreement":null},{"id":"W3023653095","doi":"10.3390/s20092628","title":"Radiation-Activated Pre-Differentiated Retinal Tissue Monitored by Acoustic Wave Biosensor","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Sanofi Pasteur; University of Toronto; Sanofi","keywords":"Electrode; Materials science; Retinal; Light intensity; Intensity (physics); Retina; Surface acoustic wave; Radiation; Interdigital transducer; Optics; Optoelectronics; Analytical Chemistry (journal); Chemistry; Physics","score_opus":0.030355195672874494,"score_gpt":0.25369006186426163,"score_spread":0.22333486619138715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023653095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983253,0.00094959425,0.014815622,0.000047664947,0.000023345281,0.00003093423,0.00017217171,0.00009275592,0.00061506714],"genre_scores_gemma":[0.9683016,0.001919543,0.026542647,0.00007549083,0.0000120194345,0.00013193525,0.00033470895,0.000032069067,0.0026500293],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999826,0.000022872548,0.000014112859,0.000046309866,0.000061878025,0.000028931952],"domain_scores_gemma":[0.9998641,0.0000375749,0.000040119492,0.000011118596,0.000032988308,0.000013960083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023301897,0.00036607077,0.00029285555,0.00016171287,0.000088139546,0.00024405424,0.00025079554,0.00034749618,0.00048008142],"category_scores_gemma":[0.00023180663,0.00017229239,0.00020797587,0.00015356562,0.00019732834,0.00023914335,0.00017883586,0.0004882975,0.0002108718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006250657,0.000001714077,0.00002316614,0.0000069826406,5.5453506e-7,0.00000471193,0.0000057338357,0.0000081150665,0.9997849,0.000006495398,0.0000017393089,0.00014970264],"study_design_scores_gemma":[0.0000015822537,0.00008777323,0.0018175237,0.0000026107734,0.000005163587,0.000036815858,0.000020164578,0.00054416445,0.99723876,0.000012732434,0.0002296484,0.0000031644183],"about_ca_topic_score_codex":0.00051753875,"about_ca_topic_score_gemma":0.000703064,"teacher_disagreement_score":0.00051753875,"about_ca_system_score_codex":0.00019909805,"about_ca_system_score_gemma":0.00018609245,"threshold_uncertainty_score":0.0016060472},"labels":[],"label_agreement":null},{"id":"W3024325364","doi":"10.3390/s20102757","title":"Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Liga Portuguesa Contra o Cancro; Natural Science Foundation of Gansu Province; Chinese Academy of Sciences","keywords":"Land use; Land cover; Driving factors; Environmental resource management; Sustainable development; Arid; Global change; Socioeconomic development; Land use, land-use change and forestry; China; Environmental science; Climate change; Geography; Remote sensing; Physical geography; Ecology; Civil engineering; Engineering","score_opus":0.03831389502142688,"score_gpt":0.22111638255278682,"score_spread":0.18280248753135994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024325364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997889,0.000057365007,0.0009893016,0.00011970087,0.000004497895,0.000018400242,0.0002959098,0.000031393734,0.0005943797],"genre_scores_gemma":[0.9984049,0.000057906913,0.0008679549,0.000009805828,0.0000034287268,0.00001415063,0.0003651734,0.0000037742477,0.00027275155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999801,0.00004811694,0.000013036335,0.00005012669,0.00003541876,0.000052430067],"domain_scores_gemma":[0.999461,0.0002470398,0.00006430041,0.000040839488,0.00013440881,0.000052411142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007274294,0.00082029507,0.00034346565,0.000760817,0.0005295039,0.0007112368,0.00088664505,0.00073434366,0.0007615112],"category_scores_gemma":[0.0011463626,0.00038162374,0.00085270155,0.0008569452,0.0004603265,0.00049091445,0.00041983696,0.00045916208,0.00009747492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016436368,0.00025686112,0.32270595,0.00009783923,0.0001338087,0.0029781514,0.00027006475,0.6589962,0.0015539565,0.0010535856,0.0012345071,0.010554774],"study_design_scores_gemma":[0.000020536565,0.000035357833,0.033797313,0.0000067359306,0.00003474502,0.000056074947,0.00022492076,0.9650549,0.00034337328,0.00019040977,0.00021932214,0.000016220354],"about_ca_topic_score_codex":0.34493285,"about_ca_topic_score_gemma":0.23114534,"teacher_disagreement_score":0.34493285,"about_ca_system_score_codex":0.002902716,"about_ca_system_score_gemma":0.0015622326,"threshold_uncertainty_score":0.68585026},"labels":[],"label_agreement":null},{"id":"W3024632411","doi":"10.3390/s20102851","title":"Nanoporous Gold as a VOC Sensor, Based on Nanoscale Electrical Phenomena and Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Nanoporous metals and alloys","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanoporous; Convolutional neural network; Computer science; Process engineering; Materials science; Process (computing); Sensitivity (control systems); Nanotechnology; Environmental science; Artificial intelligence; Electronic engineering; Engineering","score_opus":0.012021354778649773,"score_gpt":0.21723447774239585,"score_spread":0.20521312296374608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024632411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8251129,0.0039022388,0.15475588,0.00077801145,0.00028485968,0.00013118336,0.00056387536,0.0024222508,0.012048848],"genre_scores_gemma":[0.93439776,0.0007570342,0.05946097,0.00014778474,0.000020603458,0.000029589919,0.0001430855,0.000023966208,0.0050192066],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999897,0.000007803748,0.0000037197929,0.000034851382,0.00004399766,0.000012625369],"domain_scores_gemma":[0.99995756,0.000014358911,0.000007120409,0.0000051933707,0.00001051612,0.000005271131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120191944,0.00024845457,0.0002088377,0.0001705687,0.00014837751,0.00020948265,0.00042683523,0.00053361454,0.00046186356],"category_scores_gemma":[0.00017763805,0.00015019401,0.00013898464,0.0001471809,0.00021407769,0.00040318238,0.00022959929,0.00018211361,0.00017395733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102573315,0.00006110353,0.0007432045,0.00012667176,0.000021928045,0.0001254745,0.000028130991,0.009728767,0.95837724,0.0013640049,0.00054759864,0.028773263],"study_design_scores_gemma":[0.000009925864,0.0001950932,0.0026950552,0.000011350968,0.000025574338,0.00016426426,0.000015163712,0.35205403,0.64057523,0.0005100581,0.0037145556,0.000029708424],"about_ca_topic_score_codex":0.0034850966,"about_ca_topic_score_gemma":0.010227993,"teacher_disagreement_score":0.0034850966,"about_ca_system_score_codex":0.0006444314,"about_ca_system_score_gemma":0.00024157848,"threshold_uncertainty_score":0.0069295764},"labels":[],"label_agreement":null},{"id":"W3024831242","doi":"10.3390/s20102747","title":"Characterization of the Uniformity of High-Flux CdZnTe Material","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Redlen Technologies (Canada)","funders":"Science and Technology Facilities Council; UK Research and Innovation","keywords":"Cadmium zinc telluride; Characterization (materials science); Detector; Materials science; Optoelectronics; Full width at half maximum; Synchrotron; Optics; X-ray detector; Cadmium telluride photovoltaics; Tellurium; Dot pitch; Irradiation; Physics; Pixel; Nuclear physics","score_opus":0.009983677615099008,"score_gpt":0.18923592531904354,"score_spread":0.17925224770394454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024831242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9783393,0.00052793574,0.018224956,0.000047840178,0.000030205787,0.000051445226,0.00041761957,0.00013179526,0.0022289022],"genre_scores_gemma":[0.9866123,0.00023640931,0.010320243,0.000035194338,0.000005339432,0.000055185283,0.0006944511,0.000054624707,0.0019862894],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999764,0.000024280083,0.000018486435,0.00006759839,0.00010138405,0.00002422811],"domain_scores_gemma":[0.9996742,0.00006154033,0.000064866224,0.000033913147,0.00014624844,0.000019331865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032065608,0.00022129502,0.00012846036,0.00042340785,0.00017153409,0.00040644832,0.000251828,0.00033337477,0.0013622377],"category_scores_gemma":[0.00065982615,0.000113546965,0.00010196395,0.0002530087,0.00016661671,0.00022386543,0.00012001739,0.00017319502,0.0002924549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011745395,0.000007707138,0.0006429498,0.000009377185,0.0000021803223,0.000012528044,0.000010044835,0.00005259886,0.99853015,0.000033050754,0.000019491614,0.00066806487],"study_design_scores_gemma":[0.0000028226582,0.00006243566,0.009067384,0.0000031599614,0.0000092771,0.00008419033,0.000026186523,0.0009577805,0.9883003,0.000015082221,0.0014684434,0.0000029401065],"about_ca_topic_score_codex":0.00066501397,"about_ca_topic_score_gemma":0.0013864547,"teacher_disagreement_score":0.0013622377,"about_ca_system_score_codex":0.00028062004,"about_ca_system_score_gemma":0.00018030268,"threshold_uncertainty_score":0.0045571327},"labels":[],"label_agreement":null},{"id":"W3024912007","doi":"10.3390/s20102778","title":"Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":699,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Structural health monitoring; Deep learning; Popularity; Computer science; Data science; Cloud computing; Systems engineering; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.022140394563229512,"score_gpt":0.26494890173788266,"score_spread":0.24280850717465313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024912007","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026049532,0.96330506,0.02986239,0.00072250137,0.0003809325,0.000028306826,0.0001464513,0.000140267,0.002809079],"genre_scores_gemma":[0.025452081,0.95931774,0.0122056,0.00042078947,0.0008247592,0.000050375573,0.00034809898,0.000038971837,0.0013415862],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960357,0.000057107754,0.000057891568,0.00013147536,0.00012230425,0.00002766559],"domain_scores_gemma":[0.99835336,0.0011630596,0.00011094775,0.00005052484,0.0002828628,0.000039293358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010674731,0.0011670497,0.0011901044,0.0013281178,0.00018327814,0.0012489841,0.0013178084,0.0012739726,0.0021149283],"category_scores_gemma":[0.0027157448,0.0004985257,0.0009531298,0.0019794626,0.0004120918,0.001675688,0.0007418886,0.0009716455,0.00082211406],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083968254,0.000111226705,0.0013424577,0.012177399,0.0002522366,0.00011799711,0.00008580928,0.011899996,0.0020092085,0.005381312,0.0107041495,0.95583427],"study_design_scores_gemma":[0.00006446985,0.0010008521,0.0077714855,0.013895167,0.0021485146,0.0022231678,0.00042518263,0.18198252,0.015927095,0.025441995,0.7487506,0.0003688714],"about_ca_topic_score_codex":0.002139744,"about_ca_topic_score_gemma":0.0015231248,"teacher_disagreement_score":0.002139744,"about_ca_system_score_codex":0.0004162262,"about_ca_system_score_gemma":0.0010719263,"threshold_uncertainty_score":0.007075131},"labels":[],"label_agreement":null},{"id":"W3025037886","doi":"10.3390/s20102807","title":"Hollow-Core Photonic Crystal Fiber Mach–Zehnder Interferometer for Gas Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Korea Carbon Capture and Sequestration R and D Center","keywords":"Mach–Zehnder interferometer; Interferometry; Photonic-crystal fiber; Core (optical fiber); Materials science; Optical fiber; Fiber; Optoelectronics; Optics; Photonic crystal; Physics; Composite material","score_opus":0.028734870859244668,"score_gpt":0.23888807560090275,"score_spread":0.21015320474165808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025037886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45098844,0.0059975847,0.5362248,0.00055124896,0.0003717596,0.00030515002,0.00055205513,0.0020540457,0.0029549242],"genre_scores_gemma":[0.5196854,0.0010513165,0.4777077,0.00012539432,0.000058856167,0.00011525259,0.00022443329,0.000033833046,0.00099788],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990652,0.00010839568,0.000029591256,0.00016175033,0.0005893301,0.00004582598],"domain_scores_gemma":[0.9995371,0.00014119099,0.000109741224,0.000044578595,0.00013431626,0.00003306771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007570772,0.0006318023,0.0006763638,0.00053753454,0.00032585528,0.00026249173,0.0011661717,0.0007837438,0.00034225296],"category_scores_gemma":[0.00056742114,0.0003456764,0.0003099408,0.00048447045,0.00054978905,0.0010749052,0.0004982896,0.0005947885,0.00019847164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000698135,0.000040895193,0.0004396486,0.00011701343,0.0000099816025,0.00004289742,0.00002443536,0.00044720172,0.98586905,0.0007100466,0.00018560256,0.0120434025],"study_design_scores_gemma":[0.000022730794,0.00023019705,0.001979714,0.000007951183,0.000019633702,0.0003376061,0.00001724699,0.04296347,0.95136416,0.000233436,0.0027800596,0.00004385116],"about_ca_topic_score_codex":0.0010055538,"about_ca_topic_score_gemma":0.0026679263,"teacher_disagreement_score":0.0011661717,"about_ca_system_score_codex":0.0009010698,"about_ca_system_score_gemma":0.0007898065,"threshold_uncertainty_score":0.006537795},"labels":[],"label_agreement":null},{"id":"W3027468975","doi":"10.3390/s20102991","title":"Quality Control and Pre-Analysis Treatment of the Environmental Datasets Collected by an Internet Operated Deep-Sea Crawler during Its Entire 7-Year Long Deployment (2009–2016)","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ocean Networks Canada Society; University of Victoria","funders":"Helmholtz-Gemeinschaft; Ministerio de Ciencia, Innovación y Universidades","keywords":"Web crawler; Software deployment; Turbidity; Sea trial; Environmental science; Computer science; The Internet; Quality (philosophy); Remote sensing; Engineering; Marine engineering; Geography; Geology; World Wide Web; Oceanography","score_opus":0.012896534607764314,"score_gpt":0.21856111101077233,"score_spread":0.205664576403008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027468975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75624025,0.00034122352,0.1958868,0.00062931573,0.0004950794,0.003028638,0.027413316,0.006512142,0.00945315],"genre_scores_gemma":[0.66388,0.0003197491,0.2703649,0.00034238066,0.00011832388,0.0036157384,0.054494064,0.0014308734,0.0054340265],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9969047,0.00036078796,0.0003648111,0.0006483054,0.0015115939,0.00020968809],"domain_scores_gemma":[0.9938824,0.00065207836,0.0005884019,0.0013132559,0.0034239823,0.00013992294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003759019,0.00077215803,0.0004604643,0.002037822,0.0010135135,0.0010680056,0.0007242092,0.0004663028,0.0011311527],"category_scores_gemma":[0.007056458,0.0002267617,0.0006934498,0.0025355876,0.00075993664,0.00061257504,0.0011629366,0.00062871625,0.00074260694],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006890117,0.001094163,0.39459383,0.0008742684,0.00038274558,0.00095379597,0.002874615,0.018824428,0.14473289,0.0025670803,0.035577476,0.39683577],"study_design_scores_gemma":[0.00007084455,0.00042985618,0.803934,0.00011922926,0.00014281544,0.00026622668,0.0012863087,0.03300068,0.09494552,0.0008230128,0.06486375,0.00011769319],"about_ca_topic_score_codex":0.017343437,"about_ca_topic_score_gemma":0.034000646,"teacher_disagreement_score":0.017343437,"about_ca_system_score_codex":0.00074037927,"about_ca_system_score_gemma":0.0018662572,"threshold_uncertainty_score":0.034484982},"labels":[],"label_agreement":null},{"id":"W3027800744","doi":"10.3390/s20102954","title":"An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Plant Surface Properties and Treatments","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Control theory (sociology); Overshoot (microwave communication); Settling time; Fuzzy logic; Controller (irrigation); Fuzzy control system; Inertia; PID controller; Control system; Chaotic; Nonlinear system; Open-loop controller; Adaptive control; Engineering; Control engineering; Computer science; Step response; Temperature control; Closed loop; Control (management)","score_opus":0.026095971409784518,"score_gpt":0.18938897094186552,"score_spread":0.163292999532081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027800744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12547956,0.0005532654,0.8666959,0.00019316467,0.000126103,0.00012970406,0.000046758072,0.00095191126,0.005823711],"genre_scores_gemma":[0.9305838,0.0001323687,0.06638026,0.00006435074,0.000022464203,0.00012411123,0.000040067436,0.000016643078,0.0026358564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996569,0.0000406576,0.000023290942,0.00010599176,0.00014293662,0.000030356814],"domain_scores_gemma":[0.99983394,0.0000371351,0.00003307917,0.000017421027,0.00006379779,0.000014523769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003194834,0.00043899432,0.00050556735,0.0002354351,0.00039261364,0.0005383465,0.0008269094,0.0005284668,0.0007924213],"category_scores_gemma":[0.00043283735,0.00024034272,0.00034026892,0.00025953978,0.00036256554,0.00046620902,0.0005462458,0.00043960003,0.00012912699],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005759568,0.00021310423,0.0037134045,0.0005182009,0.00017515889,0.00071442476,0.0005218734,0.48699266,0.24626666,0.0105375,0.002160731,0.2476103],"study_design_scores_gemma":[0.000054676697,0.00021339224,0.0007114458,0.000008013385,0.00002308992,0.00010745816,0.000011032653,0.98399967,0.012547315,0.0005716244,0.0017296505,0.00002269958],"about_ca_topic_score_codex":0.0025589813,"about_ca_topic_score_gemma":0.001938086,"teacher_disagreement_score":0.0025589813,"about_ca_system_score_codex":0.00041108282,"about_ca_system_score_gemma":0.0005425189,"threshold_uncertainty_score":0.00508821},"labels":[],"label_agreement":null},{"id":"W3027989287","doi":"10.3390/s20102939","title":"Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Accelerometer; Kinematics; Sagittal plane; Gait; Computer science; Mean squared error; Motion capture; Convolutional neural network; Artificial intelligence; Treadmill; Simulation; Gait analysis; Calibration; Motion analysis; Computer vision; Physical medicine and rehabilitation; Motion (physics); Mathematics; Statistics; Physical therapy; Medicine","score_opus":0.028631037815318127,"score_gpt":0.20676676600613542,"score_spread":0.1781357281908173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027989287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23810868,0.0006197317,0.7586798,0.00018286647,0.000050491602,0.00006591938,0.0002390562,0.0008999226,0.0011535549],"genre_scores_gemma":[0.90315896,0.00029718791,0.093549155,0.0000841176,0.000035377685,0.00009448158,0.0005566915,0.00002918602,0.0021949082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999863,0.000020106447,0.000009293,0.000053644402,0.00002592533,0.000027888003],"domain_scores_gemma":[0.9998099,0.00006211454,0.0000320075,0.000021768039,0.000058073794,0.000016055814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034611087,0.00084556,0.00047516217,0.0004295855,0.00016892925,0.000338846,0.00064252625,0.0005911748,0.00066865585],"category_scores_gemma":[0.0008292142,0.0003705215,0.0004236359,0.00037165426,0.0001560023,0.0003939885,0.00048743526,0.000659571,0.0002670267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023187514,0.00040249404,0.011216961,0.00010105366,0.00018814603,0.00018288475,0.00008207256,0.5028123,0.025170607,0.00065481116,0.0012882511,0.4576685],"study_design_scores_gemma":[0.0000041866474,0.000053287793,0.0021155688,0.000007508146,0.000013678835,0.00002245134,0.00001185047,0.99560046,0.0016458328,0.00034877055,0.00017097102,0.0000053856193],"about_ca_topic_score_codex":0.006531417,"about_ca_topic_score_gemma":0.009143095,"teacher_disagreement_score":0.006531417,"about_ca_system_score_codex":0.00027669923,"about_ca_system_score_gemma":0.0005353359,"threshold_uncertainty_score":0.012986779},"labels":[],"label_agreement":null},{"id":"W3030263417","doi":"10.3390/s20113079","title":"Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Algorithm; Range (aeronautics); Noise (video); Estimation theory; Interferometry; Computer science; Bayesian probability; Microwave; Mathematics; Statistics; Artificial intelligence; Engineering; Physics; Optics","score_opus":0.01407588185468258,"score_gpt":0.231183703300023,"score_spread":0.2171078214453404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030263417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027973703,0.000109017106,0.9965533,0.000044786466,0.00001243655,0.000016206744,0.000008782695,0.00013058879,0.0003275284],"genre_scores_gemma":[0.09176033,0.00016186068,0.9054685,0.00011589955,0.000034614797,0.00015140172,0.000118487005,0.00013420921,0.0020546822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983925,0.00047416126,0.00010157718,0.00026738297,0.0006720392,0.00009228601],"domain_scores_gemma":[0.9980566,0.0009724669,0.00022009455,0.00015866685,0.0005474735,0.000044732576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025243137,0.001273708,0.0013271297,0.0010094278,0.00059015164,0.0008092071,0.0017183217,0.0015071771,0.0016909046],"category_scores_gemma":[0.0067292587,0.0008656081,0.00080823223,0.00082828314,0.00094028556,0.001958641,0.001254858,0.001393158,0.0006764624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028225436,0.00011085807,0.0015512268,0.0001691169,0.00013686859,0.00009776009,0.00024633575,0.5996439,0.019257925,0.019369394,0.0016409522,0.35749346],"study_design_scores_gemma":[0.000017496375,0.000032672044,0.00019153536,0.0000105242525,0.000009962776,0.00003556671,0.0000075626012,0.99229383,0.0029393933,0.0032994968,0.001143656,0.000018317849],"about_ca_topic_score_codex":0.0049759103,"about_ca_topic_score_gemma":0.005965558,"teacher_disagreement_score":0.0049759103,"about_ca_system_score_codex":0.0010225981,"about_ca_system_score_gemma":0.0017418448,"threshold_uncertainty_score":0.01335001},"labels":[],"label_agreement":null},{"id":"W3031198926","doi":"10.3390/s20113071","title":"Disjoint Spanning Tree Based Reliability Evaluation of Wireless Sensor Network","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University; Alfaisal University; Princess Nourah Bint Abdulrahman University","keywords":"Wireless sensor network; Computer science; Disjoint sets; Reliability (semiconductor); Spanning tree; Distributed computing; Computer network; Wireless; Key (lock); Key distribution in wireless sensor networks; Wireless network; Computer security; Mathematics","score_opus":0.023091904566464608,"score_gpt":0.22796485352687731,"score_spread":0.2048729489604127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031198926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10322398,0.0014741987,0.8912357,0.00013309089,0.00007406486,0.00009693146,0.00018078674,0.00037143403,0.003209724],"genre_scores_gemma":[0.87052286,0.00087562465,0.1271582,0.00003310168,0.00003861098,0.00011820312,0.0003214525,0.00004728898,0.000884819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998569,0.0006070586,0.00009962156,0.0001708784,0.0004904845,0.00006294142],"domain_scores_gemma":[0.9977865,0.0010917309,0.00022508632,0.00012761298,0.0007160607,0.000052987787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013460001,0.0008105414,0.00071467645,0.0014765384,0.00028928608,0.00061597564,0.00058958994,0.0004581183,0.000683055],"category_scores_gemma":[0.0076822517,0.0001757707,0.0005033027,0.0010547291,0.0003346794,0.0011700701,0.00052760914,0.00032286468,0.00016451847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019189327,0.00004804323,0.0036647175,0.0003117377,0.00008969017,0.00014524924,0.00011992489,0.869662,0.011568721,0.010181175,0.0008055135,0.1032114],"study_design_scores_gemma":[0.0000025343968,0.00008149872,0.0006431239,0.000012828036,0.000014811788,0.00006493897,0.00002341864,0.99369377,0.0022626393,0.0028504846,0.0003418509,0.000007974396],"about_ca_topic_score_codex":0.0010652255,"about_ca_topic_score_gemma":0.0008019583,"teacher_disagreement_score":0.0014765384,"about_ca_system_score_codex":0.0006082159,"about_ca_system_score_gemma":0.00044898703,"threshold_uncertainty_score":0.0071184635},"labels":[],"label_agreement":null},{"id":"W3033120978","doi":"10.3390/s20113240","title":"Psychophysiological Models to Identify and Monitor Elderly with a Cardiovascular Condition: The Added Value of Psychosocial Parameters to Routinely Applied Physiological Assessments","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek; H2020 Marie Skłodowska-Curie Actions; Newfoundland and Labrador; Noaber Foundation","keywords":"Psychosocial; Depression (economics); Medicine; Cardiovascular health; Population; Depressive symptoms; Elderly people; Gerontology; Physical therapy; Clinical psychology; Psychiatry; Disease; Internal medicine; Anxiety; Environmental health","score_opus":0.057120067735478876,"score_gpt":0.32481514067415196,"score_spread":0.26769507293867306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033120978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8743175,0.0015698147,0.11821915,0.00054020336,0.00015512867,0.00040277938,0.0005266029,0.00039434384,0.0038745708],"genre_scores_gemma":[0.97655964,0.0005963679,0.021524346,0.00009066555,0.00006347161,0.00028597267,0.00026385896,0.000012809757,0.0006029429],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996153,0.0002164044,0.000019803283,0.0000540012,0.000070107904,0.000024346678],"domain_scores_gemma":[0.99896705,0.0006758062,0.00010867732,0.00008384712,0.000090121925,0.00007441752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001897562,0.001002523,0.00048376236,0.0007235104,0.00015897192,0.00067461695,0.00043874411,0.00039229955,0.0012599305],"category_scores_gemma":[0.0047528874,0.0001669744,0.0004883282,0.00021387634,0.0002043202,0.00039094265,0.00046424798,0.00064783957,0.0003952866],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002885814,0.0035546138,0.56294674,0.00033800225,0.0010076583,0.00028815953,0.00057392416,0.13464098,0.017456118,0.0020963242,0.0016421599,0.2725696],"study_design_scores_gemma":[0.00018236501,0.004470857,0.25384519,0.00011406605,0.00032845244,0.00046593146,0.0002689126,0.7321061,0.0023265802,0.0041445526,0.0016713615,0.00007555746],"about_ca_topic_score_codex":0.0023691403,"about_ca_topic_score_gemma":0.002491547,"teacher_disagreement_score":0.0023691403,"about_ca_system_score_codex":0.0002680984,"about_ca_system_score_gemma":0.0005086595,"threshold_uncertainty_score":0.010035396},"labels":[],"label_agreement":null},{"id":"W3033137406","doi":"10.3390/s20113261","title":"Monitoring the Water Stress of an Indoor Living Wall System Using the “Triangle Method”","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Multispectral image; Normalized Difference Vegetation Index; Hyperspectral imaging; Remote sensing; Environmental science; Water stress; Vegetation (pathology); Modular design; Rainwater harvesting; Computer science; Ecology; Geography; Biology","score_opus":0.03182222777324198,"score_gpt":0.2550662466252933,"score_spread":0.22324401885205133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033137406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7497626,0.00019954328,0.24354972,0.000089159184,0.000067458444,0.00011287042,0.0007514404,0.0010215052,0.0044456134],"genre_scores_gemma":[0.8971377,0.00011997976,0.10113145,0.000026461532,0.000012858054,0.00007999665,0.00043399187,0.000040781408,0.0010167198],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998085,0.000023216937,0.000008645175,0.000065689324,0.00006985314,0.00002410586],"domain_scores_gemma":[0.9998215,0.000032233853,0.00003259158,0.000017275963,0.00007463754,0.000021831429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015273891,0.0003743675,0.0002869427,0.00090778625,0.0002446696,0.0003896772,0.0004537792,0.00026690454,0.0014508747],"category_scores_gemma":[0.00036645538,0.00011988071,0.00027009108,0.00081947324,0.00015188131,0.00057028007,0.00047339112,0.00023833095,0.0003045645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060846313,0.00027982643,0.13276564,0.0004727602,0.00014577179,0.00043541295,0.0009677615,0.03653712,0.44003683,0.0013172122,0.0024845474,0.3839487],"study_design_scores_gemma":[0.000034842324,0.00040832063,0.1450209,0.00003159224,0.00008897028,0.00050118123,0.00085339294,0.7369653,0.11145574,0.0010391689,0.0034669572,0.00013365551],"about_ca_topic_score_codex":0.0030361516,"about_ca_topic_score_gemma":0.0063294466,"teacher_disagreement_score":0.0030361516,"about_ca_system_score_codex":0.00018822461,"about_ca_system_score_gemma":0.00020835061,"threshold_uncertainty_score":0.0060369372},"labels":[],"label_agreement":null},{"id":"W3033353798","doi":"10.3390/s20113228","title":"NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Power Generation; Ontario Tech University","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Machine learning; Situation awareness; Recurrent neural network; Operator (biology); Artificial neural network; Natural language processing; Engineering","score_opus":0.09565670724383254,"score_gpt":0.38219653731445824,"score_spread":0.28653983007062567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033353798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038370952,0.00022379146,0.95358694,0.0002867049,0.00008191031,0.00008703962,0.0010681014,0.0036409043,0.002653632],"genre_scores_gemma":[0.81502885,0.00027535588,0.17798376,0.00022247195,0.0001007344,0.0002543735,0.0023473138,0.00016963345,0.0036173817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978155,0.000031987467,0.00001572429,0.00009494472,0.000053125692,0.000022657061],"domain_scores_gemma":[0.9995303,0.00022207056,0.00007337336,0.000035556914,0.0001187482,0.000019879517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031520304,0.00089099,0.0003699095,0.0007286821,0.0002093716,0.00043799944,0.000571083,0.0005894823,0.002057822],"category_scores_gemma":[0.0015543995,0.00026092003,0.0005073381,0.00051118236,0.00024287806,0.00068804255,0.0005284222,0.00091993925,0.00068983535],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032864918,0.00028767114,0.0053115524,0.00027445817,0.00010850855,0.0005003284,0.00021067995,0.7069695,0.026886852,0.0034746237,0.005348407,0.25029874],"study_design_scores_gemma":[0.0000031907055,0.000018734692,0.00068707275,0.0000049044697,0.000007665025,0.000021742948,0.000014085802,0.9953477,0.002243573,0.0011677836,0.000479015,0.000004543717],"about_ca_topic_score_codex":0.009111639,"about_ca_topic_score_gemma":0.009835636,"teacher_disagreement_score":0.009111639,"about_ca_system_score_codex":0.00053916784,"about_ca_system_score_gemma":0.00085830706,"threshold_uncertainty_score":0.01811719},"labels":[],"label_agreement":null},{"id":"W3033434602","doi":"10.3390/s20113171","title":"Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Face recognition and analysis","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"National Institute of Dental and Craniofacial Research; National Institutes of Health","keywords":"Landmark; Computer science; Artificial intelligence; Computer vision; Biometrics; Ground truth; Pattern recognition (psychology); Face (sociological concept); Set (abstract data type)","score_opus":0.027561599788340743,"score_gpt":0.24670990920984098,"score_spread":0.21914830942150024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033434602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17215918,0.0003046895,0.8212888,0.000117957155,0.000044168177,0.0001902096,0.0004998221,0.004407035,0.0009881331],"genre_scores_gemma":[0.46144533,0.0003379851,0.5343741,0.00007146474,0.000024799228,0.00023908885,0.0014948898,0.00058643264,0.0014258408],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992244,0.00018874623,0.00003975951,0.00018712936,0.00030656994,0.000053315238],"domain_scores_gemma":[0.99893266,0.00039822079,0.000096164105,0.00028689593,0.00026150016,0.000024468596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009359117,0.0007472823,0.00085334247,0.0012971911,0.00021050098,0.00085199985,0.0007392464,0.00059565733,0.0015359136],"category_scores_gemma":[0.0031983624,0.00049288775,0.00083708833,0.00064247055,0.00042270587,0.0006080522,0.0009180335,0.00059880514,0.0012071007],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005514908,0.00009290739,0.011495619,0.00022938156,0.00014810821,0.00041042612,0.00059620367,0.04620942,0.23179008,0.0011896648,0.002765865,0.7045209],"study_design_scores_gemma":[0.00006684455,0.00041895304,0.03967603,0.000047324367,0.00011310747,0.0021868078,0.00053194084,0.8085463,0.1397444,0.0027750868,0.0057782535,0.00011501385],"about_ca_topic_score_codex":0.0021436918,"about_ca_topic_score_gemma":0.004278241,"teacher_disagreement_score":0.0021436918,"about_ca_system_score_codex":0.00025227113,"about_ca_system_score_gemma":0.000641771,"threshold_uncertainty_score":0.005138159},"labels":[],"label_agreement":null},{"id":"W3033530497","doi":"10.3390/s20113209","title":"Muscle Synergies in Parkinson’s Disease","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Parkinson's disease; Physical medicine and rehabilitation; Gait; Disease; Balance (ability); Motor control; Neuroscience; Movement disorders; Medicine; Psychology; Pathology","score_opus":0.022139883831747604,"score_gpt":0.25237125239404695,"score_spread":0.23023136856229934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033530497","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002052255,0.9984701,0.00026925158,0.00014929575,0.000098739656,0.000005153501,0.00001777452,0.000006811425,0.000777551],"genre_scores_gemma":[0.0021289082,0.99676883,0.00030962387,0.00010954766,0.00010202049,0.0000093449635,0.00003113916,0.0000017591302,0.00053875113],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998394,0.000027823668,0.000029937648,0.000037298105,0.000055338456,0.000010203534],"domain_scores_gemma":[0.9997429,0.0001535951,0.00003689877,0.000005830305,0.00004986969,0.00001087261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045391574,0.00083734246,0.0010725919,0.0027188614,0.00019833146,0.0008335241,0.00057547935,0.00094893726,0.0030970252],"category_scores_gemma":[0.000703201,0.00029661358,0.0006161609,0.0017272843,0.00043223388,0.0008667719,0.0006527217,0.00092830876,0.0012632145],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066888,0.00004599895,0.00025274896,0.032212682,0.00016253498,0.00017260377,0.00008769392,0.00081680936,0.0018358566,0.004767293,0.009671604,0.94990736],"study_design_scores_gemma":[0.000028472572,0.00028228873,0.0047570714,0.015063126,0.00039860263,0.003213468,0.00016400265,0.00066578225,0.0014117842,0.009930879,0.964019,0.00006549163],"about_ca_topic_score_codex":0.0011577036,"about_ca_topic_score_gemma":0.0016130805,"teacher_disagreement_score":0.0030970252,"about_ca_system_score_codex":0.00046377184,"about_ca_system_score_gemma":0.00080456375,"threshold_uncertainty_score":0.010360599},"labels":[],"label_agreement":null},{"id":"W3033936082","doi":"10.3390/s20113270","title":"Robust Weighted l1,2 Norm Filtering in Passive Radar Systems","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Radar; Algorithm; Ambiguity function; Passive radar; Noise (video); Gaussian noise; Doppler radar; UMTS frequency bands; Pulse-Doppler radar; Artificial intelligence; Radar imaging; Telecommunications","score_opus":0.027761720823341416,"score_gpt":0.2066271138046825,"score_spread":0.17886539298134108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033936082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071763643,0.0002878434,0.9917481,0.00005591589,0.000026107493,0.000009332824,0.000012286899,0.00010027065,0.00058372424],"genre_scores_gemma":[0.4135602,0.0012651229,0.5805392,0.00016651239,0.00014196507,0.00014113366,0.00018937261,0.00010365398,0.0038928695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992679,0.00021174377,0.000046933368,0.00014180504,0.00028936315,0.00004227366],"domain_scores_gemma":[0.99943775,0.00030468064,0.00008505526,0.00003667424,0.000121309895,0.000014574766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009790144,0.00069990754,0.00058396015,0.00059648365,0.0002600649,0.0008555667,0.0005325789,0.00081380963,0.0006121032],"category_scores_gemma":[0.0022315322,0.00026917591,0.0004545522,0.0007533349,0.00055411534,0.0010057512,0.00048386934,0.00062177965,0.00025614706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033964743,0.000089376466,0.00088978244,0.00027963158,0.000082794,0.00011884363,0.00015090792,0.6093535,0.058278102,0.03587751,0.001530913,0.293009],"study_design_scores_gemma":[0.0000058001797,0.000054689146,0.0003021113,0.000008664904,0.0000070367146,0.000039451188,0.000008903439,0.98854136,0.006863875,0.0032528678,0.0008999071,0.000015328613],"about_ca_topic_score_codex":0.002050401,"about_ca_topic_score_gemma":0.0011471327,"teacher_disagreement_score":0.002050401,"about_ca_system_score_codex":0.00048077403,"about_ca_system_score_gemma":0.00057118176,"threshold_uncertainty_score":0.005177617},"labels":[],"label_agreement":null},{"id":"W3034179620","doi":"10.3390/s20123385","title":"A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Usability; Computer science; Human–computer interaction; Engineering","score_opus":0.018805786506243958,"score_gpt":0.2335149128733445,"score_spread":0.21470912636710054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034179620","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9758365,0.00016558963,0.022685446,0.00003715762,0.000054364464,0.00006577498,0.00016012408,0.00023622392,0.00075875566],"genre_scores_gemma":[0.98708785,0.00010602556,0.011030736,0.000022935104,0.000011386587,0.00008171874,0.00021333805,0.00003198159,0.0014139903],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99902594,0.00021500015,0.00012400158,0.00023166499,0.00031660893,0.000086836444],"domain_scores_gemma":[0.9972319,0.001240149,0.00022728674,0.00036716537,0.00083733536,0.00009613359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018344616,0.0006577533,0.0006085345,0.0004919169,0.00014782255,0.00047475923,0.00049705873,0.000649583,0.00093736907],"category_scores_gemma":[0.0056102015,0.00016847579,0.00033195014,0.00034654024,0.00026462233,0.00054663926,0.00045414746,0.00031998818,0.00037185324],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0052584447,0.0016529587,0.053852983,0.00076514075,0.00040163123,0.000399281,0.0008191721,0.05862496,0.49657908,0.00035211263,0.0010467224,0.3802475],"study_design_scores_gemma":[0.000063411935,0.013806055,0.19977722,0.00006078722,0.00026377154,0.00054607634,0.00045518472,0.56100315,0.22202389,0.00023079236,0.0016447671,0.00012486367],"about_ca_topic_score_codex":0.0012924562,"about_ca_topic_score_gemma":0.0014303372,"teacher_disagreement_score":0.0018344616,"about_ca_system_score_codex":0.00026861674,"about_ca_system_score_gemma":0.00014983298,"threshold_uncertainty_score":0.009701729},"labels":[],"label_agreement":null},{"id":"W3034477608","doi":"10.3390/s20113302","title":"Source–Detector Spectral Pairing-Related Inaccuracies in Pulse Oximetry: Evaluation of the Wavelength Shift","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Pulse oximetry; Photodetector; Detector; Photodetection; Computer science; Analyser; Optics; Pairing; Electronic engineering; Physics; Telecommunications; Engineering; Medicine","score_opus":0.023021945783396024,"score_gpt":0.22872617940941437,"score_spread":0.20570423362601833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034477608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58676136,0.0011484826,0.40697497,0.00021743495,0.00028731432,0.00010635132,0.00033094705,0.0014522405,0.002720782],"genre_scores_gemma":[0.9575184,0.00027049863,0.041077483,0.00006022261,0.000012339042,0.000046968154,0.00020933375,0.000117024145,0.0006877032],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985997,0.000270593,0.00009030082,0.0003034075,0.0006492557,0.00008671301],"domain_scores_gemma":[0.99751437,0.0014962904,0.0002396445,0.0004219584,0.00030196554,0.000025866973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001636271,0.0007028962,0.0003606775,0.00053657626,0.0003162014,0.0005960194,0.0007286914,0.00086919183,0.001128631],"category_scores_gemma":[0.00655789,0.00027980786,0.000422406,0.0006728291,0.00047144928,0.0008108523,0.00082831,0.0006494958,0.00045336958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001831779,0.0004701191,0.030953102,0.0011202161,0.00025578222,0.0007868547,0.0008140712,0.2491353,0.49591988,0.003957369,0.0016469896,0.21310847],"study_design_scores_gemma":[0.000020568314,0.0004712017,0.013965015,0.000060103313,0.00007820477,0.0007007774,0.00013363836,0.4516244,0.52830386,0.0013354287,0.0032414175,0.0000654216],"about_ca_topic_score_codex":0.0011816206,"about_ca_topic_score_gemma":0.0008528652,"teacher_disagreement_score":0.001636271,"about_ca_system_score_codex":0.0005147054,"about_ca_system_score_gemma":0.00042738975,"threshold_uncertainty_score":0.008653522},"labels":[],"label_agreement":null},{"id":"W3035164309","doi":"10.3390/s20123381","title":"Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Engineering and Physical Sciences Research Council","keywords":"Nondestructive testing; Thermography; Aerospace; SIGNAL (programming language); Signal-to-noise ratio (imaging); Computer science; Noise (video); Signal processing; Acoustics; Materials science; Artificial intelligence; Infrared; Engineering; Aerospace engineering; Computer hardware; Optics; Telecommunications; Digital signal processing","score_opus":0.01599059968888067,"score_gpt":0.2513590380386251,"score_spread":0.2353684383497444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035164309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.852013,0.002464858,0.14008151,0.00010544901,0.00011133664,0.0000927004,0.00017044962,0.000599037,0.0043617003],"genre_scores_gemma":[0.9304407,0.00058652327,0.067133166,0.00006823992,0.000019946221,0.00003882313,0.00012690885,0.00006768201,0.0015180148],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989502,0.00020155245,0.000041601383,0.00016657606,0.0005886393,0.00005149957],"domain_scores_gemma":[0.9986185,0.0005672161,0.0002188102,0.000121972065,0.00042986675,0.00004361983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010899573,0.00054124626,0.00043256363,0.000841159,0.00018210492,0.0007750785,0.0005945723,0.00061854656,0.0009777816],"category_scores_gemma":[0.002257044,0.00023435672,0.00038695472,0.00037351315,0.0004955681,0.0009745061,0.00038980652,0.00038498215,0.00031895872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006188352,0.000061497376,0.0038603283,0.00028136987,0.000039834493,0.0001104024,0.00015364852,0.0043723728,0.95071507,0.00033682262,0.00012489095,0.03932504],"study_design_scores_gemma":[0.00001991544,0.0012016653,0.019979045,0.000049527538,0.00010596258,0.00064971385,0.00028720585,0.03649843,0.9389951,0.00028678848,0.0018615671,0.000065030246],"about_ca_topic_score_codex":0.00035885617,"about_ca_topic_score_gemma":0.0011839472,"teacher_disagreement_score":0.0010899573,"about_ca_system_score_codex":0.0002809801,"about_ca_system_score_gemma":0.00017626447,"threshold_uncertainty_score":0.0057643056},"labels":[],"label_agreement":null},{"id":"W3035791417","doi":"10.3390/s20123543","title":"A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robot; Task (project management); Automation; Process (computing); Mobile robot; Artificial intelligence; Computer science; Engineering; Simulation; Real-time computing; Embedded system; Systems engineering; Operating system; Mechanical engineering","score_opus":0.02317505975707986,"score_gpt":0.24410910624552276,"score_spread":0.2209340464884429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035791417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033579025,0.00028117624,0.9601566,0.00021296191,0.00005183757,0.00006643848,0.00007320469,0.0026146297,0.0029639944],"genre_scores_gemma":[0.7074562,0.00021184582,0.2834164,0.00022142194,0.00002794484,0.00017766438,0.00024365706,0.00006727918,0.008177633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999001,0.000010396595,0.0000038551393,0.00003277981,0.000029238918,0.000023639794],"domain_scores_gemma":[0.9999311,0.00001519489,0.0000099862955,0.000008757636,0.000022515176,0.000012438497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017881846,0.000519837,0.00031724162,0.00016646205,0.0002619667,0.00034523132,0.0009384208,0.0007846861,0.0021750138],"category_scores_gemma":[0.0002807541,0.00023751159,0.00045385212,0.00012226315,0.0002970919,0.00036414491,0.0006835089,0.0007059577,0.0005065751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019040021,0.00024745348,0.0017068934,0.00013778656,0.0000786196,0.00024967178,0.00011113086,0.6190228,0.038625363,0.004760938,0.003269916,0.331599],"study_design_scores_gemma":[0.000006394694,0.000058353733,0.00021884832,0.00000511922,0.00000658437,0.000021067894,0.000006558731,0.9950912,0.0029448743,0.00079983164,0.00083627657,0.0000047956523],"about_ca_topic_score_codex":0.0067831757,"about_ca_topic_score_gemma":0.008670894,"teacher_disagreement_score":0.0067831757,"about_ca_system_score_codex":0.00040753465,"about_ca_system_score_gemma":0.0009249291,"threshold_uncertainty_score":0.013487399},"labels":[],"label_agreement":null},{"id":"W3035864131","doi":"10.3390/s20123412","title":"Reliability and Validity of a Novel Wearable Device for Measuring Elbow Strength","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Sports injuries and prevention","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Reproducibility; Reliability (semiconductor); Physical medicine and rehabilitation; Dynamometer; Repeatability; Criterion validity; Physical therapy; Population; Psychology; Medicine; Mathematics; Statistics; Construct validity; Psychometrics; Engineering; Developmental psychology","score_opus":0.07601945700677896,"score_gpt":0.301017753350837,"score_spread":0.22499829634405805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035864131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95484334,0.00082647835,0.0405542,0.00012519176,0.00020157702,0.00040865777,0.00048346014,0.00018482581,0.0023722297],"genre_scores_gemma":[0.9657976,0.0002917524,0.032057524,0.0001239022,0.00007106269,0.00036023,0.00040989532,0.000023810557,0.0008642055],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99351066,0.0022652925,0.0007680223,0.001026025,0.002298862,0.00013112662],"domain_scores_gemma":[0.9902062,0.0040709483,0.0013752449,0.0008924377,0.00328478,0.00017046276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073783067,0.00054412795,0.0004588534,0.0008231504,0.00024666192,0.00068978866,0.0004850691,0.00071713596,0.00066163094],"category_scores_gemma":[0.0136794895,0.0002640588,0.0006081385,0.00048779012,0.00049234845,0.0004967551,0.00076573144,0.00037464846,0.00046995722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022316098,0.00091602816,0.7489133,0.0007261076,0.0006430727,0.0001709388,0.0011995004,0.00219979,0.057884373,0.00048254032,0.0012425625,0.18339004],"study_design_scores_gemma":[0.00023959747,0.007431935,0.95271134,0.000200061,0.00038245556,0.0012516091,0.00059284933,0.016876593,0.016473018,0.0003612842,0.003407623,0.00007165823],"about_ca_topic_score_codex":0.0005121105,"about_ca_topic_score_gemma":0.0012011023,"teacher_disagreement_score":0.0073783067,"about_ca_system_score_codex":0.0002573362,"about_ca_system_score_gemma":0.00034103045,"threshold_uncertainty_score":0.039020717},"labels":[],"label_agreement":null},{"id":"W3035940885","doi":"10.3390/s20123464","title":"Quantification of Triple Single-Leg Hop Test Temporospatial Parameters: A Validated Method Using Body-Worn Sensors for Functional Evaluation after Knee Injury","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Canada; University of British Columbia; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hop (telecommunications); Interquartile range; Physical medicine and rehabilitation; Inertial measurement unit; Medicine; Kinematics; Osteoarthritis; Range of motion; Physical therapy; Surgery; Computer science; Artificial intelligence","score_opus":0.08542181249979021,"score_gpt":0.3461322528598707,"score_spread":0.2607104403600805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035940885","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93585193,0.0018779347,0.058987245,0.00004042143,0.00006530626,0.00025942488,0.0013394078,0.0002528868,0.0013254334],"genre_scores_gemma":[0.96673346,0.00066701474,0.030764267,0.000051040428,0.000042380147,0.00039802556,0.0006315642,0.00003142378,0.00068090187],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935704,0.00014120487,0.0000718325,0.00012670651,0.0002614663,0.000041730567],"domain_scores_gemma":[0.9992539,0.00016391503,0.00023390146,0.00006293716,0.0002338225,0.000051407067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055672636,0.0007314939,0.00049409224,0.0010995907,0.00018437485,0.0004268733,0.00036795996,0.00055588514,0.00086098723],"category_scores_gemma":[0.0014701806,0.0001764296,0.00028538867,0.00082352187,0.0002401952,0.00031185913,0.00044848936,0.00023708446,0.00031063418],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001582289,0.0003845509,0.4358832,0.0010990226,0.00025956242,0.0002524326,0.0006999052,0.0014077948,0.38223082,0.00014074479,0.00081381085,0.17524587],"study_design_scores_gemma":[0.00004825877,0.0021914865,0.9252499,0.000086244974,0.00022864644,0.0013321913,0.0005293608,0.006769517,0.062156055,0.000121635225,0.0012009628,0.00008585468],"about_ca_topic_score_codex":0.0010084847,"about_ca_topic_score_gemma":0.003096685,"teacher_disagreement_score":0.0010995907,"about_ca_system_score_codex":0.0001297399,"about_ca_system_score_gemma":0.00025512875,"threshold_uncertainty_score":0.0029442906},"labels":[],"label_agreement":null},{"id":"W3036109227","doi":"10.3390/s20123536","title":"A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"FastICA; Independent component analysis; Blind signal separation; Pattern recognition (psychology); Computer science; Artificial intelligence; SIGNAL (programming language); Principal component analysis; Filter (signal processing); Signal-to-noise ratio (imaging); Algorithm; Computer vision","score_opus":0.029130563591382844,"score_gpt":0.29166916336831167,"score_spread":0.26253859977692884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036109227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004031342,0.00013779767,0.99510324,0.00003964336,0.000021956123,0.00001712296,0.00002913144,0.0002933332,0.00032631189],"genre_scores_gemma":[0.07808296,0.0002750155,0.9188696,0.0000633243,0.00004063297,0.00008179767,0.00022769882,0.00008116252,0.0022777542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956423,0.00009433027,0.000028119617,0.00009349921,0.00019724741,0.000022606062],"domain_scores_gemma":[0.99958676,0.00016883144,0.000053484066,0.000053084503,0.00011985959,0.000017920984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004947977,0.0007774582,0.00038680888,0.0008559219,0.00032112838,0.00058731815,0.00051346386,0.0005747999,0.0016109563],"category_scores_gemma":[0.0015734582,0.00023782387,0.00058386946,0.0005388619,0.00040659634,0.0007454299,0.000655127,0.0006138798,0.00089062675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003482339,0.00007877711,0.0010047876,0.00017621198,0.00007784308,0.00012707684,0.00010639653,0.035543595,0.10419712,0.009418896,0.0016375637,0.84728336],"study_design_scores_gemma":[0.00005502241,0.00022944903,0.002679049,0.00002855851,0.00004267186,0.0007926435,0.000035075613,0.8801409,0.10173131,0.005011664,0.009183899,0.00006978466],"about_ca_topic_score_codex":0.0010374542,"about_ca_topic_score_gemma":0.0012333991,"teacher_disagreement_score":0.0016109563,"about_ca_system_score_codex":0.00030026576,"about_ca_system_score_gemma":0.00077775866,"threshold_uncertainty_score":0.0053892136},"labels":[],"label_agreement":null},{"id":"W3036640231","doi":"10.3390/s20123422","title":"Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Queen's University; National Research Foundation; Queen's University Belfast","keywords":"Particle swarm optimization; Pattern recognition (psychology); Support vector machine; Artificial intelligence; Classifier (UML); Wavelet; Relevance vector machine; Computer science; Data mining; Machine learning","score_opus":0.027545044338056126,"score_gpt":0.26546648046168136,"score_spread":0.23792143612362524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036640231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037483506,0.0003637335,0.9602814,0.00018007778,0.00008396227,0.00006812628,0.00005792515,0.0005507244,0.0009305647],"genre_scores_gemma":[0.6806901,0.0003930418,0.31522486,0.00011047951,0.00009451995,0.00016435186,0.00033316837,0.000034304037,0.0029551713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996804,0.000050400475,0.000028350429,0.00007411456,0.00013644215,0.0000303478],"domain_scores_gemma":[0.99967754,0.00011198759,0.00004177031,0.000027205566,0.00012653711,0.000014875707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005642833,0.0005060688,0.0008878392,0.00077302573,0.00024168334,0.00058779365,0.00054053095,0.000861611,0.0005798175],"category_scores_gemma":[0.0014807675,0.00017907117,0.00048703587,0.00059732515,0.00019417427,0.0006380277,0.00034913342,0.0005992262,0.00034199303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021032049,0.00023756825,0.0045477217,0.00014458438,0.000107433494,0.00018442431,0.000071406816,0.26236337,0.027939416,0.0039118007,0.0033648699,0.6969172],"study_design_scores_gemma":[0.0000059000577,0.000041613108,0.00047504698,0.0000032254752,0.0000063398247,0.000023047092,0.000005483323,0.99694246,0.0018954524,0.00026860487,0.00032946208,0.0000033570325],"about_ca_topic_score_codex":0.0022236567,"about_ca_topic_score_gemma":0.0015716426,"teacher_disagreement_score":0.0022236567,"about_ca_system_score_codex":0.00026801176,"about_ca_system_score_gemma":0.0005526749,"threshold_uncertainty_score":0.0044214725},"labels":[],"label_agreement":null},{"id":"W3037231803","doi":"10.3390/s20133637","title":"Secure Communications for Resource-Constrained IoT Devices","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Alfaisal University","keywords":"Internet of Things; Computer science; Resource (disambiguation); Computer security; Computer network; Telecommunications","score_opus":0.05058923311175807,"score_gpt":0.27317145592583625,"score_spread":0.2225822228140782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037231803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030647375,0.0016373085,0.92441547,0.002436875,0.00029942687,0.00016033587,0.0001072454,0.00039364756,0.03990243],"genre_scores_gemma":[0.85804296,0.0023295623,0.1269093,0.00049867266,0.00022627252,0.0003100176,0.00014371089,0.00007587011,0.01146366],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909556,0.00023705408,0.00005883178,0.00011125961,0.00036528002,0.0001320705],"domain_scores_gemma":[0.99910504,0.0003309993,0.00010532561,0.0002695369,0.00012743149,0.000061683866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009634928,0.0005107638,0.00037985828,0.00041140174,0.001121287,0.0021493286,0.0008374617,0.0011150924,0.0029925513],"category_scores_gemma":[0.002373414,0.00023525198,0.00037908484,0.00036462204,0.0014932802,0.0033664862,0.0026464122,0.0015662936,0.00069314224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006177132,0.000026026733,0.00038546394,0.0001291722,0.000015939953,0.0003735005,0.00030802417,0.046643157,0.0065080775,0.90807253,0.0050029336,0.032473505],"study_design_scores_gemma":[0.000024628236,0.00009890436,0.0004590195,0.00019156096,0.000026071886,0.0007270658,0.00041528873,0.5347911,0.0069296625,0.38485563,0.07143359,0.000047354828],"about_ca_topic_score_codex":0.0012301931,"about_ca_topic_score_gemma":0.0015529675,"teacher_disagreement_score":0.0029925513,"about_ca_system_score_codex":0.00087348255,"about_ca_system_score_gemma":0.0014066765,"threshold_uncertainty_score":0.010011017},"labels":[],"label_agreement":null},{"id":"W3037540650","doi":"10.3390/s20133616","title":"Ultrasound Measurement of Skeletal Muscle Contractile Parameters Using Flexible and Wearable Single-Element Ultrasonic Sensor","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletal muscle; Materials science; Muscle contraction; Ultrasonic sensor; Biomedical engineering; Contraction (grammar); Piezoelectricity; Gastrocnemius muscle; Ultrasound; Muscle tissue; Anatomy; Composite material; Acoustics; Medicine; Internal medicine","score_opus":0.04372562447803088,"score_gpt":0.23194846684397183,"score_spread":0.18822284236594095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037540650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79352015,0.003426385,0.20014107,0.00016747853,0.00011988206,0.000096265125,0.0002827094,0.00047581695,0.001770245],"genre_scores_gemma":[0.90229845,0.0015411321,0.09361839,0.00013699879,0.000051154348,0.00011683235,0.00014190759,0.000019349714,0.0020757471],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999724,0.00003140884,0.000018066861,0.00008626726,0.00012537408,0.000014839798],"domain_scores_gemma":[0.99982965,0.000040690615,0.000055525,0.000014108322,0.00004517652,0.000014819247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023265932,0.00038298406,0.00039257042,0.00035665225,0.000084653555,0.000198914,0.0004339526,0.0006144687,0.00045552428],"category_scores_gemma":[0.00039493723,0.00019913983,0.00023230401,0.0003529206,0.00018548041,0.00048375476,0.0002972633,0.00022559546,0.00017778395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007808541,0.00002955236,0.0015452001,0.00016700674,0.000016349792,0.00006789006,0.00004386929,0.00073310704,0.97513276,0.00007951313,0.000111559435,0.021994991],"study_design_scores_gemma":[0.000040187784,0.0016753657,0.028289868,0.000051729112,0.00012476677,0.0015394487,0.0001796035,0.056601122,0.90811944,0.000277885,0.0030052282,0.00009535305],"about_ca_topic_score_codex":0.00021308433,"about_ca_topic_score_gemma":0.00042989824,"teacher_disagreement_score":0.0006144687,"about_ca_system_score_codex":0.00011322649,"about_ca_system_score_gemma":0.00010935091,"threshold_uncertainty_score":0.001523912},"labels":[],"label_agreement":null},{"id":"W3038042145","doi":"10.3390/s20123594","title":"Measurement of In-Plane Motions in MEMS","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Planar; Plane (geometry); Microelectromechanical systems; Actuator; Vibration; Acoustics; Optics; Physics; Modal analysis; Modal; Laser Doppler vibrometer; Computer science; Laser; Materials science; Geometry; Mathematics; Artificial intelligence; Optoelectronics; Laser beams","score_opus":0.024529708606635635,"score_gpt":0.2168016146841712,"score_spread":0.19227190607753555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038042145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9106481,0.000834266,0.080226205,0.0003166568,0.00012254973,0.000052534942,0.00018136461,0.00019427469,0.007424055],"genre_scores_gemma":[0.959716,0.00044706865,0.037787266,0.00010033564,0.000038894614,0.00003191844,0.0000748132,0.0000144029955,0.001789212],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980384,0.000027328575,0.0000057497423,0.000025081052,0.00012258883,0.000015356192],"domain_scores_gemma":[0.9997377,0.00008600623,0.000051971063,0.00003157494,0.00007487666,0.000017965463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011080162,0.00026839814,0.00013781249,0.00028303073,0.00016893297,0.00018538088,0.00020505402,0.00028441488,0.0006125581],"category_scores_gemma":[0.0004491255,0.000119028686,0.000038536135,0.00015491071,0.0002673821,0.000281912,0.00025496047,0.00025154915,0.00020690347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030415733,0.0000118657235,0.0012352889,0.000025335254,0.000003036103,0.00003300013,0.000049965616,0.00032435992,0.98793066,0.00036593486,0.00013082325,0.009859285],"study_design_scores_gemma":[0.000009128422,0.00020711859,0.007840229,0.0000072023454,0.000007455504,0.0003204127,0.00012789128,0.00964012,0.9771171,0.00033108157,0.0043804646,0.000011885264],"about_ca_topic_score_codex":0.00031132172,"about_ca_topic_score_gemma":0.00068826723,"teacher_disagreement_score":0.0006125581,"about_ca_system_score_codex":0.00012947147,"about_ca_system_score_gemma":0.0001014419,"threshold_uncertainty_score":0.0020492077},"labels":[],"label_agreement":null},{"id":"W3038546681","doi":"10.3390/s20133760","title":"Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer security; Computer science; Supply chain; Scalability; Leverage (statistics); Adversarial system; Authentication (law); Business","score_opus":0.031638699352732634,"score_gpt":0.27016150824196816,"score_spread":0.23852280888923552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038546681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039543435,0.00086202513,0.8933831,0.0013041028,0.00013171411,0.0003140952,0.00020823143,0.00043241074,0.06382089],"genre_scores_gemma":[0.87313485,0.0014993306,0.10439038,0.0001691604,0.00006841519,0.00036783313,0.0002713595,0.00006238549,0.020036444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992719,0.000220905,0.000038235903,0.00015606386,0.00021619188,0.00009663518],"domain_scores_gemma":[0.9994311,0.00017064063,0.00006833574,0.00010903432,0.00016084581,0.000060004953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006708033,0.00038649316,0.00035755988,0.0004641223,0.0010567765,0.002230441,0.0008977089,0.0008537747,0.010982809],"category_scores_gemma":[0.0016539264,0.00027757196,0.00063425663,0.00069878093,0.0010752974,0.003114539,0.002067357,0.00089513784,0.0010227462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014608008,0.00012390636,0.004098283,0.00041626566,0.000048753373,0.0011095561,0.0009191562,0.24967799,0.017121354,0.6525224,0.0034857637,0.07033055],"study_design_scores_gemma":[0.000037792342,0.00014656535,0.0009211293,0.00008792181,0.000039450653,0.0004083454,0.00052088173,0.7586509,0.004219977,0.19935311,0.03557915,0.000034688343],"about_ca_topic_score_codex":0.0035009142,"about_ca_topic_score_gemma":0.0025593527,"teacher_disagreement_score":0.010982809,"about_ca_system_score_codex":0.00088307576,"about_ca_system_score_gemma":0.0016600033,"threshold_uncertainty_score":0.036741138},"labels":[],"label_agreement":null},{"id":"W3038962085","doi":"10.3390/s20133772","title":"Long-Period Gratings and Microcavity In-Line Mach Zehnder Interferometers as Highly Sensitive Optical Fiber Platforms for Bacteria Sensing","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Biosensor; Astronomical interferometer; Materials science; Refractive index; Mach–Zehnder interferometer; Sensitivity (control systems); Optoelectronics; Optical fiber; Escherichia coli; Bacteria; Optics; Nanotechnology; Interferometry; Chemistry; Physics; Biology; Electronic engineering","score_opus":0.024036846609715785,"score_gpt":0.2810093329446009,"score_spread":0.2569724863348851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038962085","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7996485,0.03572468,0.15441565,0.0005257645,0.00037832218,0.00022300499,0.00026951559,0.00080171507,0.008012859],"genre_scores_gemma":[0.7948184,0.010632857,0.18760811,0.00026898715,0.000158502,0.000106448846,0.00018575031,0.00007175902,0.0061491528],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99973196,0.00003889588,0.0000106236075,0.00005214452,0.00013273577,0.000033580167],"domain_scores_gemma":[0.9998604,0.000044389984,0.00004548758,0.000014069869,0.000021310494,0.000014194861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027908824,0.00042004415,0.00016864922,0.0002957509,0.00009705106,0.00023461784,0.00046458578,0.0004855993,0.00037069782],"category_scores_gemma":[0.00018902334,0.0002762037,0.0001919513,0.00024738332,0.00045676267,0.00044012227,0.00029488598,0.0003577029,0.00022584767],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025391748,0.000012613417,0.0002271406,0.00011619986,0.0000048126176,0.00004189291,0.000032530603,0.00028269723,0.9883845,0.0007848197,0.00009131214,0.009995984],"study_design_scores_gemma":[0.000009460324,0.0002771549,0.0018539814,0.00001091025,0.000012603044,0.00028866145,0.000023954222,0.004309425,0.98494935,0.00029103953,0.00795261,0.000020789592],"about_ca_topic_score_codex":0.00037872273,"about_ca_topic_score_gemma":0.0007458225,"teacher_disagreement_score":0.0004855993,"about_ca_system_score_codex":0.00030449397,"about_ca_system_score_gemma":0.000163013,"threshold_uncertainty_score":0.0022092462},"labels":[],"label_agreement":null},{"id":"W3039439308","doi":"10.3390/s20133774","title":"An Interface–Particle Interaction Approach for Evaluation of the Co-Encapsulation Efficiency of Cells in a Flow-Focusing Droplet Generator","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Sharif University of Technology","keywords":"Encapsulation (networking); Scalability; Microfluidics; Pairing; Surface tension; Computer science; Finite element method; Materials science; Nanotechnology; Volumetric flow rate; Simulation; Biological system; Mechanics; Engineering; Physics; Superconductivity; Structural engineering","score_opus":0.03591499620546605,"score_gpt":0.2959639947425991,"score_spread":0.26004899853713304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039439308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3484271,0.0006797275,0.6471947,0.000089380585,0.00007911216,0.0001755667,0.00025289526,0.00064928934,0.0024522792],"genre_scores_gemma":[0.77262676,0.0005158614,0.22499357,0.00005330536,0.0000101906835,0.00027525608,0.00021337769,0.00004792007,0.0012637351],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996989,0.000037690454,0.000020766029,0.0000604694,0.00015916378,0.000023097658],"domain_scores_gemma":[0.99982303,0.00009063866,0.000023216899,0.000015430121,0.000038195823,0.000009572506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053577515,0.0004370129,0.00034723623,0.0004891477,0.00018357667,0.00029880134,0.0006506978,0.00055612257,0.00067748607],"category_scores_gemma":[0.00046088084,0.00024118795,0.00048292495,0.00035429903,0.00018828851,0.0003631252,0.00035655944,0.00041506664,0.00018783683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007979426,0.0001062401,0.0007136124,0.000142997,0.000023993909,0.00008297329,0.000055259483,0.019526856,0.96243936,0.001978658,0.00016259022,0.014687746],"study_design_scores_gemma":[0.00001248176,0.0001434121,0.00096033025,0.000004651025,0.00002423844,0.000055167406,0.000011090585,0.5579552,0.43980193,0.0002575053,0.00075130886,0.000022706068],"about_ca_topic_score_codex":0.00077163393,"about_ca_topic_score_gemma":0.00069092755,"teacher_disagreement_score":0.00077163393,"about_ca_system_score_codex":0.0004470957,"about_ca_system_score_gemma":0.00036252322,"threshold_uncertainty_score":0.0032439232},"labels":[],"label_agreement":null},{"id":"W3040456805","doi":"10.3390/s20133736","title":"Simultaneous Clamping and Cutting Force Measurements with Built-In Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Korea Institute of Machinery and Materials","keywords":"Clamping; Strain gauge; Machining; Lead zirconate titanate; Mechanical engineering; Piezoelectricity; Slippage; Materials science; Structural engineering; Engineering; Acoustics; Composite material; Electrical engineering; Physics","score_opus":0.01743247151018199,"score_gpt":0.2295628465806924,"score_spread":0.2121303750705104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040456805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50552744,0.0014022106,0.48678067,0.0001373736,0.00029435466,0.00021885816,0.0005046644,0.002123189,0.0030112078],"genre_scores_gemma":[0.73834884,0.00051544287,0.25759932,0.000119817814,0.00007744952,0.00021096577,0.00029752814,0.00009395151,0.0027366688],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980096,0.00013383757,0.000095528536,0.00037747205,0.001282261,0.00010132077],"domain_scores_gemma":[0.9988949,0.00032139104,0.00020596919,0.00018488173,0.00033357355,0.00005937012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087530253,0.0007402054,0.0009130047,0.0008772743,0.00027819324,0.00073088636,0.0011884351,0.0010545915,0.0009862027],"category_scores_gemma":[0.0016121046,0.00051871344,0.00032363698,0.0007873252,0.00034371935,0.0010291116,0.001070309,0.0006598224,0.00032361597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001818232,0.00007648062,0.002699189,0.00014576892,0.000041456635,0.00006682825,0.00010148099,0.00081455533,0.9435789,0.00027056952,0.00025487805,0.051768165],"study_design_scores_gemma":[0.000046871806,0.0005296014,0.011179621,0.000019795865,0.00006349532,0.00050889666,0.00009985215,0.024075532,0.9603825,0.00024653383,0.0027842587,0.000062997584],"about_ca_topic_score_codex":0.000357543,"about_ca_topic_score_gemma":0.00088636513,"teacher_disagreement_score":0.0011884351,"about_ca_system_score_codex":0.00024954925,"about_ca_system_score_gemma":0.00032211083,"threshold_uncertainty_score":0.004629135},"labels":[],"label_agreement":null},{"id":"W3040538438","doi":"10.3390/s20133703","title":"Trends in Compressive Sensing for EEG Signal Processing Applications","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Compressed sensing; Brain–computer interface; Electroencephalography; Computer science; Field (mathematics); Neural engineering; Signal processing; Energy (signal processing); Interface (matter); SIGNAL (programming language); Artificial intelligence; Machine learning; Neuroscience; Digital signal processing; Psychology; Computer hardware","score_opus":0.04805201194775321,"score_gpt":0.31905179200137196,"score_spread":0.27099978005361874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040538438","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066085014,0.9805712,0.009072333,0.002238643,0.0007294769,0.000029213015,0.00006827833,0.00004967368,0.0065803146],"genre_scores_gemma":[0.0045026843,0.9872055,0.005145403,0.00065842224,0.0009158905,0.00003388435,0.00008877297,0.000014311007,0.0014351731],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993445,0.00013455524,0.000082192215,0.00013976915,0.00026583462,0.00003316039],"domain_scores_gemma":[0.99777704,0.0014582688,0.00014156837,0.00007474457,0.0004919982,0.00005641392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013318096,0.001004459,0.00074223406,0.0023944767,0.00029395352,0.0013210451,0.0009373887,0.0017243153,0.0054161907],"category_scores_gemma":[0.002607207,0.0004630121,0.0008178268,0.0032531067,0.0009625656,0.0021596444,0.0008576375,0.0026762036,0.0022052252],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070523216,0.0000912633,0.00036772375,0.015660144,0.00009746331,0.00018503808,0.00014013838,0.0022185275,0.004639144,0.03715097,0.02005116,0.91932803],"study_design_scores_gemma":[0.000022659788,0.00031101503,0.0017878658,0.0061932583,0.0001387093,0.0020057338,0.00015832658,0.0044068806,0.0036064012,0.027348138,0.9539337,0.00008725065],"about_ca_topic_score_codex":0.0011139186,"about_ca_topic_score_gemma":0.0009075531,"teacher_disagreement_score":0.0054161907,"about_ca_system_score_codex":0.0006508038,"about_ca_system_score_gemma":0.0012864684,"threshold_uncertainty_score":0.018118918},"labels":[],"label_agreement":null},{"id":"W3040643270","doi":"10.3390/s20133755","title":"Piezoelectric Energy Harvesting from Suspension Structures with Piezoelectric Layers","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Piezoelectricity; Energy harvesting; Voltage; Acoustics; Capacitor; Materials science; Generator (circuit theory); Vibration; Impedance matching; Suspension (topology); Power (physics); Electrical impedance; Power density; PMUT; Maximum power principle; Energy (signal processing); Electrical engineering; Physics; Engineering; Mathematics","score_opus":0.014045042094076064,"score_gpt":0.18875770472428668,"score_spread":0.17471266263021062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040643270","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6087708,0.0020787101,0.3768282,0.00074137707,0.00028002408,0.000087654196,0.00014672724,0.00083803374,0.0102285305],"genre_scores_gemma":[0.9436031,0.000454773,0.05107053,0.0001556927,0.000055027114,0.000056285055,0.00010519997,0.00003375637,0.004465745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999,0.000010163992,0.000005724573,0.00002280217,0.00004828868,0.000013095346],"domain_scores_gemma":[0.99992514,0.000026527761,0.000015258489,0.000008559857,0.00001658359,0.00000791286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010191661,0.00025238012,0.00021383249,0.00019506183,0.00017254884,0.0002188382,0.0005249115,0.00038779245,0.0011031254],"category_scores_gemma":[0.00015699911,0.00017837333,0.00019799515,0.00018488096,0.00023349734,0.00066723174,0.000417543,0.00030861452,0.0005013525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027935732,0.00002306931,0.00032272458,0.00009089037,0.000009029282,0.00013650769,0.000037613223,0.0015750338,0.9745129,0.001107884,0.0003485211,0.021807894],"study_design_scores_gemma":[0.000045294484,0.000411122,0.0017206643,0.000019605155,0.00002820314,0.000434814,0.000066714536,0.070137896,0.91245914,0.0018144815,0.0128302695,0.000031899664],"about_ca_topic_score_codex":0.0000924959,"about_ca_topic_score_gemma":0.00021568047,"teacher_disagreement_score":0.0011031254,"about_ca_system_score_codex":0.00014073217,"about_ca_system_score_gemma":0.00009449271,"threshold_uncertainty_score":0.003690362},"labels":[],"label_agreement":null},{"id":"W3041330968","doi":"10.3390/s20143886","title":"Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Runway; Tread; Friction coefficient; Correlation coefficient; Braking distance; Sensor fusion; Fuse (electrical); Information fusion; Artificial neural network; Automotive engineering; Engineering; Control theory (sociology); Computer science; Artificial intelligence; Materials science; Control (management); Brake; Electrical engineering","score_opus":0.009920811710217258,"score_gpt":0.19797739724285313,"score_spread":0.18805658553263588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041330968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07028827,0.00040038486,0.92758054,0.00007987074,0.000038861534,0.00003752312,0.00007376762,0.00039091642,0.0011098528],"genre_scores_gemma":[0.95044774,0.00036740277,0.048170958,0.000028071392,0.000024171422,0.00005980397,0.00014879947,0.000021185964,0.00073193177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994444,0.00006331177,0.0000357238,0.00017776727,0.00020111518,0.00007763781],"domain_scores_gemma":[0.99967206,0.00008711438,0.0000680538,0.000049086688,0.00010969294,0.000014124082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005167097,0.0008532213,0.001020992,0.0010827896,0.00037530297,0.0006587122,0.000671417,0.0006390891,0.000571515],"category_scores_gemma":[0.0016752124,0.00038692888,0.0010098158,0.0011557455,0.0003485285,0.0015331849,0.00077641464,0.0006775238,0.00018034223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016354812,0.000109400906,0.006680477,0.00020033232,0.00015007476,0.00018926231,0.00015905825,0.80205005,0.018415408,0.0035410654,0.00075159094,0.16758972],"study_design_scores_gemma":[0.000002859875,0.000027912465,0.0016102209,0.0000058420787,0.00001453348,0.00002848314,0.000011745487,0.99540037,0.0020320276,0.0006790649,0.0001755685,0.000011414925],"about_ca_topic_score_codex":0.006510759,"about_ca_topic_score_gemma":0.0046513355,"teacher_disagreement_score":0.006510759,"about_ca_system_score_codex":0.00048518382,"about_ca_system_score_gemma":0.0006368734,"threshold_uncertainty_score":0.012945712},"labels":[],"label_agreement":null},{"id":"W3042243359","doi":"10.3390/s20143951","title":"Internet of Things Based Blockchain for Temperature Monitoring and Counterfeit Pharmaceutical Prevention","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Natural Sciences and Engineering Research Council of Canada; Danmarks Tekniske Universitet","keywords":"Blockchain; Counterfeit; Counterfeit Drugs; The Internet; Internet of Things; Computer security; Internet privacy; Computer science; Nanotechnology; World Wide Web; Business; Materials science; Political science","score_opus":0.028358431883411202,"score_gpt":0.28289079545489676,"score_spread":0.25453236357148556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042243359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114875965,0.0018296168,0.8393787,0.0022000636,0.0004087979,0.00066119083,0.00051317795,0.0019938445,0.038138628],"genre_scores_gemma":[0.93917346,0.0007275667,0.051527973,0.00021289704,0.00006252068,0.00019771684,0.00035168687,0.000040181254,0.0077061346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989561,0.0003185751,0.00007671542,0.0001675515,0.000354291,0.00012675716],"domain_scores_gemma":[0.9985942,0.00046224106,0.00017249667,0.0003106477,0.0003456449,0.00011464977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013145906,0.00038183245,0.0004666772,0.0006301152,0.0009966803,0.0011080713,0.0009216829,0.000904992,0.0036768352],"category_scores_gemma":[0.0027739687,0.00021636616,0.00029387476,0.0007828007,0.000602148,0.0028008737,0.001511931,0.0006517501,0.00078489335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012756683,0.00049614627,0.008137963,0.00067584956,0.00016222594,0.0017463617,0.0009517241,0.2645527,0.044207484,0.23979242,0.017735183,0.4202662],"study_design_scores_gemma":[0.00009243617,0.00027544334,0.0012528214,0.00009731562,0.000058930924,0.00048607026,0.0001702337,0.847968,0.020097606,0.09527663,0.03417025,0.000054267555],"about_ca_topic_score_codex":0.0018966455,"about_ca_topic_score_gemma":0.003023705,"teacher_disagreement_score":0.0036768352,"about_ca_system_score_codex":0.00072277576,"about_ca_system_score_gemma":0.0018394574,"threshold_uncertainty_score":0.012300253},"labels":[],"label_agreement":null},{"id":"W3042867243","doi":"10.3390/s20143923","title":"Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Drone; Computer science; Provisioning; Scheme (mathematics); Feature (linguistics); Computer security; Artificial intelligence; Support vector machine; Deep learning; Public security; Real-time computing; Telecommunications","score_opus":0.0445550845053924,"score_gpt":0.3095517292916006,"score_spread":0.26499664478620816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042867243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42643,0.0019446607,0.56400865,0.0003594298,0.00022933957,0.00011422464,0.0005443364,0.0023978457,0.003971516],"genre_scores_gemma":[0.9281134,0.0005276238,0.068626404,0.00008690318,0.000079692785,0.000025011723,0.0006933709,0.00003307343,0.0018146128],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978215,0.000024124403,0.000010180507,0.0000544642,0.000082800616,0.000046343222],"domain_scores_gemma":[0.99973804,0.000055476354,0.000045555684,0.000041388594,0.000092756505,0.000026888109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021564243,0.00059743616,0.00043509394,0.0012391944,0.00016733061,0.00037410186,0.00041893483,0.00042658998,0.00066994206],"category_scores_gemma":[0.00067408424,0.00014776982,0.00034550953,0.0005531309,0.0002160009,0.00069765636,0.00046932354,0.0004104379,0.00041163538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006942698,0.00032626712,0.010084413,0.0001328718,0.00008060351,0.00035098975,0.00007346035,0.036773503,0.15069132,0.0008438693,0.0035794894,0.79636896],"study_design_scores_gemma":[0.000025930129,0.00028037705,0.012224412,0.000023048728,0.000074442025,0.00035805683,0.000106550935,0.93207,0.051306162,0.0011685586,0.0023374783,0.000024905483],"about_ca_topic_score_codex":0.0022240714,"about_ca_topic_score_gemma":0.0038772558,"teacher_disagreement_score":0.0022240714,"about_ca_system_score_codex":0.00022866184,"about_ca_system_score_gemma":0.00024702481,"threshold_uncertainty_score":0.004422307},"labels":[],"label_agreement":null},{"id":"W3043379336","doi":"10.3390/s20143909","title":"Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Northern Studies; Université de Sherbrooke","funders":"Goddard Space Flight Center; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Fonds de recherche du Québec – Nature et technologies; Université Laval; Crown-Indigenous Relations and Northern Affairs Canada; Institut Polaire Français Paul Emile Victor; National Aeronautics and Space Administration","keywords":"Snow; Snowpack; Remote sensing; Radar; Environmental science; Lidar; Tundra; Continuous monitoring; Meltwater; Arctic; Geology; Meteorology; Engineering; Geomorphology; Telecommunications; Geography","score_opus":0.07403066974222046,"score_gpt":0.2525180008860498,"score_spread":0.17848733114382936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043379336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40663254,0.0016904051,0.58234316,0.00033542,0.00024157966,0.00021197791,0.00040472572,0.0023299032,0.0058103306],"genre_scores_gemma":[0.7637072,0.00055323815,0.22990535,0.00025336308,0.00017642396,0.00020943652,0.0004042604,0.00008985102,0.004700896],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962926,0.00007558156,0.000017079194,0.0000653119,0.0001784135,0.0000344185],"domain_scores_gemma":[0.99944264,0.00016976365,0.00011861849,0.0000877343,0.00015242845,0.000028826189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046284441,0.0005518234,0.0003665761,0.00048341826,0.00014994017,0.00041527877,0.0005267499,0.0005555985,0.0021903983],"category_scores_gemma":[0.0008218349,0.00014765616,0.00019877958,0.00040644562,0.00016416072,0.00050609413,0.0003045465,0.00032055395,0.001108869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003605072,0.000118884185,0.005487219,0.00039455688,0.00003833687,0.00024233275,0.00016227737,0.003235232,0.75668365,0.0009647006,0.0021945839,0.23011763],"study_design_scores_gemma":[0.00026823077,0.005145978,0.04968003,0.00012552939,0.00022157392,0.004475931,0.00028158454,0.09465519,0.77768356,0.0018706153,0.06547235,0.00011949336],"about_ca_topic_score_codex":0.00020467954,"about_ca_topic_score_gemma":0.0002555772,"teacher_disagreement_score":0.0021903983,"about_ca_system_score_codex":0.00015780734,"about_ca_system_score_gemma":0.00017950333,"threshold_uncertainty_score":0.007327676},"labels":[],"label_agreement":null},{"id":"W3043779764","doi":"10.3390/s20143926","title":"Integrated LTE and Millimeter-Wave 5G MIMO Antenna System for 4G/5G Wireless Terminals","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Qatar National Library","keywords":"MIMO; Antenna (radio); Extremely high frequency; Ground plane; Electronic engineering; Wireless; Microwave; Frequency band; Engineering; Electrical engineering; Telecommunications; Physics; Computer science; Beamforming","score_opus":0.025596310707516383,"score_gpt":0.2083351601353625,"score_spread":0.1827388494278461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043779764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30748343,0.0016853432,0.65513754,0.0005552806,0.00049314636,0.00008917085,0.00032569782,0.002642911,0.03158745],"genre_scores_gemma":[0.9363728,0.00034780908,0.056433715,0.0002425886,0.00006874589,0.000039396844,0.00019557771,0.000019091683,0.006280274],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996866,0.00006241659,0.000014072415,0.000049183087,0.00012128121,0.00006645922],"domain_scores_gemma":[0.9998443,0.000015696569,0.000029382078,0.000033479344,0.000063036525,0.000014009713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017032427,0.00045513993,0.00028926812,0.00022260241,0.00015430068,0.00054138224,0.0005153003,0.0005860321,0.0016393828],"category_scores_gemma":[0.00019797003,0.00012981106,0.0003504675,0.0002575581,0.00012352831,0.0003833495,0.00032693098,0.0002818467,0.0012031192],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058956404,0.00012308352,0.006956548,0.0002829214,0.00022241278,0.0009552003,0.00013928303,0.030452507,0.7506616,0.010144853,0.006733675,0.19273844],"study_design_scores_gemma":[0.00007037324,0.003328352,0.012390522,0.00006915598,0.00027981342,0.0042389645,0.00023062395,0.33239254,0.5845665,0.0023950804,0.059929583,0.000108471795],"about_ca_topic_score_codex":0.0003900972,"about_ca_topic_score_gemma":0.00080905284,"teacher_disagreement_score":0.0016393828,"about_ca_system_score_codex":0.00031367474,"about_ca_system_score_gemma":0.00017026176,"threshold_uncertainty_score":0.0054843426},"labels":[],"label_agreement":null},{"id":"W3043973943","doi":"10.3390/s20144055","title":"Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Attitude and heading reference system; Gyroscope; Accelerometer; Control theory (sociology); Compensation (psychology); Inertial navigation system; Inertial measurement unit; Orientation (vector space); Extended Kalman filter; Heading (navigation); Computer science; Kalman filter; Filter (signal processing); Quaternion; Controller (irrigation); Engineering; Artificial intelligence; Computer vision; Mathematics; Control (management)","score_opus":0.02471713640956728,"score_gpt":0.24199858147993297,"score_spread":0.2172814450703657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043973943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06899695,0.00072648184,0.9250304,0.0001402511,0.00024149516,0.000058186673,0.0000387653,0.0018385994,0.0029287497],"genre_scores_gemma":[0.86827457,0.00040619593,0.12795243,0.00007678156,0.00006501636,0.000066993765,0.000100256606,0.000050833212,0.0030068895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994838,0.00006360597,0.00003975068,0.00011818491,0.000247892,0.000046587968],"domain_scores_gemma":[0.9993832,0.0001402879,0.000058761394,0.000076857206,0.00032841935,0.000012458481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071488594,0.00047436566,0.00045094726,0.00037941366,0.000305572,0.0005061875,0.000493665,0.00048256674,0.0008321901],"category_scores_gemma":[0.0017090752,0.00018969263,0.00024130549,0.00027512823,0.00019538963,0.0005918939,0.00030908416,0.00056236377,0.0003355924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069531647,0.00019075324,0.0065460186,0.0003140913,0.00012133909,0.0001584762,0.00029145234,0.07962463,0.093428686,0.0019080882,0.0025128704,0.8142082],"study_design_scores_gemma":[0.00008005301,0.00045044612,0.007960446,0.000030552063,0.00007767888,0.00024800174,0.000041243926,0.9281107,0.0573127,0.0004075936,0.0052447454,0.00003583464],"about_ca_topic_score_codex":0.0036556479,"about_ca_topic_score_gemma":0.0026698187,"teacher_disagreement_score":0.0036556479,"about_ca_system_score_codex":0.00028150593,"about_ca_system_score_gemma":0.0004259341,"threshold_uncertainty_score":0.007268727},"labels":[],"label_agreement":null},{"id":"W3045552396","doi":"10.3390/s20154253","title":"A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Homomorphic encryption; Computer science; Encryption; Ciphertext; Wireless ad hoc network; Vehicular ad hoc network; Computer security; Client-side encryption; Computation; Computer network; Wireless; On-the-fly encryption; Algorithm","score_opus":0.02617499428158839,"score_gpt":0.2585368395646417,"score_spread":0.2323618452830533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045552396","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001113634,0.9705771,0.016979741,0.00054209004,0.0004455976,0.00004168726,0.00006464203,0.0000802477,0.010155222],"genre_scores_gemma":[0.008143942,0.98117816,0.0075170756,0.00028911975,0.00045911144,0.000043499374,0.00012473165,0.000022571332,0.0022218125],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993967,0.00011743067,0.000077585704,0.000115080526,0.00024170696,0.000051543473],"domain_scores_gemma":[0.99915135,0.0005377717,0.000056498196,0.0000690686,0.00015910696,0.00002625246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075264316,0.0010964177,0.0012795177,0.002298323,0.0004114788,0.0012598988,0.0010190614,0.0009979374,0.003611853],"category_scores_gemma":[0.0014627039,0.0005788816,0.00086024223,0.0044304384,0.0007085476,0.0032679325,0.0008938232,0.0015137561,0.0018467295],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006263706,0.00011780244,0.00050957897,0.013155951,0.0000909106,0.00018758039,0.00014995258,0.004064603,0.0024947827,0.059645995,0.020880278,0.8986399],"study_design_scores_gemma":[0.000019192361,0.0002676136,0.00085395266,0.0036548888,0.00012846645,0.0016142798,0.00015628544,0.005005866,0.0037089204,0.025511747,0.9589999,0.00007896991],"about_ca_topic_score_codex":0.0007438772,"about_ca_topic_score_gemma":0.00055324356,"teacher_disagreement_score":0.003611853,"about_ca_system_score_codex":0.00064951275,"about_ca_system_score_gemma":0.0012698687,"threshold_uncertainty_score":0.012082875},"labels":[],"label_agreement":null},{"id":"W3045897451","doi":"10.3390/s20154220","title":"Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":469,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Perception; Computer science; Sensor fusion; Set (abstract data type); Anticipation (artificial intelligence); Human–computer interaction; Range (aeronautics); Artificial intelligence; Deep learning; Real-time computing; Engineering; Simulation","score_opus":0.035556953549532186,"score_gpt":0.3170844727122842,"score_spread":0.281527519162752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045897451","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052019143,0.8719947,0.11293607,0.001315847,0.0008788014,0.0000616087,0.0001854155,0.00033287436,0.007092734],"genre_scores_gemma":[0.05087147,0.9111677,0.031573188,0.00064484583,0.0009348132,0.00008862116,0.00057688594,0.00006032485,0.0040821075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967825,0.00004564093,0.0000427036,0.000078292214,0.00012922233,0.00002592541],"domain_scores_gemma":[0.99940825,0.00031578937,0.000050078765,0.000026534362,0.00017736747,0.000021958185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076668285,0.0010302215,0.0008366844,0.0012300548,0.0002032606,0.00096057163,0.0013224575,0.0011780587,0.0018524668],"category_scores_gemma":[0.0013045588,0.00041756948,0.0007834496,0.001935554,0.00040295397,0.0017125705,0.0007902868,0.001156754,0.0009716548],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006333235,0.000105366795,0.0009570193,0.0050517423,0.00017716202,0.00010768748,0.00008546831,0.012430829,0.003349543,0.006790935,0.011150098,0.95973086],"study_design_scores_gemma":[0.000037677128,0.0011356543,0.0063804453,0.0053496757,0.0010033393,0.0018857538,0.0003427896,0.20544192,0.020594133,0.021699136,0.7358662,0.00026328862],"about_ca_topic_score_codex":0.0021681965,"about_ca_topic_score_gemma":0.0013741808,"teacher_disagreement_score":0.0021681965,"about_ca_system_score_codex":0.00043526737,"about_ca_system_score_gemma":0.0007845467,"threshold_uncertainty_score":0.006197095},"labels":[],"label_agreement":null},{"id":"W3046252339","doi":"10.3390/s20154275","title":"Color Sensor Accuracy Index Utilizing Metamer Mismatch Radii","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Index (typography); Accuracy and precision; Computer science; Materials science; Acoustics; Mathematics; Physics; Statistics","score_opus":0.027958877803828327,"score_gpt":0.2774010981176362,"score_spread":0.24944222031380786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046252339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15251593,0.00483931,0.7948152,0.0016441416,0.00091622217,0.00018719284,0.0002739878,0.0031778533,0.04163011],"genre_scores_gemma":[0.82379055,0.0007847948,0.17106646,0.00046594543,0.00012284699,0.00009335658,0.00013888271,0.00017662591,0.0033606433],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976942,0.00034542294,0.000117934826,0.00040644835,0.0013229739,0.000112938666],"domain_scores_gemma":[0.9971712,0.0009465272,0.00048074464,0.0005336546,0.00079674803,0.000071119575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010885727,0.00061356573,0.00048587154,0.0013526775,0.00035902043,0.0012806467,0.0009863385,0.0013997096,0.0010451046],"category_scores_gemma":[0.0051852097,0.00024082948,0.00017413091,0.0012003438,0.00088236923,0.0020686963,0.00092207384,0.0008237208,0.0012007533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006303357,0.000080372396,0.008153047,0.00036053607,0.000040603223,0.00043450517,0.0002759003,0.013745723,0.6068552,0.04228822,0.0049573025,0.32217827],"study_design_scores_gemma":[0.000023636203,0.0005282155,0.008944749,0.000081518585,0.000046178735,0.0022458464,0.00020565762,0.24117151,0.68862516,0.015253321,0.04265373,0.00022041632],"about_ca_topic_score_codex":0.00038612983,"about_ca_topic_score_gemma":0.0005014873,"teacher_disagreement_score":0.0016486022,"about_ca_system_score_codex":0.0016486022,"about_ca_system_score_gemma":0.00023343881,"threshold_uncertainty_score":0.01196152},"labels":[],"label_agreement":null},{"id":"W3046303146","doi":"10.3390/s20154302","title":"Time-Domain Investigation of Switchable Filter Wide-Band Antenna for Microwave Breast Imaging","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Polytechnique Montréal","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Sejong University; National Research Foundation","keywords":"Antenna (radio); Electronic engineering; Antenna measurement; Computer science; Antenna rotator; Coaxial antenna; Telecommunications; Microstrip antenna; Engineering","score_opus":0.00817198454593608,"score_gpt":0.1844504257801894,"score_spread":0.17627844123425332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046303146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7872319,0.0024331387,0.18917131,0.0020359105,0.0004014712,0.000045778903,0.00007704086,0.00037561008,0.01822773],"genre_scores_gemma":[0.95273274,0.00081596867,0.040671308,0.00030275178,0.00006597586,0.000017815579,0.000056562258,0.000023050896,0.005313847],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987805,0.000025425023,0.000003902409,0.000020533296,0.000058628626,0.000013408961],"domain_scores_gemma":[0.9997036,0.00010272084,0.00004645374,0.000035325887,0.00009854281,0.0000132604555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018159566,0.0001566746,0.00014516631,0.00013594258,0.00007805221,0.0002807407,0.0002163678,0.000558841,0.0007497886],"category_scores_gemma":[0.00042726414,0.00007334214,0.00013638313,0.00019018813,0.00018965284,0.00034602394,0.00008141204,0.00018426658,0.00031947394],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020855425,0.000029556806,0.0010630977,0.00009475811,0.00001559605,0.0004607955,0.000113747476,0.0018620121,0.9566267,0.0019435992,0.0010406522,0.036540836],"study_design_scores_gemma":[0.000025916794,0.0011613667,0.005677119,0.000020330766,0.000044546192,0.0030580696,0.00023941028,0.10661329,0.854121,0.00073353923,0.028260803,0.000044492695],"about_ca_topic_score_codex":0.00019147166,"about_ca_topic_score_gemma":0.00029570807,"teacher_disagreement_score":0.0007497886,"about_ca_system_score_codex":0.00038269773,"about_ca_system_score_gemma":0.00008314826,"threshold_uncertainty_score":0.0027766228},"labels":[],"label_agreement":null},{"id":"W3046676219","doi":"10.3390/s20154280","title":"Validation of an IMU Suit for Military-Based Tasks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Occupational Health and Performance","field":"Health Professions","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; Defence Research and Development Canada; University of Ottawa","funders":"Ministère de la Défense Nationale","keywords":"Inertial measurement unit; Motion capture; Mean squared error; Kinematics; Artificial intelligence; Computer vision; Principal component analysis; Computer science; Motion analysis; Units of measurement; Correlation coefficient; Motion (physics); Mathematics; Statistics","score_opus":0.11692007154603015,"score_gpt":0.45252438827759883,"score_spread":0.33560431673156865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046676219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88635856,0.00022120988,0.10861594,0.00011492502,0.00015161902,0.00049874664,0.0010468025,0.00066156674,0.0023305502],"genre_scores_gemma":[0.9526226,0.00012400793,0.04419256,0.000087196815,0.000028141047,0.00046154353,0.0010167607,0.00006430305,0.0014029677],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990345,0.00037746766,0.00009195078,0.00016205106,0.00026116267,0.00007289704],"domain_scores_gemma":[0.9988507,0.0003157219,0.00012574081,0.00020475154,0.00045052925,0.000052561783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020089804,0.00085679494,0.00032340197,0.00075348816,0.00032271727,0.000450652,0.0005799587,0.00069127633,0.0014590507],"category_scores_gemma":[0.0049406895,0.0002492501,0.00034059444,0.00048439752,0.0002928042,0.00041150217,0.0006314832,0.000169667,0.000670866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004076143,0.0009975976,0.17992035,0.00091664377,0.00035234608,0.0004661034,0.0016567104,0.046539355,0.42202756,0.0010311501,0.0027888436,0.33922717],"study_design_scores_gemma":[0.00022725799,0.0059221038,0.5318561,0.00019868558,0.00024654955,0.0008324124,0.0010654634,0.23730111,0.210108,0.00036920272,0.011777011,0.00009601524],"about_ca_topic_score_codex":0.0018314562,"about_ca_topic_score_gemma":0.0020858364,"teacher_disagreement_score":0.0020089804,"about_ca_system_score_codex":0.0001957512,"about_ca_system_score_gemma":0.0002801108,"threshold_uncertainty_score":0.010624588},"labels":[],"label_agreement":null},{"id":"W3047198360","doi":"10.3390/s20154345","title":"Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GF Strong Rehabilitation Centre; Simon Fraser University; Vancouver Coastal Health Research Institute; University of British Columbia; Memorial University of Newfoundland","funders":"Canadian Institutes of Health Research","keywords":"Inertial measurement unit; Ground reaction force; Gait; Mathematics; Accelerometer; Treadmill; Force platform; Gait analysis; Units of measurement; Physical medicine and rehabilitation; Simulation; Computer science; Kinematics; Physical therapy; Medicine; Artificial intelligence; Physics","score_opus":0.022954811107475905,"score_gpt":0.22567761574816106,"score_spread":0.20272280464068515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047198360","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63558453,0.0004313882,0.36252427,0.000034190227,0.00006102675,0.00006675188,0.00027443268,0.0004892254,0.0005341763],"genre_scores_gemma":[0.9727433,0.00014955224,0.026518323,0.000011938495,0.000020561169,0.00004594269,0.00018436759,0.000009472088,0.00031656044],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980575,0.000025268197,0.000011925156,0.00008520676,0.0000514254,0.00002056899],"domain_scores_gemma":[0.9998049,0.00006817179,0.00004547226,0.000019350811,0.000050714963,0.000011232935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023991482,0.00080634176,0.00063540105,0.00061933027,0.0001325275,0.0002772657,0.0003515396,0.0004904982,0.00038477668],"category_scores_gemma":[0.0008664349,0.00022360349,0.00036106768,0.0004275581,0.00011149732,0.00027004755,0.00022555911,0.0002067601,0.00023812428],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007098366,0.00036157368,0.09949407,0.00041398895,0.00033387775,0.0006040094,0.00030268746,0.22006698,0.1438974,0.00027596357,0.0009908639,0.5325487],"study_design_scores_gemma":[0.000022081365,0.00045309257,0.075701214,0.00003551143,0.00006591336,0.0003171505,0.00006494954,0.91191745,0.010760568,0.00026698652,0.0003648807,0.000030250692],"about_ca_topic_score_codex":0.0037104927,"about_ca_topic_score_gemma":0.00446035,"teacher_disagreement_score":0.0037104927,"about_ca_system_score_codex":0.00008123128,"about_ca_system_score_gemma":0.00020685297,"threshold_uncertainty_score":0.0073777437},"labels":[],"label_agreement":null},{"id":"W3047796933","doi":"10.3390/s20164411","title":"Control System for Vertical Take-Off and Landing Vehicle’s Adaptive Landing Based on Multi-Sensor Data Fusion","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Inertial measurement unit; Landing gear; Sensor fusion; Computer science; Robot; Adaptive control; Engineering; Simulation; Control (management); Artificial intelligence; Aerospace engineering","score_opus":0.05026773989903551,"score_gpt":0.24025906955398418,"score_spread":0.18999132965494867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047796933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08579421,0.00036982718,0.90414673,0.00031114812,0.00033644194,0.00016768575,0.00007492508,0.0017176736,0.0070813773],"genre_scores_gemma":[0.9754117,0.00013979548,0.021626886,0.00009092893,0.000039330815,0.00015774676,0.000068244226,0.000011355699,0.002453989],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968255,0.000022390284,0.000022074457,0.00010075883,0.00012560535,0.000046660596],"domain_scores_gemma":[0.99980325,0.000020442278,0.00003406963,0.000015910688,0.000108040585,0.000018318922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029044287,0.00049325527,0.0004129148,0.00030615812,0.0006168678,0.0005365834,0.0006223664,0.00040150477,0.0010954217],"category_scores_gemma":[0.00032205464,0.00017698004,0.00029819604,0.00018339245,0.0003000617,0.00047372875,0.0007127642,0.00042128164,0.00030780147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085590815,0.00037543164,0.007939112,0.00053125515,0.00014042536,0.0009766457,0.0011272067,0.22371638,0.29425842,0.0117476005,0.008815947,0.44951573],"study_design_scores_gemma":[0.00010918178,0.0005638103,0.0026458935,0.000025508189,0.000049895378,0.00018746624,0.0000964001,0.96199733,0.027985092,0.0010550766,0.005242638,0.000041861178],"about_ca_topic_score_codex":0.0039131674,"about_ca_topic_score_gemma":0.0024562108,"teacher_disagreement_score":0.0039131674,"about_ca_system_score_codex":0.00032022013,"about_ca_system_score_gemma":0.00072336005,"threshold_uncertainty_score":0.0077807903},"labels":[],"label_agreement":null},{"id":"W3048394765","doi":"10.3390/s20164510","title":"Atmospheric Neutron Monitoring through Optical Fiber-Based Sensing","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"TRIUMF","funders":"","keywords":"Optical fiber; Remote sensing; Fiber; Materials science; Environmental science; Optoelectronics; Optics; Computer science; Physics; Geology; Composite material","score_opus":0.02037306240930724,"score_gpt":0.24456275884797835,"score_spread":0.2241896964386711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048394765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82604104,0.009607323,0.07922532,0.0077768425,0.00056282344,0.00025845025,0.00050442456,0.0011429882,0.07488071],"genre_scores_gemma":[0.95971733,0.002070939,0.02815239,0.0008467072,0.00013015074,0.000045456603,0.00010039063,0.000017712766,0.008918978],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996892,0.00004477935,0.000007677622,0.000045110333,0.00018822702,0.000025159743],"domain_scores_gemma":[0.9998419,0.00005599481,0.000029235014,0.000013287015,0.00005171108,0.000007842382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028054486,0.00022603532,0.0001072765,0.00023096564,0.00022379139,0.0003236339,0.00037268244,0.0007765068,0.00055975124],"category_scores_gemma":[0.00037890256,0.00009093498,0.00005567832,0.00024559934,0.0003546995,0.00034406985,0.00018215667,0.0002790084,0.00025658213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021053429,0.000055199664,0.004153997,0.00014414222,0.0000120435625,0.00061040756,0.00010290433,0.0011499601,0.9310225,0.0028059918,0.004633652,0.055098552],"study_design_scores_gemma":[0.000027615959,0.00030327492,0.0072543686,0.000043044103,0.000023503906,0.0016539951,0.00012219562,0.03801572,0.8791674,0.00095807173,0.07238374,0.000047210993],"about_ca_topic_score_codex":0.0026652236,"about_ca_topic_score_gemma":0.008552562,"teacher_disagreement_score":0.0026652236,"about_ca_system_score_codex":0.00085213257,"about_ca_system_score_gemma":0.00021689286,"threshold_uncertainty_score":0.00618273},"labels":[],"label_agreement":null},{"id":"W3048664086","doi":"10.3390/s20164525","title":"Low-Rank and Sparse Recovery of Human Gait Data","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Principal component analysis; Artificial intelligence; Computer science; Kinematics; Rank (graph theory); Gait; Reduction (mathematics); Pattern recognition (psychology); Motion capture; Data reduction; Missing data; Motion (physics); Computer vision; Data mining; Mathematics; Machine learning","score_opus":0.04105828264815586,"score_gpt":0.23660860732847358,"score_spread":0.19555032468031772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048664086","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04508676,0.00015956878,0.95346427,0.00011526754,0.000036276764,0.00003456394,0.00022059544,0.00045448157,0.00042823164],"genre_scores_gemma":[0.4992955,0.0003652787,0.4963012,0.00009577384,0.00006778118,0.000110118795,0.0014818521,0.000119114055,0.0021632598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993236,0.00019956347,0.00003725019,0.00013847026,0.00023808525,0.00006293584],"domain_scores_gemma":[0.998978,0.0003517044,0.00013804206,0.00025039277,0.0002394769,0.00004243352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008026073,0.0007782911,0.0005396645,0.0007450052,0.0002150099,0.00056760426,0.0005143262,0.00066006114,0.0010624263],"category_scores_gemma":[0.0038207013,0.00030459065,0.0005999703,0.0007783949,0.00051373587,0.0007652719,0.00068183,0.0007462551,0.00062076416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063281495,0.0003127769,0.0033169356,0.00053293805,0.00016308378,0.000427782,0.00033309154,0.40139362,0.09979516,0.007985754,0.006363854,0.4787422],"study_design_scores_gemma":[0.000012716796,0.000099581244,0.0022528907,0.000015499789,0.000011590033,0.00020599089,0.000039723225,0.9793825,0.014425769,0.0022604428,0.0012723112,0.000021078624],"about_ca_topic_score_codex":0.0020745962,"about_ca_topic_score_gemma":0.0027151753,"teacher_disagreement_score":0.0020745962,"about_ca_system_score_codex":0.00019133075,"about_ca_system_score_gemma":0.00061565085,"threshold_uncertainty_score":0.0042446256},"labels":[],"label_agreement":null},{"id":"W3048761334","doi":"10.3390/s21093091","title":"DOE-SLAM: Dynamic Object Enhanced Visual SLAM","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Robustness (evolution); Simultaneous localization and mapping; Computer science; Object (grammar); Pose; Monocular; Exploit; Trajectory; Video tracking; Robot; Mobile robot","score_opus":0.004179687517758217,"score_gpt":0.21913750380646735,"score_spread":0.21495781628870914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048761334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058022346,0.00018677124,0.99097985,0.00004015123,0.00007316732,0.000026678137,0.00006219765,0.0018402545,0.0009887094],"genre_scores_gemma":[0.4443486,0.00037942268,0.5490376,0.00020723906,0.00010268155,0.0001494478,0.0006067588,0.0003763716,0.0047919923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994092,0.00010122028,0.000024340616,0.00015414097,0.0002097155,0.000101446225],"domain_scores_gemma":[0.9996051,0.00008372395,0.0000476445,0.00014126308,0.00009396689,0.000028243701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005323224,0.00085144426,0.0008904579,0.00064081023,0.00034847134,0.0006223423,0.0014767303,0.00065849815,0.0016901176],"category_scores_gemma":[0.0012239891,0.00041864303,0.00051154965,0.0007847022,0.0005242487,0.0011265685,0.002217626,0.000822339,0.0009999716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024271554,0.0001267783,0.0013633163,0.00025316238,0.00013636405,0.00023016408,0.00017985624,0.3063943,0.051393263,0.011465683,0.009125917,0.61908853],"study_design_scores_gemma":[0.000032926146,0.00013761567,0.00063159777,0.000015638689,0.00001635465,0.00013549182,0.000045946123,0.9727297,0.011057224,0.0049566547,0.010214657,0.000026247633],"about_ca_topic_score_codex":0.0035154065,"about_ca_topic_score_gemma":0.0054595256,"teacher_disagreement_score":0.0035154065,"about_ca_system_score_codex":0.00036480319,"about_ca_system_score_gemma":0.0008327913,"threshold_uncertainty_score":0.0069898367},"labels":[],"label_agreement":null},{"id":"W3048855289","doi":"10.3390/s20164523","title":"Optimized CNT-PDMS Flexible Composite for Attachable Health-Care Device","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; Chinese Academy of Sciences; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Piezoresistive effect; Materials science; Composite material; Composite number; Resistive touchscreen; Percolation threshold; Modulus; Biocompatibility; Young's modulus; Carbon nanotube; Electrical resistivity and conductivity","score_opus":0.0332441037150906,"score_gpt":0.2753556423159231,"score_spread":0.24211153860083248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048855289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.954388,0.0037336731,0.03527523,0.00019979422,0.00032796196,0.00024766577,0.0012717267,0.00045787706,0.004097895],"genre_scores_gemma":[0.9414468,0.0015931823,0.053400423,0.00008321099,0.000031743126,0.0002146849,0.00066628744,0.000055692544,0.0025079376],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998379,0.00000959624,0.000011242909,0.00005236724,0.00006696563,0.000021955017],"domain_scores_gemma":[0.99987555,0.00002152586,0.000033753662,0.000008362963,0.00003958253,0.000021289994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017314282,0.00071469723,0.0002232788,0.00043061786,0.00018658141,0.00021270009,0.00025801134,0.00047334077,0.0008426969],"category_scores_gemma":[0.00027216552,0.00024154327,0.00023491352,0.00028498517,0.00013092463,0.00032752872,0.0002170598,0.0002944003,0.00035800805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015401562,0.000009755243,0.000064968466,0.000049023234,0.0000030539316,0.00003998852,0.0000076152496,0.0002558344,0.99811614,0.0000642466,0.00004371425,0.0013303182],"study_design_scores_gemma":[0.000007010255,0.00012759356,0.0017773206,0.0000053962845,0.000014590575,0.00007600875,0.000014832815,0.003037129,0.99283797,0.000039523293,0.0020492147,0.000013507391],"about_ca_topic_score_codex":0.00041009882,"about_ca_topic_score_gemma":0.0018404067,"teacher_disagreement_score":0.0008426969,"about_ca_system_score_codex":0.00032470693,"about_ca_system_score_gemma":0.00020899525,"threshold_uncertainty_score":0.0028190613},"labels":[],"label_agreement":null},{"id":"W3048986172","doi":"10.3390/s20164594","title":"Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities","keywords":"Point cloud; Piping; Elbow; Joint (building); Computer science; Point (geometry); Laser scanning; Translation (biology); Computer vision; Geometry; Artificial intelligence; Simulation; Algorithm; Structural engineering; Mathematics; Laser; Engineering; Mechanical engineering; Physics; Optics","score_opus":0.062276847858970134,"score_gpt":0.2233630543660574,"score_spread":0.16108620650708727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048986172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06823894,0.00015742624,0.9279022,0.00006993333,0.000030799565,0.00011291829,0.00057927,0.0019731442,0.0009353603],"genre_scores_gemma":[0.84060276,0.0003783747,0.15579616,0.00003017026,0.000015860658,0.0001965767,0.0018357096,0.0002081075,0.0009363194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990213,0.00014527634,0.00007312732,0.00025343095,0.00039941334,0.000107449094],"domain_scores_gemma":[0.999313,0.00014178062,0.00014999861,0.00021743728,0.00014623867,0.00003146601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068289915,0.0012742516,0.0008424356,0.0016421457,0.00050893426,0.0012701912,0.0017483816,0.0012248415,0.0012877065],"category_scores_gemma":[0.0019176077,0.00095958274,0.0017867074,0.0021861317,0.00095773727,0.0011019023,0.0014209975,0.00092341815,0.0008322666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004381503,0.000025800995,0.0024753986,0.00005246714,0.000026344398,0.00010920846,0.000107019696,0.9594778,0.006582088,0.00095320825,0.00033161647,0.029815236],"study_design_scores_gemma":[0.0000040412274,0.000019108997,0.0017942694,0.000008134175,0.000006782288,0.000051621417,0.000036289417,0.99419147,0.0026754814,0.0004904298,0.0007052565,0.000017098708],"about_ca_topic_score_codex":0.01656213,"about_ca_topic_score_gemma":0.013756379,"teacher_disagreement_score":0.01656213,"about_ca_system_score_codex":0.0009422523,"about_ca_system_score_gemma":0.0012283776,"threshold_uncertainty_score":0.032931447},"labels":[],"label_agreement":null},{"id":"W3077098836","doi":"10.3390/s20174673","title":"Energy-Guided Temporal Segmentation Network for Multimodal Human Action Recognition","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China-Henan Joint Fund; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Construct (python library); Pattern recognition (psychology); Task (project management); Energy (signal processing); Action recognition; Action (physics); Machine learning; Frame (networking); Class (philosophy); Mathematics","score_opus":0.09490681399533432,"score_gpt":0.3094997564524858,"score_spread":0.21459294245715146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3077098836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08639171,0.0011668482,0.9037212,0.000311416,0.00014309277,0.00009782426,0.0004969248,0.0024331862,0.005237742],"genre_scores_gemma":[0.81324315,0.00055505364,0.1742564,0.00035467205,0.00007472123,0.00014132456,0.0013321491,0.00015272741,0.009889746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997987,0.000022950167,0.000008603638,0.000086175365,0.00004294727,0.00004070267],"domain_scores_gemma":[0.9998977,0.000024487961,0.000016520169,0.000014708934,0.000034005334,0.000012572438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032671628,0.00094281806,0.0005834895,0.00064589607,0.00028938096,0.00032788457,0.001011088,0.0006667957,0.002679948],"category_scores_gemma":[0.00065435725,0.0002811894,0.0005592753,0.0006298885,0.00036364573,0.0008419513,0.000698702,0.0005860567,0.00050030317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005365421,0.000226488,0.0017744921,0.000111483685,0.00012937009,0.00022119796,0.00012581791,0.28253257,0.05745129,0.0055241776,0.008143804,0.64322275],"study_design_scores_gemma":[0.000004842144,0.000043923817,0.00070517446,0.000007079459,0.000019578898,0.0000459515,0.000017633052,0.9884162,0.007699132,0.0021071723,0.00092474866,0.000008545364],"about_ca_topic_score_codex":0.008932726,"about_ca_topic_score_gemma":0.013813165,"teacher_disagreement_score":0.008932726,"about_ca_system_score_codex":0.000852228,"about_ca_system_score_gemma":0.00064783153,"threshold_uncertainty_score":0.017761469},"labels":[],"label_agreement":null},{"id":"W3080203368","doi":"10.3390/s20174815","title":"Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Architecture; Computer science; Artificial intelligence; Computer architecture; Human–computer interaction; Real-time computing; Systems engineering; Computational biology; Engineering; Geography; Biology","score_opus":0.01943753666006451,"score_gpt":0.215106448819315,"score_spread":0.1956689121592505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080203368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04798558,0.00007670926,0.94733065,0.000076200835,0.000013486905,0.000035639045,0.000035554243,0.0015526864,0.0028934136],"genre_scores_gemma":[0.7931012,0.00007553402,0.20289451,0.00007147388,0.000008376732,0.0000805317,0.00012043924,0.00008789417,0.003560064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977285,0.00003377486,0.000013207845,0.000097291864,0.000049782277,0.00003307059],"domain_scores_gemma":[0.999749,0.00004080095,0.000028801058,0.00007821267,0.00007511145,0.000028073739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030488553,0.00051367533,0.00036820542,0.00032486703,0.00040869732,0.00058956415,0.0014488975,0.00045126418,0.0016544312],"category_scores_gemma":[0.0006221902,0.00022725215,0.0003989363,0.00026982982,0.0006785758,0.0013084238,0.001083566,0.00041607206,0.00037859497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002329743,0.00017490821,0.00300723,0.0001511333,0.00016339956,0.00036861293,0.0007679117,0.52651876,0.06272922,0.06384166,0.0025406473,0.33950356],"study_design_scores_gemma":[0.000010388384,0.00007953288,0.0005225576,0.0000060653756,0.00004480582,0.00007459149,0.00004792814,0.97046036,0.010024306,0.016319819,0.002395213,0.000014466634],"about_ca_topic_score_codex":0.009252343,"about_ca_topic_score_gemma":0.008975914,"teacher_disagreement_score":0.009252343,"about_ca_system_score_codex":0.0006215051,"about_ca_system_score_gemma":0.0007479674,"threshold_uncertainty_score":0.018396974},"labels":[],"label_agreement":null},{"id":"W3080387846","doi":"10.3390/s20226486","title":"A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects","year":2020,"lang":"en","type":"preprint","venue":"Sensors","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Power consumption; Artificial intelligence; Machine learning; Benchmark (surveying); Data stream; Data mining; Pattern recognition (psychology); Power (physics)","score_opus":0.26567517145029607,"score_gpt":0.37826166342326767,"score_spread":0.1125864919729716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080387846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78850424,0.004613808,0.15684773,0.0010114001,0.0009195849,0.00096106477,0.017392963,0.019760031,0.0099891415],"genre_scores_gemma":[0.8427881,0.0011316837,0.12255806,0.00021438421,0.00016481233,0.00044241443,0.02919039,0.00029446516,0.0032158063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986896,0.00025548387,0.00016233596,0.00036973914,0.00040565862,0.000117179276],"domain_scores_gemma":[0.9981964,0.00074844656,0.00012530565,0.00032875058,0.0004669552,0.000134197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016811106,0.0014277698,0.00090225623,0.0021531272,0.0004869588,0.0010736453,0.0014414919,0.0012195035,0.0012947478],"category_scores_gemma":[0.005829171,0.00016903464,0.0005955475,0.002597824,0.00045942853,0.0011780828,0.00058480847,0.00070398924,0.00097504427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025879915,0.0016368525,0.0268162,0.0017135999,0.00061127736,0.0006889817,0.00028844769,0.2541158,0.012597343,0.003165909,0.03985156,0.65592605],"study_design_scores_gemma":[0.00014185658,0.00083658914,0.015734965,0.00006911493,0.00006284302,0.00040148274,0.00021306223,0.9495832,0.019957155,0.004358588,0.008603224,0.000038021393],"about_ca_topic_score_codex":0.0070576975,"about_ca_topic_score_gemma":0.0045542414,"teacher_disagreement_score":0.0070576975,"about_ca_system_score_codex":0.0008639906,"about_ca_system_score_gemma":0.00083445193,"threshold_uncertainty_score":0.014033258},"labels":[],"label_agreement":null},{"id":"W3081011859","doi":"10.3390/s20174858","title":"Automated Channel Selection in High-Density sEMG for Improved Force Estimation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Pattern recognition (psychology); Computer science; Principal component analysis; Spectral density; Brachioradialis; Redundancy (engineering); Channel (broadcasting); Artificial intelligence; Dimensionality reduction; Biceps; Telecommunications","score_opus":0.010398588362813758,"score_gpt":0.21505202073581542,"score_spread":0.20465343237300165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081011859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08841662,0.0003869396,0.9077959,0.00008398428,0.000057319186,0.000123228,0.00043360045,0.0019150773,0.00078721606],"genre_scores_gemma":[0.41290638,0.00032807988,0.58308864,0.000078545636,0.000078534176,0.0003860136,0.0014254533,0.00030474263,0.0014036722],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935085,0.00019349578,0.000037969505,0.00015956334,0.00017257895,0.000085414365],"domain_scores_gemma":[0.99903655,0.0004689613,0.000069414855,0.0001244596,0.00026981367,0.00003074818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010269837,0.001213385,0.00093837385,0.001237226,0.00034348774,0.0007125237,0.0004820585,0.00054286,0.0015804289],"category_scores_gemma":[0.0033465328,0.00027666838,0.0006501795,0.0012449833,0.00029351356,0.0006998902,0.00050282653,0.00047468682,0.0007603202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073448376,0.00039518424,0.00628246,0.0003507177,0.00014323644,0.0002027759,0.0002730129,0.06049794,0.14733516,0.0019781895,0.0052523715,0.7765546],"study_design_scores_gemma":[0.00008038527,0.00024056053,0.028914759,0.00003720043,0.00009677847,0.000323851,0.00012375759,0.8837686,0.07870003,0.0032871324,0.0043587605,0.00006822138],"about_ca_topic_score_codex":0.0018949069,"about_ca_topic_score_gemma":0.004385904,"teacher_disagreement_score":0.0018949069,"about_ca_system_score_codex":0.00019295469,"about_ca_system_score_gemma":0.0007445933,"threshold_uncertainty_score":0.0054312944},"labels":[],"label_agreement":null},{"id":"W3081524940","doi":"10.3390/s20174955","title":"Integrated Lab-on-a-Chip Optical Biosensor Using Ultrathin Silicon Waveguide SOI MMI Device","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Qatar National Research Fund; American University in Cairo","keywords":"Biosensor; Silicon on insulator; Materials science; Sensitivity (control systems); Figure of merit; Waveguide; Streptavidin; Optoelectronics; Silicon; Optics; Nanotechnology; Electronic engineering; Chemistry; Biotin; Physics","score_opus":0.032016159929465667,"score_gpt":0.24568008028587374,"score_spread":0.21366392035640808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081524940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73525375,0.0012689985,0.2543515,0.0004097403,0.00032140678,0.00017728901,0.00090287963,0.0021911121,0.005123339],"genre_scores_gemma":[0.7129059,0.0007021139,0.2822039,0.00016765266,0.000023715018,0.00028679596,0.00051394454,0.00008146213,0.0031145425],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982125,0.000020141419,0.000012751535,0.000048332695,0.000074307616,0.00002335514],"domain_scores_gemma":[0.99981374,0.000047847258,0.00004770496,0.000030441102,0.000047209563,0.000013138209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030660653,0.00042812375,0.00034780838,0.00015772507,0.00013219731,0.00038354602,0.0007491551,0.000630305,0.00067328295],"category_scores_gemma":[0.00030461242,0.00029482273,0.00038230466,0.0001659568,0.0002045699,0.000435724,0.0002590145,0.0004506763,0.00040455954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045447603,0.00004161305,0.0003696385,0.00008428271,0.000025989253,0.000044018292,0.000022952372,0.002501117,0.99151284,0.00074452505,0.00028623056,0.0043213535],"study_design_scores_gemma":[0.000016509008,0.00028251638,0.0009105332,0.00000855751,0.000029298368,0.000080453916,0.000021224,0.05036144,0.94399506,0.00020277892,0.0040701125,0.000021541213],"about_ca_topic_score_codex":0.0004813601,"about_ca_topic_score_gemma":0.0009923503,"teacher_disagreement_score":0.0007491551,"about_ca_system_score_codex":0.00051099336,"about_ca_system_score_gemma":0.00029773553,"threshold_uncertainty_score":0.003707528},"labels":[],"label_agreement":null},{"id":"W3082120068","doi":"10.3390/s20174946","title":"A Hierarchical Learning Approach for Human Action Recognition","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Action recognition; Domain (mathematical analysis); Action (physics); Machine learning; Focus (optics); Inertial measurement unit; RGB color model; Activity recognition; Human–computer interaction","score_opus":0.1011026685981778,"score_gpt":0.29549425859953865,"score_spread":0.19439159000136086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082120068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008835822,0.00087414857,0.9834267,0.00019253013,0.00010129334,0.00010891727,0.0006418295,0.0026109975,0.0032077066],"genre_scores_gemma":[0.42512214,0.0010822772,0.5516167,0.00055480184,0.0001957806,0.0003193815,0.0035737737,0.00025163888,0.017283522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.000063755404,0.000024895358,0.00024165415,0.0001127389,0.00008229038],"domain_scores_gemma":[0.99975866,0.00004942993,0.000023270442,0.000061473904,0.000079586775,0.000027642533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047782421,0.0008500909,0.00071238115,0.00093005114,0.00036012538,0.00057897787,0.0017008026,0.0008169627,0.005183749],"category_scores_gemma":[0.0008334511,0.00042767343,0.000951202,0.0010218234,0.0004325154,0.00085607107,0.0009435428,0.0011691015,0.0022371844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015794128,0.00019530524,0.0014106599,0.00016082355,0.000109651846,0.000083758954,0.00007370149,0.11132542,0.015659606,0.008723881,0.012725134,0.8493741],"study_design_scores_gemma":[0.0000089582,0.00007115318,0.0011606873,0.000018243245,0.000022736478,0.000052341078,0.000018682338,0.982092,0.0039533395,0.008834432,0.003752602,0.000014743218],"about_ca_topic_score_codex":0.023794385,"about_ca_topic_score_gemma":0.036444318,"teacher_disagreement_score":0.023794385,"about_ca_system_score_codex":0.0011762471,"about_ca_system_score_gemma":0.0012139705,"threshold_uncertainty_score":0.047311783},"labels":[],"label_agreement":null},{"id":"W3082570134","doi":"10.3390/s20174967","title":"A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Trent University; Nottingham Trent University","keywords":"Case fatality rate; Epidemiology; Coronavirus disease 2019 (COVID-19); Vulnerability (computing); Population; Epidemic model; Disease; Environmental health; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Demography; Susceptible individual; Medicine; Statistics; Computer science; Infectious disease (medical specialty); Mathematics; Computer security; Pathology","score_opus":0.26813417492055724,"score_gpt":0.3060474115501728,"score_spread":0.03791323662961554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082570134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41115665,0.0020398684,0.5257638,0.0050542727,0.0005032362,0.00062642794,0.0032451516,0.0004987341,0.051111836],"genre_scores_gemma":[0.93765944,0.0010211441,0.0362986,0.00025326555,0.000072853305,0.00042793297,0.00086469366,0.000041432013,0.023360685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941385,0.00030283036,0.000029436445,0.00007997551,0.00006432067,0.00010958202],"domain_scores_gemma":[0.9990859,0.00052463374,0.00014098386,0.000034489694,0.0001384825,0.00007547812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007777384,0.0006706934,0.00076755014,0.00066079816,0.00048767158,0.0014781978,0.0016103035,0.0024379778,0.004591618],"category_scores_gemma":[0.0031735892,0.00045725706,0.0012934881,0.00059060147,0.0005529283,0.0008561992,0.0014677164,0.0011226679,0.0005236484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091931026,0.000038269085,0.0020770044,0.000049376595,0.000023779974,0.0002447889,0.000082341314,0.9850889,0.0004229142,0.008187779,0.0006596323,0.003033348],"study_design_scores_gemma":[0.000019119443,0.00004900668,0.0004947954,0.000011531968,0.000015842143,0.00003919678,0.00004599455,0.9967519,0.00005197278,0.0016476098,0.00086123205,0.000011815781],"about_ca_topic_score_codex":0.04394516,"about_ca_topic_score_gemma":0.017528277,"teacher_disagreement_score":0.04394516,"about_ca_system_score_codex":0.0014826476,"about_ca_system_score_gemma":0.0013910414,"threshold_uncertainty_score":0.08737874},"labels":[],"label_agreement":null},{"id":"W3082866720","doi":"10.3390/s20174999","title":"Assessing the Validity and Reliability of A Low-Cost Microcontroller-Based Load Cell Amplifier for Measuring Lower Limb and Upper Limb Muscular Force","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Canadian Frailty Network","keywords":"Dynamometer; Isometric exercise; Load cell; Reliability (semiconductor); Microcontroller; Amplifier; Signal conditioning; SIGNAL (programming language); Computer science; Simulation; Physical therapy; Engineering; Electrical engineering; Automotive engineering; Computer hardware; Medicine; Telecommunications; Physics; Bandwidth (computing)","score_opus":0.024995884625701507,"score_gpt":0.23598236570314465,"score_spread":0.21098648107744314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082866720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9493051,0.0005640765,0.045513034,0.00008368986,0.0001556301,0.00036976725,0.00016236148,0.00019755687,0.0036488597],"genre_scores_gemma":[0.96822584,0.00017718559,0.029592896,0.000076854754,0.00004769263,0.00026004142,0.00023657765,0.000044842065,0.0013382046],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98968923,0.0034959149,0.00086763396,0.001268607,0.004507115,0.00017158562],"domain_scores_gemma":[0.9761445,0.011019943,0.0027279966,0.0018609975,0.0079335,0.00031318795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008509352,0.00052814174,0.00045111385,0.0010890109,0.00030202343,0.00062316377,0.00069403363,0.00070475345,0.0010544012],"category_scores_gemma":[0.024109563,0.00026069573,0.0005679019,0.000618482,0.0007188168,0.0006046757,0.0007228171,0.00038644325,0.0009483334],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015390245,0.00105028,0.6869652,0.0005490317,0.00069627527,0.00015494201,0.0023596922,0.0024416388,0.10536231,0.0005861269,0.0011013922,0.19719416],"study_design_scores_gemma":[0.00012425626,0.008070113,0.9335246,0.00017940316,0.00041648885,0.0008892076,0.0010862077,0.023001824,0.028880117,0.00041278647,0.0033147107,0.000100190664],"about_ca_topic_score_codex":0.0011032583,"about_ca_topic_score_gemma":0.0021235996,"teacher_disagreement_score":0.008509352,"about_ca_system_score_codex":0.00037945202,"about_ca_system_score_gemma":0.00047651384,"threshold_uncertainty_score":0.04500228},"labels":[],"label_agreement":null},{"id":"W3083345301","doi":"10.3390/s20185098","title":"Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Pose; Artificial intelligence; Computer vision; Computer science; Object (grammar); 3D pose estimation; Point cloud; RGB color model; Histogram; Cognitive neuroscience of visual object recognition; Feature (linguistics); Pattern recognition (psychology); Point (geometry); 3D single-object recognition; Matching (statistics); Mathematics; Image (mathematics)","score_opus":0.030649383411090532,"score_gpt":0.21454142102897375,"score_spread":0.18389203761788322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083345301","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004494996,0.00030923763,0.99235743,0.000052543903,0.000035012,0.000029424467,0.00010129773,0.0019504692,0.0006695656],"genre_scores_gemma":[0.23014179,0.0012877355,0.760029,0.00026498613,0.00010721017,0.00014725003,0.001055924,0.00029290112,0.006673231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943334,0.00004298377,0.000024237912,0.00020638094,0.00022199523,0.00007118003],"domain_scores_gemma":[0.9997396,0.000024979377,0.000045131455,0.000090401656,0.000072267474,0.000027624532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031253955,0.0011481609,0.0012275846,0.0018549424,0.00030104112,0.0013391114,0.0015522853,0.0010579474,0.002859687],"category_scores_gemma":[0.00063295435,0.0007124256,0.0013056558,0.0015469549,0.0005582189,0.0009434952,0.0015076143,0.0008005126,0.0036552951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001572956,0.0001296932,0.002236392,0.00017997879,0.000126628,0.00027024254,0.00015501313,0.06892489,0.10220931,0.0027821588,0.0045434567,0.8182849],"study_design_scores_gemma":[0.000016112317,0.0001543063,0.0055938694,0.000051265084,0.00006727501,0.00084233796,0.00013207457,0.92572623,0.049186315,0.0043899077,0.013770986,0.00006935775],"about_ca_topic_score_codex":0.004347626,"about_ca_topic_score_gemma":0.006386014,"teacher_disagreement_score":0.004347626,"about_ca_system_score_codex":0.00046492333,"about_ca_system_score_gemma":0.00056284806,"threshold_uncertainty_score":0.009566605},"labels":[],"label_agreement":null},{"id":"W3083382581","doi":"10.3390/s20185081","title":"Development of the User Requirements for the Canadian WildFireSat Satellite Mission","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; Ministry of Natural Resources and Forestry; Canadian Space Agency; Canadian Forest Service","funders":"Canadian Space Agency; Natural Resources Canada; Environment and Climate Change Canada; Sight Research UK; Natural Environment Research Council; Australian Government","keywords":"Earth observation; Satellite; User requirements document; Agency (philosophy); Environmental resource management; Systems engineering; Engineering; Computer science; Environmental science","score_opus":0.03055279544195347,"score_gpt":0.2352789076843613,"score_spread":0.20472611224240783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083382581","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41055292,0.00058702205,0.3610759,0.004451257,0.00020423252,0.012196758,0.012467301,0.011808881,0.18665573],"genre_scores_gemma":[0.5872757,0.00043268086,0.3541771,0.0012951355,0.00005279578,0.0047353986,0.012310229,0.0016402185,0.038080744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99488443,0.000742243,0.00026010233,0.00025732294,0.0031405403,0.0007153329],"domain_scores_gemma":[0.98811764,0.00240565,0.00043418806,0.0009481256,0.007373345,0.0007210751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007003471,0.000740549,0.0003577486,0.0009907834,0.0016015565,0.0017914284,0.0015511977,0.00095314474,0.004319628],"category_scores_gemma":[0.013124563,0.0005378846,0.000514899,0.00046303682,0.0007846507,0.0015635197,0.0015446262,0.0013388034,0.0015900135],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001981449,0.0012358661,0.07307996,0.0019404856,0.000109327004,0.0032826033,0.027275838,0.03338481,0.25934213,0.05610166,0.097427934,0.44483805],"study_design_scores_gemma":[0.00016662109,0.0008435663,0.08627244,0.0011055194,0.000120471115,0.0012878014,0.008068119,0.11592086,0.12511694,0.005130468,0.6555633,0.00040392214],"about_ca_topic_score_codex":0.32291222,"about_ca_topic_score_gemma":0.40932643,"teacher_disagreement_score":0.6770878,"about_ca_system_score_codex":0.0055819363,"about_ca_system_score_gemma":0.011034428,"threshold_uncertainty_score":0.6420653},"labels":[],"label_agreement":null},{"id":"W3083459791","doi":"10.3390/s20185046","title":"Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Botulinum Toxin and Related Neurological Disorders","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spasticity; Rehabilitation; Physical medicine and rehabilitation; Wearable computer; Stretch reflex; Medicine; Computer science; Physical therapy; Electromyography","score_opus":0.2496515022131561,"score_gpt":0.5022267517483004,"score_spread":0.2525752495351443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083459791","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036672113,0.9951807,0.0023210014,0.0002715813,0.000225058,0.00002153436,0.00006636517,0.000020055737,0.0015269974],"genre_scores_gemma":[0.0047096815,0.9913812,0.0024701867,0.00015852983,0.00016559214,0.000043752523,0.000112741305,0.000007761751,0.00095056783],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952614,0.00010661544,0.00006636445,0.000067157234,0.00020666502,0.00002711688],"domain_scores_gemma":[0.9992488,0.00039794718,0.00009167277,0.000025360654,0.00021521778,0.000020841138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012804162,0.0012345533,0.0017403607,0.0028342176,0.00018968593,0.0011708797,0.0010975427,0.0012777658,0.0029342235],"category_scores_gemma":[0.0018063637,0.00034168916,0.0011819826,0.0018439043,0.0005044607,0.0012337579,0.00066318293,0.0011247403,0.0015926273],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008038829,0.00010239125,0.0005086504,0.029379152,0.00022132524,0.00015608736,0.00008079247,0.0020726626,0.0043960973,0.0077809556,0.012321115,0.94290024],"study_design_scores_gemma":[0.000036330766,0.000384789,0.0033871946,0.017262613,0.00075375685,0.0028574208,0.00021384221,0.0044832015,0.005962691,0.0101458775,0.95437187,0.00014040388],"about_ca_topic_score_codex":0.0017011841,"about_ca_topic_score_gemma":0.0017310929,"teacher_disagreement_score":0.0029342235,"about_ca_system_score_codex":0.0007153974,"about_ca_system_score_gemma":0.0011640087,"threshold_uncertainty_score":0.009815931},"labels":[],"label_agreement":null},{"id":"W3083611323","doi":"10.3390/s20185055","title":"Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences; National Key Research and Development Program of China; Chinese Academy of Agricultural Sciences","keywords":"Backpropagation; Yield (engineering); Support vector machine; Vegetation (pathology); Mathematics; Random forest; Mean squared error; Extreme learning machine; Chlorophyll; Chlorophyll a; Vegetation Index; Artificial intelligence; Artificial neural network; Leaf area index; Environmental science; Agronomy; Statistics; Computer science; Horticulture; Botany; Normalized Difference Vegetation Index; Biology; Materials science","score_opus":0.011295092055428734,"score_gpt":0.19747362143552136,"score_spread":0.18617852938009263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083611323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79781324,0.00085067435,0.19569482,0.000059068665,0.0000503569,0.000062873594,0.0010682289,0.0011912796,0.0032095471],"genre_scores_gemma":[0.93735594,0.00025218545,0.060619872,0.000016345957,0.000009750337,0.00004638734,0.00078657264,0.00003560785,0.00087734085],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984634,0.000024383353,0.000006555572,0.000038917322,0.00006765471,0.000016026825],"domain_scores_gemma":[0.99988425,0.000024865001,0.00002858139,0.00001061846,0.000042756492,0.000008847324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002700352,0.00045418492,0.0002314495,0.00088606955,0.00011933655,0.00023615855,0.0002369344,0.00021240783,0.0003066439],"category_scores_gemma":[0.0004486166,0.00015264879,0.0003535835,0.0006449786,0.00007756495,0.00041585497,0.00019502443,0.00024971442,0.00016385234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049861794,0.000255661,0.16569604,0.00034678096,0.00028747646,0.00030059973,0.00020108478,0.12524848,0.3685754,0.001195677,0.002871177,0.33452302],"study_design_scores_gemma":[0.000019673536,0.00017798622,0.14398693,0.000017561599,0.00006444813,0.00012936429,0.00006421708,0.81213325,0.04100602,0.0003200829,0.0020254012,0.000055048713],"about_ca_topic_score_codex":0.005487853,"about_ca_topic_score_gemma":0.0073557603,"teacher_disagreement_score":0.005487853,"about_ca_system_score_codex":0.00025993105,"about_ca_system_score_gemma":0.00018334665,"threshold_uncertainty_score":0.010911822},"labels":[],"label_agreement":null},{"id":"W3083736525","doi":"10.3390/s20185056","title":"Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Lidar; Point cloud; Photogrammetry; Computer vision; Intersection (aeronautics); Computer science; Remote sensing; Calibration; Triangulation; Artificial intelligence; Ranging; Elevation (ballistics); Digital elevation model; Point (geometry); Geography; Mathematics","score_opus":0.014146896525865123,"score_gpt":0.23145405065011768,"score_spread":0.21730715412425256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083736525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03550873,0.00015983969,0.9605637,0.00004800617,0.00006512198,0.00009120028,0.00013674905,0.0017232167,0.001703511],"genre_scores_gemma":[0.24288768,0.00023215116,0.7536638,0.00007604691,0.00004161685,0.00012532958,0.0006073405,0.00028589548,0.0020802417],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985274,0.00013840481,0.00006916543,0.00038619363,0.00078310975,0.00009569447],"domain_scores_gemma":[0.9989661,0.0000786905,0.00016645946,0.0003442761,0.00041296275,0.00003148804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006639665,0.0007903821,0.00058054365,0.0017309521,0.0005044436,0.0009855918,0.0010334477,0.00057326927,0.0021306167],"category_scores_gemma":[0.0020413364,0.00048482043,0.0005597429,0.0016749143,0.00039977825,0.0014254372,0.0015373453,0.0009672471,0.0015314062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002325809,0.00008909528,0.004469771,0.00022105573,0.000056961893,0.00018547634,0.0005501794,0.00966801,0.2147607,0.0035747602,0.0035490678,0.76264244],"study_design_scores_gemma":[0.0001581074,0.0005929438,0.037052,0.000098304816,0.00013163673,0.0020894546,0.000986342,0.34717235,0.55270004,0.007143668,0.051648404,0.00022671936],"about_ca_topic_score_codex":0.0020065743,"about_ca_topic_score_gemma":0.0026690734,"teacher_disagreement_score":0.0021306167,"about_ca_system_score_codex":0.00039120103,"about_ca_system_score_gemma":0.0009860069,"threshold_uncertainty_score":0.0071276426},"labels":[],"label_agreement":null},{"id":"W3084046865","doi":"10.3390/s20185156","title":"Contactless Capacitive Electrocardiography Using Hybrid Flexible Printed Electrodes","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; Concordia University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Capacitive sensing; Electrode; Wearable computer; Capacitance; Computer science; Noise (video); Materials science; Electronic engineering; Biomedical engineering; SIGNAL (programming language); Engineering; Artificial intelligence; Embedded system","score_opus":0.021945196553220574,"score_gpt":0.22211767677612,"score_spread":0.2001724802228994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084046865","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5542418,0.005705176,0.42956007,0.00075563596,0.0007556244,0.00024038061,0.00049258175,0.0012734168,0.0069753802],"genre_scores_gemma":[0.8969885,0.0011014878,0.09822939,0.0003716963,0.00012656295,0.00007769808,0.000117453725,0.000043131076,0.002943957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943763,0.00009258671,0.000033982065,0.00016738163,0.00023550559,0.000032830896],"domain_scores_gemma":[0.9995035,0.00018716861,0.00011450011,0.0000819645,0.00008759141,0.000025248997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031463167,0.00044058214,0.00032791554,0.0003844055,0.00009260878,0.0005659207,0.00080017204,0.00096851785,0.0006448332],"category_scores_gemma":[0.0010922087,0.00025698388,0.00025835965,0.00046806363,0.00032306573,0.0007079766,0.0005047871,0.00029766344,0.0003458917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001778848,0.000032982636,0.00086193683,0.00021912174,0.000036630376,0.0006657172,0.000053653454,0.0017423478,0.9399232,0.0004258171,0.00049697177,0.055363666],"study_design_scores_gemma":[0.000057647136,0.0010233152,0.00597388,0.000058527137,0.00006402028,0.002618075,0.00006822596,0.023288228,0.95796365,0.00065419136,0.008127696,0.00010239664],"about_ca_topic_score_codex":0.00012544967,"about_ca_topic_score_gemma":0.00027214867,"teacher_disagreement_score":0.00096851785,"about_ca_system_score_codex":0.00013924809,"about_ca_system_score_gemma":0.00008810947,"threshold_uncertainty_score":0.0021571517},"labels":[],"label_agreement":null},{"id":"W3084639145","doi":"10.3390/s20185296","title":"A Heterogeneous Edge-Fog Environment Supporting Digital Twins for Remote Inspections","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Agência Nacional de Energia Elétrica; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Point cloud; Real-time computing; Cloud computing; Enhanced Data Rates for GSM Evolution; Process (computing); Synchronization (alternating current); Sensor fusion; Computer vision; Artificial intelligence; Channel (broadcasting); Computer network","score_opus":0.01934493707809807,"score_gpt":0.2512804530448257,"score_spread":0.23193551596672765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084639145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67597467,0.0003243165,0.31163305,0.0002382073,0.00016921273,0.00024038993,0.00048683956,0.0026844956,0.00824878],"genre_scores_gemma":[0.939195,0.00009042284,0.058410417,0.000055693337,0.00001388974,0.000043820306,0.00033859268,0.000060512226,0.0017915455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997032,0.00003341682,0.00001120947,0.00006782351,0.0000739,0.0001104129],"domain_scores_gemma":[0.99976283,0.000039237235,0.000018269036,0.00006588772,0.000048245784,0.00006550366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002627501,0.00050541107,0.00044965607,0.00029649257,0.0010162818,0.0010017868,0.0014154015,0.0005996078,0.0015602086],"category_scores_gemma":[0.00043672574,0.00020526783,0.0003937439,0.00043498425,0.0003482613,0.0010145116,0.0015415627,0.00036950826,0.00041460968],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032648565,0.001058586,0.017699178,0.0005320276,0.00022627805,0.0060957144,0.0012034425,0.45475766,0.28811115,0.019794583,0.014575382,0.19268113],"study_design_scores_gemma":[0.00004981011,0.00023963914,0.0039988398,0.000015281737,0.00004422609,0.00032684143,0.0003857983,0.95161664,0.032562125,0.0031518543,0.007565507,0.000043400392],"about_ca_topic_score_codex":0.011264815,"about_ca_topic_score_gemma":0.010249816,"teacher_disagreement_score":0.011264815,"about_ca_system_score_codex":0.0006326902,"about_ca_system_score_gemma":0.0010102431,"threshold_uncertainty_score":0.022398472},"labels":[],"label_agreement":null},{"id":"W3086844398","doi":"10.3390/s20185280","title":"Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Date Palm Research Studies","field":"Agricultural and Biological Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Singapore University of Technology and Design; Agency for Science, Technology and Research","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Object detection; Field (mathematics); Trap (plumbing); Internet of Things; Real-time computing; Embedded system; Pattern recognition (psychology); Engineering","score_opus":0.06630199852606261,"score_gpt":0.2757370786748023,"score_spread":0.20943508014873968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086844398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3055314,0.0010427667,0.6698445,0.00037526214,0.00015401652,0.00022124384,0.0006951015,0.010277713,0.011857971],"genre_scores_gemma":[0.91213816,0.00034073758,0.08029594,0.00025031902,0.000036050355,0.00012585837,0.00080244534,0.000044416465,0.005965995],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998178,0.000010075588,0.000010476714,0.00005553435,0.000068340996,0.000037845646],"domain_scores_gemma":[0.9999182,0.000009834863,0.000014558207,0.000010146968,0.00003637475,0.000010866431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017944668,0.00047797977,0.0003708058,0.00040724903,0.00022248657,0.00034769517,0.0006611525,0.0003694818,0.0011991732],"category_scores_gemma":[0.00020872979,0.00020108832,0.00035924383,0.00023749711,0.0001366423,0.00060961425,0.0005184317,0.00038722114,0.00026341307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063992565,0.00076640846,0.0206732,0.0003348631,0.0001894089,0.00077389856,0.00018482865,0.12586293,0.16028875,0.0024811963,0.011664477,0.67614007],"study_design_scores_gemma":[0.00002701422,0.0002116317,0.008124449,0.000017834956,0.00005337514,0.00019811536,0.000036770114,0.9643668,0.023457533,0.00077601336,0.00270079,0.000029758412],"about_ca_topic_score_codex":0.006018248,"about_ca_topic_score_gemma":0.0074731074,"teacher_disagreement_score":0.006018248,"about_ca_system_score_codex":0.0004389742,"about_ca_system_score_gemma":0.00050905434,"threshold_uncertainty_score":0.011966467},"labels":[],"label_agreement":null},{"id":"W3087107611","doi":"10.3390/s20185341","title":"Noise Suppression in Compressive Single-Pixel Imaging","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Random lasers and scattering media","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Compressed sensing; Underdetermined system; Noise (video); Computer science; Multiplicative noise; Noise reduction; Algorithm; Inverse problem; Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Telecommunications; Transmission (telecommunications)","score_opus":0.011945422380385881,"score_gpt":0.21795656889679244,"score_spread":0.20601114651640656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087107611","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04960245,0.0059123626,0.92484134,0.0037815257,0.00041079617,0.000080511825,0.000053586424,0.00040201668,0.014915407],"genre_scores_gemma":[0.59196794,0.0047382545,0.3970956,0.0013978404,0.0004930448,0.00012023786,0.00010545211,0.00007794736,0.004003679],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995627,0.000097187214,0.000014740045,0.00006850063,0.00023731697,0.000019507454],"domain_scores_gemma":[0.9994567,0.00028819873,0.000055240096,0.00007762736,0.000100508354,0.000021691001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049873814,0.0003670739,0.00034921296,0.00032295333,0.00025029125,0.00038850863,0.00050028006,0.0010855622,0.0005659938],"category_scores_gemma":[0.0020918886,0.00019783869,0.00014435343,0.00036751613,0.0010585715,0.0009979844,0.00049803144,0.00064415997,0.0004405666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005218519,0.00008921671,0.0018913198,0.0009159659,0.0000338265,0.0012184744,0.00032923676,0.05640699,0.42771316,0.120029576,0.010958571,0.37989196],"study_design_scores_gemma":[0.00002771595,0.0001419477,0.0005185467,0.00007264063,0.000012292067,0.0020368828,0.00006761701,0.79999566,0.15383592,0.024753671,0.01849499,0.000042134172],"about_ca_topic_score_codex":0.00027190588,"about_ca_topic_score_gemma":0.0004951903,"teacher_disagreement_score":0.0010855622,"about_ca_system_score_codex":0.00044133517,"about_ca_system_score_gemma":0.00025321744,"threshold_uncertainty_score":0.0032022},"labels":[],"label_agreement":null},{"id":"W3087492702","doi":"10.3390/s20185360","title":"Fifth-Generation (5G) mmWave Spatial Channel Characterization for Urban Environments’ System Analysis","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades","keywords":"Transmitter; Beamforming; Computer science; Node (physics); Interference (communication); Channel (broadcasting); Extremely high frequency; Antenna (radio); Wireless; Electronic engineering; Telecommunications; Engineering; Acoustics; Physics","score_opus":0.028578064246295845,"score_gpt":0.19074055234614823,"score_spread":0.1621624880998524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087492702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24401449,0.00028830903,0.7421527,0.00012210567,0.000042139316,0.000060844257,0.00084223924,0.0008417016,0.01163555],"genre_scores_gemma":[0.9490692,0.00035082552,0.047031384,0.000038076665,0.000016826436,0.00008064898,0.0007312236,0.000094122544,0.0025877284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988663,0.000026588006,0.000003899719,0.000012709487,0.000048928625,0.000021202617],"domain_scores_gemma":[0.9998253,0.00006724092,0.000020301291,0.00003133483,0.000049090933,0.000006773376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017930506,0.00035296314,0.0001478981,0.0004992073,0.00016000467,0.00041612168,0.00023677584,0.00023633853,0.0018041485],"category_scores_gemma":[0.00037170865,0.000082572085,0.00029249114,0.00045978648,0.00016371241,0.00034990394,0.00023417399,0.00020530462,0.00043511117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102410995,0.00006329789,0.009954278,0.000185267,0.000064778505,0.00022370579,0.00012973722,0.85262465,0.05429865,0.013002546,0.0021441162,0.06720662],"study_design_scores_gemma":[0.0000027866995,0.00005056666,0.0059703016,0.000007949789,0.000014319517,0.000105452906,0.00007016417,0.97105557,0.018378863,0.001356205,0.0029733742,0.000014440706],"about_ca_topic_score_codex":0.0019733403,"about_ca_topic_score_gemma":0.0027513455,"teacher_disagreement_score":0.0019733403,"about_ca_system_score_codex":0.0003121048,"about_ca_system_score_gemma":0.00036582988,"threshold_uncertainty_score":0.006035447},"labels":[],"label_agreement":null},{"id":"W3087776899","doi":"10.3390/s20195564","title":"An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Innovate UK; University of Illinois at Urbana-Champaign","keywords":"Pothole (geology); Computer science; Road surface; Random forest; Robustness (evolution); Real-time computing; Global Positioning System; Artificial intelligence; Machine learning; Data mining; Engineering; Telecommunications","score_opus":0.029323833183376107,"score_gpt":0.2633845644882231,"score_spread":0.234060731304847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087776899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10723425,0.00070150633,0.88559943,0.00022537922,0.00014529334,0.0002217674,0.00065703527,0.003696321,0.0015188575],"genre_scores_gemma":[0.732861,0.00032925143,0.26283398,0.000109153145,0.00011987568,0.00023981826,0.0014305167,0.000044292374,0.002032137],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99958867,0.00005284972,0.000035120054,0.0001502893,0.00010790352,0.000065087275],"domain_scores_gemma":[0.99950767,0.00016134886,0.00006436672,0.000055208257,0.00019254052,0.000018831985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048027636,0.0009186596,0.00072019774,0.0020051158,0.00036669688,0.0005136178,0.000771845,0.00077492004,0.00084335805],"category_scores_gemma":[0.0014891096,0.00020568594,0.00071036635,0.0010296465,0.0002130758,0.00072666164,0.0004059542,0.00063271733,0.0006577975],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024489325,0.0005891364,0.012050289,0.00020272878,0.00011984898,0.0004085823,0.000107351014,0.09189677,0.034869686,0.0010173541,0.0049718344,0.85352147],"study_design_scores_gemma":[0.000011591622,0.00010531313,0.0052127964,0.000011720623,0.00002094804,0.0001330992,0.000059998572,0.98455447,0.007764093,0.0008983506,0.0012098295,0.000017835415],"about_ca_topic_score_codex":0.0042620962,"about_ca_topic_score_gemma":0.005759638,"teacher_disagreement_score":0.0042620962,"about_ca_system_score_codex":0.0003102258,"about_ca_system_score_gemma":0.0005588191,"threshold_uncertainty_score":0.008474588},"labels":[],"label_agreement":null},{"id":"W3087815357","doi":"10.3390/s20195477","title":"Millimeter Wave Multi-Port Interferometric Radar Sensors: Evolution of Fabrication and Characterization Technologies","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Miniaturization; Electronic engineering; Extremely high frequency; Amplifier; Computer science; Radar; Electrical engineering; Engineering; Fabrication; Telecommunications; CMOS","score_opus":0.046394922906310074,"score_gpt":0.25367254570344167,"score_spread":0.20727762279713158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087815357","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007531994,0.9941707,0.0023469196,0.00024363765,0.00018901807,0.000011238124,0.000016168273,0.000019139932,0.0022499487],"genre_scores_gemma":[0.004417115,0.9900849,0.0029759249,0.00023414046,0.00023244825,0.000018924338,0.000043552365,0.0000049068653,0.0019881574],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995505,0.00005841111,0.000040969386,0.00009063989,0.00022354888,0.000035917776],"domain_scores_gemma":[0.9996809,0.00011900679,0.00005525196,0.0000141791625,0.00011651994,0.000014093098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083194074,0.0007900444,0.0008347696,0.0019023429,0.00017445831,0.00079535734,0.0008495747,0.00089430285,0.00095344],"category_scores_gemma":[0.0005329639,0.00042597292,0.0003664727,0.0020012038,0.0003931213,0.0013577356,0.0004202632,0.0012555784,0.0010369552],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038414717,0.00009358117,0.0002555956,0.011314352,0.00005017841,0.00015456422,0.000082151004,0.0009672101,0.015313297,0.0099866185,0.005982233,0.95576185],"study_design_scores_gemma":[0.0000075068456,0.00019848302,0.001076828,0.0017207663,0.00008834036,0.0012840079,0.00008750989,0.00074634736,0.015133136,0.0028034819,0.9768173,0.000036306574],"about_ca_topic_score_codex":0.0005700877,"about_ca_topic_score_gemma":0.0006160696,"teacher_disagreement_score":0.0019023429,"about_ca_system_score_codex":0.0004904402,"about_ca_system_score_gemma":0.0006739284,"threshold_uncertainty_score":0.004399836},"labels":[],"label_agreement":null},{"id":"W3088355966","doi":"10.3390/s20195541","title":"Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Chongqing Municipal Education Commission; Chongqing Research Program of Basic Research and Frontier Technology; National Natural Science Foundation of China","keywords":"Hankel matrix; Singular value decomposition; Rank (graph theory); Singular value; Fault (geology); Algorithm; Computer science; Feature (linguistics); Bearing (navigation); Pattern recognition (psychology); Matrix (chemical analysis); Matrix decomposition; Feature extraction; Artificial intelligence; Mathematics; Physics","score_opus":0.012356864755249171,"score_gpt":0.26218135430902917,"score_spread":0.24982448955378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088355966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009232546,0.00013526875,0.9897826,0.0001091878,0.000018979097,0.0000146635875,0.000027294971,0.00018450912,0.00049497327],"genre_scores_gemma":[0.47267094,0.0005308496,0.5240852,0.00012831388,0.00011711067,0.00007686418,0.0002750633,0.00006675005,0.0020490123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996836,0.00007209258,0.000016044478,0.000047261892,0.00015355846,0.000027324793],"domain_scores_gemma":[0.9995442,0.0001876649,0.000073057934,0.000041561623,0.00012385311,0.000029639823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042949026,0.0005046718,0.0004600179,0.0007539076,0.00021885114,0.00040477136,0.00037592152,0.00057286595,0.000957371],"category_scores_gemma":[0.0014403146,0.00020658138,0.0004327014,0.0005836892,0.0003493137,0.00067477644,0.00054056675,0.000690402,0.0002880715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026324592,0.000093501745,0.0016603164,0.0003289473,0.000061020673,0.00029481048,0.00023548832,0.37127522,0.09057216,0.018981237,0.00482592,0.51140815],"study_design_scores_gemma":[0.0000071435516,0.000035812034,0.00027329207,0.0000049023038,0.000007746144,0.00006853812,0.000018354182,0.9905351,0.0051224483,0.0031340069,0.000784071,0.000008609999],"about_ca_topic_score_codex":0.0016155682,"about_ca_topic_score_gemma":0.0019298611,"teacher_disagreement_score":0.0016155682,"about_ca_system_score_codex":0.00023114847,"about_ca_system_score_gemma":0.00060721446,"threshold_uncertainty_score":0.0032123327},"labels":[],"label_agreement":null},{"id":"W3088620281","doi":"10.3390/s20195573","title":"Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Kinematics; Wearable computer; Simulation; Computer science; Perceived exertion; Muscle fatigue; Artificial intelligence; Physical medicine and rehabilitation; Electromyography; Medicine; Embedded system","score_opus":0.060614327258943536,"score_gpt":0.2743200975897523,"score_spread":0.21370577033080876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088620281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82322246,0.0017370008,0.17226084,0.000112922666,0.000088314395,0.00009973652,0.00024516572,0.000233747,0.001999839],"genre_scores_gemma":[0.97145736,0.00055947714,0.027090875,0.000069791626,0.000023905252,0.00004865007,0.000085201325,0.000011402279,0.0006534553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997402,0.000068281486,0.000017877028,0.00006869619,0.000084810505,0.00002008443],"domain_scores_gemma":[0.9997737,0.00007609968,0.00006116081,0.000020387939,0.000054405125,0.0000143854495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032320933,0.00046117266,0.00037519244,0.0004133382,0.00014717219,0.0003296052,0.00024472698,0.00047551107,0.00075592264],"category_scores_gemma":[0.000838881,0.00013293377,0.00026220732,0.00047854806,0.00016974886,0.00052273454,0.00028066416,0.00015925472,0.00014921732],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011124269,0.0002679009,0.04044535,0.00074985484,0.00012705143,0.00041139274,0.000492163,0.014758839,0.66149515,0.0004016383,0.00055733835,0.27918085],"study_design_scores_gemma":[0.00011897692,0.006655718,0.39379203,0.00037897212,0.0003700803,0.0035359564,0.0011950862,0.24659601,0.33797294,0.0030137196,0.006130286,0.00024026712],"about_ca_topic_score_codex":0.00046394803,"about_ca_topic_score_gemma":0.0009936616,"teacher_disagreement_score":0.00075592264,"about_ca_system_score_codex":0.00008661471,"about_ca_system_score_gemma":0.00008336294,"threshold_uncertainty_score":0.0025288463},"labels":[],"label_agreement":null},{"id":"W3088783789","doi":"10.3390/s20195546","title":"Non-Destructive Assessment of Chicken Egg Fertility","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Fertility; Hatching; Hyperspectral imaging; Biology; Agriculture; Biotechnology; Ecology; Artificial intelligence; Computer science; Population; Demography","score_opus":0.03675032404445038,"score_gpt":0.37129876012129176,"score_spread":0.33454843607684137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088783789","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022360166,0.98924595,0.0046724477,0.0002866783,0.00022888597,0.000015917618,0.000061664425,0.000033039025,0.0032194369],"genre_scores_gemma":[0.017678054,0.975999,0.0032384417,0.00021918128,0.00018685026,0.000024554034,0.000099495825,0.000007139616,0.0025472788],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996829,0.000040674135,0.000020822354,0.0000855211,0.00014834102,0.000021713733],"domain_scores_gemma":[0.99965346,0.00016197696,0.000054733187,0.000012407479,0.000105742125,0.000011710576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063367566,0.0008142215,0.00074895774,0.001866512,0.00014594104,0.00060055556,0.00063508016,0.00082293747,0.00095417514],"category_scores_gemma":[0.00063182425,0.00035413186,0.00059664116,0.001147654,0.00039787102,0.000763516,0.00034698404,0.00072869594,0.00058923126],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005616683,0.00007216807,0.0010651107,0.01361874,0.0001609215,0.00014678517,0.000061563594,0.0017103732,0.023830405,0.0038151904,0.008816565,0.9466461],"study_design_scores_gemma":[0.000014342617,0.00055473036,0.011638958,0.003664045,0.00054396404,0.003204594,0.00027575647,0.007297129,0.06006805,0.0052387924,0.90731704,0.00018250966],"about_ca_topic_score_codex":0.0013853256,"about_ca_topic_score_gemma":0.0020388616,"teacher_disagreement_score":0.001866512,"about_ca_system_score_codex":0.00034774988,"about_ca_system_score_gemma":0.00040777755,"threshold_uncertainty_score":0.0033512115},"labels":[],"label_agreement":null},{"id":"W3088789516","doi":"10.3390/s20195559","title":"Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center","keywords":"Computer science; Convolutional neural network; Utterance; Spectrogram; Speech recognition; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Bag-of-words model in computer vision; Channel (broadcasting); Generalization; Histogram; Natural language processing; Visual Word; Image (mathematics); Image retrieval; Mathematics","score_opus":0.05612792339920404,"score_gpt":0.36330295506495563,"score_spread":0.3071750316657516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088789516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35505044,0.007591441,0.59653425,0.0006375507,0.0013135284,0.00039437995,0.0025805465,0.019182771,0.01671508],"genre_scores_gemma":[0.8701001,0.0012270557,0.10384348,0.000822935,0.00024052356,0.00024771434,0.007949414,0.00037749813,0.015191262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994351,0.000058563437,0.000031302778,0.0002394339,0.00012795602,0.00010752836],"domain_scores_gemma":[0.9996321,0.00007609471,0.00003109793,0.00007185468,0.0001582653,0.000030607338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060447026,0.0020248727,0.0010100838,0.001176951,0.00026706848,0.0006385007,0.0011087577,0.00072016823,0.0025576232],"category_scores_gemma":[0.001213043,0.00031323338,0.00076095044,0.0006323936,0.00027178795,0.0013488542,0.0012795421,0.0008601651,0.0016151122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006632749,0.00038273103,0.0034536729,0.00018355765,0.0002303664,0.00023470305,0.000105664134,0.02527022,0.06404507,0.0008202214,0.011998846,0.8926116],"study_design_scores_gemma":[0.00004487735,0.00036371374,0.007405548,0.00003974556,0.00020847279,0.00023804569,0.00014291756,0.9449664,0.038031504,0.0024154952,0.006091687,0.0000516154],"about_ca_topic_score_codex":0.00945526,"about_ca_topic_score_gemma":0.014646295,"teacher_disagreement_score":0.00945526,"about_ca_system_score_codex":0.0006835359,"about_ca_system_score_gemma":0.0005182909,"threshold_uncertainty_score":0.018800437},"labels":[],"label_agreement":null},{"id":"W3088956855","doi":"10.3390/s20185442","title":"Canadian Biomass Burning Aerosol Properties Modification during a Long-Ranged Event on August 2018","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"NASA Headquarters; National Oceanic and Atmospheric Administration; Langley Research Center; National Aeronautics and Space Administration","keywords":"Aerosol; Angstrom exponent; Atmospheric sciences; Environmental science; Smoke; Lidar; Depolarization ratio; Altitude (triangle); Mineral dust; Satellite; Radiative forcing; Biomass burning; Plume; Biomass (ecology); Meteorology; Remote sensing; Geology; Geography; Physics; Oceanography","score_opus":0.02910379550164775,"score_gpt":0.20430832926433246,"score_spread":0.17520453376268472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088956855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964174,0.00016677254,0.00009910589,0.00005652967,0.000013825606,0.000013280608,0.0010396495,0.000018677114,0.0021747986],"genre_scores_gemma":[0.99732995,0.0001221156,0.00014888412,0.000024904488,0.000008109358,0.000008694058,0.0013280082,0.000005118558,0.0010242747],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982554,0.000003374047,0.0000032145924,0.00002938049,0.00007851507,0.000060010207],"domain_scores_gemma":[0.99975795,0.000007865541,0.000023635801,0.000007277156,0.00015885125,0.000044445158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108001244,0.0003373644,0.0002875408,0.0006873934,0.0012397256,0.0006824365,0.0003943656,0.00035942587,0.0008936023],"category_scores_gemma":[0.00027360168,0.00011490795,0.00021749796,0.0009509198,0.00025204563,0.00019284747,0.00039061316,0.00031646225,0.00014767503],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004654008,0.00010663875,0.93820107,0.00009398872,0.00011834866,0.0016414411,0.0011009646,0.0015251132,0.03486453,0.00024899983,0.0028923699,0.018741108],"study_design_scores_gemma":[0.0000027105082,0.000010972252,0.99625564,0.000005953791,0.00001237846,0.00005977752,0.00027572893,0.0004896503,0.0008858366,0.000008057768,0.001986718,0.000006555694],"about_ca_topic_score_codex":0.856423,"about_ca_topic_score_gemma":0.93013144,"teacher_disagreement_score":0.14357698,"about_ca_system_score_codex":0.0048836833,"about_ca_system_score_gemma":0.0030118562,"threshold_uncertainty_score":0.288845},"labels":[],"label_agreement":null},{"id":"W3089214336","doi":"10.3390/s20195487","title":"The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Fund for Distinguished Young Scholars; National Key Research and Development Program of China; Leading Edge Endowment Fund; National Natural Science Foundation of China","keywords":"Electroencephalography; Physical medicine and rehabilitation; Rehabilitation; Motor function; Computer science; Robustness (evolution); Convolutional neural network; Artificial intelligence; Psychology; Physical therapy; Medicine; Neuroscience","score_opus":0.10683624498655538,"score_gpt":0.3374622666205001,"score_spread":0.23062602163394474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089214336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.606793,0.0020826089,0.3795782,0.0006739954,0.00034300477,0.0006253734,0.0028961878,0.001197488,0.005810101],"genre_scores_gemma":[0.949375,0.0009134381,0.045899697,0.00014544209,0.00010875743,0.000470732,0.0014363129,0.00006209868,0.0015885152],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993424,0.0002484019,0.00005752811,0.00016795822,0.00014631204,0.000037457965],"domain_scores_gemma":[0.9987185,0.0004657181,0.00013528389,0.00016364605,0.00046881606,0.000048039503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015856684,0.0005973829,0.0003111983,0.00041234854,0.00015093309,0.0005214407,0.0003272082,0.000434258,0.0015768816],"category_scores_gemma":[0.004550756,0.00015536723,0.00021752014,0.00032481994,0.00023734567,0.0006934392,0.0003496932,0.0004095999,0.00053236476],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028526385,0.00089960604,0.18226293,0.0009289684,0.0004751349,0.00054539257,0.00055331015,0.012920389,0.22141887,0.0012411005,0.0045625977,0.5713391],"study_design_scores_gemma":[0.00029743262,0.0050506536,0.6293438,0.00036067035,0.00059739914,0.0025382768,0.0007195845,0.21195322,0.13057743,0.004694601,0.013711728,0.00015524353],"about_ca_topic_score_codex":0.0012832383,"about_ca_topic_score_gemma":0.002710013,"teacher_disagreement_score":0.0015856684,"about_ca_system_score_codex":0.00012736786,"about_ca_system_score_gemma":0.0003734763,"threshold_uncertainty_score":0.008385897},"labels":[],"label_agreement":null},{"id":"W3089601434","doi":"10.3390/s20195655","title":"A Novel Hardware–Software Co-Design and Implementation of the HOG Algorithm","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria","keywords":"Computer science; Algorithm; Histogram; Field-programmable gate array; Software; Hardware acceleration; Computation; Normalization (sociology); Computer hardware; Frame rate; Artificial intelligence; Image (mathematics)","score_opus":0.015592308392946234,"score_gpt":0.23994745386045743,"score_spread":0.2243551454675112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089601434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0111978315,0.0003069397,0.97283226,0.0001489003,0.00021033222,0.0003229971,0.00011846057,0.009781837,0.0050805495],"genre_scores_gemma":[0.3136089,0.00026492414,0.6703122,0.0005521738,0.00015260823,0.000709722,0.000571571,0.0006480594,0.013179896],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991134,0.0000685642,0.000062302075,0.00020464294,0.0004056404,0.0001454128],"domain_scores_gemma":[0.9994136,0.000074773125,0.000071634204,0.000112978036,0.0002768394,0.000050218783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003921313,0.00085412257,0.00056826684,0.0009327187,0.0003850128,0.0010018011,0.0030458085,0.00062396814,0.00882676],"category_scores_gemma":[0.0009293522,0.00056079763,0.00042593392,0.0005799035,0.00029230939,0.0011425851,0.0007880383,0.0008735508,0.0035616206],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000799867,0.00064614293,0.0027499313,0.0006199063,0.00021730294,0.0005570435,0.00019766796,0.017792279,0.25948176,0.011407984,0.024283443,0.68124676],"study_design_scores_gemma":[0.0004057936,0.0019400629,0.0037731512,0.000098533674,0.00020290387,0.0022822637,0.000075069715,0.51192176,0.3658867,0.0038647368,0.10938353,0.00016542159],"about_ca_topic_score_codex":0.0014830403,"about_ca_topic_score_gemma":0.0020108875,"teacher_disagreement_score":0.00882676,"about_ca_system_score_codex":0.00056209613,"about_ca_system_score_gemma":0.0011546324,"threshold_uncertainty_score":0.029528439},"labels":[],"label_agreement":null},{"id":"W3090386704","doi":"10.3390/s20195689","title":"Particle Filter for Randomly Delayed Measurements with Unknown Latency Probability","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Probability density function; Recursion (computer science); Latency (audio); Control theory (sociology); Particle filter; Filter (signal processing); Noise (video); Algorithm; Mathematics; Convergence (economics); Computer science; Applied mathematics; Statistics; Kalman filter; Artificial intelligence; Telecommunications","score_opus":0.06808194585835943,"score_gpt":0.24597077906044293,"score_spread":0.17788883320208349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090386704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002982428,0.00010275045,0.9963857,0.00007031908,0.00003269296,0.000011872582,0.000016111717,0.00007675336,0.00032140047],"genre_scores_gemma":[0.5633097,0.0013477529,0.42491853,0.00020832868,0.00018455542,0.00029564614,0.00032652155,0.00008320556,0.009325743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945015,0.00011795574,0.000029623327,0.00015381146,0.00018593286,0.000062451836],"domain_scores_gemma":[0.99920446,0.0004268049,0.00011312807,0.00006264601,0.00017137438,0.000021596576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009943026,0.0007859872,0.00091310777,0.0004784546,0.00041969772,0.0007649689,0.0010902497,0.0013911587,0.00096784503],"category_scores_gemma":[0.002970784,0.00052227656,0.00074379327,0.0007994,0.00066332455,0.0015306291,0.0007911925,0.0016337546,0.00027462377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079458994,0.000028070426,0.0006746891,0.00012289632,0.000037195678,0.000105537045,0.00008289301,0.92867756,0.004096313,0.021449545,0.00083241775,0.043813482],"study_design_scores_gemma":[0.0000052333216,0.000011119652,0.00008319062,0.0000030315769,0.0000036255137,0.000011282155,0.000004123986,0.99751306,0.00044663745,0.0015213236,0.00039289877,0.0000044831886],"about_ca_topic_score_codex":0.010931449,"about_ca_topic_score_gemma":0.0060570515,"teacher_disagreement_score":0.010931449,"about_ca_system_score_codex":0.0009908085,"about_ca_system_score_gemma":0.0017508826,"threshold_uncertainty_score":0.021735668},"labels":[],"label_agreement":null},{"id":"W3091328183","doi":"10.3390/s20195657","title":"A Real-Time Thermal Monitoring System Intended for Embedded Sensors Interfaces","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Thermal properties of materials","field":"Materials Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec en Outaouais","funders":"","keywords":"Overheating (electricity); Integrated circuit; Microelectronics; Printed circuit board; Thermal; Electronic circuit; Electronic engineering; Computer science; Heat transfer; Chip; Embedded system; Engineering; Electrical engineering","score_opus":0.02969706762929608,"score_gpt":0.2504748935009035,"score_spread":0.22077782587160744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091328183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038591925,0.0003236585,0.9468057,0.00007849188,0.00016468861,0.00016379189,0.00017714214,0.010584871,0.0031097822],"genre_scores_gemma":[0.55455005,0.00022887134,0.43726397,0.00020631359,0.00009066529,0.00033744553,0.00034641262,0.00043527625,0.0065409634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994259,0.000089063695,0.000024039779,0.00016437077,0.00026603814,0.00003063615],"domain_scores_gemma":[0.99956113,0.00010436858,0.00006285662,0.000093784794,0.00015182786,0.000025923906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039953005,0.00071457605,0.0005521165,0.0005133538,0.00019047379,0.0005243661,0.0013272618,0.0005707929,0.003416857],"category_scores_gemma":[0.00090156955,0.00023548875,0.00023561473,0.00024463484,0.00021093636,0.0005881461,0.00030111574,0.0004418348,0.0010215056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072823925,0.0002590829,0.003921865,0.00050647807,0.00007301319,0.00024138865,0.00027991028,0.022120459,0.5748141,0.004432627,0.005481216,0.38714162],"study_design_scores_gemma":[0.00011749398,0.00094274204,0.0060153566,0.000058061374,0.000103226725,0.0008439349,0.00004885115,0.5382429,0.41619202,0.000863662,0.036483318,0.00008845538],"about_ca_topic_score_codex":0.0003577312,"about_ca_topic_score_gemma":0.00039359808,"teacher_disagreement_score":0.003416857,"about_ca_system_score_codex":0.00028599115,"about_ca_system_score_gemma":0.00028807452,"threshold_uncertainty_score":0.011430562},"labels":[],"label_agreement":null},{"id":"W3091533160","doi":"10.3390/s20195574","title":"Hardware Impaired Self-Energized Bidirectional Sensor Networks over Complex Fading Channels","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Fading; Rician fading; Throughput; Computer science; Energy harvesting; Path loss; Wireless sensor network; Context (archaeology); Wireless; Energy (signal processing); Electronic engineering; Computer network; Channel (broadcasting); Telecommunications; Engineering; Mathematics; Statistics","score_opus":0.02161220797257123,"score_gpt":0.21501882926396404,"score_spread":0.1934066212913928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091533160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69414407,0.00035004236,0.2997316,0.000101250786,0.000023186338,0.000018204617,0.00010729662,0.00019317758,0.005331272],"genre_scores_gemma":[0.9966774,0.00017737166,0.00259616,0.000006868355,0.0000028829743,0.0000044584813,0.000017090892,0.0000046561277,0.00051320303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987054,0.000035003548,0.000006052603,0.000020426523,0.000045892408,0.00002203973],"domain_scores_gemma":[0.9996928,0.00013565744,0.00006310126,0.000042175918,0.00005604682,0.000010342881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020655832,0.0002710794,0.00020378146,0.00014085784,0.00017659504,0.00036565744,0.00025675565,0.00020532361,0.0003266699],"category_scores_gemma":[0.00062153436,0.00006522249,0.00011374822,0.00022486852,0.00046303374,0.0004332572,0.0003167484,0.00024640944,0.00007926259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011488403,0.000035100864,0.0030630587,0.00013295097,0.00003607582,0.00053833687,0.0003056729,0.856597,0.08715436,0.028245568,0.0002955514,0.023481525],"study_design_scores_gemma":[0.00000304601,0.000050550643,0.0011465895,0.0000060776783,0.00001122486,0.00014601795,0.000075478725,0.97857064,0.0134936515,0.0058849305,0.00060072495,0.00001107225],"about_ca_topic_score_codex":0.0007607399,"about_ca_topic_score_gemma":0.0008447679,"teacher_disagreement_score":0.0007607399,"about_ca_system_score_codex":0.00026992915,"about_ca_system_score_gemma":0.00021347885,"threshold_uncertainty_score":0.001958549},"labels":[],"label_agreement":null},{"id":"W3092021092","doi":"10.3390/s20195722","title":"Evaluation of Inertial Sensor Data by a Comparison with Optical Motion Capture Data of Guitar Strumming Gestures","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Motion capture; Gesture; Computer science; Inertial measurement unit; Flexibility (engineering); Acceleration; Data acquisition; Computer vision; Motion (physics); Artificial intelligence; Mathematics","score_opus":0.1227566141360068,"score_gpt":0.3237782535071972,"score_spread":0.20102163937119039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092021092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8290773,0.0008562571,0.16180734,0.00012583687,0.000258453,0.00020404079,0.0014656436,0.0012660533,0.0049391487],"genre_scores_gemma":[0.9422938,0.00032053492,0.05375483,0.00006246037,0.000053611882,0.00017899164,0.0018127183,0.000102680046,0.0014203211],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829525,0.000300952,0.00017971419,0.00031359904,0.0008072387,0.000103236125],"domain_scores_gemma":[0.9969674,0.0012913286,0.0002913758,0.00030600466,0.0010913279,0.000052533574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015953203,0.00079558213,0.00042064357,0.0022291935,0.00022389338,0.0006935856,0.00037386996,0.0005814728,0.0016300024],"category_scores_gemma":[0.008911956,0.00014091923,0.00032622175,0.0015415876,0.00027258854,0.00061638793,0.0005105279,0.00016361472,0.000633865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018811926,0.00040927716,0.07842909,0.0012018066,0.0003600149,0.0004498221,0.0008699197,0.03662731,0.3334719,0.0012629644,0.0019167167,0.54312],"study_design_scores_gemma":[0.00014603462,0.0022754273,0.5465442,0.00017026413,0.00030161493,0.00096078945,0.00083561137,0.22788772,0.21042457,0.0004653391,0.009846806,0.00014170841],"about_ca_topic_score_codex":0.0017795635,"about_ca_topic_score_gemma":0.0023046767,"teacher_disagreement_score":0.0022291935,"about_ca_system_score_codex":0.00019888574,"about_ca_system_score_gemma":0.0002939014,"threshold_uncertainty_score":0.008436978},"labels":[],"label_agreement":null},{"id":"W3092821014","doi":"10.3390/s20205777","title":"Study of Three Interface Pressure Measurement Systems Used in the Treatment of Venous Disease","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Diagnosis and Treatment of Venous Diseases","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Repeatability; Pressure measurement; Pressure sensor; Compression Bandage; Piezoresistive effect; Interface (matter); Biomedical engineering; Linearity; Compression (physics); System of measurement; Work (physics); Pressure system; Simulation; Medicine; Computer science; Materials science; Engineering; Mechanical engineering; Electronic engineering; Electrical engineering; Composite material; Mathematics","score_opus":0.10804207612945219,"score_gpt":0.3044558478983926,"score_spread":0.1964137717689404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092821014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8726577,0.014321542,0.107322134,0.00049918494,0.0005265479,0.0003347552,0.0005031231,0.0004961313,0.0033389593],"genre_scores_gemma":[0.94174314,0.0028346193,0.05298728,0.00024670525,0.00014892589,0.00017642161,0.0003541109,0.000054405333,0.001454399],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9925452,0.001989913,0.0004513341,0.00088995387,0.0038873404,0.00023622396],"domain_scores_gemma":[0.9936534,0.0029586505,0.0009080617,0.00048592204,0.0018879599,0.0001058801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035238387,0.00060115027,0.0006514352,0.00091763644,0.00033480642,0.0011070131,0.0008795652,0.0011747059,0.00083058747],"category_scores_gemma":[0.009237414,0.0003138719,0.00057458325,0.001179761,0.00050713937,0.00081167714,0.000603403,0.00071290584,0.0003838234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013005538,0.0003987631,0.023974117,0.0012351963,0.00022729473,0.00039294283,0.00086144335,0.0013161617,0.79727197,0.0006641746,0.0006223426,0.17173497],"study_design_scores_gemma":[0.000055429693,0.009902275,0.062777855,0.00011207425,0.00044616594,0.0024395417,0.00054669124,0.011234886,0.9042815,0.0002509022,0.007834588,0.00011805801],"about_ca_topic_score_codex":0.0006021406,"about_ca_topic_score_gemma":0.0005251497,"teacher_disagreement_score":0.0035238387,"about_ca_system_score_codex":0.0003078041,"about_ca_system_score_gemma":0.00040887482,"threshold_uncertainty_score":0.018636107},"labels":[],"label_agreement":null},{"id":"W3093055699","doi":"10.3390/s20205866","title":"Multi-Constellation Software-Defined Receiver for Doppler Positioning with LEO Satellites","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Doppler effect; Satellite; Computer science; Remote sensing; Orbit determination; Constellation; Telecommunications link; Extended Kalman filter; Satellite constellation; Transmitter; Global Positioning System; Kalman filter; Telecommunications; Physics; Engineering; Geography; Artificial intelligence; Aerospace engineering","score_opus":0.023892022877797563,"score_gpt":0.21311513048725328,"score_spread":0.1892231076094557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093055699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05346026,0.00039863228,0.93860096,0.00008101513,0.00012348103,0.00011461678,0.00008735748,0.0030607162,0.0040729395],"genre_scores_gemma":[0.69195265,0.000167174,0.3007372,0.00013195525,0.00008245682,0.00022197641,0.0004313644,0.00013894333,0.0061361943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991196,0.00016562844,0.000040087172,0.00014205795,0.00046334887,0.00006925781],"domain_scores_gemma":[0.9992767,0.000113453796,0.00010020659,0.00013485196,0.0003413657,0.000033442917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007605612,0.00048169584,0.0005354015,0.00075783435,0.0002623131,0.00045644012,0.0010168793,0.00045295185,0.0017737445],"category_scores_gemma":[0.0010846406,0.00020083807,0.00028864417,0.00042490245,0.00021316866,0.0004325296,0.00042747942,0.0005416919,0.0011205877],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093241845,0.0002851613,0.015844071,0.00041734948,0.0001537623,0.00032884153,0.0005615821,0.03743667,0.49101636,0.010245127,0.004739809,0.43803886],"study_design_scores_gemma":[0.00027851792,0.0019595942,0.010189483,0.000088878696,0.00019508526,0.0013444788,0.00009801219,0.5414813,0.38194582,0.0010818703,0.061195653,0.00014129108],"about_ca_topic_score_codex":0.0008582237,"about_ca_topic_score_gemma":0.00084666687,"teacher_disagreement_score":0.0017737445,"about_ca_system_score_codex":0.00036327567,"about_ca_system_score_gemma":0.00068098644,"threshold_uncertainty_score":0.0059337616},"labels":[],"label_agreement":null},{"id":"W3093140226","doi":"10.3390/s20205882","title":"Power-Saving Design of Radio Frequency Identification Sensor Networks in Bus Seatbelt Monitoring Systems","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"RFID technology advancements","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Radio-frequency identification; Concentrator; Identification (biology); Software; Node (physics); Embedded system; Power (physics); Engineering; Wireless sensor network; Computer science; Software design; Real-time computing; Computer hardware; Electrical engineering; Computer network; Computer security; Software development; Operating system","score_opus":0.01746271121188555,"score_gpt":0.22273062310745956,"score_spread":0.205267911895574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093140226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0648032,0.000889111,0.92335874,0.00033281144,0.000104719475,0.00013328991,0.000052551008,0.00073833996,0.009587292],"genre_scores_gemma":[0.89099014,0.000520529,0.10258935,0.00012695167,0.00004847454,0.00015867413,0.000064632135,0.000054601172,0.005446692],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967563,0.00009177177,0.000017948301,0.00007612894,0.00010503229,0.000033441145],"domain_scores_gemma":[0.9997689,0.00004048409,0.000056459197,0.00002131816,0.00010082175,0.0000119462975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028518058,0.00041401133,0.00026080312,0.0002527401,0.00025203553,0.0004166899,0.0009623161,0.0003254281,0.0012494856],"category_scores_gemma":[0.00036917182,0.00024706297,0.00022736353,0.00016621462,0.00018619558,0.00063713023,0.00023456103,0.00022126698,0.00039937694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046725993,0.00018142264,0.0028315259,0.0007549626,0.00015757719,0.00041351325,0.00041331962,0.26551458,0.48058563,0.021039948,0.0046519726,0.22298822],"study_design_scores_gemma":[0.00004326195,0.0009004637,0.0021397702,0.000046284273,0.00009018302,0.00037435637,0.00008828131,0.8071768,0.1614355,0.0030799825,0.024591044,0.00003407874],"about_ca_topic_score_codex":0.00046388793,"about_ca_topic_score_gemma":0.0011171529,"teacher_disagreement_score":0.0012494856,"about_ca_system_score_codex":0.0005450293,"about_ca_system_score_gemma":0.00031963724,"threshold_uncertainty_score":0.0041799545},"labels":[],"label_agreement":null},{"id":"W3093511015","doi":"10.3390/s20215991","title":"Policy-Gradient and Actor-Critic Based State Representation Learning for Safe Driving of Autonomous Vehicles","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Computer science; Representation (politics); Artificial intelligence; Reinforcement learning; Perception; Scheme (mathematics); Object (grammar); Function (biology); Deep learning; Machine learning; Simulation; Mathematics; Psychology; Law","score_opus":0.02501525026013139,"score_gpt":0.2744572081329379,"score_spread":0.24944195787280649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093511015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017363431,0.0001983119,0.98031473,0.00020176632,0.000042258933,0.000025821973,0.000024244964,0.0004875186,0.0013419504],"genre_scores_gemma":[0.92115957,0.0001176712,0.07542698,0.00012270366,0.000029867188,0.00008359972,0.000079164434,0.00006764691,0.0029128222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963105,0.00010443641,0.00001612687,0.000101230005,0.00008642475,0.00006077569],"domain_scores_gemma":[0.9993511,0.00029938426,0.00009253606,0.00005277813,0.0001492943,0.00005486665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090862514,0.0007567728,0.00073190575,0.00033734366,0.00027297347,0.00069178874,0.001026065,0.00089644076,0.0011568514],"category_scores_gemma":[0.0022956282,0.0005027005,0.0004257413,0.00027060116,0.0007911104,0.0008641977,0.0008799076,0.0014094663,0.00027348037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003425473,0.000030647494,0.00041364113,0.000027322416,0.00002000087,0.000039073107,0.000040297935,0.9680609,0.0015613043,0.004843573,0.0004498851,0.024479112],"study_design_scores_gemma":[0.000001702205,0.000009291646,0.000028795175,0.0000012566004,0.0000011497283,0.0000029026926,0.0000015882162,0.9988568,0.00017305418,0.0008363866,0.00008538989,0.0000015655045],"about_ca_topic_score_codex":0.006227026,"about_ca_topic_score_gemma":0.004945404,"teacher_disagreement_score":0.006227026,"about_ca_system_score_codex":0.00086655305,"about_ca_system_score_gemma":0.0014442173,"threshold_uncertainty_score":0.012381554},"labels":[],"label_agreement":null},{"id":"W3093664364","doi":"10.3390/s20216070","title":"Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Bounding overwatch; Artificial intelligence; Minimum bounding box; Object detection; Intersection (aeronautics); Convolutional neural network; Pixel; Sample (material); Aerial imagery; Computer vision; Task (project management); Object (grammar); Aerial image; Deep learning; Pattern recognition (psychology); Image (mathematics); Geography; Cartography; Engineering","score_opus":0.03114746921412371,"score_gpt":0.2789104352584913,"score_spread":0.2477629660443676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093664364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2498493,0.0009736953,0.7280068,0.00034959617,0.0002150491,0.00015503653,0.0011991068,0.011892937,0.0073585487],"genre_scores_gemma":[0.78054065,0.00030826696,0.20893988,0.0002450947,0.000054370958,0.000068656715,0.0029605203,0.00019969337,0.00668284],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973756,0.000023896915,0.000010870188,0.000098597586,0.00006910669,0.000059962935],"domain_scores_gemma":[0.99973136,0.000042726642,0.00003235796,0.00005468919,0.00011589721,0.000022973145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033318918,0.0011217641,0.0006444225,0.0012876788,0.00025031154,0.0007144439,0.0010524964,0.0007970022,0.002291724],"category_scores_gemma":[0.0008537601,0.00036864274,0.0007106809,0.00074636005,0.0003367816,0.00068654394,0.00085977267,0.0006609185,0.0012960235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003038919,0.00016915261,0.006783935,0.00018413899,0.00012487744,0.0002712782,0.00011946905,0.28115094,0.025063813,0.0016305168,0.0108493585,0.6733486],"study_design_scores_gemma":[0.000010954219,0.000036059282,0.0014745154,0.0000147985165,0.0000160155,0.00006378882,0.00004146228,0.99205667,0.0042469543,0.00074432837,0.0012867398,0.000007798492],"about_ca_topic_score_codex":0.011751581,"about_ca_topic_score_gemma":0.019059602,"teacher_disagreement_score":0.011751581,"about_ca_system_score_codex":0.0004916026,"about_ca_system_score_gemma":0.00084279454,"threshold_uncertainty_score":0.023366392},"labels":[],"label_agreement":null},{"id":"W3093830561","doi":"10.3390/s20205919","title":"New Method and Portable Measurement Device for the Calibration of Industrial Robots","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Canada Research Chairs","keywords":"Laser tracker; Robot; Calibration; Robot end effector; Geodetic datum; Computer vision; Artificial intelligence; Industrial robot; Robot calibration; Position (finance); Engineering; Computer science; Simulation; Laser; Robot kinematics; Mobile robot; Mathematics; Optics","score_opus":0.11367451516008917,"score_gpt":0.2937295085908522,"score_spread":0.180054993430763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093830561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007255463,0.00062747335,0.98719716,0.00005502969,0.00020261957,0.00018059534,0.0000867307,0.0022572253,0.002137669],"genre_scores_gemma":[0.12732504,0.00056989613,0.8668538,0.00017341545,0.00016211986,0.0005293669,0.0003168603,0.00026326074,0.0038063163],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.991625,0.00077199505,0.00036064704,0.0011789821,0.0058928253,0.00017054868],"domain_scores_gemma":[0.99582267,0.0007212497,0.00060021144,0.001343294,0.0014060119,0.00010662363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018003801,0.0015231031,0.0011791297,0.0027903162,0.0004624737,0.0009679497,0.0024971352,0.001235412,0.0035044427],"category_scores_gemma":[0.0046611987,0.000729948,0.00068585045,0.0018308382,0.00088538,0.0013963844,0.0016965623,0.0013323411,0.002104346],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026269254,0.00019281986,0.0048801247,0.0011762682,0.00010278392,0.00027653432,0.0003860386,0.0064657927,0.3403226,0.005964293,0.006166817,0.6338032],"study_design_scores_gemma":[0.0002732198,0.002968958,0.02431444,0.00038571344,0.00035765863,0.0073804846,0.00028825252,0.10947358,0.65228415,0.0030421342,0.19861604,0.00061533815],"about_ca_topic_score_codex":0.00056886673,"about_ca_topic_score_gemma":0.00062269944,"teacher_disagreement_score":0.0035044427,"about_ca_system_score_codex":0.000554365,"about_ca_system_score_gemma":0.0009060705,"threshold_uncertainty_score":0.011723578},"labels":[],"label_agreement":null},{"id":"W3094094799","doi":"10.3390/s20205940","title":"An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; National Research Council Canada; Defence Research and Development Canada","funders":"","keywords":"Drone; Radar; Context (archaeology); Propeller; Doppler effect; Engineering; Computer science; Acoustics; Electronic engineering; Aerospace engineering; Marine engineering; Physics; Geography","score_opus":0.01805957038011774,"score_gpt":0.24972124090676567,"score_spread":0.23166167052664793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094094799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98435915,0.00033896687,0.012011216,0.000043017077,0.00003089222,0.000015571195,0.00016701258,0.00009639233,0.00293788],"genre_scores_gemma":[0.99559385,0.00019104652,0.0034388457,0.000023654391,0.000012797893,0.0000070391798,0.00011562158,0.000012156269,0.0006049515],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99989796,0.000013929689,0.0000028625086,0.000025466976,0.000040477036,0.000019295114],"domain_scores_gemma":[0.9997408,0.00008979222,0.0000529201,0.000019469853,0.00007720425,0.000019833233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011263808,0.00023332865,0.0001994334,0.00077565043,0.00018520262,0.00022591962,0.00017395086,0.0003874678,0.0010626465],"category_scores_gemma":[0.00037518365,0.000073094554,0.00009661089,0.00039021327,0.00023644084,0.0002970675,0.00013853068,0.0002643986,0.00026654723],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004596974,0.00011023566,0.029552625,0.00022068177,0.000040790037,0.001178968,0.000584734,0.0037257976,0.8926817,0.00041190154,0.0007038946,0.07032897],"study_design_scores_gemma":[0.000034365603,0.0016193539,0.50523907,0.00009786656,0.00009854285,0.003371054,0.0027064108,0.076269396,0.40231252,0.00086127094,0.007291045,0.00009913811],"about_ca_topic_score_codex":0.0005339381,"about_ca_topic_score_gemma":0.001037471,"teacher_disagreement_score":0.0010626465,"about_ca_system_score_codex":0.00007047133,"about_ca_system_score_gemma":0.000054464377,"threshold_uncertainty_score":0.0035549402},"labels":[],"label_agreement":null},{"id":"W3094102925","doi":"10.3390/s20216006","title":"Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"China Scholarship Council; Agriculture and Agri-Food Canada; Department of Education, Fujian Province; National Natural Science Foundation of China","keywords":"Leaf area index; Data assimilation; Multispectral image; Remote sensing; Environmental science; Mean squared error; Growing season; Scale (ratio); Mathematics; Meteorology; Agronomy; Geography; Statistics; Cartography","score_opus":0.04014866636613158,"score_gpt":0.2754612758633959,"score_spread":0.23531260949726435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094102925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8204359,0.00035530323,0.17581764,0.00007082142,0.000050299856,0.00005805035,0.0005137986,0.0011487468,0.001549485],"genre_scores_gemma":[0.9406321,0.00009656034,0.058208518,0.000015016689,0.000010624367,0.000034398996,0.0005295689,0.000036365338,0.0004368381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998988,0.000011118368,0.0000066186426,0.00003953946,0.00002882494,0.000014978774],"domain_scores_gemma":[0.999828,0.000038190254,0.000036604262,0.000022575461,0.000062378815,0.000012349234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003137977,0.0006787504,0.00029138618,0.00089303456,0.00018707878,0.00029828332,0.00040368122,0.00034486406,0.00034514364],"category_scores_gemma":[0.00067381765,0.00025309363,0.00073905516,0.00061009725,0.00010487948,0.0005115121,0.0002868002,0.00025203504,0.00012665005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031550342,0.0001579541,0.048171595,0.00018764795,0.00025894627,0.00031650122,0.0002744382,0.6622015,0.05564171,0.00063783425,0.00097647286,0.23085989],"study_design_scores_gemma":[0.000011589226,0.000029292725,0.010607433,0.000004397541,0.000024822579,0.00002762746,0.000030850246,0.9843106,0.0045520714,0.00014509686,0.00024104098,0.000015057881],"about_ca_topic_score_codex":0.0136645455,"about_ca_topic_score_gemma":0.01407752,"teacher_disagreement_score":0.0136645455,"about_ca_system_score_codex":0.00034632295,"about_ca_system_score_gemma":0.0003778158,"threshold_uncertainty_score":0.027170002},"labels":[],"label_agreement":null},{"id":"W3094230952","doi":"10.3390/s20215976","title":"New Multi-Step Iterative Methods for Solving Systems of Nonlinear Equations and Their Application on GNSS Pseudorange Equations","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Iterative Methods for Nonlinear Equations","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Khalifa University of Science, Technology and Research","keywords":"Dilution of precision; Pseudorange; GNSS applications; Computer science; Nonlinear system; Position (finance); Algorithm; Iterative method; Satellite navigation; Satellite system; Mathematical optimization; Precise Point Positioning; Computation; Control theory (sociology); Mathematics; Global Positioning System","score_opus":0.19645392000374845,"score_gpt":0.4366977276897148,"score_spread":0.24024380768596637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094230952","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014509134,0.0002834129,0.9966878,0.000039948667,0.0000442691,0.00004505591,0.00001392009,0.0000905506,0.0013441247],"genre_scores_gemma":[0.041846436,0.00076411455,0.95220166,0.000043272783,0.00004592849,0.00031086864,0.0000797551,0.00008118384,0.004626755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99944824,0.0001577277,0.000041087424,0.000054700664,0.0002681872,0.000029999113],"domain_scores_gemma":[0.9991985,0.00034572426,0.00007057444,0.00005038449,0.00031132135,0.0000235338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010651428,0.0007886423,0.0006229084,0.00069872825,0.00044419337,0.0006303383,0.0010898868,0.0009980558,0.0022791266],"category_scores_gemma":[0.0024282918,0.00042240255,0.0011641579,0.0007355676,0.00048115666,0.00086820347,0.00088060286,0.0017339026,0.0008578766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009699359,0.00011317282,0.0020470547,0.00090769824,0.00014626485,0.00029992804,0.0006948058,0.5325314,0.022757359,0.068842776,0.004234959,0.3673277],"study_design_scores_gemma":[0.000009771557,0.000032256306,0.00018534521,0.00002747952,0.000008003634,0.000074997304,0.00002758865,0.9893021,0.0023359875,0.0025785307,0.005400947,0.000016998529],"about_ca_topic_score_codex":0.004230149,"about_ca_topic_score_gemma":0.0055370447,"teacher_disagreement_score":0.004230149,"about_ca_system_score_codex":0.00046547878,"about_ca_system_score_gemma":0.0013105484,"threshold_uncertainty_score":0.00841105},"labels":[],"label_agreement":null},{"id":"W3094281574","doi":"10.3390/s20205936","title":"Digital Twin Coaching for Physical Activities: A Survey","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coaching; Psychology; Applied psychology; Engineering; Computer science; Medical education; Medicine; Psychotherapist","score_opus":0.059784789513470486,"score_gpt":0.30124055471929057,"score_spread":0.24145576520582007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094281574","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010520128,0.992732,0.00052138645,0.0003925733,0.00017921753,0.000028722454,0.000090206515,0.000017472828,0.0049863546],"genre_scores_gemma":[0.005237061,0.9925211,0.0007018088,0.00029802433,0.00011022642,0.00003379076,0.00011208125,0.000007787381,0.0009780031],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920195,0.00016840678,0.00013620501,0.00015578441,0.00029041056,0.000047267644],"domain_scores_gemma":[0.9978104,0.0015709918,0.00020590145,0.00005212964,0.00029044817,0.00007013853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012012246,0.00071206864,0.0011505088,0.0047751293,0.00035303988,0.0017894615,0.000896477,0.001142788,0.0073018204],"category_scores_gemma":[0.0033160676,0.00033643923,0.00084695336,0.0057351687,0.00054612366,0.0027156803,0.0010434978,0.0009848957,0.0020385096],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006532327,0.000083003964,0.0008867877,0.036806315,0.000096879376,0.000075551616,0.0002289469,0.00016849092,0.000409293,0.0043317345,0.0086383475,0.9482092],"study_design_scores_gemma":[0.000020842683,0.00022474854,0.0070071453,0.03305803,0.0003209644,0.0013137409,0.00089353666,0.00023427836,0.00068725715,0.0023609095,0.9538297,0.00004901594],"about_ca_topic_score_codex":0.002024021,"about_ca_topic_score_gemma":0.0032805451,"teacher_disagreement_score":0.0073018204,"about_ca_system_score_codex":0.0006706,"about_ca_system_score_gemma":0.0013464546,"threshold_uncertainty_score":0.024426997},"labels":[],"label_agreement":null},{"id":"W3094312606","doi":"10.3390/s20216008","title":"Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Discriminative model; Convolutional neural network; Speech recognition; Random forest; Support vector machine; Feature selection; Artificial intelligence; Context (archaeology); Emotion recognition; Feature (linguistics); Feature extraction; Artificial neural network; Emotion classification; Task (project management); Pattern recognition (psychology); Selection (genetic algorithm)","score_opus":0.05434416167619265,"score_gpt":0.32461674049573197,"score_spread":0.2702725788195393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094312606","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69025743,0.0069904625,0.28212103,0.0009945896,0.0009423058,0.000373834,0.0012511252,0.0115076415,0.005561601],"genre_scores_gemma":[0.91334957,0.00075984845,0.07773701,0.00035778157,0.00007474494,0.00023165473,0.003765081,0.00021743443,0.0035069636],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990175,0.00019546047,0.000089865476,0.00025962899,0.00025885442,0.00017856178],"domain_scores_gemma":[0.99904615,0.0004306765,0.000050752165,0.00006772737,0.0003638636,0.000040861563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016911373,0.0018070611,0.0011245913,0.0007290328,0.00040615644,0.00065707037,0.0007999757,0.00065366505,0.0015108681],"category_scores_gemma":[0.0040002684,0.00021329452,0.00068104645,0.00053398317,0.0002270254,0.0009456191,0.00054695824,0.000959831,0.0007172802],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016321378,0.0008116058,0.011126498,0.00020473087,0.00023827373,0.00036891978,0.000094872245,0.07637151,0.030299827,0.00048381544,0.013932843,0.86443496],"study_design_scores_gemma":[0.00008145933,0.00024021542,0.004959562,0.000030302474,0.000093782364,0.0001429488,0.000074720636,0.9659285,0.026360556,0.00042256596,0.0016405919,0.000024826071],"about_ca_topic_score_codex":0.008776374,"about_ca_topic_score_gemma":0.007963705,"teacher_disagreement_score":0.008776374,"about_ca_system_score_codex":0.0005339981,"about_ca_system_score_gemma":0.0009286189,"threshold_uncertainty_score":0.017450571},"labels":[],"label_agreement":null},{"id":"W3094510469","doi":"10.3390/s20205945","title":"Smart Sensors and Devices in Artificial Intelligence","year":2020,"lang":"en","type":"editorial","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Artificial intelligence; Engineering; Computer science; Robotics; Intelligent sensor; Embedded system; Real-time computing; Wireless sensor network; Operating system","score_opus":0.035308097347787344,"score_gpt":0.27945372257220674,"score_spread":0.2441456252244194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094510469","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002608093,0.043241613,0.00044118185,0.04139863,0.9086835,0.000014177278,0.00004588255,0.00008501032,0.006064027],"genre_scores_gemma":[0.00092623534,0.037081204,0.00041022227,0.040201005,0.88852197,0.000038070142,0.000064067746,0.000080781785,0.032676343],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9922774,0.0016766139,0.0006831431,0.0007734646,0.0042650155,0.00032438606],"domain_scores_gemma":[0.9847435,0.008541405,0.0005982339,0.00052171515,0.0041348306,0.0014603635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061321924,0.0027033736,0.002647437,0.0030106124,0.0022817652,0.010335198,0.0025797088,0.015156592,0.010468538],"category_scores_gemma":[0.01388254,0.0010527907,0.0014338722,0.0017712185,0.004891128,0.006603402,0.0026843525,0.024272978,0.01411622],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018110884,0.000006056328,0.000012028181,0.00023442246,0.000008794536,0.000034178778,0.000017640636,0.000024618847,0.000059521135,0.0037111065,0.98105246,0.014821008],"study_design_scores_gemma":[0.000007784112,0.00001340657,0.00004285866,0.00017284101,0.000006105166,0.00004563007,0.000015889966,0.00004753305,0.00003418708,0.002323796,0.997283,0.0000068877043],"about_ca_topic_score_codex":0.0011348001,"about_ca_topic_score_gemma":0.0038692385,"teacher_disagreement_score":0.015156592,"about_ca_system_score_codex":0.0025264001,"about_ca_system_score_gemma":0.0029026596,"threshold_uncertainty_score":0.03502077},"labels":[],"label_agreement":null},{"id":"W3095140182","doi":"10.3390/s20216346","title":"Colocalized Sensing and Intelligent Computing in Micro-Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Division of Electrical, Communications and Cyber Systems","keywords":"Microelectromechanical systems; Waveform; SIGNAL (programming language); Electronic engineering; Noise (video); Computer science; Acceleration; Analog signal; Sampling (signal processing); Signal processing; Digital signal processing; Engineering; Electrical engineering; Voltage; Artificial intelligence; Physics; Telecommunications; Detector","score_opus":0.023753095383638494,"score_gpt":0.24512262310841335,"score_spread":0.22136952772477486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095140182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18269563,0.0027579814,0.80421716,0.00045990603,0.00012906597,0.000059186754,0.000047452966,0.00072977896,0.008903866],"genre_scores_gemma":[0.8753021,0.00048599113,0.121364795,0.00010655303,0.00004325767,0.00004024778,0.00001999054,0.00003095103,0.0026061146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999828,0.00003440292,0.000009092441,0.000054822416,0.000053577438,0.000020028046],"domain_scores_gemma":[0.99981266,0.00009056497,0.00002637283,0.00003990048,0.00001793396,0.000012713662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022185002,0.00019346573,0.0002902383,0.00022717957,0.00018014063,0.000531285,0.000575118,0.00041491847,0.0011896515],"category_scores_gemma":[0.00058864866,0.00013247329,0.00016735455,0.00021584897,0.00088569726,0.0011366431,0.0007965003,0.00043165826,0.00020467644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022477348,0.00010595628,0.00091514015,0.00036777486,0.00006456263,0.0002558452,0.00022546887,0.05631231,0.5738789,0.21498832,0.0009448132,0.15171619],"study_design_scores_gemma":[0.000024462248,0.00030232093,0.00085501384,0.000028087647,0.000026835793,0.0002830763,0.000087471526,0.66580456,0.2705585,0.052471135,0.009510697,0.000047859325],"about_ca_topic_score_codex":0.00021674132,"about_ca_topic_score_gemma":0.00042408422,"teacher_disagreement_score":0.0011896515,"about_ca_system_score_codex":0.0003894518,"about_ca_system_score_gemma":0.00021078154,"threshold_uncertainty_score":0.003979802},"labels":[],"label_agreement":null},{"id":"W3095786911","doi":"10.3390/s20216081","title":"MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Software deployment; Software; Argo; Python (programming language); Float (project management); Real-time computing; Systems engineering; Software engineering; Engineering; Oceanography; Geology; Programming language","score_opus":0.026355859743327666,"score_gpt":0.24268399506432128,"score_spread":0.21632813532099363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095786911","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021533265,0.00012756347,0.9597542,0.00024306924,0.00006677722,0.00023130391,0.0013810715,0.032375623,0.0036670913],"genre_scores_gemma":[0.030979492,0.00028294438,0.94350666,0.00064996234,0.000056577115,0.001437039,0.003404806,0.0113947345,0.0082877865],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99921167,0.00017000109,0.000119637974,0.00018713572,0.00021959278,0.000091982954],"domain_scores_gemma":[0.99867684,0.0007356185,0.00012131931,0.00015712467,0.00018483696,0.00012413687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018640094,0.0016240901,0.00074461807,0.0006944047,0.00075099064,0.0018465417,0.003413556,0.0012096415,0.017958876],"category_scores_gemma":[0.0030387565,0.0015283797,0.0019410305,0.0005364796,0.0011588477,0.0035727646,0.00299003,0.0031255349,0.0062639993],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019348804,0.0005225939,0.0064942967,0.0029399383,0.00049587735,0.001509163,0.002415823,0.06499426,0.047448833,0.22401273,0.21989441,0.42733717],"study_design_scores_gemma":[0.00047844418,0.00020815335,0.0010221318,0.00046189872,0.00009797975,0.0008433983,0.00014948694,0.3062083,0.027242912,0.05367546,0.60944337,0.00016835243],"about_ca_topic_score_codex":0.0021582055,"about_ca_topic_score_gemma":0.003254653,"teacher_disagreement_score":0.017958876,"about_ca_system_score_codex":0.0007290453,"about_ca_system_score_gemma":0.001357927,"threshold_uncertainty_score":0.060078442},"labels":[],"label_agreement":null},{"id":"W3096387972","doi":"10.3390/s20216145","title":"Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Installation; Session (web analytics); Control (management); Engineering; Computer science; World Wide Web; Artificial intelligence; Operating system","score_opus":0.054461236350253094,"score_gpt":0.29327767319971626,"score_spread":0.23881643684946316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096387972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978822,0.00010874073,0.00023520387,0.000027886796,0.000007169726,0.00003912162,0.000021730815,0.000011819492,0.0016661586],"genre_scores_gemma":[0.99885404,0.00006508356,0.00037511895,0.000018477642,0.000005774396,0.00002835524,0.00004062026,0.0000039824545,0.00060874666],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967462,0.0014440884,0.0002671351,0.00043145954,0.0008915158,0.00021958118],"domain_scores_gemma":[0.86178225,0.112704754,0.011415146,0.0051467903,0.0047508804,0.0042003035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044541545,0.00033514336,0.00053799973,0.00078404334,0.00039935624,0.0010313949,0.0007287043,0.00077584444,0.0032533323],"category_scores_gemma":[0.06658892,0.00031502248,0.000415222,0.00032462025,0.0005636729,0.0013435885,0.0007685987,0.0008590144,0.00033466125],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013058652,0.051041603,0.56092936,0.0011104235,0.00064839766,0.0006960206,0.017320942,0.0025080987,0.03405694,0.00057085016,0.001133814,0.31692478],"study_design_scores_gemma":[0.00034363478,0.03153019,0.94343805,0.00023428604,0.0005910247,0.00053578813,0.0043956107,0.0061943964,0.010394532,0.00036956888,0.0018829588,0.00008991124],"about_ca_topic_score_codex":0.0024056092,"about_ca_topic_score_gemma":0.0023138952,"teacher_disagreement_score":0.0044541545,"about_ca_system_score_codex":0.00049435743,"about_ca_system_score_gemma":0.0005426032,"threshold_uncertainty_score":0.023556054},"labels":[],"label_agreement":null},{"id":"W3096898106","doi":"10.3390/s20216282","title":"Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Biconjugate gradient stabilized method; Biconjugate gradient method; Algorithm; Computer science; Block (permutation group theory); Computation; Microwave imaging; Context (archaeology); Microwave; Conjugate gradient method; Mathematics; Artificial intelligence; Nonlinear conjugate gradient method; Artificial neural network; Gradient descent; Geometry","score_opus":0.01216704744413416,"score_gpt":0.20647104001485142,"score_spread":0.19430399257071726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096898106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034813827,0.000093179646,0.99551815,0.000047575395,0.0000057368743,0.000010276476,0.000028630695,0.0002753911,0.0005397859],"genre_scores_gemma":[0.0813007,0.0003015396,0.9167011,0.000030349987,0.000016655642,0.00009707468,0.00015926067,0.00021489953,0.0011783955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997702,0.0000703475,0.000009553214,0.000016765965,0.00011819185,0.0000148494455],"domain_scores_gemma":[0.9992631,0.00042039697,0.00006399297,0.000102170205,0.00012119608,0.000029175377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493114,0.0005040204,0.0005502669,0.00042886613,0.00020564925,0.00069509336,0.0006856776,0.0006253604,0.002001435],"category_scores_gemma":[0.0022808902,0.00037773512,0.00031241082,0.00064730144,0.0003054811,0.0008412953,0.0007947039,0.0007619431,0.0012285396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021600365,0.00007444113,0.0009577396,0.00028541265,0.00006766594,0.00010993642,0.00017939533,0.63460106,0.054369275,0.051924452,0.0040553017,0.2531593],"study_design_scores_gemma":[0.0000061202827,0.000008692158,0.000077656696,0.0000035229584,0.0000021546753,0.00002065306,0.000005427826,0.9932312,0.0025940242,0.002729646,0.0013162164,0.0000046653017],"about_ca_topic_score_codex":0.002323546,"about_ca_topic_score_gemma":0.002654073,"teacher_disagreement_score":0.002323546,"about_ca_system_score_codex":0.00027359175,"about_ca_system_score_gemma":0.00056733325,"threshold_uncertainty_score":0.0066954494},"labels":[],"label_agreement":null},{"id":"W3097060730","doi":"10.3390/s20216230","title":"Federated Learning in Smart City Sensing: Challenges and Opportunities","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":303,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Smart city; Data science; Field (mathematics); Scale (ratio); Big data; Internet of Things; Computer security","score_opus":0.21785258297780635,"score_gpt":0.325802333237122,"score_spread":0.10794975025931564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097060730","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006746143,0.98643273,0.006446285,0.0016515494,0.00037389,0.000016459462,0.000021988968,0.000034309174,0.0043481835],"genre_scores_gemma":[0.007523463,0.9866172,0.0035946881,0.00067679374,0.00038398814,0.000023331917,0.000043300235,0.000008073291,0.0011291847],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934286,0.0001805165,0.00005158599,0.00011749244,0.00024954407,0.000058006266],"domain_scores_gemma":[0.9982938,0.001207913,0.0000961357,0.000073010815,0.00027595597,0.00005308814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014886531,0.0006228699,0.00092668185,0.0017907165,0.0004062217,0.0017620844,0.0011219281,0.0016470467,0.0021996496],"category_scores_gemma":[0.0025470778,0.00031737625,0.0006361497,0.0030664108,0.0008515165,0.00392094,0.00096165034,0.002071986,0.0010826509],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036695088,0.00009068896,0.0003755377,0.008014558,0.00007140777,0.00010354989,0.00013439001,0.0029422466,0.0006658127,0.060212355,0.0116122225,0.9157406],"study_design_scores_gemma":[0.00001278966,0.00018807554,0.0009878622,0.007875977,0.000113437745,0.0008669472,0.00038213638,0.005334591,0.0014544313,0.057985585,0.92473674,0.00006149314],"about_ca_topic_score_codex":0.0011094644,"about_ca_topic_score_gemma":0.0013886397,"teacher_disagreement_score":0.0021996496,"about_ca_system_score_codex":0.0008818704,"about_ca_system_score_gemma":0.0016130666,"threshold_uncertainty_score":0.0078728795},"labels":[],"label_agreement":null},{"id":"W3097310758","doi":"10.3390/s20216211","title":"Virtual Reality as a Portable Alternative to Chromotherapy Rooms for Stress Relief: A Preliminary Study","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Olfactory and Sensory Function Studies","field":"Neuroscience","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministerio de Ciencia, Innovación y Universidades","keywords":"Virtual reality; Stress (linguistics); Stress reduction; Stress test; Session (web analytics); Test (biology); Task (project management); Stress relief; Computer science; Simulation; Human–computer interaction; Psychology; Engineering; Applied psychology","score_opus":0.1609912625934656,"score_gpt":0.3286567950692677,"score_spread":0.1676655324758021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097310758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944337,0.000451215,0.0035375424,0.000085821965,0.000030653864,0.00030623813,0.00006204517,0.000015569061,0.001077089],"genre_scores_gemma":[0.9880675,0.0011186248,0.00882459,0.000120029916,0.0000794726,0.00034129308,0.00009201082,0.000011118705,0.0013453737],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995394,0.00021597995,0.000023945418,0.000063374326,0.000086406435,0.00007091425],"domain_scores_gemma":[0.9995116,0.00021011035,0.000039990085,0.000056811572,0.00008147946,0.000099935314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007313323,0.00046576106,0.00037345386,0.00024938537,0.00024489735,0.00042854782,0.00046426966,0.00039185662,0.0034351286],"category_scores_gemma":[0.0012140267,0.00013535006,0.0004611212,0.00013496175,0.00040699576,0.0004420876,0.0005125827,0.00042892378,0.0003167475],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013795758,0.04853301,0.0380523,0.0033357975,0.00045691643,0.003305819,0.0061996677,0.0018471846,0.5143768,0.0011846324,0.001399762,0.3675123],"study_design_scores_gemma":[0.0018362529,0.5918816,0.24183321,0.00042165798,0.0013313601,0.005618232,0.009621034,0.0066973823,0.11744929,0.00082827243,0.02225194,0.00022975751],"about_ca_topic_score_codex":0.000522405,"about_ca_topic_score_gemma":0.00070861884,"teacher_disagreement_score":0.0034351286,"about_ca_system_score_codex":0.00010222905,"about_ca_system_score_gemma":0.00027555352,"threshold_uncertainty_score":0.011491656},"labels":[],"label_agreement":null},{"id":"W3097558037","doi":"10.3390/s20216057","title":"Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"McMaster University; Institut Français de Recherche pour l'Exploitation de la Mer","keywords":"Orthogonal frequency-division multiplexing; Bit error rate; Optical wireless; Transmitter; Electronic engineering; Computer science; Wireless; Optical wireless communications; Electrical engineering; Engineering; Telecommunications; Channel (broadcasting)","score_opus":0.0284050025206825,"score_gpt":0.24267229863787212,"score_spread":0.21426729611718962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097558037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81124055,0.0018280791,0.17290118,0.00022309614,0.000059412094,0.000040939904,0.00008199143,0.00011142403,0.013513268],"genre_scores_gemma":[0.9802639,0.000475421,0.018285071,0.000034512574,0.000016645683,0.000017162612,0.000029093308,0.000005798171,0.00087255286],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992466,0.000015295953,0.0000025074935,0.0000114284885,0.000033094228,0.000012925991],"domain_scores_gemma":[0.9998354,0.00007819802,0.00004415232,0.000011651967,0.000024370345,0.0000063925368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015352346,0.00023596702,0.00012557654,0.00014084628,0.00015042863,0.00021607813,0.00018117315,0.00019844457,0.00088948774],"category_scores_gemma":[0.0004235388,0.00004901935,0.00009989449,0.00016969416,0.00017790943,0.00020896038,0.00018910482,0.00019445646,0.00016611876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032543775,0.00012160744,0.0036112124,0.0005061051,0.00003744321,0.00053258706,0.00013991378,0.08837159,0.76874226,0.020465303,0.0007336193,0.11641295],"study_design_scores_gemma":[0.000019270248,0.00054254755,0.0031758244,0.000047584694,0.000037771042,0.00042032995,0.00009626905,0.7463926,0.23839645,0.003000335,0.007845147,0.00002587199],"about_ca_topic_score_codex":0.00022186653,"about_ca_topic_score_gemma":0.00044478633,"teacher_disagreement_score":0.00088948774,"about_ca_system_score_codex":0.0002307783,"about_ca_system_score_gemma":0.0001564632,"threshold_uncertainty_score":0.002975583},"labels":[],"label_agreement":null},{"id":"W3097653348","doi":"10.3390/s20216071","title":"Random Fiber Grating Characterization Based on OFDR and Transfer Matrix Method","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Grating; Randomness; Optics; Laser linewidth; Reflectometry; Materials science; Wavelength; Time domain; Physics; Laser; Mathematics","score_opus":0.011287878052672324,"score_gpt":0.23816604275889802,"score_spread":0.2268781647062257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097653348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24079116,0.00065410923,0.7532088,0.00012712888,0.000038830916,0.000113746195,0.0003891693,0.0012462286,0.0034307535],"genre_scores_gemma":[0.67983395,0.0004554616,0.3185523,0.000043175216,0.00001723482,0.000093659364,0.00029058417,0.00006922012,0.00064437604],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997304,0.00005632999,0.000015348622,0.000064433894,0.00011062712,0.000022833834],"domain_scores_gemma":[0.9995678,0.0001639293,0.00009891063,0.000051556777,0.000104969295,0.00001277715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035713692,0.00056017976,0.00026332668,0.0010057555,0.0001418124,0.00035350415,0.000418162,0.00037177175,0.000864745],"category_scores_gemma":[0.0007983476,0.00020247315,0.00033237666,0.0006768719,0.00025981138,0.0007638409,0.00022419865,0.00026158858,0.00021137163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023543813,0.000105781044,0.0037371283,0.0005435071,0.00008047665,0.00033336022,0.00023674889,0.09616603,0.6913495,0.011615028,0.0008707031,0.19472626],"study_design_scores_gemma":[0.000012133932,0.00008194619,0.0016801934,0.000010684451,0.000020562278,0.00021355314,0.00003618055,0.90526956,0.090345375,0.001160977,0.0011178713,0.000051023242],"about_ca_topic_score_codex":0.00124654,"about_ca_topic_score_gemma":0.0013387875,"teacher_disagreement_score":0.00124654,"about_ca_system_score_codex":0.00034446974,"about_ca_system_score_gemma":0.0002403748,"threshold_uncertainty_score":0.0028928518},"labels":[],"label_agreement":null},{"id":"W3097835771","doi":"10.3390/s20216088","title":"Application of Wireless Accelerometer Mounted on Wheel Rim for Parked Car Monitoring","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Transport Systems and Technology","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Accelerometer; Wireless; Head (geology); Interface (matter); Computer science; Interference (communication); Engineering; Automotive engineering; Embedded system; Real-time computing; Electrical engineering; Telecommunications; Channel (broadcasting)","score_opus":0.018596410800632086,"score_gpt":0.2295545615375265,"score_spread":0.21095815073689442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097835771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7309731,0.0031959221,0.25394,0.00025491332,0.00047933782,0.00022495011,0.0006432099,0.0019861807,0.008302401],"genre_scores_gemma":[0.96278244,0.0009092323,0.03254695,0.00005493668,0.000049281614,0.00005101744,0.0001413291,0.000015802356,0.0034490728],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998203,0.000031935666,0.000013047644,0.000041570253,0.00007611353,0.00001709566],"domain_scores_gemma":[0.99982077,0.0000345283,0.00003130879,0.000021072714,0.0000835854,0.000008788513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015728366,0.00033575913,0.00022888283,0.0003260756,0.000071543276,0.00024280792,0.00042739935,0.00029206314,0.0012580958],"category_scores_gemma":[0.00042809334,0.00012701614,0.00012141481,0.00022335842,0.000073094074,0.000237713,0.00017470018,0.00009151015,0.00039662205],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005430667,0.000091586764,0.016294349,0.0007251877,0.00006674272,0.0009050387,0.0002596851,0.0023616077,0.8087317,0.0005548845,0.001841422,0.16762482],"study_design_scores_gemma":[0.00008885449,0.003103304,0.07527052,0.00017121989,0.00025676447,0.0033342687,0.00051582826,0.09481812,0.7966902,0.00035580117,0.025311256,0.00008381678],"about_ca_topic_score_codex":0.00028984787,"about_ca_topic_score_gemma":0.00045554858,"teacher_disagreement_score":0.0012580958,"about_ca_system_score_codex":0.000062997424,"about_ca_system_score_gemma":0.00009377326,"threshold_uncertainty_score":0.004208803},"labels":[],"label_agreement":null},{"id":"W3098149900","doi":"10.3390/s20226527","title":"Ka Band Holographic Imaging System Based on Linear Frequency Modulation Radar","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Terahertz technology and applications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Southwest Jiaotong University; Fundamental Research Funds for the Central Universities; Ministry of Science and Technology of the People's Republic of China; Ministry of Education of the People's Republic of China","keywords":"Sensitivity (control systems); Radar; Computer science; Image resolution; Extremely high frequency; Holography; Spatial frequency; Calibration; Modulation (music); Millimeter; Planar; Optics; Remote sensing; Computer vision; Electronic engineering; Acoustics; Physics; Engineering; Computer graphics (images); Telecommunications; Geology","score_opus":0.008170350113656159,"score_gpt":0.1943685738832411,"score_spread":0.18619822376958495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098149900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3739321,0.0028097034,0.6041012,0.0006798627,0.00027735668,0.00034720145,0.00043481716,0.0026302915,0.014787526],"genre_scores_gemma":[0.6995192,0.0010851286,0.28951848,0.0003975306,0.00012562664,0.00010325245,0.00023521572,0.000036716287,0.008978881],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997445,0.000039225968,0.00001000295,0.000054935383,0.00012710014,0.00002426438],"domain_scores_gemma":[0.999785,0.000051155544,0.000048663253,0.000049230868,0.000050232895,0.000015817972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017422657,0.00025059236,0.00043013704,0.00027105975,0.00012490085,0.0003886268,0.0006356318,0.0004239577,0.0016041823],"category_scores_gemma":[0.00026028245,0.00020015732,0.00019906105,0.00025120738,0.000284291,0.0005668509,0.00042525984,0.00029345482,0.0006266473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016746968,0.000054871176,0.0010260083,0.00023200584,0.000025955174,0.00023873239,0.000094503586,0.00085563096,0.9079262,0.0016668321,0.0012339076,0.08647786],"study_design_scores_gemma":[0.00013429778,0.0016901173,0.012719905,0.00004849765,0.00013957797,0.004909845,0.00015143071,0.03705041,0.92279357,0.00066431426,0.01958437,0.000113625734],"about_ca_topic_score_codex":0.00029534881,"about_ca_topic_score_gemma":0.00048267626,"teacher_disagreement_score":0.0016041823,"about_ca_system_score_codex":0.00016578218,"about_ca_system_score_gemma":0.00028221455,"threshold_uncertainty_score":0.005366504},"labels":[],"label_agreement":null},{"id":"W3098919339","doi":"10.3390/s20226648","title":"A Study of the Radiation Tolerance of CVD Diamond to 70 MeV Protons, Fast Neutrons and 200 MeV Pions","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Diamond and Carbon-based Materials Research","field":"Materials Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Eidgenössische Technische Hochschule Zürich; Science and Technology Facilities Council; European Commission; CERN; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; U.S. Department of Energy; Horizon 2020 Framework Programme; Institut \"Jožef Stefan\"; National Science Foundation","keywords":"Pion; Irradiation; Neutron; Nuclear physics; Physics; Diamond; Fluence; Radiation damage; Hadron; Neutron temperature; Electron; Atomic physics; Materials science; Analytical Chemistry (journal); Chemistry","score_opus":0.019260546337610852,"score_gpt":0.2683417469169572,"score_spread":0.24908120057934638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098919339","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980627,0.00041965334,0.0008304764,0.000013210211,0.0000071779054,0.000010120558,0.00014180377,0.000015999714,0.0004989025],"genre_scores_gemma":[0.99752635,0.00020897314,0.0013020136,0.00001609402,0.0000036405575,0.0000137844945,0.00023170738,0.000016249074,0.0006811054],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999718,0.000027766138,0.000016275151,0.000076922115,0.000122254,0.000038686983],"domain_scores_gemma":[0.9994485,0.00021492704,0.00010966726,0.000065458094,0.00012819776,0.000033355205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028163826,0.00023165371,0.0003003511,0.00023986792,0.00022624317,0.00024287944,0.0002746494,0.0003678786,0.00088072923],"category_scores_gemma":[0.0008747035,0.00016884986,0.00021303722,0.00024130892,0.00020156341,0.00014942777,0.00018839502,0.00032153216,0.00012384476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012716367,0.000018405071,0.0022068738,0.00006631778,0.000021587735,0.00006543111,0.000085592306,0.00091836246,0.99407357,0.000037688405,0.000030262005,0.002348766],"study_design_scores_gemma":[0.000012828344,0.0006868201,0.026114507,0.000009985809,0.000027346496,0.00018860608,0.00013533451,0.0016724329,0.9700189,0.00003674781,0.0010801452,0.000016413102],"about_ca_topic_score_codex":0.0014834101,"about_ca_topic_score_gemma":0.0009338412,"teacher_disagreement_score":0.0014834101,"about_ca_system_score_codex":0.000293693,"about_ca_system_score_gemma":0.00017792845,"threshold_uncertainty_score":0.0029494762},"labels":[],"label_agreement":null},{"id":"W3101967666","doi":"10.3390/s20226540","title":"Estimating Energy Dissipation Rate from Breaking Waves Using Polarimetric SAR Images","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; Ministry of Science and Technology of the People's Republic of China; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; European Space Agency","keywords":"Dissipation; Polarimetry; Energy (signal processing); Remote sensing; Breaking wave; Physics; Geology; Optics; Scattering; Wave propagation","score_opus":0.021819777203868556,"score_gpt":0.2230309393086794,"score_spread":0.20121116210481085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101967666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9620213,0.00015359395,0.03623949,0.000012252534,0.000009494689,0.00003536905,0.00042721213,0.00015676903,0.0009444843],"genre_scores_gemma":[0.98373324,0.00026203028,0.014900932,0.000005171724,0.0000062997256,0.000029006525,0.0007024569,0.000028494143,0.00033229656],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99987864,0.000011704415,0.000014206848,0.000034156597,0.00004215678,0.000019116093],"domain_scores_gemma":[0.999777,0.00007931182,0.000053621683,0.000027750015,0.00004961229,0.000012737262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032589285,0.0005944686,0.00031812963,0.00156113,0.00010979917,0.00048640696,0.00023073627,0.0002110967,0.0003966064],"category_scores_gemma":[0.0006757611,0.00024982038,0.00040318203,0.0010183452,0.00015731891,0.0006928421,0.0002833615,0.00024169715,0.00017673032],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005396322,0.00026603354,0.25631464,0.00042254227,0.00023743082,0.0005409784,0.00034644382,0.27739504,0.28890264,0.0008471429,0.00062466605,0.17356278],"study_design_scores_gemma":[0.000021389504,0.00017886785,0.23924734,0.000024680241,0.00009576325,0.0001582271,0.00016258804,0.71468145,0.044492923,0.000399043,0.00049002096,0.0000477296],"about_ca_topic_score_codex":0.0014937331,"about_ca_topic_score_gemma":0.0012903282,"teacher_disagreement_score":0.00156113,"about_ca_system_score_codex":0.00013388824,"about_ca_system_score_gemma":0.00013709921,"threshold_uncertainty_score":0.0029700994},"labels":[],"label_agreement":null},{"id":"W3103087864","doi":"10.3390/s20226567","title":"Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Inertial navigation system; Odometry; Real-time computing; Air navigation; Computer science; Kalman filter; Navigation system; Heading (navigation); GNSS augmentation; Extended Kalman filter; Satellite system; Simulation; Engineering; Artificial intelligence; Global Positioning System; Mobile robot; Inertial frame of reference; Telecommunications; Robot; Aerospace engineering","score_opus":0.01343364616968989,"score_gpt":0.19656640238257125,"score_spread":0.18313275621288136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103087864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09873736,0.0026307458,0.88380325,0.00037640805,0.0006060544,0.00015378486,0.00042947402,0.0026433656,0.010619594],"genre_scores_gemma":[0.7536469,0.001457208,0.23493603,0.00027053422,0.00017019671,0.00009817968,0.00047291472,0.00005720951,0.008890713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999828,0.000015708893,0.0000054721368,0.00004405105,0.00008549443,0.000021267067],"domain_scores_gemma":[0.99989474,0.000014905582,0.000017720662,0.000008335756,0.000056386874,0.000007882823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001668143,0.0006690281,0.00031228695,0.00090665644,0.00023602505,0.00042569824,0.00056303554,0.00054859294,0.001182784],"category_scores_gemma":[0.00032606162,0.00023822715,0.00030759818,0.00046895954,0.00021990808,0.00090486225,0.00042418003,0.00041450927,0.0004262948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024635834,0.00018294709,0.0070903995,0.0003905781,0.00006175415,0.0002938695,0.00021524222,0.035989564,0.21139446,0.0076475153,0.0057466007,0.7307406],"study_design_scores_gemma":[0.0000600863,0.0004755659,0.013075882,0.00014724268,0.0001705509,0.00068415166,0.0002016119,0.7849313,0.15112063,0.0049719797,0.044032622,0.00012840287],"about_ca_topic_score_codex":0.0040710038,"about_ca_topic_score_gemma":0.0048993086,"teacher_disagreement_score":0.0040710038,"about_ca_system_score_codex":0.0003644813,"about_ca_system_score_gemma":0.00045857488,"threshold_uncertainty_score":0.008094609},"labels":[],"label_agreement":null},{"id":"W3103496703","doi":"10.3390/s20226407","title":"Distributed High Temperature Monitoring of SMF under Electrical Arc Discharges Based on OFDR","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reflectometry; Materials science; Electric arc; Rayleigh scattering; Temperature measurement; Optical fiber; Optics; Fiber; Wavelength; Correlation coefficient; Optical path; Single-mode optical fiber; Sensitivity (control systems); Spectral line; Atmospheric temperature range; Fiber optic sensor; Optoelectronics; Electrode; Time domain; Electronic engineering; Chemistry; Physics; Composite material","score_opus":0.011205576086455702,"score_gpt":0.21696432856654826,"score_spread":0.20575875248009257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103496703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846245,0.00028819114,0.013967347,0.00003731614,0.000012614902,0.000015072682,0.00014409385,0.00014580657,0.00076510006],"genre_scores_gemma":[0.99318343,0.00016391133,0.0059393127,0.000016329792,0.000007952952,0.000016526776,0.000053005446,0.000014828385,0.00060477055],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999744,0.000025505637,0.0000059643,0.00009647765,0.00010075426,0.000027306802],"domain_scores_gemma":[0.99973196,0.00007019276,0.00009251564,0.00003102215,0.00005939902,0.000014975489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022534026,0.00040424135,0.00021283256,0.00030924098,0.00014019909,0.00018366282,0.00034098575,0.00028293979,0.00067111244],"category_scores_gemma":[0.00046854734,0.000117642696,0.00014293104,0.00026693175,0.00037669786,0.00033945934,0.0002562318,0.00041227997,0.00014073016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005362609,0.000013222007,0.00073391735,0.000031719548,0.000005267392,0.000036418387,0.000059961138,0.00023310557,0.99416476,0.00005646058,0.00002897422,0.004582672],"study_design_scores_gemma":[0.000005917214,0.00017649296,0.008455169,0.0000048531565,0.000011671064,0.0001629792,0.00004917808,0.004867869,0.98581046,0.000049142734,0.00039558628,0.000010702373],"about_ca_topic_score_codex":0.0006065319,"about_ca_topic_score_gemma":0.0009495111,"teacher_disagreement_score":0.00067111244,"about_ca_system_score_codex":0.00033095884,"about_ca_system_score_gemma":0.00012784485,"threshold_uncertainty_score":0.0024012327},"labels":[],"label_agreement":null},{"id":"W3104919786","doi":"10.3390/s20226601","title":"An Efficient Topology Discovery Protocol with Node ID Assignment Based on Layered Model for Underwater Acoustic Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer network; Computer science; Network packet; Ring network; Node (physics); Network topology; Topology (electrical circuits); Neighbor Discovery Protocol; Distributed computing; Engineering; The Internet; Internet Protocol","score_opus":0.0255298101451932,"score_gpt":0.24913761914147786,"score_spread":0.22360780899628466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104919786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00920386,0.00033600145,0.98778665,0.00023069172,0.00008406917,0.00013587803,0.00008196842,0.0006802311,0.0014606768],"genre_scores_gemma":[0.54882866,0.0014992146,0.44315928,0.00018780293,0.000090728216,0.0010082615,0.0007282696,0.00010601793,0.004391795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987625,0.00032526048,0.00013705167,0.00016146014,0.00046898518,0.00014476934],"domain_scores_gemma":[0.9988123,0.00042244216,0.00017784351,0.00019781517,0.000293675,0.0000959521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274285,0.00072306965,0.0008984374,0.0010879791,0.0010938242,0.0016821722,0.002082701,0.0007284248,0.00077013246],"category_scores_gemma":[0.0030843006,0.0005209069,0.0008766113,0.0013705877,0.000664663,0.003373937,0.00185181,0.0012881744,0.00026429805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032208863,0.0002185733,0.0018301809,0.000410117,0.0001875777,0.0006476076,0.00058873225,0.67175543,0.034621585,0.1340639,0.0073865084,0.14796768],"study_design_scores_gemma":[0.000020056608,0.000073850424,0.00013965675,0.000012594474,0.000038510614,0.00014379383,0.000031267373,0.98426455,0.0029893254,0.008690394,0.0035590457,0.000036901674],"about_ca_topic_score_codex":0.0052297865,"about_ca_topic_score_gemma":0.005151027,"teacher_disagreement_score":0.0052297865,"about_ca_system_score_codex":0.0014845359,"about_ca_system_score_gemma":0.0023639065,"threshold_uncertainty_score":0.010771155},"labels":[],"label_agreement":null},{"id":"W3105410536","doi":"10.3390/s20226459","title":"Effect of Surface and Interfacial Tension on the Resonance Frequency of Microfluidic Channel Cantilever","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cantilever; Surface tension; Materials science; Laser Doppler vibrometer; Resonance (particle physics); Surface stress; Tension (geology); Microfluidics; Resonator; Mechanics; Chemistry; Composite material; Optoelectronics; Nanotechnology; Physics; Ultimate tensile strength; Thermodynamics","score_opus":0.012673261833467718,"score_gpt":0.23195387926359445,"score_spread":0.21928061743012672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105410536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99541533,0.00031822282,0.0035038532,0.000052306328,0.000022804506,0.000007196048,0.000030850875,0.000029078281,0.0006204027],"genre_scores_gemma":[0.9981958,0.00018423979,0.0013824293,0.000017107106,0.000006332347,0.00000782444,0.000015666776,0.0000074521326,0.00018320413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997224,0.000050500603,0.000015064385,0.00007261428,0.00008038542,0.00005901028],"domain_scores_gemma":[0.9990909,0.0006902337,0.000084515224,0.000041203963,0.000059751696,0.000033304565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043015892,0.00039551614,0.00023646026,0.00014713572,0.00017591038,0.00021621375,0.00024049355,0.0004322446,0.0007554718],"category_scores_gemma":[0.0011993068,0.00020432468,0.00019725667,0.00010685129,0.0003541955,0.00038448977,0.00023348065,0.0002875071,0.000117535725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000745239,0.000019241974,0.00034023108,0.000030206842,0.0000047962653,0.000060441722,0.000039485054,0.0017214239,0.995846,0.00007703788,0.000018915638,0.0017677555],"study_design_scores_gemma":[0.000021711185,0.00031719898,0.0035993778,0.000009967891,0.000018899073,0.00007356352,0.000055867284,0.034947075,0.9604372,0.00009102301,0.0004031408,0.0000248896],"about_ca_topic_score_codex":0.00037324918,"about_ca_topic_score_gemma":0.0003760065,"teacher_disagreement_score":0.0007554718,"about_ca_system_score_codex":0.00017611228,"about_ca_system_score_gemma":0.00013838083,"threshold_uncertainty_score":0.002527237},"labels":[],"label_agreement":null},{"id":"W3106149304","doi":"10.3390/s20226532","title":"The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Cruise control; Perception; Computer science; Radar; Transport engineering; Environmental science; Engineering; Meteorology; Control (management); Artificial intelligence; Geography; Telecommunications","score_opus":0.022801953109040467,"score_gpt":0.2779844097190068,"score_spread":0.2551824566099663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106149304","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005018454,0.99779,0.0005500393,0.000095937474,0.00008265304,0.000018180512,0.00005555719,0.000010054913,0.0008957882],"genre_scores_gemma":[0.0046215937,0.99381506,0.0009267416,0.000094187664,0.000075135715,0.000024204208,0.000099669836,0.0000042398656,0.00033913268],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99951875,0.00008468029,0.0001045984,0.00010637425,0.00015366585,0.00003182162],"domain_scores_gemma":[0.99779606,0.0014639058,0.00021240521,0.000050144772,0.0004390419,0.0000384762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004433,0.0010092523,0.0013595168,0.0037953786,0.0003588654,0.0016417345,0.0010416732,0.0010499371,0.003229215],"category_scores_gemma":[0.0031075971,0.00046866635,0.0013489153,0.0036745477,0.00044122603,0.001892778,0.00057164935,0.00054356497,0.00077343616],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009907775,0.00008505393,0.0010152786,0.14668167,0.00038236784,0.00017976508,0.00024542637,0.000942559,0.0009476564,0.0030675007,0.00847898,0.8378747],"study_design_scores_gemma":[0.000030089594,0.0005676028,0.00821321,0.11144019,0.004258146,0.0020992165,0.0009873816,0.0016109572,0.002504682,0.004532797,0.8636265,0.00012919922],"about_ca_topic_score_codex":0.0041175424,"about_ca_topic_score_gemma":0.004389985,"teacher_disagreement_score":0.0041175424,"about_ca_system_score_codex":0.0005719837,"about_ca_system_score_gemma":0.0029352377,"threshold_uncertainty_score":0.010802746},"labels":[],"label_agreement":null},{"id":"W3106359073","doi":"10.3390/s20216377","title":"Classification of Aggressive Movements Using Smartwatches","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Random forest; Naive Bayes classifier; Machine learning; Computer science; Smartwatch; Support vector machine; Sensitivity (control systems); Multilayer perceptron; Decision tree; Feature selection; Pattern recognition (psychology); Artificial neural network; Wearable computer; Engineering","score_opus":0.08680730585647801,"score_gpt":0.28165801964757586,"score_spread":0.19485071379109786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106359073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8695765,0.00046661578,0.11517303,0.00017073899,0.00016780618,0.00044513628,0.0043113255,0.0032647077,0.0064241565],"genre_scores_gemma":[0.94286114,0.00029229635,0.04713442,0.00009333366,0.000038457718,0.00033480156,0.0037278736,0.00006115346,0.0054565435],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996978,0.00005445488,0.000024807288,0.000094030474,0.00009401648,0.000034969406],"domain_scores_gemma":[0.99953175,0.0001573711,0.00009448936,0.00003968403,0.00014122242,0.000035490768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030427313,0.0007215828,0.0005594645,0.0010568681,0.0001287994,0.00040044406,0.00032249923,0.0004042681,0.0019110552],"category_scores_gemma":[0.0011689544,0.00014390225,0.0004603829,0.00058101275,0.0001495187,0.0003421323,0.0003002878,0.00023581946,0.0016304757],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001424983,0.000539801,0.21305302,0.0006181004,0.00029029217,0.0007550675,0.00051628565,0.023835959,0.114820994,0.00067830883,0.008406411,0.6350608],"study_design_scores_gemma":[0.000080918144,0.0018672632,0.56566966,0.00016663717,0.0001483039,0.0011216009,0.000888801,0.3704117,0.04842897,0.0014518676,0.009648502,0.00011584116],"about_ca_topic_score_codex":0.0017264977,"about_ca_topic_score_gemma":0.0048569404,"teacher_disagreement_score":0.0019110552,"about_ca_system_score_codex":0.00015238876,"about_ca_system_score_gemma":0.00014695791,"threshold_uncertainty_score":0.006393075},"labels":[],"label_agreement":null},{"id":"W3107016918","doi":"10.3390/s20236767","title":"Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Université Laval","keywords":"Intensity (physics); Acceleration; Wearable computer; Computer science; Open source; Wearable technology; Algorithm; Artificial intelligence; Embedded system; Physics; Optics; Software","score_opus":0.0984754701246471,"score_gpt":0.353847378920971,"score_spread":0.25537190879632393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107016918","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07004595,0.0006765255,0.9060122,0.00026966282,0.00025654663,0.000834462,0.0016409765,0.018176677,0.0020870694],"genre_scores_gemma":[0.2704899,0.00036420987,0.7126282,0.00028019332,0.00010668849,0.0025070265,0.009437461,0.0010699176,0.003116328],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996123,0.0009049718,0.00043772304,0.00097636384,0.001378406,0.0001794624],"domain_scores_gemma":[0.99050903,0.003267031,0.0009301585,0.0010575951,0.0039286185,0.00030764248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005367337,0.0015876793,0.0009779675,0.0023260377,0.00038556484,0.0015646829,0.0034565313,0.0015375152,0.0035239812],"category_scores_gemma":[0.019079654,0.00046523748,0.0011818019,0.0012197661,0.0005447514,0.0016477447,0.0020994183,0.0011253539,0.0019844954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012828321,0.0016560088,0.0317303,0.0013552698,0.00072464027,0.00021970973,0.00044075938,0.03335881,0.028485356,0.0037815827,0.0149371065,0.8820277],"study_design_scores_gemma":[0.0006906768,0.0015842736,0.09596671,0.00070860254,0.00034889567,0.00089816,0.00038096678,0.77272075,0.08153836,0.010821394,0.034081273,0.0002600263],"about_ca_topic_score_codex":0.0029619064,"about_ca_topic_score_gemma":0.003038764,"teacher_disagreement_score":0.005367337,"about_ca_system_score_codex":0.00075846474,"about_ca_system_score_gemma":0.0013624564,"threshold_uncertainty_score":0.02838552},"labels":[],"label_agreement":null},{"id":"W3108487942","doi":"10.3390/s20236870","title":"A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Natural Sciences and Engineering Research Council of Canada; Six Talent Peaks Project in Jiangsu Province","keywords":"RGB color model; Multispectral image; Environmental science; Remote sensing; Normalized Difference Vegetation Index; Soil crust; Grassland; Vegetation (pathology); Computer science; Artificial intelligence; Mathematics; Soil science; Soil water; Ecology; Geography; Leaf area index; Biology","score_opus":0.008951985998901405,"score_gpt":0.2392451686722125,"score_spread":0.2302931826733111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108487942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35075623,0.0003967132,0.64266634,0.000048673508,0.000039652692,0.00018624347,0.00077532424,0.0027119296,0.0024189048],"genre_scores_gemma":[0.6556333,0.00022315986,0.34101897,0.00004696604,0.000023623283,0.00019352138,0.0009645334,0.000107475586,0.0017884766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973243,0.00003552851,0.000017339538,0.00009688757,0.0000909975,0.00002682792],"domain_scores_gemma":[0.99978024,0.00004279439,0.000048112688,0.000027786356,0.00008699628,0.000014059085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032647405,0.00050810154,0.000275569,0.0018329622,0.00018614784,0.0005727404,0.00041110048,0.00027637096,0.0008893543],"category_scores_gemma":[0.00045968234,0.00024792523,0.00037359828,0.0006415362,0.00018373271,0.0005386583,0.00037207984,0.00017863953,0.00034665212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024000742,0.00023032277,0.040220782,0.00045820663,0.00018977694,0.00018743724,0.00034706036,0.017566212,0.24050792,0.00076553086,0.0018256435,0.6974611],"study_design_scores_gemma":[0.000045204957,0.00028840665,0.2236224,0.00006973606,0.00017437084,0.0007731157,0.00040936744,0.663498,0.10392254,0.0012780917,0.005767649,0.0001512007],"about_ca_topic_score_codex":0.0027396402,"about_ca_topic_score_gemma":0.0057958867,"teacher_disagreement_score":0.0027396402,"about_ca_system_score_codex":0.00020850879,"about_ca_system_score_gemma":0.00033868517,"threshold_uncertainty_score":0.0054474473},"labels":[],"label_agreement":null},{"id":"W3108692063","doi":"10.3390/s20236883","title":"Deep Neural Network for Slip Detection on Ice Surface","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Renewable Energy Research Center, University of Dhaka","keywords":"Slip (aerodynamics); Convolutional neural network; Artificial neural network; Computer science; Artificial intelligence; Engineering; Structural engineering; Simulation","score_opus":0.024246081608303813,"score_gpt":0.2729327240311163,"score_spread":0.24868664242281247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108692063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6002566,0.005780505,0.3748143,0.0011104735,0.000548365,0.00015573492,0.002977718,0.004539741,0.009816597],"genre_scores_gemma":[0.9621688,0.00076785753,0.028501015,0.00015507947,0.00006377372,0.000064835425,0.0023512715,0.000052900985,0.0058743935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998802,0.000014287742,0.000009193804,0.00003072397,0.000029033938,0.00003653632],"domain_scores_gemma":[0.9998517,0.000040115854,0.000024015346,0.00001010553,0.000063166255,0.00001088064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025998038,0.00081858964,0.00058754324,0.00058073597,0.00020478085,0.00044422937,0.000685689,0.00061023916,0.0017417761],"category_scores_gemma":[0.0007660311,0.00032470166,0.00053770957,0.0005864294,0.00018853319,0.00044204548,0.0005040719,0.0007848282,0.0005022594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004554036,0.00023316874,0.00868245,0.00015728013,0.00013058381,0.00037095958,0.00006395895,0.6788194,0.008669443,0.0008942577,0.007193338,0.2943297],"study_design_scores_gemma":[0.0000031356872,0.000018537494,0.0009105811,0.0000075870357,0.0000049234977,0.0000124578455,0.000008242603,0.99780387,0.0006893452,0.0002908587,0.00024756786,0.0000029207145],"about_ca_topic_score_codex":0.017794836,"about_ca_topic_score_gemma":0.01307748,"teacher_disagreement_score":0.017794836,"about_ca_system_score_codex":0.000657926,"about_ca_system_score_gemma":0.00061677286,"threshold_uncertainty_score":0.03538257},"labels":[],"label_agreement":null},{"id":"W3110182387","doi":"10.3390/s20236772","title":"Development of a Wireless Telemetry Sensor Device to Measure Load and Deformation in Orthopaedic Applications","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Transducer; Capacitive sensing; Resistive touchscreen; Deformation (meteorology); SIGNAL (programming language); Load cell; Acoustics; Pressure sensor; Computer science; Electrical engineering; Materials science; Engineering; Mechanical engineering; Physics","score_opus":0.014210837971743391,"score_gpt":0.21861176526575987,"score_spread":0.2044009272940165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110182387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2627554,0.0019249085,0.7241517,0.00070931204,0.0004375405,0.0008481058,0.0005147307,0.0018596244,0.0067986953],"genre_scores_gemma":[0.47836962,0.0014603218,0.5068305,0.00041127464,0.000120698045,0.0006029204,0.00032961048,0.00007255179,0.011802494],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965525,0.000037116177,0.000022536844,0.00007584491,0.00019221668,0.00001710187],"domain_scores_gemma":[0.9997346,0.00007738946,0.000050827504,0.000029718669,0.00009144013,0.000016176324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042181532,0.00035857147,0.00022647993,0.00028089035,0.00010280436,0.0002783397,0.00078818324,0.00050136825,0.0019238528],"category_scores_gemma":[0.0005898165,0.00022169396,0.00016915765,0.00023898153,0.0002513612,0.0006272508,0.00029313238,0.0003855748,0.0006306896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007002921,0.00007251477,0.0012059993,0.00022553295,0.000011466152,0.00017114333,0.00005745434,0.0006062688,0.9252501,0.00072940066,0.00078259315,0.070817485],"study_design_scores_gemma":[0.000041753305,0.0016733638,0.0064582527,0.000039104973,0.000043878354,0.0017426278,0.00006085437,0.018845359,0.95137596,0.00022112325,0.019459235,0.000038512662],"about_ca_topic_score_codex":0.00017481498,"about_ca_topic_score_gemma":0.00028854868,"teacher_disagreement_score":0.0019238528,"about_ca_system_score_codex":0.00018323165,"about_ca_system_score_gemma":0.0003556247,"threshold_uncertainty_score":0.0064359307},"labels":[],"label_agreement":null},{"id":"W3110660569","doi":"10.3390/s20247197","title":"Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Motion planning; Potential field; Sigmoid function; Path (computing); Trajectory; Stability (learning theory); Collision avoidance; Field (mathematics); Computer science; Simulation; Control theory (sociology); Engineering; Collision; Artificial intelligence; Mathematics; Control (management); Robot; Artificial neural network","score_opus":0.01892239436582381,"score_gpt":0.2408737988330617,"score_spread":0.22195140446723788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110660569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014251145,0.00023127488,0.9831322,0.0000964374,0.000023203122,0.00004087211,0.000032981585,0.0003515326,0.0018403943],"genre_scores_gemma":[0.68797904,0.00043813858,0.30725202,0.00008647313,0.000029525927,0.00026173392,0.00014973596,0.000119026365,0.003684383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997669,0.000057307505,0.000012357291,0.000046152338,0.00008377544,0.000033474356],"domain_scores_gemma":[0.99965274,0.00015261368,0.000028125862,0.000026687554,0.000108547276,0.00003126409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051221374,0.0008421812,0.00062739075,0.001087174,0.00047553625,0.0005986771,0.0010995886,0.00071687123,0.0015155078],"category_scores_gemma":[0.001067676,0.00043508672,0.000543472,0.0009505506,0.0005140851,0.0013586947,0.0010557002,0.00053765625,0.00027948737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070299306,0.000034028682,0.000497373,0.00007667026,0.000022821387,0.00008765836,0.00005883123,0.8923687,0.0056073344,0.007951296,0.0010611342,0.09216391],"study_design_scores_gemma":[0.0000051804395,0.000021788659,0.000051016985,0.000003208988,0.0000023964747,0.000023709923,0.0000064798182,0.99638015,0.00066012325,0.0023027526,0.0005351852,0.000008060235],"about_ca_topic_score_codex":0.006315849,"about_ca_topic_score_gemma":0.0030692853,"teacher_disagreement_score":0.006315849,"about_ca_system_score_codex":0.00072554866,"about_ca_system_score_gemma":0.0014291669,"threshold_uncertainty_score":0.012558162},"labels":[],"label_agreement":null},{"id":"W3110757659","doi":"10.3390/s20247304","title":"Sub-Canopy Topography Estimation from TanDEM-X DEM by Fusing ALOS-2 PARSAR-2 InSAR Coherence and GEDI Data","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Interferometric synthetic aperture radar; Remote sensing; Canopy; Synthetic aperture radar; Environmental science; Coherence (philosophical gambling strategy); Digital elevation model; Tree canopy; Taiga; Geography; Forestry; Mathematics","score_opus":0.019336851000556512,"score_gpt":0.2230783783864216,"score_spread":0.2037415273858651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110757659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41161692,0.00040290944,0.5780013,0.00011306897,0.00005648881,0.000066039654,0.002564114,0.0028256814,0.004353626],"genre_scores_gemma":[0.7830503,0.00033526603,0.21071672,0.000066142675,0.000031691507,0.00004747383,0.0039863144,0.000080322585,0.001685753],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999007,0.000009230839,0.000005594074,0.000029009156,0.00004042268,0.000015003865],"domain_scores_gemma":[0.9998833,0.000010229168,0.000023315686,0.000028106335,0.000047672078,0.0000072824982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012403917,0.0004418702,0.00023537492,0.0009684362,0.00010524124,0.00030022083,0.00034283736,0.00019524456,0.0007262633],"category_scores_gemma":[0.00032916272,0.00020205833,0.0003297507,0.00078833505,0.00009994165,0.000639447,0.00045397974,0.0002544881,0.0005386275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001398973,0.0001323887,0.04538865,0.00025718822,0.00016659383,0.00053090724,0.00029571596,0.17387216,0.15242411,0.0027052185,0.0053829206,0.61870426],"study_design_scores_gemma":[0.00002359118,0.00006763581,0.058487773,0.000023043372,0.0000596423,0.00035685697,0.00021899921,0.91194665,0.02204272,0.0017483284,0.0049800156,0.00004467658],"about_ca_topic_score_codex":0.0030345786,"about_ca_topic_score_gemma":0.0063888263,"teacher_disagreement_score":0.0030345786,"about_ca_system_score_codex":0.00015203217,"about_ca_system_score_gemma":0.0002706742,"threshold_uncertainty_score":0.006033838},"labels":[],"label_agreement":null},{"id":"W3110873163","doi":"10.3390/s20247159","title":"Enhanced Infrared Sparse Pattern Extraction and Usage for Impact Evaluation of Basalt-Carbon Hybrid Composites by Pulsed Thermography","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Sapienza Università di Roma; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Thermography; Materials science; Extraction (chemistry); Preprocessor; Infrared; Computer science; Pattern recognition (psychology); Artificial intelligence; Optics","score_opus":0.01744014415245765,"score_gpt":0.25822521857316283,"score_spread":0.24078507442070518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110873163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44782898,0.00041774206,0.5490216,0.000069086906,0.000017698974,0.000045680943,0.00013506063,0.0007336934,0.0017304478],"genre_scores_gemma":[0.7816098,0.00034915414,0.21692008,0.000021328846,0.000011044723,0.00003245299,0.00014051581,0.000043326498,0.0008722916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999894,0.000011908183,0.0000033842778,0.000016462454,0.000064775755,0.000009523422],"domain_scores_gemma":[0.99984264,0.00004447441,0.00003185006,0.000017861137,0.000054232383,0.000008968116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017969376,0.00032091845,0.00018714319,0.00061457703,0.000076128534,0.0002616472,0.00016796635,0.00021783772,0.00053299026],"category_scores_gemma":[0.0003358105,0.00013375453,0.000177737,0.00036218655,0.00022595926,0.00035797188,0.00023678422,0.0002001913,0.00013630211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106682215,0.000023714201,0.00079522375,0.000067021196,0.0000057805773,0.0000649526,0.00003546572,0.005884227,0.93625283,0.00037292362,0.0000837197,0.056307387],"study_design_scores_gemma":[0.000008351732,0.00014983772,0.007167387,0.0000102991335,0.000020657371,0.0003255645,0.000044069733,0.21651547,0.77429014,0.00046649904,0.0009808823,0.000020876054],"about_ca_topic_score_codex":0.00037456988,"about_ca_topic_score_gemma":0.0007675469,"teacher_disagreement_score":0.00061457703,"about_ca_system_score_codex":0.00012464267,"about_ca_system_score_gemma":0.00011077162,"threshold_uncertainty_score":0.0017830133},"labels":[],"label_agreement":null},{"id":"W3110937812","doi":"10.3390/s20247130","title":"Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Science and ICT, South Korea","keywords":"Normalization (sociology); Biometrics; Artificial intelligence; QRS complex; Pattern recognition (psychology); Computer science; Linear interpolation; Speech recognition; Cardiology; Medicine","score_opus":0.07190090664378719,"score_gpt":0.30309289036154263,"score_spread":0.23119198371775546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110937812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110092506,0.0006419861,0.8824252,0.00015291147,0.00025260198,0.00012995112,0.00016210417,0.0039270124,0.0022158525],"genre_scores_gemma":[0.74059343,0.00046248076,0.25431606,0.00021940187,0.00011813313,0.00015781538,0.0003712064,0.000108942,0.003652582],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911493,0.00009684916,0.00007549528,0.00029882154,0.00034253785,0.00007134844],"domain_scores_gemma":[0.9995202,0.00007677762,0.00006395433,0.00008570613,0.00022738134,0.000025988636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006982021,0.00058102683,0.0008717771,0.0009074485,0.0003444973,0.0005922531,0.00057740044,0.00067305093,0.0015837785],"category_scores_gemma":[0.0012254483,0.00019291532,0.0006481665,0.0006701164,0.00025256522,0.00096180284,0.0005130361,0.0003426083,0.000979577],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076840597,0.00019428272,0.0052934247,0.00015519999,0.00010813225,0.00020570446,0.00016196804,0.006406587,0.3382675,0.0011607171,0.0020579922,0.64522004],"study_design_scores_gemma":[0.0000945284,0.0013768928,0.046056114,0.0000510048,0.0004054055,0.0023944338,0.00014066878,0.49746183,0.4391423,0.002038019,0.010646181,0.00019264924],"about_ca_topic_score_codex":0.00070930837,"about_ca_topic_score_gemma":0.0006677016,"teacher_disagreement_score":0.0015837785,"about_ca_system_score_codex":0.00026198517,"about_ca_system_score_gemma":0.00028609234,"threshold_uncertainty_score":0.0052983165},"labels":[],"label_agreement":null},{"id":"W3111083811","doi":"10.3390/s20247023","title":"Numerical Analysis of MIM-Based Log-Spiral Rectennas for Efficient Infrared Energy Harvesting","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"King Abdulaziz University","keywords":"Rectenna; Spiral antenna; Insulator (electricity); Optoelectronics; Energy harvesting; Materials science; Rectification; Responsivity; Electrode; Feed horn; Antenna (radio); Electrical engineering; Radiation pattern; Physics; Energy (signal processing); Engineering; Voltage; Coaxial antenna","score_opus":0.018353677689719176,"score_gpt":0.2178612936513876,"score_spread":0.1995076159616684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111083811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4229694,0.00185684,0.4996224,0.0008345379,0.00015277161,0.00009447345,0.00032731285,0.0004450737,0.07369722],"genre_scores_gemma":[0.9528545,0.00032715642,0.040751033,0.00005074228,0.0000149715215,0.000057681427,0.00005418937,0.00002371376,0.005866112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99995136,0.000010422284,0.0000020333314,0.000008784616,0.00001976288,0.0000076089354],"domain_scores_gemma":[0.9998827,0.000053825792,0.00002291264,0.000010880727,0.000024922392,0.0000048219345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017318214,0.00022846855,0.00021110455,0.00019553797,0.00013991223,0.00039886386,0.000283615,0.00039664345,0.0021973262],"category_scores_gemma":[0.00053952844,0.00016970743,0.0002861439,0.00018705237,0.00021579298,0.00036067376,0.0001988827,0.00016242838,0.0003228533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013620619,0.000048126145,0.0016985361,0.00026778202,0.000031264834,0.0002307886,0.0000894157,0.8713059,0.07394058,0.034221724,0.001146431,0.01688332],"study_design_scores_gemma":[0.0000029903595,0.000018853478,0.00012998162,0.0000035175349,0.0000022754791,0.000022965096,0.000008996495,0.9970011,0.0013686471,0.00086094846,0.00057692576,0.0000027978278],"about_ca_topic_score_codex":0.00045466292,"about_ca_topic_score_gemma":0.00069781096,"teacher_disagreement_score":0.0021973262,"about_ca_system_score_codex":0.00032666864,"about_ca_system_score_gemma":0.0002159346,"threshold_uncertainty_score":0.0073508024},"labels":[],"label_agreement":null},{"id":"W3111215764","doi":"10.3390/s20247143","title":"Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Inertial measurement unit; Osteoarthritis; Gait; Physical medicine and rehabilitation; Gait analysis; Wearable technology; Physical therapy; Medicine; Ankle; Inertial frame of reference; Computer science; Artificial intelligence; Surgery; Alternative medicine","score_opus":0.016805246377392752,"score_gpt":0.267911858537327,"score_spread":0.25110661215993424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111215764","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005046628,0.9970252,0.00042161794,0.0005742891,0.00032786824,0.0004774018,0.00016294321,0.00000904224,0.0004969062],"genre_scores_gemma":[0.0031565845,0.99341995,0.0015512283,0.0005398233,0.00018443212,0.0007809683,0.0001789901,0.0000061435994,0.00018181658],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9846491,0.004573548,0.007306499,0.0006481344,0.0025417446,0.00028095522],"domain_scores_gemma":[0.9321152,0.04860432,0.009929654,0.0009815664,0.007985107,0.0003842678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022071438,0.0017805336,0.0058037178,0.020732539,0.0012324824,0.004856576,0.002447323,0.0042873896,0.003760755],"category_scores_gemma":[0.07232971,0.0013657653,0.0062210597,0.017332487,0.0012204447,0.004015767,0.0021999308,0.0015966471,0.00088715],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014088913,0.000054813467,0.0007561229,0.80833423,0.0020065303,0.00018221163,0.0005071237,0.00016402526,0.0004152246,0.0007801661,0.005243325,0.18141535],"study_design_scores_gemma":[0.000040934152,0.00013946423,0.0014979882,0.96182305,0.0073275226,0.0003013117,0.00039465903,0.00009473104,0.00017149682,0.00041373976,0.027770799,0.000024312636],"about_ca_topic_score_codex":0.0079401275,"about_ca_topic_score_gemma":0.019055111,"teacher_disagreement_score":0.022071438,"about_ca_system_score_codex":0.0030749254,"about_ca_system_score_gemma":0.017750785,"threshold_uncertainty_score":0.11672628},"labels":[],"label_agreement":null},{"id":"W3111377738","doi":"10.3390/s20247137","title":"Towards a Simulation Framework for Smart Indoor Spaces","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"AGE-WELL","keywords":"Software deployment; Computer science; Context (archaeology); Smart environment; Wireless sensor network; Process (computing); Real-time computing; Event (particle physics); Home automation; Window (computing); Building automation; Embedded system; Work (physics); Human–computer interaction; Simulation; Distributed computing; Engineering; Internet of Things; Telecommunications; Computer network; Software engineering; Operating system","score_opus":0.06935534494269459,"score_gpt":0.3098953563799878,"score_spread":0.2405400114372932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111377738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016846508,0.000109450055,0.9926104,0.00016334906,0.000043442902,0.00010830436,0.00018801005,0.0036340544,0.0014584207],"genre_scores_gemma":[0.07876846,0.00070458377,0.9140076,0.0001688236,0.00006732517,0.00090517895,0.0014457272,0.0013906213,0.002541762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998075,0.0008852439,0.00019635039,0.0001981423,0.0005187773,0.00012652019],"domain_scores_gemma":[0.99670947,0.0017508046,0.00019331758,0.0004762363,0.0006157939,0.0002542896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004115804,0.0016679482,0.0015253185,0.0014402788,0.0009716196,0.0036642014,0.0052417796,0.002155893,0.0051885657],"category_scores_gemma":[0.0074181734,0.0015061515,0.0028179474,0.0011093023,0.0020834834,0.003162649,0.004197306,0.0034807536,0.0016767915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100034704,0.00011969195,0.0013164156,0.00028580325,0.00011017247,0.00016488293,0.0006179627,0.817428,0.002440632,0.15243554,0.003563573,0.021417335],"study_design_scores_gemma":[0.000027981532,0.000017309561,0.00006588237,0.000052577525,0.000015187679,0.000027904862,0.000046593406,0.9554233,0.0008801476,0.026870323,0.016554179,0.000018624882],"about_ca_topic_score_codex":0.012662082,"about_ca_topic_score_gemma":0.013465026,"teacher_disagreement_score":0.012662082,"about_ca_system_score_codex":0.0016825397,"about_ca_system_score_gemma":0.002833059,"threshold_uncertainty_score":0.025176764},"labels":[],"label_agreement":null},{"id":"W3112072088","doi":"10.3390/s20247252","title":"Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"","keywords":"Computer science; Function (biology); Clinical Practice; Respiratory monitoring; Systems engineering; Pulmonary function testing; Medical physics; Medicine; Respiratory system; Engineering; Physical therapy","score_opus":0.08478164190484434,"score_gpt":0.3955250350447758,"score_spread":0.3107433931399315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112072088","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010784726,0.97815335,0.013579561,0.00070268963,0.00076740654,0.000052749292,0.00016736214,0.00008531733,0.005413166],"genre_scores_gemma":[0.008468435,0.9740104,0.013089858,0.00054878514,0.00078172504,0.00008611922,0.00024569465,0.000027223154,0.0027416751],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975339,0.0003798388,0.00021389022,0.0006006765,0.0011836085,0.00008804464],"domain_scores_gemma":[0.9960711,0.00235282,0.00033451893,0.0001525017,0.0010280856,0.000061055354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025062715,0.0015769377,0.0015478389,0.0051142583,0.00029510565,0.0015589664,0.0018511164,0.0019139205,0.0038379398],"category_scores_gemma":[0.004143541,0.00074271945,0.0014851983,0.003843557,0.00094570953,0.0028289603,0.0010915571,0.0023622112,0.0023500891],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012153239,0.000099651326,0.0010897217,0.0263938,0.00019423006,0.00018713888,0.00014744193,0.0011213251,0.019969108,0.008513082,0.010616949,0.931546],"study_design_scores_gemma":[0.000019425876,0.0004445063,0.004834086,0.007406718,0.00043995338,0.0031562722,0.00024933435,0.0030391633,0.02664756,0.0053484677,0.9482115,0.00020295345],"about_ca_topic_score_codex":0.0015027485,"about_ca_topic_score_gemma":0.0013554868,"teacher_disagreement_score":0.0051142583,"about_ca_system_score_codex":0.00081965985,"about_ca_system_score_gemma":0.001249522,"threshold_uncertainty_score":0.013254583},"labels":[],"label_agreement":null},{"id":"W3112397268","doi":"10.3390/s20247214","title":"A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"University of Tabriz; Universiti Teknologi Malaysia","keywords":"Shuttle Radar Topography Mission; Digital elevation model; Remote sensing; Synthetic aperture radar; Interferometric synthetic aperture radar; Terrain; Geology; Environmental science; Cartography; Geography","score_opus":0.07198353228449207,"score_gpt":0.28647556687273706,"score_spread":0.214492034588245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112397268","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643966,0.0004939885,0.029441534,0.0001512506,0.000059037884,0.000056400357,0.0015861036,0.00039936675,0.0034156437],"genre_scores_gemma":[0.9877259,0.00022544673,0.010457003,0.000014697328,0.000012033409,0.000023542594,0.0012140664,0.000027190268,0.0003001177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991928,0.00027347647,0.000083615545,0.00013724924,0.00024263332,0.00007021085],"domain_scores_gemma":[0.9970114,0.0013741249,0.00023090478,0.00037630633,0.00093561306,0.000071533555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031688649,0.00058422575,0.0003948757,0.0023808992,0.0002811953,0.0007881232,0.00043089563,0.00057239766,0.00075182994],"category_scores_gemma":[0.0047513223,0.00024040574,0.0010066968,0.0018612754,0.00028681388,0.0018469415,0.00043840025,0.00024268526,0.00018664422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001732631,0.00046735845,0.16748147,0.000617639,0.0010498546,0.0006899769,0.0005382906,0.6148848,0.022077855,0.0032464287,0.002832755,0.18438092],"study_design_scores_gemma":[0.00004267431,0.00042902172,0.14586207,0.00006137832,0.00028127685,0.0001950227,0.00051352545,0.83935535,0.010134033,0.00099177,0.0020322094,0.00010164463],"about_ca_topic_score_codex":0.005078526,"about_ca_topic_score_gemma":0.0062597054,"teacher_disagreement_score":0.005078526,"about_ca_system_score_codex":0.00051540375,"about_ca_system_score_gemma":0.0003289306,"threshold_uncertainty_score":0.01675874},"labels":[],"label_agreement":null},{"id":"W3112893815","doi":"10.3390/s20247003","title":"Indoor Positioning System Using Dynamic Model Estimation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Samsung; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade Federal do Amazonas","keywords":"Computer science; Computation; Node (physics); Position (finance); SIGNAL (programming language); Bluetooth; RSS; Real-time computing; Set (abstract data type); Scale (ratio); Simulation; Algorithm; Wireless; Engineering; Telecommunications","score_opus":0.013765885562010546,"score_gpt":0.21448787437234454,"score_spread":0.20072198881033398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112893815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004148407,0.00011153845,0.99252254,0.00003569239,0.0000394716,0.000015433881,0.00009901116,0.0018929447,0.0011349325],"genre_scores_gemma":[0.44340444,0.00050879427,0.549281,0.000120223434,0.00009334303,0.00013602496,0.0014592939,0.0002225601,0.004774314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945086,0.00010045276,0.000024434778,0.00017176323,0.00021001724,0.00004240602],"domain_scores_gemma":[0.99962425,0.00007174852,0.000052509466,0.000113168906,0.00012349537,0.000014835459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031222412,0.0010346043,0.0010132263,0.0009294057,0.00037899218,0.0007400559,0.0009522653,0.000712014,0.0019110215],"category_scores_gemma":[0.0014924998,0.00040919497,0.0006597875,0.0013343391,0.00021985442,0.0011970687,0.0011254541,0.00077466393,0.0019730548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015083882,0.00008492301,0.0029594016,0.00018007991,0.00016281437,0.00015505408,0.00008750035,0.4529163,0.027411342,0.0064470833,0.005519231,0.50392544],"study_design_scores_gemma":[0.00001820086,0.000066069675,0.0010079688,0.000012905728,0.000028114253,0.0001822477,0.000020892898,0.98492277,0.005810714,0.0028692242,0.005032042,0.000028907554],"about_ca_topic_score_codex":0.0038196542,"about_ca_topic_score_gemma":0.0039133197,"teacher_disagreement_score":0.0038196542,"about_ca_system_score_codex":0.00034821048,"about_ca_system_score_gemma":0.0005417345,"threshold_uncertainty_score":0.007594824},"labels":[],"label_agreement":null},{"id":"W3113020781","doi":"10.3390/s20247019","title":"Mechanical Flexibility of DNA: A Quintessential Tool for DNA Nanotechnology","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; Natural Sciences and Engineering Research Council of Canada; Arkansas Biosciences Institute; Michael Smith Health Research BC; National Science Foundation","keywords":"DNA nanotechnology; Nanotechnology; DNA; Rigidity (electromagnetism); Flexural rigidity; Biomolecule; DNA origami; Structural rigidity; Base pair; Flexibility (engineering); Biophysics; Materials science; Chemistry; Nanostructure; Biology; Engineering; Biochemistry","score_opus":0.027093989863399455,"score_gpt":0.3377379397010784,"score_spread":0.31064394983767896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113020781","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007826083,0.99658906,0.0002642747,0.00068222213,0.00053060014,0.0000023478417,0.000007894739,0.0000061633045,0.0018391992],"genre_scores_gemma":[0.0009048547,0.9961689,0.0003192087,0.0005108365,0.00049634936,0.0000073671854,0.00001654586,0.0000028389356,0.0015729822],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997311,0.000043248678,0.000024877725,0.000048587317,0.00012515421,0.000026943573],"domain_scores_gemma":[0.99952686,0.00026750352,0.000033706063,0.000021612734,0.000107797394,0.000042524956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007112469,0.0008302428,0.0008158607,0.0019113382,0.00043555247,0.0011852167,0.0008681543,0.0019202589,0.0027693573],"category_scores_gemma":[0.00086234394,0.00035744862,0.00033077496,0.0017171967,0.0014565497,0.002557059,0.00091622944,0.0027279828,0.0024082104],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005573809,0.00006488358,0.00014709726,0.009876172,0.000051035688,0.00020037209,0.00013057649,0.0007127837,0.004492255,0.046855524,0.049174402,0.88823915],"study_design_scores_gemma":[0.000004172357,0.00003951109,0.00015120518,0.0010437865,0.000010460873,0.00030676468,0.000029494024,0.00005636901,0.0005355845,0.004776643,0.99303424,0.000011857624],"about_ca_topic_score_codex":0.0010205052,"about_ca_topic_score_gemma":0.001382274,"teacher_disagreement_score":0.0027693573,"about_ca_system_score_codex":0.0011299391,"about_ca_system_score_gemma":0.001215344,"threshold_uncertainty_score":0.00926441},"labels":[],"label_agreement":null},{"id":"W3113115661","doi":"10.3390/s20236995","title":"Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Brain–computer interface; Finger tapping; Motor imagery; Support vector machine; Interface (matter); Channel (broadcasting); Functional near-infrared spectroscopy; Computer science; Artificial intelligence; Pattern recognition (psychology); Brain activity and meditation; Similarity (geometry); Tapping; Correlation; Noise (video); Feature extraction; Speech recognition; Electroencephalography; Mathematics; Psychology; Cognition; Engineering; Medicine; Audiology","score_opus":0.11707996508810879,"score_gpt":0.3427002334304569,"score_spread":0.2256202683423481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113115661","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49445873,0.001750291,0.49270105,0.000352411,0.00025663298,0.00015850706,0.00092167733,0.0040028603,0.005397882],"genre_scores_gemma":[0.84841,0.00061630935,0.14544578,0.00012354278,0.000081718725,0.000107154185,0.001706928,0.00016152879,0.0033470502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919647,0.00013673944,0.0000634934,0.00016274893,0.0003310362,0.00010958097],"domain_scores_gemma":[0.9985896,0.00051225215,0.00012474429,0.00008385038,0.000648262,0.000041264786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012319321,0.00113796,0.00073535345,0.001890862,0.00032841266,0.00088884856,0.00053214055,0.0005212521,0.0013495544],"category_scores_gemma":[0.003750183,0.00011601976,0.0004986031,0.001103241,0.00026827378,0.0005894633,0.00052246306,0.00042677217,0.000999834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009249107,0.00022593302,0.019525502,0.00022140297,0.00023932247,0.00017233742,0.00013681696,0.017529512,0.07534118,0.0006937353,0.0063307765,0.8786586],"study_design_scores_gemma":[0.00009683234,0.0004454623,0.09175658,0.0000476086,0.00023195136,0.000632315,0.00027637355,0.803946,0.09644129,0.0014803244,0.0045303693,0.00011489269],"about_ca_topic_score_codex":0.0061179376,"about_ca_topic_score_gemma":0.00790396,"teacher_disagreement_score":0.0061179376,"about_ca_system_score_codex":0.00027821763,"about_ca_system_score_gemma":0.000631399,"threshold_uncertainty_score":0.012164652},"labels":[],"label_agreement":null},{"id":"W3113118202","doi":"10.3390/s20247265","title":"An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Non-line-of-sight propagation; Precise Point Positioning; Computer science; Satellite system; Global Positioning System; Multipath propagation; Real-time computing; Artificial intelligence; Telecommunications; Wireless","score_opus":0.009213815214801649,"score_gpt":0.20948553768550043,"score_spread":0.2002717224706988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113118202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12532422,0.00020429127,0.8718848,0.000095393385,0.000086052954,0.000041609313,0.000042759053,0.00089661276,0.0014242611],"genre_scores_gemma":[0.89330935,0.00010749152,0.10415021,0.00006446911,0.000041512634,0.000052243893,0.00013522118,0.000040753213,0.0020986723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955386,0.000060478276,0.000034989323,0.000094523806,0.00020310021,0.000053046064],"domain_scores_gemma":[0.9992907,0.00009758236,0.00007583071,0.00006613264,0.00044155755,0.000028055892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056008983,0.0005648997,0.0004278834,0.00053092255,0.0002795732,0.00040930946,0.0007417943,0.00050761504,0.00078711746],"category_scores_gemma":[0.0017097684,0.00016349339,0.00026184108,0.0005226317,0.00022519035,0.00082652573,0.0004474971,0.0004426611,0.00047918168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000296091,0.00017433704,0.0055660284,0.000087104905,0.000052429205,0.000088784356,0.00008597196,0.19460385,0.06511787,0.0018979152,0.0020379238,0.72999173],"study_design_scores_gemma":[0.000010360745,0.00011544504,0.002080581,0.000004520787,0.000013259736,0.000033734155,0.00001208732,0.9863923,0.010073551,0.00033003988,0.00092522166,0.000008836108],"about_ca_topic_score_codex":0.0023763345,"about_ca_topic_score_gemma":0.0018735816,"teacher_disagreement_score":0.0023763345,"about_ca_system_score_codex":0.00030546365,"about_ca_system_score_gemma":0.00037031103,"threshold_uncertainty_score":0.0047249794},"labels":[],"label_agreement":null},{"id":"W3113160741","doi":"10.3390/s20247204","title":"A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; University of Calgary; University of Victoria","funders":"Alberta Innovates","keywords":"Flume; Particle image velocimetry; Metre; Flow measurement; Advection; Flow velocity; Velocimetry; Calibration; Doppler effect; Current meter; Ultrasonic flow meter; Flow (mathematics); Range (aeronautics); Geodesy; Particle velocity; Flow visualization; Remote sensing; Environmental science; Geology; Optics; Acoustics; Physics; Meteorology; Materials science; Mechanics; Mathematics; Statistics; Turbulence","score_opus":0.052290773694557764,"score_gpt":0.24748817499038914,"score_spread":0.19519740129583138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113160741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09150394,0.00070889137,0.89510345,0.00015482762,0.00033625646,0.0008994276,0.0021695283,0.0039756047,0.00514803],"genre_scores_gemma":[0.19531962,0.0006925003,0.7954916,0.000109518376,0.000045585224,0.0012672186,0.0016928246,0.00015989754,0.0052212756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991092,0.00010305174,0.000063977925,0.00016789084,0.0005047682,0.00005112409],"domain_scores_gemma":[0.9989073,0.00031629374,0.00012712191,0.00010819173,0.00043845348,0.00010256897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014426035,0.0006827452,0.0005256462,0.0021845035,0.0005012309,0.0008054674,0.0009042357,0.00071801303,0.003479196],"category_scores_gemma":[0.0022564738,0.0004338771,0.0002222919,0.0014843533,0.00036220194,0.0010917437,0.0008111232,0.00081311207,0.0008950074],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023017653,0.00021117815,0.026191115,0.0005749046,0.00004469279,0.00012188961,0.00023921406,0.005077252,0.6907056,0.003860644,0.0070266826,0.2657166],"study_design_scores_gemma":[0.00018484818,0.0017281442,0.09526193,0.0002236917,0.00015123443,0.0009207044,0.00040203036,0.23967168,0.58334893,0.0024710605,0.07535775,0.00027793253],"about_ca_topic_score_codex":0.0018329753,"about_ca_topic_score_gemma":0.004924302,"teacher_disagreement_score":0.003479196,"about_ca_system_score_codex":0.0006408261,"about_ca_system_score_gemma":0.0011649394,"threshold_uncertainty_score":0.011638999},"labels":[],"label_agreement":null},{"id":"W3114050539","doi":"10.3390/s20247342","title":"Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Load balancing (electrical power); Computer science; Cloud computing; Server; Distributed computing; Virtual machine; Virtualization; Algorithm; Round-robin DNS; Weighted round robin; Live migration; Cloud testing; Network Load Balancing Services; Operating system; The Internet; Computer network; Dynamic priority scheduling; Cloud computing security; Quality of service; Round-robin scheduling","score_opus":0.023885169296156413,"score_gpt":0.2485551648887074,"score_spread":0.224669995592551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114050539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8956216,0.00023596975,0.08772102,0.00022521158,0.0003274329,0.0014651989,0.0026061882,0.0034938052,0.008303554],"genre_scores_gemma":[0.9394021,0.0002263271,0.053093527,0.00008668854,0.000036873353,0.001582033,0.0020076854,0.00018973961,0.0033750068],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998865,0.00018736896,0.00010933649,0.0002160313,0.00035971514,0.00026256617],"domain_scores_gemma":[0.99783224,0.00055167224,0.00019755153,0.00048611418,0.0007214862,0.0002109427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009554201,0.00072954193,0.00056597433,0.0008466149,0.0010150264,0.00064073276,0.001361702,0.0006866457,0.0062002256],"category_scores_gemma":[0.0021599631,0.00023732688,0.0003166391,0.0012735322,0.000571286,0.00087945873,0.00069637265,0.0007832992,0.0011686822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007476457,0.009454485,0.00996648,0.0016475333,0.00023508645,0.00060245587,0.00054593966,0.15784334,0.6869363,0.00820032,0.011912536,0.10517902],"study_design_scores_gemma":[0.0007662592,0.009260387,0.016546473,0.00007356418,0.00014011498,0.0002797137,0.00072494056,0.39137095,0.5609389,0.0040261014,0.015718183,0.00015440588],"about_ca_topic_score_codex":0.0017364449,"about_ca_topic_score_gemma":0.001282005,"teacher_disagreement_score":0.0062002256,"about_ca_system_score_codex":0.0005588226,"about_ca_system_score_gemma":0.0006431224,"threshold_uncertainty_score":0.02074182},"labels":[],"label_agreement":null},{"id":"W3114394575","doi":"10.3390/s21010074","title":"Binaural Heterophasic Superdirective Beamforming","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Binaural recording; Beamforming; Acoustics; Monaural; White noise; Computer science; Noise (video); Psychoacoustics; Coherence (philosophical gambling strategy); SIGNAL (programming language); Speech recognition; Telecommunications; Physics; Artificial intelligence","score_opus":0.01934372228444671,"score_gpt":0.2284918040599598,"score_spread":0.20914808177551308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114394575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004192124,0.00021589136,0.9929276,0.000040021598,0.00004104201,0.000016000617,0.000018990371,0.00018638601,0.0023619162],"genre_scores_gemma":[0.2414566,0.0017539565,0.74063754,0.00046249153,0.00013052342,0.00012654756,0.00021488848,0.00009254781,0.015124936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995192,0.00009583768,0.000030717594,0.00011195555,0.00021007609,0.00003221627],"domain_scores_gemma":[0.99965084,0.000079670215,0.00005552852,0.000061860286,0.00012049375,0.000031612417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000331296,0.0007892411,0.0004943949,0.0003790619,0.00021050479,0.00044262104,0.0005228566,0.0005186032,0.0032681979],"category_scores_gemma":[0.00048033887,0.00029094645,0.000367479,0.0005815779,0.00043472063,0.00077914033,0.00073674053,0.00040388983,0.0013262464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002917665,0.000061385355,0.0007794692,0.00025251394,0.00011145757,0.00012774495,0.00012670478,0.03714099,0.437241,0.015660943,0.0016818624,0.5065242],"study_design_scores_gemma":[0.00012242168,0.00076193566,0.004972235,0.000093699135,0.00018752241,0.0026168623,0.00016601702,0.5358092,0.37504068,0.019507363,0.060587794,0.00013432294],"about_ca_topic_score_codex":0.00043754,"about_ca_topic_score_gemma":0.0014506778,"teacher_disagreement_score":0.0032681979,"about_ca_system_score_codex":0.00027129296,"about_ca_system_score_gemma":0.00045121255,"threshold_uncertainty_score":0.01093328},"labels":[],"label_agreement":null},{"id":"W3115723390","doi":"10.3390/s21010113","title":"Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Transfer of learning; Deep learning; Pattern recognition (psychology); Tactile sensor; Cognitive neuroscience of visual object recognition; Computer vision; Object (grammar); Robot","score_opus":0.05689374446467004,"score_gpt":0.2834457575522588,"score_spread":0.22655201308758877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115723390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22617982,0.0012237979,0.7608365,0.00035718887,0.00021905577,0.00017146757,0.0004324463,0.005516642,0.0050630914],"genre_scores_gemma":[0.8872196,0.00029539847,0.105842516,0.0003637575,0.000035530175,0.0001396495,0.0006198508,0.000092331735,0.0053913672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972945,0.000024418943,0.000010297946,0.00008768502,0.00009432105,0.000053877367],"domain_scores_gemma":[0.9997923,0.0000635229,0.000022630182,0.00004080447,0.000058984413,0.000021729678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046256388,0.00084443274,0.00050761504,0.0004297026,0.0001888792,0.0005307164,0.0012700423,0.00088227756,0.001222366],"category_scores_gemma":[0.0008765507,0.00032984317,0.00069934415,0.00038199953,0.00044088883,0.00080620806,0.0012181916,0.0008004699,0.0004462258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034955065,0.00045766603,0.002720519,0.00017585482,0.00021773276,0.00035333424,0.00009747311,0.44398287,0.09097005,0.0018973566,0.0032792308,0.4554983],"study_design_scores_gemma":[0.000007028977,0.00011006941,0.00073895167,0.000007795318,0.000016413507,0.00004777685,0.000008162766,0.9857202,0.011947535,0.0008578802,0.00052773417,0.000010388036],"about_ca_topic_score_codex":0.0043905894,"about_ca_topic_score_gemma":0.0044846493,"teacher_disagreement_score":0.0043905894,"about_ca_system_score_codex":0.00072189124,"about_ca_system_score_gemma":0.00048776457,"threshold_uncertainty_score":0.008730054},"labels":[],"label_agreement":null},{"id":"W3116851389","doi":"10.3390/s21010130","title":"Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal comfort; HVAC; Computer science; Demand response; Energy consumption; Scheduling (production processes); Linear programming; Efficient energy use; Air conditioning; Energy management; Mathematical optimization; Reliability engineering; Engineering; Energy (signal processing); Electricity; Operations management; Algorithm","score_opus":0.021128531340889115,"score_gpt":0.2179576694929108,"score_spread":0.1968291381520217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116851389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08482944,0.00011659783,0.9075413,0.0001923259,0.000033752298,0.000077316974,0.00010574733,0.00025485264,0.0068487],"genre_scores_gemma":[0.92633057,0.00009335565,0.069765896,0.00006492857,0.000017009956,0.00011687086,0.000115857016,0.00006906919,0.0034264817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996487,0.00010209107,0.000013517654,0.000078745696,0.000086695065,0.000070239],"domain_scores_gemma":[0.9997825,0.0000893119,0.000039676383,0.000015833642,0.00005237233,0.000020337473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005055671,0.0007571003,0.0006297211,0.00027169625,0.0003440969,0.00077591394,0.0005819583,0.00044014474,0.0023840521],"category_scores_gemma":[0.0007588548,0.0004076788,0.00061616534,0.00047904646,0.00030639465,0.000655518,0.000562479,0.0005153165,0.00019123519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038282662,0.00003580038,0.0003167571,0.000022686203,0.000015700043,0.0000203046,0.00001931238,0.9885323,0.001949699,0.002184323,0.00028824402,0.006576618],"study_design_scores_gemma":[0.0000035646049,0.00001788016,0.00006692842,9.247455e-7,0.0000031950533,0.000002666534,0.0000063734524,0.9985745,0.00025771343,0.0009105008,0.00015427507,0.0000014517549],"about_ca_topic_score_codex":0.0073579885,"about_ca_topic_score_gemma":0.007428675,"teacher_disagreement_score":0.0073579885,"about_ca_system_score_codex":0.0008115519,"about_ca_system_score_gemma":0.0011472803,"threshold_uncertainty_score":0.014630318},"labels":[],"label_agreement":null},{"id":"W3117058866","doi":"10.3390/s21010225","title":"Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Inductive sensor; Wearable computer; Textile; Inductance; Resistive touchscreen; Induction loop; Reliability (semiconductor); Computer science; Process (computing); Electronic engineering; Electrical engineering; Engineering; Embedded system; Voltage; Materials science; Physics","score_opus":0.038460863187059054,"score_gpt":0.2611675008761204,"score_spread":0.22270663768906135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117058866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33475775,0.0012093281,0.65238583,0.00044661155,0.0005389805,0.001421473,0.00074482197,0.0013565082,0.0071386974],"genre_scores_gemma":[0.77042836,0.0008485932,0.21844894,0.00024742004,0.00006033721,0.001068692,0.0005843642,0.0001107686,0.008202562],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985839,0.0003066704,0.00010152594,0.0002468621,0.00069173594,0.00006926407],"domain_scores_gemma":[0.9985582,0.000293737,0.00014823816,0.00022724202,0.00071935856,0.000053295953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017108627,0.00064575294,0.0004098215,0.00061296637,0.0003165736,0.0005172742,0.0010839553,0.00088078674,0.0016143642],"category_scores_gemma":[0.002595755,0.00020615892,0.00046532546,0.00039327255,0.00036635846,0.0005811944,0.00046668368,0.0002909481,0.00081353675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035317504,0.0002903121,0.0040578465,0.00084327906,0.00006572523,0.0005033464,0.00038002207,0.011123305,0.91049147,0.0020952136,0.001676262,0.06812005],"study_design_scores_gemma":[0.00005371205,0.0035615494,0.005838957,0.000116892625,0.00010680715,0.0008857562,0.00021061531,0.056545153,0.9078033,0.0007547706,0.0240576,0.00006489998],"about_ca_topic_score_codex":0.0002838885,"about_ca_topic_score_gemma":0.00031784043,"teacher_disagreement_score":0.0017108627,"about_ca_system_score_codex":0.00039168764,"about_ca_system_score_gemma":0.00044355047,"threshold_uncertainty_score":0.009047985},"labels":[],"label_agreement":null},{"id":"W3117750865","doi":"10.3390/s21010050","title":"An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interoperability; Geospatial analysis; Computer science; World Wide Web; Reuse; Computer security; Data science; Engineering","score_opus":0.03793967303771522,"score_gpt":0.31258557389045366,"score_spread":0.27464590085273843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117750865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3110161,0.000769779,0.5925332,0.0059131775,0.0003808748,0.0016097559,0.00032030596,0.0023983805,0.0850584],"genre_scores_gemma":[0.76034045,0.000595732,0.22565614,0.0007621105,0.000033707285,0.00045413038,0.0005445769,0.00025192642,0.0113613345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977471,0.0006818246,0.00018485465,0.00021131389,0.00076735887,0.00040747088],"domain_scores_gemma":[0.9981382,0.00039786915,0.00014150378,0.0005448144,0.00047289266,0.00030475372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032760703,0.00041122924,0.00024679388,0.0007205328,0.0016524572,0.003041926,0.0014320783,0.0031576362,0.001227881],"category_scores_gemma":[0.002886339,0.00028754192,0.00074394455,0.0009719326,0.001938527,0.0048754243,0.0045443834,0.0020601372,0.00045414252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036325282,0.0011495207,0.028571054,0.0008298344,0.0001072448,0.020878086,0.015129713,0.06979378,0.05556206,0.5831373,0.020498551,0.20397964],"study_design_scores_gemma":[0.00010887984,0.0009838918,0.0138619095,0.000753805,0.00016741295,0.009210138,0.017123112,0.24450304,0.047658894,0.09925696,0.5660671,0.00030490014],"about_ca_topic_score_codex":0.0056023407,"about_ca_topic_score_gemma":0.006880518,"teacher_disagreement_score":0.0056023407,"about_ca_system_score_codex":0.0017281809,"about_ca_system_score_gemma":0.0019807194,"threshold_uncertainty_score":0.0173257},"labels":[],"label_agreement":null},{"id":"W3118367691","doi":"10.3390/s21020364","title":"Development of Integrative Methodologies for Effective Excavation Progress Monitoring","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Excavator; Excavation; Volume (thermodynamics); Automation; Lidar; Computer science; Engineering; Remote sensing; Civil engineering; Geotechnical engineering; Geology; Mechanical engineering","score_opus":0.027962734501508876,"score_gpt":0.3082845495425588,"score_spread":0.2803218150410499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118367691","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016451537,0.00022656283,0.9971975,0.000038465518,0.000013515175,0.000035843583,0.000038644543,0.00029128024,0.00051310466],"genre_scores_gemma":[0.09166987,0.0009053675,0.9056462,0.000052940653,0.00005159487,0.00020109006,0.00029148004,0.00009278641,0.0010886414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982735,0.00033423,0.00016287515,0.0005293978,0.0006132201,0.00008672073],"domain_scores_gemma":[0.99767476,0.00055323093,0.00044901157,0.0003771927,0.0008850315,0.00006091663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019899823,0.0012043535,0.0008577944,0.0028903505,0.00038786675,0.0018078217,0.0020337144,0.0008788296,0.001565791],"category_scores_gemma":[0.0055520306,0.00064450176,0.00089497707,0.0019326352,0.00067375664,0.0029227757,0.0021607515,0.0007699237,0.00069668936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057260706,0.00011233256,0.004128919,0.0006651515,0.00019196358,0.00017374678,0.00041827338,0.102395914,0.032568946,0.026113803,0.0019375214,0.8312362],"study_design_scores_gemma":[0.00001371967,0.00013867652,0.003911762,0.0001818559,0.000116376665,0.00038223414,0.00044138124,0.9142851,0.025038546,0.03834606,0.017086133,0.000058182697],"about_ca_topic_score_codex":0.0017686413,"about_ca_topic_score_gemma":0.0024106402,"teacher_disagreement_score":0.0028903505,"about_ca_system_score_codex":0.0006565227,"about_ca_system_score_gemma":0.0011787377,"threshold_uncertainty_score":0.010524094},"labels":[],"label_agreement":null},{"id":"W3118496521","doi":"10.3390/s21020443","title":"Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Software; Satellite; Computer science; Satellite system; Remote sensing; Global Positioning System; Physics; Telecommunications; Geology","score_opus":0.04055418666080145,"score_gpt":0.2740928223853225,"score_spread":0.23353863572452105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118496521","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67957705,0.00035114316,0.31815886,0.000043384298,0.000029987585,0.000023080771,0.00013486083,0.00048894086,0.0011927128],"genre_scores_gemma":[0.93871135,0.000106885665,0.060520366,0.000012756643,0.000008536094,0.000011678567,0.00017827135,0.000032091397,0.00041819105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972326,0.000033012733,0.000010335406,0.000060803268,0.0001506771,0.000021907974],"domain_scores_gemma":[0.9994535,0.00014524552,0.00011883264,0.00007279621,0.00019088139,0.000018848157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003201139,0.00043985868,0.0002290483,0.00073971634,0.00014652305,0.00032685118,0.00029475498,0.0003176593,0.00018500122],"category_scores_gemma":[0.0012811573,0.00017912683,0.0002121166,0.00071043504,0.00021684034,0.0003778349,0.00033729654,0.00026668655,0.00012077879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030477688,0.00009002566,0.14200205,0.00013197462,0.0001477058,0.00017381329,0.00014461302,0.4649592,0.1483655,0.0020379892,0.0003418941,0.2413004],"study_design_scores_gemma":[0.000018988072,0.00011891699,0.0378047,0.000012625475,0.000041625368,0.00013208923,0.00003186722,0.9141026,0.04640809,0.00040084874,0.00089191744,0.0000358081],"about_ca_topic_score_codex":0.0071701105,"about_ca_topic_score_gemma":0.009237022,"teacher_disagreement_score":0.0071701105,"about_ca_system_score_codex":0.00040369297,"about_ca_system_score_gemma":0.00048302417,"threshold_uncertainty_score":0.014256775},"labels":[],"label_agreement":null},{"id":"W3118840623","doi":"10.3390/s21020344","title":"Weakening Investigation of Reservoir Rock by Coupled Uniaxial Compression, Computed Tomography and Digital Image Correlation Methods: A Case Study","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Digital image correlation; Deformation (meteorology); Compression (physics); Geotechnical engineering; Cracking; Geology; Materials science; Tension (geology); Failure mode and effects analysis; Wetting; Compressive strength; Composite material","score_opus":0.015608644822137484,"score_gpt":0.2572079462965583,"score_spread":0.24159930147442082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118840623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97963333,0.00040251348,0.018394925,0.00004892076,0.000008233159,0.00007600299,0.000088966895,0.00007793307,0.0012690271],"genre_scores_gemma":[0.983799,0.00022879527,0.015294839,0.000012089068,0.000005511287,0.000014034776,0.000030202888,0.000010232284,0.00060531584],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99968696,0.000032419375,0.000018006918,0.000052798285,0.00017129745,0.000038653514],"domain_scores_gemma":[0.9995105,0.00013555771,0.00008324583,0.00006413774,0.00016399167,0.000042578347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045206863,0.00036089958,0.00029954806,0.0012825564,0.00024408383,0.0004021385,0.00048339658,0.000722131,0.0009011316],"category_scores_gemma":[0.0005868778,0.0002758332,0.00026439133,0.0008942909,0.00048969395,0.00039858944,0.00047891773,0.00027445232,0.00012018987],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005089047,0.00040132037,0.066543095,0.00060018577,0.000080462574,0.008200735,0.0010350217,0.013211305,0.82681894,0.0008006618,0.00037653954,0.08142288],"study_design_scores_gemma":[0.000047663947,0.0018163077,0.1397563,0.0000704578,0.0001988585,0.013959616,0.0018119565,0.21450187,0.6231837,0.00058703026,0.003919915,0.00014621977],"about_ca_topic_score_codex":0.0020760067,"about_ca_topic_score_gemma":0.0053471955,"teacher_disagreement_score":0.0020760067,"about_ca_system_score_codex":0.00025861547,"about_ca_system_score_gemma":0.00031571218,"threshold_uncertainty_score":0.0041279197},"labels":[],"label_agreement":null},{"id":"W3118872891","doi":"10.3390/s21020506","title":"Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Toronto Rehabilitation Institute","keywords":"Computer science; Convolutional neural network; Sensory system; Cuff; Artificial intelligence; Biomedical engineering; SIGNAL (programming language); Pattern recognition (psychology); Neuroscience; Medicine; Psychology; Surgery","score_opus":0.057329570043128115,"score_gpt":0.31520474445834634,"score_spread":0.25787517441521823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118872891","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9211967,0.00034539844,0.07508982,0.00022112945,0.000038898113,0.000106639134,0.00018264253,0.0001662527,0.002652464],"genre_scores_gemma":[0.9933095,0.00009002191,0.0059109586,0.000024431421,0.0000026613916,0.000041647927,0.000043565386,0.0000056945532,0.0005715738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998907,0.000027336866,0.000008420479,0.000021496933,0.00002369272,0.000028337578],"domain_scores_gemma":[0.9992742,0.0004471236,0.000091582304,0.000055013737,0.000090494446,0.0000415458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004350228,0.0003418615,0.0002561761,0.00023337716,0.00014234238,0.00032882462,0.00040770503,0.0006604638,0.0010365335],"category_scores_gemma":[0.0014820236,0.0001209992,0.00037095518,0.00011877551,0.00034293282,0.00026116092,0.00037121255,0.00036160005,0.000100690464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052378897,0.00029563383,0.005782467,0.00023681966,0.000050557068,0.00045506962,0.00018266318,0.93525076,0.032718733,0.00080072816,0.00042217667,0.023280706],"study_design_scores_gemma":[0.000032970416,0.00060779037,0.0026084185,0.000020372468,0.000024204859,0.00011339067,0.000056611676,0.98559433,0.01016854,0.00036550043,0.00039661117,0.000011214902],"about_ca_topic_score_codex":0.003612754,"about_ca_topic_score_gemma":0.003908529,"teacher_disagreement_score":0.003612754,"about_ca_system_score_codex":0.00031641667,"about_ca_system_score_gemma":0.0003155539,"threshold_uncertainty_score":0.0071834326},"labels":[],"label_agreement":null},{"id":"W3119313302","doi":"10.3390/s21010251","title":"Scanning and Actuation Techniques for Cantilever-Based Fiber Optic Endoscopic Scanners—A Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cantilever; Miniaturization; Microelectromechanical systems; Endoscope; Optical fiber; Biomedical engineering; Fiber; Computer science; Nanotechnology; Materials science; Engineering; Surgery; Medicine","score_opus":0.0373901510147054,"score_gpt":0.3234619873615111,"score_spread":0.2860718363468057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119313302","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029970266,0.9965659,0.00073980435,0.00014536719,0.00021963129,0.000011336285,0.000024904544,0.000016519636,0.0019769534],"genre_scores_gemma":[0.0012005252,0.9963432,0.0009645493,0.00010524503,0.0001254821,0.000015547645,0.000039800412,0.0000036319245,0.0012019046],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974936,0.000025922038,0.000031455562,0.00005176609,0.00011925035,0.000022140366],"domain_scores_gemma":[0.99971956,0.00012687618,0.00004520645,0.000010906949,0.000080204394,0.000017290833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052885606,0.0011382505,0.0009973219,0.0033596458,0.00035323802,0.00087429816,0.0009953338,0.0010834081,0.0039417096],"category_scores_gemma":[0.0005474978,0.0004891586,0.00058362994,0.0028197193,0.00042472951,0.001686456,0.0006016255,0.0012526512,0.0027905204],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000355578,0.00011534426,0.00016145597,0.023891078,0.000052849962,0.00022143763,0.0000829708,0.0005757854,0.009626245,0.0045986637,0.015815485,0.9448232],"study_design_scores_gemma":[0.000005493558,0.00014184242,0.00058653165,0.0025850758,0.000063402826,0.001295816,0.00006255752,0.00023735431,0.0031184808,0.001335479,0.9905335,0.000034576944],"about_ca_topic_score_codex":0.0008158766,"about_ca_topic_score_gemma":0.0011212352,"teacher_disagreement_score":0.0039417096,"about_ca_system_score_codex":0.00035846554,"about_ca_system_score_gemma":0.0007762105,"threshold_uncertainty_score":0.013186336},"labels":[],"label_agreement":null},{"id":"W3119627781","doi":"10.3390/s21020559","title":"A Camera Intrinsic Matrix-Free Calibration Method for Laser Triangulation Sensor","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Huazhong University of Science and Technology; National Natural Science Foundation of China","keywords":"Triangulation; Displacement (psychology); Calibration; Camera matrix; Camera auto-calibration; Computer vision; Camera resectioning; Matrix (chemical analysis); Computer science; Artificial intelligence; Position (finance); Laser; Fundamental matrix (linear differential equation); Essential matrix; Algorithm; Optics; Mathematics; Pinhole camera model; Physics; Geometry; Mathematical analysis; Symmetric matrix; Materials science; State-transition matrix","score_opus":0.0367602804662067,"score_gpt":0.31569808618110795,"score_spread":0.27893780571490123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119627781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018988395,0.00017098464,0.996567,0.00004013608,0.000040324652,0.000021969008,0.000019752253,0.0002653062,0.0009757106],"genre_scores_gemma":[0.17644997,0.0008301473,0.81762135,0.00013371996,0.000104400955,0.00020073898,0.00026740564,0.00019674111,0.0041954485],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984549,0.00019642833,0.000060584527,0.00039449244,0.0008378516,0.00005573776],"domain_scores_gemma":[0.9993656,0.000078596015,0.000085271575,0.000102469334,0.00034406257,0.000024015804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056982966,0.0010695313,0.0006457154,0.0009396036,0.0005653183,0.00087246485,0.0011740979,0.0010165457,0.003247022],"category_scores_gemma":[0.001964755,0.0006046171,0.00083914207,0.0011114978,0.00054456736,0.0018065484,0.0013081752,0.0015631946,0.0013677373],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016686473,0.00007158825,0.0016115631,0.0005915868,0.00010975081,0.00021535328,0.00035361896,0.10005319,0.18200952,0.031069197,0.005685587,0.6780621],"study_design_scores_gemma":[0.00004648897,0.00017398527,0.0015846262,0.00006477651,0.000060662776,0.00095064647,0.000099110286,0.88107246,0.089638904,0.0054963897,0.02064177,0.00017013281],"about_ca_topic_score_codex":0.002075501,"about_ca_topic_score_gemma":0.0018287306,"teacher_disagreement_score":0.003247022,"about_ca_system_score_codex":0.0007115971,"about_ca_system_score_gemma":0.0011530014,"threshold_uncertainty_score":0.01086235},"labels":[],"label_agreement":null},{"id":"W3119782209","doi":"10.3390/s21020598","title":"3D Photon-To-Digital Converter for Radiation Instrumentation: Motivation and Future Works","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Silicon photomultiplier; Avalanche photodiode; Converters; Time-to-digital converter; Photomultiplier; Instrumentation (computer programming); Physics; Avalanche diode; Electronic engineering; Single-photon avalanche diode; Computer science; Electrical engineering; Optics; Detector; Engineering; Scintillator; Electronic circuit; Voltage","score_opus":0.020147357619762468,"score_gpt":0.2916354053975135,"score_spread":0.27148804777775104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119782209","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043996648,0.99117786,0.0017513697,0.000456922,0.00037722837,0.000011578401,0.00003211224,0.000019148707,0.005733759],"genre_scores_gemma":[0.0025680196,0.9915378,0.0018167828,0.00037164174,0.00034524332,0.000019519803,0.00005084696,0.0000067839082,0.003283296],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976677,0.000026547019,0.00002188585,0.00006144012,0.000101587095,0.000021759026],"domain_scores_gemma":[0.99970716,0.00012826738,0.00002995145,0.000016235937,0.00010047814,0.000017889228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052948453,0.00064364355,0.00062237075,0.001500835,0.00030004454,0.0011892426,0.00064134545,0.001077652,0.003643497],"category_scores_gemma":[0.00059352146,0.00042823414,0.00049505604,0.0020866224,0.0004324484,0.001958232,0.0005975051,0.0017686078,0.00264131],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038585844,0.00009288043,0.00017987577,0.012706909,0.000034791086,0.00023484402,0.0000969201,0.001076618,0.0072213393,0.03701783,0.021040203,0.9202591],"study_design_scores_gemma":[0.000004019621,0.000079347694,0.00028076713,0.0012229802,0.00003246878,0.00090339035,0.000041952535,0.00037137858,0.0023062588,0.004575942,0.9901613,0.000020193882],"about_ca_topic_score_codex":0.000758772,"about_ca_topic_score_gemma":0.00075284473,"teacher_disagreement_score":0.003643497,"about_ca_system_score_codex":0.00048652638,"about_ca_system_score_gemma":0.00088479725,"threshold_uncertainty_score":0.012188673},"labels":[],"label_agreement":null},{"id":"W3119833346","doi":"10.3390/s21020461","title":"A Multilane Tracking Algorithm Using IPDA with Intensity Feature","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada); McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; False positive paradox; Frame (networking); Filter (signal processing); Feature (linguistics); Set (abstract data type); Pixel; Pattern recognition (psychology); Tracking (education); Probabilistic logic; Computer vision","score_opus":0.009998182942996715,"score_gpt":0.20527415104938307,"score_spread":0.19527596810638637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119833346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009721804,0.00018181973,0.9870006,0.000052286287,0.00006318555,0.000046407004,0.00006056963,0.0022132727,0.0006599676],"genre_scores_gemma":[0.16000411,0.00015066782,0.8352073,0.00011577558,0.00005490015,0.0001750797,0.0005977877,0.00017017974,0.0035242597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992143,0.00005463104,0.000037530182,0.00036791878,0.00022853618,0.00009699504],"domain_scores_gemma":[0.9993967,0.00010741988,0.00007572217,0.00013253585,0.00023733186,0.000050258754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000852457,0.0010345752,0.0013789003,0.0018206943,0.00065070053,0.0010474833,0.0019415055,0.0009334583,0.001969477],"category_scores_gemma":[0.0015394943,0.0006136102,0.0009479939,0.0013513454,0.00044416502,0.001120094,0.0016040121,0.0017515228,0.0016709428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021210688,0.00019678363,0.0023023884,0.000056169618,0.00011016571,0.00009534229,0.00006753995,0.061622374,0.021901777,0.0017327103,0.002532433,0.9091703],"study_design_scores_gemma":[0.000024297413,0.000101388985,0.0007922851,0.0000062432528,0.00002098711,0.00009366492,0.000018490915,0.98809576,0.0067524146,0.0011704927,0.002904202,0.000019814826],"about_ca_topic_score_codex":0.005658561,"about_ca_topic_score_gemma":0.006894465,"teacher_disagreement_score":0.005658561,"about_ca_system_score_codex":0.00063354464,"about_ca_system_score_gemma":0.0011809889,"threshold_uncertainty_score":0.011251271},"labels":[],"label_agreement":null},{"id":"W3120297356","doi":"10.3390/s21020401","title":"Tablet Technology for Writing and Drawing during Functional Magnetic Resonance Imaging: A Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Sunnybrook Health Science Centre; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto","keywords":"Functional magnetic resonance imaging; Neuropsychology; Modality (human–computer interaction); Psychology; Neurosurgery; Magnetic resonance imaging; Neuroimaging; Computer science; Neuroscience; Medical physics; Physical medicine and rehabilitation; Medicine; Human–computer interaction; Radiology; Cognition","score_opus":0.043979078372211114,"score_gpt":0.32385068728765404,"score_spread":0.27987160891544294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120297356","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007249317,0.99913496,0.000094836905,0.00006292186,0.00005864649,0.000005483619,0.000013248312,0.0000060825096,0.00055135984],"genre_scores_gemma":[0.00047818685,0.99880373,0.00023251332,0.000061361796,0.00008486466,0.000010862324,0.000027424772,0.0000023437358,0.00029871307],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997044,0.000054707954,0.00006871325,0.000058458445,0.000094638905,0.000019030913],"domain_scores_gemma":[0.9993193,0.00044973756,0.00008592048,0.000017388782,0.00009802401,0.000029627052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007321778,0.0012707792,0.0016002435,0.0042728065,0.0003162707,0.0012353914,0.0010342977,0.0013449372,0.0049887747],"category_scores_gemma":[0.0012932023,0.000446192,0.00075529906,0.0030281113,0.0006610211,0.0019126693,0.0007855394,0.0013278788,0.0030695847],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005041425,0.000056797468,0.00014120754,0.028861472,0.0000771548,0.00019149377,0.00006447552,0.000193888,0.0009541981,0.0015094916,0.012649791,0.95524955],"study_design_scores_gemma":[0.000029204517,0.00015967,0.0021822096,0.01266363,0.00023829409,0.004073366,0.00011389379,0.00016568144,0.000777109,0.002065447,0.9774779,0.00005363783],"about_ca_topic_score_codex":0.0015660282,"about_ca_topic_score_gemma":0.0021369043,"teacher_disagreement_score":0.0049887747,"about_ca_system_score_codex":0.00040208487,"about_ca_system_score_gemma":0.0009003421,"threshold_uncertainty_score":0.016689062},"labels":[],"label_agreement":null},{"id":"W3120327648","doi":"10.3390/s21020447","title":"In-Situ Estimation of Soil Water Retention Curve in Silt Loam and Loamy Sand Soils at Different Soil Depths","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Economic Development, Job Creation and Trade; Ministero dello Sviluppo Economico","keywords":"Soil water; Loam; Environmental science; Soil science; Silt; Pedotransfer function; In situ; Water content; Hydrology (agriculture); Geology; Geotechnical engineering; Hydraulic conductivity; Chemistry","score_opus":0.009401545817597166,"score_gpt":0.21717297850243786,"score_spread":0.2077714326848407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120327648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99668026,0.00003854296,0.0027246247,0.0000059403937,0.0000028017355,0.0000055030905,0.00018597595,0.000038033406,0.00031832926],"genre_scores_gemma":[0.9970036,0.00004331498,0.0026028054,0.0000062638323,0.0000016575547,0.000008326792,0.00014758889,0.000005220333,0.00018127273],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999232,0.0000072498797,0.0000037510686,0.000032385567,0.000021368656,0.000012075939],"domain_scores_gemma":[0.9998838,0.000031356183,0.000027558097,0.000009542679,0.000038674658,0.0000091262145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001840959,0.00031408263,0.00017530992,0.00031147402,0.000099405974,0.00020110788,0.00028336202,0.00022656436,0.0002766723],"category_scores_gemma":[0.00022977294,0.00011518133,0.00017047586,0.00030796498,0.00009642618,0.00028815665,0.00015937594,0.0001777931,0.00007000395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003488445,0.00018575838,0.10132786,0.00017269417,0.000075128504,0.00014671145,0.00033649654,0.0096277455,0.8575039,0.00009836177,0.00020147409,0.02997511],"study_design_scores_gemma":[0.00003444722,0.00056553725,0.4185063,0.000021441376,0.000144679,0.0002050268,0.000526858,0.16626693,0.4123736,0.00019903021,0.0011015811,0.00005460969],"about_ca_topic_score_codex":0.003216475,"about_ca_topic_score_gemma":0.00668049,"teacher_disagreement_score":0.003216475,"about_ca_system_score_codex":0.0001760824,"about_ca_system_score_gemma":0.00011654623,"threshold_uncertainty_score":0.006395519},"labels":[],"label_agreement":null},{"id":"W3120369513","doi":"10.3390/s21020532","title":"Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Beamforming; Blind signal separation; Microphone array; Acoustic source localization; Computer science; Weighting; Source separation; Direction of arrival; Microphone; Interference (communication); Angle of arrival; Noise (video); Acoustics; Algorithm; Artificial intelligence; Sound (geography); Telecommunications","score_opus":0.033277745721011276,"score_gpt":0.2739286567683367,"score_spread":0.24065091104732544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120369513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00094904745,0.00010214081,0.99850416,0.000029241988,0.000014254777,0.000006842192,0.000007828798,0.00016912179,0.00021738648],"genre_scores_gemma":[0.043286767,0.00035007493,0.9546671,0.00009266787,0.000065428525,0.00009260718,0.000093016184,0.00007898105,0.0012734829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987658,0.00038320097,0.00007020187,0.00025239063,0.00043784967,0.000090621776],"domain_scores_gemma":[0.9986305,0.000603115,0.00016980196,0.00016189455,0.00037686658,0.000057849444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012590224,0.0018930518,0.0010988949,0.0014125461,0.0004358665,0.0010794521,0.0012130066,0.0018291849,0.0019090527],"category_scores_gemma":[0.0039031527,0.00068782654,0.0010899298,0.00126093,0.001080275,0.002098733,0.0027305163,0.0014887062,0.0022330075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003738966,0.00011339101,0.00058144203,0.0004874513,0.00017169079,0.0001876289,0.00029251428,0.24807522,0.16439685,0.031536195,0.003785041,0.5499986],"study_design_scores_gemma":[0.00006197807,0.00015789703,0.0002741256,0.000058451053,0.000031811247,0.0002892719,0.00006266321,0.93590176,0.036745567,0.020166444,0.0061750156,0.00007507318],"about_ca_topic_score_codex":0.0010749482,"about_ca_topic_score_gemma":0.0010118604,"teacher_disagreement_score":0.0019090527,"about_ca_system_score_codex":0.00040785898,"about_ca_system_score_gemma":0.0011507482,"threshold_uncertainty_score":0.006658435},"labels":[],"label_agreement":null},{"id":"W3120547797","doi":"10.3390/s21020334","title":"Real-Time Coseismic Displacement Retrieval Based on Temporal Point Positioning with IGS RTS Correction Products","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; China Postdoctoral Science Foundation; Centre National d’Etudes Spatiales; National Natural Science Foundation of China; Natural Resources Canada; Earthquake Commission","keywords":"Precise Point Positioning; GNSS applications; Geodesy; Orbit (dynamics); Displacement (psychology); Satellite; Epoch (astronomy); Computer science; Satellite system; Global Positioning System; Convergence (economics); Orbit determination; Ionosphere; Real-time computing; Remote sensing; Geology; Aerospace engineering; Telecommunications; Computer vision; Engineering; Geophysics","score_opus":0.005451507158837166,"score_gpt":0.19457085725408796,"score_spread":0.1891193500952508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120547797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61804724,0.0017262666,0.34730545,0.00035471306,0.00081248395,0.00023738758,0.003619195,0.0094161965,0.018481039],"genre_scores_gemma":[0.80109495,0.00053988106,0.18526725,0.00015819287,0.0001116249,0.00010311424,0.005790449,0.00052317686,0.0064113294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896073,0.00008653647,0.000047321308,0.00017545516,0.00062112143,0.00010889294],"domain_scores_gemma":[0.9995685,0.00004171345,0.000046675344,0.000070918955,0.00025004835,0.000022183403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006663767,0.0009822184,0.0005395051,0.0012345403,0.00021338585,0.0007987353,0.00088815484,0.00050730474,0.002742813],"category_scores_gemma":[0.0015035257,0.00021300165,0.0004076959,0.001604562,0.00024375196,0.001119736,0.00073983765,0.00051708636,0.0016655537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015610657,0.00033829175,0.034884155,0.0007145049,0.00029860093,0.0006545978,0.00037526598,0.12466922,0.26893342,0.0031360018,0.014546121,0.5498887],"study_design_scores_gemma":[0.0002008943,0.00048738025,0.03804091,0.000047475507,0.00016056729,0.00042940417,0.00027306672,0.81589603,0.12306307,0.0007793508,0.02045237,0.00016952964],"about_ca_topic_score_codex":0.006918369,"about_ca_topic_score_gemma":0.0072985515,"teacher_disagreement_score":0.006918369,"about_ca_system_score_codex":0.00034207065,"about_ca_system_score_gemma":0.0006223257,"threshold_uncertainty_score":0.013756216},"labels":[],"label_agreement":null},{"id":"W3120756540","doi":"10.3390/s21020580","title":"Finite Element Modelling of Bandgap Engineered Graphene FET with the Application in Sensing Methanethiol Biomarker","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Graphene research and applications","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Graphene; Materials science; Nanotechnology; Biosensor; Biomarker; Graphene nanoribbons; Graphene foam; Field-effect transistor; Methanethiol; Transistor; Chemistry","score_opus":0.03521452045032097,"score_gpt":0.27088779589924744,"score_spread":0.23567327544892647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120756540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62440807,0.001092636,0.3286749,0.001044229,0.00023752506,0.0002057176,0.0021008223,0.0011280853,0.041108068],"genre_scores_gemma":[0.9379957,0.00032928796,0.05321077,0.000091378504,0.00001567322,0.00021149185,0.0006134995,0.00009901133,0.0074332403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998822,0.00003021207,0.000005158856,0.000017718989,0.000045434903,0.000019342344],"domain_scores_gemma":[0.99963784,0.0002358461,0.000028464685,0.000023164323,0.000060301358,0.000014388872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024938348,0.00050549966,0.00048754545,0.00048062493,0.0003907065,0.0007298733,0.00083300413,0.0022187615,0.0033525913],"category_scores_gemma":[0.00089582993,0.00043604148,0.00078777404,0.00047067276,0.00044135054,0.00040800014,0.00031877268,0.00042832235,0.0003272194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030456304,0.00002281955,0.0005268059,0.00005640691,0.000013491786,0.0000848858,0.000036778678,0.9923354,0.0042220885,0.00092433864,0.00015804508,0.0015885292],"study_design_scores_gemma":[0.0000036783044,0.000012227676,0.00013482868,0.000005952421,0.0000028273284,0.000010055373,0.000013792044,0.9986344,0.00068657385,0.00016584921,0.00032602306,0.0000037944185],"about_ca_topic_score_codex":0.009570783,"about_ca_topic_score_gemma":0.00919266,"teacher_disagreement_score":0.009570783,"about_ca_system_score_codex":0.0006682698,"about_ca_system_score_gemma":0.0007877868,"threshold_uncertainty_score":0.019030154},"labels":[],"label_agreement":null},{"id":"W3121652263","doi":"10.3390/s21030750","title":"Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Ministerio de Ciencia, Innovación y Universidades","keywords":"Artificial intelligence; Thermography; Computer science; Automation; Segmentation; Convolution (computer science); Data processing; Field (mathematics); Image processing; Convolutional neural network; Process (computing); Computer vision; Deep learning; Artificial neural network; Pattern recognition (psychology); Machine learning; Image (mathematics); Engineering; Infrared; Database; Mechanical engineering","score_opus":0.004473439342449172,"score_gpt":0.21467846487735867,"score_spread":0.2102050255349095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121652263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015651679,0.00486196,0.9729375,0.00075285876,0.0001568829,0.000051914536,0.00017468086,0.0014211838,0.0039913147],"genre_scores_gemma":[0.46991447,0.008639485,0.5058317,0.00092760654,0.00033926827,0.00023863016,0.0011750242,0.00030847528,0.012625325],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995702,0.00008401184,0.00003283851,0.00013811191,0.0001306144,0.000044246517],"domain_scores_gemma":[0.999488,0.00018731212,0.0000437596,0.000059856222,0.00019426903,0.000026848744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009169246,0.0008010513,0.0005202492,0.0006133181,0.00018050395,0.00088290876,0.0010155685,0.0010380821,0.0017917652],"category_scores_gemma":[0.002029056,0.0004006468,0.00069691904,0.00072366145,0.000459609,0.0009692689,0.0008185129,0.00164403,0.0007186942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121210964,0.00012081823,0.0020115683,0.000346005,0.00013899783,0.000112294634,0.000096629694,0.20069386,0.01597165,0.011919675,0.0060582105,0.76240915],"study_design_scores_gemma":[0.0000061258634,0.0000640996,0.0008195467,0.000047066977,0.000022203583,0.000048899412,0.000012474054,0.97984767,0.006887188,0.005694828,0.0065335254,0.000016303029],"about_ca_topic_score_codex":0.0051553026,"about_ca_topic_score_gemma":0.0042344276,"teacher_disagreement_score":0.0051553026,"about_ca_system_score_codex":0.00072137953,"about_ca_system_score_gemma":0.00076264486,"threshold_uncertainty_score":0.010250568},"labels":[],"label_agreement":null},{"id":"W3121729622","doi":"10.3390/s21030722","title":"Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; École de Technologie Supérieure","funders":"","keywords":"Radar; STRIDE; Wearable computer; Computer science; Falling (accident); Simulation; Acceleration; Embedded system; Medicine; Telecommunications; Computer security","score_opus":0.03530191360557053,"score_gpt":0.3566749921455065,"score_spread":0.321373078539936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121729622","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99235725,0.00028918745,0.0066087334,0.00003804795,0.000029038394,0.000020286185,0.00024131751,0.00004310506,0.00037308998],"genre_scores_gemma":[0.9910006,0.00017266005,0.008073183,0.000027091874,0.00003070204,0.000024890604,0.00039188826,0.0000055332366,0.0002734951],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99918693,0.000252394,0.00007254605,0.00019012259,0.00024267494,0.000055320434],"domain_scores_gemma":[0.9992918,0.00024265551,0.00019933876,0.000044957786,0.00015444818,0.000066735614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080861547,0.000550124,0.0006938329,0.0011527132,0.000112011716,0.000539375,0.00022524812,0.0005427077,0.00051756395],"category_scores_gemma":[0.0029967458,0.00014402263,0.00051116117,0.00072197686,0.00015124082,0.00034978605,0.00047613334,0.00020838557,0.00018146775],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042280904,0.00083352363,0.78647363,0.00044804448,0.00082791585,0.00056222564,0.00041469088,0.005663749,0.035719812,0.00015251436,0.0005984051,0.16407743],"study_design_scores_gemma":[0.00007326443,0.0024602776,0.92705786,0.00007402449,0.00047450032,0.0016226014,0.00039877882,0.061572272,0.005430187,0.00017705704,0.00059524924,0.000064007465],"about_ca_topic_score_codex":0.001267155,"about_ca_topic_score_gemma":0.0025086245,"teacher_disagreement_score":0.001267155,"about_ca_system_score_codex":0.000099866105,"about_ca_system_score_gemma":0.00016695131,"threshold_uncertainty_score":0.004276395},"labels":[],"label_agreement":null},{"id":"W3123500501","doi":"10.3390/s21030778","title":"Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Neuroimaging; Computer science; Pipeline (software); Cognition; Convolutional neural network; Artificial intelligence; Disease; Alzheimer's disease; Machine learning; Cognitive impairment; Medicine; Neuroscience; Psychology; Pathology","score_opus":0.03181282074798456,"score_gpt":0.31219696525981666,"score_spread":0.2803841445118321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123500501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4638376,0.0042694234,0.5119956,0.0011857814,0.0003694399,0.00045233953,0.0023744765,0.006201678,0.009313685],"genre_scores_gemma":[0.8556112,0.00079100265,0.13928072,0.00013484793,0.00010297515,0.0001188993,0.0015610764,0.000049996524,0.0023492936],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997162,0.000069386864,0.000026554691,0.00006259841,0.00006919978,0.000055968656],"domain_scores_gemma":[0.999592,0.00013212088,0.000052981657,0.000053273645,0.0001381381,0.000031473774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071806397,0.0007527873,0.00051162887,0.0016881797,0.0002334014,0.0005249267,0.0004586549,0.0005915265,0.0023165022],"category_scores_gemma":[0.0016654831,0.0001814745,0.00045196828,0.0007431301,0.00015191884,0.0005045131,0.00034312054,0.00043034105,0.0011856129],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008738768,0.00040948216,0.027001068,0.00016130369,0.00012320753,0.00038686793,0.00008065027,0.012353781,0.06509283,0.0014642613,0.006552808,0.8854999],"study_design_scores_gemma":[0.000084039886,0.0005125537,0.07129601,0.00009777773,0.00016159704,0.0012514677,0.0002294907,0.82758766,0.08303757,0.008342645,0.0073288037,0.000070397786],"about_ca_topic_score_codex":0.0028653108,"about_ca_topic_score_gemma":0.00352677,"teacher_disagreement_score":0.0028653108,"about_ca_system_score_codex":0.00036399654,"about_ca_system_score_gemma":0.000522977,"threshold_uncertainty_score":0.007749498},"labels":[],"label_agreement":null},{"id":"W3124201954","doi":"10.3390/s21030759","title":"Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biceps; Wearable computer; Muscle fatigue; Physical medicine and rehabilitation; Medicine; Dumbbell; Computer science; Physical therapy; Electromyography","score_opus":0.0794489189513806,"score_gpt":0.3334175912594966,"score_spread":0.253968672308116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124201954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.700166,0.006778718,0.27465975,0.00082398386,0.0005863326,0.0005960469,0.0063502654,0.004063976,0.0059749624],"genre_scores_gemma":[0.9105029,0.0018340473,0.07603336,0.00035937285,0.0002446068,0.00045134992,0.0068111024,0.000074029136,0.0036891294],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99931395,0.00010862459,0.000047391564,0.00026481564,0.00015948761,0.000105711966],"domain_scores_gemma":[0.99948514,0.00014542809,0.00008588673,0.000061833045,0.00017266472,0.000048995014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004922447,0.0012078311,0.0010602954,0.0019010867,0.00026192222,0.0007984499,0.0006447914,0.0011399009,0.0007190518],"category_scores_gemma":[0.0017660259,0.0002166659,0.00083214097,0.001020847,0.00022315024,0.00061466673,0.000754662,0.00060477597,0.0008997687],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001416347,0.0012834162,0.1269684,0.0013667818,0.00039099043,0.00097358954,0.00045795052,0.036480274,0.1214876,0.0008417467,0.013903022,0.69442993],"study_design_scores_gemma":[0.00010885637,0.0016423058,0.34634614,0.00040605164,0.00031407087,0.0017939664,0.00094869966,0.59991586,0.03008206,0.0026930817,0.015616529,0.00013234036],"about_ca_topic_score_codex":0.003588107,"about_ca_topic_score_gemma":0.0058382777,"teacher_disagreement_score":0.003588107,"about_ca_system_score_codex":0.00027619553,"about_ca_system_score_gemma":0.0003011166,"threshold_uncertainty_score":0.0071344376},"labels":[],"label_agreement":null},{"id":"W3124609511","doi":"10.3390/s21030702","title":"D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; EnodeB; Mobile device; Throughput; Cellular network; Distributed computing; Set (abstract data type); Nash equilibrium; Channel (broadcasting); Computer network; User equipment; Wireless; Base station; Telecommunications; Mathematical optimization","score_opus":0.010039853034039347,"score_gpt":0.22758217888156212,"score_spread":0.21754232584752278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124609511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07223281,0.00029121674,0.9050888,0.0009231586,0.00006943157,0.00016025516,0.00022956719,0.00007721452,0.020927513],"genre_scores_gemma":[0.9440586,0.00043528396,0.043700367,0.00022159924,0.000056342204,0.0002087203,0.00009975448,0.000020674783,0.011198704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929273,0.00029179093,0.000025521076,0.00009433901,0.0001423226,0.00015331687],"domain_scores_gemma":[0.99876463,0.0007107352,0.00016509785,0.000050474067,0.00016735538,0.00014162283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014496725,0.0010593039,0.00071587786,0.0005944182,0.00051467493,0.0013233848,0.0008536112,0.0011458157,0.0028701976],"category_scores_gemma":[0.0025773053,0.0002462211,0.0008542113,0.00039021234,0.0013982574,0.001043551,0.0010877941,0.001242499,0.00024512722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007239369,0.00008721593,0.0014210305,0.000085117696,0.00004401653,0.00028178346,0.00013693201,0.70618516,0.0031657922,0.2765487,0.0018798917,0.010092007],"study_design_scores_gemma":[0.0000055791415,0.000023570947,0.00017425742,0.000006405833,0.0000063117186,0.000030610485,0.000020565161,0.9790324,0.00016740634,0.020104263,0.00042288343,0.00000577702],"about_ca_topic_score_codex":0.009661722,"about_ca_topic_score_gemma":0.005529362,"teacher_disagreement_score":0.009661722,"about_ca_system_score_codex":0.0022511017,"about_ca_system_score_gemma":0.0012626011,"threshold_uncertainty_score":0.019210994},"labels":[],"label_agreement":null},{"id":"W3124627994","doi":"10.3390/s21030730","title":"Towards an Alternative to Time of Flight Diffraction Using Instantaneous Phase Coherence Imaging for Characterization of Crack-Like Defects","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sizing; Nondestructive testing; Ultrasonic sensor; Phase (matter); Time of flight; Diffraction; Characterization (materials science); Coherence (philosophical gambling strategy); Rendering (computer graphics); Computer science; Phased array; Calibration; Acoustics; Optics; Computer vision; Physics; Telecommunications","score_opus":0.01259873140147607,"score_gpt":0.25723678473604406,"score_spread":0.24463805333456798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124627994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05205358,0.0014388616,0.9445303,0.00013593063,0.000050433628,0.000054000728,0.00007213908,0.00042965708,0.0012349496],"genre_scores_gemma":[0.17730704,0.00092312525,0.8203669,0.000080095495,0.000035588695,0.000045609107,0.00013860632,0.000060716382,0.0010422386],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994499,0.000098063465,0.000019928351,0.00012222202,0.00027324728,0.00003674667],"domain_scores_gemma":[0.99920136,0.00024737258,0.00011739506,0.00011726683,0.0002729733,0.000043587137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006446326,0.0005351163,0.00039328772,0.00146068,0.00010322833,0.00091708795,0.00080235495,0.00090384757,0.00073955604],"category_scores_gemma":[0.0010134334,0.00027185195,0.0003166231,0.0010145772,0.00050622347,0.0013353823,0.00059163984,0.0004999766,0.0003668427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001271215,0.0000838416,0.002200277,0.00029657801,0.000032449338,0.0001099006,0.000117652744,0.004180632,0.8072957,0.0058248052,0.00047074672,0.17926036],"study_design_scores_gemma":[0.000054579647,0.00075698795,0.0058657136,0.00007078843,0.00007382414,0.0018014006,0.00018320525,0.41213894,0.5606838,0.0037663674,0.014489115,0.0001152427],"about_ca_topic_score_codex":0.00056698726,"about_ca_topic_score_gemma":0.0008664593,"teacher_disagreement_score":0.00146068,"about_ca_system_score_codex":0.00029981407,"about_ca_system_score_gemma":0.0004138655,"threshold_uncertainty_score":0.0034092069},"labels":[],"label_agreement":null},{"id":"W3125818097","doi":"10.3390/s21020612","title":"Marine Icing Sensor with Phase Discrimination","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Ministry of Education, Libya","keywords":"Icing; SIGNAL (programming language); Phase (matter); Layer (electronics); Signal processing; Dimension (graph theory); Acoustics; Field (mathematics); Decision tree; Tree (set theory); Biological system; Remote sensing; Computer science; Electronic engineering; Environmental science; Materials science; Meteorology; Engineering; Geology; Artificial intelligence; Physics; Mathematics; Digital signal processing; Nanotechnology","score_opus":0.010443073082178957,"score_gpt":0.22694777998528004,"score_spread":0.21650470690310109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125818097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19105984,0.0007816181,0.80313194,0.00022301835,0.0001423442,0.00010692988,0.00018927302,0.0005315614,0.0038334467],"genre_scores_gemma":[0.7576606,0.0003263202,0.23993827,0.00022375895,0.00005500361,0.00006201148,0.00014063196,0.00002284216,0.0015704901],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995763,0.00005410843,0.000015735086,0.000078238845,0.000242266,0.000033402637],"domain_scores_gemma":[0.9996692,0.00011053746,0.00004855855,0.000035181787,0.000114190174,0.000022401797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025638798,0.00032735453,0.00031255922,0.00033342338,0.00016631513,0.0003372623,0.00054685475,0.0005036895,0.0006109154],"category_scores_gemma":[0.00077721785,0.00017290757,0.00025166597,0.00045082413,0.00024197939,0.0005383483,0.0003799387,0.00040298852,0.0002638162],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022220693,0.00008157668,0.0021122645,0.0001643249,0.000039066483,0.00015413653,0.000067322435,0.014910758,0.8820073,0.0015361294,0.00053464563,0.09817022],"study_design_scores_gemma":[0.000032880274,0.0004683453,0.0042598736,0.000016429502,0.000040571682,0.0007499573,0.00003733545,0.38914195,0.5998578,0.0010290046,0.0043158713,0.000050111954],"about_ca_topic_score_codex":0.00047749936,"about_ca_topic_score_gemma":0.000610204,"teacher_disagreement_score":0.0006109154,"about_ca_system_score_codex":0.00028154513,"about_ca_system_score_gemma":0.00029687586,"threshold_uncertainty_score":0.002043724},"labels":[],"label_agreement":null},{"id":"W3125996450","doi":"10.3390/s21030769","title":"Positional Differences in Pre-Season Scrimmage Performance of Division I Collegiate Football Players","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Coaching; Football players; Match play; Heart rate; Football; Medicine; Physical therapy; Animal science; Psychology; Internal medicine; Blood pressure; Geography; Biology","score_opus":0.0204761866314764,"score_gpt":0.26009244221952943,"score_spread":0.23961625558805302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125996450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99944574,0.000042951237,0.00012043997,0.000004628336,0.000002149906,0.0000046318282,0.000051392566,0.000003942519,0.00032418992],"genre_scores_gemma":[0.99861896,0.00006519172,0.00020453641,0.000011207814,0.0000077790555,0.00001288235,0.00024299147,0.000003844901,0.0008326959],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998062,0.000021602164,0.00001390407,0.00005775341,0.000055678745,0.000044848577],"domain_scores_gemma":[0.99952507,0.00008302418,0.00018942702,0.000023648014,0.00007028713,0.000108492844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001949774,0.00038263973,0.00022632493,0.0005594264,0.00027944127,0.0003719362,0.00018906298,0.0003663263,0.0016322976],"category_scores_gemma":[0.0007414102,0.00015505096,0.00013032042,0.0003846531,0.00016066711,0.00017013063,0.0002986649,0.00020578715,0.0006012411],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005070077,0.00021467917,0.964589,0.00003749161,0.00005108695,0.00020411467,0.00079198624,0.00016660975,0.019995015,0.000018616933,0.00014104808,0.013283313],"study_design_scores_gemma":[8.384146e-7,0.000098129014,0.9995839,0.0000010604823,0.00000195102,0.000042148367,0.00008910556,0.000042438693,0.00009490097,0.0000016568677,0.000042556592,0.0000012685849],"about_ca_topic_score_codex":0.0068060937,"about_ca_topic_score_gemma":0.015923584,"teacher_disagreement_score":0.0068060937,"about_ca_system_score_codex":0.00016148234,"about_ca_system_score_gemma":0.00013220022,"threshold_uncertainty_score":0.013532996},"labels":[],"label_agreement":null},{"id":"W3126217834","doi":"10.3390/s21041234","title":"Tactile and Thermal Sensors Built from Carbon–Polymer Nanocomposites—A Critical Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Context (archaeology); Computer science; Robot; Mechanical engineering; Function (biology); Nanotechnology; Materials science; Engineering; Artificial intelligence","score_opus":0.03393675628437317,"score_gpt":0.2958733730880587,"score_spread":0.2619366168036856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126217834","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021669797,0.9982792,0.0002616817,0.00018253333,0.00021479662,0.000007401227,0.000017824876,0.0000054787497,0.0008144441],"genre_scores_gemma":[0.0010935861,0.9978676,0.00033984616,0.00018970176,0.000119352684,0.000011680472,0.000025384476,0.0000015200604,0.00035126353],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996867,0.000047083162,0.00005487189,0.00006501028,0.000118470736,0.00002797858],"domain_scores_gemma":[0.9992731,0.0004087344,0.000088865134,0.000016806298,0.00018412757,0.00002835921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007613871,0.0009433181,0.001079177,0.002612762,0.00031853144,0.000811922,0.00073416345,0.0009461377,0.0022102094],"category_scores_gemma":[0.001260879,0.00048609992,0.0006343482,0.0021633643,0.00041117828,0.0015293157,0.000529033,0.0011453398,0.00085752556],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064378095,0.00009934939,0.0002899924,0.07587565,0.00019080604,0.0002799072,0.00013872316,0.00073236896,0.0070994426,0.0074134297,0.028367301,0.87944865],"study_design_scores_gemma":[0.000008607596,0.00017771017,0.00083073915,0.0066454858,0.00025072097,0.0008886691,0.000096139825,0.00017869602,0.0027284478,0.0015499144,0.986605,0.000039856848],"about_ca_topic_score_codex":0.00089708716,"about_ca_topic_score_gemma":0.0015542322,"teacher_disagreement_score":0.002612762,"about_ca_system_score_codex":0.0004433577,"about_ca_system_score_gemma":0.001267274,"threshold_uncertainty_score":0.007393837},"labels":[],"label_agreement":null},{"id":"W3126233134","doi":"10.3390/s21041070","title":"On the Impact of Biceps Muscle Fatigue in Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biceps; Wearable computer; Muscle fatigue; Physical medicine and rehabilitation; Feed forward; Work (physics); Computer science; Muscular fatigue; Artificial neural network; Physical therapy; Electromyography; Medicine; Artificial intelligence; Engineering","score_opus":0.08800449911368347,"score_gpt":0.3233516225808209,"score_spread":0.23534712346713743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126233134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.940167,0.009561871,0.044787455,0.0005041605,0.00032478516,0.00007522569,0.0010239305,0.0006391552,0.002916509],"genre_scores_gemma":[0.99054176,0.0011141967,0.005648596,0.00013188875,0.00008869855,0.00002602416,0.0013996365,0.000032234966,0.0010169835],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99872965,0.0003662644,0.00011675217,0.0003351294,0.00032365503,0.00012857174],"domain_scores_gemma":[0.99613756,0.0027455476,0.00028761392,0.00023176929,0.00047979536,0.00011779485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019977593,0.0008145696,0.00063486386,0.00078687636,0.0002530396,0.00049090205,0.00032123405,0.0006469338,0.0009301627],"category_scores_gemma":[0.007485351,0.00014090406,0.0004050881,0.0005922774,0.0002721842,0.0006396744,0.00051348394,0.0004722387,0.00040979713],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023235225,0.0007548562,0.16809568,0.0011594574,0.0008642909,0.0007479682,0.00044248588,0.15106666,0.05227522,0.00041432306,0.0050257747,0.61682975],"study_design_scores_gemma":[0.00003869668,0.0032352584,0.4729895,0.00023356076,0.00035413585,0.0012524758,0.00038424094,0.49163854,0.024200065,0.0010979304,0.004487841,0.000087774184],"about_ca_topic_score_codex":0.0074978955,"about_ca_topic_score_gemma":0.011265782,"teacher_disagreement_score":0.0074978955,"about_ca_system_score_codex":0.00028642558,"about_ca_system_score_gemma":0.0003014021,"threshold_uncertainty_score":0.014908552},"labels":[],"label_agreement":null},{"id":"W3126300220","doi":"10.3390/s21041053","title":"A Color Restoration Algorithm for Diffractive Optical Images of Membrane Camera","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; York University","keywords":"Chromatic aberration; Robustness (evolution); Computer science; Chromatic scale; Artificial intelligence; Image restoration; Computer vision; Optics; Algorithm; Image processing; Image (mathematics); Physics","score_opus":0.011357170736262013,"score_gpt":0.28089860863040184,"score_spread":0.2695414378941398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126300220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010499408,0.00019178331,0.9881467,0.00006665048,0.000035284294,0.000026297565,0.00001452367,0.00033379282,0.0006855408],"genre_scores_gemma":[0.09482745,0.00035244037,0.90217304,0.000059857884,0.000027116263,0.00005391072,0.000083398765,0.00006227638,0.0023605786],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964166,0.000035912795,0.00001797421,0.00007634469,0.00019335376,0.00003465419],"domain_scores_gemma":[0.9997141,0.000035629717,0.000039575345,0.00004098617,0.00014872159,0.000021007405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038456256,0.0005825721,0.0005515285,0.0006370522,0.00039718888,0.00069128483,0.0008640931,0.0007962571,0.0011984388],"category_scores_gemma":[0.00077887456,0.0003083901,0.0006915264,0.0006863088,0.00041104853,0.0006963913,0.00055326486,0.0010137096,0.00069145556],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023826199,0.000083415136,0.0009782879,0.00020878311,0.00006886572,0.00014387204,0.00016705722,0.08629097,0.22323796,0.010619538,0.0022654324,0.67569757],"study_design_scores_gemma":[0.000016495442,0.00007228898,0.0005169146,0.000010656363,0.000018989376,0.0003029889,0.00003617993,0.92668957,0.06720011,0.0014231071,0.003678904,0.00003388934],"about_ca_topic_score_codex":0.003980086,"about_ca_topic_score_gemma":0.002710102,"teacher_disagreement_score":0.003980086,"about_ca_system_score_codex":0.000686577,"about_ca_system_score_gemma":0.0011438276,"threshold_uncertainty_score":0.0079138875},"labels":[],"label_agreement":null},{"id":"W3126384481","doi":"10.3390/s21030962","title":"Comparison of Heating Strategies on Soil Water Measurement Using Actively Heated Fiber Optics on Contrasting Textured Soils","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Soil water; Environmental science; Optical fiber; Intensity (physics); Sensitivity (control systems); Power (physics); Water content; Materials science; Optics; Soil science; Geotechnical engineering; Geology; Physics","score_opus":0.057885402015054294,"score_gpt":0.29681978047284374,"score_spread":0.23893437845778945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126384481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99179614,0.00019092279,0.0076754596,0.000012427995,0.00000711364,0.000020811885,0.000049216815,0.000039342372,0.00020851412],"genre_scores_gemma":[0.9887727,0.00027047176,0.010596212,0.000016961716,0.0000050946082,0.000032954194,0.00005179283,0.00001430498,0.00023965989],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997336,0.00005220601,0.00001383757,0.000086651344,0.00007856013,0.000035146393],"domain_scores_gemma":[0.99954754,0.00026081427,0.00004788504,0.00002933934,0.00009845474,0.00001597934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058258645,0.00047719746,0.00029788972,0.00032036516,0.00016632972,0.0002627882,0.00028376537,0.00031774287,0.00023396489],"category_scores_gemma":[0.00092523365,0.00022265378,0.00032394877,0.000253974,0.0003214102,0.0004257009,0.00031169533,0.0002823817,0.00006654718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044253073,0.000067286215,0.008743198,0.00013901466,0.000039509905,0.00003984163,0.00018430702,0.004724613,0.96264756,0.00004484875,0.000036062527,0.022891378],"study_design_scores_gemma":[0.00001783691,0.0006448428,0.04098768,0.000013380828,0.00007682825,0.00004671428,0.00014334582,0.031195093,0.9263564,0.00006104102,0.00042409837,0.000032639422],"about_ca_topic_score_codex":0.0029847594,"about_ca_topic_score_gemma":0.0059859594,"teacher_disagreement_score":0.0029847594,"about_ca_system_score_codex":0.00025247905,"about_ca_system_score_gemma":0.00015583874,"threshold_uncertainty_score":0.005934775},"labels":[],"label_agreement":null},{"id":"W3127188496","doi":"10.3390/s21041047","title":"Devising Digital Twins DNA Paradigm for Modeling ISO-Based City Services","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Interoperability; Smart city; 3D city models; Computer science; Matching (statistics); Inner city; Data science; Data mining; Geography; World Wide Web; Mathematics; Environmental planning","score_opus":0.02774886307517578,"score_gpt":0.23605973275554604,"score_spread":0.20831086968037027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127188496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015416518,0.0000809828,0.97548556,0.000248666,0.00006291663,0.00018048976,0.0007068027,0.0005074697,0.007310589],"genre_scores_gemma":[0.16158685,0.00032090722,0.82951456,0.00016218846,0.00002819238,0.0004668166,0.002698425,0.00019074164,0.0050312956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985415,0.00035055453,0.00014896976,0.00030724224,0.0005253752,0.00012644407],"domain_scores_gemma":[0.99912447,0.00019133955,0.000111976355,0.00015293664,0.00033605835,0.000083198844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012938695,0.000660551,0.00045762598,0.0027796873,0.0010735517,0.002733395,0.0020065487,0.0012706782,0.0028793705],"category_scores_gemma":[0.0041257036,0.00040157555,0.001708004,0.0031771616,0.0013132028,0.003655068,0.0025717132,0.0013266703,0.0009904121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010331347,0.00012504996,0.009707808,0.00020204025,0.00007182351,0.00058275834,0.001287263,0.3923017,0.0071860817,0.5047097,0.0037626675,0.079959676],"study_design_scores_gemma":[0.00001565293,0.000047836886,0.0008674264,0.000054668075,0.000036637506,0.00019737054,0.00058789586,0.8711709,0.0039351406,0.07117784,0.05187357,0.000035086676],"about_ca_topic_score_codex":0.05952722,"about_ca_topic_score_gemma":0.057172008,"teacher_disagreement_score":0.05952722,"about_ca_system_score_codex":0.0029943075,"about_ca_system_score_gemma":0.0039582993,"threshold_uncertainty_score":0.11836147},"labels":[],"label_agreement":null},{"id":"W3127765628","doi":"10.3390/s21030901","title":"QSMVM: QoS-Aware and Social-Aware Multimetric Routing Protocol for Video-Streaming Services over MANETS","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"European Regional Development Fund; Atlantic Research Center for Information and Communication Technologies","keywords":"Computer science; Mobile ad hoc network; Computer network; Quality of service; Routing protocol; Node (physics); Routing (electronic design automation); Service (business); Metric (unit); Multimedia; Network packet; Business","score_opus":0.018840223683593747,"score_gpt":0.2896469990988489,"score_spread":0.27080677541525516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127765628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021117339,0.0025224334,0.96286523,0.001531848,0.00077478803,0.0007447064,0.0003683426,0.0028846997,0.007190615],"genre_scores_gemma":[0.74299824,0.0023300755,0.24347529,0.0010733342,0.0004479762,0.0010582014,0.0009573766,0.00016033216,0.007499025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906653,0.00031524725,0.00008718616,0.00011314462,0.00031602598,0.00010192279],"domain_scores_gemma":[0.99898475,0.00035175745,0.00013691871,0.00013313946,0.00029915496,0.00009425833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001337608,0.00061553845,0.0008669812,0.0013001877,0.0011879003,0.0011485158,0.0015549243,0.0010311926,0.0015661295],"category_scores_gemma":[0.003674296,0.0002074287,0.0003967635,0.0011513699,0.00068449613,0.0014278004,0.0019262972,0.0010176018,0.0005423496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070888107,0.00031341688,0.0018534738,0.0009931357,0.0002185573,0.0012649205,0.0009671816,0.21457879,0.048422582,0.2056352,0.048011847,0.477032],"study_design_scores_gemma":[0.00008372315,0.00031214886,0.00050604565,0.00008202952,0.0000601138,0.0005916199,0.00019562001,0.90292406,0.0068902983,0.041442677,0.046833795,0.00007791637],"about_ca_topic_score_codex":0.0018715364,"about_ca_topic_score_gemma":0.0028696586,"teacher_disagreement_score":0.0018715364,"about_ca_system_score_codex":0.0012236532,"about_ca_system_score_gemma":0.0014143122,"threshold_uncertainty_score":0.008878291},"labels":[],"label_agreement":null},{"id":"W3127795108","doi":"10.3390/s21030988","title":"Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"RGB color model; Computer science; Computer vision; The Internet; Artificial intelligence; Automation; Activity recognition; Thermal; Engineering; Geography","score_opus":0.018458118149390374,"score_gpt":0.2649695963617268,"score_spread":0.2465114782123364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127795108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02752016,0.00018367165,0.96934503,0.000066939196,0.00004402893,0.000042552667,0.000050160197,0.00084640615,0.001900999],"genre_scores_gemma":[0.5346547,0.00050642696,0.46121615,0.00012640347,0.000060926497,0.00014702854,0.000335219,0.000115469855,0.0028376232],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976915,0.000043499174,0.0000139596095,0.000063964704,0.000080991405,0.00002836544],"domain_scores_gemma":[0.99985266,0.000043477256,0.00002021221,0.000024111821,0.000047115147,0.000012384897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020731858,0.00048389245,0.00044260774,0.00076772395,0.00021919947,0.0004982677,0.0005385908,0.00043320324,0.0010295454],"category_scores_gemma":[0.0005629567,0.00025605183,0.0006988691,0.0005458286,0.00024522564,0.00070875254,0.00038011442,0.0002635683,0.00059469696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023414785,0.00022441888,0.008953991,0.00026382654,0.00011710268,0.0004781028,0.000227623,0.121479444,0.0805012,0.0040732245,0.0028873284,0.78055966],"study_design_scores_gemma":[0.000014447148,0.000121429024,0.010172476,0.000036387006,0.00005384733,0.00089010975,0.00017110752,0.92839545,0.04966908,0.0048231375,0.0056147277,0.000037720558],"about_ca_topic_score_codex":0.0011343207,"about_ca_topic_score_gemma":0.0028292357,"teacher_disagreement_score":0.0011343207,"about_ca_system_score_codex":0.0002242461,"about_ca_system_score_gemma":0.00020846148,"threshold_uncertainty_score":0.0034441948},"labels":[],"label_agreement":null},{"id":"W3127797135","doi":"10.3390/s21031020","title":"A Spectral-Based Approach for BCG Signal Content Classification","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université de Sherbrooke","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Segmentation; Centroid; SIGNAL (programming language)","score_opus":0.06176392422137034,"score_gpt":0.23815293487633857,"score_spread":0.17638901065496823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127797135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006747516,0.0002757097,0.99054563,0.000067956,0.000058382182,0.00007750938,0.00011450524,0.0008245171,0.0012882621],"genre_scores_gemma":[0.17642185,0.0006853759,0.81606525,0.00017842546,0.00030971455,0.0002803638,0.0011050398,0.00032297353,0.004631026],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992052,0.00015062974,0.00005649887,0.00021037168,0.00029040765,0.000087045395],"domain_scores_gemma":[0.99925286,0.00018622584,0.00007937766,0.0001054225,0.0003317344,0.000044401993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008386999,0.0010500721,0.0008554962,0.0034344348,0.0004761381,0.0010291649,0.0009976907,0.0012093597,0.0023516852],"category_scores_gemma":[0.0018743647,0.00026247595,0.0010586068,0.0019090009,0.00047370192,0.000905711,0.0007622343,0.000922967,0.002747451],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029821013,0.00028175034,0.0015189474,0.00021941398,0.000071146525,0.00011775462,0.00015671004,0.024215924,0.10641598,0.0063803596,0.003289161,0.8570346],"study_design_scores_gemma":[0.00002133039,0.00028934877,0.00564892,0.000074073316,0.000103539816,0.0004659941,0.0001389357,0.9301925,0.041962076,0.007998252,0.013040281,0.0000648042],"about_ca_topic_score_codex":0.0016088738,"about_ca_topic_score_gemma":0.001828318,"teacher_disagreement_score":0.0034344348,"about_ca_system_score_codex":0.00040202547,"about_ca_system_score_gemma":0.00061037997,"threshold_uncertainty_score":0.007867098},"labels":[],"label_agreement":null},{"id":"W3127847014","doi":"10.3390/s21041089","title":"Acoustic Emission Signal Entropy as a Means to Estimate Loads in Fiber Reinforced Polymer Rods","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"Rod; Acoustic emission; Fibre-reinforced plastic; Materials science; Composite material; Structural engineering; Engineering","score_opus":0.01051976805617845,"score_gpt":0.2947541379938566,"score_spread":0.28423436993767814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127847014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92627084,0.00045984468,0.07119964,0.000040784973,0.00002310107,0.000027618478,0.00022523162,0.00031737692,0.0014355755],"genre_scores_gemma":[0.99233055,0.00012082863,0.006985746,0.000010761987,0.000013558058,0.000011900046,0.0001292511,0.0000141788605,0.00038316532],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996896,0.00003773089,0.000014641626,0.000054284723,0.00017147475,0.00003227432],"domain_scores_gemma":[0.99914026,0.00034601433,0.00025660492,0.000049659244,0.00016783508,0.000039714254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004325373,0.00046580235,0.00021971443,0.001478965,0.00013516343,0.0002707573,0.00023829895,0.0002907199,0.0005279223],"category_scores_gemma":[0.0011368066,0.0001580837,0.00015739935,0.0006686976,0.00030291794,0.0004987838,0.0003931563,0.0002685092,0.00017104704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074573595,0.00016485427,0.062352,0.00022088947,0.00006986589,0.0003683913,0.00037410625,0.058249243,0.764296,0.00066712644,0.00035166123,0.11214016],"study_design_scores_gemma":[0.000011813779,0.00060082006,0.24377488,0.000034439927,0.00005679105,0.00045570816,0.00025441742,0.43353692,0.3190468,0.0008058207,0.0013144034,0.00010716876],"about_ca_topic_score_codex":0.000589828,"about_ca_topic_score_gemma":0.0007904157,"teacher_disagreement_score":0.001478965,"about_ca_system_score_codex":0.0001812046,"about_ca_system_score_gemma":0.00008974547,"threshold_uncertainty_score":0.002287507},"labels":[],"label_agreement":null},{"id":"W3128086051","doi":"10.3390/s21030900","title":"Methodology for the Implementation of Internal Standard to Laser-Induced Breakdown Spectroscopy Analysis of Soft Tissues","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Masarykova Univerzita; Grantová Agentura České Republiky; Strategic Innovation Fund","keywords":"Laser-induced breakdown spectroscopy; SIGNAL (programming language); Laser ablation; Elemental analysis; Laser; Materials science; Biological system; Biomedical engineering; Biological tissue; Computer science; Soft tissue; Optics; Chemistry; Engineering; Pathology; Medicine; Physics","score_opus":0.031391703752110645,"score_gpt":0.34646378364411173,"score_spread":0.3150720798920011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128086051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014493835,0.00078500266,0.9770529,0.00014646235,0.00024826612,0.0014800723,0.0009659295,0.0030597816,0.0017677677],"genre_scores_gemma":[0.032022685,0.00090100314,0.9568441,0.00017502277,0.00005892145,0.005013884,0.001749893,0.00069616456,0.0025383623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99528766,0.00089120853,0.00049437484,0.001082085,0.0020136337,0.00023095158],"domain_scores_gemma":[0.99591774,0.0006902372,0.00040278808,0.0014501816,0.0014259393,0.00011302823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00618603,0.001921216,0.0011329608,0.002725549,0.0012447514,0.0012876254,0.0022341413,0.0016684644,0.0071520265],"category_scores_gemma":[0.0058926833,0.0011604312,0.0011279747,0.0013752517,0.0013291257,0.0008287517,0.0018772163,0.0026520619,0.005264702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016280789,0.00019639524,0.00075687474,0.0008226562,0.00006360957,0.00021742328,0.00023561563,0.0008918342,0.9440544,0.0040719137,0.001908666,0.04661781],"study_design_scores_gemma":[0.000021577784,0.00042638983,0.0014039794,0.00009548016,0.000052361036,0.00049869734,0.000064539396,0.004221691,0.95285875,0.001557433,0.038729742,0.00006945925],"about_ca_topic_score_codex":0.0005653977,"about_ca_topic_score_gemma":0.0012767136,"teacher_disagreement_score":0.0071520265,"about_ca_system_score_codex":0.0007012432,"about_ca_system_score_gemma":0.0025523044,"threshold_uncertainty_score":0.0327152},"labels":[],"label_agreement":null},{"id":"W3129004031","doi":"10.3390/s21030976","title":"Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"National Institute on Disability, Independent Living, and Rehabilitation Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intersection (aeronautics); Tripping; Mean squared error; Artificial intelligence; Sagittal plane; Motion capture; Simulation; Computer vision; Foot (prosody); Motion (physics); Mathematics; Statistics; Engineering; Transport engineering; Medicine","score_opus":0.03466145204536756,"score_gpt":0.3025154258463826,"score_spread":0.267853973801015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129004031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029186694,0.00013492192,0.9626735,0.00034222848,0.00020498343,0.00051414286,0.00027345138,0.0043323752,0.0023376092],"genre_scores_gemma":[0.3300333,0.00023487423,0.6636873,0.00039279452,0.00009566195,0.0007900923,0.0007227953,0.00023235317,0.0038108297],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894124,0.00008137831,0.000041926894,0.00023138194,0.00063831,0.00006575269],"domain_scores_gemma":[0.9988263,0.00017340905,0.00008121281,0.0001102847,0.0007396895,0.00006909132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010449253,0.00070153095,0.00068954256,0.00042734414,0.00035250935,0.0007424003,0.0015770752,0.0011116094,0.0033431724],"category_scores_gemma":[0.0024569272,0.0005774161,0.0003503402,0.0002851711,0.00033520948,0.001112378,0.0010847237,0.0011638536,0.0023521194],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005058732,0.0007112625,0.010416799,0.0005719228,0.00012715801,0.0008438818,0.00026516416,0.025010148,0.40310183,0.004628986,0.016656136,0.5371609],"study_design_scores_gemma":[0.00027701925,0.0015208798,0.0129856905,0.00013213133,0.00008682864,0.0017397124,0.00014613016,0.7023148,0.24719982,0.002383298,0.031041594,0.00017215483],"about_ca_topic_score_codex":0.0031468975,"about_ca_topic_score_gemma":0.0025177512,"teacher_disagreement_score":0.0033431724,"about_ca_system_score_codex":0.00043649206,"about_ca_system_score_gemma":0.0013638786,"threshold_uncertainty_score":0.011183977},"labels":[],"label_agreement":null},{"id":"W3129409897","doi":"10.3390/s21041454","title":"Fiber Bragg Grating Wavelength Drift in Long-Term High Temperature Annealing","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fiber Bragg grating; Materials science; Wavelength; Temperature measurement; Atmospheric temperature range; Optoelectronics; Optics; Thermocouple; Annealing (glass); Survivability; Optical fiber; Computer science; Physics; Meteorology; Composite material","score_opus":0.016851506649643317,"score_gpt":0.2762034620009415,"score_spread":0.2593519553512982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129409897","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004744453,0.99803025,0.00036176102,0.00010082106,0.00013730978,0.000004365735,0.000008168247,0.0000045068623,0.00087831967],"genre_scores_gemma":[0.0038252291,0.99443465,0.0004500396,0.000109734996,0.00014195118,0.00001016912,0.000021742506,0.0000021669402,0.0010043476],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997179,0.000036274803,0.00002765016,0.00006716717,0.00012219862,0.000028846782],"domain_scores_gemma":[0.9996113,0.00016125575,0.00007460918,0.000013046061,0.00012303906,0.00001670948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006568653,0.00078628684,0.0008257755,0.0017417033,0.00025667585,0.0007131545,0.0006936289,0.0011653639,0.001304677],"category_scores_gemma":[0.00069928385,0.00038365825,0.00047313125,0.0021213405,0.00046071655,0.0011345639,0.0004508864,0.0009353234,0.0010039251],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082680635,0.00012146963,0.0006306556,0.0255645,0.00010397332,0.0003199725,0.00013345348,0.0015148913,0.012506659,0.009736973,0.011800387,0.93748444],"study_design_scores_gemma":[0.000009264656,0.00028048633,0.0027000234,0.004312274,0.00021533012,0.0022438108,0.00013949108,0.00086985395,0.014258123,0.0041782986,0.97073203,0.000061137405],"about_ca_topic_score_codex":0.0010989037,"about_ca_topic_score_gemma":0.0014064984,"teacher_disagreement_score":0.0017417033,"about_ca_system_score_codex":0.0005681368,"about_ca_system_score_gemma":0.00082450727,"threshold_uncertainty_score":0.0043646097},"labels":[],"label_agreement":null},{"id":"W3130750914","doi":"10.3390/s21041447","title":"Ultrafast Laser Processing of Optical Fibers for Sensing Applications","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Infineon Technologies (Canada); National Research Council Canada","funders":"","keywords":"Fiber Bragg grating; Materials science; PHOSFOS; Optics; Femtosecond; Fiber laser; Optoelectronics; Laser; Fiber optic sensor; Cladding (metalworking); Optical fiber; Plastic optical fiber; Long-period fiber grating; Photonic-crystal fiber; Physics","score_opus":0.02961174767305978,"score_gpt":0.30569236192836774,"score_spread":0.27608061425530794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130750914","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006037721,0.99408877,0.0011643817,0.00012957671,0.0002477917,0.000016974767,0.00002462018,0.000021068097,0.0037030876],"genre_scores_gemma":[0.0031093678,0.99051535,0.0015003412,0.00018662887,0.00018063093,0.000028953473,0.000070226524,0.000005090793,0.004403444],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998209,0.000019015932,0.000013125228,0.00003953068,0.00008702338,0.000020442834],"domain_scores_gemma":[0.9998596,0.000053047974,0.000024560604,0.000007509322,0.00004639161,0.000008982254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037301498,0.0009258876,0.0005624691,0.0019878575,0.00026427818,0.0005601806,0.0005767511,0.0007896379,0.0035102488],"category_scores_gemma":[0.00031264446,0.00038797548,0.00044235104,0.0016131987,0.0002659857,0.0011264537,0.0005103404,0.0010591822,0.0028756573],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003322703,0.000112780115,0.00016017139,0.011798669,0.00005687476,0.00019723618,0.00006404855,0.0006346729,0.03233685,0.0049149846,0.013828055,0.9358625],"study_design_scores_gemma":[0.0000081946,0.00014546765,0.0007793138,0.0017571867,0.000046671776,0.0011505517,0.000046018667,0.00030895724,0.013647956,0.0022196097,0.979861,0.000028979579],"about_ca_topic_score_codex":0.00082916085,"about_ca_topic_score_gemma":0.0011064769,"teacher_disagreement_score":0.0035102488,"about_ca_system_score_codex":0.0004106326,"about_ca_system_score_gemma":0.0005186096,"threshold_uncertainty_score":0.01174289},"labels":[],"label_agreement":null},{"id":"W3130840916","doi":"10.3390/s21041383","title":"A New Perspective on Low-Cost MEMS-Based AHRS Determination","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Attitude and heading reference system; Accelerometer; Initialization; Kalman filter; Orientation (vector space); Control theory (sociology); Gyroscope; Computer science; Process (computing); Engineering; Artificial intelligence; Mathematics; Aerospace engineering","score_opus":0.007991293188096035,"score_gpt":0.24234716060787004,"score_spread":0.234355867419774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130840916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0083802305,0.011768561,0.9556457,0.0027896632,0.0012990737,0.00005174886,0.00010287446,0.00080840604,0.019153766],"genre_scores_gemma":[0.23202875,0.016619528,0.71161425,0.0015770266,0.0033827098,0.00012525453,0.00034728646,0.00018402078,0.034121133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999374,0.00008713301,0.000023542827,0.00010859713,0.00038030563,0.000026505832],"domain_scores_gemma":[0.99935824,0.00016331545,0.000046862293,0.00010414299,0.00030249287,0.000024956278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006896356,0.00054421456,0.00048831635,0.00048930437,0.000222529,0.0011089832,0.0009697796,0.0008692133,0.0027518873],"category_scores_gemma":[0.0012245079,0.00026652895,0.00029746545,0.0005722329,0.0005765285,0.0017027581,0.0005580088,0.0013217818,0.0014315847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020128876,0.00010604683,0.0022084792,0.00078866223,0.000083692976,0.00018876157,0.00022010661,0.021855567,0.106961034,0.16157329,0.013911108,0.69190186],"study_design_scores_gemma":[0.0000700712,0.0015167181,0.005387239,0.000337043,0.00017834871,0.0010810517,0.000281308,0.29919583,0.11133652,0.042216137,0.538231,0.000168679],"about_ca_topic_score_codex":0.0008468759,"about_ca_topic_score_gemma":0.00092030136,"teacher_disagreement_score":0.0027518873,"about_ca_system_score_codex":0.00042347563,"about_ca_system_score_gemma":0.00052013464,"threshold_uncertainty_score":0.009205997},"labels":[],"label_agreement":null},{"id":"W3131127964","doi":"10.3390/s21041476","title":"Cortical Effects of Noisy Galvanic Vestibular Stimulation Using Functional Near-Infrared Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"California HIV/AIDS Research Program","keywords":"Galvanic vestibular stimulation; Functional near-infrared spectroscopy; Stimulation; Neuroscience; Vestibular system; Medicine; Psychology; Cognition; Prefrontal cortex","score_opus":0.015398680230193657,"score_gpt":0.30215272360759465,"score_spread":0.286754043377401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131127964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99533725,0.00027863876,0.0037916668,0.00005034261,0.000012080038,0.000027775859,0.00005251274,0.00001947667,0.00043023395],"genre_scores_gemma":[0.9981767,0.00014472914,0.0013267517,0.00003379534,0.000009520158,0.0000330458,0.000030289468,0.0000046899577,0.00024061845],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99988556,0.000031028725,0.0000054945413,0.000027748501,0.000030220353,0.000019871093],"domain_scores_gemma":[0.9998938,0.000048767462,0.000020983307,0.0000068947793,0.000015506612,0.000014022999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014306656,0.00034659097,0.00021372487,0.0001500915,0.00011810791,0.00017763772,0.00014115674,0.00023883619,0.001316255],"category_scores_gemma":[0.0006063637,0.00013308735,0.000127494,0.000071359034,0.00037154037,0.00019729034,0.00026015023,0.0001623731,0.00008749916],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020258273,0.00009131711,0.003355717,0.0001232784,0.000029251016,0.000097201366,0.00011661539,0.00039499515,0.9782427,0.000057535774,0.00007601606,0.015389504],"study_design_scores_gemma":[0.00028359116,0.008977377,0.496065,0.000053283475,0.00019857532,0.0009298907,0.0008330356,0.006556374,0.48325065,0.0015764766,0.0012252657,0.000050448358],"about_ca_topic_score_codex":0.0009795355,"about_ca_topic_score_gemma":0.002341881,"teacher_disagreement_score":0.001316255,"about_ca_system_score_codex":0.00013550669,"about_ca_system_score_gemma":0.00013795633,"threshold_uncertainty_score":0.004403293},"labels":[],"label_agreement":null},{"id":"W3131178462","doi":"10.3390/s21041391","title":"Elucidating the Quenching Mechanism in Carbon Dot-Metal Interactions–Designing Sensitive and Selective Optical Probes","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Fluorescence; Metal ions in aqueous solution; Nanotechnology; Materials science; Characterization (materials science); Quenching (fluorescence); Biosensor; Carbon fibers; Metal; Formamide; Chemistry; Organic chemistry","score_opus":0.012929442146629597,"score_gpt":0.25811022803137834,"score_spread":0.24518078588474873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131178462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92531,0.002514241,0.068453506,0.0003185206,0.000031092495,0.00011362873,0.000110311856,0.00015268006,0.0029960375],"genre_scores_gemma":[0.9692308,0.0011977175,0.027790241,0.00009599671,0.0000052647797,0.000059029106,0.0000912354,0.000014153277,0.0015155361],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988604,0.000014018638,0.0000059641116,0.00004082647,0.000031052983,0.000022050457],"domain_scores_gemma":[0.99991024,0.00004083751,0.000016101632,0.000006349596,0.000018835959,0.000007684755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023693823,0.0002553899,0.00016333928,0.00011255101,0.00019248445,0.00020350904,0.00029201776,0.0005596165,0.00052331557],"category_scores_gemma":[0.00024196414,0.00012736909,0.000121102385,0.000120744124,0.00033822036,0.00032609675,0.00015167566,0.00035910172,0.0001621121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014515851,0.000012013194,0.00010048764,0.000072117946,0.000002289677,0.00003314065,0.000023429551,0.0004052841,0.9966934,0.0008322836,0.00003352795,0.0017775773],"study_design_scores_gemma":[0.0000026611758,0.000056337714,0.00026527434,0.000002555428,0.0000027085848,0.00004415959,0.000010623731,0.0049248505,0.9935417,0.00012599769,0.0010187195,0.000004427629],"about_ca_topic_score_codex":0.00097437983,"about_ca_topic_score_gemma":0.0017974508,"teacher_disagreement_score":0.00097437983,"about_ca_system_score_codex":0.00051076396,"about_ca_system_score_gemma":0.00021610512,"threshold_uncertainty_score":0.0037059188},"labels":[],"label_agreement":null},{"id":"W3131684588","doi":"10.3390/s21041509","title":"Fruit Quality Monitoring with Smart Packaging","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Postharvest Quality and Shelf Life Management","field":"Agricultural and Biological Sciences","cited_by":205,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Active packaging; Shelf life; Quality (philosophy); Food packaging; Plastic packaging; Risk analysis (engineering); Business; Computer science; Engineering","score_opus":0.1579636865543911,"score_gpt":0.3505862213528389,"score_spread":0.1926225347984478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131684588","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024381853,0.9869441,0.0034546654,0.00032377313,0.0003573937,0.000037487254,0.00014360894,0.00007135805,0.006229471],"genre_scores_gemma":[0.016377082,0.9744349,0.0043376083,0.0004040615,0.00019495506,0.00005081867,0.00025325434,0.000013397998,0.0039339294],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995952,0.000050562987,0.00003070716,0.000098440556,0.0001936934,0.000031366395],"domain_scores_gemma":[0.99973184,0.0001061051,0.000046464887,0.00001245622,0.00009142288,0.0000116856345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000544351,0.00093844585,0.001118858,0.0019896254,0.00019663286,0.0010420062,0.0008146736,0.001368123,0.0023223115],"category_scores_gemma":[0.00069698115,0.0003680105,0.0010309436,0.0017620778,0.00028821654,0.0015488119,0.00056707294,0.0011256862,0.0012752849],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010825123,0.00013074424,0.0010253376,0.031281527,0.00019302563,0.000315519,0.00010622491,0.0011622321,0.042823814,0.0055388412,0.013442463,0.9038721],"study_design_scores_gemma":[0.000017521905,0.00043404227,0.0040041353,0.0041559814,0.00037329015,0.0022404278,0.00014475812,0.0016584647,0.0360217,0.00300176,0.94786435,0.000083517036],"about_ca_topic_score_codex":0.00074013486,"about_ca_topic_score_gemma":0.0010287238,"teacher_disagreement_score":0.0023223115,"about_ca_system_score_codex":0.0004305171,"about_ca_system_score_gemma":0.00044060728,"threshold_uncertainty_score":0.007768929},"labels":[],"label_agreement":null},{"id":"W3131832053","doi":"10.3390/s21041327","title":"Accelerometer-Based Wheel Odometer for Kinematics Determination","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometer; Accelerometer; Odometry; Kinematics; Inertial measurement unit; Computer science; Inertial navigation system; Automotive engineering; Process (computing); Simulation; Inertial frame of reference; Engineering; Computer vision; Artificial intelligence; Mobile robot; Robot","score_opus":0.019214425353031812,"score_gpt":0.24452054738724383,"score_spread":0.22530612203421202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131832053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09392608,0.0018195562,0.87106884,0.00013295568,0.0011525856,0.0005684722,0.0042836634,0.006530602,0.020517247],"genre_scores_gemma":[0.7044633,0.0020619244,0.2712314,0.00018591613,0.0002043015,0.00048074426,0.0044710687,0.00021445843,0.016686868],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929595,0.000056122273,0.00004946769,0.00013522687,0.0004106939,0.000052477953],"domain_scores_gemma":[0.99938154,0.000048409536,0.000069887,0.00008633811,0.0003893336,0.000024451685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025253659,0.0011535244,0.0007341568,0.0013452978,0.0003332038,0.0006310369,0.0008763428,0.00044713658,0.004111692],"category_scores_gemma":[0.0010422877,0.00037054234,0.0003081406,0.0011728621,0.00015604425,0.00069433753,0.00073436677,0.00047360596,0.0036239212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005049641,0.0002957128,0.022721965,0.0017114381,0.0001716465,0.00022265795,0.00027967425,0.007010999,0.28326914,0.005291208,0.015906582,0.662614],"study_design_scores_gemma":[0.0002186885,0.0014115283,0.09386804,0.00054236245,0.000484241,0.001955081,0.00060165476,0.28434688,0.43111804,0.003987509,0.18109158,0.00037448658],"about_ca_topic_score_codex":0.0023451329,"about_ca_topic_score_gemma":0.004676883,"teacher_disagreement_score":0.004111692,"about_ca_system_score_codex":0.00020686915,"about_ca_system_score_gemma":0.0007262698,"threshold_uncertainty_score":0.013754904},"labels":[],"label_agreement":null},{"id":"W3132698244","doi":"10.3390/s22103617","title":"SCD: A Stacked Carton Dataset for Detection and Segmentation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Carton; Computer science; Classifier (UML); Artificial intelligence; Segmentation; Computer vision; Engineering","score_opus":0.018622672787664,"score_gpt":0.27118897448788987,"score_spread":0.25256630170022587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132698244","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07560414,0.00525608,0.050243303,0.0010349726,0.0013885079,0.0012239047,0.74265975,0.102436736,0.020152535],"genre_scores_gemma":[0.040730603,0.00058934337,0.04630344,0.000371708,0.000087703054,0.00034680121,0.9057047,0.0017601146,0.004105629],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845326,0.00011813694,0.00009050827,0.00057793694,0.00045298654,0.0003071905],"domain_scores_gemma":[0.9990478,0.00011361771,0.00007936985,0.00032012022,0.00033257663,0.00010642842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007271649,0.0054903734,0.0017953602,0.0048225857,0.0013322973,0.0023828482,0.0044855988,0.0029920023,0.010557575],"category_scores_gemma":[0.0019500998,0.0010606681,0.0023571039,0.0041373693,0.0006886494,0.001904932,0.0021153905,0.002147134,0.012318538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091424107,0.00062641315,0.005696862,0.0013893633,0.00036855706,0.0008188902,0.00013377817,0.015670646,0.017571768,0.001880438,0.8027446,0.15218447],"study_design_scores_gemma":[0.0005740017,0.0005529847,0.03235344,0.00056222756,0.00037133694,0.0029794597,0.0007010279,0.31313404,0.060376775,0.007883869,0.580013,0.0004978282],"about_ca_topic_score_codex":0.053477515,"about_ca_topic_score_gemma":0.12116904,"teacher_disagreement_score":0.053477515,"about_ca_system_score_codex":0.0020493143,"about_ca_system_score_gemma":0.0022419496,"threshold_uncertainty_score":0.10633248},"labels":[],"label_agreement":null},{"id":"W3132794641","doi":"10.3390/s21041459","title":"An Investigation of Silica Aerogel to Reduce Acoustic Crosstalk in CMUT Arrays","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Capacitive micromachined ultrasonic transducers; Materials science; Aerogel; Ultrasonic sensor; Capacitive sensing; Crosstalk; Nanoporous; Acoustics; Transducer; Optoelectronics; Electronic engineering; Nanotechnology; Electrical engineering; Engineering","score_opus":0.015372929236709314,"score_gpt":0.25138848023903715,"score_spread":0.23601555100232782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132794641","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9526787,0.0017041373,0.042164892,0.00010456782,0.000058115787,0.000023794508,0.000055402772,0.00021689407,0.0029934992],"genre_scores_gemma":[0.96895695,0.00078963104,0.028558582,0.00004876032,0.00001612421,0.000017843739,0.000051598305,0.000029419518,0.0015310565],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997826,0.000023188588,0.0000075641583,0.00004657302,0.000109962435,0.000030172521],"domain_scores_gemma":[0.99985313,0.000043866272,0.000039636907,0.000010346178,0.00003863112,0.0000143038515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016063663,0.00029430768,0.00015905972,0.00018379842,0.00019928171,0.00020606379,0.00021236451,0.00023151062,0.00031548113],"category_scores_gemma":[0.00029241448,0.00012799172,0.00015864242,0.00023998963,0.00020293296,0.00037512768,0.00023822261,0.00019739967,0.00008465849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012887024,0.000005454686,0.00011296192,0.000029727786,0.0000025437525,0.000044224456,0.000022677887,0.00047074916,0.99737346,0.00011645705,0.000016461972,0.0017925149],"study_design_scores_gemma":[0.0000010948464,0.00007143263,0.00035064502,0.0000016166206,0.000003911911,0.000065424116,0.00002253656,0.0027530678,0.99592197,0.000017656856,0.00078718294,0.0000033935348],"about_ca_topic_score_codex":0.0003985013,"about_ca_topic_score_gemma":0.0009261324,"teacher_disagreement_score":0.0003985013,"about_ca_system_score_codex":0.0002444131,"about_ca_system_score_gemma":0.0001634153,"threshold_uncertainty_score":0.0017732978},"labels":[],"label_agreement":null},{"id":"W3132839237","doi":"10.3390/s21041472","title":"Improved Accuracy of a Single-Slit Digital Sun Sensor Design for CubeSat Application Using Sub-Pixel Interpolation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"CubeSat; Computer science; Pixel; Interpolation (computer graphics); Mean squared error; Artificial intelligence; Satellite; Engineering; Mathematics","score_opus":0.024774224177689996,"score_gpt":0.24529909396441218,"score_spread":0.2205248697867222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132839237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32334,0.0010404799,0.66197985,0.00038770548,0.00019745223,0.00009841323,0.0005137116,0.0036393942,0.008802948],"genre_scores_gemma":[0.5656094,0.00024025376,0.43049303,0.00012109572,0.00003289092,0.000051243038,0.0003525456,0.00013102787,0.002968535],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964714,0.00002591516,0.000019003344,0.00009889414,0.00018620625,0.000022799491],"domain_scores_gemma":[0.9994407,0.00009810148,0.00011845943,0.0000942262,0.0002251284,0.000023273316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003450247,0.00032411836,0.0002734053,0.00033186562,0.00016614758,0.000514413,0.0008016622,0.00039748583,0.0015834754],"category_scores_gemma":[0.0006856969,0.00022344384,0.00023253202,0.000291826,0.00019429115,0.000635309,0.00043388887,0.0003588851,0.00053134933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030073646,0.000060496706,0.0038829548,0.0002894138,0.00003648733,0.00014042585,0.00017874532,0.0115795005,0.8897795,0.0033005776,0.0013435268,0.08910768],"study_design_scores_gemma":[0.000030335135,0.00043466382,0.0069327126,0.000021486474,0.000044519522,0.00071395753,0.00006198485,0.16008185,0.8122318,0.0008109557,0.018574564,0.000061264516],"about_ca_topic_score_codex":0.00084986165,"about_ca_topic_score_gemma":0.0012947329,"teacher_disagreement_score":0.0015834754,"about_ca_system_score_codex":0.0005567529,"about_ca_system_score_gemma":0.00051142264,"threshold_uncertainty_score":0.005297303},"labels":[],"label_agreement":null},{"id":"W3133343134","doi":"10.3390/s21041504","title":"A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Gesture recognition; Computer science; Robustness (evolution); Pipeline (software); Artificial intelligence; Pattern recognition (psychology); Speech recognition; Machine learning","score_opus":0.013742939736035761,"score_gpt":0.2235269307472226,"score_spread":0.20978399101118683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133343134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009855787,0.00007504175,0.98767614,0.00006849683,0.000027129274,0.00006009383,0.000073544215,0.0018553042,0.00030840116],"genre_scores_gemma":[0.20979227,0.00011129649,0.7869288,0.00009242303,0.000055676042,0.00027909767,0.00061311934,0.00019947389,0.0019279403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933404,0.00009485571,0.00004800209,0.00022980984,0.00022356314,0.00006960379],"domain_scores_gemma":[0.9991423,0.00028188777,0.000076179516,0.00013395268,0.00032847107,0.000037089205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014347292,0.0009780579,0.0007990356,0.0007903169,0.0005036149,0.00062680355,0.0009996431,0.0007855675,0.0026075963],"category_scores_gemma":[0.003375653,0.00046370312,0.0008518334,0.00070647226,0.00043024714,0.00090109644,0.0010929815,0.0013759499,0.0016053445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038842863,0.00022068512,0.0022074524,0.00014078878,0.00010998604,0.00013132332,0.0001642464,0.050779305,0.1230031,0.003605119,0.0034491476,0.8158004],"study_design_scores_gemma":[0.00002071333,0.00018766004,0.0035081187,0.000013936251,0.000024209687,0.0001029236,0.000022412412,0.956528,0.03388718,0.0029692852,0.002698815,0.000036712434],"about_ca_topic_score_codex":0.0035351822,"about_ca_topic_score_gemma":0.0045533595,"teacher_disagreement_score":0.0035351822,"about_ca_system_score_codex":0.00047076587,"about_ca_system_score_gemma":0.0012192461,"threshold_uncertainty_score":0.008723259},"labels":[],"label_agreement":null},{"id":"W3133510726","doi":"10.3390/s21051680","title":"Fiber Bragg Sensors Embedded in Cast Aluminum Parts: Axial Strain and Temperature Response","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Deutsche Forschungsgemeinschaft","keywords":"Materials science; Ultimate tensile strength; Composite material; Tensile testing; Casting; Thermal; Fiber Bragg grating; Fiber; Deformation (meteorology); Optical fiber; Aluminium; Optics; Optoelectronics","score_opus":0.008427848228171676,"score_gpt":0.22751258425607773,"score_spread":0.21908473602790607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133510726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98988557,0.0016141762,0.007628112,0.00002647819,0.00003988247,0.000016026128,0.000065610875,0.00010937478,0.00061481877],"genre_scores_gemma":[0.99226475,0.00045718686,0.0057213637,0.000021590786,0.000010736158,0.000009377183,0.00006776252,0.000016675684,0.0014306206],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966526,0.000035437264,0.000011834268,0.00005111324,0.00020297413,0.000033398093],"domain_scores_gemma":[0.99963117,0.0000934314,0.000114837465,0.00003813688,0.00009759737,0.000024755253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020828228,0.00043919624,0.00022999542,0.0002682183,0.00009869136,0.00018792281,0.00025979232,0.00034386947,0.0005109556],"category_scores_gemma":[0.00048475363,0.00019080244,0.0001953181,0.00016928982,0.0002820526,0.00025109245,0.00014502493,0.00022512443,0.00021416998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048903774,0.000006393489,0.00018622269,0.000032825832,0.0000031883396,0.00002078432,0.000018010023,0.00011835059,0.99813014,0.000010329088,0.0000075122284,0.0014172866],"study_design_scores_gemma":[0.0000031019263,0.00018673793,0.0034354718,0.0000024624428,0.00001079186,0.000076200835,0.000021773647,0.001525164,0.9944647,0.00001059862,0.00025725216,0.000005762326],"about_ca_topic_score_codex":0.0006258359,"about_ca_topic_score_gemma":0.0012035862,"teacher_disagreement_score":0.0006258359,"about_ca_system_score_codex":0.0001454283,"about_ca_system_score_gemma":0.00010054157,"threshold_uncertainty_score":0.001709342},"labels":[],"label_agreement":null},{"id":"W3134122111","doi":"10.3390/s21051839","title":"FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Centroid; Feature (linguistics); Computer vision; Distortion (music); Matching (statistics); Perspective (graphical); Image (mathematics); Computer science; Pattern recognition (psychology); Robustness (evolution); Mathematics","score_opus":0.02400388270059812,"score_gpt":0.29331466453529154,"score_spread":0.2693107818346934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134122111","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018694766,0.00055041356,0.9782063,0.00006761041,0.00009091158,0.000070556445,0.00035882884,0.0006133077,0.0013473732],"genre_scores_gemma":[0.52897066,0.0022179026,0.45701873,0.00026741624,0.00019683615,0.00026250284,0.0039240425,0.00022859298,0.0069132573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957603,0.000045385917,0.000027717962,0.000064914624,0.00024666375,0.00003929809],"domain_scores_gemma":[0.9996495,0.000039829225,0.000050558843,0.000090718226,0.00014659378,0.000022844533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024046765,0.00053863984,0.00066945766,0.0011889555,0.00016053511,0.0006229494,0.00078335026,0.00037567312,0.0020457027],"category_scores_gemma":[0.00096700166,0.00019548634,0.0005968098,0.001250998,0.00032974916,0.0012649291,0.0007385398,0.0005582508,0.00096392335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004929841,0.00006371635,0.0027822715,0.00044718667,0.00013094283,0.00032434327,0.00012365125,0.037854012,0.17475072,0.01985592,0.010864694,0.7523096],"study_design_scores_gemma":[0.000068812646,0.00060635194,0.008389751,0.00005475465,0.00012907639,0.0023209422,0.00022963222,0.80929404,0.13010907,0.00862927,0.039976854,0.00019138794],"about_ca_topic_score_codex":0.002308501,"about_ca_topic_score_gemma":0.0018060731,"teacher_disagreement_score":0.002308501,"about_ca_system_score_codex":0.00039935243,"about_ca_system_score_gemma":0.00047319237,"threshold_uncertainty_score":0.0068435073},"labels":[],"label_agreement":null},{"id":"W3134304696","doi":"10.3390/s21051730","title":"Man Down Situation Detection Using an in-Ear Inertial Platform","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Accelerometer; Inertial measurement unit; Gyroscope; Computer science; ALARM; Orientation (vector space); Simulation; Real-time computing; Computer security; Artificial intelligence; Reliability engineering; Engineering","score_opus":0.02386389541948984,"score_gpt":0.2382443769197668,"score_spread":0.21438048150027694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134304696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32552335,0.0007842788,0.66233516,0.000143793,0.0004200663,0.0004769758,0.0009908357,0.0038644532,0.005461133],"genre_scores_gemma":[0.8703346,0.00051750377,0.12322834,0.00012146306,0.0000941785,0.0002736732,0.0006599979,0.000052146188,0.004718007],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971384,0.00003631277,0.00001865033,0.000087497785,0.00010863044,0.000035061235],"domain_scores_gemma":[0.9997948,0.000033694527,0.00004386522,0.00002418608,0.000083185136,0.000020345034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021817548,0.0009334752,0.00048690476,0.00096039724,0.00016201244,0.00043989305,0.00050431094,0.0004815875,0.0017447735],"category_scores_gemma":[0.00062015303,0.00018261492,0.0002479082,0.0003481929,0.000119248514,0.00040708852,0.00071556895,0.00021814843,0.0010592716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011358117,0.00030628656,0.022251362,0.0006355836,0.00016273878,0.00095977215,0.00036968756,0.008553847,0.49065483,0.001049099,0.004083741,0.4698372],"study_design_scores_gemma":[0.0002091386,0.0033046855,0.11391832,0.00017082153,0.00040346268,0.004311738,0.00047238465,0.4748466,0.37640968,0.0014480669,0.024309162,0.0001959026],"about_ca_topic_score_codex":0.0006417576,"about_ca_topic_score_gemma":0.00092336314,"teacher_disagreement_score":0.0017447735,"about_ca_system_score_codex":0.000118884294,"about_ca_system_score_gemma":0.00026715023,"threshold_uncertainty_score":0.0058368444},"labels":[],"label_agreement":null},{"id":"W3134989913","doi":"10.3390/s21051699","title":"Cicada Wing Inspired Template-Stripped SERS Active 3D Metallic Nanostructures for the Detection of Toxic Substances","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología; Canada Foundation for Innovation","keywords":"Rhodamine 6G; Materials science; Raman scattering; Nanotechnology; Substrate (aquarium); Nanostructure; Grating; Plasmon; Optoelectronics; Raman spectroscopy; Polarization (electrochemistry); Optics; Chemistry; Fluorescence","score_opus":0.02144226048259574,"score_gpt":0.2475017547346107,"score_spread":0.22605949425201496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134989913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93572176,0.0033072827,0.052541435,0.00022815156,0.00017521904,0.00008398255,0.0002470264,0.0007630799,0.006932051],"genre_scores_gemma":[0.94990575,0.0010753678,0.045460142,0.00011604493,0.00001983045,0.000055783727,0.00021980086,0.000048795486,0.0030984888],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989986,0.000009283891,0.000004759008,0.000028002243,0.00004591927,0.0000122393885],"domain_scores_gemma":[0.9999416,0.000009619978,0.00001858187,0.0000088721745,0.000010800119,0.000010416846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007753944,0.00040826917,0.00019620846,0.00017716055,0.00009382085,0.00029436155,0.0003924782,0.00052904536,0.00028924938],"category_scores_gemma":[0.00011829209,0.00021301303,0.00022827128,0.00010459423,0.0002059697,0.00017894912,0.00021977918,0.00033624153,0.00030783698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000846279,0.0000063096236,0.000046891622,0.000035160785,0.0000030480107,0.00006171345,0.000010268228,0.00036395458,0.9972172,0.00010906757,0.000054494456,0.0020834927],"study_design_scores_gemma":[0.0000048829284,0.000072986804,0.00046660667,0.0000027421875,0.000005327125,0.00019852968,0.000011719637,0.0062124548,0.9901106,0.000039310882,0.002865422,0.0000093899735],"about_ca_topic_score_codex":0.00035911798,"about_ca_topic_score_gemma":0.0008047102,"teacher_disagreement_score":0.00052904536,"about_ca_system_score_codex":0.0002505238,"about_ca_system_score_gemma":0.00015000638,"threshold_uncertainty_score":0.0018176436},"labels":[],"label_agreement":null},{"id":"W3135088000","doi":"10.3390/s21051604","title":"Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Image and Video Stabilization","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; National Research Council Canada","funders":"Innovate UK","keywords":"Thermography; Computer science; Process (computing); Pipeline (software); Real-time computing; Computer vision; Profiling (computer programming); Artificial intelligence; Infrared; Simulation; Engineering","score_opus":0.015005461459265798,"score_gpt":0.2836921448242378,"score_spread":0.26868668336497203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135088000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5774438,0.0033741617,0.41251487,0.00016514127,0.00013930156,0.0004519435,0.00019851813,0.0012549103,0.0044573797],"genre_scores_gemma":[0.85782826,0.00094288995,0.1392018,0.000029487084,0.000016750277,0.00013458027,0.00024215717,0.00008014129,0.0015239104],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960107,0.00008120972,0.000028772667,0.00007745103,0.00017296377,0.000038572573],"domain_scores_gemma":[0.9988254,0.0003409107,0.0001516056,0.00007595009,0.0005581034,0.00004810319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060261856,0.00051870156,0.000366061,0.00087956846,0.0002614871,0.0003979076,0.00040865253,0.0004430628,0.0009739005],"category_scores_gemma":[0.0025048424,0.0001207508,0.00027278223,0.0003968104,0.0001550387,0.00041819966,0.00021033346,0.0002458936,0.00024389189],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014945817,0.00043944916,0.005273229,0.0009179863,0.00009400927,0.00015458735,0.00015509283,0.101031095,0.35793793,0.0008744663,0.0013283531,0.5302992],"study_design_scores_gemma":[0.00007794845,0.0034708043,0.01177683,0.00010597562,0.00014984603,0.0002766347,0.0002476286,0.63746494,0.3410473,0.00033611633,0.0049988776,0.000047092453],"about_ca_topic_score_codex":0.0016992205,"about_ca_topic_score_gemma":0.0021151376,"teacher_disagreement_score":0.0016992205,"about_ca_system_score_codex":0.00036889387,"about_ca_system_score_gemma":0.00034941826,"threshold_uncertainty_score":0.0033786297},"labels":[],"label_agreement":null},{"id":"W3135290515","doi":"10.3390/s21051755","title":"D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Université Mohammed VI Polytechnique","keywords":"Reinforcement learning; Computer science; EnodeB; Distributed computing; Fading; Noma; Perspective (graphical); Mobile device; Channel (broadcasting); Telecommunications link; Computer network; User equipment; Artificial intelligence","score_opus":0.01131867992594842,"score_gpt":0.2403919438019107,"score_spread":0.22907326387596227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135290515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034715123,0.00064965093,0.94777185,0.0012448799,0.00008389411,0.000093056915,0.00004558487,0.00009408579,0.015301935],"genre_scores_gemma":[0.96569085,0.00058273046,0.029384388,0.00013728137,0.00008859318,0.00008544586,0.000017470222,0.000017446446,0.0039957813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994343,0.00023552547,0.000020943982,0.000089775654,0.00011564475,0.000103748636],"domain_scores_gemma":[0.9982753,0.0010772347,0.00023884788,0.00010049558,0.0001924767,0.000115779076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012536902,0.00066060043,0.0007553394,0.00031940328,0.0004592162,0.0015029265,0.000798435,0.0012268106,0.0020302383],"category_scores_gemma":[0.0030909332,0.00024526234,0.0005219812,0.0003192669,0.0012676766,0.0011775352,0.00086228765,0.001270303,0.00025906658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004735947,0.000051324598,0.0007236962,0.000074192016,0.000036949496,0.00013960962,0.00006371424,0.8812572,0.0024758696,0.09829874,0.0007362027,0.016095249],"study_design_scores_gemma":[0.0000063487414,0.00004016039,0.00015679494,0.0000071116774,0.0000064896603,0.00003282588,0.0000141061355,0.9817426,0.00030192046,0.017192742,0.0004932379,0.0000056279946],"about_ca_topic_score_codex":0.0037229464,"about_ca_topic_score_gemma":0.0019139588,"teacher_disagreement_score":0.0037229464,"about_ca_system_score_codex":0.0012628356,"about_ca_system_score_gemma":0.0010789884,"threshold_uncertainty_score":0.009162486},"labels":[],"label_agreement":null},{"id":"W3135317838","doi":"10.3390/s21051671","title":"A Sensitive and Fast Fiber Bragg Grating-Based Investigation of the Biomechanical Dynamics of In Vitro Spinal Cord Injuries","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hôpital du Sacré-Cœur de Montréal","funders":"","keywords":"Fiber Bragg grating; Biomechanics; Spinal cord injury; Compression (physics); Optical fiber; Materials science; Spinal cord; Biomedical engineering; Computer science; Engineering; Medicine; Telecommunications; Anatomy","score_opus":0.027217722460498917,"score_gpt":0.3279550121098276,"score_spread":0.30073728964932867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135317838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9342132,0.0019393155,0.061392996,0.00015194637,0.000096409334,0.000097490265,0.00027720872,0.00018639171,0.001645051],"genre_scores_gemma":[0.950914,0.0011681836,0.04615093,0.00009007004,0.000020645128,0.000070364906,0.00014117335,0.000013475923,0.0014311125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985695,0.000025874317,0.000007303895,0.000029859986,0.0000603098,0.00001971591],"domain_scores_gemma":[0.99987495,0.000038143564,0.000030901916,0.000011733318,0.000027832379,0.00001633858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003146173,0.00027455622,0.00016947846,0.00020888906,0.00014489719,0.00014940968,0.00023022186,0.000380352,0.0003864646],"category_scores_gemma":[0.0002600426,0.00013398506,0.00012367564,0.00016431326,0.00025228894,0.00022803352,0.00016379656,0.00026676335,0.000117485964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014453116,0.000010741596,0.00011487079,0.000019434794,0.0000014269978,0.000010525781,0.000011968086,0.000073314535,0.9984357,0.000035354242,0.000015019992,0.0012572135],"study_design_scores_gemma":[0.00000349543,0.00020325804,0.0031804806,0.00000346095,0.0000068640097,0.00009338716,0.000029021627,0.003920361,0.9919592,0.000032155243,0.0005605301,0.000007758738],"about_ca_topic_score_codex":0.0013493161,"about_ca_topic_score_gemma":0.0028542818,"teacher_disagreement_score":0.0013493161,"about_ca_system_score_codex":0.00023348912,"about_ca_system_score_gemma":0.0002985448,"threshold_uncertainty_score":0.0026829243},"labels":[],"label_agreement":null},{"id":"W3135521100","doi":"10.3390/s21051844","title":"A Cost-Effective Inertial Measurement System for Tracking Movement and Triggering Kinesthetic Feedback in Lower-Limb Prosthesis Users","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; Alberta Health Services; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Kinesthetic learning; Tracking (education); Prosthesis; Computer science; Inertial frame of reference; Movement (music); Inertial measurement unit; Physical medicine and rehabilitation; Simulation; Human–computer interaction; Engineering; Computer vision; Artificial intelligence; Medicine; Psychology; Acoustics; Physics","score_opus":0.025840364680722178,"score_gpt":0.2278836478633658,"score_spread":0.2020432831826436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135521100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53859633,0.0010137475,0.45557547,0.00034663783,0.00013245708,0.00059939525,0.00025160494,0.0012763718,0.002208014],"genre_scores_gemma":[0.89427125,0.00029980895,0.10253601,0.00012872026,0.000044179204,0.00035325787,0.000111935464,0.000041286898,0.002213532],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995468,0.00013064947,0.000050712075,0.0000782988,0.00015736507,0.00003598189],"domain_scores_gemma":[0.9994954,0.0001764876,0.0000868133,0.00005193607,0.00014726866,0.0000420929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005378498,0.00044275395,0.0003978873,0.00031858368,0.00021540241,0.0003312682,0.0007534756,0.00059255125,0.0023619672],"category_scores_gemma":[0.0013895789,0.00021899956,0.00018333792,0.00020186731,0.00021247771,0.00048714987,0.0005092604,0.00024369688,0.00054607657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007374729,0.00031451244,0.007795248,0.0005343512,0.000033727905,0.00059727987,0.00046265995,0.0006493104,0.83041394,0.0004925452,0.0009695692,0.15699938],"study_design_scores_gemma":[0.0005686696,0.015608064,0.14349483,0.00029909093,0.00068044744,0.014488273,0.0006285244,0.047054972,0.7471298,0.0008026435,0.028962383,0.0002823118],"about_ca_topic_score_codex":0.00040841184,"about_ca_topic_score_gemma":0.0006698706,"teacher_disagreement_score":0.0023619672,"about_ca_system_score_codex":0.00017937979,"about_ca_system_score_gemma":0.0003475767,"threshold_uncertainty_score":0.007901549},"labels":[],"label_agreement":null},{"id":"W3135542633","doi":"10.3390/s21051669","title":"Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Workplace Safety and Insurance Board","keywords":"Smartwatch; Artificial intelligence; Machine learning; Deep learning; Activity recognition; Computer science; Context (archaeology); Inertial measurement unit; Wearable computer","score_opus":0.03629879010880702,"score_gpt":0.26016735076695724,"score_spread":0.22386856065815022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135542633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8444947,0.0006762831,0.1405598,0.00049556175,0.00035303133,0.00021262442,0.004236039,0.0038421093,0.005129808],"genre_scores_gemma":[0.9581001,0.0001849463,0.034916244,0.00017235785,0.00006792637,0.00010711744,0.0047867117,0.000045569348,0.0016189715],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994711,0.00009262726,0.000029884752,0.00018461264,0.00015158215,0.00007018743],"domain_scores_gemma":[0.9994479,0.00019801996,0.00009456391,0.00009337045,0.00011423372,0.000051859068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005217927,0.0009449733,0.00052260474,0.0006716333,0.00014635143,0.00036277922,0.00062132557,0.00049868703,0.00069930387],"category_scores_gemma":[0.0024180238,0.00016356917,0.0003384463,0.0005592489,0.00031750154,0.0004849598,0.0007507481,0.0005590661,0.00050172966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001846966,0.0013318644,0.14957446,0.00044915054,0.00035535646,0.0004894939,0.00027408096,0.13463926,0.038556684,0.0015837521,0.018529175,0.65236986],"study_design_scores_gemma":[0.00010796879,0.00069910847,0.11093421,0.000052790583,0.0000446695,0.00045659413,0.00020693218,0.8533415,0.026992586,0.0021306493,0.0049821977,0.00005074008],"about_ca_topic_score_codex":0.0035996963,"about_ca_topic_score_gemma":0.0072988276,"teacher_disagreement_score":0.0035996963,"about_ca_system_score_codex":0.00029608523,"about_ca_system_score_gemma":0.00038762594,"threshold_uncertainty_score":0.0071575046},"labels":[],"label_agreement":null},{"id":"W3135857679","doi":"10.3390/s21061968","title":"Frontal Electroencephalogram Alpha Asymmetry during Mental Stress Related to Workplace Noise","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Noise Effects and Management","field":"Health Professions","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Higher Education, Malaysia","keywords":"Noise (video); Electroencephalography; Prefrontal cortex; Audiology; Stress (linguistics); Psychology; Brain activity and meditation; QUIET; Alpha (finance); Cognition; Clinical psychology; Neuroscience; Medicine; Internal consistency; Psychometrics; Artificial intelligence; Computer science; Physics","score_opus":0.010477516307046434,"score_gpt":0.33966726589531476,"score_spread":0.32918974958826835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135857679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99824834,0.00023859966,0.00074219843,0.000024643754,0.000010470768,0.0000123123555,0.00009116568,0.0000064125416,0.0006259995],"genre_scores_gemma":[0.9992735,0.00016508574,0.00022528636,0.000018048993,0.000021008884,0.00001055973,0.00005935734,0.0000025779548,0.00022449924],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99989676,0.000022510538,0.000007106904,0.000024111989,0.00003003683,0.000019389017],"domain_scores_gemma":[0.9996742,0.00009696642,0.00011769539,0.00002172622,0.000053238,0.00003612743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013681725,0.00028088555,0.00021284149,0.00023591258,0.00012595393,0.00024959518,0.00008006104,0.00021090881,0.0012785048],"category_scores_gemma":[0.0010299005,0.0000904674,0.00013114543,0.00011097641,0.00019934442,0.00011449768,0.0002049465,0.00016691646,0.00012114097],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006160131,0.00034286338,0.2931484,0.00037945836,0.00025072152,0.0011620775,0.0020558261,0.0006007119,0.61914116,0.00014274474,0.00039087576,0.07622508],"study_design_scores_gemma":[0.000015166664,0.0005374155,0.99330115,0.000009380266,0.00003482946,0.0003572889,0.00029048213,0.00019762233,0.0050127925,0.00007361655,0.00016346727,0.0000067578094],"about_ca_topic_score_codex":0.0009372968,"about_ca_topic_score_gemma":0.001377042,"teacher_disagreement_score":0.0012785048,"about_ca_system_score_codex":0.00009213163,"about_ca_system_score_gemma":0.00008371026,"threshold_uncertainty_score":0.0042770505},"labels":[],"label_agreement":null},{"id":"W3135876039","doi":"10.3390/s21051750","title":"Comparison of the Performance of the Leap Motion ControllerTM with a Standard Marker-Based Motion Capture System","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"EIT Health","keywords":"Motion capture; Gold standard (test); Kinematics; Computer science; Motion analysis; Interphalangeal Joint; Motion (physics); Middle finger; Wrist; Thumb; Artificial intelligence; Computer vision; Mathematics; Medicine; Anatomy; Physics; Statistics","score_opus":0.013088711113265738,"score_gpt":0.22960507945237935,"score_spread":0.2165163683391136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135876039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89948213,0.0038927128,0.0854038,0.00025383118,0.0003950409,0.0005206624,0.001988666,0.0014389418,0.006624248],"genre_scores_gemma":[0.9480182,0.0009407244,0.045657266,0.00024962754,0.000083863044,0.0003681713,0.001598536,0.00013366698,0.00295004],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967644,0.00076406304,0.00036881,0.00065559905,0.0012550947,0.00019196136],"domain_scores_gemma":[0.9945787,0.0021210683,0.00035312778,0.00032925696,0.0024891417,0.00012877039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036372945,0.00071284024,0.0005748697,0.0016660708,0.0002683797,0.0011088654,0.00084968674,0.0010854546,0.0023927153],"category_scores_gemma":[0.012993226,0.00028595244,0.0004382342,0.000865611,0.0003896369,0.00085631834,0.0009248873,0.00032554226,0.0010398558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011658907,0.0006872379,0.11524326,0.003558568,0.0011954244,0.0005160282,0.0018436587,0.012525727,0.33765265,0.0010428322,0.0064023905,0.50767326],"study_design_scores_gemma":[0.00045841548,0.011783385,0.6670718,0.0004800915,0.0013298739,0.002770337,0.0012761177,0.12839451,0.16898727,0.0006312125,0.016381638,0.0004352468],"about_ca_topic_score_codex":0.0040130443,"about_ca_topic_score_gemma":0.0041520186,"teacher_disagreement_score":0.0040130443,"about_ca_system_score_codex":0.000502375,"about_ca_system_score_gemma":0.0005500488,"threshold_uncertainty_score":0.019236088},"labels":[],"label_agreement":null},{"id":"W3136382407","doi":"10.3390/s21072296","title":"A Signal Processing Algorithm of Two-Phase Staggered PRI and Slow Time Signal Integration for MTI Triangular FMCW Multi-Target Tracking Radars","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"Ministère de la Défense Nationale","keywords":"Continuous-wave radar; Pulse-Doppler radar; Radar; Computer science; SIGNAL (programming language); Pulse repetition frequency; Fire-control radar; Moving target indication; Low probability of intercept radar; Waveform; Electronic engineering; Radar imaging; Engineering; Telecommunications","score_opus":0.018460547517222658,"score_gpt":0.26718672249400743,"score_spread":0.2487261749767848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136382407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023063682,0.00005630194,0.9969633,0.000021584952,0.00002822585,0.000024353423,0.0000056239123,0.00018015485,0.0004141462],"genre_scores_gemma":[0.0868089,0.00015288097,0.9102305,0.000076412245,0.000051392613,0.000106703286,0.00008332884,0.00005809374,0.0024317387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995322,0.000055046632,0.00003348747,0.000118022705,0.00022867169,0.000032539796],"domain_scores_gemma":[0.9996512,0.00007608266,0.000042095766,0.000049670518,0.00016404933,0.00001687088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046888422,0.00053961464,0.000364042,0.00048477302,0.000326588,0.00064970163,0.0008125161,0.00061574735,0.0022600342],"category_scores_gemma":[0.0011870007,0.0002653049,0.00046681357,0.00053867546,0.00035020866,0.0007708108,0.00044011156,0.0009500413,0.0010569844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037830806,0.0000879901,0.0006645386,0.00018119423,0.00005311684,0.00013844197,0.00021011443,0.06972227,0.16044347,0.019069316,0.0014938128,0.7475576],"study_design_scores_gemma":[0.00004626719,0.00031103953,0.000718625,0.000018404484,0.000026433947,0.00033425153,0.000028000393,0.93875545,0.04813601,0.0024775648,0.009113175,0.00003482134],"about_ca_topic_score_codex":0.000886613,"about_ca_topic_score_gemma":0.0008122768,"teacher_disagreement_score":0.0022600342,"about_ca_system_score_codex":0.00035588432,"about_ca_system_score_gemma":0.00056321506,"threshold_uncertainty_score":0.007560551},"labels":[],"label_agreement":null},{"id":"W3137225947","doi":"10.3390/s21062152","title":"Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Internet of Things; Building automation; Energy consumption; Computer science; Efficient energy use; Greenhouse gas; Systems engineering; Architectural engineering; Risk analysis (engineering); Engineering; Computer security","score_opus":0.0171714034897836,"score_gpt":0.28067637496583303,"score_spread":0.26350497147604945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137225947","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002657094,0.99705493,0.00027936435,0.00028428025,0.00024668893,0.00001077787,0.000045818633,0.000011459083,0.0018009591],"genre_scores_gemma":[0.00086548936,0.9982079,0.0003196806,0.00011455591,0.00010142696,0.0000069486605,0.00004031382,0.0000016996011,0.0003419913],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996338,0.00005806417,0.00007149866,0.0000604031,0.00014529166,0.000031019725],"domain_scores_gemma":[0.9987588,0.0007517085,0.0001572287,0.000026482849,0.00026557726,0.000040105308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007132656,0.0010000527,0.0010936919,0.004229619,0.00032256288,0.0012404603,0.0007066877,0.00088850607,0.0057208193],"category_scores_gemma":[0.001281477,0.00039087576,0.0008690846,0.006021044,0.00030696567,0.0017722633,0.00062250666,0.0010643308,0.0015876166],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005265885,0.00007763808,0.00035571295,0.09479613,0.00013633705,0.00026049296,0.00021635904,0.0007970898,0.0019025103,0.005663566,0.023881312,0.8718602],"study_design_scores_gemma":[0.0000062995728,0.00009931762,0.0013563624,0.017532492,0.00031042573,0.00073249504,0.00022609209,0.00013855069,0.00053689774,0.0019940222,0.97703755,0.00002955169],"about_ca_topic_score_codex":0.0017127808,"about_ca_topic_score_gemma":0.0033827687,"teacher_disagreement_score":0.0057208193,"about_ca_system_score_codex":0.0005896956,"about_ca_system_score_gemma":0.0019762716,"threshold_uncertainty_score":0.019138038},"labels":[],"label_agreement":null},{"id":"W3137764579","doi":"10.3390/s21062219","title":"Virtual Reality Customized 360-Degree Experiences for Stress Relief","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Virtual reality; Task (project management); Stress (linguistics); Electroencephalography; Wilcoxon signed-rank test; Computer science; Degree (music); Session (web analytics); Human–computer interaction; Stress reduction; Simulation; Multimedia; Psychology; Applied psychology; Medicine; Engineering; Mann–Whitney U test; World Wide Web","score_opus":0.05521706194402299,"score_gpt":0.31134711614766,"score_spread":0.256130054203637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137764579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84228045,0.0021504678,0.13960914,0.00032035,0.00020251397,0.0002923512,0.00028105022,0.00063009025,0.01423357],"genre_scores_gemma":[0.95644546,0.0009966255,0.038716275,0.00013949389,0.00008091308,0.00015730514,0.00014322538,0.00005513137,0.0032655804],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997329,0.00011992766,0.000010149074,0.000037034515,0.00005836452,0.000041614294],"domain_scores_gemma":[0.99981326,0.00007415143,0.000024948149,0.000032495536,0.000019471265,0.000035824236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002708226,0.00042590834,0.00015646813,0.0002483352,0.00011248074,0.00044173942,0.00034162594,0.00026229233,0.0066192416],"category_scores_gemma":[0.0007929969,0.000099163044,0.00039913374,0.00013243553,0.0002602604,0.00033496605,0.0007517443,0.0002727167,0.0004978158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002305477,0.0017041026,0.0066018165,0.0016870375,0.00019230282,0.0006557732,0.0051365937,0.006457508,0.41454005,0.0064716134,0.0047695213,0.54947823],"study_design_scores_gemma":[0.0017850169,0.048412416,0.2952419,0.0012307556,0.0012195811,0.016565057,0.016303621,0.039351407,0.26136294,0.021139987,0.296476,0.0009113384],"about_ca_topic_score_codex":0.00015321371,"about_ca_topic_score_gemma":0.00028073374,"teacher_disagreement_score":0.0066192416,"about_ca_system_score_codex":0.00006419337,"about_ca_system_score_gemma":0.000089037785,"threshold_uncertainty_score":0.022143602},"labels":[],"label_agreement":null},{"id":"W3137788206","doi":"10.3390/s21062020","title":"Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Motor imagery; Brain–computer interface; SMA*; Supplementary motor area; Electroencephalography; Primary motor cortex; Rhythm; Neuroscience; Motor cortex; Psychology; Sensory system; Brain activity and meditation; Physical medicine and rehabilitation; Computer science; Medicine; Functional magnetic resonance imaging; Stimulation","score_opus":0.0169050385500774,"score_gpt":0.29156036499166055,"score_spread":0.27465532644158314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137788206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99714524,0.00005768958,0.0021434883,0.000022671831,0.000009940587,0.000023301118,0.000033768756,0.000038976807,0.00052492437],"genre_scores_gemma":[0.9983053,0.00004947335,0.0012798093,0.000026316075,0.000007983801,0.00002359455,0.000050315983,0.000008284882,0.0002488826],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998822,0.000044748886,0.000010190609,0.00002308452,0.00001937203,0.0000204128],"domain_scores_gemma":[0.99940276,0.00043043352,0.000045482117,0.00003404069,0.000041122712,0.000046106055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013775351,0.00034024488,0.00015473386,0.0001729788,0.00006131863,0.00020521127,0.00011633704,0.00020211788,0.0016451449],"category_scores_gemma":[0.0019942,0.00007287504,0.000094099225,0.00008331161,0.00018632149,0.00013113805,0.00020867567,0.00013858154,0.00014359562],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008350805,0.0009147321,0.011833985,0.00035516397,0.000123088,0.00043955905,0.00056293583,0.002096492,0.87491,0.00015455429,0.0003021306,0.09995666],"study_design_scores_gemma":[0.00063022756,0.020426186,0.75842047,0.00005108291,0.00044240098,0.0017943165,0.0006395057,0.025037229,0.18976031,0.0005842738,0.0021668805,0.000047268484],"about_ca_topic_score_codex":0.0003077736,"about_ca_topic_score_gemma":0.0004497232,"teacher_disagreement_score":0.0016451449,"about_ca_system_score_codex":0.000054118533,"about_ca_system_score_gemma":0.00006598188,"threshold_uncertainty_score":0.005503595},"labels":[],"label_agreement":null},{"id":"W3138144160","doi":"10.3390/s21062137","title":"Evaluation of the Metrological Performance of a Handheld 3D Laser Scanner Using a Pseudo-3D Ball-Lattice Artifact","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Pratt and Whitney Canada","keywords":"Scanner; Artifact (error); Laser scanning; Computer science; Computer vision; Metrology; Artificial intelligence; Mathematics; Optics; Laser; Statistics; Physics","score_opus":0.06911169219631128,"score_gpt":0.263806761352626,"score_spread":0.1946950691563147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138144160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6610678,0.0006034139,0.33533055,0.00006380567,0.00007400472,0.0001740104,0.00019401826,0.00095321384,0.001539134],"genre_scores_gemma":[0.84473187,0.0001460089,0.15393028,0.000032577158,0.000013683309,0.00005556606,0.00014069714,0.00009144038,0.00085797167],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99792576,0.0003492276,0.00010255891,0.00025733,0.0012917966,0.00007328038],"domain_scores_gemma":[0.9957599,0.0019273846,0.00046913407,0.0008449819,0.0008936865,0.00010486631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001458384,0.0005334004,0.0005174081,0.00084098027,0.0003107278,0.0009212677,0.0012152982,0.00090462563,0.00097542204],"category_scores_gemma":[0.0059778444,0.00023534508,0.0004350846,0.00079004664,0.000553631,0.00069748383,0.00068458094,0.0003046098,0.00034195016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012769073,0.00018354841,0.023944627,0.00074238284,0.0001767073,0.00083497405,0.00047417168,0.034953613,0.78788424,0.0013327955,0.00028705547,0.14790908],"study_design_scores_gemma":[0.000075581855,0.0026373693,0.053532355,0.00006909512,0.00018790549,0.0043951757,0.00028871046,0.2033928,0.729326,0.0009746023,0.0048981183,0.00022227672],"about_ca_topic_score_codex":0.0010396448,"about_ca_topic_score_gemma":0.0013535854,"teacher_disagreement_score":0.001458384,"about_ca_system_score_codex":0.00041442952,"about_ca_system_score_gemma":0.00049806066,"threshold_uncertainty_score":0.007712722},"labels":[],"label_agreement":null},{"id":"W3138575731","doi":"10.3390/s21062246","title":"Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Waterloo","funders":"Network for Aging Research, University of Waterloo; Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Gait; Accelerometer; Gyroscope; Simulation; Computer science; Artificial intelligence; Environmental science; Engineering; Physical medicine and rehabilitation; Medicine","score_opus":0.07368207087295042,"score_gpt":0.334120845771478,"score_spread":0.26043877489852757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138575731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97674984,0.000254654,0.021354072,0.00004577401,0.000033148986,0.00004909617,0.0007378531,0.00020537585,0.00057012],"genre_scores_gemma":[0.9942616,0.000076587035,0.005062234,0.000010492077,0.000009801849,0.000012379537,0.00041950808,0.0000040521995,0.00014335234],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982566,0.000022616396,0.000016610953,0.00004806832,0.00004929926,0.00003782794],"domain_scores_gemma":[0.9996977,0.00009167417,0.00006901377,0.000020334428,0.00008771602,0.000033669283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003057582,0.00061569834,0.00043424184,0.0011441071,0.00014737176,0.0003993922,0.00024871694,0.00031220508,0.0003786092],"category_scores_gemma":[0.0013282937,0.00015426274,0.00047375227,0.00057082635,0.000088803754,0.0003642753,0.00032857235,0.00028081136,0.00020445173],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014473695,0.0004229398,0.6206864,0.00031810623,0.00032302822,0.00092675915,0.00034392896,0.027798707,0.05492125,0.0001814303,0.0019572026,0.29067287],"study_design_scores_gemma":[0.000023831255,0.0005839817,0.6555169,0.0000766939,0.00014840189,0.00082474557,0.00031562586,0.3283008,0.013000721,0.00036210622,0.0008011788,0.000044929737],"about_ca_topic_score_codex":0.0052095237,"about_ca_topic_score_gemma":0.008010269,"teacher_disagreement_score":0.0052095237,"about_ca_system_score_codex":0.00013154144,"about_ca_system_score_gemma":0.00020214255,"threshold_uncertainty_score":0.010358453},"labels":[],"label_agreement":null},{"id":"W3138688069","doi":"10.3390/s21072302","title":"Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Department of Education and Knowledge","keywords":"Reinforcement learning; Computer science; SIGNAL (programming language); Queue; Duration (music); Artificial intelligence; Control (management); Process (computing); Set (abstract data type); Traffic signal; Real-time computing; Computer network","score_opus":0.011009002275098394,"score_gpt":0.20725298886913723,"score_spread":0.19624398659403883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138688069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05484178,0.0005705801,0.9389641,0.00032301724,0.00009616178,0.000051289313,0.00008400812,0.0012884943,0.0037804754],"genre_scores_gemma":[0.9661103,0.00011632093,0.03176849,0.00015960973,0.000028271998,0.000058641424,0.000097928605,0.000030158912,0.0016302938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967325,0.000069019974,0.00001564302,0.00009505764,0.000077250515,0.00006985141],"domain_scores_gemma":[0.99942505,0.0002658776,0.000074765005,0.000041668027,0.00014131256,0.000051258216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007030536,0.00076881,0.0007048196,0.00028255593,0.00019191543,0.0006342288,0.001085192,0.0006883292,0.0014277331],"category_scores_gemma":[0.0014683099,0.00029032133,0.00037894794,0.00024202737,0.00060290407,0.0006029342,0.0006753326,0.0012241604,0.00020682221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006314525,0.0000793802,0.0007455089,0.000034768713,0.000035346162,0.000037473314,0.000025732246,0.95000964,0.001618824,0.002344422,0.00066473254,0.04434103],"study_design_scores_gemma":[0.0000037449315,0.000015143357,0.00003873318,0.0000014612283,0.0000022975069,0.000002361732,0.0000011670957,0.9991197,0.00014876334,0.0005845993,0.00008079758,0.000001277357],"about_ca_topic_score_codex":0.00912424,"about_ca_topic_score_gemma":0.008025481,"teacher_disagreement_score":0.00912424,"about_ca_system_score_codex":0.00090281083,"about_ca_system_score_gemma":0.0010585069,"threshold_uncertainty_score":0.018142283},"labels":[],"label_agreement":null},{"id":"W3138708451","doi":"10.3390/s21061942","title":"Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Dalhousie University","funders":"","keywords":"Computer network; Routing protocol; Computer science; Network packet; Packet forwarding; Underwater; Zone Routing Protocol; Dynamic Source Routing; Energy consumption; Engineering; Electrical engineering; Geography","score_opus":0.03867682744291683,"score_gpt":0.2709720644209989,"score_spread":0.23229523697808208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138708451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022813972,0.0068161692,0.9407131,0.0010975621,0.0006408205,0.0006570414,0.00050711783,0.0015594407,0.0251948],"genre_scores_gemma":[0.6869084,0.009520089,0.2770578,0.00093330245,0.00026036627,0.0019416767,0.0016169987,0.00015880333,0.021602528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997309,0.00007648786,0.00002282172,0.00002793473,0.00011283202,0.000029074128],"domain_scores_gemma":[0.9997092,0.000101947604,0.000049810737,0.000035564703,0.000085671585,0.000017759092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037962807,0.00031202802,0.00031190846,0.00043489865,0.0005126887,0.00040382784,0.0008416726,0.0003220399,0.00072067836],"category_scores_gemma":[0.00081630156,0.000118044074,0.00023296897,0.0005473653,0.00029797154,0.0006111676,0.0006175755,0.00047211893,0.00020153864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029829773,0.00017050099,0.002257939,0.0016899242,0.00023099163,0.001622636,0.0006658796,0.133996,0.08897345,0.14684644,0.047463104,0.5757849],"study_design_scores_gemma":[0.00012371059,0.00042458024,0.002489072,0.00024684836,0.00016485159,0.0022686603,0.00038897115,0.5844551,0.023593975,0.05820762,0.32746804,0.00016860667],"about_ca_topic_score_codex":0.0020371345,"about_ca_topic_score_gemma":0.0038471974,"teacher_disagreement_score":0.0020371345,"about_ca_system_score_codex":0.0003798628,"about_ca_system_score_gemma":0.0011354651,"threshold_uncertainty_score":0.0040504932},"labels":[],"label_agreement":null},{"id":"W3138800326","doi":"10.3390/s21062046","title":"Exploring Signals on L5/E5a/B2a for Dual-Frequency GNSS Precise Point Positioning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Precise Point Positioning; GNSS applications; Global Positioning System; Galileo (satellite navigation); Computer science; BeiDou Navigation Satellite System; Satellite; Geodesy; Remote sensing; Real-time computing; Telecommunications; Geography; Engineering; Aerospace engineering","score_opus":0.07240185208758275,"score_gpt":0.24628092514265704,"score_spread":0.17387907305507427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138800326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72967887,0.0009771964,0.23697437,0.0002995328,0.00015807062,0.00009441104,0.0013726297,0.0019186268,0.028526297],"genre_scores_gemma":[0.9284467,0.00035325423,0.065345325,0.00007316205,0.000042039243,0.000036211386,0.0019094594,0.00021150528,0.0035823027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995864,0.000058321697,0.000012833563,0.00007217527,0.00021036748,0.000059946095],"domain_scores_gemma":[0.999795,0.000044693807,0.000026486647,0.000033056123,0.00008320404,0.000017643706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035147998,0.0005645211,0.00016501131,0.0011728068,0.00024835556,0.0006250461,0.00031168672,0.00038435622,0.003368762],"category_scores_gemma":[0.000599379,0.00009690966,0.00023901684,0.0010196038,0.00016193152,0.0004498762,0.00039520167,0.00034811176,0.0015560716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012972858,0.00015800032,0.044172674,0.00040972666,0.00010794751,0.0007288102,0.00059779244,0.030639213,0.3632116,0.006210953,0.005520863,0.5469451],"study_design_scores_gemma":[0.00014798706,0.0017472161,0.2807078,0.00020403466,0.00027025767,0.0013080272,0.0008547588,0.26222917,0.3635472,0.0029210874,0.0859084,0.00015410836],"about_ca_topic_score_codex":0.0027236664,"about_ca_topic_score_gemma":0.004853531,"teacher_disagreement_score":0.003368762,"about_ca_system_score_codex":0.00026234236,"about_ca_system_score_gemma":0.0003345381,"threshold_uncertainty_score":0.011269629},"labels":[],"label_agreement":null},{"id":"W3139045596","doi":"10.3390/s21062076","title":"Optimal Access Point Power Management for Green IEEE 802.11 Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Key (lock); Nonlinear programming; Software deployment; Decomposition; Integer programming; Transmission (telecommunications); Power (physics); Point (geometry); Nonlinear system; Distributed computing; Computer network; Algorithm; Telecommunications; Mathematics","score_opus":0.022858203817522155,"score_gpt":0.2862406702470449,"score_spread":0.2633824664295228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139045596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014242527,0.0002830658,0.9828015,0.00013115614,0.000025479421,0.00003244757,0.000018221792,0.00020170984,0.002263847],"genre_scores_gemma":[0.6901238,0.00045985106,0.30340448,0.00013072038,0.000044211527,0.0001227885,0.00007161759,0.00008922955,0.0055533154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996006,0.00012751354,0.000012253078,0.00007480687,0.000106096835,0.00007876616],"domain_scores_gemma":[0.99968493,0.00018415935,0.00004079612,0.000022006743,0.0000461541,0.000022036922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008291439,0.0007872345,0.0007286012,0.0005912909,0.0004397172,0.0009199445,0.0008511267,0.0007477981,0.0020195078],"category_scores_gemma":[0.0011482353,0.00041193233,0.00033962785,0.0006890387,0.00075527315,0.0011289546,0.0007726387,0.00077606965,0.00025849146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054477514,0.000053090436,0.00023499377,0.00004007461,0.000019564623,0.000038387847,0.000056694458,0.918238,0.0024331461,0.013289628,0.0010588227,0.064483136],"study_design_scores_gemma":[0.000009462174,0.000021962647,0.00008424677,0.000003973704,0.0000052322903,0.000012074243,0.000015569809,0.9896459,0.00063890475,0.008878559,0.0006802384,0.0000039566503],"about_ca_topic_score_codex":0.0027288625,"about_ca_topic_score_gemma":0.004359252,"teacher_disagreement_score":0.0027288625,"about_ca_system_score_codex":0.0011880187,"about_ca_system_score_gemma":0.0009621639,"threshold_uncertainty_score":0.008619785},"labels":[],"label_agreement":null},{"id":"W3143368883","doi":"10.3390/s21072493","title":"OSCAR: An Optimized Scheduling Cell Allocation Algorithm for Convergecast in IEEE 802.15.4e TSCH Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; École de technologie supérieure","keywords":"Computer network; IEEE 802.15; Computer science; Wireless sensor network; Superframe; Latency (audio); Scheduling (production processes); Duty cycle; Energy consumption; Real-time computing; Engineering; Telecommunications; Voltage","score_opus":0.014176517895612674,"score_gpt":0.24053088846730608,"score_spread":0.22635437057169341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143368883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056517452,0.00028261752,0.93637675,0.00012693483,0.00011879815,0.00013280423,0.00007746328,0.00276778,0.0035993706],"genre_scores_gemma":[0.6186729,0.00014612965,0.37780765,0.00011872974,0.000042719308,0.00017445616,0.00019207409,0.00019132142,0.0026540244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996568,0.00007060762,0.000017995884,0.00005599816,0.00014227685,0.000056330464],"domain_scores_gemma":[0.9996325,0.00013552744,0.00005605092,0.000048080576,0.00008657972,0.000041257408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007159748,0.00045702702,0.00053002837,0.00057146983,0.00066370773,0.0005732682,0.0009955894,0.00037576875,0.0011990217],"category_scores_gemma":[0.0013925835,0.00017444468,0.0002092379,0.00032776018,0.0003945299,0.00046328275,0.0007407474,0.0004884695,0.00027160766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039867265,0.00019675854,0.0017417326,0.000078215075,0.00005401966,0.00009744258,0.00017235294,0.6661899,0.018866923,0.012095123,0.006624964,0.29348388],"study_design_scores_gemma":[0.000024230832,0.000037111357,0.00010870469,0.000002318688,0.0000042600414,0.000021155804,0.00001737786,0.9939865,0.0032940358,0.0011435972,0.0013542342,0.0000064072947],"about_ca_topic_score_codex":0.0047157723,"about_ca_topic_score_gemma":0.0075309854,"teacher_disagreement_score":0.0047157723,"about_ca_system_score_codex":0.0006767852,"about_ca_system_score_gemma":0.0011757206,"threshold_uncertainty_score":0.009376645},"labels":[],"label_agreement":null},{"id":"W3145875890","doi":"10.3390/s21072467","title":"Characterization of Shear Horizontal Waves Using a 1D Laser Doppler Vibrometer","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Laser Doppler vibrometer; Acoustics; Laser scanning vibrometry; Optics; Transducer; Rayleigh wave; Surface wave; Materials science; Doppler effect; Finite element method; Displacement (psychology); Shear waves; Shear (geology); Geology; Laser; Physics; Engineering; Structural engineering","score_opus":0.012628057428662132,"score_gpt":0.21025284943442674,"score_spread":0.1976247920057646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145875890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8470594,0.0006644267,0.14953656,0.00014755473,0.0000965166,0.00008934144,0.000647646,0.0004555373,0.0013030051],"genre_scores_gemma":[0.89787066,0.00042126473,0.10029345,0.000057015266,0.000028960609,0.000120298544,0.00020245327,0.000031617023,0.00097417575],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995552,0.000034016706,0.000025929214,0.00010943344,0.00022761908,0.000047737216],"domain_scores_gemma":[0.9994553,0.00025575486,0.00007663424,0.00005532949,0.00011660457,0.00004036088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046673705,0.00038523896,0.00040547524,0.0009764959,0.00020696857,0.00045906735,0.0005532367,0.000574753,0.0013009203],"category_scores_gemma":[0.00084145524,0.00025408395,0.00016630671,0.00038242497,0.00041188303,0.00050156855,0.00044728155,0.00043395278,0.000305811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037493064,0.000015518192,0.0018243417,0.000041547926,0.0000035164367,0.000036306894,0.00007265545,0.00042434593,0.9883084,0.00023402125,0.00006446113,0.008937319],"study_design_scores_gemma":[0.000020578325,0.00028102982,0.0116073005,0.0000112405605,0.000013929251,0.00020253213,0.00011007942,0.03875694,0.9468704,0.00029710875,0.001785488,0.000043499924],"about_ca_topic_score_codex":0.0007652284,"about_ca_topic_score_gemma":0.0012612585,"teacher_disagreement_score":0.0013009203,"about_ca_system_score_codex":0.00029112902,"about_ca_system_score_gemma":0.00035800994,"threshold_uncertainty_score":0.004352033},"labels":[],"label_agreement":null},{"id":"W3149985325","doi":"10.3390/s21082590","title":"A System in Package Based on a Piezoelectric Micromachined Ultrasonic Transducer Matrix for Ranging Applications","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure; Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"PMUT; Ultrasonic sensor; Transimpedance amplifier; Piezoelectricity; Ranging; Capacitive micromachined ultrasonic transducers; Acoustics; Transducer; Voltage; Electronic engineering; Materials science; Amplifier; Electrical engineering; Engineering; Operational amplifier; CMOS; Physics; Telecommunications","score_opus":0.005120464614366213,"score_gpt":0.21382856323887436,"score_spread":0.20870809862450815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149985325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1397035,0.0015152749,0.83897954,0.00026828144,0.0005600415,0.0003629387,0.00026905545,0.007171471,0.0111700175],"genre_scores_gemma":[0.57685125,0.00063927856,0.4127872,0.00020345302,0.00016862812,0.00024210675,0.00028776174,0.0002498032,0.00857051],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974436,0.000042684234,0.000011062587,0.000049250513,0.00012686735,0.000025713167],"domain_scores_gemma":[0.99978524,0.000049009494,0.00003115658,0.000042817424,0.00006565648,0.000026133137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002947331,0.0006647241,0.00047845408,0.00031002058,0.00023435446,0.00039972088,0.0011846551,0.00047004438,0.004292586],"category_scores_gemma":[0.00038292163,0.00031687465,0.0004304711,0.00020418035,0.00019065404,0.00068972853,0.00043445735,0.00049485435,0.0018315485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018755124,0.00010256115,0.0012696179,0.00054747006,0.00012173643,0.00038145922,0.00016940449,0.007518986,0.8893138,0.0049963226,0.0024886404,0.092902556],"study_design_scores_gemma":[0.00011710736,0.0039056868,0.0043071536,0.00008575434,0.0004220526,0.0035179656,0.00013873065,0.13109946,0.756144,0.0015898338,0.09855274,0.00011960766],"about_ca_topic_score_codex":0.000260826,"about_ca_topic_score_gemma":0.000445364,"teacher_disagreement_score":0.004292586,"about_ca_system_score_codex":0.00016188552,"about_ca_system_score_gemma":0.00039097868,"threshold_uncertainty_score":0.01436013},"labels":[],"label_agreement":null},{"id":"W3150857133","doi":"10.3390/s21072524","title":"Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Feature extraction; Bearing (navigation); Pattern recognition (psychology); Artificial intelligence; Support vector machine; Singular value decomposition; Fault (geology); Engineering; Singular value; Computer science; Feature (linguistics); Classifier (UML); Wavelet packet decomposition; Data mining; Entropy (arrow of time); Control theory (sociology); Wavelet; Wavelet transform; Eigenvalues and eigenvectors","score_opus":0.009626207601137408,"score_gpt":0.29475933511531116,"score_spread":0.28513312751417375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150857133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027900325,0.00017811943,0.9702519,0.00007828033,0.000059441612,0.000066089866,0.00006834742,0.0008422875,0.0005552117],"genre_scores_gemma":[0.59882706,0.00037350968,0.39842024,0.000074633324,0.00008156271,0.00013481833,0.00042420288,0.000069189045,0.0015946705],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992508,0.000056515626,0.00007092272,0.00017594804,0.00036614828,0.00007965444],"domain_scores_gemma":[0.9993531,0.00015892585,0.00008885564,0.00006836816,0.00030362452,0.000027106676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006745176,0.00072225626,0.001122044,0.0021992205,0.00043194764,0.0006682548,0.00068168255,0.00074913434,0.00097190804],"category_scores_gemma":[0.0020656048,0.00029345677,0.0009324472,0.0013513338,0.00035164706,0.0015788873,0.0007375663,0.00070625654,0.0004177345],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024552003,0.00016499824,0.0036841794,0.0001782485,0.00007623037,0.00026296408,0.00021926255,0.048676495,0.10692782,0.0029068452,0.0018637193,0.83479375],"study_design_scores_gemma":[0.0000380188,0.00022678744,0.007222315,0.000019406976,0.00006352643,0.00058470096,0.00008677569,0.927239,0.05805643,0.0030969554,0.003308618,0.000057541292],"about_ca_topic_score_codex":0.0019484079,"about_ca_topic_score_gemma":0.0009386489,"teacher_disagreement_score":0.0021992205,"about_ca_system_score_codex":0.00035115445,"about_ca_system_score_gemma":0.00060225854,"threshold_uncertainty_score":0.003874123},"labels":[],"label_agreement":null},{"id":"W3150896365","doi":"10.3390/s21072427","title":"Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; University of Calgary","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Avid Radiopharmaceuticals; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health; Heart and Stroke Foundation of Canada","keywords":"Convolutional neural network; Computer science; Protocol (science); Artificial intelligence; Atlas (anatomy); Segmentation; Pattern recognition (psychology); Data mining; Medicine; Pathology; Anatomy","score_opus":0.03235015929178652,"score_gpt":0.2744477638356134,"score_spread":0.2420976045438269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150896365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11281425,0.0022060042,0.86263573,0.00037628136,0.00026543767,0.0017162109,0.0040930915,0.010917347,0.004975649],"genre_scores_gemma":[0.25402436,0.0011761757,0.7263565,0.0003775055,0.00006772213,0.0015396604,0.010337916,0.0013219684,0.004798231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877685,0.00021494736,0.000163453,0.0004161776,0.00034219507,0.000086389446],"domain_scores_gemma":[0.9988238,0.00017819717,0.00015825668,0.00035878504,0.00043473483,0.00004615909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002217014,0.0013613072,0.0007376457,0.0018257431,0.00078906777,0.0012428287,0.0016812459,0.0011548355,0.0020209278],"category_scores_gemma":[0.0042900816,0.00060742284,0.0009913996,0.001168389,0.0006904181,0.0011159036,0.0015174884,0.0010906907,0.0012388162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013253967,0.00034180115,0.01849726,0.0014387354,0.0009937921,0.0008317551,0.00083266385,0.06367612,0.18002097,0.0078597395,0.023588868,0.7005929],"study_design_scores_gemma":[0.00025953696,0.0009402859,0.04874484,0.00040672295,0.0008753582,0.0058242567,0.00043463183,0.59782386,0.27440372,0.015369232,0.054561447,0.0003560845],"about_ca_topic_score_codex":0.009710044,"about_ca_topic_score_gemma":0.019679103,"teacher_disagreement_score":0.009710044,"about_ca_system_score_codex":0.0010677222,"about_ca_system_score_gemma":0.0027844447,"threshold_uncertainty_score":0.019307077},"labels":[],"label_agreement":null},{"id":"W3151220847","doi":"10.3390/s21072450","title":"Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Taif University","keywords":"Discriminative model; Artificial intelligence; Categorization; Computer science; Support vector machine; Pattern recognition (psychology); Machine learning; Mixture model; Generative model; Generative grammar","score_opus":0.0763559978046889,"score_gpt":0.3081030517821675,"score_spread":0.23174705397747863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151220847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032911594,0.00030980332,0.99582666,0.000067327164,0.000020137677,0.000016638025,0.000028675231,0.00025482316,0.00018459406],"genre_scores_gemma":[0.41728035,0.0010127749,0.57720244,0.0003447545,0.00020455492,0.00020800666,0.0007647688,0.00017949175,0.0028028598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821424,0.00051318057,0.00013454283,0.0004893638,0.0005087763,0.00013987947],"domain_scores_gemma":[0.9987023,0.00057881034,0.00011875035,0.00026472274,0.00027620897,0.00005923946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020609717,0.00080309156,0.0013899317,0.0018784935,0.00031538878,0.00090522657,0.0019005617,0.0011563705,0.0011867135],"category_scores_gemma":[0.0035712013,0.00045233354,0.0016505726,0.0017546722,0.00072105235,0.0018303023,0.0014249656,0.0013628106,0.0010399761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028394963,0.00027312327,0.0037364669,0.0003177787,0.00032654565,0.00016452624,0.00021385416,0.22023043,0.025326498,0.030530775,0.0035389285,0.71505713],"study_design_scores_gemma":[0.000006600976,0.00005382093,0.0005920808,0.000008084755,0.00002530676,0.00014362669,0.00001725603,0.98472774,0.003206588,0.009904988,0.0012945116,0.000019352932],"about_ca_topic_score_codex":0.0016127512,"about_ca_topic_score_gemma":0.001615016,"teacher_disagreement_score":0.0020609717,"about_ca_system_score_codex":0.00060358224,"about_ca_system_score_gemma":0.0006050158,"threshold_uncertainty_score":0.010899544},"labels":[],"label_agreement":null},{"id":"W3152110224","doi":"10.3390/s21072532","title":"Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Outlier; Data mining; Identification (biology); Computer science; Set (abstract data type); Data set; Anomaly (physics); Probabilistic logic; Statistical model; Local outlier factor; Artificial intelligence","score_opus":0.05592338187970599,"score_gpt":0.2884915138427008,"score_spread":0.23256813196299478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152110224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021254899,0.00018327516,0.9772561,0.00011681563,0.000031059604,0.000048682487,0.0001590501,0.00035794193,0.00059219525],"genre_scores_gemma":[0.9231177,0.0006045659,0.07052289,0.00007320395,0.00006226107,0.00037911808,0.0006329497,0.000102184466,0.0045050853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804676,0.0005011845,0.00012260913,0.0006237952,0.0005162205,0.00018932877],"domain_scores_gemma":[0.9965641,0.0018851057,0.0006734548,0.00021137898,0.0005806437,0.0000852468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027219884,0.0010479445,0.001385371,0.0014155364,0.00038952086,0.0014767479,0.003118207,0.0013836029,0.0012694956],"category_scores_gemma":[0.008291018,0.00063240743,0.0015320379,0.0014452353,0.0012562153,0.0017385639,0.0013873734,0.0019416894,0.00031824247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009646139,0.0000373337,0.0026427675,0.000093021954,0.00007624715,0.00014107446,0.00010165138,0.9644761,0.0013626849,0.018047089,0.00033098372,0.012594575],"study_design_scores_gemma":[0.000002443375,0.000015012776,0.00021200938,0.0000033971985,0.0000053846125,0.000021516975,0.0000046129735,0.9973908,0.00014801176,0.0020655172,0.00012596384,0.0000053702324],"about_ca_topic_score_codex":0.010003691,"about_ca_topic_score_gemma":0.0053820526,"teacher_disagreement_score":0.010003691,"about_ca_system_score_codex":0.0012020584,"about_ca_system_score_gemma":0.0011401719,"threshold_uncertainty_score":0.019890904},"labels":[],"label_agreement":null},{"id":"W3152187070","doi":"10.3390/s21144805","title":"OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection","year":2021,"lang":"en","type":"preprint","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Anomaly detection; Deep learning; Latency (audio); Computer science; Software deployment; Low latency (capital markets); Artificial intelligence; Convolutional neural network; Real-time computing; Computer network; Telecommunications; Software engineering","score_opus":0.01895605087259754,"score_gpt":0.27069476755233673,"score_spread":0.2517387166797392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152187070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20810561,0.00079900824,0.7726889,0.00053044746,0.00018959414,0.0001143055,0.00072024093,0.009640978,0.0072109355],"genre_scores_gemma":[0.8386311,0.00030178207,0.15352067,0.00023430213,0.00003302241,0.00011127233,0.0011075055,0.0002419117,0.005818541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981993,0.000028363042,0.000008057913,0.000038209106,0.00006948545,0.000035914407],"domain_scores_gemma":[0.99968433,0.0001018075,0.000040268656,0.000051547988,0.00010253057,0.000019560164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036824722,0.0006620731,0.00023080427,0.00028834792,0.00015349974,0.00039306414,0.0010712148,0.00037175845,0.0020500654],"category_scores_gemma":[0.0011141786,0.00023062344,0.00022535509,0.00023517707,0.00026231134,0.0011545758,0.00054864737,0.0007620484,0.0005577615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006450087,0.00027145757,0.006849702,0.00028981452,0.00016712367,0.00041842886,0.00013587876,0.49197814,0.077107236,0.015374931,0.020445671,0.38631654],"study_design_scores_gemma":[0.000016574731,0.00017230646,0.0007032048,0.000010878873,0.000016919894,0.000074251766,0.000020650534,0.97169703,0.019813074,0.0036415807,0.003823577,0.000010028941],"about_ca_topic_score_codex":0.002042238,"about_ca_topic_score_gemma":0.006339042,"teacher_disagreement_score":0.0020500654,"about_ca_system_score_codex":0.0004889295,"about_ca_system_score_gemma":0.00050990056,"threshold_uncertainty_score":0.00685817},"labels":[],"label_agreement":null},{"id":"W3153216549","doi":"10.3390/s21082824","title":"Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Drone; Artificial intelligence; Computer science; Visibility; Constant false alarm rate; ALARM; False alarm; Machine learning; Position (finance); Point (geometry); False positive rate; Deep learning; Computer vision; Geography; Engineering; Mathematics","score_opus":0.017918550219570718,"score_gpt":0.2437758308296785,"score_spread":0.22585728061010776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153216549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90613294,0.017248178,0.04117297,0.0037445123,0.0019323891,0.00036285876,0.008358564,0.006242018,0.014805569],"genre_scores_gemma":[0.88418806,0.002012079,0.061663765,0.0010247588,0.00028465193,0.0001566378,0.040634464,0.00035173632,0.009683771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794096,0.0005729957,0.00013318795,0.00048860663,0.0005160486,0.00034815015],"domain_scores_gemma":[0.99672556,0.0015626806,0.00015630807,0.0004363855,0.0008403754,0.0002785709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002686117,0.003068079,0.001385284,0.0015321946,0.00066124636,0.0010548929,0.001718932,0.0026944024,0.0015559181],"category_scores_gemma":[0.0068401066,0.00039780728,0.0010202087,0.0009394769,0.00069984957,0.0016874246,0.001423317,0.0024598856,0.0009859288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00358908,0.0038831956,0.03251936,0.0018464037,0.0013499428,0.0008919658,0.00034102873,0.24865319,0.012845649,0.003276748,0.13315396,0.55764943],"study_design_scores_gemma":[0.00023487992,0.0009084322,0.015227972,0.00013023957,0.00016414956,0.00029092166,0.00039355355,0.95893914,0.011671711,0.0033592815,0.008601401,0.00007829983],"about_ca_topic_score_codex":0.0248675,"about_ca_topic_score_gemma":0.034925003,"teacher_disagreement_score":0.0248675,"about_ca_system_score_codex":0.0014965265,"about_ca_system_score_gemma":0.0010125773,"threshold_uncertainty_score":0.04944551},"labels":[],"label_agreement":null},{"id":"W3153641163","doi":"10.3390/s21082682","title":"Robust Principal Component Thermography for Defect Detection in Composites","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Jaccard index; Principal component analysis; Robust principal component analysis; Outlier; Thermography; Pattern recognition (psychology); Noise (video); Computer science; Sparse PCA; Artificial intelligence; Materials science; Optics; Physics","score_opus":0.014718832661576454,"score_gpt":0.20696050456751364,"score_spread":0.1922416719059372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153641163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075699404,0.0011761229,0.9189786,0.00016324685,0.0000690492,0.000071493225,0.00019920498,0.0022987346,0.0013441868],"genre_scores_gemma":[0.45359612,0.000851415,0.54310775,0.000051965726,0.00004634993,0.00010619971,0.00037838283,0.00032435125,0.0015374847],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995436,0.00009913138,0.000018872575,0.000078100165,0.0002379708,0.000022345579],"domain_scores_gemma":[0.99912447,0.00033338272,0.0001251673,0.00012822796,0.00026011828,0.000028607787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005932842,0.00070031086,0.0005111977,0.0018451895,0.0002311734,0.00055120024,0.00040016527,0.0006063091,0.0017054684],"category_scores_gemma":[0.0019533508,0.00033403235,0.0005578915,0.0014370079,0.00043262116,0.0006336802,0.00036684933,0.0006716922,0.0006288478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004891566,0.00011904066,0.002823053,0.00043064498,0.00009579153,0.00019724171,0.00017520018,0.16197063,0.41299298,0.003870291,0.0026480753,0.41418785],"study_design_scores_gemma":[0.000011730242,0.00009400966,0.0044976645,0.000017080785,0.00002529121,0.0001678485,0.00003457571,0.9188398,0.07213486,0.001902732,0.0022278915,0.000046435744],"about_ca_topic_score_codex":0.0018186034,"about_ca_topic_score_gemma":0.002372342,"teacher_disagreement_score":0.0018451895,"about_ca_system_score_codex":0.00038541752,"about_ca_system_score_gemma":0.0007104148,"threshold_uncertainty_score":0.0057053566},"labels":[],"label_agreement":null},{"id":"W3154879496","doi":"10.3390/s21082780","title":"Vibration-Sensing Electronic Yarns for the Monitoring of Hand Transmitted Vibrations","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Trent University; Nottingham Trent University","keywords":"Vibration; Accelerometer; Yarn; Transducer; Acoustics; Textile; Engineering; Computer science; Materials science; Mechanical engineering; Electrical engineering; Composite material; Physics","score_opus":0.017905018352928126,"score_gpt":0.2919474810473947,"score_spread":0.2740424626944666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154879496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8530763,0.009035673,0.13375698,0.00019447676,0.00023758606,0.00019656775,0.0002664245,0.00039070437,0.0028452757],"genre_scores_gemma":[0.894875,0.0028842373,0.09906888,0.000102746344,0.00003715304,0.000095447984,0.000104816,0.000031630043,0.0028001587],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953127,0.000105605955,0.000027083704,0.00008388272,0.00022459507,0.000027624888],"domain_scores_gemma":[0.99928635,0.00026743842,0.00021434078,0.00005511797,0.00015315045,0.000023618659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058907125,0.00040875576,0.0002854635,0.00041159062,0.00013257362,0.0002837548,0.0003614856,0.000577347,0.0012129229],"category_scores_gemma":[0.0008526495,0.00029968176,0.00026548002,0.00032046993,0.00023713961,0.00034024523,0.00021922874,0.00034287217,0.000386866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107972905,0.000031490843,0.0013814574,0.0002022188,0.000009099738,0.00006348404,0.000049106096,0.00033907758,0.982681,0.00008256902,0.00005799946,0.014994443],"study_design_scores_gemma":[0.000024538256,0.0023845911,0.04216852,0.00010498336,0.0000698598,0.0015043903,0.00022796442,0.008085025,0.93975824,0.00017629692,0.0054478236,0.00004775706],"about_ca_topic_score_codex":0.0002443311,"about_ca_topic_score_gemma":0.0010222513,"teacher_disagreement_score":0.0012129229,"about_ca_system_score_codex":0.00014069326,"about_ca_system_score_gemma":0.00012958016,"threshold_uncertainty_score":0.004057646},"labels":[],"label_agreement":null},{"id":"W3155622372","doi":"10.3390/s21082806","title":"Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology, Taiwan","keywords":"Thermography; Principal component analysis; Hilbert–Huang transform; Zoom; Computer science; Pattern recognition (psychology); Artificial intelligence; Infrared; Biological system; Engineering; Computer vision; Optics","score_opus":0.007908393227064922,"score_gpt":0.21922601504414008,"score_spread":0.21131762181707514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155622372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23516245,0.00048058302,0.7609483,0.000097534314,0.00003340213,0.000033988275,0.0003081386,0.0007140342,0.0022215685],"genre_scores_gemma":[0.8466564,0.00042769202,0.15182468,0.000029420544,0.00003294511,0.000044024007,0.0002081024,0.00007930059,0.0006975505],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992144,0.000009116371,0.0000032820597,0.000020529527,0.00003625559,0.000009283188],"domain_scores_gemma":[0.9998826,0.000028723736,0.000024081266,0.000019781432,0.000034898738,0.000009906183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013226869,0.0002984984,0.00021206443,0.00070187036,0.000092545924,0.00025777848,0.00016410422,0.00022851976,0.0010399697],"category_scores_gemma":[0.00039954015,0.000116166324,0.00022386157,0.0005847586,0.00017547816,0.0003161161,0.0002814847,0.00026320596,0.00019210821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118456184,0.000046732363,0.0039326767,0.00017699612,0.000032103864,0.00018846855,0.00016998741,0.0453781,0.7527697,0.0034437624,0.0013451864,0.1923978],"study_design_scores_gemma":[0.000005948031,0.00008257255,0.018707376,0.000020652687,0.00003322177,0.00020667733,0.00012144778,0.86461234,0.11052141,0.0021929727,0.003456386,0.000039069066],"about_ca_topic_score_codex":0.00060409616,"about_ca_topic_score_gemma":0.0010896402,"teacher_disagreement_score":0.0010399697,"about_ca_system_score_codex":0.00010640137,"about_ca_system_score_gemma":0.00015078609,"threshold_uncertainty_score":0.0034790635},"labels":[],"label_agreement":null},{"id":"W3155692624","doi":"10.3390/s21082786","title":"From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université du Québec à Chicoutimi","funders":"","keywords":"Activity recognition; Computer science; Wireless sensor network; Context (archaeology); Field (mathematics); Wireless; Artificial intelligence; Machine learning; Feature extraction; Ambient intelligence; Real-time computing; Computer network; Telecommunications","score_opus":0.08266494054558529,"score_gpt":0.3516915973087813,"score_spread":0.26902665676319604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155692624","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032467264,0.9943956,0.0025643103,0.00032243895,0.000288742,0.000017808983,0.000037289654,0.000025952082,0.0020232147],"genre_scores_gemma":[0.0020889747,0.99513924,0.0016150783,0.00018693286,0.00029421627,0.000019658171,0.0000623385,0.000006459293,0.0005871175],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996556,0.000061327046,0.000049179358,0.00008288944,0.00012581008,0.000025154484],"domain_scores_gemma":[0.99880254,0.000810559,0.000085351225,0.000033806144,0.0002325534,0.000035134428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008501549,0.0010661732,0.0011511127,0.0018812902,0.00024626596,0.0011465843,0.0011028112,0.0010422623,0.0032593452],"category_scores_gemma":[0.0018075575,0.00047026196,0.0006285452,0.0027902965,0.00040147433,0.0022220395,0.00076229754,0.0010356609,0.0020604667],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040023417,0.000060204038,0.00029827515,0.017245753,0.000071745206,0.000107752654,0.00006967306,0.0012693526,0.0012308826,0.0054300833,0.013487059,0.9606891],"study_design_scores_gemma":[0.000016418,0.00032676966,0.0014208623,0.010502917,0.00032775497,0.0015847818,0.00021142267,0.0024832974,0.0019312893,0.00708881,0.9740216,0.00008410615],"about_ca_topic_score_codex":0.0009995869,"about_ca_topic_score_gemma":0.0011851444,"teacher_disagreement_score":0.0032593452,"about_ca_system_score_codex":0.00036691927,"about_ca_system_score_gemma":0.0010884948,"threshold_uncertainty_score":0.010903537},"labels":[],"label_agreement":null},{"id":"W3155975840","doi":"10.3390/s21082638","title":"Planetary Gearbox Dynamic Modeling Considering Bearing Clearance and Sun Gear Tooth Crack","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bearing (navigation); Vibration; Structural engineering; Dynamic load testing; Engineering; Automotive engineering; Fault (geology); Planet; Computer science; Geology; Acoustics; Physics","score_opus":0.00936633840377679,"score_gpt":0.19630232458598565,"score_spread":0.18693598618220886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155975840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45115104,0.0006114299,0.5365322,0.00014669714,0.000072155475,0.000076995675,0.00026695704,0.00040991406,0.010732554],"genre_scores_gemma":[0.99152875,0.00019630056,0.0064988495,0.000008827954,0.0000070988203,0.00003313561,0.0000697877,0.000019131332,0.0016381026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999145,0.000013764177,0.0000065678214,0.000020549835,0.00003261209,0.000011893889],"domain_scores_gemma":[0.9998838,0.0000403815,0.000024531117,0.0000148234685,0.000029741166,0.00000667611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001580299,0.00044329828,0.0005044869,0.0003094008,0.00021051825,0.0004419787,0.00050791417,0.0006634763,0.0012556382],"category_scores_gemma":[0.0004553491,0.0002507551,0.0006753597,0.00013823497,0.0002589189,0.0005022438,0.0003302991,0.00032360796,0.00022702626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034569228,0.00002043297,0.002151231,0.00006709081,0.000015417278,0.00021044051,0.000071277485,0.97437245,0.014656425,0.0009304404,0.00013358769,0.0073366463],"study_design_scores_gemma":[0.0000020863342,0.000012196348,0.00043148652,0.0000031193795,0.0000035198582,0.00001806713,0.000010402167,0.99873346,0.0005349085,0.000094030656,0.00015451536,0.0000022852444],"about_ca_topic_score_codex":0.0055872537,"about_ca_topic_score_gemma":0.004596697,"teacher_disagreement_score":0.0055872537,"about_ca_system_score_codex":0.00015953103,"about_ca_system_score_gemma":0.0004612997,"threshold_uncertainty_score":0.011109471},"labels":[],"label_agreement":null},{"id":"W3156200692","doi":"10.3390/s21082700","title":"Blindness and the Reliability of Downwards Sensors to Avoid Obstacles: A Study with the EyeCane","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Obstacle; Blindness; Computer science; Rendering (computer graphics); Computer vision; Reliability (semiconductor); Artificial intelligence; Obstacle avoidance; Human–computer interaction; Simulation; Optometry; Geography; Medicine","score_opus":0.023369151916140384,"score_gpt":0.2828719460258323,"score_spread":0.25950279410969196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156200692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99962234,0.000027077389,0.00016322608,0.0000075971957,0.0000031509912,0.000019416704,0.000016034472,0.0000020607104,0.00013914732],"genre_scores_gemma":[0.9989304,0.000044919332,0.0004154631,0.000037544432,0.000005715979,0.000032685057,0.00003113849,0.0000050631984,0.0004971175],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989405,0.0003399365,0.00008088285,0.00022151155,0.00029868048,0.000118464755],"domain_scores_gemma":[0.99305505,0.0032151868,0.0008296058,0.0005921458,0.0017030102,0.00060497294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025774206,0.00053717114,0.00048260996,0.0007603478,0.00048370866,0.0006043389,0.00040845666,0.00067425316,0.001465537],"category_scores_gemma":[0.013591819,0.0003140141,0.00033177485,0.00021954963,0.00091736944,0.0005355661,0.0005947086,0.00054903893,0.0003196214],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019195097,0.029142369,0.68104607,0.0007685236,0.00042856648,0.0035291014,0.03223665,0.0011053786,0.15886958,0.0002944413,0.0009488298,0.07243536],"study_design_scores_gemma":[0.0003803431,0.055406157,0.9133747,0.0000727558,0.00029042768,0.0036571708,0.0056026983,0.0031303538,0.015669415,0.00029600956,0.002003903,0.00011613345],"about_ca_topic_score_codex":0.0052598147,"about_ca_topic_score_gemma":0.0038338276,"teacher_disagreement_score":0.0052598147,"about_ca_system_score_codex":0.00019200733,"about_ca_system_score_gemma":0.00037669137,"threshold_uncertainty_score":0.013630867},"labels":[],"label_agreement":null},{"id":"W3156506161","doi":"10.3390/s21092908","title":"A Review of Corrosion in Aircraft Structures and Graphene-Based Sensors for Advanced Corrosion Monitoring","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; National Research Council Canada","funders":"National Research Council Canada; Ministère de la Défense Nationale","keywords":"Corrosion; Corrosion monitoring; Downtime; Materials science; Aircraft maintenance; Rivet; Metallurgy; Engineering; Reliability engineering; Composite material","score_opus":0.03274253329167756,"score_gpt":0.3222362493039734,"score_spread":0.2894937160122958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156506161","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016794322,0.98923606,0.0026217506,0.0006847902,0.0009266201,0.00002443543,0.000102144586,0.000059163896,0.004665544],"genre_scores_gemma":[0.008180651,0.98421395,0.0022089474,0.00079546834,0.0005762356,0.000038936636,0.00015236883,0.00001610546,0.0038174035],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995407,0.000055163135,0.000044608863,0.00010878588,0.00020836125,0.000042469004],"domain_scores_gemma":[0.9995141,0.00022421671,0.00008380068,0.000025166326,0.00012959316,0.000023172988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057411817,0.0012839326,0.0009882518,0.0022362024,0.0004706754,0.0010364515,0.0010208193,0.0016228704,0.0028927757],"category_scores_gemma":[0.000865289,0.00059869076,0.00088589726,0.0025327671,0.00044307168,0.00212394,0.00067882484,0.0014840807,0.0015407453],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011346423,0.00018171122,0.0008872751,0.05662329,0.00021883888,0.0009348904,0.0003485204,0.001557387,0.07895478,0.013102826,0.062664285,0.7844127],"study_design_scores_gemma":[0.0000061445253,0.00031409724,0.001514769,0.0026884114,0.00017659245,0.002217429,0.00014490153,0.0009456925,0.021999514,0.0021610865,0.96774757,0.00008381819],"about_ca_topic_score_codex":0.0010468963,"about_ca_topic_score_gemma":0.0015993539,"teacher_disagreement_score":0.0028927757,"about_ca_system_score_codex":0.0005039943,"about_ca_system_score_gemma":0.0005195304,"threshold_uncertainty_score":0.009677231},"labels":[],"label_agreement":null},{"id":"W3157141087","doi":"10.3390/s21093131","title":"Characteristics of Bow-Tie Antenna Structures for Semi-Insulating GaAs and InP Photoconductive Terahertz Emitters","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Terahertz technology and applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; King Abdulaziz City for Science and Technology; Canada Foundation for Innovation","keywords":"Terahertz radiation; Optoelectronics; Common emitter; Photoconductivity; Materials science; Bow tie; Bandwidth (computing); Antenna (radio); Amplitude; Optics; Electrical engineering; Physics; Telecommunications; Engineering","score_opus":0.010653875207708845,"score_gpt":0.23119758225002868,"score_spread":0.22054370704231982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157141087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9926386,0.0006057825,0.0051476313,0.000045324585,0.000018020606,0.000013117473,0.000063686755,0.00004330371,0.0014245987],"genre_scores_gemma":[0.9968141,0.00028433438,0.001938393,0.000010544916,0.000005547478,0.000010213205,0.00005730753,0.000013561057,0.0008659284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998493,0.000017108681,0.0000065120053,0.00003652533,0.00006783304,0.00002264339],"domain_scores_gemma":[0.9995852,0.00014203771,0.00010491687,0.000043568674,0.000107206586,0.000017123568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018764833,0.00018733939,0.00017719687,0.00017925327,0.00010445479,0.00041309907,0.00035675496,0.00039218998,0.0007078919],"category_scores_gemma":[0.0006587844,0.0001261276,0.00015965552,0.00018785812,0.0001875476,0.000304131,0.0001805332,0.00026507076,0.00023023039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005033817,0.000008916589,0.00036994892,0.000036010973,0.000006252078,0.000048598245,0.00007923589,0.00031667892,0.99632543,0.00024088101,0.000038849274,0.0024787493],"study_design_scores_gemma":[0.0000052113537,0.00022270657,0.0026872598,0.0000067733463,0.000014738793,0.00022736668,0.000070860595,0.003564141,0.99155784,0.000069828624,0.0015668639,0.0000065048184],"about_ca_topic_score_codex":0.00010859743,"about_ca_topic_score_gemma":0.00013311261,"teacher_disagreement_score":0.0007078919,"about_ca_system_score_codex":0.00022816508,"about_ca_system_score_gemma":0.00004691703,"threshold_uncertainty_score":0.0023681521},"labels":[],"label_agreement":null},{"id":"W3157906910","doi":"10.3390/s21092998","title":"Adversarial Gaussian Denoiser for Multiple-Level Image Denoising","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Noise reduction; Artificial intelligence; Computer science; Convolutional neural network; Adversarial system; Image (mathematics); Pattern recognition (psychology); Noise (video); Non-local means; Image denoising; Inpainting; Computer vision","score_opus":0.043032376356473334,"score_gpt":0.2963711017674756,"score_spread":0.2533387254110023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157906910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004234612,0.00015369589,0.99471843,0.00008671802,0.000017144977,0.000008461162,0.000012058718,0.00013317785,0.00063561654],"genre_scores_gemma":[0.60089487,0.0007966254,0.38941494,0.00036434465,0.00009283897,0.00007647972,0.00017875156,0.00017584993,0.008005335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954164,0.00012496841,0.000017211098,0.00010620902,0.00016242705,0.000047702895],"domain_scores_gemma":[0.99933463,0.0003676744,0.000068268244,0.000086282445,0.00011146744,0.000031598767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013058498,0.0007063659,0.0008257629,0.00043129406,0.00026408597,0.0005171159,0.0010043858,0.0010788635,0.00132738],"category_scores_gemma":[0.0025356817,0.00036121346,0.0007606148,0.0003843291,0.0009728145,0.0009019872,0.0012217619,0.0019056776,0.00041289828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012995316,0.000051912324,0.0006397952,0.00010570378,0.000081203725,0.00012556057,0.00010022618,0.85593873,0.022588706,0.02567672,0.0017139682,0.09284753],"study_design_scores_gemma":[0.0000024901722,0.000016603719,0.00007321052,0.000004407506,0.0000068401773,0.000032026128,0.000004832806,0.992438,0.0031049284,0.0037235664,0.00058806525,0.000004951926],"about_ca_topic_score_codex":0.0017757338,"about_ca_topic_score_gemma":0.0025208786,"teacher_disagreement_score":0.0017757338,"about_ca_system_score_codex":0.00054705754,"about_ca_system_score_gemma":0.00048666462,"threshold_uncertainty_score":0.006906092},"labels":[],"label_agreement":null},{"id":"W3158020234","doi":"10.3390/s21093047","title":"Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; National Research Council Canada","funders":"","keywords":"Remote sensing; Mean squared error; Computer science; Radiance; Global Positioning System; Georeference; Process (computing); Environmental science; Satellite imagery; Inertial measurement unit; Thematic Mapper; Computer vision; Artificial intelligence; Geography; Mathematics; Telecommunications; Statistics","score_opus":0.016677034559518352,"score_gpt":0.2274540539143586,"score_spread":0.21077701935484025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158020234","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56181175,0.00082590536,0.42153665,0.00019082194,0.00021986858,0.00021720924,0.0016565219,0.0043649203,0.009176333],"genre_scores_gemma":[0.7117347,0.0005252708,0.28228027,0.000058800728,0.00003830552,0.00009992198,0.002386989,0.00041065327,0.0024651277],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996679,0.000038028236,0.000015943146,0.000064904365,0.00017975751,0.000033534718],"domain_scores_gemma":[0.9996575,0.00004516383,0.000058791262,0.0000822062,0.00014582888,0.000010458065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047413673,0.0005175494,0.00024746705,0.0011289014,0.00028172985,0.0006162296,0.0004923445,0.00020679065,0.0009694351],"category_scores_gemma":[0.0014282485,0.00020641992,0.0003190174,0.001143309,0.0002270191,0.0006005567,0.0004723731,0.00036926422,0.0005355222],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031639583,0.00016749337,0.030052781,0.00037958086,0.00014137376,0.00020114757,0.00061729265,0.053644847,0.13724299,0.0019632343,0.004956254,0.7703165],"study_design_scores_gemma":[0.00008583133,0.00024497858,0.20010525,0.00013527654,0.0002006964,0.00043180073,0.0008980253,0.59846044,0.16385649,0.0024853537,0.03298062,0.00011519179],"about_ca_topic_score_codex":0.017589437,"about_ca_topic_score_gemma":0.039223973,"teacher_disagreement_score":0.017589437,"about_ca_system_score_codex":0.00033969452,"about_ca_system_score_gemma":0.00092370325,"threshold_uncertainty_score":0.034974158},"labels":[],"label_agreement":null},{"id":"W3158216587","doi":"10.3390/s21092984","title":"Quantization and Deployment of Deep Neural Networks on Microcontrollers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Computer science; Microcontroller; Quantization (signal processing); Artificial neural network; Deep learning; Software deployment; MNIST database; Inference; Embedded system; Artificial intelligence; Computer engineering; Computer hardware; Algorithm","score_opus":0.011372284754760318,"score_gpt":0.24267625202229698,"score_spread":0.23130396726753666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158216587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10913154,0.0010569752,0.84802604,0.0006204861,0.00033972767,0.00030793308,0.00081981375,0.027439078,0.012258321],"genre_scores_gemma":[0.7738956,0.00039912987,0.21734533,0.00027135256,0.000033636992,0.00025829306,0.0008539952,0.0005720364,0.006370615],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948406,0.00007534376,0.000046493526,0.000118209624,0.00021009233,0.000065803666],"domain_scores_gemma":[0.9993954,0.00018081907,0.000053648364,0.0001287743,0.00020827634,0.000033208005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040453853,0.00084087014,0.00033286694,0.00051398494,0.000188674,0.00063093763,0.0015938856,0.00032486825,0.007972012],"category_scores_gemma":[0.0022880852,0.00028650177,0.0002449186,0.00036412236,0.00033362594,0.0013373813,0.00082692044,0.00082703034,0.0011441675],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007499409,0.0001800684,0.0024278862,0.0006903511,0.00009962604,0.0005825898,0.0002943719,0.2566348,0.093739785,0.017480243,0.025020603,0.6020997],"study_design_scores_gemma":[0.00009072095,0.00047643797,0.0017063487,0.00008199621,0.000042178697,0.00022561445,0.000106844134,0.83368933,0.13359661,0.008501443,0.021437012,0.00004537135],"about_ca_topic_score_codex":0.0025930789,"about_ca_topic_score_gemma":0.0029702813,"teacher_disagreement_score":0.007972012,"about_ca_system_score_codex":0.00084655115,"about_ca_system_score_gemma":0.000615429,"threshold_uncertainty_score":0.026669025},"labels":[],"label_agreement":null},{"id":"W3159339089","doi":"10.3390/s21093178","title":"Development and Evaluation of a Quantitative Fluorescent Lateral Flow Immunoassay for Cystatin-C, a Renal Dysfunction Biomarker","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Chronic Kidney Disease and Diabetes","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Erciyes Üniversitesi","keywords":"Cystatin C; Detection limit; Renal function; Immunoassay; Chromatography; Repeatability; Urine; Cystatin; Chemistry; Biomarker; Creatinine; Urology; Medicine; Immunology; Antibody; Biochemistry","score_opus":0.05172834456784865,"score_gpt":0.3261845313101435,"score_spread":0.2744561867422949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159339089","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3224283,0.0049095876,0.6661057,0.00068083126,0.00038775284,0.001047036,0.0007747366,0.00089428306,0.0027718137],"genre_scores_gemma":[0.43665984,0.0017251151,0.55443805,0.00065703393,0.000099288,0.0012227661,0.0009829114,0.000043880398,0.0041711256],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99784684,0.00053222623,0.000092956594,0.00046478029,0.0009566615,0.00010668366],"domain_scores_gemma":[0.9990982,0.00026188043,0.0001328343,0.00005599901,0.00038096728,0.00007016585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002593477,0.00082791783,0.00045395378,0.0007294575,0.00030058224,0.00047888735,0.00095256924,0.0014634834,0.0005481484],"category_scores_gemma":[0.0022170565,0.00028811407,0.0005050525,0.0003733558,0.00047963407,0.00047976983,0.00044759098,0.00067599164,0.00045193097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010041946,0.00015866899,0.0011518304,0.00018132823,0.000022921238,0.000051313848,0.000049659735,0.00039306286,0.9806717,0.00035833058,0.00023791725,0.016622787],"study_design_scores_gemma":[0.000047172787,0.0009473702,0.0033187103,0.000022726628,0.000058712412,0.0005015528,0.000037557133,0.017368952,0.9731414,0.00017975338,0.0043314057,0.000044632365],"about_ca_topic_score_codex":0.0007035687,"about_ca_topic_score_gemma":0.0009932404,"teacher_disagreement_score":0.002593477,"about_ca_system_score_codex":0.00061039656,"about_ca_system_score_gemma":0.0009107787,"threshold_uncertainty_score":0.013715744},"labels":[],"label_agreement":null},{"id":"W3159555583","doi":"10.3390/s21093098","title":"Identification of the Optimal Season and Spectral Regions for Shrub Cover Estimation in Grasslands","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Shrub; Grassland; Vegetation (pathology); Remote sensing; Environmental science; Transect; Grassland ecosystem; Growing season; Ecosystem; Agroforestry; Ecology; Geography; Biology","score_opus":0.006939480324472521,"score_gpt":0.22128211532056077,"score_spread":0.21434263499608824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159555583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9780869,0.00038270606,0.019771537,0.000024655348,0.000010452124,0.000021294189,0.00044901972,0.00019772672,0.0010556559],"genre_scores_gemma":[0.9765014,0.00015375535,0.022000598,0.00001592174,0.000012557484,0.000025222394,0.00096174993,0.000029387573,0.0002993487],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99987733,0.000026475363,0.0000096713775,0.000037965827,0.000017685727,0.000030954077],"domain_scores_gemma":[0.9997247,0.00009693275,0.000058572525,0.000018026689,0.0000584399,0.000043377768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048184555,0.00027714702,0.0003061453,0.001745865,0.00023390622,0.00044939748,0.00019169177,0.00029631765,0.00069235824],"category_scores_gemma":[0.00065330166,0.00011680768,0.00037121074,0.0007743421,0.00014394958,0.00033665032,0.00018629148,0.00014733043,0.00032222233],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008833477,0.00029174308,0.6234502,0.00035147267,0.00021846568,0.00050130056,0.0005700456,0.018877042,0.10097015,0.00044468118,0.0017261592,0.2517154],"study_design_scores_gemma":[0.000022522638,0.000099687386,0.891738,0.000033577708,0.0000862924,0.00029208136,0.00042507713,0.09903204,0.006824355,0.00040711198,0.0010102941,0.000028853576],"about_ca_topic_score_codex":0.004273452,"about_ca_topic_score_gemma":0.007805383,"teacher_disagreement_score":0.004273452,"about_ca_system_score_codex":0.00012772507,"about_ca_system_score_gemma":0.00025442115,"threshold_uncertainty_score":0.008497119},"labels":[],"label_agreement":null},{"id":"W3159701637","doi":"10.3390/s21092896","title":"Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Inertial measurement unit; STRIDE; Global Positioning System; Gait; Computer science; Step detection; Wearable computer; Simulation; Gait analysis; Units of measurement; Physical medicine and rehabilitation; Artificial intelligence; Telecommunications; Physics; Medicine; Embedded system","score_opus":0.022088582734206115,"score_gpt":0.20261481478219542,"score_spread":0.18052623204798932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159701637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8533693,0.0007243953,0.14088225,0.00009111093,0.00009495991,0.00030410654,0.00097557594,0.00038530614,0.0031731813],"genre_scores_gemma":[0.85919195,0.0004516335,0.13649672,0.000114751296,0.00005085341,0.0005648515,0.00065154315,0.000038975162,0.0024386928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914813,0.00025607084,0.00008870771,0.00017825127,0.0002838414,0.000044926164],"domain_scores_gemma":[0.9988998,0.0002446118,0.00023805126,0.00012788907,0.00041471684,0.000074839954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079492014,0.00047442672,0.000554736,0.00100297,0.00021499308,0.00046246315,0.0004003457,0.00044837504,0.002512945],"category_scores_gemma":[0.0022539245,0.00023499058,0.00023280132,0.0009071447,0.00021907056,0.00044184073,0.00052128837,0.00026958555,0.0008468368],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022864426,0.0006587954,0.3238352,0.00096784026,0.00048086658,0.00022764862,0.0011107143,0.0017663062,0.20655881,0.00079285126,0.002110022,0.4592045],"study_design_scores_gemma":[0.00013654085,0.0034730267,0.936539,0.00013420377,0.00025559595,0.001272245,0.001107118,0.012956686,0.03941918,0.00072903046,0.0038863148,0.00009108515],"about_ca_topic_score_codex":0.0009392635,"about_ca_topic_score_gemma":0.0027722835,"teacher_disagreement_score":0.002512945,"about_ca_system_score_codex":0.00012516338,"about_ca_system_score_gemma":0.00020224434,"threshold_uncertainty_score":0.0084065795},"labels":[],"label_agreement":null},{"id":"W3159885913","doi":"10.3390/s21093073","title":"State of the Art of Telecommunication Systems in Isolated and Constrained Areas","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Rimouski","funders":"","keywords":"Telecommunications; State (computer science); Field (mathematics); Isolation (microbiology); Computer science; Remote sensing; Geography; Biology","score_opus":0.018284960485905895,"score_gpt":0.2446595049696322,"score_spread":0.22637454448372632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159885913","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005807453,0.97993124,0.0017809792,0.00090744137,0.00044942647,0.000016610544,0.0000310303,0.000019869774,0.016282722],"genre_scores_gemma":[0.0036845969,0.99150497,0.0011193431,0.00034092666,0.00034807864,0.000015283811,0.000038691152,0.0000063566745,0.0029418261],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996006,0.00005830818,0.00003565232,0.00007128523,0.00019236295,0.00004183866],"domain_scores_gemma":[0.9993957,0.00033288889,0.00006007646,0.000036981022,0.00014875748,0.000025598201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064976356,0.00067880575,0.00070856855,0.0019133551,0.00048380386,0.0015077716,0.0007965375,0.0013102388,0.0041837813],"category_scores_gemma":[0.0006807107,0.00035921845,0.00045592783,0.0029745717,0.0008703417,0.0021215896,0.0007169382,0.0016531604,0.0021377627],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003268155,0.000077235774,0.00019517807,0.013462377,0.0000408554,0.00016007271,0.0001887748,0.0010042199,0.003855353,0.040518064,0.022983994,0.9174811],"study_design_scores_gemma":[0.0000019785782,0.00007322688,0.000494608,0.002493536,0.000030560313,0.00049766334,0.00011100098,0.00026150479,0.0011119699,0.005087572,0.9898201,0.000016095708],"about_ca_topic_score_codex":0.0013319668,"about_ca_topic_score_gemma":0.0014029806,"teacher_disagreement_score":0.0041837813,"about_ca_system_score_codex":0.00075147283,"about_ca_system_score_gemma":0.0011782022,"threshold_uncertainty_score":0.013996124},"labels":[],"label_agreement":null},{"id":"W3159928448","doi":"10.3390/s21092953","title":"Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"Mitacs","keywords":"Electroencephalography; Wearable computer; Ear canal; Computer science; Latency (audio); Silicone; Acoustics; Biomedical engineering; Engineering; Materials science; Embedded system; Telecommunications; Neuroscience; Psychology; Physics","score_opus":0.03463747338590123,"score_gpt":0.2744181826277855,"score_spread":0.23978070924188427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159928448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51524496,0.001307661,0.4753262,0.00018732746,0.00036792402,0.0027550128,0.0010462655,0.0016105388,0.0021541014],"genre_scores_gemma":[0.6478238,0.0011266156,0.3435868,0.00015704756,0.000052117728,0.0017918913,0.000685203,0.0001802425,0.004596254],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888617,0.00021137483,0.00009748947,0.00021450518,0.0005196824,0.00007084243],"domain_scores_gemma":[0.99852365,0.00033398744,0.0002628151,0.0003317589,0.00045277618,0.000094965166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013108442,0.0010827674,0.00039763158,0.0005816363,0.00016627548,0.00038290012,0.0015217765,0.00088559097,0.0020709706],"category_scores_gemma":[0.0027853718,0.00033491306,0.00038996205,0.00028075758,0.00047919448,0.00050688494,0.0006163453,0.00033822522,0.00063039846],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003040038,0.00041829236,0.0029993514,0.0010152584,0.000056157445,0.00038811323,0.00027327627,0.002505452,0.93489647,0.00048715182,0.0006535469,0.056002818],"study_design_scores_gemma":[0.00010842832,0.0050060977,0.018320238,0.000115784496,0.00013834311,0.0023138733,0.00018221124,0.011678653,0.947952,0.00022261206,0.01389479,0.00006705466],"about_ca_topic_score_codex":0.00035408948,"about_ca_topic_score_gemma":0.0008594749,"teacher_disagreement_score":0.0020709706,"about_ca_system_score_codex":0.00023233012,"about_ca_system_score_gemma":0.00046915872,"threshold_uncertainty_score":0.006932497},"labels":[],"label_agreement":null},{"id":"W3160663836","doi":"10.3390/s24041123","title":"Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Banatao Institute; Center for Information Technology Research in the Interest of Society; Muscular Dystrophy Association; U.S. Department of Defense","keywords":"Accelerometer; Gait; Physical medicine and rehabilitation; Duchenne muscular dystrophy; Gait analysis; Ambulatory; Medicine; Physical therapy; Artificial intelligence; Computer science; Surgery","score_opus":0.02520528235298479,"score_gpt":0.23570596768350666,"score_spread":0.21050068533052185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160663836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9634703,0.00063899974,0.034509163,0.00007429983,0.000015063663,0.000031115003,0.00055963226,0.00013731282,0.0005641506],"genre_scores_gemma":[0.9771333,0.0002905863,0.021636527,0.000024909159,0.000010676659,0.000035336296,0.00048608042,0.000008062855,0.0003744484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998274,0.000036008438,0.000026867467,0.000055346973,0.0000336946,0.000020673846],"domain_scores_gemma":[0.999757,0.000073968,0.00006759792,0.00001867762,0.000061337065,0.000021467775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036193026,0.00049929775,0.00031109355,0.0010895057,0.00012297348,0.00031597447,0.0001939084,0.00035507182,0.00036093706],"category_scores_gemma":[0.0009335926,0.0001395924,0.00025686118,0.0005140709,0.000106359774,0.0001849365,0.00026626582,0.00017221963,0.00013204575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060028624,0.00034243843,0.42814234,0.00036934362,0.00027875797,0.00055213424,0.0002676034,0.027831675,0.082565695,0.00026686687,0.0009682019,0.4578147],"study_design_scores_gemma":[0.000027308628,0.0005407364,0.7386141,0.00011549894,0.00008928523,0.0008116487,0.00037086578,0.24611141,0.012037689,0.00046255387,0.0007821331,0.000036759557],"about_ca_topic_score_codex":0.002954985,"about_ca_topic_score_gemma":0.007478542,"teacher_disagreement_score":0.002954985,"about_ca_system_score_codex":0.00015461056,"about_ca_system_score_gemma":0.00013897855,"threshold_uncertainty_score":0.0058755875},"labels":[],"label_agreement":null},{"id":"W3161019910","doi":"10.3390/s21103555","title":"A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii","keywords":"Multilinear map; Kalman filter; Adaptive filter; Tensor (intrinsic definition); Algorithm; Extended Kalman filter; Computer science; Context (archaeology); Mathematics; Invariant extended Kalman filter; Filter (signal processing); Artificial intelligence","score_opus":0.04993205294080435,"score_gpt":0.3074780558820816,"score_spread":0.2575460029412773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161019910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007874169,0.00022090055,0.9982936,0.000071481976,0.000034493285,0.000008076299,0.000015806776,0.000053347216,0.00051488256],"genre_scores_gemma":[0.26111576,0.0031373207,0.7277779,0.00024794685,0.0003353219,0.00020634857,0.0002157015,0.00013510155,0.006828698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905604,0.00022256294,0.00007127548,0.0003088254,0.00026971757,0.000071556904],"domain_scores_gemma":[0.9987412,0.00057171297,0.0002272904,0.0001331931,0.00027974718,0.00004688685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014948167,0.0009160579,0.0007051243,0.000687998,0.00062087306,0.0013323792,0.0008955619,0.0012128252,0.002056929],"category_scores_gemma":[0.004832562,0.00044567572,0.001025999,0.0010438632,0.0013179509,0.0018084748,0.0011620761,0.0020154428,0.0007226477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115956296,0.000039357892,0.00113631,0.00024586983,0.00008847094,0.00015410194,0.00033884763,0.39636242,0.01010987,0.4280143,0.0024977257,0.16089691],"study_design_scores_gemma":[0.000010495476,0.00005988569,0.00034203482,0.000038051396,0.00002385473,0.000093415365,0.000022381937,0.915793,0.0022406338,0.07263946,0.008692542,0.00004421787],"about_ca_topic_score_codex":0.00562154,"about_ca_topic_score_gemma":0.0029757018,"teacher_disagreement_score":0.00562154,"about_ca_system_score_codex":0.0009257871,"about_ca_system_score_gemma":0.0011134386,"threshold_uncertainty_score":0.011177659},"labels":[],"label_agreement":null},{"id":"W3161097911","doi":"10.3390/s21103472","title":"A People-Counting and Speed-Estimation System Using Wi-Fi Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fuzhou University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Subcarrier; Outlier; Computer science; Statistics; Variance (accounting); Estimation; Filter (signal processing); Dynamic time warping; Real-time computing; Simulation; Artificial intelligence; Mathematics; Computer vision; Engineering; Estimator","score_opus":0.010979301212375654,"score_gpt":0.2134409662752489,"score_spread":0.20246166506287325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161097911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16190685,0.00042393678,0.8119252,0.0002088925,0.0003213076,0.0003301708,0.0007669215,0.018964201,0.00515251],"genre_scores_gemma":[0.70563656,0.0003623009,0.28212515,0.00027234506,0.00023248715,0.00042032232,0.0012000654,0.00014304921,0.00960774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995584,0.000055060886,0.000033585962,0.00014004405,0.00015639486,0.000056386507],"domain_scores_gemma":[0.99952316,0.00007227954,0.000058741833,0.00006181819,0.00022964473,0.0000544314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004524629,0.0007781353,0.0008723168,0.0014287838,0.00049662543,0.000593094,0.0011092954,0.00083261245,0.0019396796],"category_scores_gemma":[0.0008689392,0.00029136005,0.00027986336,0.0007251114,0.00019408282,0.0010820244,0.0006712903,0.0004591433,0.0016645122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011022474,0.0007309873,0.02447803,0.00035678686,0.00020191011,0.0005974264,0.00040502532,0.01162703,0.16779818,0.002157507,0.012477002,0.7780679],"study_design_scores_gemma":[0.00027432086,0.0013982133,0.040823974,0.000106018226,0.00035687996,0.0020656155,0.00033456925,0.7026973,0.216966,0.0025832672,0.032055136,0.00033879856],"about_ca_topic_score_codex":0.0028417355,"about_ca_topic_score_gemma":0.002292619,"teacher_disagreement_score":0.0028417355,"about_ca_system_score_codex":0.00022302942,"about_ca_system_score_gemma":0.0004477582,"threshold_uncertainty_score":0.0064889193},"labels":[],"label_agreement":null},{"id":"W3161606486","doi":"10.3390/s21103375","title":"Impact of Safety Message Generation Rules on the Awareness of Vulnerable Road Users","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agencia Nacional de Investigación y Desarrollo; Comisión Nacional de Investigación Científica y Tecnológica; Cisco Systems","keywords":"Context (archaeology); Channel (broadcasting); Network packet; Engineering; Computer science; Metric (unit); Computer security; Computer network; Operations management; Geography","score_opus":0.01782125384098874,"score_gpt":0.2464202466193014,"score_spread":0.22859899277831267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161606486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87715346,0.0006214769,0.11522119,0.00046415214,0.00011261862,0.00023878974,0.0001627003,0.0004470805,0.005578571],"genre_scores_gemma":[0.996289,0.000056514997,0.0034427145,0.000030209072,0.000009596446,0.000016258959,0.00003079444,0.000012804923,0.00011211376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994076,0.0027497867,0.0003533455,0.0007328685,0.0014111894,0.00067677133],"domain_scores_gemma":[0.92731494,0.05577109,0.0072125397,0.003796157,0.0048158285,0.0010893435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005704317,0.0008050416,0.00067666906,0.0011505926,0.00064718083,0.0019149028,0.0013414435,0.0010476101,0.0005138806],"category_scores_gemma":[0.04154063,0.00031609286,0.00038111492,0.00051952095,0.0010484448,0.0023133778,0.0011102739,0.0009170517,0.00009099682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013782192,0.0005486493,0.063646674,0.0004334148,0.0002554252,0.0006537195,0.00072320516,0.8069362,0.019512063,0.013611905,0.0011308858,0.09116963],"study_design_scores_gemma":[0.000027145814,0.00062529097,0.014253086,0.000053096992,0.00018977372,0.00033563646,0.0005609682,0.9624368,0.014851398,0.0055958615,0.0010183608,0.00005252075],"about_ca_topic_score_codex":0.003504078,"about_ca_topic_score_gemma":0.0022776772,"teacher_disagreement_score":0.005704317,"about_ca_system_score_codex":0.0013173705,"about_ca_system_score_gemma":0.0013994942,"threshold_uncertainty_score":0.03016764},"labels":[],"label_agreement":null},{"id":"W3162246789","doi":"10.3390/s21103561","title":"Current Trends and Challenges in Pediatric Access to Sensorless and Sensor-Based Upper Limb Exoskeletons","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Physical medicine and rehabilitation; Computer science; Lower limb; Activities of daily living; Population; Medicine; Physical therapy","score_opus":0.07776700705986463,"score_gpt":0.33216888912201664,"score_spread":0.25440188206215203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162246789","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021289877,0.9982583,0.0001986741,0.00031675087,0.00008746327,0.000003858103,0.000024740217,0.000005721478,0.00089166133],"genre_scores_gemma":[0.0009930057,0.9982033,0.000298838,0.00016510296,0.0000728655,0.0000074874515,0.000034868015,0.0000021560115,0.00022227713],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924904,0.0001268108,0.00019205558,0.00013472291,0.00024536398,0.000052036296],"domain_scores_gemma":[0.9975351,0.0017254952,0.00028876192,0.000034404384,0.00035928536,0.00005700915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013555507,0.0006413473,0.0013596474,0.002300513,0.00024241465,0.0014258784,0.0007941369,0.0012791448,0.004067384],"category_scores_gemma":[0.0027009773,0.000341964,0.00091815816,0.002371068,0.0005320013,0.0020470016,0.0007604949,0.0014059185,0.0011924697],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007893324,0.000054958546,0.00047100705,0.055726066,0.00010622652,0.000234542,0.00017799642,0.0002890065,0.0010958018,0.004313078,0.009424558,0.92802775],"study_design_scores_gemma":[0.000017904618,0.00020736364,0.0023768032,0.029820593,0.00036546032,0.002797193,0.0003578232,0.00015515408,0.0008615196,0.0026499953,0.9603522,0.000038050217],"about_ca_topic_score_codex":0.0013503021,"about_ca_topic_score_gemma":0.0020668083,"teacher_disagreement_score":0.004067384,"about_ca_system_score_codex":0.00058074173,"about_ca_system_score_gemma":0.0020151965,"threshold_uncertainty_score":0.013606787},"labels":[],"label_agreement":null},{"id":"W3162432519","doi":"10.3390/s21103423","title":"A Review on Advanced Sensing Materials for Agricultural Gas Sensors","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Research Nova Scotia; University of Windsor; Dalhousie University; CMC Microsystems","keywords":"Materials science; Graphene; Nanotechnology; Analyte; Hydrogen sulfide; Process engineering; Chemistry; Sulfur","score_opus":0.030157048629689952,"score_gpt":0.2824729229858081,"score_spread":0.2523158743561182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162432519","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011935836,0.98887897,0.0025314873,0.00042166814,0.0008519089,0.00002816684,0.00013505881,0.00007830945,0.0058808653],"genre_scores_gemma":[0.005393372,0.9821786,0.0038296985,0.00063647714,0.00076420204,0.00006297516,0.00029870096,0.000027446005,0.0068084886],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959713,0.00005186825,0.0000490394,0.000090202935,0.00016977823,0.000041926345],"domain_scores_gemma":[0.9996916,0.0001293862,0.000055920966,0.000015434922,0.00008615265,0.000021573953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045037243,0.0012618636,0.0010340307,0.0023630823,0.00036564717,0.0010194906,0.0009425756,0.0013970521,0.0085698245],"category_scores_gemma":[0.00059416675,0.0006219646,0.0007412879,0.0022825154,0.0002569807,0.0018658796,0.0006794073,0.0013464584,0.004596097],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001339097,0.00020350887,0.00034798388,0.047836177,0.00013614606,0.0005908548,0.00016059176,0.0011685678,0.07251953,0.007834369,0.05883514,0.8102332],"study_design_scores_gemma":[0.0000063176403,0.00017719978,0.00049146364,0.0014323633,0.00007212375,0.001034279,0.00004076201,0.00036107784,0.009197643,0.0009493309,0.98620623,0.000031258503],"about_ca_topic_score_codex":0.0004947011,"about_ca_topic_score_gemma":0.00077238656,"teacher_disagreement_score":0.0085698245,"about_ca_system_score_codex":0.00041697317,"about_ca_system_score_gemma":0.0006730115,"threshold_uncertainty_score":0.02866894},"labels":[],"label_agreement":null},{"id":"W3163035523","doi":"10.3390/s21103316","title":"A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Electroencephalography; Polysomnography; Sleep (system call); Dementia; Sleep Stages; Computer science; Deep learning; Slow-wave sleep; Artificial intelligence; Medicine; Disease; Internal medicine; Psychiatry","score_opus":0.06948215918722901,"score_gpt":0.32041485116429164,"score_spread":0.25093269197706264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163035523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056278665,0.00034036336,0.938795,0.00028572118,0.00007790731,0.000089189154,0.0002564747,0.0023345763,0.0015420803],"genre_scores_gemma":[0.78180534,0.00026505673,0.20937471,0.00032338395,0.00004977002,0.00020234534,0.0008631092,0.00011221035,0.007004058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984634,0.000020202408,0.000011314596,0.00005735542,0.0000330946,0.00003168017],"domain_scores_gemma":[0.99976414,0.00006448131,0.000025416333,0.000031390966,0.00009617607,0.000018437842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040711765,0.00082045695,0.0003275106,0.00046623545,0.00024124153,0.00040447686,0.0008034942,0.0005128278,0.0015086613],"category_scores_gemma":[0.0013397359,0.00029620779,0.00042597755,0.0004022265,0.00024360907,0.00054752233,0.0006709497,0.00089146005,0.00056119356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027717173,0.00024786018,0.0040280162,0.000100572484,0.00009007112,0.00017144687,0.000108129156,0.18128322,0.03651055,0.0024760312,0.0054593035,0.76924753],"study_design_scores_gemma":[0.000006609542,0.000040874573,0.0008745902,0.000008174393,0.0000108530285,0.00002583785,0.000009128869,0.9911343,0.0061747916,0.0010097066,0.0006991195,0.0000060021457],"about_ca_topic_score_codex":0.009751145,"about_ca_topic_score_gemma":0.014207993,"teacher_disagreement_score":0.009751145,"about_ca_system_score_codex":0.0005334169,"about_ca_system_score_gemma":0.0007974276,"threshold_uncertainty_score":0.019388795},"labels":[],"label_agreement":null},{"id":"W3163465263","doi":"10.3390/s21093275","title":"Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Metamaterial Technologies (Canada)","funders":"Innovate UK","keywords":"Aqueous solution; Transmission (telecommunications); Extremely high frequency; Infrared; Near-infrared spectroscopy; Materials science; Chemistry; Computer science; Optics; Physics; Telecommunications","score_opus":0.01871313825425471,"score_gpt":0.2972324395231779,"score_spread":0.2785193012689232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163465263","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8475792,0.0029100014,0.14641279,0.00040764536,0.0002237665,0.000046433444,0.00013886865,0.00049404654,0.0017873091],"genre_scores_gemma":[0.93152285,0.0009648673,0.066373356,0.00015970287,0.000050881772,0.000031309883,0.00006851974,0.00004537896,0.0007830937],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921894,0.00017092653,0.000050232757,0.00017317555,0.0003356561,0.00005099832],"domain_scores_gemma":[0.9990527,0.000562533,0.0001295882,0.00007418906,0.00016620866,0.000014758653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008840507,0.00065927306,0.0004602359,0.00034731833,0.00014775997,0.0005916187,0.0005770629,0.0007712925,0.00055354263],"category_scores_gemma":[0.0020739785,0.00024907527,0.00025211196,0.00040890952,0.00041467827,0.00071606314,0.0004639618,0.0005789781,0.00024830134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021308071,0.000039112776,0.0011566696,0.00012354027,0.000022946928,0.00007095815,0.00006383887,0.0012080442,0.9756944,0.000113943875,0.00006800798,0.021225432],"study_design_scores_gemma":[0.000010326055,0.00029223977,0.003778898,0.000013687541,0.00004401385,0.00015853668,0.000038664723,0.02596321,0.96898896,0.00013460622,0.00055354484,0.000023415783],"about_ca_topic_score_codex":0.00039459518,"about_ca_topic_score_gemma":0.00059597223,"teacher_disagreement_score":0.0008840507,"about_ca_system_score_codex":0.00019459685,"about_ca_system_score_gemma":0.00013107754,"threshold_uncertainty_score":0.0046753883},"labels":[],"label_agreement":null},{"id":"W3164160402","doi":"10.3390/s21113775","title":"A Low-Cost Multi-Parameter Water Quality Monitoring System","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedded system; Interconnection; Computer science; Wireless; Wireless sensor network; Android (operating system); Real-time computing; Computer hardware; Engineering; Telecommunications; Computer network","score_opus":0.06319825220918913,"score_gpt":0.3008643341703084,"score_spread":0.23766608196111927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164160402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14198524,0.0017295595,0.7738955,0.0017822983,0.0010404614,0.0020972446,0.0028106982,0.048224825,0.026434157],"genre_scores_gemma":[0.7358036,0.00067044084,0.22717772,0.0017041106,0.00042063848,0.0014183808,0.0023813914,0.0003423333,0.0300813],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987048,0.00008432053,0.000094525516,0.00036899935,0.00066875294,0.00007859553],"domain_scores_gemma":[0.9995259,0.000039010403,0.000063055406,0.000087062195,0.00022808394,0.000056973855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004672247,0.0006473909,0.00090004393,0.0008459351,0.0005253006,0.0007677272,0.0020081517,0.0010445615,0.0073657036],"category_scores_gemma":[0.0005569073,0.00040160352,0.00032985353,0.00053724996,0.00023193285,0.001670018,0.0015648789,0.00061005016,0.0042909617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000628033,0.00047220927,0.006832169,0.00078498176,0.00009322734,0.0006535121,0.0001911524,0.0034420674,0.61688787,0.0031276753,0.03716159,0.3297255],"study_design_scores_gemma":[0.0003485458,0.0028513218,0.0179928,0.00011653629,0.00027981208,0.0036035185,0.00015793982,0.19332604,0.5322396,0.0018170938,0.24686398,0.00040288523],"about_ca_topic_score_codex":0.00088140083,"about_ca_topic_score_gemma":0.00078427687,"teacher_disagreement_score":0.0073657036,"about_ca_system_score_codex":0.00047477413,"about_ca_system_score_gemma":0.00067686517,"threshold_uncertainty_score":0.024640739},"labels":[],"label_agreement":null},{"id":"W3164616601","doi":"10.3390/s21113759","title":"A High-Resolution Reflective Microwave Planar Sensor for Sensing of Vanadium Electrolyte","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; CMC Microsystems","keywords":"Planar; Microwave; Materials science; Optoelectronics; Split-ring resonator; Electrolyte; Resonator; Dielectric; Vanadium; Optics; Electrode; Computer science; Chemistry; Telecommunications; Physics","score_opus":0.0162184691050035,"score_gpt":0.2268568390116048,"score_spread":0.2106383699066013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164616601","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67772907,0.0028251475,0.31050897,0.0004420102,0.0003379286,0.0001727584,0.00040577003,0.0019527678,0.005625543],"genre_scores_gemma":[0.79808474,0.0010649413,0.19572389,0.00018585156,0.00005051017,0.0000479654,0.00027589116,0.000056714885,0.0045094728],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959284,0.00004206819,0.000014851272,0.000114826384,0.00020340955,0.000031975113],"domain_scores_gemma":[0.99977356,0.000043515174,0.00006115818,0.00002604853,0.00007172811,0.000023955597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002944876,0.00047756746,0.00038223836,0.0003186885,0.00015068734,0.00056661613,0.00096573104,0.00089331326,0.00063306594],"category_scores_gemma":[0.0004912468,0.00031060903,0.00030200614,0.0002276451,0.00027000508,0.0005069016,0.00038802507,0.00058898533,0.0004713352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019505325,0.000010561753,0.00010076909,0.000032514654,0.0000052288656,0.000019996787,0.0000090110325,0.00006173611,0.99465925,0.00009204349,0.000049092247,0.0049401987],"study_design_scores_gemma":[0.0000043093396,0.00017455399,0.00060712866,0.000002406964,0.000010962,0.00035364192,0.00001714638,0.0023229106,0.9950029,0.00003206658,0.0014615003,0.000010495695],"about_ca_topic_score_codex":0.00020799135,"about_ca_topic_score_gemma":0.00052459206,"teacher_disagreement_score":0.00096573104,"about_ca_system_score_codex":0.0002874997,"about_ca_system_score_gemma":0.00028533046,"threshold_uncertainty_score":0.0021178126},"labels":[],"label_agreement":null},{"id":"W3165049227","doi":"10.3390/s21113801","title":"LTCC-Integrated Dielectric Resonant Antenna Array for 5G Applications","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dielectric resonator antenna; Materials science; Antenna array; Antenna (radio); Microstrip antenna; Stack (abstract data type); Extremely high frequency; Resonator; Dielectric; Optoelectronics; Ceramic; Acoustics; Electrical engineering; Electronic engineering; Engineering; Computer science; Telecommunications; Physics; Composite material","score_opus":0.008905616562901546,"score_gpt":0.20954136070487633,"score_spread":0.2006357441419748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165049227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.640826,0.0013710454,0.31843564,0.00042552873,0.0007388924,0.00017154311,0.0011043072,0.0074233846,0.029503698],"genre_scores_gemma":[0.87691945,0.0002516844,0.11404424,0.00016937384,0.00009280194,0.00011206261,0.00094860233,0.00016027261,0.007301482],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995178,0.00004185487,0.000027784308,0.00012370016,0.0002124619,0.000076425335],"domain_scores_gemma":[0.9996082,0.000027266116,0.00013139512,0.00006835874,0.0001327324,0.00003201979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026375207,0.00048332106,0.00047525731,0.000364702,0.00015953301,0.000582118,0.00091920013,0.0005856912,0.0019975111],"category_scores_gemma":[0.00035754792,0.00025296817,0.00050082925,0.0004322306,0.000116838775,0.0004436788,0.00031627342,0.0003776417,0.002542389],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012782979,0.00003306553,0.00081455905,0.00008390864,0.00003260118,0.00020246867,0.000027154698,0.0012918719,0.97883385,0.0008093875,0.0019078188,0.015835578],"study_design_scores_gemma":[0.000041508778,0.00092182285,0.0045724586,0.000010058661,0.00009788191,0.0013905192,0.00003747296,0.0284064,0.9398914,0.00010868369,0.024448937,0.00007280246],"about_ca_topic_score_codex":0.0005884551,"about_ca_topic_score_gemma":0.0013570609,"teacher_disagreement_score":0.0019975111,"about_ca_system_score_codex":0.00056785776,"about_ca_system_score_gemma":0.00034574332,"threshold_uncertainty_score":0.0066822767},"labels":[],"label_agreement":null},{"id":"W3165321300","doi":"10.3390/s21113724","title":"Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RANSAC; Computer science; Computer vision; Artificial intelligence; Surface (topology); Mathematics; Geometry; Image (mathematics)","score_opus":0.048256802410126795,"score_gpt":0.23907085419213867,"score_spread":0.19081405178201188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165321300","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034369327,0.00044002675,0.9579157,0.00007282359,0.000067887006,0.000090237576,0.00018343062,0.0061433595,0.00071712746],"genre_scores_gemma":[0.16736208,0.0005277285,0.8279221,0.00013008778,0.00003573987,0.00010481187,0.0018950553,0.00073558505,0.0012868341],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99746907,0.00020323579,0.00009711499,0.0004957999,0.0014721059,0.0002627008],"domain_scores_gemma":[0.99830997,0.0003114571,0.00023216355,0.0004806558,0.0005981576,0.0000676492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010521775,0.0028490126,0.0027009756,0.003171736,0.00072586787,0.0014780492,0.0025898952,0.00131885,0.0011537127],"category_scores_gemma":[0.0023539236,0.0010058044,0.0026052624,0.0026052946,0.0007966107,0.0014063142,0.0023614336,0.0016810858,0.002073718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026358815,0.00022693549,0.004710687,0.00038022635,0.00025734838,0.0008532091,0.0004531321,0.13436207,0.12486544,0.0015435352,0.0067141526,0.7253697],"study_design_scores_gemma":[0.000016190723,0.00007439413,0.0033902714,0.000029960353,0.000049941613,0.0006202672,0.00017759939,0.9494688,0.0410396,0.0013920434,0.0036839198,0.00005710865],"about_ca_topic_score_codex":0.006795398,"about_ca_topic_score_gemma":0.012242584,"teacher_disagreement_score":0.006795398,"about_ca_system_score_codex":0.00045348,"about_ca_system_score_gemma":0.0013295006,"threshold_uncertainty_score":0.013511717},"labels":[],"label_agreement":null},{"id":"W3165351430","doi":"10.3390/s21113575","title":"Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Decompensation; Heart failure; Reliability (semiconductor); Cardiac decompensation; Medicine; Intensive care medicine; Failure rate; Reliability engineering; Computer science; Engineering; Cardiology","score_opus":0.04798363613563003,"score_gpt":0.4014561628817521,"score_spread":0.35347252674612206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165351430","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000051207007,0.99964225,0.00003672637,0.00012290214,0.00005086606,0.0000035580047,0.000012142332,0.000001730354,0.00007854073],"genre_scores_gemma":[0.00043399812,0.9992798,0.00009472473,0.00007465802,0.00006551036,0.000005703922,0.0000148321005,5.464327e-7,0.000030223915],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906856,0.00022657591,0.00028554111,0.00014468643,0.00023130685,0.00004335025],"domain_scores_gemma":[0.9950754,0.003661323,0.0005748395,0.00005799862,0.00054766395,0.0000827545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023775366,0.0012854419,0.0033521752,0.006198063,0.000400199,0.0018153993,0.0014731382,0.0014660221,0.0038255884],"category_scores_gemma":[0.0055042813,0.00046927837,0.0023964937,0.006147046,0.00067119306,0.0020130407,0.0008679095,0.001408177,0.00066565856],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014350865,0.0000898893,0.0010680154,0.2103972,0.0008799106,0.00019993715,0.00017512224,0.00032090305,0.00027687248,0.001518462,0.010660062,0.7742702],"study_design_scores_gemma":[0.00009905417,0.00042972548,0.014512084,0.3521238,0.009015438,0.0041323714,0.00075565,0.0004933729,0.000499014,0.0038085787,0.6139854,0.0001455022],"about_ca_topic_score_codex":0.0032074586,"about_ca_topic_score_gemma":0.0052722963,"teacher_disagreement_score":0.006198063,"about_ca_system_score_codex":0.0010789344,"about_ca_system_score_gemma":0.0033733633,"threshold_uncertainty_score":0.0127978325},"labels":[],"label_agreement":null},{"id":"W3165471237","doi":"10.3390/s21113706","title":"GPS Swept Anti-Jamming Technique Based on Fast Orthogonal Search (FOS)","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Jamming; Computer science; GPS signals; Precision Lightweight GPS Receiver; Assisted GPS; Time to first fix; SIGNAL (programming language); Interference (communication); Real-time computing; Telecommunications; Gps receiver","score_opus":0.010258985696331229,"score_gpt":0.2225929232804146,"score_spread":0.21233393758408337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165471237","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05518583,0.00040632018,0.9419094,0.000055584776,0.000047350786,0.000035394027,0.000035736797,0.00042292342,0.001901606],"genre_scores_gemma":[0.50249976,0.0006024328,0.49406835,0.000062886786,0.00004300342,0.00007932208,0.00010114082,0.000038216604,0.0025049609],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979013,0.00004990897,0.000009367981,0.00003119657,0.0001010449,0.000018289024],"domain_scores_gemma":[0.99974066,0.00008685978,0.000058997626,0.000019948904,0.000084640946,0.000008863788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477161,0.0004552404,0.0002574361,0.0006803296,0.00019529632,0.00023108789,0.00030876757,0.00029825434,0.00070052914],"category_scores_gemma":[0.0008369939,0.00013635255,0.0002547651,0.00043631648,0.0002394525,0.0005628574,0.00027238225,0.0002416808,0.00023298185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005041814,0.000099868426,0.0024672565,0.0003410852,0.000081058235,0.0002339216,0.00033625428,0.0931769,0.25223976,0.011961182,0.0012921402,0.6372664],"study_design_scores_gemma":[0.00004499066,0.00068601547,0.0028083655,0.00004925524,0.00006484131,0.00092785404,0.000104570114,0.85047024,0.13383503,0.0025468424,0.008397822,0.00006415584],"about_ca_topic_score_codex":0.00081554503,"about_ca_topic_score_gemma":0.0012738634,"teacher_disagreement_score":0.00081554503,"about_ca_system_score_codex":0.00017896398,"about_ca_system_score_gemma":0.0003642088,"threshold_uncertainty_score":0.0023435354},"labels":[],"label_agreement":null},{"id":"W3165482683","doi":"10.3390/s21113615","title":"Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Inertial measurement unit; Heading (navigation); Computer science; Extended Kalman filter; Sensor fusion; Inertial navigation system; Kalman filter; Calibration; Dead reckoning; Artificial intelligence; Computer vision; Pedestrian; Simulation; Real-time computing; Global Positioning System; Inertial frame of reference; Engineering; Telecommunications; Aerospace engineering; Physics","score_opus":0.020925970490885636,"score_gpt":0.29154042474274966,"score_spread":0.27061445425186403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165482683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01349483,0.0002482126,0.9849133,0.00002991722,0.000051382383,0.000016614751,0.000019837766,0.00043074452,0.00079513824],"genre_scores_gemma":[0.6166393,0.0004949397,0.38049236,0.0000768155,0.00009572369,0.00007665268,0.00012369057,0.000045359717,0.0019551606],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960476,0.00007742798,0.000023182783,0.00011180391,0.00013620315,0.00004661227],"domain_scores_gemma":[0.9998198,0.000025216434,0.000029745222,0.000032269443,0.00008096986,0.00001202695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048296482,0.0006488817,0.00051981123,0.00065195194,0.00035358063,0.00037077186,0.00052988814,0.0005411421,0.0007947395],"category_scores_gemma":[0.0005989242,0.00026424517,0.0006297559,0.0006821098,0.0002423821,0.00081111817,0.0010031272,0.00036813013,0.00035075226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002687534,0.00008933641,0.004606686,0.00022284412,0.00012423482,0.00027721358,0.00040813402,0.17393205,0.06803743,0.007986576,0.0014634051,0.7425834],"study_design_scores_gemma":[0.000014995619,0.00027124712,0.004175636,0.00003027809,0.00007161184,0.00025756413,0.00010323105,0.9585851,0.026215803,0.0038586052,0.0063663344,0.00004963019],"about_ca_topic_score_codex":0.0018293479,"about_ca_topic_score_gemma":0.0014208381,"teacher_disagreement_score":0.0018293479,"about_ca_system_score_codex":0.00022083196,"about_ca_system_score_gemma":0.00041759457,"threshold_uncertainty_score":0.0036373734},"labels":[],"label_agreement":null},{"id":"W3165542947","doi":"10.3390/s21113690","title":"A Bi-Spectral Microbolometer Sensor for Wildfire Measurement","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service; University of Toronto; Institut National d'Optique","funders":"Mitacs","keywords":"Microbolometer; Remote sensing; Detector; Environmental science; Infrared; Radiometry; Computer science; Bolometer; Optics; Geology; Physics; Telecommunications","score_opus":0.016689818146288494,"score_gpt":0.21550757112356772,"score_spread":0.19881775297727922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165542947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18475291,0.0021298327,0.7909235,0.00035356462,0.0005434228,0.0006160866,0.0016606837,0.005154147,0.013865868],"genre_scores_gemma":[0.44567737,0.0007155911,0.5373269,0.00053801056,0.00012704225,0.000718806,0.0015070378,0.00017267861,0.013216659],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994742,0.0000424725,0.000012137224,0.0000927253,0.00034509192,0.00003345077],"domain_scores_gemma":[0.99971384,0.00004657221,0.000035284298,0.00003948998,0.00012640482,0.000038363036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004024016,0.00050960924,0.00040672155,0.00081526383,0.00037059354,0.0005533253,0.0010748104,0.000645302,0.0023720225],"category_scores_gemma":[0.00054206897,0.00030623053,0.00021879376,0.0005136257,0.00017428456,0.0008286198,0.00063541107,0.0005857786,0.0018040197],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016309992,0.00013280811,0.0026939823,0.00021023628,0.000019730265,0.000041758412,0.000065098735,0.00094138784,0.899128,0.001204727,0.0023837925,0.09301527],"study_design_scores_gemma":[0.000090567184,0.00089627574,0.022072557,0.00007228348,0.000075852855,0.0010025362,0.00012008643,0.108377494,0.8049485,0.0009635036,0.06122272,0.0001576579],"about_ca_topic_score_codex":0.00075439847,"about_ca_topic_score_gemma":0.0021989776,"teacher_disagreement_score":0.0023720225,"about_ca_system_score_codex":0.00033100555,"about_ca_system_score_gemma":0.00049525686,"threshold_uncertainty_score":0.007935226},"labels":[],"label_agreement":null},{"id":"W3165583696","doi":"10.3390/s21113723","title":"Uncertainties in Measuring Soil Moisture Content with Actively Heated Fiber-Optic Distributed Temperature Sensing","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Water content; Environmental science; Calibration; Soil science; Soil water; Groundwater recharge; Hydrogeology; Moisture; Parametric statistics; Optical fiber; Fiber; Remote sensing; Hydrology (agriculture); Geotechnical engineering; Materials science; Geology; Engineering; Meteorology; Groundwater; Mathematics; Statistics; Geography","score_opus":0.017640776067119994,"score_gpt":0.20057916928601124,"score_spread":0.18293839321889124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165583696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84986985,0.00018459975,0.14647268,0.00013259245,0.000026524962,0.000040021376,0.00033416558,0.00053939794,0.0024001643],"genre_scores_gemma":[0.9824769,0.00005551819,0.017213684,0.000011987041,0.0000025393863,0.000014326088,0.0000910563,0.000018269135,0.00011559437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99908674,0.00021628894,0.00004701983,0.00019173692,0.0004152987,0.000042840795],"domain_scores_gemma":[0.9979323,0.0010936194,0.00025728135,0.00043005627,0.00026045938,0.000026234238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001172101,0.0004400587,0.00026914818,0.00034767794,0.00028166993,0.0006431739,0.0009231287,0.00042777782,0.00027765083],"category_scores_gemma":[0.004872372,0.00024650744,0.0003115798,0.00071105384,0.00054163695,0.0010111207,0.0005877376,0.00035869746,0.00008488046],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003998703,0.00014796613,0.078980565,0.00019205592,0.00013063694,0.00018443294,0.00025622372,0.7063956,0.154895,0.0027395766,0.00033850508,0.055339545],"study_design_scores_gemma":[0.000028311024,0.00016220786,0.020200107,0.000024418012,0.00003588835,0.00010568856,0.00008791553,0.8815533,0.095382534,0.0015375835,0.00082427065,0.000057873018],"about_ca_topic_score_codex":0.0059936303,"about_ca_topic_score_gemma":0.0078513445,"teacher_disagreement_score":0.0059936303,"about_ca_system_score_codex":0.0007476328,"about_ca_system_score_gemma":0.0005252215,"threshold_uncertainty_score":0.011917472},"labels":[],"label_agreement":null},{"id":"W3165739532","doi":"10.3390/s21113738","title":"Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Calibration; Remote sensing; Full spectral imaging; Spectral imaging; Asphalt; Environmental science; Spectral power distribution; Radiant intensity; Principal component analysis; Optics; Spectral sensitivity; Materials science; Geology; Computer science; Artificial intelligence; Mathematics; Physics; Wavelength","score_opus":0.0252940560242713,"score_gpt":0.23380164776359652,"score_spread":0.20850759173932523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165739532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09884186,0.00063711,0.89287996,0.00025550582,0.00019712349,0.00030045665,0.0001608372,0.00321675,0.003510422],"genre_scores_gemma":[0.3452871,0.00049243146,0.64625555,0.00044201128,0.00009015675,0.0003378475,0.0005067851,0.0003160767,0.006271924],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983822,0.00024183195,0.000059735397,0.00045167728,0.00079581677,0.00006869839],"domain_scores_gemma":[0.9987664,0.00026356932,0.00012273488,0.0003236206,0.00047472937,0.000048984213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016278481,0.0006449334,0.0005851954,0.0010864452,0.0005054084,0.00084006443,0.0017320393,0.001476312,0.002945727],"category_scores_gemma":[0.0021825545,0.00035593638,0.00032592984,0.00074481254,0.0005927001,0.0013801516,0.0008632734,0.00078285555,0.0027200216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023752074,0.00015567336,0.0033073907,0.00026660744,0.000036447258,0.00015321895,0.00014007911,0.0030033598,0.83570284,0.0017843207,0.0014654093,0.15374711],"study_design_scores_gemma":[0.00004545373,0.0007908963,0.009725047,0.00005786958,0.0000940258,0.001328672,0.00010450455,0.053302824,0.91405827,0.00059700554,0.019793767,0.00010159935],"about_ca_topic_score_codex":0.0006308724,"about_ca_topic_score_gemma":0.0009444319,"teacher_disagreement_score":0.002945727,"about_ca_system_score_codex":0.00051039655,"about_ca_system_score_gemma":0.0006312684,"threshold_uncertainty_score":0.009854436},"labels":[],"label_agreement":null},{"id":"W3165851732","doi":"10.3390/s21113614","title":"Wideband Circular Polarized Dielectric Resonator Antenna Array for Millimeter-Wave Applications","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wideband; Dielectric resonator antenna; Extremely high frequency; Antenna array; Circular polarization; Bandwidth (computing); Optics; Axial ratio; Physics; Antenna (radio); Resonator; Engineering; Telecommunications; Microstrip","score_opus":0.012891007678335632,"score_gpt":0.20815171050075437,"score_spread":0.19526070282241875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165851732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30335364,0.0029056068,0.6716847,0.000704837,0.00045085413,0.000048958686,0.00028966134,0.002157379,0.018404422],"genre_scores_gemma":[0.7428356,0.0011421754,0.2487317,0.00027632446,0.00012833944,0.00007087023,0.00030985204,0.00010239961,0.006402802],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997564,0.000048729475,0.000009854393,0.00007290944,0.00007809296,0.000034021607],"domain_scores_gemma":[0.9997918,0.000037743812,0.00005300913,0.00003142994,0.00006467087,0.00002133447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015303704,0.00041657648,0.00037509212,0.00019920307,0.000119573844,0.0003976198,0.00045650126,0.0004335515,0.0010098298],"category_scores_gemma":[0.00022513753,0.00022016739,0.00033468133,0.00029574838,0.00012799879,0.00043452487,0.0002949872,0.00029128368,0.001722395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009165927,0.00002654363,0.0003216713,0.00006363467,0.000019207735,0.00010651874,0.000021457467,0.00218,0.9708166,0.0009872677,0.00085001736,0.024515385],"study_design_scores_gemma":[0.00003267043,0.0006063066,0.0016087345,0.000011296478,0.000052812215,0.0013207746,0.000061138344,0.07524925,0.8950841,0.0005546993,0.025357911,0.000060303384],"about_ca_topic_score_codex":0.000067709916,"about_ca_topic_score_gemma":0.00014521366,"teacher_disagreement_score":0.0010098298,"about_ca_system_score_codex":0.00014956377,"about_ca_system_score_gemma":0.00015362859,"threshold_uncertainty_score":0.0033782125},"labels":[],"label_agreement":null},{"id":"W3167308378","doi":"10.3390/s21124026","title":"Aircraft Fuselage Corrosion Detection Using Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Nvidia","keywords":"Fuselage; Corrosion; Aerospace; Automation; Identification (biology); Engineering; Economic shortage; Airworthiness; Artificial intelligence; Aircraft maintenance; Task (project management); Computer science; Systems engineering; Structural engineering; Aeronautics; Aerospace engineering; Mechanical engineering; Materials science","score_opus":0.03583568003301097,"score_gpt":0.2642771769685478,"score_spread":0.22844149693553684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167308378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26004374,0.0011361284,0.7289595,0.00029442692,0.00009694116,0.00008695445,0.00033695006,0.0039135944,0.0051317397],"genre_scores_gemma":[0.8737368,0.0004184555,0.12182148,0.000121367186,0.000049966326,0.000044863784,0.00067551335,0.000057019326,0.0030745252],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964166,0.000048733782,0.000018229215,0.000113521004,0.00014003288,0.000037857317],"domain_scores_gemma":[0.99953556,0.00011034933,0.00010838219,0.000051206232,0.0001763601,0.00001809337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003336011,0.0008356339,0.00043331375,0.0011261393,0.00015824688,0.0005410554,0.0005381764,0.00070727593,0.0005717498],"category_scores_gemma":[0.001034967,0.00024170961,0.0005752268,0.000435245,0.00027433067,0.00037338553,0.00040659792,0.00046708132,0.00035379897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022546099,0.00023610519,0.009986412,0.00022958749,0.00017788184,0.00022371774,0.000098619974,0.2633648,0.10312924,0.0011550596,0.0047213496,0.61645174],"study_design_scores_gemma":[0.0000035354194,0.00005819976,0.004134888,0.000009661083,0.000018354194,0.000055856315,0.000014497418,0.97901046,0.015219608,0.00056386314,0.00090188114,0.000009112],"about_ca_topic_score_codex":0.0035325496,"about_ca_topic_score_gemma":0.0039542783,"teacher_disagreement_score":0.0035325496,"about_ca_system_score_codex":0.00045185065,"about_ca_system_score_gemma":0.0003151946,"threshold_uncertainty_score":0.00702399},"labels":[],"label_agreement":null},{"id":"W3167504941","doi":"10.3390/s21113835","title":"Design and Implementation of an Enhanced Matched Filter for Sidelobe Reduction of Pulsed Linear Frequency Modulation Radar","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Matched filter; Frequency modulation; Radar; Reduction (mathematics); Pulse repetition frequency; Filter (signal processing); Impulse response; Modulation (music); Bandwidth (computing); Acoustics; Electronic engineering; Mathematics; Physics; Telecommunications; Engineering","score_opus":0.01887970702146798,"score_gpt":0.26854305211232743,"score_spread":0.24966334509085944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167504941","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021054795,0.0002698154,0.97648025,0.00006399663,0.000055048822,0.000052917072,0.000026171803,0.0004901547,0.0015068231],"genre_scores_gemma":[0.4249085,0.00035955769,0.57134426,0.00012211024,0.000054729848,0.00013063713,0.00010822197,0.000043861946,0.0029281988],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995665,0.00006440184,0.00002887175,0.000077425466,0.00022066476,0.00004209453],"domain_scores_gemma":[0.9996213,0.000083045554,0.00007074233,0.00003506668,0.00017077665,0.00001899932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005481074,0.00059497735,0.00047612793,0.0004955853,0.00020972645,0.0005196577,0.000941525,0.0010857763,0.0011979435],"category_scores_gemma":[0.00076917675,0.00025214322,0.0004952406,0.0003031791,0.00020202134,0.0006686265,0.00030594307,0.0004252051,0.0006683306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050887256,0.00019598207,0.0022261706,0.00042965353,0.00013297122,0.00045918408,0.0001711442,0.06602791,0.5396639,0.011712195,0.001637885,0.37683406],"study_design_scores_gemma":[0.0000722649,0.0009810524,0.0013249962,0.000039332936,0.00007953447,0.00063808996,0.00003154191,0.67936736,0.29982337,0.0010536193,0.016527316,0.00006154669],"about_ca_topic_score_codex":0.000829983,"about_ca_topic_score_gemma":0.00058705686,"teacher_disagreement_score":0.0011979435,"about_ca_system_score_codex":0.00038918754,"about_ca_system_score_gemma":0.00056410395,"threshold_uncertainty_score":0.0040075183},"labels":[],"label_agreement":null},{"id":"W3168331296","doi":"10.3390/s21123948","title":"Grouping and Sponsoring Centric Green Coverage Model for Internet of Things","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Jawaharlal Nehru University; Nottingham Trent University","keywords":"Computer science; Internet of Things; Software deployment; Wireless sensor network; Cover (algebra); Scheduling (production processes); Set (abstract data type); Energy consumption; Distributed computing; Real-time computing; Set cover problem; Computer network; Engineering; Embedded system","score_opus":0.015331583627531885,"score_gpt":0.22076120610080524,"score_spread":0.20542962247327334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168331296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013743497,0.0004905976,0.97966236,0.00035594567,0.00006337648,0.000055066335,0.00008356168,0.00016143198,0.005383997],"genre_scores_gemma":[0.8754194,0.0016442242,0.11310547,0.00022644928,0.00014352404,0.00027225245,0.00026312354,0.000118703836,0.008806905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990219,0.0002833039,0.00003516816,0.0001992528,0.00031343845,0.00014699604],"domain_scores_gemma":[0.99928904,0.0003114205,0.00012932744,0.0000952383,0.00012014806,0.000054713655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082943623,0.0009891255,0.0008230566,0.00094935956,0.00065382523,0.0010452952,0.0020073222,0.0013640221,0.001787712],"category_scores_gemma":[0.0019073517,0.00041048857,0.0010710985,0.0013675231,0.0010953101,0.0019605723,0.0011966976,0.0011045807,0.00035372813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004546409,0.000029356788,0.00047763495,0.00007449802,0.000027619973,0.00021106552,0.00013189501,0.91584086,0.003819852,0.06272414,0.0016173144,0.015000147],"study_design_scores_gemma":[0.0000024824296,0.000026831342,0.00010528304,0.000005922219,0.000007266705,0.000065217355,0.000021840911,0.98973256,0.0003314586,0.008571295,0.0011241601,0.0000057556504],"about_ca_topic_score_codex":0.0045241453,"about_ca_topic_score_gemma":0.0035375988,"teacher_disagreement_score":0.0045241453,"about_ca_system_score_codex":0.0017064802,"about_ca_system_score_gemma":0.00068408,"threshold_uncertainty_score":0.012381434},"labels":[],"label_agreement":null},{"id":"W3168902558","doi":"10.3390/s21123967","title":"Acousto-Optic Comb Interrogation System for Random Fiber Grating Sensors with Sub-nm Resolution","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Educación, Cultura y Deporte; Ministerio de Ciencia, Innovación y Universidades; Comunidad de Madrid; University of Ottawa","keywords":"Optics; Demodulation; Heterodyne (poetry); Heterodyne detection; Materials science; Interferometry; Fiber Bragg grating; Fiber optic sensor; Optical fiber; Optoelectronics; Acoustics; Physics; Telecommunications; Laser; Computer science","score_opus":0.009179927832652802,"score_gpt":0.21063250293982025,"score_spread":0.20145257510716744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168902558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70221233,0.0016955195,0.28629616,0.0005952336,0.0002904213,0.0004283032,0.00044437544,0.0019238963,0.006113818],"genre_scores_gemma":[0.77120835,0.00024252925,0.22361614,0.00022106792,0.00011892784,0.00025308004,0.00019230945,0.00004532921,0.0041022087],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996043,0.00005099697,0.000019880135,0.000089326386,0.00021679986,0.000018714954],"domain_scores_gemma":[0.9995826,0.00010476292,0.00010731166,0.000044913868,0.00013536181,0.00002495605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031727733,0.0003040052,0.0002770049,0.0004988689,0.00032056344,0.0002465011,0.0005702635,0.0004615581,0.0015674744],"category_scores_gemma":[0.00056539156,0.00022416658,0.00011839828,0.00023236558,0.00024122406,0.00050099543,0.00032238703,0.00022120474,0.00050621596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013661411,0.00011028764,0.0013477121,0.0000782884,0.000013176578,0.00007142946,0.00006586191,0.00037012438,0.9583402,0.0008880469,0.0006046916,0.037973575],"study_design_scores_gemma":[0.00007692792,0.0006293406,0.0067924145,0.000016222883,0.000044378034,0.00087877153,0.000031706753,0.051799238,0.92869824,0.00033361165,0.010643591,0.000055525157],"about_ca_topic_score_codex":0.0005290736,"about_ca_topic_score_gemma":0.001387941,"teacher_disagreement_score":0.0015674744,"about_ca_system_score_codex":0.00039995974,"about_ca_system_score_gemma":0.0003307551,"threshold_uncertainty_score":0.005243778},"labels":[],"label_agreement":null},{"id":"W3169434620","doi":"10.3390/s21124007","title":"PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Science and Technology Major Project of Guangxi; Natural Sciences and Engineering Research Council of Canada; National Science and Technology Major Project; Guilin University of Electronic Technology; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Computer science; Image stitching; Toolbox; Waveform; Artificial intelligence; MATLAB; Pattern recognition (psychology); SIGNAL (programming language); Speech recognition","score_opus":0.017485895760015243,"score_gpt":0.23748883688702052,"score_spread":0.22000294112700527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169434620","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004580213,0.0003490497,0.8940451,0.00011405407,0.0001270665,0.0002198156,0.0051295217,0.09165405,0.0037811468],"genre_scores_gemma":[0.078189224,0.00096592744,0.8692578,0.00048564002,0.000114137496,0.0027932266,0.01214037,0.021269977,0.014783751],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964,0.00006398091,0.00004406873,0.0000782816,0.00014475621,0.000028824843],"domain_scores_gemma":[0.99878305,0.00066064484,0.00011840813,0.00012210273,0.00026082838,0.000055037595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008476288,0.0011587252,0.00051618053,0.0011080776,0.0002042668,0.00081863144,0.0013210563,0.0006453453,0.051515352],"category_scores_gemma":[0.0042994656,0.00046166594,0.0005151952,0.00048251433,0.00025191062,0.00068096473,0.0011374847,0.0009317504,0.013734439],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009833251,0.00024070984,0.002774776,0.0027154996,0.0002320155,0.0013747218,0.00052899297,0.039342538,0.0757633,0.010390714,0.16680792,0.6988455],"study_design_scores_gemma":[0.00040743555,0.00049556134,0.0075333077,0.0005701369,0.000117080424,0.0021083998,0.00020031465,0.54254854,0.12885077,0.019253576,0.29764268,0.00027212204],"about_ca_topic_score_codex":0.00076912466,"about_ca_topic_score_gemma":0.0014658296,"teacher_disagreement_score":0.051515352,"about_ca_system_score_codex":0.00022389233,"about_ca_system_score_gemma":0.0006584903,"threshold_uncertainty_score":0.17233604},"labels":[],"label_agreement":null},{"id":"W3169871308","doi":"10.3390/s21113864","title":"Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Intersection (aeronautics); Speed limit; Computer science; Reinforcement learning; Ranging; Real-time computing; Simulation; Engineering; Transport engineering; Artificial intelligence","score_opus":0.007188595518160619,"score_gpt":0.19526986928836815,"score_spread":0.18808127377020753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169871308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18020985,0.00023967843,0.8149114,0.00021838021,0.00006733435,0.00009229295,0.00006390666,0.001450769,0.0027463483],"genre_scores_gemma":[0.9672842,0.0000320634,0.03162506,0.00003472734,0.0000085310385,0.000040805295,0.00005982685,0.000021786578,0.0008929941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997811,0.000046467176,0.000012649622,0.000065516346,0.000049917035,0.00004436145],"domain_scores_gemma":[0.9992181,0.0003705891,0.00012137152,0.00004705768,0.0001872262,0.00005562737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006785716,0.00070122286,0.00059333845,0.0003834471,0.00027841638,0.0005512037,0.00076027523,0.0005760029,0.00087639043],"category_scores_gemma":[0.0020627475,0.0003342907,0.00030936097,0.0002360469,0.00047760337,0.00046454437,0.000560313,0.0007897324,0.00020749021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066759305,0.00005990535,0.000983747,0.00001555169,0.000018475663,0.000027688475,0.00002054178,0.9653397,0.0011540932,0.0005628891,0.00023230641,0.031518273],"study_design_scores_gemma":[0.000003774335,0.0000138010855,0.00006420216,0.0000011297032,0.0000014204452,0.000002401778,0.0000014432763,0.99939775,0.00026870615,0.00020326594,0.000040939383,0.0000010754136],"about_ca_topic_score_codex":0.010710332,"about_ca_topic_score_gemma":0.007961698,"teacher_disagreement_score":0.010710332,"about_ca_system_score_codex":0.0008970197,"about_ca_system_score_gemma":0.0011699403,"threshold_uncertainty_score":0.021295965},"labels":[],"label_agreement":null},{"id":"W3170294632","doi":"10.3390/s21124076","title":"Smartphone Screen Integrated Optical Breathalyzer","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"Canada First Research Excellence Fund","keywords":"Mobile phone; Computer science; Phone; Wearable computer; Simulation; Embedded system; Telecommunications","score_opus":0.009932763274905351,"score_gpt":0.20633996761666373,"score_spread":0.19640720434175837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170294632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7951613,0.0034742956,0.16383919,0.001249061,0.0011355814,0.0007937788,0.0029057036,0.008129027,0.023312064],"genre_scores_gemma":[0.89111245,0.0009460332,0.071896255,0.0007298056,0.0001013528,0.00020710326,0.00061220577,0.00014459918,0.034250226],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997404,0.000021824206,0.000014515335,0.00006997332,0.0001264552,0.000026816033],"domain_scores_gemma":[0.9996562,0.00007607811,0.000040707815,0.00005031123,0.00015018598,0.000026539425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016110693,0.00041232503,0.00027539043,0.00028166003,0.0001497567,0.00037536718,0.0005817128,0.0005777871,0.008038034],"category_scores_gemma":[0.00038596315,0.00023534712,0.00023870164,0.00013603893,0.000130418,0.00045622044,0.00035858498,0.00021010774,0.0014066369],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002585239,0.00008699563,0.0026147244,0.0003055455,0.000033397988,0.00044963707,0.00010177689,0.0002424183,0.9446152,0.0003898771,0.0039225095,0.046979386],"study_design_scores_gemma":[0.00010262696,0.0021323073,0.024740813,0.00006995214,0.00016729195,0.0027340152,0.00018244878,0.016546158,0.9067395,0.00020873139,0.046275735,0.000100493315],"about_ca_topic_score_codex":0.0008632104,"about_ca_topic_score_gemma":0.001995814,"teacher_disagreement_score":0.008038034,"about_ca_system_score_codex":0.0001951167,"about_ca_system_score_gemma":0.00015132275,"threshold_uncertainty_score":0.02688992},"labels":[],"label_agreement":null},{"id":"W3170610488","doi":"10.3390/s21124100","title":"Circularly Polarized Hybrid Dielectric Resonator Antennas: A Brief Review and Perspective Analysis","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Dielectric resonator antenna; Circular polarization; Dielectric resonator; Dielectric; Resonator; Polarization (electrochemistry); Bandwidth (computing); Electronic engineering; Computer science; Telecommunications; Optics; Engineering; Physics; Optoelectronics; Microstrip; Chemistry","score_opus":0.018021236652404134,"score_gpt":0.2690569480076358,"score_spread":0.25103571135523167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170610488","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071935984,0.993258,0.0023619148,0.00030222055,0.00025412513,0.000011101899,0.00002732032,0.000021375035,0.0030445703],"genre_scores_gemma":[0.004551098,0.990226,0.0024616322,0.00028881634,0.0003817127,0.000021096297,0.000051731324,0.0000065996064,0.0020112842],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980956,0.000028314425,0.000023931343,0.00005209166,0.000066812616,0.000019231482],"domain_scores_gemma":[0.9997223,0.00013961198,0.000041648236,0.000009463134,0.00007349384,0.000013429343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047062204,0.00084813003,0.0008196118,0.0017651612,0.00022626086,0.00089255313,0.0006431855,0.001012562,0.002292292],"category_scores_gemma":[0.0005538242,0.0005714641,0.0005507662,0.0017990198,0.00032068204,0.001305302,0.00044402195,0.0010546397,0.0020647831],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010261658,0.00017538366,0.00041899257,0.029212521,0.000095515716,0.000585314,0.00019683405,0.0023919286,0.022634372,0.023925014,0.02521507,0.8950465],"study_design_scores_gemma":[0.000007953495,0.00036469504,0.0008100394,0.0025317508,0.000097580836,0.0022168497,0.00013605595,0.0012568242,0.0065381266,0.0047406247,0.9812407,0.0000588148],"about_ca_topic_score_codex":0.0004132757,"about_ca_topic_score_gemma":0.00049131684,"teacher_disagreement_score":0.002292292,"about_ca_system_score_codex":0.00035251674,"about_ca_system_score_gemma":0.00043821725,"threshold_uncertainty_score":0.007668495},"labels":[],"label_agreement":null},{"id":"W3170677518","doi":"10.3390/s21113874","title":"An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Office of the Privacy Commissioner of Canada","keywords":"Deep learning; Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Pattern recognition (psychology); Inversion (geology); Similarity (geometry); Correlation coefficient; Artificial neural network; Computer vision; Image (mathematics); Machine learning; Geology","score_opus":0.020476217532274604,"score_gpt":0.3739590765604225,"score_spread":0.3534828590281479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170677518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7554087,0.0025476946,0.23744549,0.0006104596,0.00011699762,0.00014982816,0.0003905697,0.001022621,0.0023077847],"genre_scores_gemma":[0.9533602,0.0006969266,0.04445749,0.000082011895,0.00002020473,0.000041943138,0.0004995113,0.00006392809,0.00077777036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.00024877413,0.0001048605,0.00016178859,0.0004917556,0.000107595944],"domain_scores_gemma":[0.9948822,0.0030250584,0.0006363132,0.00061508507,0.0007313799,0.000109935776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151333,0.00069894374,0.00042076872,0.0014480746,0.00023220367,0.00056104304,0.0004526342,0.0009602289,0.0007511213],"category_scores_gemma":[0.010302003,0.00020193007,0.00056569336,0.0006826171,0.0006702205,0.000946625,0.00062964787,0.00063805905,0.00017761807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019126618,0.000281682,0.018413153,0.000728735,0.0005994105,0.00087150687,0.00033960713,0.489022,0.1374338,0.004241104,0.0019393582,0.34421694],"study_design_scores_gemma":[0.000014136954,0.00067471527,0.010562,0.000048634523,0.000074971154,0.00070570444,0.00007320533,0.9137797,0.07179063,0.0012392718,0.0010035844,0.000033493765],"about_ca_topic_score_codex":0.0016800521,"about_ca_topic_score_gemma":0.001496803,"teacher_disagreement_score":0.002151333,"about_ca_system_score_codex":0.00061307795,"about_ca_system_score_gemma":0.000400558,"threshold_uncertainty_score":0.011377513},"labels":[],"label_agreement":null},{"id":"W3170700550","doi":"10.3390/s21124039","title":"An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Peking University","keywords":"Geospatial analysis; Environmental monitoring; Exposure assessment; Environmental epidemiology; Environmental data; Beijing; Computer science; Field (mathematics); Human health; Data collection; Environmental impact assessment; Data science; Environmental science; Environmental resource management; Risk analysis (engineering); Remote sensing; Environmental health; Geography; Business; Environmental engineering","score_opus":0.026565117929955136,"score_gpt":0.33055407241167,"score_spread":0.30398895448171487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170700550","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09733761,0.0004920168,0.8869862,0.00037957798,0.00020664878,0.0005237844,0.0010661259,0.0069109313,0.0060970993],"genre_scores_gemma":[0.60088664,0.00025276587,0.39423934,0.0002842259,0.00009301077,0.0007393261,0.00083092105,0.00010136136,0.002572349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913186,0.00029424447,0.00006294879,0.00022047563,0.00024705456,0.00004344434],"domain_scores_gemma":[0.9991385,0.00022503713,0.00008331061,0.00012206817,0.00037577687,0.000055318756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014376554,0.0006233782,0.0006463795,0.0012379687,0.00036269656,0.0009934466,0.00085304456,0.0007841707,0.0028226946],"category_scores_gemma":[0.0018089483,0.00023758365,0.0004750827,0.00096913724,0.00024243617,0.0010350504,0.0012525963,0.00048355004,0.0007984199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001398678,0.0008147368,0.06483618,0.0008132813,0.0004517169,0.00056242605,0.001232541,0.038259573,0.11957103,0.009293027,0.012238518,0.7505283],"study_design_scores_gemma":[0.00018506033,0.001426628,0.07706742,0.00019698181,0.0005474363,0.0010974196,0.00091513316,0.7916923,0.063766316,0.01036828,0.05240747,0.0003295928],"about_ca_topic_score_codex":0.0014142279,"about_ca_topic_score_gemma":0.0023443867,"teacher_disagreement_score":0.0028226946,"about_ca_system_score_codex":0.00034239236,"about_ca_system_score_gemma":0.00052476116,"threshold_uncertainty_score":0.009442866},"labels":[],"label_agreement":null},{"id":"W3171100995","doi":"10.3390/s21124152","title":"Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Wireless sensor network; Hop (telecommunications); Algorithm; Computer science; Signal strength; Computation; Wireless; Distance measurement; Artificial intelligence; Computer network; Telecommunications","score_opus":0.010516637327522673,"score_gpt":0.19947968225147655,"score_spread":0.18896304492395388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171100995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026460555,0.00036880712,0.99592716,0.000045305485,0.000030599207,0.000013452183,0.000012442304,0.00034448394,0.00061161484],"genre_scores_gemma":[0.25402945,0.0012414864,0.7398971,0.00008411424,0.00007250244,0.00013160499,0.00027773972,0.00009108754,0.0041749734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937576,0.00016147658,0.000033330616,0.00013119217,0.00026577653,0.000032411666],"domain_scores_gemma":[0.99966,0.0001078606,0.00004207366,0.00005037888,0.00012918239,0.000010523021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044232598,0.0005941933,0.0007103525,0.0006502886,0.0003194578,0.0005407404,0.0013381961,0.0006167296,0.0008637168],"category_scores_gemma":[0.0014226265,0.00024393445,0.0004639224,0.0011540377,0.00033331278,0.0008565056,0.0005849278,0.0006170213,0.00056380697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014032194,0.000055727327,0.0010998214,0.0002771187,0.00007117975,0.00015974196,0.00017631124,0.30648246,0.035384145,0.017172046,0.0033065057,0.63567466],"study_design_scores_gemma":[0.00002380482,0.00011837975,0.000509201,0.000014575204,0.000024943929,0.00029417308,0.000028393044,0.97816044,0.009610219,0.0030344215,0.008150426,0.000031026735],"about_ca_topic_score_codex":0.002364274,"about_ca_topic_score_gemma":0.0021440357,"teacher_disagreement_score":0.002364274,"about_ca_system_score_codex":0.0003911701,"about_ca_system_score_gemma":0.0005947239,"threshold_uncertainty_score":0.0047010183},"labels":[],"label_agreement":null},{"id":"W3172258546","doi":"10.3390/s21113858","title":"A MEMS Ultra-Wideband (UWB) Power Sensor with a Fe-Co-B Core Planar Inductor and a Vibrating Diaphragm Capacitor","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Capacitance; Capacitor; Microfabrication; Microelectromechanical systems; Variable capacitor; Materials science; Electrical engineering; Inductor; Planar; Diaphragm (acoustics); Voltage; Wideband; Amplifier; Optoelectronics; Electronic engineering; Engineering; CMOS; Fabrication; Computer science; Physics; Electrode","score_opus":0.01191008994921281,"score_gpt":0.20841235564329075,"score_spread":0.19650226569407794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172258546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32073024,0.0035787974,0.66593415,0.0008970703,0.00030979546,0.00032627,0.0004290727,0.0025785705,0.0052159964],"genre_scores_gemma":[0.6391732,0.0007692093,0.3537258,0.00040676002,0.000079021105,0.00013825163,0.00028836602,0.00005768987,0.005361685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967146,0.00003565029,0.000017381923,0.00009462585,0.00015388205,0.000027083834],"domain_scores_gemma":[0.99982697,0.00003070713,0.000046891422,0.000020470094,0.00005147197,0.000023612241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025380333,0.00043792243,0.00052326097,0.00023971336,0.00018349479,0.0003734485,0.0013018679,0.00092810934,0.00056874554],"category_scores_gemma":[0.00025178312,0.00035853166,0.00028255404,0.00021913993,0.00025812502,0.00068491866,0.0003608175,0.00038196915,0.0004674035],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044453333,0.000028697863,0.000346639,0.00010903346,0.000014396982,0.00012981142,0.000014345285,0.00064271095,0.9843077,0.0003723342,0.00027541476,0.013714312],"study_design_scores_gemma":[0.000029637136,0.00079997914,0.002727688,0.000013033259,0.000043939704,0.0016555368,0.000020382091,0.021935813,0.9638427,0.00015278423,0.008744128,0.00003446829],"about_ca_topic_score_codex":0.00026794558,"about_ca_topic_score_gemma":0.00045944165,"teacher_disagreement_score":0.0013018679,"about_ca_system_score_codex":0.00036972828,"about_ca_system_score_gemma":0.00036557292,"threshold_uncertainty_score":0.0026825666},"labels":[],"label_agreement":null},{"id":"W3173262317","doi":"10.3390/s21217309","title":"Semi-Supervised Training for Positioning of Welding Seams","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Mitacs","keywords":"Robustness (evolution); Artificial intelligence; Computer science; Welding; Computer vision; Robot; Machine learning; Supervised learning; Process (computing); Artificial neural network; Pattern recognition (psychology); Engineering","score_opus":0.026085332143715174,"score_gpt":0.24964637500038003,"score_spread":0.22356104285666487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173262317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03292729,0.00012003889,0.96313894,0.00007285227,0.000028322005,0.000045615885,0.00008307258,0.0026195585,0.00096424296],"genre_scores_gemma":[0.65645385,0.000087708766,0.33998904,0.00014404493,0.000038029746,0.00015306124,0.00054150104,0.0002820639,0.0023106092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999213,0.00018272742,0.000049589882,0.00028913066,0.00018629605,0.00007928628],"domain_scores_gemma":[0.9974694,0.00079356274,0.00040971363,0.0006303154,0.00061136903,0.00008553085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011585238,0.00091781357,0.0009362266,0.00069950416,0.0005759482,0.00059575756,0.0022550335,0.0012579323,0.002397512],"category_scores_gemma":[0.004218385,0.0006952974,0.0007592736,0.00055301003,0.0009023383,0.0010393601,0.0010289542,0.0012156692,0.0010683297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026992065,0.000158345,0.0019962697,0.0001887277,0.00006849402,0.000110116285,0.00026214457,0.5326807,0.03293932,0.002824728,0.0033935644,0.42510772],"study_design_scores_gemma":[0.000006838505,0.000039980754,0.0004978497,0.000007859017,0.0000048091124,0.00003540704,0.000021346752,0.990317,0.0068943067,0.0016409009,0.0005262528,0.000007449999],"about_ca_topic_score_codex":0.0033915318,"about_ca_topic_score_gemma":0.006150205,"teacher_disagreement_score":0.0033915318,"about_ca_system_score_codex":0.0005514522,"about_ca_system_score_gemma":0.0010841205,"threshold_uncertainty_score":0.008020461},"labels":[],"label_agreement":null},{"id":"W3173270563","doi":"10.3390/s21134490","title":"Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; Toronto Metropolitan University; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Segmentation; Computer science; Artificial intelligence; Magnetic resonance imaging; Deep learning; Image segmentation; Process (computing); Pattern recognition (psychology); Machine learning; Medicine; Radiology","score_opus":0.0180560113677698,"score_gpt":0.2959185990786765,"score_spread":0.2778625877109067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173270563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07984772,0.0017797825,0.9104036,0.00075267866,0.00013854312,0.0001321868,0.00030043157,0.0027639295,0.0038811648],"genre_scores_gemma":[0.7689995,0.0007665608,0.22058025,0.0010038374,0.00013157439,0.0001795444,0.0010418656,0.0002330041,0.007063846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996132,0.00009486094,0.000025815147,0.00012184279,0.00009283101,0.00005146077],"domain_scores_gemma":[0.99929583,0.00036690233,0.00006680159,0.00006465994,0.00015381176,0.000051983276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013391204,0.0009334454,0.000614696,0.00091128994,0.00036360082,0.0008066607,0.0012420476,0.0016871281,0.002660957],"category_scores_gemma":[0.0026773645,0.00047892088,0.0006288161,0.0004204834,0.0005647654,0.0013242264,0.0014422258,0.0010744367,0.0005068678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009469673,0.00028987465,0.0068464596,0.0002718398,0.00028977485,0.0009960475,0.00016567705,0.37201634,0.033703834,0.007637347,0.006454948,0.57038087],"study_design_scores_gemma":[0.000020471234,0.00016529621,0.0013720158,0.000022850973,0.00006912739,0.00035968435,0.000020095435,0.9803935,0.012038918,0.003587563,0.0019279637,0.00002246604],"about_ca_topic_score_codex":0.003979417,"about_ca_topic_score_gemma":0.0068040085,"teacher_disagreement_score":0.003979417,"about_ca_system_score_codex":0.00077672105,"about_ca_system_score_gemma":0.00086913136,"threshold_uncertainty_score":0.008901775},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W3174141261","doi":"10.3390/s21134356","title":"Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spinal cord injury; Pressure sensor; Pipeline (software); Computer science; Wheelchair; Interface (matter); Snapshot (computer storage); Simulation; Real-time computing; Engineering; Medicine; Spinal cord; Mechanical engineering","score_opus":0.050728947695011906,"score_gpt":0.41840446970707657,"score_spread":0.36767552201206466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174141261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026690261,0.00032600624,0.9697233,0.0001042733,0.00008183787,0.00007422018,0.00046942566,0.0017680016,0.0007626335],"genre_scores_gemma":[0.4356864,0.00086068246,0.5579471,0.00012539233,0.0001659956,0.00022971327,0.0013414982,0.00029048868,0.003352692],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99976736,0.000036030662,0.000017453624,0.000059606093,0.00008503456,0.00003463403],"domain_scores_gemma":[0.99974555,0.00006050014,0.000040132796,0.000027961765,0.000100833044,0.000024937659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003055414,0.0008068979,0.00041379489,0.0011409911,0.00021145244,0.0005291445,0.0004006743,0.00042040925,0.0024387876],"category_scores_gemma":[0.0010665485,0.00025096943,0.00049051637,0.000830066,0.00014220577,0.00047111802,0.0004630646,0.0005364801,0.001172508],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046526553,0.0003536368,0.014647463,0.00031999193,0.0001142615,0.00037051065,0.00023452472,0.032586638,0.1263533,0.0023285428,0.008210678,0.8140151],"study_design_scores_gemma":[0.00002002336,0.0002625537,0.031361725,0.00004135088,0.0000620288,0.00042046726,0.00012841994,0.9186045,0.038787734,0.0029010593,0.007359001,0.000051131054],"about_ca_topic_score_codex":0.0023256638,"about_ca_topic_score_gemma":0.002907834,"teacher_disagreement_score":0.0024387876,"about_ca_system_score_codex":0.00014309495,"about_ca_system_score_gemma":0.00037971075,"threshold_uncertainty_score":0.008158565},"labels":[],"label_agreement":null},{"id":"W3174276973","doi":"10.3390/s21134559","title":"PSON: A Serialization Format for IoT Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Serialization; Computer science; Embedded system; Internet of Things; Data transmission; The Internet; Computer network; Real-time computing; Operating system","score_opus":0.01566106071222411,"score_gpt":0.2385990237611456,"score_spread":0.2229379630489215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174276973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023392575,0.0007833461,0.9208793,0.0005559228,0.0011867137,0.001241302,0.004822959,0.02691037,0.020227553],"genre_scores_gemma":[0.29626185,0.0020531288,0.6398822,0.0009932142,0.0005376036,0.0027681396,0.02073309,0.0039867675,0.03278394],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986785,0.00030092546,0.00031670896,0.00015225208,0.00046666266,0.00008498627],"domain_scores_gemma":[0.9959494,0.000852193,0.00039703588,0.0013780487,0.0012936954,0.00012970192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002040508,0.0012371711,0.0005623681,0.0011015497,0.00057835877,0.0017738044,0.0012772188,0.00050063856,0.007956622],"category_scores_gemma":[0.006247878,0.00041153707,0.00034820536,0.0016063107,0.0005390314,0.0032869736,0.0011261785,0.0011435411,0.002725532],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040879236,0.00043327015,0.003091466,0.0012863468,0.00009422434,0.0010565097,0.000843283,0.041055843,0.04934616,0.070198506,0.11594658,0.7125599],"study_design_scores_gemma":[0.000590004,0.0014305072,0.0026911022,0.00043919345,0.000112532914,0.0017521902,0.00048422863,0.34166193,0.13319395,0.046942126,0.47042957,0.00027258752],"about_ca_topic_score_codex":0.0013880143,"about_ca_topic_score_gemma":0.0013224499,"teacher_disagreement_score":0.007956622,"about_ca_system_score_codex":0.00069608504,"about_ca_system_score_gemma":0.00083122915,"threshold_uncertainty_score":0.026617587},"labels":[],"label_agreement":null},{"id":"W3175156399","doi":"10.3390/s21124202","title":"A Machine Learning Approach as an Aid for Early COVID-19 Detection","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Receiver operating characteristic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Learning curve; Computer science; Population; Limit (mathematics); Test (biology); Scale (ratio); Machine learning; Artificial intelligence; Sensitivity (control systems); Order (exchange); Pandemic; Risk analysis (engineering); Medicine; Business; Virology; Engineering; Disease; Geography; Environmental health; Infectious disease (medical specialty); Mathematics; Operating system; Cartography","score_opus":0.043053620042464755,"score_gpt":0.3360731855659155,"score_spread":0.2930195655234507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175156399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36481127,0.003974942,0.59458447,0.0069760843,0.0010361876,0.00054164045,0.0018122562,0.005471141,0.02079209],"genre_scores_gemma":[0.88148814,0.0005111111,0.11207975,0.00086633046,0.0002022439,0.00014813211,0.0008274833,0.000039991035,0.003836727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986356,0.0005996431,0.00011555112,0.00021148451,0.00028387253,0.00015372466],"domain_scores_gemma":[0.9951218,0.0034216393,0.00031498598,0.00016110786,0.00080799684,0.00017255267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002517315,0.0011346444,0.00085717265,0.0028233672,0.00041533497,0.0014172373,0.0008896688,0.0013853486,0.002990984],"category_scores_gemma":[0.009042215,0.00017400394,0.00067187846,0.0011053734,0.00031393653,0.0010298162,0.0006452694,0.0013020366,0.0016099219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009530579,0.0016758143,0.111317895,0.0002944986,0.00036286842,0.0004261452,0.0001517724,0.12049553,0.012997496,0.002926814,0.0113159325,0.73708206],"study_design_scores_gemma":[0.000023824037,0.0004598613,0.010996716,0.000051133575,0.00005764843,0.00029580877,0.000093468465,0.97802633,0.0045783175,0.0030456667,0.002331722,0.00003953611],"about_ca_topic_score_codex":0.0027291349,"about_ca_topic_score_gemma":0.002663522,"teacher_disagreement_score":0.002990984,"about_ca_system_score_codex":0.0005974917,"about_ca_system_score_gemma":0.0007478687,"threshold_uncertainty_score":0.013312995},"labels":[],"label_agreement":null},{"id":"W3175562493","doi":"10.3390/s21134310","title":"Li-Pos: A Light Positioning Framework Leveraging OFDM for Visible Light Communication","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"","keywords":"Visible light communication; Light-emitting diode; Orthogonal frequency-division multiplexing; Quadrature amplitude modulation; Brightness; Electronic engineering; Modulation (music); Estimator; Computer science; Transmitter; Optics; Telecommunications; Physics; Bit error rate; Engineering; Mathematics; Acoustics; Statistics","score_opus":0.013704930742525143,"score_gpt":0.24582687484092983,"score_spread":0.2321219440984047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175562493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017865436,0.0006143038,0.9727192,0.00012026996,0.00013216127,0.00007216166,0.000090728965,0.0042752787,0.0041104327],"genre_scores_gemma":[0.59415644,0.00055457227,0.39388898,0.00032320834,0.00017002219,0.00017408004,0.0002473294,0.00018575789,0.01029964],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997063,0.000047733058,0.000013038147,0.000056572026,0.00014075829,0.000035620233],"domain_scores_gemma":[0.9998128,0.000034760025,0.000040494015,0.000031909094,0.0000624668,0.000017574212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032710307,0.0004048254,0.00032671195,0.0004689041,0.0002806674,0.00060047256,0.00089426496,0.0005242412,0.0023017258],"category_scores_gemma":[0.00049489277,0.00014835008,0.00020274111,0.0002860986,0.0002207044,0.00068707386,0.00053534686,0.00046580788,0.0011762846],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056166755,0.0002337732,0.001963601,0.0003884972,0.000084619176,0.00049012486,0.00018787212,0.03695146,0.3111283,0.023213306,0.008944775,0.615852],"study_design_scores_gemma":[0.00010888587,0.0015125937,0.0024311773,0.000064932894,0.000097441625,0.0012286304,0.00006759865,0.67538804,0.23109697,0.006707001,0.08116867,0.00012807643],"about_ca_topic_score_codex":0.0006743856,"about_ca_topic_score_gemma":0.0010679304,"teacher_disagreement_score":0.0023017258,"about_ca_system_score_codex":0.0003251818,"about_ca_system_score_gemma":0.00038625614,"threshold_uncertainty_score":0.007700026},"labels":[],"label_agreement":null},{"id":"W3175705516","doi":"10.3390/s21134386","title":"Estimation of Soil Surface Roughness Using Stereo Vision Approach","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Tillage; Surface finish; Plough; Soil science; Surface roughness; Pixel; Soil water; Remote sensing; Computer science; Environmental science; Mathematics; Computer vision; Artificial intelligence; Geology; Engineering; Geography; Materials science","score_opus":0.015132241313397523,"score_gpt":0.2411903717261491,"score_spread":0.22605813041275158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175705516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13741037,0.00029240717,0.8563544,0.00004612685,0.00003430767,0.00010500219,0.0005124069,0.001776939,0.0034680879],"genre_scores_gemma":[0.71984696,0.00030174537,0.2769102,0.00004001381,0.00002859475,0.00008307128,0.00084480393,0.000069843576,0.001874705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996519,0.000026469144,0.000013399538,0.000079145655,0.00018117709,0.000047989437],"domain_scores_gemma":[0.9997347,0.00003463772,0.000041433042,0.000030215593,0.00014679025,0.000012122431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017019552,0.0004268743,0.00042085355,0.0022898188,0.0001598514,0.00047962557,0.00044590476,0.00043988173,0.001243558],"category_scores_gemma":[0.00048106653,0.00023081417,0.0006063432,0.0008799202,0.00013884026,0.00051115145,0.00032007118,0.0002487404,0.0006031802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016262094,0.00012599719,0.010572713,0.0002815084,0.0001076953,0.00022668704,0.0001453571,0.06544168,0.2755485,0.0015464635,0.0023436395,0.6434971],"study_design_scores_gemma":[0.00003181209,0.00017842404,0.039770275,0.000024690367,0.00006158301,0.00041486847,0.00017909077,0.89707935,0.057879962,0.0013111418,0.0029938465,0.00007501999],"about_ca_topic_score_codex":0.0045395694,"about_ca_topic_score_gemma":0.005822496,"teacher_disagreement_score":0.0045395694,"about_ca_system_score_codex":0.00026437014,"about_ca_system_score_gemma":0.0004264196,"threshold_uncertainty_score":0.009026289},"labels":[],"label_agreement":null},{"id":"W3176609169","doi":"10.3390/s21134492","title":"Internal Consistency of Sway Measures via Embedded Head-Mounted Accelerometers: Implications for Neuromotor Investigations","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Accelerometer; Intraclass correlation; Reliability (semiconductor); Jerk; Acceleration; Computer science; Physical medicine and rehabilitation; Simulation; Medicine; Reproducibility; Mathematics; Physics; Statistics","score_opus":0.08462872517302295,"score_gpt":0.4063343644875738,"score_spread":0.3217056393145509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176609169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85889256,0.0047864094,0.12397999,0.0006827287,0.0007274696,0.00095201505,0.0009913184,0.00036080598,0.008626719],"genre_scores_gemma":[0.9682548,0.000526804,0.028924339,0.00017146158,0.00015461365,0.00076569105,0.00059116853,0.00008943932,0.000521632],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.96791023,0.015391474,0.005083027,0.0034997875,0.0076412936,0.00047415585],"domain_scores_gemma":[0.83715093,0.11189383,0.015578322,0.015681287,0.01906492,0.0006306975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06277175,0.0009921214,0.0012668915,0.001725773,0.00064921076,0.0021430564,0.0011711859,0.0008905763,0.000789466],"category_scores_gemma":[0.13628772,0.0006216259,0.00092849403,0.001876691,0.0019096641,0.0013769738,0.0016499897,0.0009199353,0.00036196617],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053574075,0.00032959195,0.88584214,0.00083793525,0.0018155404,0.000111967885,0.0030774672,0.0032476631,0.004609369,0.0016882784,0.0013223039,0.09658189],"study_design_scores_gemma":[0.00005710177,0.0013762603,0.97158605,0.00048534424,0.0004437844,0.00029123362,0.0012120525,0.0139539465,0.003574536,0.0034720555,0.0034680066,0.00007951617],"about_ca_topic_score_codex":0.0014857542,"about_ca_topic_score_gemma":0.002744474,"teacher_disagreement_score":0.06277175,"about_ca_system_score_codex":0.0005066601,"about_ca_system_score_gemma":0.00075639354,"threshold_uncertainty_score":0.33197272},"labels":[],"label_agreement":null},{"id":"W3176616755","doi":"10.3390/s21134473","title":"Validation of a 3D Camera System for Cycling Analysis","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Intraclass correlation; Gold standard (test); Kinematics; Motion capture; Motion analysis; Amateur; Cycling; Computer science; Video camera; Artificial intelligence; Computer vision; Mathematics; Statistics; Motion (physics); Reproducibility; Geography; Physics","score_opus":0.033298793254108,"score_gpt":0.36654560989728313,"score_spread":0.3332468166431751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176616755","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6855525,0.0014960415,0.30409688,0.00018942486,0.00022458725,0.0012025229,0.00090082147,0.0009321311,0.0054051713],"genre_scores_gemma":[0.84095764,0.0003932723,0.15556306,0.00017157299,0.00005071668,0.000626309,0.0009472215,0.00011086832,0.0011792916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99214315,0.0026109589,0.0006697198,0.0015413585,0.0028161083,0.00021863656],"domain_scores_gemma":[0.98938984,0.0027186791,0.0010662489,0.0014786891,0.0050618816,0.0002846844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067311204,0.0008613852,0.0004545306,0.0014359881,0.00042777575,0.0007773978,0.001063018,0.00089463353,0.002258664],"category_scores_gemma":[0.012529878,0.0002909792,0.0004900306,0.0006682067,0.0008139449,0.0006380864,0.0011834195,0.0003461356,0.00096602464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017851059,0.00047570013,0.33108696,0.0012148952,0.00034378542,0.00027599602,0.0013619973,0.0028584872,0.3562366,0.0011004669,0.0019443958,0.3013157],"study_design_scores_gemma":[0.00013991274,0.0048659756,0.74145645,0.0005154446,0.00056679663,0.0037447503,0.0010083502,0.02728757,0.2065409,0.0006818803,0.01304542,0.00014652609],"about_ca_topic_score_codex":0.0012178834,"about_ca_topic_score_gemma":0.0019518418,"teacher_disagreement_score":0.0067311204,"about_ca_system_score_codex":0.00046066954,"about_ca_system_score_gemma":0.0008503737,"threshold_uncertainty_score":0.03559798},"labels":[],"label_agreement":null},{"id":"W3177322353","doi":"10.3390/s21134291","title":"Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Support vector machine; Artificial intelligence; Radar; Computer science; Pattern recognition (psychology); Kernel (algebra); Feature extraction; Machine learning; Computer vision; Telecommunications; Mathematics","score_opus":0.03986333637574761,"score_gpt":0.24795949936813103,"score_spread":0.20809616299238343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177322353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16928616,0.00030715024,0.8258344,0.00014803265,0.00009682716,0.00011467049,0.00009882264,0.002024122,0.0020897281],"genre_scores_gemma":[0.769276,0.00015837503,0.22690654,0.00008134849,0.000036107125,0.00011991461,0.00023178602,0.000025396394,0.0031644509],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960405,0.00007328156,0.0000359061,0.000102881786,0.00014725392,0.000036634476],"domain_scores_gemma":[0.9995147,0.00012467519,0.00008073146,0.00008292574,0.00017814313,0.000018803206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005857847,0.00040152163,0.00055535766,0.0006378037,0.00021526446,0.0004075343,0.00058260787,0.00056048395,0.00091849983],"category_scores_gemma":[0.0010042929,0.00011212681,0.00028271895,0.00046876757,0.0002043537,0.0006859174,0.00031799925,0.00034606698,0.00061846635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060361647,0.0004731825,0.0060076886,0.00014130157,0.00008693763,0.00017467278,0.00009335234,0.058495056,0.1777204,0.0024053175,0.0016051829,0.7521933],"study_design_scores_gemma":[0.00001434244,0.00024837366,0.0039215926,0.000009585676,0.000021309406,0.00017747248,0.00002166712,0.94171387,0.051811114,0.00066793454,0.001377248,0.00001552146],"about_ca_topic_score_codex":0.00049847504,"about_ca_topic_score_gemma":0.00049359933,"teacher_disagreement_score":0.00091849983,"about_ca_system_score_codex":0.00027645452,"about_ca_system_score_gemma":0.00025592296,"threshold_uncertainty_score":0.0030979514},"labels":[],"label_agreement":null},{"id":"W3177364874","doi":"10.3390/s21134376","title":"Routing with Renewable Energy Management in Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Routing protocol; Computer network; Renewable energy; Energy consumption; Geographic routing; Dynamic Source Routing; Wireless sensor network; Routing (electronic design automation); Zone Routing Protocol; Engineering; Electrical engineering","score_opus":0.007111604986765874,"score_gpt":0.19754435135941445,"score_spread":0.19043274637264856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177364874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03157321,0.006908407,0.9515622,0.0009056614,0.0004387793,0.00006277331,0.000045226767,0.00090222515,0.00760147],"genre_scores_gemma":[0.72897404,0.006803181,0.25601745,0.00034644458,0.00029778562,0.00013850904,0.00018465328,0.00017147453,0.0070664883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961346,0.0001369196,0.000025003945,0.00007918489,0.00011806573,0.000027334969],"domain_scores_gemma":[0.99968946,0.00014024379,0.000055634082,0.000057425175,0.00004775429,0.000009417306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005614306,0.0005547001,0.00047412066,0.00052132335,0.00038904327,0.00095449475,0.0007257087,0.00076865836,0.0007763196],"category_scores_gemma":[0.0012727663,0.00019970495,0.00035596048,0.0008182279,0.00044787308,0.0018428251,0.00073411484,0.0004650807,0.0002498366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007025457,0.00005903083,0.00071175955,0.0002761276,0.00007247308,0.00020648671,0.00011801511,0.6904986,0.0143286325,0.062537566,0.0032514133,0.22786963],"study_design_scores_gemma":[0.000011796981,0.00006213114,0.00025123573,0.00002776838,0.00003134093,0.00018623307,0.00005577468,0.94356924,0.0060651056,0.03767358,0.012038626,0.000027223554],"about_ca_topic_score_codex":0.0008474075,"about_ca_topic_score_gemma":0.0012510343,"teacher_disagreement_score":0.00095449475,"about_ca_system_score_codex":0.0004074822,"about_ca_system_score_gemma":0.00029954637,"threshold_uncertainty_score":0.0029691458},"labels":[],"label_agreement":null},{"id":"W3177628377","doi":"10.3390/s21134617","title":"SARS-CoV-2 Receptor Binding Domain as a Stable-Potential Target for SARS-CoV-2 Detection by Surface—Enhanced Raman Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"King Abdulaziz City for Science and Technology","keywords":"Detection limit; Raman spectroscopy; Surface-enhanced Raman spectroscopy; Bovine serum albumin; Materials science; Chemistry; Nanotechnology; Analytical Chemistry (journal); Raman scattering; Chromatography; Optics; Physics","score_opus":0.026780929599781483,"score_gpt":0.3355038699902462,"score_spread":0.30872294039046466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177628377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90774614,0.0036659313,0.085803814,0.00025896984,0.00011108685,0.00007027547,0.00015792542,0.00025123832,0.0019346529],"genre_scores_gemma":[0.91775054,0.0011800297,0.07859552,0.00013187177,0.00002944158,0.000041501306,0.00026803862,0.000029657667,0.0019733126],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968255,0.00008215105,0.000013180204,0.000071977505,0.00011179875,0.000038379872],"domain_scores_gemma":[0.9999207,0.000023380286,0.000016329013,0.000009692574,0.000020500167,0.000009409949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041774107,0.00057040213,0.0003860618,0.00017617157,0.00015854336,0.00026161826,0.00040525998,0.0006939793,0.000530041],"category_scores_gemma":[0.0001708319,0.0002291621,0.0003489203,0.00012755151,0.00018696449,0.00034005014,0.00026445635,0.0003681519,0.0003729995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001743003,0.000009837921,0.000111684014,0.000024883191,0.0000031871875,0.000029111823,0.0000074567815,0.00013762125,0.998749,0.00005458722,0.000021103615,0.00083404785],"study_design_scores_gemma":[0.0000046077853,0.00013624733,0.00065478164,0.000003075058,0.000009987427,0.00015066112,0.000017569482,0.008924682,0.9889187,0.000042817705,0.0011274165,0.000009333486],"about_ca_topic_score_codex":0.00032210347,"about_ca_topic_score_gemma":0.00074193603,"teacher_disagreement_score":0.0006939793,"about_ca_system_score_codex":0.00020085015,"about_ca_system_score_gemma":0.000133524,"threshold_uncertainty_score":0.0022092462},"labels":[],"label_agreement":null},{"id":"W3178681001","doi":"10.3390/s21144713","title":"Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal; Institut National de la Recherche Scientifique; Université TÉLUQ","funders":"Canada Research Chairs","keywords":"Activity recognition; Wearable computer; Computer science; Artificial intelligence; Machine learning; Feature engineering; Accelerometer; Classifier (UML); Wearable technology; Feature (linguistics); Deep learning; Embedded system","score_opus":0.0546264820847625,"score_gpt":0.2832454706733922,"score_spread":0.22861898858862967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178681001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037324514,0.00026939518,0.9583147,0.00018817058,0.0001526866,0.00013342501,0.000098764904,0.0017631532,0.001755088],"genre_scores_gemma":[0.5363932,0.00027846106,0.45526072,0.00018448515,0.00017339042,0.0003529658,0.00047251486,0.00010593504,0.00677821],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948514,0.000070130605,0.000036799796,0.0001869133,0.00015128432,0.0000697147],"domain_scores_gemma":[0.99921227,0.00018482939,0.00005113112,0.0001301719,0.00037815425,0.00004336248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010475956,0.00080053846,0.0010016847,0.0007368455,0.00051607203,0.0009317638,0.0012052583,0.0007281927,0.0022291976],"category_scores_gemma":[0.0018557494,0.0005240044,0.0007533568,0.00089802966,0.00027758995,0.00135251,0.0007663428,0.0014906299,0.0010582308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002935349,0.0005110576,0.004365596,0.00006254,0.00020970951,0.00009522088,0.00007902645,0.17866722,0.016989643,0.0028873822,0.0025192313,0.7933199],"study_design_scores_gemma":[0.000007790081,0.00005723576,0.00064559514,0.0000036824,0.0000231444,0.000028735738,0.000009145651,0.9946471,0.002834723,0.0010703425,0.00066573353,0.0000067278424],"about_ca_topic_score_codex":0.008680999,"about_ca_topic_score_gemma":0.013716203,"teacher_disagreement_score":0.008680999,"about_ca_system_score_codex":0.0007418925,"about_ca_system_score_gemma":0.0013088943,"threshold_uncertainty_score":0.017260969},"labels":[],"label_agreement":null},{"id":"W3180852400","doi":"10.3390/s21144731","title":"GPS-Free, Error Tolerant Path Planning for Swarms of Micro Aerial Vehicles with Quality Amplification ‡","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Global Affairs Canada","keywords":"Terrain; Global Positioning System; Computer vision; Path (computing); Computer science; A priori and a posteriori; Artificial intelligence; Motion planning; Real-time computing; Geography; Cartography; Telecommunications; Robot","score_opus":0.05485880521274164,"score_gpt":0.3117381080264911,"score_spread":0.2568793028137495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180852400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032898176,0.0001523273,0.9649577,0.000119254684,0.00002258452,0.000044064607,0.00003440183,0.0002490257,0.001522472],"genre_scores_gemma":[0.80529714,0.00015010961,0.19171517,0.00004916226,0.000024875664,0.00013728825,0.00009211703,0.000053029937,0.0024812324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997528,0.0000440843,0.000012635472,0.00005774471,0.000093963215,0.00003884694],"domain_scores_gemma":[0.9994081,0.00024609698,0.00014280526,0.00006331865,0.00010230204,0.000037306487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000383646,0.000671202,0.00039616067,0.0003205778,0.0003968144,0.0004671835,0.00096514705,0.0004753984,0.00085278274],"category_scores_gemma":[0.0016778606,0.00029256145,0.00032178272,0.00034653742,0.00060068496,0.0005593624,0.0010822518,0.0005656664,0.00017580188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040533738,0.000010245191,0.00044938465,0.000026679743,0.0000123474565,0.00003452856,0.00005618291,0.97782284,0.0016909405,0.0039161914,0.00035190067,0.015588159],"study_design_scores_gemma":[0.000006725151,0.000020372385,0.00008128766,0.0000021448636,0.0000026911341,0.000009946809,0.000007334954,0.9975962,0.0003800836,0.0016243753,0.00026678498,0.0000019815457],"about_ca_topic_score_codex":0.00634439,"about_ca_topic_score_gemma":0.004161418,"teacher_disagreement_score":0.00634439,"about_ca_system_score_codex":0.0007830089,"about_ca_system_score_gemma":0.00079843844,"threshold_uncertainty_score":0.012614906},"labels":[],"label_agreement":null},{"id":"W3181436270","doi":"10.3390/s21144633","title":"Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Education University of Hong Kong","keywords":"Kinematics; Accelerometer; Gyroscope; Inertial measurement unit; Treadmill; Sagittal plane; Computer science; Gait analysis; Gait; Simulation; Artificial intelligence; Engineering; Physical medicine and rehabilitation; Physics; Physical therapy; Medicine","score_opus":0.09835852312838059,"score_gpt":0.29494792689202715,"score_spread":0.19658940376364656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181436270","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9459837,0.0008575808,0.05134684,0.00006822643,0.000084000094,0.000034438148,0.00022166225,0.00022935207,0.0011742103],"genre_scores_gemma":[0.99236125,0.00029585543,0.006679903,0.000014876908,0.00000921684,0.000014780404,0.00017781783,0.000008988704,0.00043721826],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996916,0.00006308331,0.000023134662,0.00010741215,0.000071459304,0.000043318625],"domain_scores_gemma":[0.99951696,0.00021386136,0.00005813063,0.000041009756,0.0001454839,0.000024565234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006473387,0.00083360163,0.0003482427,0.00050447776,0.00010683783,0.00037460309,0.00023450133,0.00046482074,0.00063332665],"category_scores_gemma":[0.0022827392,0.00021628942,0.00028732896,0.00032063405,0.00012091823,0.0005287628,0.0003081729,0.00019520713,0.00024475312],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021296348,0.0005472908,0.32282937,0.00033733973,0.00057500077,0.00036118933,0.00017758696,0.14378826,0.06948339,0.0003694826,0.0008566339,0.45854482],"study_design_scores_gemma":[0.00002364812,0.0006648032,0.1713142,0.00005703234,0.00019188118,0.00017956851,0.000100845304,0.81114686,0.015590389,0.00016209751,0.00053978985,0.000028917526],"about_ca_topic_score_codex":0.0064630825,"about_ca_topic_score_gemma":0.0115069235,"teacher_disagreement_score":0.0064630825,"about_ca_system_score_codex":0.00018264887,"about_ca_system_score_gemma":0.000273195,"threshold_uncertainty_score":0.01285094},"labels":[],"label_agreement":null},{"id":"W3183098377","doi":"10.3390/s21144799","title":"Accuracy of a Low-Cost 3D-Printed Wearable Goniometer for Measuring Wrist Motion","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Standard deviation; Wrist; Computer science; Goniometer; Motion capture; Ulnar deviation; Simulation; Artificial intelligence; Computer vision; Motion (physics); Mathematics; Statistics; Medicine; Surgery; Embedded system","score_opus":0.027037256633450298,"score_gpt":0.2979984732854896,"score_spread":0.2709612166520393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183098377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44076735,0.0031750435,0.5470217,0.00050748105,0.00070379773,0.00046492752,0.0010381899,0.0017310493,0.0045903903],"genre_scores_gemma":[0.7755762,0.0008878478,0.21994078,0.0003230031,0.00013472383,0.00037928243,0.00057752646,0.00015895221,0.0020217472],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9944753,0.0014533476,0.0004638239,0.0009437378,0.002561749,0.000102028425],"domain_scores_gemma":[0.9937172,0.0032445316,0.0008173395,0.0009168892,0.0012107868,0.0000931723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035359096,0.00094844727,0.00066035555,0.0010307196,0.00024019566,0.001109834,0.0012772378,0.0013563973,0.0018227225],"category_scores_gemma":[0.014569654,0.00049512705,0.0006113767,0.0006086343,0.0005860446,0.000540424,0.00085211516,0.00055518857,0.0009988438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001137078,0.0003711176,0.09952126,0.0012695473,0.00033945485,0.00024281756,0.0005713099,0.0046481527,0.5879236,0.00069680513,0.002095992,0.30118293],"study_design_scores_gemma":[0.00031239775,0.0049431454,0.62424827,0.00049188774,0.0005256168,0.0050838017,0.0004284133,0.06833899,0.28227532,0.0012370496,0.011810751,0.0003043628],"about_ca_topic_score_codex":0.0008151745,"about_ca_topic_score_gemma":0.0016804177,"teacher_disagreement_score":0.0035359096,"about_ca_system_score_codex":0.00025552823,"about_ca_system_score_gemma":0.00044489664,"threshold_uncertainty_score":0.018699884},"labels":[],"label_agreement":null},{"id":"W3183492561","doi":"10.3390/s21144940","title":"Wearable Robotic Gait Training in Persons with Multiple Sclerosis: A Satisfaction Study","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gait; Physical therapy; Rehabilitation; Physical medicine and rehabilitation; Medicine; Powered exoskeleton; Gait training; Wearable computer; Patient satisfaction; Quality of life (healthcare); Exoskeleton; Neurology; Multiple sclerosis; Wearable technology; Psychology; Nursing; Computer science","score_opus":0.10257410242606917,"score_gpt":0.30818444178791965,"score_spread":0.20561033936185047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183492561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99925286,0.00008711207,0.000091432245,0.000030364026,0.0000031925288,0.00002584739,0.0000896349,0.000001800855,0.0004177155],"genre_scores_gemma":[0.9992416,0.00010151649,0.000104348044,0.00006326254,0.000008136082,0.000034652676,0.00011023379,0.0000010971274,0.00033536484],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99941635,0.00021584342,0.00007480171,0.00005697978,0.00016127722,0.00007471516],"domain_scores_gemma":[0.9985056,0.00032779906,0.00048499095,0.000043263764,0.00036962904,0.000268897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011051149,0.00020311493,0.0004941595,0.00051170326,0.00045541467,0.0004651953,0.00012862805,0.0003979446,0.0023595754],"category_scores_gemma":[0.0021215545,0.00014108837,0.0007942811,0.0005612899,0.00022089492,0.00031012256,0.000390873,0.00034965551,0.00032056557],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008425671,0.0015317057,0.95807797,0.00028052257,0.0002818782,0.000351761,0.0037672056,0.00013318742,0.0015009536,0.00004457256,0.00053755543,0.032650106],"study_design_scores_gemma":[0.000060560636,0.0040397462,0.9899901,0.00003068396,0.000100579484,0.0005323512,0.0037977982,0.0003785289,0.00019052732,0.000014847335,0.0008480226,0.000016167985],"about_ca_topic_score_codex":0.0023387047,"about_ca_topic_score_gemma":0.0033093274,"teacher_disagreement_score":0.0023595754,"about_ca_system_score_codex":0.00025635472,"about_ca_system_score_gemma":0.00024592807,"threshold_uncertainty_score":0.007893562},"labels":[],"label_agreement":null},{"id":"W3183751646","doi":"10.3390/s21155028","title":"FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Resource allocation; Computer science; Cloud computing; Resource management (computing); Quality of service; Intelligent transportation system; Resource (disambiguation); SAFER; Set (abstract data type); Service (business); Distributed computing; Computer network; Transport engineering; Operations research; Computer security; Engineering; Business","score_opus":0.018973038800964265,"score_gpt":0.23982955644818152,"score_spread":0.22085651764721725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183751646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07393254,0.0013369719,0.9075107,0.00076279993,0.0006024199,0.0003766448,0.00025341293,0.002351701,0.0128727965],"genre_scores_gemma":[0.92474794,0.0003166372,0.07180719,0.00023753713,0.000098777804,0.00010075869,0.00011851528,0.000042174946,0.0025304053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999553,0.00006992372,0.000035485024,0.0001112176,0.0001038156,0.00012656843],"domain_scores_gemma":[0.9996829,0.000056844565,0.000047427922,0.00007036477,0.00008566941,0.00005674355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077498064,0.00052411476,0.00046913658,0.0005255565,0.0009838837,0.0011324115,0.0017196696,0.00063141645,0.0013197836],"category_scores_gemma":[0.0008626851,0.00019918445,0.0005764873,0.00036865292,0.00046798892,0.0014326184,0.0013335922,0.00065779063,0.00022567913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013540699,0.0006950798,0.0059996815,0.00066494837,0.0005140337,0.0015684419,0.0008058531,0.30695534,0.084804006,0.2149392,0.033407617,0.3482918],"study_design_scores_gemma":[0.00006438823,0.00023863815,0.0009680666,0.000034884462,0.00007812978,0.00037155062,0.00016397967,0.93551046,0.016394498,0.025718704,0.020384202,0.00007253966],"about_ca_topic_score_codex":0.0037010498,"about_ca_topic_score_gemma":0.0049007945,"teacher_disagreement_score":0.0037010498,"about_ca_system_score_codex":0.0008597964,"about_ca_system_score_gemma":0.0012784956,"threshold_uncertainty_score":0.007359028},"labels":[],"label_agreement":null},{"id":"W3184647894","doi":"10.3390/s21155097","title":"A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mel-frequency cepstrum; Classifier (UML); Mixture model; Discriminative model; Artificial intelligence; Pattern recognition (psychology); Computer science; Speech recognition; Support vector machine; Speaker recognition; Feature extraction","score_opus":0.03279552816942155,"score_gpt":0.2527752848545654,"score_spread":0.21997975668514386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184647894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03071605,0.0003221927,0.9598012,0.0002202654,0.00015363868,0.00013905988,0.00008825101,0.005043284,0.0035160736],"genre_scores_gemma":[0.6073149,0.00017958584,0.38143554,0.00042356364,0.00016183172,0.00027757033,0.00036065967,0.00013187816,0.009714424],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991134,0.00013172255,0.00004625202,0.00030319006,0.00027014155,0.00013531439],"domain_scores_gemma":[0.9995474,0.000073455834,0.000042745232,0.00006855343,0.00020976749,0.000058000383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010688356,0.0007051673,0.0010936637,0.0006304522,0.000603733,0.0010097601,0.0018817631,0.0013624539,0.0029141563],"category_scores_gemma":[0.0010891425,0.0005364241,0.00069132214,0.00039462207,0.0003708369,0.0012728379,0.0019008758,0.0013188092,0.0025444091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079752953,0.00039548782,0.0036246607,0.00019987804,0.00026955156,0.0005452053,0.0004475406,0.029263075,0.20670241,0.006332052,0.0069251917,0.74449754],"study_design_scores_gemma":[0.000054798253,0.0004101885,0.0031080502,0.00003278994,0.00018116402,0.0005471342,0.00007461344,0.9179538,0.0637245,0.0050755437,0.008748998,0.00008838218],"about_ca_topic_score_codex":0.0020319624,"about_ca_topic_score_gemma":0.002330926,"teacher_disagreement_score":0.0029141563,"about_ca_system_score_codex":0.00051792787,"about_ca_system_score_gemma":0.00080943183,"threshold_uncertainty_score":0.0097488165},"labels":[],"label_agreement":null},{"id":"W3184663704","doi":"10.3390/s21155157","title":"Dosimetric Application of Phosphorus Doped Fibre for X-ray and Proton Therapy","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic Crystal and Fiber Optics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF","funders":"","keywords":"Dosimeter; Dosimetry; Materials science; Bragg peak; Linear energy transfer; Proton therapy; Photon; Attenuation; Proton; Radiation; Doping; Radiochemistry; Saturation (graph theory); Optics; Radiation therapy; Nuclear medicine; Optoelectronics; Chemistry; Physics; Nuclear physics; Medicine","score_opus":0.009417842920872322,"score_gpt":0.21791629219248412,"score_spread":0.2084984492716118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184663704","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.980213,0.0016740558,0.015834995,0.00006916035,0.000032215597,0.000051917654,0.00012945152,0.000099816454,0.0018952712],"genre_scores_gemma":[0.9869445,0.00066694076,0.0109248115,0.000023488954,0.000008035329,0.000028735141,0.000073755764,0.00002007684,0.001309737],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999724,0.00004858317,0.00001322651,0.000064205364,0.000116660725,0.000033272885],"domain_scores_gemma":[0.9995407,0.00015213087,0.00010243794,0.00003921408,0.00013487396,0.00003061683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005558594,0.0003433499,0.0001754902,0.00030360086,0.00027572093,0.00022197359,0.0002481397,0.00045587745,0.00072093663],"category_scores_gemma":[0.0007726089,0.00020750663,0.00021088005,0.00021786866,0.00033956245,0.0002937126,0.0002475926,0.00020351057,0.00013706353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016997744,0.000013497654,0.0010244173,0.00006527124,0.000008682008,0.000024900157,0.00005681376,0.0008803466,0.9935143,0.00010607863,0.00002139534,0.0041143172],"study_design_scores_gemma":[0.000006500717,0.00038817778,0.00416662,0.000011896141,0.000020845468,0.00012722296,0.000028804878,0.0016025772,0.99271375,0.000043907952,0.00088002067,0.0000097524435],"about_ca_topic_score_codex":0.0012649676,"about_ca_topic_score_gemma":0.0016602261,"teacher_disagreement_score":0.0012649676,"about_ca_system_score_codex":0.0005052203,"about_ca_system_score_gemma":0.00033585596,"threshold_uncertainty_score":0.0036656857},"labels":[],"label_agreement":null},{"id":"W3185544562","doi":"10.3390/s21144883","title":"Evaluation of Precise Microwave Ranging Technology for Low Earth Orbit Formation Missions with Beidou Time-Synchronize Receiver","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Shanghai Jiao Tong University; National Natural Science Foundation of China","keywords":"Ranging; Computer science; Geostationary orbit; Orbit determination; GNSS applications; Orbit (dynamics); BeiDou Navigation Satellite System; Real-time computing; Range (aeronautics); Remote sensing; Aerospace engineering; Global Positioning System; Engineering; Telecommunications; Geology","score_opus":0.023143196606863908,"score_gpt":0.22878723603547638,"score_spread":0.20564403942861248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185544562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7662897,0.0011258465,0.22243756,0.00023121476,0.00009230329,0.00012109537,0.00012794418,0.0010418785,0.008532447],"genre_scores_gemma":[0.9551357,0.00023561211,0.04292761,0.00003586547,0.000014092845,0.000035470588,0.000113397786,0.000031498348,0.0014708523],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994307,0.000116391384,0.000022194923,0.00008609445,0.00029892055,0.000045743913],"domain_scores_gemma":[0.9997093,0.000058095593,0.00004825489,0.000057004963,0.000109255816,0.00001802531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006787641,0.00033719494,0.00021268122,0.00040738724,0.00019918047,0.0004246712,0.00046339582,0.00030817202,0.00079531566],"category_scores_gemma":[0.00087541767,0.00009908316,0.00017093291,0.00025735438,0.00016764906,0.00081267784,0.00035994043,0.00022746329,0.00024014458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071934983,0.00012996775,0.022236979,0.00049762515,0.00009238657,0.0004067514,0.00064782833,0.06152829,0.65777713,0.006478568,0.0009638541,0.24852133],"study_design_scores_gemma":[0.0001416314,0.0052729654,0.04358596,0.000080527854,0.00029933968,0.001344565,0.00051182834,0.26515862,0.65390885,0.0011200526,0.028465435,0.00011022209],"about_ca_topic_score_codex":0.0006885746,"about_ca_topic_score_gemma":0.0006570901,"teacher_disagreement_score":0.00079531566,"about_ca_system_score_codex":0.0002938916,"about_ca_system_score_gemma":0.0002949571,"threshold_uncertainty_score":0.0035896897},"labels":[],"label_agreement":null},{"id":"W3186864674","doi":"10.3390/s21155146","title":"A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward Distribution","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Crowdsourcing; Computer science; Transparency (behavior); Traceability; Spatial analysis; Blockchain; Data science; Data mining; Computer security; World Wide Web; Geography","score_opus":0.011702696420383518,"score_gpt":0.2219854707020919,"score_spread":0.21028277428170838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186864674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11246978,0.00071985327,0.8538567,0.0013466546,0.00030781122,0.001248526,0.0009578648,0.011616353,0.017476514],"genre_scores_gemma":[0.8700506,0.00035089662,0.111925974,0.00023693814,0.00007168404,0.0007058813,0.00091050466,0.00012229817,0.015625164],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876106,0.00025641164,0.000109012675,0.00028216606,0.0004260601,0.00016535993],"domain_scores_gemma":[0.9981415,0.00042770497,0.00017384104,0.00040643016,0.0004933123,0.0003571197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001139045,0.00057590875,0.00089658337,0.0006838402,0.0020950323,0.0011744228,0.0019312863,0.0011883678,0.0074683307],"category_scores_gemma":[0.0026130017,0.00037124442,0.00041737626,0.0011033877,0.00065533805,0.0019988785,0.002987555,0.00078157464,0.0020719336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034946427,0.0014612922,0.011815858,0.0012457193,0.00027034935,0.0034833197,0.0022690084,0.27470458,0.09483875,0.047478676,0.036521032,0.52241683],"study_design_scores_gemma":[0.00031964178,0.0002975411,0.0010650952,0.00004050858,0.000055350185,0.00031652613,0.00015957058,0.9434747,0.015681738,0.012036864,0.026459403,0.0000930483],"about_ca_topic_score_codex":0.010732191,"about_ca_topic_score_gemma":0.008088472,"teacher_disagreement_score":0.010732191,"about_ca_system_score_codex":0.0010688631,"about_ca_system_score_gemma":0.003579748,"threshold_uncertainty_score":0.024984121},"labels":[],"label_agreement":null},{"id":"W3187189595","doi":"10.3390/s21165402","title":"Sensors for Fire and Smoke Monitoring","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service; York University","funders":"","keywords":"Smoke; Civilization; Engineering; Architectural engineering; Environmental science; Computer science; Forensic engineering; Computer security; Waste management; History; Archaeology","score_opus":0.01736827008493812,"score_gpt":0.22712565780175037,"score_spread":0.20975738771681224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187189595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048133776,0.25600567,0.46460253,0.020181792,0.011943455,0.00048635688,0.0037673393,0.006426224,0.18845281],"genre_scores_gemma":[0.5486876,0.13305569,0.19719313,0.0077857,0.0034007072,0.00048026722,0.002943586,0.0004202093,0.10603303],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990128,0.00010733471,0.000024107152,0.00018034488,0.0006032746,0.00007213248],"domain_scores_gemma":[0.99955994,0.00010581868,0.00006763063,0.000047577556,0.0001865542,0.000032328204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057494343,0.00064632925,0.0005628652,0.0008947371,0.0005444815,0.0014630717,0.0010297514,0.0018102127,0.0066597043],"category_scores_gemma":[0.0013168287,0.00041641766,0.00046098232,0.0009682001,0.0006506351,0.0018257126,0.0012133024,0.0018436532,0.003546844],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042542646,0.00017463206,0.0030493152,0.0021733083,0.00016272128,0.0002950773,0.000326225,0.004142554,0.26361963,0.06091904,0.08861357,0.57609856],"study_design_scores_gemma":[0.00004569234,0.00056604383,0.004892653,0.0006747776,0.00015461692,0.0014887464,0.00030984564,0.023585467,0.2444828,0.031992186,0.6916323,0.00017485008],"about_ca_topic_score_codex":0.00068063574,"about_ca_topic_score_gemma":0.0013081552,"teacher_disagreement_score":0.0066597043,"about_ca_system_score_codex":0.00052022265,"about_ca_system_score_gemma":0.0005084596,"threshold_uncertainty_score":0.022278965},"labels":[],"label_agreement":null},{"id":"W3187632501","doi":"10.3390/s21165259","title":"Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hyperspectral imaging; Snow; Principal component analysis; Remote sensing; Snowpack; Similarity (geometry); Pattern recognition (psychology); Confusion matrix; Multivariate statistics; Artificial intelligence; Computer science; Environmental science; Geology; Meteorology; Machine learning; Geography; Image (mathematics)","score_opus":0.11222106713352416,"score_gpt":0.2669410624222175,"score_spread":0.15471999528869335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187632501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67725694,0.0005916354,0.31606188,0.00008323777,0.00005241417,0.0001324464,0.0006363258,0.0006603563,0.004524809],"genre_scores_gemma":[0.91341037,0.00037046778,0.08393935,0.000028338878,0.000039379134,0.0000488463,0.0009231796,0.000035459154,0.0012045762],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998758,0.000019714533,0.000009364095,0.00002897376,0.000050581715,0.000015538175],"domain_scores_gemma":[0.9998286,0.000040905186,0.000042100346,0.000016600517,0.000058323363,0.000013531025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002465474,0.00043292542,0.00029937364,0.0018860956,0.00023583835,0.00048973877,0.00020517546,0.00021841505,0.00042902643],"category_scores_gemma":[0.0003671481,0.00013483236,0.00037262897,0.0008139733,0.00016364639,0.00047309586,0.00023868571,0.00022698838,0.00024860076],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034622138,0.00035979805,0.13034917,0.00039228544,0.00019355232,0.00021103765,0.00041472536,0.038757544,0.24365069,0.0010617303,0.0012306732,0.58303255],"study_design_scores_gemma":[0.000021717982,0.00020613192,0.29230297,0.000064136955,0.0001591276,0.00032060125,0.0005962475,0.63645613,0.06379853,0.001814031,0.0041957875,0.000064590866],"about_ca_topic_score_codex":0.0022609937,"about_ca_topic_score_gemma":0.004619656,"teacher_disagreement_score":0.0022609937,"about_ca_system_score_codex":0.000142325,"about_ca_system_score_gemma":0.00019229314,"threshold_uncertainty_score":0.0044956207},"labels":[],"label_agreement":null},{"id":"W3188437996","doi":"10.3390/s21165254","title":"The Effects of Knee Flexion on Tennis Serve Performance of Intermediate Level Tennis Players","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Racket; Knee flexion; Physical medicine and rehabilitation; Significant difference; Tennis ball; Orthodontics; Mathematics; Medicine; sports equipment; Physics; Engineering; Swing; Mechanical engineering; Acoustics","score_opus":0.018216787578195188,"score_gpt":0.25735090997821614,"score_spread":0.23913412240002094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188437996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99926716,0.00011121326,0.00014720147,0.000009058615,0.000003747828,0.000007118289,0.000062819316,0.0000048720426,0.0003869158],"genre_scores_gemma":[0.99920326,0.00007881336,0.00018190239,0.000010147547,0.000004505318,0.000010719756,0.00012117634,0.0000026752427,0.0003868361],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99974555,0.0000516307,0.000027603788,0.00004997952,0.000057072088,0.000068090936],"domain_scores_gemma":[0.99935025,0.00013089838,0.00022365121,0.000023551856,0.00008857864,0.00018303648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026410687,0.00055711146,0.00030196813,0.0004276928,0.00019122918,0.00037679606,0.00014559938,0.0003520598,0.0016744633],"category_scores_gemma":[0.0012072691,0.00015993677,0.0002025363,0.00017696184,0.00017416362,0.00016723873,0.00034983808,0.00019449297,0.00041105424],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012225422,0.0017900598,0.6956967,0.00058640627,0.00031447868,0.0008731534,0.0016770165,0.0010921503,0.22968556,0.000070731156,0.00026338577,0.05572492],"study_design_scores_gemma":[0.000008527065,0.0015411929,0.9959216,0.000014343271,0.000017938086,0.000102836864,0.00032587964,0.0001567139,0.0017667082,0.000011899486,0.00012445457,0.000007848176],"about_ca_topic_score_codex":0.0018415994,"about_ca_topic_score_gemma":0.004808639,"teacher_disagreement_score":0.0018415994,"about_ca_system_score_codex":0.0001225323,"about_ca_system_score_gemma":0.0001376234,"threshold_uncertainty_score":0.0056016445},"labels":[],"label_agreement":null},{"id":"W3188531125","doi":"10.3390/s21165377","title":"Optical Fiber Array Sensor for Force Estimation and Localization in TAVI Procedure: Design, Modeling, Analysis and Validation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Optical fiber; Intensity modulation; Parametric statistics; Materials science; Finite element method; Deflection (physics); Fiber optic sensor; Acoustics; Light intensity; Optics; Biomedical engineering; Structural engineering; Engineering; Physics; Phase modulation; Mathematics","score_opus":0.01972864175020085,"score_gpt":0.32468234787170464,"score_spread":0.3049537061215038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188531125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20820723,0.0017532068,0.7846236,0.00025791273,0.00010714176,0.00022561033,0.00017722482,0.00067989586,0.00396813],"genre_scores_gemma":[0.8683364,0.000928245,0.12837021,0.000046405297,0.000017561119,0.00016007973,0.00007454835,0.000020230233,0.0020462223],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995454,0.00007568933,0.000016112945,0.000067333356,0.00027468402,0.000020768603],"domain_scores_gemma":[0.9996877,0.000090666246,0.000057653895,0.000029130044,0.0001246844,0.000010229255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056684355,0.00043348043,0.00029191363,0.00033088564,0.00018888082,0.00031949067,0.0006180303,0.0007751683,0.0005541074],"category_scores_gemma":[0.0005453109,0.00020169934,0.00038845657,0.00021981752,0.00023212378,0.00046316552,0.0002155859,0.00023726489,0.00017579705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037071592,0.00028721112,0.010268055,0.0007051691,0.000129211,0.00029272816,0.00029204832,0.30726716,0.5278908,0.004349317,0.0008672091,0.14728035],"study_design_scores_gemma":[0.000011189822,0.0006711162,0.0035667573,0.0000288623,0.00004757577,0.00016481489,0.00004181779,0.86010855,0.131218,0.00031802396,0.0037885958,0.000034724486],"about_ca_topic_score_codex":0.0013538754,"about_ca_topic_score_gemma":0.0016306232,"teacher_disagreement_score":0.0013538754,"about_ca_system_score_codex":0.00038575602,"about_ca_system_score_gemma":0.00051319995,"threshold_uncertainty_score":0.002997756},"labels":[],"label_agreement":null},{"id":"W3188896455","doi":"10.3390/s21165394","title":"TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ericsson (Canada); École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial neural network; Multispectral image; RGB color model; Convolutional neural network; Generator (circuit theory); Image translation; Artificial intelligence; Transformation (genetics); Domain (mathematical analysis); Process (computing); Pattern recognition (psychology); Polarity (international relations); Translation (biology); Computer vision; Image (mathematics); Mathematics","score_opus":0.01992038593944519,"score_gpt":0.27433744674136945,"score_spread":0.2544170608019243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188896455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03743995,0.0007294141,0.95099574,0.00027016416,0.00015333155,0.00008100876,0.00022290935,0.0027427722,0.0073647415],"genre_scores_gemma":[0.70525223,0.0006411869,0.2749898,0.000721013,0.00006534837,0.00021927731,0.0016670002,0.0004839443,0.01596015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981207,0.00002811082,0.0000085020565,0.00006733397,0.000056481127,0.000027518041],"domain_scores_gemma":[0.9998248,0.000048571485,0.000021290865,0.000036709163,0.000051276962,0.000017284507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004087505,0.0008277506,0.0004844273,0.00041059696,0.00024831152,0.00053869013,0.0016796779,0.0006639669,0.002292255],"category_scores_gemma":[0.00087160897,0.00038096146,0.00062799314,0.00046798048,0.00062965386,0.0011315461,0.0010502362,0.0011517659,0.0006862172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022097834,0.000117114396,0.0023982131,0.00018769594,0.00013957612,0.00029509063,0.00016696076,0.45695376,0.032453295,0.018585147,0.00848505,0.47999716],"study_design_scores_gemma":[0.000008633776,0.000029216071,0.00028109568,0.000011927036,0.00001613771,0.00006209564,0.000009877248,0.98842657,0.0046023163,0.0042683817,0.0022712774,0.000012447039],"about_ca_topic_score_codex":0.0045358473,"about_ca_topic_score_gemma":0.007007019,"teacher_disagreement_score":0.0045358473,"about_ca_system_score_codex":0.00071430736,"about_ca_system_score_gemma":0.00062758446,"threshold_uncertainty_score":0.009018898},"labels":[],"label_agreement":null},{"id":"W3189560484","doi":"10.3390/s21165401","title":"Steel Wire Rope Surface Defect Detection Based on Segmentation Template and Spatiotemporal Gray Sample Set","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Taif University; National Natural Science Foundation of China","keywords":"Wire rope; Segmentation; Rope; Artificial intelligence; Pixel; Computer vision; Image segmentation; Computer science; Structural engineering; Engineering; Pattern recognition (psychology)","score_opus":0.020847870209880805,"score_gpt":0.2366309627363069,"score_spread":0.2157830925264261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189560484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114427306,0.00015124773,0.8828012,0.00006326881,0.000025886693,0.000051041465,0.00009730149,0.0012416545,0.001141051],"genre_scores_gemma":[0.68061227,0.00027215763,0.3166088,0.00005004959,0.00002408059,0.00008202549,0.00034313902,0.00011901156,0.0018884577],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995327,0.000030515812,0.000024354003,0.00016311978,0.00020827219,0.00004103763],"domain_scores_gemma":[0.9995647,0.00006383293,0.00007540461,0.000090146525,0.00016653824,0.000039297684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024039336,0.00045998156,0.00046445848,0.0014034917,0.00018751704,0.0005362115,0.0007139345,0.00055624946,0.00058401644],"category_scores_gemma":[0.0010004281,0.00029256087,0.00062701054,0.00074610685,0.00043186446,0.0009020332,0.00048567358,0.0003598438,0.00025634348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003568907,0.00008673196,0.005770263,0.00018487561,0.00007286366,0.0005341219,0.0003258607,0.08139881,0.4996541,0.0060885474,0.0014898765,0.40403706],"study_design_scores_gemma":[0.0000129486725,0.00012861972,0.006857498,0.000011110095,0.00004346614,0.0005565632,0.00007861189,0.860606,0.12861094,0.0013679988,0.0016849017,0.000041345516],"about_ca_topic_score_codex":0.0025831342,"about_ca_topic_score_gemma":0.002246204,"teacher_disagreement_score":0.0025831342,"about_ca_system_score_codex":0.00040053242,"about_ca_system_score_gemma":0.0005325523,"threshold_uncertainty_score":0.005136192},"labels":[],"label_agreement":null},{"id":"W3190933319","doi":"10.3390/s21155173","title":"Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault (geology); Generative grammar; Computer science; Adversarial system; Data mining; Grid; Artificial intelligence; Set (abstract data type); Power (physics); Pattern recognition (psychology); Machine learning","score_opus":0.009949094336087155,"score_gpt":0.26851798752549316,"score_spread":0.258568893189406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190933319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016535275,0.00021399817,0.9815181,0.00015890639,0.000031324736,0.0000412273,0.000027740643,0.00041769163,0.0010558055],"genre_scores_gemma":[0.9474351,0.00012708355,0.050842293,0.00013552184,0.000036602258,0.00006149117,0.000065247536,0.000029354642,0.0012674012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992706,0.00028266784,0.000029330262,0.00014393555,0.00019295362,0.00008044716],"domain_scores_gemma":[0.9986939,0.0008617217,0.00016574144,0.00008563544,0.00014139972,0.00005161063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014718089,0.00091776275,0.00074227905,0.00052837335,0.00026913176,0.0005217228,0.0012696293,0.0008497682,0.0011762388],"category_scores_gemma":[0.0026108504,0.00026099788,0.0005470471,0.00031790437,0.0010091392,0.0009525932,0.0010558974,0.0012551753,0.00017296759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007542997,0.000020228434,0.00033499836,0.000026316739,0.00002175526,0.00007175208,0.000032026324,0.9724478,0.0013263667,0.004393341,0.00028105878,0.020968834],"study_design_scores_gemma":[0.000002212628,0.000014579856,0.000049663046,0.0000018246775,0.0000029244707,0.000013856425,0.0000016668154,0.9982338,0.0004111957,0.0012072967,0.00005869029,0.0000022826944],"about_ca_topic_score_codex":0.0020137469,"about_ca_topic_score_gemma":0.001560459,"teacher_disagreement_score":0.0020137469,"about_ca_system_score_codex":0.00088773307,"about_ca_system_score_gemma":0.00048211342,"threshold_uncertainty_score":0.007783711},"labels":[],"label_agreement":null},{"id":"W3191028459","doi":"10.3390/s21165391","title":"An Advanced Noise Reduction and Edge Enhancement Algorithm","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Noise reduction; Computer science; Robustness (evolution); Noise (video); Artificial intelligence; Computer vision; Image fusion; Image quality; Contourlet; Image noise; Image (mathematics); Enhanced Data Rates for GSM Evolution; Pyramid (geometry); Algorithm; Mathematics; Wavelet transform; Wavelet","score_opus":0.014234904960649582,"score_gpt":0.28681545530375857,"score_spread":0.27258055034310896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191028459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059722555,0.00020564685,0.991975,0.00005450066,0.000044006356,0.00003551143,0.000030963285,0.00042820728,0.0012539378],"genre_scores_gemma":[0.05690821,0.0003171896,0.9351649,0.00011649873,0.000058661197,0.000071168346,0.00025491178,0.00009960114,0.0070088045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996251,0.000025892874,0.000021932621,0.0000995402,0.00019215734,0.0000353281],"domain_scores_gemma":[0.99978036,0.000033937264,0.000016952883,0.000032952088,0.00012238258,0.000013492411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041379037,0.0007040439,0.00078294845,0.000882569,0.00034922312,0.00061180047,0.0011386338,0.0010201441,0.0030226891],"category_scores_gemma":[0.0006701141,0.0003278305,0.0008659607,0.00079570187,0.00034124378,0.00088994985,0.00074518874,0.00085633056,0.0016286054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021104263,0.00010988874,0.0006561397,0.00014873192,0.000067764755,0.00013435849,0.00009519033,0.063097686,0.13709034,0.009948675,0.0043661143,0.78407407],"study_design_scores_gemma":[0.00003393681,0.00014512712,0.0006671326,0.000016242611,0.000040376053,0.00033370312,0.000020286407,0.9390599,0.0456879,0.0032136922,0.010758378,0.00002343388],"about_ca_topic_score_codex":0.0017230618,"about_ca_topic_score_gemma":0.0024083343,"teacher_disagreement_score":0.0030226891,"about_ca_system_score_codex":0.00028183567,"about_ca_system_score_gemma":0.0006951947,"threshold_uncertainty_score":0.010111928},"labels":[],"label_agreement":null},{"id":"W3191837149","doi":"10.3390/s21165269","title":"Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Chongqing Science and Technology Foundation; National Natural Science Foundation of China","keywords":"Brain–computer interface; Computer science; Independent component analysis; Canonical correlation; Component analysis; Pattern recognition (psychology); Artificial intelligence; Stimulus (psychology); Modulation (music); Task (project management); Speech recognition; Electroencephalography; Engineering; Psychology; Acoustics","score_opus":0.048800027603505304,"score_gpt":0.2878423620373506,"score_spread":0.2390423344338453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191837149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16744879,0.0005131865,0.8283163,0.00009239353,0.000099942015,0.00018699531,0.00018246788,0.0011658309,0.0019941803],"genre_scores_gemma":[0.60684633,0.000673095,0.39017126,0.00008126271,0.00007594634,0.00024139954,0.0003210164,0.00012262979,0.0014669931],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973494,0.00005449888,0.000017573713,0.000075171956,0.00008712934,0.00003077443],"domain_scores_gemma":[0.9995472,0.00020067085,0.000039241793,0.0000377984,0.00015582662,0.000019271281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040513268,0.0007397435,0.00036104635,0.00084382744,0.00014882884,0.0002896611,0.000280432,0.00038546894,0.0010810323],"category_scores_gemma":[0.0019010304,0.00012923699,0.0003894672,0.00068807247,0.00021707035,0.0005201302,0.00041232677,0.00038666176,0.00031733388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003401587,0.00015637885,0.002818892,0.00031518503,0.000089577494,0.00014734127,0.00012459182,0.008722453,0.40002853,0.0013722541,0.0012061457,0.5846786],"study_design_scores_gemma":[0.000048851405,0.00044770702,0.052205212,0.000044404947,0.00025889682,0.0010074619,0.00007984373,0.6294699,0.30929682,0.0019645772,0.0050773164,0.000099031844],"about_ca_topic_score_codex":0.0012952905,"about_ca_topic_score_gemma":0.0024330565,"teacher_disagreement_score":0.0012952905,"about_ca_system_score_codex":0.000113211485,"about_ca_system_score_gemma":0.00039033435,"threshold_uncertainty_score":0.0036163926},"labels":[],"label_agreement":null},{"id":"W3192508862","doi":"10.3390/s21155184","title":"Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences; Chinese Academy of Sciences; Agriculture and Agri-Food Canada; National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Remote sensing; Moderate-resolution imaging spectroradiometer; Image resolution; Environmental science; Spectroradiometer; Pixel; Vegetation (pathology); Sensor fusion; Geography; Leaf area index; Reflectivity; Computer science; Satellite; Artificial intelligence","score_opus":0.03181684931154277,"score_gpt":0.24711633152322296,"score_spread":0.2152994822116802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192508862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98372304,0.0002579984,0.015070565,0.000045174304,0.000015647685,0.000018088713,0.00018300881,0.00016027401,0.00052633],"genre_scores_gemma":[0.9900243,0.00006268837,0.009591214,0.000007866382,0.0000048137795,0.000007709827,0.00021602822,0.000012577649,0.00007271264],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986571,0.0003260942,0.00012221525,0.00034112285,0.00043744475,0.00011596251],"domain_scores_gemma":[0.99676126,0.0014227536,0.00038394952,0.00044040228,0.0009016995,0.00008995896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004988569,0.00062064285,0.0003473175,0.00095438515,0.0003695084,0.0006157077,0.0004375054,0.00049134495,0.0002067467],"category_scores_gemma":[0.0098703485,0.00024439726,0.0007912053,0.0007175506,0.0003030965,0.0011905076,0.00060333026,0.0003263611,0.00009599077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002408851,0.00033954176,0.48439857,0.00020300038,0.00075476686,0.0002604569,0.00065247715,0.23582795,0.044819828,0.0009805525,0.00058402726,0.22877002],"study_design_scores_gemma":[0.00005969997,0.00040903702,0.27071795,0.0000335944,0.00020618805,0.00015361472,0.00024650712,0.7030943,0.02379406,0.0003143138,0.0008811056,0.00008967763],"about_ca_topic_score_codex":0.014216484,"about_ca_topic_score_gemma":0.011702579,"teacher_disagreement_score":0.014216484,"about_ca_system_score_codex":0.000597842,"about_ca_system_score_gemma":0.00039869227,"threshold_uncertainty_score":0.028267443},"labels":[],"label_agreement":null},{"id":"W3193484423","doi":"10.3390/s21165452","title":"Multi-Modal Residual Perceptron Network for Audio–Video Emotion Recognition","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Modal; Computer science; Modality (human–computer interaction); Residual; Perceptron; Artificial intelligence; Speech recognition; Artificial neural network; Feature (linguistics); Multilayer perceptron; Representation (politics); Pattern recognition (psychology); Machine learning; Algorithm","score_opus":0.03740230738645329,"score_gpt":0.27245008496535633,"score_spread":0.23504777757890305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193484423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02701251,0.0006574069,0.9683364,0.0002789416,0.00012126653,0.000043106502,0.00011349969,0.0013820904,0.0020547817],"genre_scores_gemma":[0.7872725,0.00041625838,0.20508096,0.00035636075,0.00008740311,0.00011216213,0.00055468676,0.00009711526,0.006022645],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959654,0.00013215613,0.000022009623,0.0001108939,0.00008082357,0.00005761143],"domain_scores_gemma":[0.99962246,0.00015874961,0.000033744283,0.000044766453,0.00011860239,0.000021687772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010156551,0.0007338414,0.00046160741,0.00026277467,0.00017157826,0.0004945485,0.0010778101,0.00091151445,0.0021137132],"category_scores_gemma":[0.0018327858,0.00022824692,0.00063697057,0.0003091381,0.00036370225,0.00093925407,0.0006307482,0.001494732,0.0007382625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057963096,0.00032996264,0.0014105631,0.00021245818,0.00019586307,0.00017823504,0.00013673917,0.44461107,0.0453467,0.0073682424,0.0063746125,0.4932559],"study_design_scores_gemma":[0.0000030551353,0.000041406976,0.0001788523,0.0000045369516,0.000010651009,0.000013863583,0.000007371381,0.99541384,0.0027552128,0.0012066321,0.00035900788,0.0000056192325],"about_ca_topic_score_codex":0.0024469877,"about_ca_topic_score_gemma":0.0030013965,"teacher_disagreement_score":0.0024469877,"about_ca_system_score_codex":0.00048373677,"about_ca_system_score_gemma":0.00033518168,"threshold_uncertainty_score":0.007071018},"labels":[],"label_agreement":null},{"id":"W3193591751","doi":"10.3390/s21175709","title":"Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; University of Calgary","funders":"","keywords":"GNSS applications; Precise Point Positioning; Zenith; GLONASS; Ambiguity resolution; Global Positioning System; Remote sensing; Environmental science; Computer science; Meteorology; Geodesy; Troposphere; Geography; Telecommunications","score_opus":0.005156779793212379,"score_gpt":0.1939856677406841,"score_spread":0.1888288879474717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193591751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9648041,0.00017443922,0.031484067,0.00004077499,0.000011946932,0.00002582189,0.0006073605,0.00036651918,0.0024849328],"genre_scores_gemma":[0.98068535,0.00009317015,0.018124826,0.0000049542327,0.0000063564976,0.000008329363,0.0006937007,0.00001806482,0.00036532735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998343,0.00002761324,0.0000069330526,0.0000391578,0.000059113205,0.000032920474],"domain_scores_gemma":[0.99981064,0.000033348584,0.000037938753,0.00003318871,0.00006889596,0.000016037355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025068413,0.00044586588,0.0002591296,0.00070919143,0.00020969186,0.0005155902,0.0003918251,0.00021198094,0.00030545608],"category_scores_gemma":[0.0006547653,0.00015912464,0.0002158753,0.0013549054,0.00013775312,0.00040954674,0.00039327572,0.0002383214,0.00017627624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000668412,0.00016621454,0.16397086,0.0001937284,0.0001763716,0.0009911499,0.0005495898,0.53281546,0.061840244,0.0013561224,0.0016152287,0.23565657],"study_design_scores_gemma":[0.000052824875,0.00024441516,0.16580325,0.000021355088,0.00006103826,0.00023532852,0.00036196274,0.8142672,0.01569734,0.0005915282,0.0026207434,0.00004295549],"about_ca_topic_score_codex":0.033774044,"about_ca_topic_score_gemma":0.028128529,"teacher_disagreement_score":0.033774044,"about_ca_system_score_codex":0.00027682984,"about_ca_system_score_gemma":0.00051907694,"threshold_uncertainty_score":0.067154944},"labels":[],"label_agreement":null},{"id":"W3193714209","doi":"10.3390/s21165596","title":"Sensor-Based Gait Retraining Lowers Knee Adduction Moment and Improves Symptoms in Patients with Knee Osteoarthritis: A Randomized Controlled Trial","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Innovation and Technology Commission - Hong Kong","keywords":"Osteoarthritis; Medicine; Gait; Knee pain; Physical medicine and rehabilitation; Physical therapy; Randomized controlled trial; Visual analogue scale; Knee Joint; Gait analysis; Surgery","score_opus":0.0050306345220574615,"score_gpt":0.21122368019331864,"score_spread":0.20619304567126118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193714209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762615,0.01675681,0.0006687263,0.00047561777,0.0008238439,0.003204217,0.00048504872,0.00009304469,0.0012311718],"genre_scores_gemma":[0.9836533,0.007726354,0.0014644373,0.00073512236,0.00071583275,0.0040173405,0.0004241468,0.000009733821,0.001253671],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99924576,0.00035664742,0.00012782862,0.000120659446,0.00007462082,0.000074518255],"domain_scores_gemma":[0.9993513,0.00024081065,0.0001791274,0.000036063408,0.00004762246,0.0001449289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012102841,0.0009833078,0.0032917373,0.0004890206,0.00040319105,0.00075230544,0.00080488395,0.0016080055,0.004375223],"category_scores_gemma":[0.0016115714,0.00050269574,0.0020241868,0.00054271467,0.0006676825,0.00064365094,0.0003340562,0.0018423569,0.00039363807],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.964898,0.009713177,0.0006085784,0.0023787473,0.0025237422,0.00003455071,0.00003398201,0.00013991397,0.0013278662,0.00005730247,0.00025352958,0.01803067],"study_design_scores_gemma":[0.9287923,0.065424085,0.0033612647,0.000120853925,0.0014978291,0.000023384495,0.00002207493,0.00024474543,0.00021472135,0.000049793278,0.00023749657,0.000011513001],"about_ca_topic_score_codex":0.0010104814,"about_ca_topic_score_gemma":0.0017024413,"teacher_disagreement_score":0.004375223,"about_ca_system_score_codex":0.00037213715,"about_ca_system_score_gemma":0.00070939417,"threshold_uncertainty_score":0.014636576},"labels":[],"label_agreement":null},{"id":"W3194592945","doi":"10.3390/s21165560","title":"Distributed Architecture for an Integrated Development Environment, Large Trace Analysis, and Visualization","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Polytechnique Montréal","funders":"","keywords":"Computer science; Debugging; Debugger; Plug-in; Visualization; Software engineering; Modular design; Compiler; Tracing; TRACE (psycholinguistics); Interoperability; Reuse; Programming language; Scalability; Operating system","score_opus":0.010559355624014997,"score_gpt":0.252192091021201,"score_spread":0.241632735397186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194592945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028015084,0.00029953342,0.9675106,0.0004143434,0.000115184004,0.0002961457,0.0001459787,0.023935948,0.004480783],"genre_scores_gemma":[0.055598285,0.00062040344,0.9319354,0.00026274423,0.00008268474,0.000719946,0.0014117593,0.0021875491,0.0071811583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747145,0.00060868135,0.00023510304,0.00039903607,0.0011062823,0.00017943014],"domain_scores_gemma":[0.9966119,0.0005359278,0.0002299659,0.0011234477,0.0010639612,0.00043482077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037004978,0.0013970329,0.0006809196,0.0014898075,0.0007283641,0.0032586434,0.0024936919,0.0014490989,0.0073137027],"category_scores_gemma":[0.005921981,0.00072131323,0.0008244708,0.0012557654,0.0007308989,0.0033006428,0.003626188,0.0023337742,0.0044769687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049651496,0.00055716495,0.003483108,0.0007256136,0.00017176958,0.000952197,0.0011102792,0.024842123,0.05851163,0.07501009,0.07210752,0.762032],"study_design_scores_gemma":[0.00038500252,0.0006462689,0.0032659227,0.000295455,0.00019280174,0.0012882614,0.00032932925,0.41270882,0.043339416,0.067533836,0.46978033,0.000234574],"about_ca_topic_score_codex":0.0018338532,"about_ca_topic_score_gemma":0.001595069,"teacher_disagreement_score":0.0073137027,"about_ca_system_score_codex":0.0007631828,"about_ca_system_score_gemma":0.0027089666,"threshold_uncertainty_score":0.024466753},"labels":[],"label_agreement":null},{"id":"W3194854785","doi":"10.3390/s21165489","title":"RAVA: Region-Based Average Video Quality Assessment","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Video quality; Subjective video quality; Computer science; PEVQ; Quality (philosophy); Quality assessment; Video processing; Artificial intelligence; Quality Score; Video tracking; Measure (data warehouse); Image quality; Mean opinion score; Computer vision; Data mining; Evaluation methods; Image (mathematics); Reliability engineering; Metric (unit)","score_opus":0.05981894774332796,"score_gpt":0.3504582328021495,"score_spread":0.29063928505882153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194854785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02156343,0.003104634,0.9655798,0.00010165421,0.000143602,0.00024344728,0.0011491332,0.0063701677,0.0017441089],"genre_scores_gemma":[0.34364024,0.001910445,0.6471872,0.00020811435,0.00015423002,0.00037636707,0.0031230447,0.0006890975,0.0027112737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981103,0.00034869625,0.00014949494,0.00045456103,0.0008386366,0.00009844367],"domain_scores_gemma":[0.9972229,0.0007487449,0.0005839317,0.00025799347,0.001078927,0.000107495696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017927522,0.0016100208,0.0014482649,0.003662403,0.00033223917,0.0013130158,0.001706789,0.00087008305,0.0023266398],"category_scores_gemma":[0.0064581423,0.00032903004,0.0012833196,0.0020255656,0.00038545937,0.0016674045,0.00085022574,0.0011261265,0.001701418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007818251,0.00028226184,0.01096388,0.00070325186,0.00048312137,0.00032738168,0.00018660675,0.09417646,0.078295045,0.003166507,0.014440151,0.7961935],"study_design_scores_gemma":[0.000056536945,0.0006070201,0.014396463,0.00009587752,0.00017088137,0.0012278832,0.000106060164,0.9218378,0.04607527,0.0033135714,0.011940278,0.00017231524],"about_ca_topic_score_codex":0.0039953906,"about_ca_topic_score_gemma":0.0038157753,"teacher_disagreement_score":0.0039953906,"about_ca_system_score_codex":0.0006104285,"about_ca_system_score_gemma":0.00059238914,"threshold_uncertainty_score":0.009481132},"labels":[],"label_agreement":null},{"id":"W3195070756","doi":"10.3390/s21175697","title":"Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. Department of Agriculture","keywords":"Deep learning; Odocoileus; Artificial intelligence; Convolutional neural network; Computer science; Machine learning; Identification (biology); Residual neural network; Cartography; Pattern recognition (psychology); Geography; Ecology; Biology","score_opus":0.013521004342469365,"score_gpt":0.22279731634706298,"score_spread":0.20927631200459362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195070756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4120074,0.0017291083,0.57550114,0.0006551367,0.00015391,0.00009486924,0.0004296467,0.0042167446,0.0052119973],"genre_scores_gemma":[0.8785757,0.00054493576,0.11780228,0.00017141507,0.000031595435,0.00003856061,0.00047284327,0.000053899992,0.0023087491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997602,0.00003150514,0.0000122856,0.00006836679,0.000091416405,0.00003625694],"domain_scores_gemma":[0.9995958,0.0001199501,0.0000732962,0.000046202476,0.00014677685,0.000017977345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004130026,0.000759744,0.00033840744,0.0006088075,0.00017861805,0.00040474164,0.0004907989,0.00044087725,0.0009320385],"category_scores_gemma":[0.0011741677,0.00020110693,0.00034663064,0.00043213402,0.00021444912,0.0010526001,0.00043100532,0.00044960037,0.000353628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020847055,0.00022443506,0.021279838,0.00019404074,0.00012595982,0.00016191264,0.00012490586,0.33419028,0.061661582,0.001152949,0.0035403292,0.5771352],"study_design_scores_gemma":[0.0000044971043,0.00012609105,0.006952858,0.000014580405,0.000024594781,0.000041091833,0.000042789834,0.9779553,0.012415413,0.0006322094,0.0017800394,0.000010609666],"about_ca_topic_score_codex":0.011246767,"about_ca_topic_score_gemma":0.017938225,"teacher_disagreement_score":0.011246767,"about_ca_system_score_codex":0.0004776207,"about_ca_system_score_gemma":0.00037971977,"threshold_uncertainty_score":0.02236259},"labels":[],"label_agreement":null},{"id":"W3196037996","doi":"10.3390/s21165547","title":"Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Motion planning; Global Positioning System; Computer science; Nonlinear system; Obstacle avoidance; Planner; Feedback linearization; Control engineering; Drone; Path (computing); Computation; Linearization; Control theory (sociology); Robot; Artificial intelligence; Engineering; Mobile robot; Control (management); Algorithm","score_opus":0.033027708570848247,"score_gpt":0.26957752315714023,"score_spread":0.23654981458629198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196037996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02056452,0.00011014357,0.9768703,0.00008362891,0.000010162585,0.00002308105,0.000019217854,0.00025506606,0.0020639508],"genre_scores_gemma":[0.8748402,0.00016968582,0.123119466,0.000035442055,0.000010738076,0.000085434534,0.00005569515,0.00003898775,0.0016443096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989164,0.000029855328,0.0000039264205,0.000019980214,0.000041540643,0.000013005812],"domain_scores_gemma":[0.9997937,0.00012362943,0.000030140991,0.000014043891,0.000028139486,0.000010328587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002826893,0.00041391427,0.00034498112,0.00021673534,0.00026281876,0.00032649896,0.00046476827,0.00032785485,0.00070215634],"category_scores_gemma":[0.0006609717,0.00023233345,0.00023751319,0.00022490561,0.0004723931,0.00044982388,0.0004363315,0.00044988244,0.00011710054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020689591,0.000008218982,0.00011372474,0.00002036275,0.0000044772337,0.000022692037,0.000032551932,0.98140705,0.0012114042,0.0020344725,0.00015717035,0.01496728],"study_design_scores_gemma":[0.000004162268,0.000014859612,0.00004082944,0.0000015769526,0.0000015736505,0.000004489789,0.0000058191463,0.9982027,0.00035617387,0.001205653,0.00016022872,0.0000018743697],"about_ca_topic_score_codex":0.009781908,"about_ca_topic_score_gemma":0.011202623,"teacher_disagreement_score":0.009781908,"about_ca_system_score_codex":0.0005559082,"about_ca_system_score_gemma":0.0008614669,"threshold_uncertainty_score":0.01944995},"labels":[],"label_agreement":null},{"id":"W3196085244","doi":"10.3390/s21175743","title":"Image Generation for 2D-CNN Using Time-Series Signal Features from Foot Gesture Applied to Select Cobot Operating Mode","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Cegep de Sept Iles; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation","keywords":"Computer science; Artificial intelligence; Gesture; Gesture recognition; Convolutional neural network; Computer vision; Robot; Robotics; Modality (human–computer interaction); Representation (politics); Inertial measurement unit; Pattern recognition (psychology)","score_opus":0.02444352356204602,"score_gpt":0.26723495163075356,"score_spread":0.24279142806870754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196085244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14642005,0.00028628283,0.8424917,0.0001945065,0.00025579054,0.00017752817,0.0010115941,0.004588505,0.00457406],"genre_scores_gemma":[0.7380557,0.00027877232,0.25331476,0.00011381994,0.000044698183,0.00024377457,0.0015147362,0.00020361408,0.006230204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999366,0.0000031440022,0.0000026517048,0.000022724807,0.000017708262,0.000017190629],"domain_scores_gemma":[0.99994683,0.000010638587,0.0000070838532,0.0000094989255,0.000020512787,0.0000055088453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011134953,0.00075763435,0.00025561132,0.00039972737,0.00013882357,0.0003184097,0.00043066422,0.00035074892,0.0034207138],"category_scores_gemma":[0.00033971254,0.00022142379,0.00043637346,0.00036423065,0.00015604855,0.00030534662,0.0003313039,0.0003957696,0.0007699061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003603499,0.0001195161,0.002665467,0.00017995916,0.00008525927,0.00033888593,0.00010634453,0.10739432,0.26081046,0.0022169761,0.005619452,0.620103],"study_design_scores_gemma":[0.000008653909,0.000088267676,0.0032001196,0.0000092268965,0.000020857977,0.00009342853,0.000025004523,0.93183666,0.061688464,0.0008164582,0.0021997488,0.000013106963],"about_ca_topic_score_codex":0.005303208,"about_ca_topic_score_gemma":0.007377392,"teacher_disagreement_score":0.005303208,"about_ca_system_score_codex":0.00042534072,"about_ca_system_score_gemma":0.00035912567,"threshold_uncertainty_score":0.011443436},"labels":[],"label_agreement":null},{"id":"W3196338472","doi":"10.3390/s21175961","title":"Multi-Excitation Infrared Fusion for Impact Evaluation of Aluminium-BFRP/GFRP Hybrid Composites","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Università degli Studi dell'Aquila; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Materials science; Thermography; Composite material; Fibre-reinforced plastic; Deformation (meteorology); Aluminium; Composite number; Fusion; Glass fiber; Infrared; Optics","score_opus":0.027154008780065412,"score_gpt":0.29484500403575087,"score_spread":0.26769099525568546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196338472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9618185,0.0009230212,0.036059342,0.00003507627,0.0000114839295,0.00002014841,0.00007905637,0.00018503693,0.0008683307],"genre_scores_gemma":[0.9852367,0.00022841903,0.014204688,0.000007732483,0.000003282944,0.000008961544,0.000029447534,0.000009658095,0.00027109345],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981004,0.000020504584,0.0000056316076,0.000027142874,0.00011528755,0.000021428332],"domain_scores_gemma":[0.9998324,0.000043781612,0.000052292344,0.000015347408,0.000046530182,0.000009644389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035200696,0.0003088423,0.00021472818,0.0009374602,0.00016476902,0.00020031596,0.00019550779,0.00044776272,0.0004846938],"category_scores_gemma":[0.00027947273,0.00019403212,0.00026810172,0.00039737253,0.00027022578,0.0004017243,0.00027389423,0.00023708376,0.00009064615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010273878,0.000012981225,0.0007678908,0.00005733892,0.0000051860584,0.000054073793,0.000048122994,0.0016351982,0.9882235,0.000070471935,0.00003133792,0.008991185],"study_design_scores_gemma":[0.00000435428,0.0002222367,0.018450877,0.000012896519,0.000029464849,0.00035282958,0.00009750734,0.03575736,0.94450253,0.000088647095,0.0004587971,0.000022413346],"about_ca_topic_score_codex":0.00049738487,"about_ca_topic_score_gemma":0.0010575146,"teacher_disagreement_score":0.0009374602,"about_ca_system_score_codex":0.00023112335,"about_ca_system_score_gemma":0.000105813306,"threshold_uncertainty_score":0.0018616319},"labels":[],"label_agreement":null},{"id":"W3196886157","doi":"10.3390/s21175766","title":"Computer Game-Based Telerehabilitation Platform Targeting Manual Dexterity: Exercise Is Fun. “You Are Kidding—Right?”","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Telerehabilitation; Task (project management); Rehabilitation; Computer science; Physical medicine and rehabilitation; Modalities; Human–computer interaction; Variety (cybernetics); Software; Medicine; Telemedicine; Health care; Physical therapy; Engineering; Artificial intelligence; Systems engineering","score_opus":0.013391669798173302,"score_gpt":0.26687544023013715,"score_spread":0.25348377043196385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196886157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42763793,0.0016586634,0.4957055,0.0018767813,0.0006772993,0.0032475782,0.0027568592,0.016202928,0.050236434],"genre_scores_gemma":[0.67909205,0.0014238966,0.276068,0.0016996268,0.0001405853,0.0027879658,0.0023920033,0.00049928064,0.03589655],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.999861,0.00003258681,0.000010067705,0.000030109462,0.000042168336,0.000024069197],"domain_scores_gemma":[0.99973494,0.00008503691,0.00002595216,0.000023689234,0.000069068294,0.00006126516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027911007,0.00057415874,0.0002521372,0.0003720123,0.00011287377,0.0004643633,0.0007504334,0.00036954173,0.006794818],"category_scores_gemma":[0.0008892475,0.00012838481,0.0002984469,0.00013128393,0.00020851038,0.00045759283,0.0005102387,0.00030268903,0.0017759706],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001797172,0.0018991634,0.010749227,0.0013449964,0.00021227138,0.0010095811,0.0007201972,0.004526489,0.23971416,0.0052383803,0.042794246,0.68999404],"study_design_scores_gemma":[0.0013286168,0.01599752,0.15191999,0.0010339657,0.0009517747,0.010588954,0.0010171651,0.16084857,0.2266037,0.010343016,0.41886187,0.0005048211],"about_ca_topic_score_codex":0.00089165673,"about_ca_topic_score_gemma":0.001980007,"teacher_disagreement_score":0.006794818,"about_ca_system_score_codex":0.00014064551,"about_ca_system_score_gemma":0.00028204752,"threshold_uncertainty_score":0.022730887},"labels":[],"label_agreement":null},{"id":"W3197158243","doi":"10.3390/s21175778","title":"TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Wuhan Institute of Technology","keywords":"Point cloud; Rigid transformation; Artificial intelligence; Embedding; Invariant (physics); Singular value decomposition; Computer science; Translation (biology); Image registration; Rotation (mathematics); Algorithm; Transformation (genetics); Feature extraction; Mean squared error; Computer vision; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.008711257501875996,"score_gpt":0.20585077292645385,"score_spread":0.19713951542457786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197158243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015774319,0.00038665484,0.9536392,0.00015882809,0.00015808384,0.00021535641,0.0013396847,0.026795482,0.0015324124],"genre_scores_gemma":[0.13495357,0.00038929365,0.84639966,0.00028263193,0.00008363998,0.0004813135,0.011426214,0.002285927,0.0036977886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865854,0.000099626304,0.0000703545,0.00044636257,0.00056839874,0.00015674136],"domain_scores_gemma":[0.99944633,0.00006480575,0.00007151,0.00022418184,0.00016247059,0.000030725903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091799675,0.0022162874,0.0017179365,0.002686738,0.00070826913,0.0013792648,0.0035316867,0.0018954067,0.0045949887],"category_scores_gemma":[0.002823829,0.000852656,0.0027172603,0.0028077185,0.0007347829,0.0021429402,0.0028994896,0.0023728125,0.00476973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003517882,0.00022996795,0.002886468,0.0002633341,0.00029129756,0.0003104995,0.00017331565,0.07514721,0.026291747,0.0052877977,0.039708722,0.8490578],"study_design_scores_gemma":[0.000106510306,0.00018583382,0.0025143377,0.00003998996,0.00006362982,0.0007819629,0.000101531616,0.93286425,0.03520136,0.008096033,0.019948196,0.00009636189],"about_ca_topic_score_codex":0.010332652,"about_ca_topic_score_gemma":0.012717792,"teacher_disagreement_score":0.010332652,"about_ca_system_score_codex":0.0007689155,"about_ca_system_score_gemma":0.0016324967,"threshold_uncertainty_score":0.020545006},"labels":[],"label_agreement":null},{"id":"W3197420377","doi":"10.3390/s21175858","title":"An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Case Western Reserve University; University of Washington; York University; Johns Hopkins University; National Heart, Lung, and Blood Institute; University of California, Davis; Akademia Górniczo-Hutnicza im. Stanislawa Staszica; University of Minnesota","keywords":"Polysomnography; Hypopnea; Apnea; Sleep apnea; Medicine; Breathing; Apnea–hypopnea index; Obstructive sleep apnea; Computer science; Artificial intelligence; Cardiology; Internal medicine; Anesthesia","score_opus":0.03184273103426783,"score_gpt":0.32309974318487844,"score_spread":0.2912570121506106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197420377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18630713,0.00392503,0.7939222,0.0009048691,0.0005893677,0.00012241838,0.0013460664,0.005294919,0.0075879632],"genre_scores_gemma":[0.9166996,0.00083512254,0.07478302,0.00020897704,0.000087823464,0.00012878976,0.0011037966,0.00006332435,0.0060895653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985886,0.000024828612,0.000010574737,0.000052734595,0.0000273189,0.000025663805],"domain_scores_gemma":[0.9998789,0.00004498158,0.000013960316,0.000008715089,0.00004668205,0.000006693214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032683345,0.0006768707,0.00028790566,0.0003302207,0.00024312412,0.00044335093,0.00078078365,0.0006319977,0.0018733277],"category_scores_gemma":[0.0007461342,0.00024834066,0.00042121718,0.00038590076,0.00017606761,0.00057886145,0.0004261254,0.0008460625,0.00057718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004791081,0.00022972452,0.003968436,0.00026525944,0.00019506119,0.00040436853,0.00017283228,0.4360099,0.044364486,0.002666239,0.006978066,0.5042665],"study_design_scores_gemma":[0.0000040835384,0.00005504109,0.00065116043,0.000013696474,0.00002063234,0.00003591083,0.0000118460775,0.9945692,0.0032367115,0.00081146683,0.0005832818,0.0000070160263],"about_ca_topic_score_codex":0.0068278536,"about_ca_topic_score_gemma":0.0076544313,"teacher_disagreement_score":0.0068278536,"about_ca_system_score_codex":0.0005258234,"about_ca_system_score_gemma":0.00053726847,"threshold_uncertainty_score":0.01357621},"labels":[],"label_agreement":null},{"id":"W3197697358","doi":"10.3390/s21175968","title":"ROBINA: Rotational Orbit-Based Inter-Node Adjustment for Acoustic Routing Path in the Internet of Underwater Things (IoUTs)","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Underwater acoustic communication; Underwater; Computer science; Computer network; Node (physics); Communication source; Equal-cost multi-path routing; Path (computing); The Internet; Routing (electronic design automation); Network packet; Path loss; Bandwidth (computing); Routing protocol; Collision; Population; Real-time computing; Routing table; Wireless; Telecommunications; Computer security; Acoustics; Geography; Physics","score_opus":0.01919590584586194,"score_gpt":0.23090612627728097,"score_spread":0.21171022043141902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197697358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04131186,0.001061164,0.94881594,0.0002659695,0.0003157087,0.00017430967,0.000093066716,0.002588252,0.005373667],"genre_scores_gemma":[0.8043561,0.00069182285,0.19044276,0.00018016365,0.00008223744,0.00021697779,0.00023104656,0.00012271211,0.0036760694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935156,0.00013332177,0.000041978612,0.00012582053,0.00023333743,0.00011392528],"domain_scores_gemma":[0.9992867,0.00018002446,0.00016376228,0.00014731052,0.00016754812,0.00005472464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000605492,0.000802084,0.00047810102,0.00083469844,0.00094735407,0.0006177341,0.0016756554,0.0004794213,0.001240041],"category_scores_gemma":[0.0018876052,0.00022510577,0.0005787158,0.0006979865,0.00068866386,0.0012509885,0.0014183464,0.00079500576,0.0005131598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004714482,0.00015442795,0.0045342627,0.00041573172,0.00015700348,0.0006543735,0.00060323445,0.42422548,0.061600316,0.03174904,0.009412759,0.46602198],"study_design_scores_gemma":[0.000038934693,0.00041158873,0.00086179824,0.000025318763,0.00006430948,0.0005324488,0.00015054335,0.96770436,0.013850722,0.004688075,0.011600966,0.00007104013],"about_ca_topic_score_codex":0.004347345,"about_ca_topic_score_gemma":0.005636013,"teacher_disagreement_score":0.004347345,"about_ca_system_score_codex":0.00058031786,"about_ca_system_score_gemma":0.0010076967,"threshold_uncertainty_score":0.008644104},"labels":[],"label_agreement":null},{"id":"W3198411447","doi":"10.3390/s22030853","title":"Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Kalman filter; Energy consumption; Computer science; Quantization (signal processing); Energy (signal processing); Computation; Filter (signal processing); Reduction (mathematics); Algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Engineering; Statistics","score_opus":0.021895682857355485,"score_gpt":0.25795511357823603,"score_spread":0.23605943072088054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198411447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08911776,0.0004509215,0.9080405,0.00014256734,0.00002858603,0.000024031824,0.000036003657,0.00030240658,0.0018571984],"genre_scores_gemma":[0.9311483,0.0001859,0.06755589,0.000028554648,0.000015450643,0.000042520216,0.000019433082,0.000028219958,0.00097578135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996729,0.00008832949,0.000020540776,0.000047215428,0.00012519969,0.00004582705],"domain_scores_gemma":[0.999368,0.00035683124,0.0000867914,0.00009054921,0.00008630516,0.000011513523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051291334,0.00044948916,0.00047573232,0.0002794578,0.00033840857,0.0006157356,0.0009147475,0.00040039403,0.0009968218],"category_scores_gemma":[0.0021609848,0.00018716318,0.00016434318,0.00047136509,0.00051925937,0.0011206393,0.0005146568,0.00037710206,0.00013511047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023066667,0.00004563971,0.0007362414,0.000081531536,0.00003286744,0.00007265298,0.0000827213,0.88432986,0.019655421,0.02007462,0.00061398966,0.07404372],"study_design_scores_gemma":[0.000011901681,0.000046141977,0.0001365906,0.0000064021506,0.000010690546,0.00001397865,0.000010986605,0.9853898,0.010465378,0.0034068546,0.0004938127,0.0000074678246],"about_ca_topic_score_codex":0.0025946503,"about_ca_topic_score_gemma":0.0031366067,"teacher_disagreement_score":0.0025946503,"about_ca_system_score_codex":0.0006875693,"about_ca_system_score_gemma":0.00053612865,"threshold_uncertainty_score":0.00515908},"labels":[],"label_agreement":null},{"id":"W3198616177","doi":"10.3390/s21175957","title":"A Tutorial on Hardware-Implemented Fault Injection and Online Fault Diagnosis for High-Speed Trains","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Department of Science and Technology of Jilin Province; National Natural Science Foundation of China","keywords":"Train; Fault injection; Viewpoints; Computer science; Fault (geology); Embedded system; Reliability (semiconductor); Transmission (telecommunications); Work (physics); Simulation; Computer hardware; Power (physics); Reliability engineering; Engineering; Software; Telecommunications; Operating system","score_opus":0.018136026655983982,"score_gpt":0.2960380657694979,"score_spread":0.27790203911351397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198616177","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004599332,0.12381578,0.7879794,0.0010351096,0.0040142396,0.00023995058,0.0005064899,0.0043214983,0.07348824],"genre_scores_gemma":[0.10767733,0.24445672,0.4270928,0.0022784816,0.0070479237,0.0005882915,0.0027297242,0.00206325,0.20606548],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956065,0.000050387178,0.00003517572,0.000087006956,0.000228126,0.000038646125],"domain_scores_gemma":[0.9995647,0.00020196746,0.000034015797,0.000040777715,0.00013037425,0.000028270477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003732574,0.0016760294,0.00073422433,0.001441868,0.0003053533,0.0011573556,0.0011235152,0.0013215321,0.01816083],"category_scores_gemma":[0.0009169835,0.0005546266,0.0007385278,0.0014574031,0.0004695417,0.00241152,0.00073530164,0.0017327145,0.00857865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015825004,0.00030324183,0.00059517,0.0039802105,0.0000814801,0.00047386606,0.00033582625,0.02849587,0.042039994,0.05089592,0.07513729,0.797503],"study_design_scores_gemma":[0.00002409703,0.00044768568,0.0014622113,0.0009831799,0.000064947846,0.002041535,0.00010235062,0.055765484,0.017481474,0.023962798,0.8975764,0.00008776841],"about_ca_topic_score_codex":0.0008256713,"about_ca_topic_score_gemma":0.00090034324,"teacher_disagreement_score":0.01816083,"about_ca_system_score_codex":0.00054213457,"about_ca_system_score_gemma":0.0006358012,"threshold_uncertainty_score":0.060754},"labels":[],"label_agreement":null},{"id":"W3198823527","doi":"10.3390/s21175812","title":"One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"RFID technology advancements","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Social Sciences and Humanities Research Council of Canada; Canada Research Chairs","keywords":"Schematic; Comparability; Computer science; Standardization; Process (computing); Open platform; Conceptualization; Field (mathematics); Systems engineering; Software engineering; Engineering; Electrical engineering; Operating system; Software","score_opus":0.02363915947681955,"score_gpt":0.25390090259053166,"score_spread":0.23026174311371211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198823527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4421633,0.013068716,0.48773122,0.0007577751,0.0010888587,0.0012992657,0.0036549692,0.015502244,0.034733713],"genre_scores_gemma":[0.6885458,0.0023176956,0.26895815,0.00058476726,0.00014468099,0.00060044084,0.0024925917,0.00048585777,0.035869986],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992169,0.00010615316,0.000034360266,0.0002109912,0.00037888193,0.000052716117],"domain_scores_gemma":[0.99970156,0.000066958484,0.00007137801,0.000045506866,0.0000856029,0.000028986566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037908342,0.00085159315,0.00069101056,0.001024694,0.00025290935,0.00056114653,0.0011977827,0.0012481802,0.0039235093],"category_scores_gemma":[0.00073971495,0.00037211142,0.0003854816,0.0005491174,0.00035578816,0.000906624,0.0007193602,0.00046025522,0.001813218],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007178722,0.0001386803,0.0026040738,0.0016067149,0.00004434032,0.0006075154,0.00034245302,0.00087966357,0.78159153,0.0025332535,0.00624829,0.20268556],"study_design_scores_gemma":[0.00009016486,0.0033006705,0.019441662,0.00030221514,0.00020378522,0.006945534,0.00035785005,0.018904636,0.7477241,0.0011040921,0.20130162,0.00032364964],"about_ca_topic_score_codex":0.00042721565,"about_ca_topic_score_gemma":0.0008547071,"teacher_disagreement_score":0.0039235093,"about_ca_system_score_codex":0.00023775178,"about_ca_system_score_gemma":0.00028852612,"threshold_uncertainty_score":0.01312542},"labels":[],"label_agreement":null},{"id":"W3198848557","doi":"10.3390/s21175947","title":"Characterisation of SiPM Photon Emission in the Dark","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Snolab; Queen's University; Simon Fraser University; TRIUMF","funders":"Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Silicon photomultiplier; Physics; Photonics; Photon; Photomultiplier; Optics; Biasing; Optoelectronics; Voltage; Detector; Scintillator","score_opus":0.01808948104218054,"score_gpt":0.2929667466393373,"score_spread":0.27487726559715675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198848557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930032,0.00013475647,0.00605223,0.000011577113,0.00000551425,0.000008370807,0.00012854753,0.000046177604,0.0006095976],"genre_scores_gemma":[0.9939599,0.0001472655,0.0042746556,0.000020345453,0.000005088076,0.00003293715,0.00032372822,0.00003630298,0.0011998405],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987626,0.000014199717,0.000005017325,0.000032427062,0.00005003957,0.000022006001],"domain_scores_gemma":[0.9996526,0.00014408537,0.000060660903,0.000054713884,0.000060475482,0.000027511081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017263294,0.00025549828,0.00015234778,0.0003219527,0.00018889901,0.00022784139,0.00032397284,0.00025895718,0.0015472362],"category_scores_gemma":[0.00037090335,0.000092195085,0.00012839584,0.0002294065,0.00025678673,0.00020499965,0.00025707652,0.000313795,0.00021530548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009087647,0.000012379977,0.0017988643,0.00003560413,0.000008790094,0.00014132453,0.00011609207,0.00013337139,0.99470866,0.00019155047,0.00003603176,0.002726536],"study_design_scores_gemma":[0.000004467552,0.00016030832,0.011023937,0.0000046170353,0.000009103182,0.00023494751,0.00006091806,0.0015945296,0.98581123,0.00010550218,0.0009834161,0.000007128739],"about_ca_topic_score_codex":0.00024934305,"about_ca_topic_score_gemma":0.00021140708,"teacher_disagreement_score":0.0015472362,"about_ca_system_score_codex":0.00019046689,"about_ca_system_score_gemma":0.0001020013,"threshold_uncertainty_score":0.0051760674},"labels":[],"label_agreement":null},{"id":"W3199719110","doi":"10.3390/s21186040","title":"Dimensioning of Wide-Area Alternate Wetting and Drying (AWD) System for IoT-Based Automation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Dimensioning; Automation; Scalability; Irrigation; Wireless sensor network; Software deployment; Computer science; Engineering; Real-time computing; Computer network; Database","score_opus":0.014598377816704807,"score_gpt":0.20333581387855074,"score_spread":0.18873743606184593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199719110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16073337,0.00064708927,0.8134029,0.00046606266,0.0003827882,0.00038157738,0.00047735352,0.008622494,0.014886325],"genre_scores_gemma":[0.77920264,0.00031319074,0.21353479,0.00021791608,0.00003848803,0.00019750264,0.0004927102,0.00011478104,0.0058880122],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966955,0.000035822228,0.000026604856,0.00007567621,0.00015257389,0.000039830556],"domain_scores_gemma":[0.9997205,0.000034429024,0.000037655987,0.000083881714,0.00009410447,0.000029395898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024745762,0.00036057012,0.0002883743,0.0003615754,0.00024519127,0.00042063338,0.0006973336,0.0003138997,0.0023861614],"category_scores_gemma":[0.00037662763,0.00017880193,0.0003373358,0.00026552405,0.00020704675,0.0007940585,0.000589692,0.00032293552,0.0005731666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003152423,0.00020109503,0.008132389,0.0004108272,0.00006047488,0.0005790032,0.00032608616,0.020905176,0.5572542,0.005430708,0.011286619,0.3950982],"study_design_scores_gemma":[0.00014085889,0.0011942988,0.030410267,0.000099435776,0.00013932833,0.001499972,0.00031255526,0.32710814,0.4792844,0.004498414,0.15511622,0.000196079],"about_ca_topic_score_codex":0.00093088264,"about_ca_topic_score_gemma":0.001217571,"teacher_disagreement_score":0.0023861614,"about_ca_system_score_codex":0.00032435422,"about_ca_system_score_gemma":0.00033947828,"threshold_uncertainty_score":0.007982492},"labels":[],"label_agreement":null},{"id":"W3200315881","doi":"10.3390/s21186250","title":"Simultaneous Absorbance and Fluorescence Measurements Using an Inlaid Microfluidic Approach","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Marine Environmental Observation Prediction and Response Network","keywords":"Absorbance; Fluorescence; Microfluidics; Rhodamine B; Materials science; Rhodamine; Lab-on-a-chip; Opacity; Polycarbonate; Methyl methacrylate; Methacrylate; Fabrication; Fluidics; Analytical Chemistry (journal); Optoelectronics; Optics; Nanotechnology; Chemistry; Chromatography; Polymer","score_opus":0.023671948190547614,"score_gpt":0.22735091109192782,"score_spread":0.2036789629013802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200315881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49122265,0.006957812,0.48825696,0.0010882956,0.0010903983,0.0005202917,0.0014266301,0.0024754107,0.0069615715],"genre_scores_gemma":[0.559135,0.0046942835,0.4258597,0.00053478393,0.00028017786,0.00081936806,0.00072453584,0.00008963345,0.007862543],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877805,0.00016752626,0.00008574252,0.00032913763,0.0005033695,0.00013614309],"domain_scores_gemma":[0.99949193,0.00014627811,0.00011063892,0.000038359056,0.0001439076,0.000068839916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065377535,0.0009833041,0.00088502833,0.0008505385,0.0006261241,0.0011497288,0.0010393898,0.0009876672,0.00070273073],"category_scores_gemma":[0.00082263915,0.0005278509,0.00027683645,0.0004890749,0.0003883598,0.0007434524,0.0009925715,0.0012227318,0.00047716993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004670918,0.000029749966,0.00011391067,0.00005079691,0.000005678086,0.00002294291,0.000026661748,0.00007734937,0.99487793,0.00024583898,0.00013172084,0.0043705986],"study_design_scores_gemma":[0.000008289227,0.000087348235,0.00037800192,0.000004567276,0.000008760322,0.0001347463,0.000017213573,0.0025815992,0.99355984,0.00013649478,0.0030607732,0.000022343806],"about_ca_topic_score_codex":0.00063989934,"about_ca_topic_score_gemma":0.0014152756,"teacher_disagreement_score":0.0011497288,"about_ca_system_score_codex":0.0007968229,"about_ca_system_score_gemma":0.0009242046,"threshold_uncertainty_score":0.005781412},"labels":[],"label_agreement":null},{"id":"W3200483757","doi":"10.3390/s21186129","title":"Development and Validation of a Railway Safety System for Nordic Trains in Isolated Territories of Northern Quebec Based on IEEE 802.15.4 Protocol","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Rimouski","funders":"","keywords":"Train; Context (archaeology); Transport engineering; Telecommunications; Track (disk drive); Intelligent transportation system; Computer science; Engineering; Geography; Archaeology","score_opus":0.009907278438614962,"score_gpt":0.21539322460771507,"score_spread":0.2054859461691001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200483757","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.670424,0.0004267806,0.28062537,0.0006299221,0.00028727236,0.0030312985,0.0013156153,0.014130973,0.029128816],"genre_scores_gemma":[0.93536067,0.00015041021,0.048602633,0.00015465761,0.0000144877185,0.00046867903,0.0012071235,0.00013480434,0.013906507],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904615,0.000116702984,0.000046297624,0.00015442922,0.0004663136,0.000170066],"domain_scores_gemma":[0.99872893,0.00007709853,0.00007815672,0.00008755976,0.00094611006,0.000082099294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012522336,0.00070401974,0.00028680265,0.00057621946,0.000706513,0.0008213472,0.0013755013,0.00058502425,0.0023059235],"category_scores_gemma":[0.0011634501,0.00015069639,0.00024343678,0.00027450858,0.0004974358,0.00049949624,0.0004483528,0.00046649328,0.00066614663],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011665716,0.0013355666,0.05540472,0.0007639876,0.00025979243,0.002874964,0.0023730139,0.09310534,0.46844125,0.0056390697,0.021307386,0.3473284],"study_design_scores_gemma":[0.0004077844,0.0027354476,0.09256075,0.00021794427,0.00032763864,0.00083612575,0.0015294504,0.61116797,0.21022381,0.0004911225,0.07927411,0.00022782301],"about_ca_topic_score_codex":0.30390245,"about_ca_topic_score_gemma":0.27079365,"teacher_disagreement_score":0.69609755,"about_ca_system_score_codex":0.0029078934,"about_ca_system_score_gemma":0.005929863,"threshold_uncertainty_score":0.6042671},"labels":[],"label_agreement":null},{"id":"W3200605617","doi":"10.3390/s21186297","title":"Energy Harvesting Materials and Structures for Smart Textile Applications: Recent Progress and Path Forward","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Council Canada","keywords":"Energy harvesting; Triboelectric effect; Textile; Photovoltaic system; Materials science; Nanotechnology; Electronics; Wearable technology; Engineering physics; Electrical engineering; Wearable computer; Energy (signal processing); Computer science; Engineering; Composite material","score_opus":0.026949112270937128,"score_gpt":0.283588731291799,"score_spread":0.2566396190208619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200605617","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039056395,0.9969855,0.00040558388,0.00025169196,0.00013232294,0.0000049558175,0.000012766571,0.000009034821,0.0018074786],"genre_scores_gemma":[0.0016894535,0.9961026,0.00064419,0.00017249196,0.00011619712,0.0000070500546,0.000019817184,0.0000029593382,0.0012453003],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99983,0.000020796957,0.000018035327,0.000037802314,0.00007187856,0.000021530856],"domain_scores_gemma":[0.999701,0.00015903336,0.00004534577,0.000010261127,0.000064737134,0.000019537636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005903246,0.00082384056,0.00079602725,0.0016566159,0.0002777533,0.0011063495,0.0005867383,0.0011212059,0.0032223326],"category_scores_gemma":[0.0005051693,0.0004967523,0.00044995383,0.0021485067,0.00037538257,0.001782899,0.00060734496,0.0014935114,0.0018372157],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043794902,0.00012417357,0.00022481692,0.023553647,0.00004820735,0.00022451952,0.00013697811,0.00067040976,0.013085964,0.013649953,0.013975398,0.93426204],"study_design_scores_gemma":[0.0000056665162,0.00013009159,0.00046340685,0.0023660304,0.000053728683,0.00081254763,0.0000949598,0.00025542817,0.0034128965,0.003483004,0.9888998,0.00002227541],"about_ca_topic_score_codex":0.00048248874,"about_ca_topic_score_gemma":0.0010008523,"teacher_disagreement_score":0.0032223326,"about_ca_system_score_codex":0.00037218645,"about_ca_system_score_gemma":0.00058959244,"threshold_uncertainty_score":0.010779798},"labels":[],"label_agreement":null},{"id":"W3201628757","doi":"10.3390/s21186179","title":"The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Sherbrooke","funders":"","keywords":"Inertial measurement unit; Gait; Treadmill; Cadence; Preferred walking speed; STRIDE; Motion capture; Physical medicine and rehabilitation; Gait analysis; Simulation; Medicine; Physical therapy; Computer science; Artificial intelligence; Motion (physics)","score_opus":0.012082068718608494,"score_gpt":0.2941151626397499,"score_spread":0.2820330939211414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201628757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99136996,0.00050383253,0.0075459294,0.000015097357,0.000019542313,0.000030124604,0.00019192745,0.000051651597,0.00027198045],"genre_scores_gemma":[0.99468356,0.0001449195,0.004792692,0.000020440928,0.000015085885,0.000018427398,0.000205444,0.0000054130533,0.000113969465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99893636,0.0002966662,0.0001689594,0.00025088477,0.0002853569,0.00006176104],"domain_scores_gemma":[0.9980107,0.00067920034,0.00046049632,0.00018849269,0.0005807118,0.00008043535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013563372,0.0005271314,0.0005246736,0.001137151,0.00014703334,0.00053017336,0.00029500914,0.0007262359,0.0004090772],"category_scores_gemma":[0.005600123,0.00019855244,0.00033708132,0.00048413672,0.00021645159,0.00037672103,0.0004648008,0.00019502817,0.00023322101],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028201125,0.00038018567,0.8058378,0.0005452079,0.00062442553,0.00043304078,0.00087330816,0.0032553042,0.05571403,0.000056937042,0.00028787376,0.12917171],"study_design_scores_gemma":[0.000051054874,0.001163043,0.9790518,0.000046146673,0.00022446799,0.0013732116,0.00047743274,0.011685085,0.005513801,0.000052293228,0.00032525187,0.000036442278],"about_ca_topic_score_codex":0.0011826691,"about_ca_topic_score_gemma":0.0023373787,"teacher_disagreement_score":0.0013563372,"about_ca_system_score_codex":0.00011177975,"about_ca_system_score_gemma":0.00014177116,"threshold_uncertainty_score":0.0071730614},"labels":[],"label_agreement":null},{"id":"W3202328561","doi":"10.3390/s21196419","title":"Static Attitude Determination Using Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Quaternion; Singular value decomposition; Convolutional neural network; Estimator; Computer science; Algorithm; Artificial intelligence; Orientation (vector space); Artificial neural network; Noise (video); Mathematics; Statistics; Image (mathematics)","score_opus":0.014613187114758872,"score_gpt":0.2404750566686975,"score_spread":0.22586186955393864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202328561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087433055,0.0009633944,0.9059628,0.00019155665,0.00009462823,0.000024319232,0.00020476943,0.0017975445,0.0033279404],"genre_scores_gemma":[0.9323455,0.00041465656,0.062940136,0.00006572217,0.00003949037,0.000026442636,0.0003790781,0.000059587128,0.0037294757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985087,0.000016750726,0.0000070785004,0.000052968546,0.00004216654,0.000030159219],"domain_scores_gemma":[0.999723,0.00009165227,0.000044288536,0.000035680227,0.000092290284,0.0000131080205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032813818,0.00061949523,0.00039128104,0.00050240185,0.00021069293,0.0005145577,0.0006581317,0.00047712514,0.0009975415],"category_scores_gemma":[0.0009100184,0.00039239644,0.00040458926,0.00057652185,0.00027122366,0.00064025074,0.0005221631,0.0005855073,0.0003199461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006135266,0.000025596772,0.0010272936,0.000034585828,0.00005303592,0.00004787715,0.000022438777,0.84511876,0.006866236,0.0022720597,0.000806989,0.1436638],"study_design_scores_gemma":[6.3522e-7,0.000005855831,0.00020399923,0.000002254999,0.0000035104624,0.00000458416,0.0000015998527,0.99829084,0.000806401,0.00050682755,0.00017123354,0.0000021346034],"about_ca_topic_score_codex":0.025703877,"about_ca_topic_score_gemma":0.022886103,"teacher_disagreement_score":0.025703877,"about_ca_system_score_codex":0.0007256274,"about_ca_system_score_gemma":0.00064382,"threshold_uncertainty_score":0.05110854},"labels":[],"label_agreement":null},{"id":"W3202962879","doi":"10.3390/s21196507","title":"HEAD Metamodel: Hierarchical, Extensible, Advanced, and Dynamic Access Control Metamodel for Dynamic and Heterogeneous Structures","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Access Control and Trust","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Rimouski","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Metamodeling; Distributed computing; Cloud computing; Honeypot; Software engineering; Computer security; Operating system","score_opus":0.02041968706409162,"score_gpt":0.35509672069705,"score_spread":0.33467703363295837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202962879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007296382,0.000248147,0.9745095,0.000650762,0.00012436451,0.00063453754,0.0014137547,0.0058688545,0.009253632],"genre_scores_gemma":[0.14647667,0.000991873,0.82515466,0.0011009134,0.00011851261,0.0017881999,0.005947184,0.0020428312,0.016379135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99639684,0.0009066487,0.0006518517,0.0005993708,0.0010163256,0.00042907093],"domain_scores_gemma":[0.9969375,0.0010015573,0.00024330473,0.0010499607,0.00057643774,0.00019136148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004501879,0.0011772936,0.0007777065,0.0019706,0.0012810633,0.004364176,0.0025811202,0.0026465433,0.0042208596],"category_scores_gemma":[0.005664744,0.0010893688,0.0030489147,0.001159603,0.0024810939,0.0061369836,0.0032547212,0.0037921348,0.0016754024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025796302,0.00030310193,0.0035550836,0.0007184967,0.00013932394,0.0013640049,0.0030468202,0.075918205,0.014828144,0.8080396,0.016371792,0.075457476],"study_design_scores_gemma":[0.00016169489,0.00019505207,0.0009754902,0.0007776411,0.00024150763,0.0011512244,0.0007674204,0.2316269,0.01942506,0.30290857,0.4415425,0.00022689166],"about_ca_topic_score_codex":0.014027112,"about_ca_topic_score_gemma":0.016794818,"teacher_disagreement_score":0.014027112,"about_ca_system_score_codex":0.002090986,"about_ca_system_score_gemma":0.005616885,"threshold_uncertainty_score":0.02789092},"labels":[],"label_agreement":null},{"id":"W3204143339","doi":"10.3390/s21196463","title":"Design and Optimization of a Linear Wavenumber Spectrometer with Cylindrical Optics for Line Scanning Optical Coherence Tomography","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optics; Spectrometer; Zemax; Imaging spectrometer; Physics; Prism; Optical coherence tomography; Grating; Wavenumber; Hyperspectral imaging; Computer science","score_opus":0.01802904688062293,"score_gpt":0.23912704946340083,"score_spread":0.2210980025827779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204143339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10777514,0.0008124583,0.8820288,0.0004333435,0.00009182481,0.00039400536,0.0004030578,0.0017698549,0.006291554],"genre_scores_gemma":[0.27585822,0.00036240835,0.7206565,0.00011443165,0.000019060486,0.00032632076,0.00023094633,0.00013897309,0.0022931613],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994087,0.00007662022,0.000030058667,0.0001299598,0.000304433,0.00005026914],"domain_scores_gemma":[0.99938035,0.00008896355,0.00018988266,0.000044303302,0.00024437893,0.00005209617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007157947,0.00083887344,0.00042485038,0.00044735658,0.0003249934,0.0006516193,0.0008249592,0.00059188885,0.0012716588],"category_scores_gemma":[0.00064076297,0.00042225403,0.0004411175,0.0004005521,0.00039301484,0.0005626563,0.00046904734,0.0003145411,0.00069469895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027450593,0.00012245795,0.0031772372,0.00050139043,0.00007301978,0.0004977425,0.0001927324,0.03865158,0.88200647,0.0059492183,0.0020966846,0.06645711],"study_design_scores_gemma":[0.00016160829,0.0014795393,0.004725752,0.000065735825,0.00014559198,0.001294214,0.00014867395,0.44037414,0.5133348,0.0012293597,0.036824364,0.00021620258],"about_ca_topic_score_codex":0.0012988162,"about_ca_topic_score_gemma":0.0017825774,"teacher_disagreement_score":0.0012988162,"about_ca_system_score_codex":0.0009017478,"about_ca_system_score_gemma":0.0013501014,"threshold_uncertainty_score":0.006542623},"labels":[],"label_agreement":null},{"id":"W3204676295","doi":"10.3390/s21196446","title":"A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Short-time Fourier transform; Initialization; Computer science; Artificial intelligence; Latency (audio); Convolutional neural network; Time–frequency analysis; Deep learning; Continuous wavelet transform; Wearable computer; Gait; Pattern recognition (psychology); Wavelet transform; Discrete wavelet transform; Real-time computing; Speech recognition; Wavelet; Fourier transform; Embedded system; Radar; Fourier analysis; Physical medicine and rehabilitation; Telecommunications","score_opus":0.018309079620926046,"score_gpt":0.2592286735701982,"score_spread":0.24091959394927215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204676295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6541463,0.007443496,0.33055967,0.0003938071,0.00044166975,0.00014270014,0.0008760638,0.00228762,0.0037086688],"genre_scores_gemma":[0.8734153,0.0020817092,0.12075888,0.0000758842,0.00010762878,0.00007321511,0.0015827267,0.000084679086,0.0018199177],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994937,0.00010533545,0.00005189826,0.000134405,0.00015071723,0.000063858235],"domain_scores_gemma":[0.9986313,0.0007742285,0.00011141151,0.00010355165,0.00032268593,0.000056767814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011919655,0.0009046601,0.0006094702,0.0015026197,0.00020249924,0.00056043133,0.00043111647,0.0006667852,0.000793204],"category_scores_gemma":[0.0033901215,0.00016097625,0.0006559385,0.0010421611,0.00011936798,0.00088881474,0.00038245067,0.0005808519,0.0003393395],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009062962,0.0003244465,0.011091505,0.0001769827,0.00029851182,0.0001268178,0.00009528218,0.049198244,0.01823073,0.0004404047,0.0021194615,0.9169913],"study_design_scores_gemma":[0.00003082951,0.0006573099,0.020741863,0.000044033175,0.00015434732,0.00026229734,0.00009658117,0.9659529,0.010325465,0.00044374538,0.0012525457,0.000038090424],"about_ca_topic_score_codex":0.0050093145,"about_ca_topic_score_gemma":0.004347698,"teacher_disagreement_score":0.0050093145,"about_ca_system_score_codex":0.00025230073,"about_ca_system_score_gemma":0.00034357913,"threshold_uncertainty_score":0.009960353},"labels":[],"label_agreement":null},{"id":"W3204715910","doi":"10.3390/s21196646","title":"Alteration in HDEMG Spatial Parameters of Trunk Muscle Due to Handle Design during Pushing","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"New Brunswick Innovation Foundation","keywords":"Trunk; Computer science; Physical medicine and rehabilitation; Engineering; Medicine; Biology; Ecology","score_opus":0.01507611885093136,"score_gpt":0.20931877527721188,"score_spread":0.19424265642628052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204715910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99872214,0.00007530802,0.0008123355,0.000008056215,0.000004678691,0.000015774536,0.00008268719,0.000008196851,0.00027069455],"genre_scores_gemma":[0.9982887,0.000062823136,0.0010357043,0.000015745167,0.000006250422,0.000031140335,0.00013395702,0.000004745679,0.00042081435],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998223,0.000031066582,0.000012019137,0.000046213212,0.00005738233,0.000031036026],"domain_scores_gemma":[0.9997037,0.0000881834,0.000106649524,0.000019799238,0.000049022885,0.00003261473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021126402,0.00032134063,0.0001810987,0.00020492324,0.00012763911,0.00020915532,0.00011379573,0.00025520247,0.0016599566],"category_scores_gemma":[0.00085755135,0.00015120766,0.00015467277,0.00013674892,0.00019309051,0.00015281147,0.0003016841,0.00013907757,0.0002153671],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022577764,0.00026444255,0.17964005,0.00027109793,0.000120714074,0.0005152223,0.0009409047,0.00028777518,0.77219677,0.0000342185,0.00020179187,0.04326924],"study_design_scores_gemma":[0.000009715085,0.0008105009,0.9893959,0.000009324481,0.000017345426,0.00030504056,0.00031975235,0.00027116772,0.008637649,0.00001985006,0.00019776227,0.0000059085064],"about_ca_topic_score_codex":0.00063945557,"about_ca_topic_score_gemma":0.0017166522,"teacher_disagreement_score":0.0016599566,"about_ca_system_score_codex":0.000069182235,"about_ca_system_score_gemma":0.000071882816,"threshold_uncertainty_score":0.0055531263},"labels":[],"label_agreement":null},{"id":"W3204823025","doi":"10.3390/s21196453","title":"Identification of Distributed Denial of Services Anomalies by Using Combination of Entropy and Sequential Probabilities Ratio Test Methods","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Denial-of-service attack; Computer science; Entropy (arrow of time); Network packet; Confusion; False positive rate; Data mining; Conditional entropy; Algorithm; Computer security; Principle of maximum entropy; Artificial intelligence; The Internet; Operating system","score_opus":0.013670085581864316,"score_gpt":0.2753907659335646,"score_spread":0.26172068035170026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204823025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31248364,0.00041693405,0.6760755,0.0001670871,0.0000901393,0.00027308453,0.0006689582,0.007159378,0.0026652804],"genre_scores_gemma":[0.77032894,0.0001283418,0.22776812,0.000048371825,0.00005499876,0.00012468494,0.00065776985,0.00017994145,0.0007088235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485624,0.00132056,0.0005001031,0.0008951293,0.0021865917,0.00024141216],"domain_scores_gemma":[0.9827221,0.010578928,0.0026060557,0.0008470332,0.0029073707,0.00033843753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034036266,0.0012960779,0.0013467341,0.009367065,0.00031054506,0.0012505017,0.0011273759,0.0006969928,0.0008053558],"category_scores_gemma":[0.013417729,0.0003445939,0.00097629084,0.0026649893,0.0004691887,0.0015843131,0.0008945994,0.00055126066,0.00039392975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013109413,0.000740525,0.1358291,0.00041837868,0.000752615,0.0010650343,0.000396542,0.07603754,0.06527314,0.0024337377,0.0021936598,0.7135487],"study_design_scores_gemma":[0.00005813071,0.00043195963,0.033185497,0.000026918422,0.00014200842,0.0013352828,0.00010110184,0.9249165,0.035036206,0.003266829,0.0013768503,0.00012274002],"about_ca_topic_score_codex":0.0014582478,"about_ca_topic_score_gemma":0.0012653255,"teacher_disagreement_score":0.009367065,"about_ca_system_score_codex":0.00045459773,"about_ca_system_score_gemma":0.00061698805,"threshold_uncertainty_score":0.018000364},"labels":[],"label_agreement":null},{"id":"W3205137388","doi":"10.3390/s21216997","title":"Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activity recognition; Accelerometer; Random forest; Artificial intelligence; Computer science; Photoplethysmogram; Pattern recognition (psychology); Feature selection; Classifier (UML); Curse of dimensionality; Inertial measurement unit; Support vector machine; Speech recognition; Machine learning; Computer vision","score_opus":0.39542541375855733,"score_gpt":0.3934748632998047,"score_spread":0.0019505504587526423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205137388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9657766,0.0052998113,0.02258596,0.0001644791,0.0002355156,0.0001189662,0.0014084856,0.0006901175,0.0037199147],"genre_scores_gemma":[0.98576885,0.0011127199,0.008913508,0.00007026023,0.00012551091,0.000056946672,0.0028335957,0.00004184801,0.0010767457],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99820447,0.00042670034,0.0001910744,0.00056844245,0.00046406867,0.0001451916],"domain_scores_gemma":[0.9970523,0.00149719,0.00027107674,0.00024254595,0.00072690856,0.00020987079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023522715,0.00082553324,0.0009083758,0.0018056183,0.00018039091,0.00081780064,0.00037077238,0.0008026766,0.00085097266],"category_scores_gemma":[0.0046467483,0.000108707885,0.0006652479,0.0010538413,0.0002549449,0.00091343536,0.00053148536,0.00033158605,0.0006091975],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046096025,0.00131776,0.22749437,0.0015718518,0.0018236495,0.0006375949,0.00051045936,0.013317936,0.07387303,0.00033977506,0.0044209175,0.67008305],"study_design_scores_gemma":[0.00014208061,0.0062476224,0.822182,0.00015768099,0.0011350779,0.0026767435,0.0011064025,0.12618552,0.034166053,0.00052410713,0.005362723,0.00011401396],"about_ca_topic_score_codex":0.0013552365,"about_ca_topic_score_gemma":0.0022176767,"teacher_disagreement_score":0.0023522715,"about_ca_system_score_codex":0.00014721641,"about_ca_system_score_gemma":0.00017892472,"threshold_uncertainty_score":0.012440145},"labels":[],"label_agreement":null},{"id":"W3205417097","doi":"10.3390/s21216974","title":"Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Accelerometer; Computer science; Gyroscope; Decision tree; Artificial intelligence; STRIDE; Gait; Foot (prosody); Gait analysis; Step detection; Computer vision; Simulation; Engineering; Physical medicine and rehabilitation; Medicine; Filter (signal processing)","score_opus":0.0857416529600927,"score_gpt":0.35383915263727855,"score_spread":0.2680974996771859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205417097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5942501,0.0070661195,0.3849536,0.0019986923,0.00065425,0.00045263072,0.0018420896,0.002510259,0.0062723537],"genre_scores_gemma":[0.9077886,0.0011270904,0.0865539,0.00061118347,0.00011587869,0.00022933897,0.0016199423,0.00007122894,0.0018828773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838746,0.00066458934,0.0001551612,0.00036270107,0.00026608177,0.00016400749],"domain_scores_gemma":[0.992683,0.0051630223,0.00035178039,0.00024556814,0.0012666039,0.0002900899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004501178,0.0012824776,0.0012216165,0.0021821517,0.00046370464,0.0012922868,0.0012215739,0.0013771856,0.0019443635],"category_scores_gemma":[0.012816398,0.0003443256,0.0012283273,0.0011396583,0.0002236555,0.001868906,0.00082393474,0.0014686392,0.00097599975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028117285,0.0010574898,0.05618165,0.0005391701,0.000940253,0.00028129554,0.00047401153,0.1837107,0.002453225,0.0016735708,0.006983834,0.7428931],"study_design_scores_gemma":[0.00006889915,0.00049742375,0.00850593,0.00009695723,0.00013400888,0.00011214429,0.00017330266,0.9855805,0.0011995478,0.0028716358,0.00072077685,0.00003886737],"about_ca_topic_score_codex":0.010992986,"about_ca_topic_score_gemma":0.0115619255,"teacher_disagreement_score":0.010992986,"about_ca_system_score_codex":0.0010284616,"about_ca_system_score_gemma":0.0013359745,"threshold_uncertainty_score":0.023804784},"labels":[],"label_agreement":null},{"id":"W3205651708","doi":"10.3390/s21196665","title":"Probe Standoff Optimization Method for Phased Array Ultrasonic TFM Imaging of Curved Parts","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Phased array; Side lobe; Main lobe; Point spread function; Ultrasonic sensor; Acoustics; Artifact (error); Image quality; Optics; Materials science; Computer science; Physics; Image (mathematics); Artificial intelligence; Telecommunications","score_opus":0.010825487099366735,"score_gpt":0.24628367988065575,"score_spread":0.23545819278128902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205651708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016077872,0.00007327003,0.9830719,0.000025390167,0.000005698507,0.000016414479,0.000009505274,0.00018142084,0.00053849496],"genre_scores_gemma":[0.3053623,0.00013029078,0.6926744,0.00003582057,0.000010014536,0.00015280841,0.00006982162,0.000101926795,0.0014624802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983454,0.00003967603,0.000008176375,0.000029091621,0.00007472219,0.000013654252],"domain_scores_gemma":[0.99980503,0.00009221287,0.00003443811,0.00001439622,0.000047299374,0.0000066018274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003470284,0.00071716367,0.00034731734,0.00034407276,0.00016649648,0.0002761163,0.00033795944,0.0004873198,0.0008202298],"category_scores_gemma":[0.00085040124,0.00026284458,0.00037899197,0.00026892527,0.00024331796,0.00032393023,0.00033479396,0.00034230357,0.00023054366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016976998,0.00007753839,0.0011210692,0.00014974822,0.000044252316,0.00011947646,0.00014663715,0.54991454,0.16701463,0.004122996,0.0009704641,0.27614892],"study_design_scores_gemma":[0.000006383457,0.00006916438,0.0004695709,0.0000044536346,0.0000084763105,0.000042583273,0.000011655895,0.9835344,0.014410443,0.00049868907,0.0009332318,0.000010970625],"about_ca_topic_score_codex":0.0010501207,"about_ca_topic_score_gemma":0.0012719611,"teacher_disagreement_score":0.0010501207,"about_ca_system_score_codex":0.00029834136,"about_ca_system_score_gemma":0.00047616762,"threshold_uncertainty_score":0.0027438998},"labels":[],"label_agreement":null},{"id":"W3205778325","doi":"10.3390/s21206851","title":"Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Wireless sensor network; Heuristic; Real-time computing; Wireless; Algorithm; Graphics processing unit; Parallel computing; Artificial intelligence; Computer network","score_opus":0.027414181248970576,"score_gpt":0.26246163522928984,"score_spread":0.23504745398031926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205778325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03354685,0.00030029227,0.95260715,0.00021032608,0.00012614553,0.000112553884,0.0001742602,0.0062771235,0.0066451617],"genre_scores_gemma":[0.20468111,0.00019147444,0.7905059,0.00008233407,0.000025011857,0.0003153165,0.00062519865,0.00035321436,0.0032205267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996731,0.000050012135,0.0000204929,0.000063762825,0.00014644142,0.000046146026],"domain_scores_gemma":[0.99972874,0.000059568105,0.000024096054,0.0000487997,0.00011498654,0.00002374722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027838667,0.00080344407,0.0006387813,0.000550129,0.0005459626,0.000740598,0.0012603243,0.00046223993,0.003891692],"category_scores_gemma":[0.00085023104,0.00030215876,0.000680725,0.00080674945,0.0002754758,0.00070659444,0.00066694856,0.00076114334,0.0011066213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039970898,0.00018912055,0.0037288982,0.0003220344,0.00020375788,0.0004288165,0.0004046845,0.56376207,0.018444797,0.018479913,0.016430005,0.3772062],"study_design_scores_gemma":[0.00003852324,0.000033140044,0.00027467974,0.0000049691175,0.000009260984,0.00002834029,0.000031860287,0.9910913,0.0020043117,0.0021505347,0.004326855,0.0000060784505],"about_ca_topic_score_codex":0.013770508,"about_ca_topic_score_gemma":0.01526507,"teacher_disagreement_score":0.013770508,"about_ca_system_score_codex":0.0006495654,"about_ca_system_score_gemma":0.0014098429,"threshold_uncertainty_score":0.027380705},"labels":[],"label_agreement":null},{"id":"W3206014156","doi":"10.3390/s21206840","title":"A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous Phenomena with Vague Boundaries Using Sensor Data","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Spatial analysis; Fuzzy logic; Node (physics); Representation (politics); Spatial relation; Artificial intelligence; Kernel (algebra); Data mining; Topology (electrical circuits); Mathematics; Geography; Remote sensing; Engineering","score_opus":0.04115397266296152,"score_gpt":0.2839326605503584,"score_spread":0.24277868788739684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206014156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008990228,0.000060329963,0.98986316,0.000057640264,0.00001058143,0.000032162483,0.00006741182,0.00011936622,0.00079913554],"genre_scores_gemma":[0.4781301,0.00017663796,0.51985836,0.00006779298,0.00003076368,0.00018363117,0.00031120598,0.000036303518,0.0012050793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991567,0.000157462,0.000069361915,0.00027062674,0.00028737928,0.00005844035],"domain_scores_gemma":[0.99906176,0.00039999446,0.00013477681,0.000116089956,0.00023995491,0.000047327718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000818164,0.0005208046,0.0010703625,0.00092539773,0.0005887622,0.0014745003,0.0018113693,0.00086574175,0.0013605795],"category_scores_gemma":[0.0027858326,0.0004649484,0.0011152549,0.0012300917,0.00075880456,0.0014256149,0.0009095039,0.00088423473,0.0001975047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057875655,0.000043666347,0.0007268025,0.00007373747,0.00004224585,0.00017222282,0.00012143557,0.9208549,0.0038929805,0.019517586,0.0006374728,0.053859044],"study_design_scores_gemma":[0.000004980983,0.000009556335,0.00007864249,0.0000035424357,0.000006607515,0.000016686168,0.00000979146,0.99474144,0.00044950965,0.0043852828,0.0002892286,0.0000047014314],"about_ca_topic_score_codex":0.0108426735,"about_ca_topic_score_gemma":0.010601223,"teacher_disagreement_score":0.0108426735,"about_ca_system_score_codex":0.0011284819,"about_ca_system_score_gemma":0.0015104976,"threshold_uncertainty_score":0.02155912},"labels":[],"label_agreement":null},{"id":"W3206083514","doi":"10.3390/s21216949","title":"Microwave Hydration Monitoring: System Assessment Using Fasting Volunteers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermoregulation and physiological responses","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Permittivity; Microwave; Population; Metric (unit); Gold standard (test); Medicine; Materials science; Computer science; Environmental health; Internal medicine; Telecommunications; Engineering; Dielectric","score_opus":0.05529905723648038,"score_gpt":0.338966800321233,"score_spread":0.2836677430847526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206083514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98219556,0.00045484587,0.0154539365,0.00008453466,0.000039055285,0.00022519541,0.0002849983,0.00015672791,0.0011050351],"genre_scores_gemma":[0.9817841,0.00041090455,0.015681189,0.00016196656,0.00003621867,0.00033416864,0.00039787762,0.000019493353,0.0011740216],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967563,0.00011958285,0.000024869205,0.00009018672,0.00006542565,0.000024286785],"domain_scores_gemma":[0.99972874,0.000053713775,0.00005285551,0.000027602533,0.00009981242,0.000037096186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062533835,0.00035616144,0.00037503778,0.00028480781,0.00026105563,0.00035935515,0.00024848702,0.00052798673,0.0009214424],"category_scores_gemma":[0.0009117664,0.000100281795,0.00014508356,0.00022294672,0.00022548558,0.00023076532,0.0004044036,0.00019274102,0.000359436],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0069821076,0.002108274,0.21974006,0.0009794395,0.00027091862,0.0008541063,0.003026159,0.0020676602,0.5729354,0.00035471458,0.0025120298,0.1881691],"study_design_scores_gemma":[0.00031859605,0.028531015,0.78007686,0.00015675052,0.000511954,0.0050514443,0.0034012615,0.022892887,0.14888752,0.00056736014,0.009444795,0.00015957118],"about_ca_topic_score_codex":0.00083977834,"about_ca_topic_score_gemma":0.0010844728,"teacher_disagreement_score":0.0009214424,"about_ca_system_score_codex":0.00010302448,"about_ca_system_score_gemma":0.00013524736,"threshold_uncertainty_score":0.0033071637},"labels":[],"label_agreement":null},{"id":"W3206162810","doi":"10.3390/s21206745","title":"Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Preprocessor; Algorithm; Spectroscopy; Computer science; Data mining; Artificial intelligence; Physics","score_opus":0.126389807894402,"score_gpt":0.34428437905299875,"score_spread":0.21789457115859676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206162810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50835276,0.0015490135,0.4799356,0.00028526958,0.000112542824,0.00030175765,0.0009139006,0.0062558088,0.0022934345],"genre_scores_gemma":[0.5389868,0.00058580673,0.45695022,0.00008970976,0.000022314687,0.0002518803,0.0016748606,0.00033457973,0.0011038678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993144,0.0002151804,0.000064614054,0.00019384999,0.00014763964,0.00006432416],"domain_scores_gemma":[0.9979184,0.0012297675,0.00015468859,0.000102990976,0.0005502008,0.000043991913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002811459,0.0016383453,0.00083397224,0.00091551256,0.00045025718,0.000983574,0.001204408,0.0009296431,0.0007963725],"category_scores_gemma":[0.004911361,0.00041201245,0.0009000911,0.0010197156,0.0002227712,0.0009076781,0.00042022634,0.001113577,0.00048296066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010874076,0.0010053788,0.01822482,0.00060478516,0.00041756572,0.00024399006,0.00021585802,0.5775573,0.047328144,0.0011064411,0.001999368,0.35020897],"study_design_scores_gemma":[0.000026271759,0.0001906206,0.0047545685,0.000016713158,0.00006344728,0.00003535899,0.000045494853,0.97743535,0.016527764,0.00019904831,0.00068400294,0.000021400423],"about_ca_topic_score_codex":0.014612189,"about_ca_topic_score_gemma":0.011291023,"teacher_disagreement_score":0.014612189,"about_ca_system_score_codex":0.0006941563,"about_ca_system_score_gemma":0.0015024615,"threshold_uncertainty_score":0.029054284},"labels":[],"label_agreement":null},{"id":"W3206887055","doi":"10.3390/s21206849","title":"Application of Prandtl’s Theory in the Design of an Experimental Chamber for Static Pressure Measurements","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Dynamics and Kinetic Theory","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mechanics; Supersonic speed; Vacuum chamber; Prandtl number; Nozzle; Optics; Flow (mathematics); Chamber pressure; Boundary value problem; Physics; Materials science; Classical mechanics; Thermodynamics; Convection","score_opus":0.06496268998645964,"score_gpt":0.3316992898842393,"score_spread":0.26673659989777965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206887055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020895752,0.00018684762,0.9954086,0.00010032547,0.000032672862,0.0000735544,0.000019933093,0.00015099424,0.0019375226],"genre_scores_gemma":[0.125462,0.0006671493,0.87060136,0.00010599172,0.000047635178,0.00062565034,0.00006083192,0.000113094335,0.0023162537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977943,0.0005000922,0.00009230885,0.00046898026,0.0010495121,0.000094911],"domain_scores_gemma":[0.99862194,0.0007830158,0.0001712444,0.0001872905,0.00020578002,0.000030773783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029390235,0.00072624214,0.0007991106,0.0008746914,0.00065600977,0.0015256113,0.002505119,0.00084335444,0.0012178766],"category_scores_gemma":[0.00375748,0.00074189954,0.0005508137,0.00052828976,0.0024463837,0.0021255517,0.0012662092,0.0012713985,0.0007236297],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015537477,0.00012058977,0.0016080508,0.00076362776,0.000039874554,0.00049159117,0.00036907895,0.19135015,0.14250998,0.57095563,0.0014750591,0.090160966],"study_design_scores_gemma":[0.000040482573,0.00036491486,0.0008501356,0.000083031824,0.000029285722,0.00042219833,0.0000627385,0.766311,0.1313273,0.07050042,0.029872004,0.00013653023],"about_ca_topic_score_codex":0.0006080559,"about_ca_topic_score_gemma":0.0006456028,"teacher_disagreement_score":0.0029390235,"about_ca_system_score_codex":0.0013982793,"about_ca_system_score_gemma":0.0018853445,"threshold_uncertainty_score":0.015543282},"labels":[],"label_agreement":null},{"id":"W3207079979","doi":"10.3390/s21196657","title":"Yield Estimation and Visualization Solution for Precision Agriculture","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Global Positioning System; Visualization; Leverage (statistics); Computer vision; Artificial intelligence; Object detection; Set (abstract data type); Data mining; Real-time computing; Pattern recognition (psychology)","score_opus":0.019977607834349476,"score_gpt":0.23804424470740668,"score_spread":0.2180666368730572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207079979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0150668435,0.0002259672,0.9663444,0.00024433207,0.000056103254,0.000034022243,0.000569441,0.014523759,0.0029351714],"genre_scores_gemma":[0.3799786,0.0005179646,0.60747,0.00020576072,0.00007425945,0.00012845796,0.0022071672,0.0013820729,0.008035839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996879,0.000025043752,0.000012409101,0.000119497,0.000115592375,0.000039572118],"domain_scores_gemma":[0.9998018,0.000041003495,0.000024990488,0.000046774076,0.00006834421,0.000017088903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027868754,0.0015862739,0.0006924721,0.00064679544,0.0002930078,0.0010250777,0.0012781499,0.0007607993,0.006097134],"category_scores_gemma":[0.0009165267,0.00040647638,0.00067297404,0.0006175072,0.00019463192,0.0015486341,0.00142987,0.0007722739,0.0017165558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005974532,0.00015556645,0.0039719054,0.00028720548,0.00011463554,0.00060782844,0.0002735656,0.16595736,0.075849734,0.009561609,0.029796012,0.71282715],"study_design_scores_gemma":[0.000033831086,0.00012014987,0.002475153,0.000029094548,0.000049317397,0.00021594146,0.000102972415,0.93823564,0.027953757,0.0127256345,0.018016592,0.000041886877],"about_ca_topic_score_codex":0.0031135175,"about_ca_topic_score_gemma":0.004159324,"teacher_disagreement_score":0.006097134,"about_ca_system_score_codex":0.00056457735,"about_ca_system_score_gemma":0.0004418047,"threshold_uncertainty_score":0.020396948},"labels":[],"label_agreement":null},{"id":"W3207592558","doi":"10.3390/s21206823","title":"Hardware Implementation and RF High-Fidelity Modeling and Simulation of Compressive Sensing Based 2D Angle-of-Arrival Measurement System for 2–18 GHz Radar Electronic Support Measures","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Radar; Angle of arrival; Radio frequency; Compressed sensing; Wideband; System of measurement; Electronic engineering; Measure (data warehouse); Computer science; Acoustics; Antenna (radio); Engineering; Remote sensing; Electrical engineering; Telecommunications; Physics; Geology","score_opus":0.03497229661259433,"score_gpt":0.26476729742946425,"score_spread":0.22979500081686993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207592558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.154279,0.00012234444,0.8363793,0.00022472325,0.000054338132,0.0001228262,0.00021042234,0.0019078865,0.006699228],"genre_scores_gemma":[0.9179656,0.000106362124,0.079536006,0.000058589427,0.000010310096,0.00016358901,0.00021602484,0.00005386501,0.0018896725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998048,0.000039835715,0.000012246351,0.000027494862,0.000097953576,0.000017612241],"domain_scores_gemma":[0.99978775,0.000064497784,0.000032770688,0.000037318805,0.0000656015,0.000012136065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027227373,0.0003924058,0.00024829328,0.00019409448,0.00022875627,0.00039447838,0.00059000734,0.00039078525,0.0021694996],"category_scores_gemma":[0.00056128774,0.00020088423,0.00036081063,0.00014746678,0.00020757392,0.0004661046,0.0003697267,0.0003880147,0.0003583075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012617244,0.00006608848,0.004567827,0.00011752278,0.000033457283,0.0001429526,0.00016052864,0.92984164,0.029195055,0.0025720175,0.0006915684,0.032485202],"study_design_scores_gemma":[0.00000810297,0.000053418295,0.00047533217,0.000003553164,0.000005848007,0.000023193106,0.00001262306,0.99371064,0.0048244405,0.00015519146,0.00072117866,0.0000064963697],"about_ca_topic_score_codex":0.0038413357,"about_ca_topic_score_gemma":0.002601601,"teacher_disagreement_score":0.0038413357,"about_ca_system_score_codex":0.0003458145,"about_ca_system_score_gemma":0.00058761355,"threshold_uncertainty_score":0.0076379776},"labels":[],"label_agreement":null},{"id":"W3208187939","doi":"10.3390/s21217258","title":"Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Fraser Health; National Research Council Canada; Surrey Memorial Hospital; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Pooling; Channel (broadcasting); Event (particle physics); Computer science; SIGNAL (programming language); Real-time computing; Algorithm; Telecommunications; Artificial intelligence; Physics","score_opus":0.014096403075064472,"score_gpt":0.2549578668104209,"score_spread":0.2408614637353564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208187939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1042063,0.000703026,0.8910383,0.00016543044,0.00008062474,0.00009458693,0.00018285878,0.0010561796,0.0024725993],"genre_scores_gemma":[0.6358644,0.00062777067,0.3610383,0.00020536012,0.00015067142,0.0002548883,0.00046911385,0.00026137946,0.0011281691],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953556,0.00012236722,0.000034233108,0.00012116983,0.00013897318,0.00004785151],"domain_scores_gemma":[0.99877816,0.0007069869,0.00011854901,0.00017541424,0.00018050072,0.000040297356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014095643,0.0008364214,0.00051320874,0.0008585476,0.00021280677,0.0006758702,0.0007515767,0.00041770106,0.0023917072],"category_scores_gemma":[0.0051572938,0.0003152502,0.00088229694,0.0008732832,0.00037573755,0.0017078039,0.0012208354,0.00045742994,0.0005347912],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091777067,0.00026320902,0.0036685315,0.0004878971,0.0005205037,0.00045321966,0.00024099555,0.05698636,0.33625862,0.0036141942,0.0025453826,0.59404325],"study_design_scores_gemma":[0.00014896574,0.0012369043,0.036529206,0.0000930392,0.00094247976,0.0016140975,0.00014000403,0.69977975,0.22908965,0.017818023,0.0123999985,0.00020792912],"about_ca_topic_score_codex":0.0005012044,"about_ca_topic_score_gemma":0.0008127852,"teacher_disagreement_score":0.0023917072,"about_ca_system_score_codex":0.0003048899,"about_ca_system_score_gemma":0.00032070497,"threshold_uncertainty_score":0.0080010295},"labels":[],"label_agreement":null},{"id":"W3208714659","doi":"10.3390/s21217248","title":"A Generic Sequential Stimulation Adapter for Reducing Muscle Fatigue during Functional Electrical Stimulation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Adapter (computing); Functional electrical stimulation; Stimulation; Pulse (music); Rehabilitation engineering; Biomedical engineering; Computer science; SIGNAL (programming language); Medicine; Voltage; Engineering; Computer hardware; Electrical engineering; Rehabilitation; Internal medicine; Physical therapy","score_opus":0.03757853240323436,"score_gpt":0.2457987993286769,"score_spread":0.20822026692544254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208714659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5622448,0.0031406614,0.4240135,0.0003650599,0.00030653883,0.0012282139,0.0006250667,0.0020784691,0.0059976513],"genre_scores_gemma":[0.83067566,0.001157399,0.16248618,0.0004294441,0.00012552003,0.00069044385,0.00058964104,0.00014421869,0.0037013816],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997594,0.00003535833,0.000034322373,0.000055313874,0.00009547905,0.000020208588],"domain_scores_gemma":[0.9997147,0.00008017586,0.00006426504,0.00004450181,0.00006881475,0.000027591987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059110497,0.0004699044,0.0002927693,0.0005077225,0.00010834412,0.00018623713,0.0007963773,0.0004723762,0.004011381],"category_scores_gemma":[0.0010270493,0.00018348952,0.00028731802,0.0002596445,0.00027314815,0.0004737265,0.00051223516,0.00022473757,0.0005521199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013710081,0.00032437188,0.0013106165,0.0008596347,0.000041276555,0.00017706185,0.00009675837,0.0015718791,0.80847585,0.00041231452,0.0011926536,0.18416661],"study_design_scores_gemma":[0.0011017543,0.029089699,0.06745379,0.00032617725,0.0005562696,0.011384467,0.00017276643,0.06017117,0.78037214,0.0016862812,0.047561344,0.00012415284],"about_ca_topic_score_codex":0.00015681285,"about_ca_topic_score_gemma":0.00042647772,"teacher_disagreement_score":0.004011381,"about_ca_system_score_codex":0.00011508756,"about_ca_system_score_gemma":0.00020628828,"threshold_uncertainty_score":0.013419449},"labels":[],"label_agreement":null},{"id":"W3208758953","doi":"10.3390/s21217185","title":"Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Thermography; Materials science; Principal component analysis; Image processing; Computer science; Artificial intelligence; Optics; Infrared","score_opus":0.010926308906337006,"score_gpt":0.23553639059146467,"score_spread":0.22461008168512767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208758953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0939852,0.000560299,0.902949,0.00022982305,0.00003909405,0.000046155732,0.0001608692,0.0009806714,0.0010488288],"genre_scores_gemma":[0.35504466,0.0006556199,0.64148,0.00008916281,0.000032534546,0.00008224645,0.00048685586,0.00018172049,0.0019471477],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959475,0.000098381664,0.00002031648,0.000075750395,0.0001784429,0.000032457407],"domain_scores_gemma":[0.99902666,0.00042356,0.00015620793,0.00011237135,0.00024698616,0.00003411083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007749157,0.00077811826,0.00034545222,0.00084280496,0.00021026547,0.00076399563,0.00042744784,0.0005978158,0.0014015851],"category_scores_gemma":[0.0023214002,0.0002782155,0.00052155723,0.00089299586,0.00056658994,0.0009588978,0.00060074654,0.0008951174,0.0005765532],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064177706,0.00016633242,0.0021140375,0.00059601787,0.0000774273,0.000490029,0.0004033478,0.13135493,0.46481007,0.0047492865,0.0014520541,0.3931447],"study_design_scores_gemma":[0.00001493771,0.00021269973,0.0030147552,0.000026625616,0.000025026506,0.00034287016,0.00009994395,0.8620394,0.12901275,0.0022712443,0.002894448,0.000045275505],"about_ca_topic_score_codex":0.0011211856,"about_ca_topic_score_gemma":0.0019942257,"teacher_disagreement_score":0.0014015851,"about_ca_system_score_codex":0.00025513873,"about_ca_system_score_gemma":0.000530277,"threshold_uncertainty_score":0.00468874},"labels":[],"label_agreement":null},{"id":"W3208761829","doi":"10.3390/s21217310","title":"Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada; University of Saskatchewan; China Scholarship Council; U.S. Department of Agriculture","keywords":"Vegetation (pathology); Rangeland; Quadrat; Remote sensing; Environmental science; Transect; Geography; Ecology; Agroforestry","score_opus":0.030472089842078755,"score_gpt":0.32363474899859784,"score_spread":0.29316265915651907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208761829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66486305,0.0019417768,0.32486096,0.00009677122,0.0001026527,0.00042096237,0.00086135505,0.0011537923,0.005698696],"genre_scores_gemma":[0.68250585,0.0015890594,0.31254128,0.00008433988,0.00006041495,0.00026512574,0.0010585589,0.00023899636,0.0016562366],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990884,0.00016249916,0.00008679815,0.00021499759,0.00040151057,0.000045818346],"domain_scores_gemma":[0.99726224,0.0010290593,0.0003348014,0.0002618775,0.0010457042,0.000066323846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015777812,0.00062355056,0.0005515384,0.0037603462,0.00024117992,0.000973483,0.00067150337,0.00043974337,0.0010986641],"category_scores_gemma":[0.0035339699,0.00026999452,0.000398685,0.002079755,0.00028262788,0.0014466962,0.00043029553,0.0002687069,0.00046615992],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009363779,0.0003883583,0.1284473,0.0009800128,0.00042797913,0.00009677352,0.0011915835,0.0056508733,0.05201344,0.00069160026,0.0011436975,0.80803204],"study_design_scores_gemma":[0.00016733892,0.0011334142,0.680024,0.00025761922,0.000890416,0.0015611073,0.0029108585,0.21461518,0.08550521,0.0016130548,0.011109532,0.00021228942],"about_ca_topic_score_codex":0.002620414,"about_ca_topic_score_gemma":0.006765155,"teacher_disagreement_score":0.0037603462,"about_ca_system_score_codex":0.00030998868,"about_ca_system_score_gemma":0.00024745383,"threshold_uncertainty_score":0.008344173},"labels":[],"label_agreement":null},{"id":"W3209051008","doi":"10.3390/s21217008","title":"Comparison of Manual Wheelchair and Pushrim-Activated Power-Assisted Wheelchair Propulsion Characteristics during Common Over-Ground Maneuvers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Collaboration On Repair Discoveries; GF Strong Rehabilitation Centre; British Columbia Institute of Technology; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wheelchair; Kinematics; Manual wheelchair; Propulsion; Dynamometer; Simulation; Work (physics); Ground reaction force; Treadmill; Physical medicine and rehabilitation; Engineering; Computer science; Automotive engineering; Physical therapy; Aerospace engineering; Medicine; Physics; Mechanical engineering","score_opus":0.04029859665219566,"score_gpt":0.3821732749515821,"score_spread":0.34187467829938645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209051008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99646795,0.00024253814,0.0018877554,0.000011508317,0.000011385256,0.00004340237,0.0005060618,0.00006184419,0.00076744065],"genre_scores_gemma":[0.9969483,0.00019068623,0.0012475793,0.000016049238,0.0000067114443,0.00006188054,0.00059710187,0.000023622122,0.00090814324],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997249,0.000055524164,0.000031593925,0.00004355669,0.000081734484,0.0000626579],"domain_scores_gemma":[0.99922705,0.00022051137,0.000107092055,0.00004736264,0.00032402377,0.00007393591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037366097,0.00043618717,0.00041633513,0.001134001,0.00017431415,0.00033115578,0.00027039897,0.0003403948,0.0017494339],"category_scores_gemma":[0.0019085421,0.00017267295,0.00029925237,0.0005127601,0.0002096103,0.0003318954,0.00033414955,0.00016870357,0.0005434091],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009615039,0.0011866847,0.24316251,0.0024851419,0.0006009979,0.0008884053,0.0022578735,0.0064073247,0.50843704,0.00027367147,0.0016616887,0.2230236],"study_design_scores_gemma":[0.00006384073,0.0035499742,0.9339207,0.00013208768,0.00019152866,0.0006213381,0.0011354062,0.010981811,0.046780292,0.00013112689,0.00241224,0.00007973494],"about_ca_topic_score_codex":0.002459035,"about_ca_topic_score_gemma":0.0042123343,"teacher_disagreement_score":0.002459035,"about_ca_system_score_codex":0.00008461184,"about_ca_system_score_gemma":0.00021421409,"threshold_uncertainty_score":0.0058524013},"labels":[],"label_agreement":null},{"id":"W3209349540","doi":"10.3390/s21217158","title":"Low Back Pain—Behavior Correction by Providing Haptic Feedbacks: A Preliminary Investigation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Session (web analytics); Haptic technology; Wearable computer; Physical medicine and rehabilitation; Low back pain; Rehabilitation; Simulation; Physical therapy; Computer science; Medicine","score_opus":0.011362730006939943,"score_gpt":0.2498523286476271,"score_spread":0.23848959864068717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209349540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99500245,0.0011798159,0.0025009655,0.00010399419,0.0000349263,0.00032365826,0.00006348453,0.000018127874,0.0007726276],"genre_scores_gemma":[0.9904366,0.0016004648,0.006059731,0.00015364791,0.00006342965,0.0002831016,0.000119308206,0.000008493505,0.0012752585],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992908,0.0002812473,0.00005948009,0.00007431255,0.0001958706,0.00009830215],"domain_scores_gemma":[0.9987268,0.00069576956,0.00011903684,0.00006453726,0.00030093282,0.00009281196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011453737,0.00047609067,0.0004907526,0.00031441578,0.00024988255,0.00036710032,0.0004150864,0.00067574915,0.0028250457],"category_scores_gemma":[0.0031375235,0.00019076423,0.0005938528,0.00018482721,0.00031038848,0.00049171294,0.00030375837,0.00043886752,0.00037510568],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0168316,0.042176537,0.041642766,0.007478326,0.00042568566,0.0020963135,0.0047854553,0.0010663064,0.5696465,0.00032930347,0.000690304,0.31283084],"study_design_scores_gemma":[0.0011726433,0.55084795,0.33588496,0.0006133866,0.00089424313,0.0037716494,0.0047237766,0.005964125,0.08760044,0.0002811327,0.008137069,0.00010858745],"about_ca_topic_score_codex":0.00071435876,"about_ca_topic_score_gemma":0.00078790507,"teacher_disagreement_score":0.0028250457,"about_ca_system_score_codex":0.00011857589,"about_ca_system_score_gemma":0.00030350708,"threshold_uncertainty_score":0.009450734},"labels":[],"label_agreement":null},{"id":"W3209538529","doi":"10.3390/s21217057","title":"Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Manitoba Hydro; University of Manitoba","funders":"","keywords":"Iterative reconstruction; Fast Fourier transform; Optical coherence tomography; Frequency domain; Algorithm; Fourier transform; Image (mathematics); Fourier domain; Computer science; Mathematics; Computer vision; Artificial intelligence; Optics; Physics; Mathematical analysis","score_opus":0.01653516065131657,"score_gpt":0.24151247013713523,"score_spread":0.22497730948581865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209538529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025302228,0.00023413356,0.97315705,0.000068561094,0.000044077857,0.000019282781,0.000026414511,0.00008774141,0.0010604771],"genre_scores_gemma":[0.28038645,0.000634462,0.7164959,0.00010473982,0.00006858936,0.000049264087,0.000116680894,0.00007746272,0.0020665582],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995402,0.00013198245,0.000024636141,0.00006922488,0.00020466204,0.000029226276],"domain_scores_gemma":[0.9995096,0.00019746248,0.00007349306,0.00011882492,0.000084172105,0.000016547068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070606906,0.00048131213,0.00045418105,0.00045261043,0.00017922063,0.00050964596,0.0005218036,0.00063799124,0.0006461651],"category_scores_gemma":[0.001652756,0.00023994729,0.00043416466,0.00035781713,0.0007271435,0.0009535636,0.00048251887,0.0006530253,0.00019688517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003919132,0.000087888395,0.0017279797,0.0005575596,0.00009201247,0.0011517932,0.00040138763,0.2167976,0.31481838,0.23875454,0.0018489892,0.22337003],"study_design_scores_gemma":[0.000019792491,0.000108390785,0.0005999919,0.000030311128,0.00002206707,0.0005866629,0.000047456542,0.920869,0.059148405,0.014797381,0.0037266037,0.000043888995],"about_ca_topic_score_codex":0.0006804814,"about_ca_topic_score_gemma":0.0009721493,"teacher_disagreement_score":0.00070606906,"about_ca_system_score_codex":0.0003148603,"about_ca_system_score_gemma":0.00031283466,"threshold_uncertainty_score":0.0037340522},"labels":[],"label_agreement":null},{"id":"W3210293801","doi":"10.3390/s21217060","title":"Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Distance-vector routing protocol; Computer science; Routing protocol; Computer network; Ad hoc On-Demand Distance Vector Routing; Zone Routing Protocol; Dynamic Source Routing; Wireless Routing Protocol; Link-state routing protocol; Destination-Sequenced Distance Vector routing; Optimized Link State Routing Protocol; Network packet; Algorithm; Distributed computing","score_opus":0.012930803677698319,"score_gpt":0.2464573937682473,"score_spread":0.233526590090549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210293801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017969634,0.00162057,0.97354096,0.00031339846,0.0000945413,0.00015248962,0.00005362648,0.0005737261,0.0056810696],"genre_scores_gemma":[0.2786636,0.0033860144,0.71236753,0.00017502064,0.000057365476,0.00049518084,0.00020528559,0.00007000388,0.004579995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996811,0.000121118734,0.000016987258,0.000042687436,0.00011871368,0.000019369092],"domain_scores_gemma":[0.9998294,0.00008339033,0.00002580672,0.000018725079,0.000036299167,0.00000624481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005546245,0.0004047536,0.0004434101,0.0005945536,0.0003900104,0.00074020895,0.0006266046,0.000680568,0.0005730027],"category_scores_gemma":[0.00096391916,0.00017220239,0.00042082224,0.00103238,0.00047850568,0.0007322978,0.00043641406,0.0005874131,0.0002008668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004173782,0.00008822863,0.0012628656,0.00027519264,0.00013003334,0.00021335503,0.00014887567,0.72131366,0.018973136,0.05930037,0.001952917,0.19629969],"study_design_scores_gemma":[0.000041227853,0.00015751543,0.00049522874,0.000069625676,0.00005713248,0.00022961653,0.000060242204,0.9380416,0.0065310095,0.036999486,0.017283317,0.000033975262],"about_ca_topic_score_codex":0.0020244708,"about_ca_topic_score_gemma":0.0014089792,"teacher_disagreement_score":0.0020244708,"about_ca_system_score_codex":0.00046413118,"about_ca_system_score_gemma":0.00075952476,"threshold_uncertainty_score":0.0040253997},"labels":[],"label_agreement":null},{"id":"W3210452977","doi":"10.3390/s21217133","title":"Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Taif University","keywords":"Microgrid; Renewable energy; Photovoltaic system; Smart grid; Energy storage; Automotive engineering; Distributed generation; Energy management; Demand response; Grid; MATLAB; Computer science; Electric vehicle; Fossil fuel; Environmental economics; Engineering; Power (physics); Energy (signal processing); Electrical engineering; Electricity; Waste management; Operating system","score_opus":0.005551346291421594,"score_gpt":0.18973258059303724,"score_spread":0.18418123430161565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210452977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57552963,0.0008667566,0.39913845,0.000649423,0.0001183547,0.00020699421,0.00033891166,0.00035445223,0.022797002],"genre_scores_gemma":[0.9909837,0.00009132355,0.007650628,0.000012320992,0.0000075459784,0.000026810028,0.00004819839,0.000010502783,0.001168927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998184,0.00006793575,0.0000064663373,0.00003170519,0.000021578624,0.00005394875],"domain_scores_gemma":[0.99980396,0.00007004133,0.000037219288,0.000009026717,0.000041090796,0.00003863456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003788921,0.000538883,0.0006283497,0.0002882171,0.00032903755,0.0008258597,0.0004203291,0.00042199288,0.0023458404],"category_scores_gemma":[0.00077339413,0.00029656212,0.00028181044,0.00039539102,0.0003124603,0.00044059535,0.00049102656,0.00036341578,0.00015318536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005538811,0.00001557851,0.000392739,0.000021960672,0.0000106359175,0.000040074694,0.000013611577,0.9940725,0.0004044739,0.0012047207,0.00030141044,0.0034668203],"study_design_scores_gemma":[0.0000062325753,0.000024733956,0.00018016754,0.0000017938436,0.000005063092,0.0000048181682,0.000024218138,0.99878746,0.00016110903,0.00063920155,0.00016319711,0.000001986354],"about_ca_topic_score_codex":0.01185325,"about_ca_topic_score_gemma":0.01100761,"teacher_disagreement_score":0.01185325,"about_ca_system_score_codex":0.00091135857,"about_ca_system_score_gemma":0.0011184285,"threshold_uncertainty_score":0.023568511},"labels":[],"label_agreement":null},{"id":"W3210590824","doi":"10.3390/s21217145","title":"Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Genetics and Physical Performance","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; University of Calgary","keywords":"Movement (music); Sagittal plane; Swing; Artificial intelligence; Computer science; Similarity (geometry); Artificial neural network; Relevance (law); Physical medicine and rehabilitation; Pattern recognition (psychology); Engineering; Medicine; Anatomy; Physics","score_opus":0.007194953104356758,"score_gpt":0.20922816933896285,"score_spread":0.2020332162346061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210590824","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99199295,0.00007263979,0.007442334,0.00001605801,0.0000059919203,0.000013729827,0.00007842251,0.000023334385,0.00035448986],"genre_scores_gemma":[0.9955356,0.000038434897,0.0040382585,0.000008132947,0.0000034591503,0.000007765636,0.00009598792,0.0000066942625,0.00026567644],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997856,0.00004040577,0.000019077253,0.00009275587,0.00003531979,0.000026950207],"domain_scores_gemma":[0.9993538,0.00021607397,0.00018376649,0.000069649584,0.00009733135,0.000079269026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049224496,0.00030808622,0.00035964904,0.0004761313,0.00014481708,0.00025175288,0.00015112376,0.00029808944,0.00095003325],"category_scores_gemma":[0.0018310726,0.00013158246,0.00019475611,0.00033319776,0.0003261645,0.00029283826,0.00032498784,0.00022818685,0.00015853718],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008906667,0.00014549533,0.46157375,0.0004718439,0.0002960362,0.00046795505,0.00082626817,0.0054135076,0.39409938,0.00030866748,0.00025959793,0.13524677],"study_design_scores_gemma":[0.000007068437,0.00038609552,0.9764064,0.000016072254,0.000057752142,0.0007067382,0.0002445255,0.009956551,0.011681696,0.00027899788,0.00024002725,0.000018091798],"about_ca_topic_score_codex":0.0008438267,"about_ca_topic_score_gemma":0.002873427,"teacher_disagreement_score":0.00095003325,"about_ca_system_score_codex":0.00009753545,"about_ca_system_score_gemma":0.00014843314,"threshold_uncertainty_score":0.0031781793},"labels":[],"label_agreement":null},{"id":"W3210658244","doi":"10.3390/s21217321","title":"The X-ray Sensitivity of an Amorphous Lead Oxide Photoconductor","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"X-ray detector; Tomosynthesis; Detector; X-ray; Amorphous solid; Electric field; Materials science; Sensitivity (control systems); Optoelectronics; Optics; Electron; Physics; Chemistry; Electronic engineering; Nuclear physics","score_opus":0.014982598700772714,"score_gpt":0.2590388815111122,"score_spread":0.2440562828103395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210658244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99409467,0.000982259,0.003398694,0.00005344653,0.0000148129175,0.00001619858,0.00012400598,0.00008170713,0.0012342521],"genre_scores_gemma":[0.9967784,0.0003884321,0.0016678461,0.00003700873,0.0000039304896,0.000010758503,0.00006914153,0.000012330744,0.0010321644],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997912,0.00002167673,0.000010916971,0.000073187155,0.00007664742,0.00002634268],"domain_scores_gemma":[0.99967265,0.00014606491,0.000063402375,0.000026177084,0.00007721666,0.00001450602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024872523,0.00028727227,0.00017136015,0.00023505156,0.00014090889,0.00037929884,0.00034162367,0.00032611942,0.0008985041],"category_scores_gemma":[0.00059303653,0.00016394079,0.00013497422,0.000250956,0.00023423762,0.00029170944,0.00023255861,0.00022636032,0.0001746787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010598397,0.00001040671,0.0012076325,0.000054532826,0.000009233543,0.00006157501,0.00004278188,0.00021106555,0.9950389,0.000082504506,0.000038068127,0.003137362],"study_design_scores_gemma":[0.0000053189915,0.00023074448,0.0063156704,0.000009231698,0.000016344475,0.00012786573,0.00004732553,0.0015880668,0.9909785,0.000037386093,0.0006375682,0.000006012934],"about_ca_topic_score_codex":0.0005102273,"about_ca_topic_score_gemma":0.00046048823,"teacher_disagreement_score":0.0008985041,"about_ca_system_score_codex":0.00026568206,"about_ca_system_score_gemma":0.0001313468,"threshold_uncertainty_score":0.0030058026},"labels":[],"label_agreement":null},{"id":"W3210766530","doi":"10.3390/s22041476","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":489,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wearable computer; Activity recognition; Deep learning; Human–computer interaction; Wearable technology; Computer science; Artificial intelligence; Embedded system","score_opus":0.07817396725530723,"score_gpt":0.32966179546578067,"score_spread":0.25148782821047344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210766530","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000453224,0.9893756,0.006102656,0.00079874415,0.00036357314,0.000013128317,0.000056426317,0.000042507323,0.002794166],"genre_scores_gemma":[0.003433286,0.9920854,0.0026092534,0.00032951077,0.00035926458,0.000016431179,0.000086007865,0.000010716952,0.0010700403],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997032,0.00005483649,0.00003376336,0.000072616276,0.000111662426,0.000023903298],"domain_scores_gemma":[0.9990446,0.000608401,0.000060149014,0.000026873788,0.00022311676,0.00003691867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096496264,0.001081127,0.00091802864,0.001574631,0.00019310358,0.0009431981,0.0010331785,0.0011657737,0.0030793722],"category_scores_gemma":[0.0018309916,0.0004291649,0.0005857928,0.0026313323,0.00049409905,0.0019233997,0.0008057737,0.001668895,0.0018589328],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034741595,0.00007205294,0.0003830842,0.007401881,0.0000789863,0.000054120243,0.000053509368,0.002014118,0.00070815795,0.008677985,0.018701086,0.9618204],"study_design_scores_gemma":[0.00001668583,0.00022489895,0.0016578486,0.007835197,0.0002630609,0.00061971997,0.000113601025,0.0066285776,0.0022671786,0.015878318,0.96442676,0.00006817307],"about_ca_topic_score_codex":0.001989012,"about_ca_topic_score_gemma":0.0017459216,"teacher_disagreement_score":0.0030793722,"about_ca_system_score_codex":0.0005334312,"about_ca_system_score_gemma":0.0012249885,"threshold_uncertainty_score":0.01030159},"labels":[],"label_agreement":null},{"id":"W3210917154","doi":"10.3390/s21217025","title":"A Hybrid Speech Enhancement Algorithm for Voice Assistance Application","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Computer science; Voice activity detection; Speech enhancement; Audio mining; Speech coding; Intelligibility (philosophy); Speech processing; Acoustic model; Background noise; Linear predictive coding; Noise (video); Hidden Markov model; Artificial intelligence","score_opus":0.011598353662183536,"score_gpt":0.2574067036717646,"score_spread":0.24580835000958107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210917154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020592868,0.0007305297,0.97411966,0.00006149967,0.00007256528,0.00006137498,0.00003178804,0.0015396825,0.002790017],"genre_scores_gemma":[0.3647673,0.0009107199,0.6179834,0.00019295198,0.00006594553,0.00017343492,0.00030607928,0.00012633445,0.015473736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998115,0.000023936731,0.000013796112,0.000053425272,0.000083488296,0.000013918962],"domain_scores_gemma":[0.9998627,0.000033350607,0.0000102839285,0.00001269358,0.000075239725,0.0000058634682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027974678,0.0005432156,0.00032494383,0.00039866124,0.00020591944,0.00036646146,0.00046502487,0.00048392193,0.0026036706],"category_scores_gemma":[0.0004111551,0.00014806222,0.00037872075,0.0002646044,0.00017551916,0.00048373168,0.00028616434,0.00038727946,0.00148946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033418488,0.00010083982,0.0005064897,0.000107752036,0.000046410718,0.000118799755,0.00007474835,0.041220337,0.1740502,0.0030664217,0.0021272239,0.7782466],"study_design_scores_gemma":[0.000033495002,0.00033298,0.0011255775,0.000020560594,0.00004821133,0.00050656655,0.000031527732,0.88633305,0.09468619,0.0009578408,0.015895097,0.000028896564],"about_ca_topic_score_codex":0.0009075346,"about_ca_topic_score_gemma":0.00093304174,"teacher_disagreement_score":0.0026036706,"about_ca_system_score_codex":0.00018013532,"about_ca_system_score_gemma":0.00028947063,"threshold_uncertainty_score":0.008710206},"labels":[],"label_agreement":null},{"id":"W3210957019","doi":"10.3390/s21217124","title":"Internet of Things Based Contact Tracing Systems","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Scalability; Computer science; Architecture; Internet of Things; Contact tracing; Key (lock); Coronavirus disease 2019 (COVID-19); Tracing; The Internet; Data science; Wireless sensor network; Computer security; Distributed computing; Infectious disease (medical specialty); World Wide Web; Computer network; Medicine; Disease","score_opus":0.0717762194684523,"score_gpt":0.3146985119870763,"score_spread":0.24292229251862402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210957019","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003940894,0.9117931,0.018724551,0.002260097,0.002100977,0.00031075018,0.00044406377,0.0003113516,0.060114127],"genre_scores_gemma":[0.041747533,0.9237954,0.015499153,0.0016748146,0.00078038423,0.0002802903,0.00077171443,0.00003704462,0.015413674],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995939,0.00006806295,0.000036673115,0.000078399775,0.00017980352,0.00004314639],"domain_scores_gemma":[0.99953103,0.00022107524,0.000051592062,0.00003780536,0.00013414532,0.000024289247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565844,0.00066749065,0.00058985216,0.0016902158,0.0003547913,0.0010735175,0.0011560968,0.0011617197,0.0060800747],"category_scores_gemma":[0.0014187287,0.0002573821,0.0006686667,0.0018496606,0.00031589044,0.0019414158,0.00080514053,0.0008888164,0.0025565177],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052720745,0.00007826057,0.0004469799,0.008096601,0.00007742209,0.00019350031,0.00009094589,0.0012128883,0.003052231,0.01779605,0.02400242,0.9448999],"study_design_scores_gemma":[0.000012500659,0.00015595446,0.0008460943,0.002070116,0.00011716351,0.0009985719,0.00012668346,0.0015711117,0.0035764594,0.005975214,0.98451614,0.000034095876],"about_ca_topic_score_codex":0.00083310687,"about_ca_topic_score_gemma":0.0011453764,"teacher_disagreement_score":0.0060800747,"about_ca_system_score_codex":0.00056469406,"about_ca_system_score_gemma":0.0008669639,"threshold_uncertainty_score":0.020339847},"labels":[],"label_agreement":null},{"id":"W3211019912","doi":"10.3390/s21217153","title":"Suppression of Continuous Wave Interference in Loran-C Signal Based on Sparse Optimization Using Tunable Q-Factor Wavelet Transform and Discrete Cosine Transform","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Radio Wave Propagation Studies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"GNSS applications; Computer science; Interference (communication); Wavelet transform; Discrete cosine transform; Filter (signal processing); SIGNAL (programming language); Sparse approximation; Algorithm; Electronic engineering; Wavelet; Telecommunications; Artificial intelligence; Engineering; Computer vision; Global Positioning System","score_opus":0.02166426773101171,"score_gpt":0.22958075527262606,"score_spread":0.20791648754161435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211019912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046206832,0.00013999818,0.95238054,0.0001000686,0.000024903891,0.000014387695,0.00002442167,0.00009271114,0.0010161389],"genre_scores_gemma":[0.44350973,0.0006237681,0.5532431,0.00008590535,0.00007349269,0.000058734127,0.00022383672,0.00007724535,0.002104238],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998124,0.000028245302,0.000009356333,0.000034224373,0.000101148195,0.000014482778],"domain_scores_gemma":[0.9996922,0.00011384701,0.00005771357,0.000030056413,0.00009090934,0.000015356118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034399814,0.00041309238,0.0003793701,0.0004275114,0.00019398645,0.00033022102,0.00033303103,0.0003086978,0.0005391445],"category_scores_gemma":[0.0009262423,0.00014618185,0.000430802,0.00073791825,0.00036854538,0.00063818006,0.00046499757,0.00046582852,0.0001610323],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002991886,0.00015499246,0.0027584857,0.00024061697,0.00009128993,0.00023419193,0.00026753594,0.27286,0.27476418,0.019758424,0.0016815217,0.4268896],"study_design_scores_gemma":[0.000014378618,0.00006504273,0.0011655488,0.0000067785254,0.00001623188,0.00010199367,0.0000322163,0.97371054,0.021574035,0.0017483031,0.0015477745,0.000017105249],"about_ca_topic_score_codex":0.001151122,"about_ca_topic_score_gemma":0.0010661783,"teacher_disagreement_score":0.001151122,"about_ca_system_score_codex":0.00018649107,"about_ca_system_score_gemma":0.00034922943,"threshold_uncertainty_score":0.002288878},"labels":[],"label_agreement":null},{"id":"W3211288915","doi":"10.3390/s21217018","title":"Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University; St. Michael's Hospital","funders":"Ryerson University","keywords":"Generalizability theory; Computer science; Medical imaging; Artificial intelligence; Segmentation; CMA-ES; Machine learning; Set (abstract data type); Deep learning; Adaptation (eye); Selection (genetic algorithm); Variety (cybernetics); Data mining; Evolution strategy; Evolutionary algorithm; Mathematics","score_opus":0.03043058301822709,"score_gpt":0.33652721590187035,"score_spread":0.30609663288364325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211288915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014981158,0.0004553107,0.97977966,0.00061866944,0.0000975606,0.000099053774,0.000120971614,0.0017829894,0.0020645505],"genre_scores_gemma":[0.34554678,0.00033002277,0.6487495,0.00084659475,0.000095473086,0.00043778683,0.0004737077,0.0004029571,0.0031172372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955195,0.00018077584,0.000033190943,0.00009490145,0.000093715615,0.000045401055],"domain_scores_gemma":[0.9989287,0.0006039517,0.00011964255,0.00014695276,0.00014539162,0.000055317643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017181026,0.00086951314,0.00072279776,0.00062483404,0.00043252614,0.00079902075,0.0011487823,0.0013772708,0.0032990777],"category_scores_gemma":[0.00578788,0.00048037988,0.00068180694,0.00060149445,0.00095448707,0.0013739367,0.0016946025,0.0020562606,0.0006764966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041390647,0.00015842867,0.0020634427,0.00019902622,0.000061463295,0.00013870597,0.00015990862,0.5935039,0.010180561,0.030833047,0.009687783,0.3525999],"study_design_scores_gemma":[0.000011588605,0.000039768704,0.00010782208,0.0000146625225,0.0000049545965,0.000036281854,0.0000071627337,0.9890416,0.0030123899,0.0059495233,0.0017681362,0.0000061370083],"about_ca_topic_score_codex":0.001780065,"about_ca_topic_score_gemma":0.0027781297,"teacher_disagreement_score":0.0032990777,"about_ca_system_score_codex":0.00082927855,"about_ca_system_score_gemma":0.0014577536,"threshold_uncertainty_score":0.011036575},"labels":[],"label_agreement":null},{"id":"W3211790039","doi":"10.3390/s21217369","title":"B2 Thickness Parameter Response to Equinoctial Geomagnetic Storms","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Geomagnetic storm; Storm; Ionogram; Total electron content; Atmospheric sciences; Ionosphere; Longitude; Earth's magnetic field; Ionosonde; Latitude; VTEC; Inflection point; TEC; Electron density; Environmental science; Meteorology; Geology; Physics; Geodesy; Mathematics; Electron; Geophysics; Chemistry","score_opus":0.007211103482715496,"score_gpt":0.23427416189853467,"score_spread":0.22706305841581917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211790039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906583,0.0001690673,0.006290494,0.000088478766,0.00001638926,0.000017103614,0.00076271425,0.00020304354,0.0017943694],"genre_scores_gemma":[0.99883586,0.000047577516,0.0004796318,0.000014406426,0.0000046211194,0.000006483442,0.0004080435,0.000027112135,0.00017621864],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979085,0.00003281714,0.000014324993,0.000066014356,0.000041941872,0.000054043398],"domain_scores_gemma":[0.99902797,0.00042247795,0.00019675973,0.00015415803,0.00014180521,0.000056831756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005746035,0.0004521372,0.0002529484,0.0005433834,0.00017018206,0.0005840804,0.0003365746,0.0004136196,0.0009353047],"category_scores_gemma":[0.002982819,0.00019026795,0.00030041923,0.00047761112,0.00025905253,0.00050828804,0.0005232293,0.00036339567,0.00019251254],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009955387,0.00010526697,0.5157992,0.00022470824,0.00030549656,0.0007328236,0.00045425553,0.3596399,0.087571494,0.0008818007,0.0017837718,0.03150575],"study_design_scores_gemma":[0.00005039935,0.00016430583,0.66229504,0.000040354214,0.000067168156,0.00045491676,0.0004033192,0.30203074,0.030819673,0.001053026,0.002544133,0.000076964905],"about_ca_topic_score_codex":0.00702014,"about_ca_topic_score_gemma":0.0029358058,"teacher_disagreement_score":0.00702014,"about_ca_system_score_codex":0.0004479382,"about_ca_system_score_gemma":0.00021410875,"threshold_uncertainty_score":0.013958573},"labels":[],"label_agreement":null},{"id":"W3212647012","doi":"10.3390/s21227467","title":"Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan; University of Ottawa","keywords":"Bearing (navigation); Fault (geology); Feature extraction; Hilbert–Huang transform; Artificial intelligence; Feature (linguistics); Computer science; Engineering; SIGNAL (programming language); Pattern recognition (psychology); Artificial neural network; Hilbert transform; Computer vision","score_opus":0.011905538595498033,"score_gpt":0.2510966843795389,"score_spread":0.23919114578404088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212647012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24863155,0.0006152706,0.7468174,0.0003924819,0.00013559492,0.00004265288,0.00018757372,0.0012166856,0.001960727],"genre_scores_gemma":[0.9621147,0.00011477343,0.03624855,0.00005877481,0.000020598141,0.000020459296,0.00021034283,0.000019488914,0.0011923354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990153,0.000013841979,0.000005312297,0.00003055751,0.000025292173,0.000023369628],"domain_scores_gemma":[0.9997631,0.0000971713,0.000029523526,0.000016868038,0.000073417745,0.000019928284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003123617,0.00061531435,0.000494931,0.0005240876,0.00021009592,0.00037621052,0.0006316978,0.000522745,0.0007147568],"category_scores_gemma":[0.00090811064,0.00029382043,0.00034567295,0.00030139578,0.0002865698,0.00076915993,0.00037980144,0.0005702325,0.00011914304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019105255,0.00012680564,0.005919523,0.00006379934,0.00005054275,0.00012577792,0.000057019886,0.8315849,0.008423008,0.0023282692,0.0018891797,0.14924014],"study_design_scores_gemma":[0.0000013629407,0.0000068701106,0.00017972263,8.961582e-7,0.0000017084448,0.0000040266,0.000002545284,0.9989298,0.00048402633,0.00034483042,0.000043066844,0.0000011157434],"about_ca_topic_score_codex":0.012228437,"about_ca_topic_score_gemma":0.012598608,"teacher_disagreement_score":0.012228437,"about_ca_system_score_codex":0.00065715896,"about_ca_system_score_gemma":0.00061321555,"threshold_uncertainty_score":0.024314523},"labels":[],"label_agreement":null},{"id":"W3213012484","doi":"10.3390/s21227577","title":"Field Decorrelation and Isolation Improvement in an MIMO Antenna Using an All-Dielectric Device Based on Transformation Electromagnetics","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"MIMO; Electronic engineering; Electromagnetics; Microstrip antenna; Broadband; Antenna efficiency; Engineering; Antenna (radio); Acoustics; Electrical engineering; Physics; Telecommunications","score_opus":0.014241195775747532,"score_gpt":0.2345345013213816,"score_spread":0.22029330554563406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213012484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77147335,0.00058358914,0.22140405,0.00014411779,0.00012930247,0.000024812982,0.000043545067,0.00041021095,0.005787045],"genre_scores_gemma":[0.96321726,0.00016735482,0.035587206,0.00005912527,0.000020590089,0.000010567288,0.0000272219,0.000018309956,0.00089244626],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998677,0.000021635387,0.0000072435682,0.000031986434,0.00005074713,0.000020634023],"domain_scores_gemma":[0.99980456,0.000053864565,0.000054195007,0.000042623953,0.000030888958,0.000013838764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013167245,0.0003243882,0.00021206017,0.00017200889,0.000100822006,0.0003419531,0.0003039368,0.0002628754,0.00037680237],"category_scores_gemma":[0.00026573197,0.00014005585,0.00024110518,0.00021052365,0.00021687425,0.00035823372,0.00031729002,0.00025295274,0.00024748122],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067649955,0.00002878124,0.00038238487,0.000048379665,0.000011793507,0.0001221358,0.000037364447,0.0023403212,0.9838479,0.0013692566,0.000092105416,0.0116519015],"study_design_scores_gemma":[0.000012567827,0.00037260243,0.0010344186,0.000004792607,0.000020273957,0.00046363514,0.000040215968,0.03853723,0.95639503,0.0003597403,0.0027451797,0.000014295779],"about_ca_topic_score_codex":0.000051909185,"about_ca_topic_score_gemma":0.00010965801,"teacher_disagreement_score":0.00037680237,"about_ca_system_score_codex":0.00013348593,"about_ca_system_score_gemma":0.00007962835,"threshold_uncertainty_score":0.001260519},"labels":[],"label_agreement":null},{"id":"W3213739421","doi":"10.3390/s21227491","title":"Electronic Sensing Platform (ESP) Based on Open-Gate Junction Field-Effect Transistor (OG-JFET) for Life Science Applications: Design, Modeling and Experimental Results","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Mitacs; York University","keywords":"JFET; Multiphysics; Biosensor; Field-effect transistor; Optoelectronics; Materials science; Transistor; Electrical engineering; Electronic engineering; Computer science; Nanotechnology; Engineering; Finite element method; Voltage","score_opus":0.02790111039531349,"score_gpt":0.2812334536903978,"score_spread":0.2533323432950843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213739421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.839411,0.0023233464,0.14630541,0.0003416971,0.0002319167,0.00022431258,0.0010583536,0.0010823287,0.009021493],"genre_scores_gemma":[0.93386304,0.0006667563,0.060754158,0.00006227391,0.000020612022,0.000110173176,0.00043591417,0.000045949135,0.004041088],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998542,0.000008276659,0.000004413696,0.00003315523,0.000085245185,0.000014672387],"domain_scores_gemma":[0.99992335,0.00001491009,0.000012821787,0.00000747941,0.000033658907,0.000007792613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020490274,0.00039139742,0.00024835934,0.00017824666,0.0001276188,0.00026890886,0.0006116897,0.00056618237,0.00090095727],"category_scores_gemma":[0.00022216098,0.000100108664,0.00022987922,0.00015203231,0.0001422135,0.0004961686,0.00014994173,0.00016683435,0.00027987617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012195855,0.00008307789,0.0018645533,0.00028020132,0.000029790703,0.00035605635,0.000063760206,0.012068066,0.95706296,0.0012562484,0.00097418163,0.025839139],"study_design_scores_gemma":[0.000032606116,0.0010357,0.003926808,0.000024228842,0.00005871216,0.0004521056,0.000059735394,0.09490455,0.8865241,0.00048665397,0.01246004,0.00003477974],"about_ca_topic_score_codex":0.00046792845,"about_ca_topic_score_gemma":0.0005499652,"teacher_disagreement_score":0.00090095727,"about_ca_system_score_codex":0.00025742408,"about_ca_system_score_gemma":0.00020131911,"threshold_uncertainty_score":0.0030139685},"labels":[],"label_agreement":null},{"id":"W3213888950","doi":"10.3390/s21227562","title":"A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Motion planning; Obstacle; Computer science; Metric (unit); Path (computing); Context (archaeology); Probabilistic logic; Field (mathematics); Robot; Trajectory; Artificial intelligence; Data mining; Mathematics; Engineering; Geography","score_opus":0.030593060565217826,"score_gpt":0.27300780558228427,"score_spread":0.24241474501706645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213888950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017255349,0.000086865155,0.997335,0.000045785982,0.000014297794,0.000013407921,0.000037028265,0.00013686584,0.00060512964],"genre_scores_gemma":[0.30478582,0.00054077717,0.6893924,0.0001257843,0.000107911466,0.0002815855,0.00035116897,0.0002668409,0.004147776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992743,0.00019379139,0.000031841093,0.0001362367,0.00026612417,0.00009772474],"domain_scores_gemma":[0.9993741,0.00027962698,0.000079059544,0.00006272403,0.00013285338,0.00007158794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009938225,0.0008910711,0.0009065135,0.0007479453,0.00053225266,0.0012312322,0.0021499312,0.00086225895,0.0023037088],"category_scores_gemma":[0.0023887225,0.00059837813,0.0010452096,0.00086123415,0.0009123511,0.0021817912,0.0025232898,0.001471762,0.00045265962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006092703,0.00004253384,0.000558586,0.00010254904,0.00003775772,0.00012298446,0.00015762239,0.8577599,0.002303962,0.09253331,0.0016529164,0.044666972],"study_design_scores_gemma":[0.0000061421592,0.000035315028,0.000107308944,0.000009942326,0.000007974602,0.000048710386,0.000019542073,0.9705889,0.00037188517,0.026684761,0.0021067676,0.000012732761],"about_ca_topic_score_codex":0.0052855844,"about_ca_topic_score_gemma":0.005325083,"teacher_disagreement_score":0.0052855844,"about_ca_system_score_codex":0.001089348,"about_ca_system_score_gemma":0.001668328,"threshold_uncertainty_score":0.01050967},"labels":[],"label_agreement":null},{"id":"W3214202416","doi":"10.3390/s21227630","title":"Multiple Cylinder Extraction from Organized Point Clouds","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Cylinder; Geometric primitive; Point cloud; Ellipsoid; Computer science; Computer vision; Point (geometry); Artificial intelligence; Feature extraction; Object detection; Detector; Algorithm; Extraction (chemistry); Mathematics; Pattern recognition (psychology); Geometry; Physics","score_opus":0.020641714348937457,"score_gpt":0.22224752713766577,"score_spread":0.2016058127887283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214202416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065013275,0.0005694867,0.9289004,0.00007691576,0.00005402871,0.00013911557,0.00043615012,0.0027724474,0.0020381212],"genre_scores_gemma":[0.46089724,0.00093130546,0.5323945,0.00004813628,0.000054241056,0.00011538333,0.0032604653,0.00027542387,0.0020233274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928147,0.000040878516,0.000024566145,0.00010844096,0.0004435119,0.00010125156],"domain_scores_gemma":[0.9994517,0.00007637824,0.00007056633,0.000087233755,0.000278888,0.000035237423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027653202,0.0012724792,0.0010901474,0.0039444813,0.000490421,0.0010146521,0.0010664876,0.0006385889,0.00096769974],"category_scores_gemma":[0.0010774483,0.0005942655,0.0008999784,0.0029162227,0.0003371281,0.001168149,0.0014193653,0.0005629939,0.00097378035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038234377,0.00009360377,0.006232722,0.0003727541,0.00013926638,0.0009456942,0.0003041823,0.12552874,0.18163283,0.006166392,0.00591195,0.6722895],"study_design_scores_gemma":[0.0000140935435,0.000039780636,0.0042044744,0.000030374918,0.000025643558,0.00038762498,0.00013863874,0.9416963,0.045526434,0.003791219,0.004104482,0.000041016647],"about_ca_topic_score_codex":0.006392717,"about_ca_topic_score_gemma":0.010789981,"teacher_disagreement_score":0.006392717,"about_ca_system_score_codex":0.00047482643,"about_ca_system_score_gemma":0.0010816241,"threshold_uncertainty_score":0.0127109885},"labels":[],"label_agreement":null},{"id":"W3214208593","doi":"10.3390/s21217353","title":"Inertial Motion Capture-Based Whole-Body Inverse Dynamics","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse dynamics; Dynamics (music); Inertial frame of reference; Motion (physics); Motion capture; Computer science; Inverse; Classical mechanics; Physics; Artificial intelligence; Mathematics; Kinematics; Acoustics; Geometry","score_opus":0.005709587193053678,"score_gpt":0.19545159269854223,"score_spread":0.18974200550548856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214208593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07298426,0.0002433902,0.91726583,0.00012763776,0.00006732166,0.00019442715,0.0016061211,0.0018750723,0.0056359777],"genre_scores_gemma":[0.78089356,0.0004375282,0.20724486,0.00015631122,0.000036447625,0.00047562493,0.0030892417,0.00018223815,0.007484165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998777,0.000017310364,0.0000072523358,0.000037250546,0.000051043717,0.000009486826],"domain_scores_gemma":[0.99987495,0.000033952387,0.000013667964,0.000024132765,0.00004828765,0.0000049408036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024192885,0.0005207808,0.00042858007,0.0003782937,0.0001836954,0.00040650915,0.00044899795,0.00037469043,0.0028819263],"category_scores_gemma":[0.00067002286,0.00022798135,0.0003562657,0.00033737515,0.00016911943,0.0003008509,0.00040752318,0.00028935695,0.0009871366],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032530754,0.00023545553,0.019354917,0.00046698563,0.00022846808,0.0002554262,0.00025711302,0.40944386,0.16292205,0.004649861,0.009593283,0.39226723],"study_design_scores_gemma":[0.00003878462,0.00014764871,0.024857936,0.00002617967,0.0000592655,0.00019420183,0.0000352115,0.94509155,0.019615589,0.0012152886,0.008679117,0.000039223018],"about_ca_topic_score_codex":0.007952351,"about_ca_topic_score_gemma":0.009428541,"teacher_disagreement_score":0.007952351,"about_ca_system_score_codex":0.0002365588,"about_ca_system_score_gemma":0.00046634924,"threshold_uncertainty_score":0.015812159},"labels":[],"label_agreement":null},{"id":"W3214583795","doi":"10.3390/s21227438","title":"Data-Driven Model-Free Adaptive Control of Z-Source Inverters","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Converters; PID controller; Controller (irrigation); Computer science; Inverter; Quadratic equation; Control engineering; Engineering; Control (management); Mathematics; Voltage","score_opus":0.032639442585045836,"score_gpt":0.21882714733630212,"score_spread":0.1861877047512563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214583795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041062187,0.00025265192,0.9519644,0.000117131356,0.00006127684,0.000045385164,0.000069283225,0.00079212536,0.0056355507],"genre_scores_gemma":[0.98632324,0.0001187606,0.012315952,0.000025245738,0.000012824236,0.000052358348,0.000046948047,0.000017040955,0.0010875702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998246,0.000024843868,0.000010625768,0.00004208641,0.00008179769,0.000016103462],"domain_scores_gemma":[0.9998016,0.00006006877,0.00004989188,0.000025036663,0.00005711536,0.0000062684785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023833256,0.0004305934,0.00040927864,0.00018843955,0.00022982554,0.0006306484,0.0007641689,0.00030026032,0.0010524779],"category_scores_gemma":[0.0006344371,0.00016480297,0.00028689404,0.00020555967,0.00032533967,0.0004258568,0.00049538555,0.0005185627,0.00021062102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014529997,0.000058489502,0.0006628143,0.00021450692,0.00004516939,0.00010713422,0.00016440809,0.8707925,0.02923336,0.013201879,0.0009185308,0.084455974],"study_design_scores_gemma":[0.0000090379,0.00004520679,0.00014814906,0.000004940002,0.000005112929,0.000010108925,0.0000047422886,0.99596524,0.0023802184,0.0008562684,0.0005668877,0.0000040576024],"about_ca_topic_score_codex":0.0032105716,"about_ca_topic_score_gemma":0.0029609306,"teacher_disagreement_score":0.0032105716,"about_ca_system_score_codex":0.00034292252,"about_ca_system_score_gemma":0.00036524984,"threshold_uncertainty_score":0.0063837767},"labels":[],"label_agreement":null},{"id":"W3214904466","doi":"10.3390/s21227712","title":"Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"SAFER; Emerging technologies; Standardization; Key (lock); Service (business); Intelligent transportation system; Transport engineering; Traffic congestion; Engineering; Computer science; Telecommunications; Computer security; Systems engineering; Business; Marketing","score_opus":0.04870287235557407,"score_gpt":0.2589536534810525,"score_spread":0.2102507811254784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214904466","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060988235,0.99133986,0.0012861222,0.0007107507,0.0007293275,0.000018218881,0.00006822032,0.000030951458,0.0052066795],"genre_scores_gemma":[0.0031795413,0.9936884,0.0006853801,0.00036061718,0.000476994,0.000014551234,0.00015442,0.000007612143,0.0014324579],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942625,0.00008536812,0.00007395968,0.00010114384,0.00024664894,0.00006666101],"domain_scores_gemma":[0.99896204,0.0005137717,0.00008341211,0.000032676584,0.00036132243,0.00004683308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007941384,0.0008384695,0.0008870135,0.0026274144,0.0004452097,0.0018847487,0.0010477833,0.001452501,0.005592147],"category_scores_gemma":[0.0016445232,0.00046807277,0.0006951241,0.0036124075,0.0004623461,0.0040194322,0.00080399786,0.0014483649,0.0026624901],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008706347,0.00009692784,0.00062521937,0.023087287,0.00008583867,0.00029166907,0.00016350579,0.0019442334,0.0026876929,0.020713031,0.062243275,0.8879743],"study_design_scores_gemma":[0.0000066956286,0.00013918399,0.0005715701,0.0041999877,0.00012194961,0.00083838444,0.00020383041,0.0007541277,0.0010324615,0.004525519,0.9875688,0.00003743522],"about_ca_topic_score_codex":0.0018306116,"about_ca_topic_score_gemma":0.001911057,"teacher_disagreement_score":0.005592147,"about_ca_system_score_codex":0.000755506,"about_ca_system_score_gemma":0.0015589322,"threshold_uncertainty_score":0.018707573},"labels":[],"label_agreement":null},{"id":"W3214921239","doi":"10.3390/s21237891","title":"Design of a Novel Wearable System for Foot Clearance Estimation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Wearable computer; Computer science; Tripping; Simulation; Motion capture; Gait analysis; Gait; Offset (computer science); Physical medicine and rehabilitation; Artificial intelligence; Motion (physics); Medicine; Engineering; Embedded system","score_opus":0.05961263713640275,"score_gpt":0.35800786498879966,"score_spread":0.2983952278523969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214921239","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06450012,0.00096426177,0.9214509,0.00035508003,0.00064536795,0.00086331076,0.0004903367,0.0046673436,0.0060632606],"genre_scores_gemma":[0.63510686,0.00084195804,0.34918773,0.0007309522,0.00033693822,0.0013234925,0.0007639742,0.00011841493,0.011589717],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937636,0.000070132795,0.00006080223,0.00023437828,0.0002016394,0.0000566563],"domain_scores_gemma":[0.99960774,0.000047384485,0.000058518,0.00004262166,0.00020689626,0.0000369392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042102367,0.0007903724,0.0010012725,0.0008176967,0.00034447826,0.0006585932,0.001490331,0.00095719495,0.0035999634],"category_scores_gemma":[0.00072890706,0.00040845448,0.00044221405,0.00049891666,0.00019789152,0.0008061911,0.0007523256,0.0003730884,0.0021017971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009794482,0.0004073755,0.01197572,0.0014129669,0.00021804491,0.0018452998,0.0004915743,0.004703343,0.46527332,0.0021425325,0.011586341,0.498964],"study_design_scores_gemma":[0.0008557282,0.008915962,0.07703472,0.0005958237,0.0009875557,0.013628779,0.0006233653,0.45275888,0.33682394,0.0029999309,0.10421085,0.00056439004],"about_ca_topic_score_codex":0.0006772652,"about_ca_topic_score_gemma":0.0007260639,"teacher_disagreement_score":0.0035999634,"about_ca_system_score_codex":0.00019173033,"about_ca_system_score_gemma":0.00036743435,"threshold_uncertainty_score":0.0120431185},"labels":[],"label_agreement":null},{"id":"W3215261403","doi":"10.3390/s21227706","title":"Intelligent Transport System Using Time Delay-Based Multipath Routing Protocol for Vehicular Ad Hoc Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Computer network; Wireless ad hoc network; Vehicular ad hoc network; Multipath propagation; Multipath routing; Protocol (science); Routing protocol; Optimized Link State Routing Protocol; Intelligent transportation system; Routing (electronic design automation); Dynamic Source Routing; Telecommunications; Engineering; Medicine; Transport engineering; Wireless","score_opus":0.018382983642418436,"score_gpt":0.24676917709535462,"score_spread":0.22838619345293618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215261403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010705479,0.0058290474,0.96374047,0.0005069092,0.00075868337,0.0004010923,0.00014829196,0.0051122806,0.012797741],"genre_scores_gemma":[0.5905792,0.010599387,0.37290484,0.00045279303,0.0006078458,0.00089631526,0.0013591354,0.00026601861,0.022334518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943703,0.00012574157,0.00005642673,0.00009200985,0.00022967401,0.00005896728],"domain_scores_gemma":[0.9997073,0.000047473153,0.000049832783,0.00004310953,0.00013205902,0.00002027154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041297387,0.0006332636,0.0006627227,0.0009549942,0.00057078066,0.0011308107,0.0012824391,0.0007158792,0.00130033],"category_scores_gemma":[0.00086049637,0.00016931952,0.0005146016,0.0009882047,0.0003278505,0.0011722004,0.0009357107,0.0006981938,0.00079891225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002523072,0.00014752753,0.0017036113,0.0007992189,0.00023440858,0.0009467129,0.00033125328,0.1587841,0.059069537,0.05415394,0.025498545,0.6980788],"study_design_scores_gemma":[0.000107427455,0.000758921,0.0011447907,0.00014523987,0.0002998471,0.0015618467,0.00018000942,0.737472,0.029433878,0.024813807,0.20387022,0.00021203114],"about_ca_topic_score_codex":0.002382882,"about_ca_topic_score_gemma":0.0016102062,"teacher_disagreement_score":0.002382882,"about_ca_system_score_codex":0.00056673394,"about_ca_system_score_gemma":0.00095425296,"threshold_uncertainty_score":0.004738033},"labels":[],"label_agreement":null},{"id":"W3215291346","doi":"10.3390/s21238011","title":"A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voronoi diagram; Software deployment; Computer science; Genetic algorithm; Wireless sensor network; CMA-ES; Adaptation (eye); Real-time computing; Evolution strategy; Optimization problem; Distributed computing; Evolutionary algorithm; Artificial intelligence; Algorithm; Machine learning; Computer network","score_opus":0.015565399974670479,"score_gpt":0.24256489336369133,"score_spread":0.22699949338902084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215291346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032097395,0.000136486,0.99565095,0.000039808278,0.000014620337,0.000021627828,0.000029027804,0.000118409436,0.00077928556],"genre_scores_gemma":[0.29526764,0.00045026606,0.70079505,0.000076389275,0.000043682772,0.00032305674,0.00019421188,0.00014948196,0.00270019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996935,0.00010137863,0.000013444001,0.00005542449,0.00010622759,0.000030080482],"domain_scores_gemma":[0.9995983,0.0002100833,0.00004137586,0.000020908521,0.000102535436,0.000026858366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005441362,0.0005829919,0.00077304523,0.000932421,0.00042064578,0.00062515895,0.0010826038,0.0006720815,0.0018858829],"category_scores_gemma":[0.0013464034,0.000347851,0.0007341981,0.0009402618,0.00040245763,0.0007206584,0.0008391072,0.0004536257,0.00037407386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028525901,0.000013932969,0.0003783751,0.000051958472,0.00001943128,0.000032062133,0.000043841843,0.9505816,0.0017072284,0.006948218,0.00070521166,0.039489653],"study_design_scores_gemma":[0.0000037608077,0.0000073262295,0.000036227902,0.000002447727,0.0000020777136,0.0000122074425,0.0000052304504,0.99821484,0.00027405168,0.0008922353,0.0005460962,0.0000034482623],"about_ca_topic_score_codex":0.008402646,"about_ca_topic_score_gemma":0.008787532,"teacher_disagreement_score":0.008402646,"about_ca_system_score_codex":0.0008376717,"about_ca_system_score_gemma":0.00120145,"threshold_uncertainty_score":0.01670748},"labels":[],"label_agreement":null},{"id":"W3215348250","doi":"10.3390/s21227690","title":"Validity and Sensitivity of an Inertial Measurement Unit-Driven Biomechanical Model of Motor Variability for Gait","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Inertial measurement unit; Intraclass correlation; Swing; Kinematics; Gait analysis; Force platform; Treadmill; Trunk; Physical medicine and rehabilitation; Simulation; Ankle; Motion capture; Mathematics; Computer science; Statistics; Physical therapy; Medicine; Physics; Reproducibility; Motion (physics); Artificial intelligence; Acoustics","score_opus":0.11162472681167174,"score_gpt":0.36625119437213804,"score_spread":0.2546264675604663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215348250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94829434,0.0002399769,0.050008196,0.000045393852,0.000037015514,0.000088410045,0.0003938831,0.00013400015,0.00075875147],"genre_scores_gemma":[0.99482673,0.000039688246,0.0046222876,0.0000164575,0.000008906021,0.000038510923,0.000341135,0.00001287527,0.000093358736],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99731964,0.0012307684,0.00020271525,0.00065775454,0.0004926157,0.00009656345],"domain_scores_gemma":[0.9909937,0.0056969,0.0009650179,0.0011308013,0.0010564722,0.00015712103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006068605,0.0006560267,0.000553906,0.0009994659,0.00023245443,0.00082644506,0.0005968832,0.0006674134,0.00047599286],"category_scores_gemma":[0.028570835,0.00038551138,0.0007879182,0.00049418194,0.00032269207,0.0005231542,0.0009074243,0.0003223452,0.00026740224],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014048702,0.00034219265,0.84038746,0.00017189444,0.0011678705,0.00011684581,0.00059621315,0.08042506,0.008383233,0.00039999373,0.00042171398,0.06618267],"study_design_scores_gemma":[0.000049545528,0.0012196152,0.6271403,0.000057495963,0.00015274303,0.00036040894,0.00017840294,0.36729214,0.0018680873,0.0010391974,0.00058061996,0.00006153833],"about_ca_topic_score_codex":0.0042010867,"about_ca_topic_score_gemma":0.003859466,"teacher_disagreement_score":0.006068605,"about_ca_system_score_codex":0.00038486713,"about_ca_system_score_gemma":0.0003963745,"threshold_uncertainty_score":0.03209424},"labels":[],"label_agreement":null},{"id":"W3215581610","doi":"10.3390/s21238048","title":"Assessing Patient-Specific Microwave Breast Imaging in Clinical Case Studies","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Irish Research Council","keywords":"Microwave imaging; Microwave; Computer science; Image quality; Medical physics; Artificial intelligence; Focus (optics); Breast imaging; Computation; Dielectric; Iterative reconstruction; Computer vision; Image (mathematics); Medicine; Mammography; Breast cancer; Telecommunications; Algorithm; Materials science; Physics; Optics","score_opus":0.031881221843171055,"score_gpt":0.3113706714359228,"score_spread":0.27948944959275174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215581610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9506998,0.0019558715,0.04177319,0.00040388072,0.000027267239,0.00024313018,0.00021205725,0.00010254624,0.0045820996],"genre_scores_gemma":[0.9688352,0.0014307922,0.028861886,0.000066709916,0.000028398148,0.00004267179,0.00019102042,0.000029227349,0.0005140784],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99866223,0.00070758734,0.00012374784,0.00014390761,0.00025452065,0.00010806064],"domain_scores_gemma":[0.99697757,0.0017315008,0.00043429725,0.00029912812,0.00036184708,0.0001956098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021806476,0.0005179067,0.00037389292,0.0022289497,0.00032821452,0.00129401,0.00061868713,0.0014122972,0.0020790414],"category_scores_gemma":[0.00985187,0.0003409804,0.00035362432,0.00080339354,0.0005850429,0.0007392203,0.000982958,0.00042787282,0.00054296793],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017334672,0.00049810647,0.5084152,0.0010150436,0.0002138608,0.15634856,0.005619202,0.029301766,0.06449062,0.0041051703,0.0032196552,0.2250394],"study_design_scores_gemma":[0.00005571707,0.0013428392,0.26813376,0.00045488073,0.00030883576,0.5641269,0.011147673,0.089774884,0.04089371,0.005137653,0.018444551,0.00017861662],"about_ca_topic_score_codex":0.0006473989,"about_ca_topic_score_gemma":0.0012341593,"teacher_disagreement_score":0.0022289497,"about_ca_system_score_codex":0.00029042736,"about_ca_system_score_gemma":0.00033568311,"threshold_uncertainty_score":0.011532545},"labels":[],"label_agreement":null},{"id":"W3216769124","doi":"10.3390/s21237868","title":"Simulation of RSO Images for Space Situation Awareness (SSA) Using Parallel Processing","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Computer science; Space (punctuation); Simulation; Real-time computing; Artificial intelligence; Computer vision","score_opus":0.024575658555275316,"score_gpt":0.2773869684751273,"score_spread":0.25281130991985196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216769124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5539912,0.00025492438,0.41426775,0.0006559176,0.00022929184,0.00028305312,0.0019214579,0.0042075505,0.024188912],"genre_scores_gemma":[0.9240961,0.00013020847,0.07211052,0.000081174934,0.000023058617,0.00014540565,0.0011634106,0.0001700457,0.0020801728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998933,0.000021415019,0.000005985569,0.00002217648,0.000035803023,0.000021427439],"domain_scores_gemma":[0.9997131,0.00013549741,0.000025685871,0.000028001074,0.00006434307,0.000033235516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019565846,0.0005793596,0.00038256444,0.00031438703,0.00036393703,0.0005706874,0.00084570545,0.0006897552,0.0025088163],"category_scores_gemma":[0.0008662872,0.00025806593,0.00049419183,0.00042283023,0.00038053677,0.00048745668,0.0005414478,0.00061572593,0.000264849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034998124,0.000022717857,0.0011201515,0.000017307591,0.000011905456,0.000041213843,0.000035766327,0.9936504,0.0010653852,0.00073041196,0.00044867926,0.0028210103],"study_design_scores_gemma":[0.0000052712303,0.000005849741,0.00013230738,8.7269217e-7,0.0000013637949,0.0000048989955,0.000009351792,0.9991365,0.00028864352,0.00022526814,0.00018772436,0.0000019012965],"about_ca_topic_score_codex":0.01898886,"about_ca_topic_score_gemma":0.011501002,"teacher_disagreement_score":0.01898886,"about_ca_system_score_codex":0.00052590354,"about_ca_system_score_gemma":0.00078094105,"threshold_uncertainty_score":0.03775662},"labels":[],"label_agreement":null},{"id":"W3217184820","doi":"10.3390/s21237889","title":"A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; International Development Research Centre","keywords":"Agriculture; Data science; Field (mathematics); Thematic analysis; Thematic map; Productivity; Knowledge management; Computer science; The Internet; World Wide Web; Geography; Social science; Sociology; Qualitative research","score_opus":0.06000251885966591,"score_gpt":0.28716946665674037,"score_spread":0.22716694779707447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217184820","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22125378,0.5507387,0.014685141,0.004607499,0.00086354255,0.002676453,0.11452388,0.00088636857,0.0897646],"genre_scores_gemma":[0.48682985,0.40466046,0.038656875,0.00052017526,0.00064691866,0.0035819223,0.05546805,0.00015729196,0.009478499],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959611,0.0008344743,0.0009862713,0.0004598834,0.0015408624,0.00021735247],"domain_scores_gemma":[0.98568463,0.009036889,0.0020145075,0.00031844876,0.0027007612,0.00024472587],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0039332546,0.00089367933,0.0015227303,0.1338573,0.0011724846,0.002616266,0.00080976286,0.0005922311,0.007299864],"category_scores_gemma":[0.0156915,0.00028297352,0.0016815884,0.1760334,0.00051929377,0.002568365,0.0014070361,0.00050100544,0.0010042024],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036509451,0.00014318104,0.06532309,0.1939028,0.0031809527,0.0016329362,0.0072157276,0.003091143,0.006504949,0.014795048,0.038497847,0.6653472],"study_design_scores_gemma":[0.00009601991,0.00038284395,0.29087415,0.04905137,0.011056917,0.003462027,0.011759676,0.0055587017,0.0052802092,0.012207831,0.610039,0.00023129427],"about_ca_topic_score_codex":0.0049870876,"about_ca_topic_score_gemma":0.009317211,"teacher_disagreement_score":0.99606675,"about_ca_system_score_codex":0.001905507,"about_ca_system_score_gemma":0.005410681,"threshold_uncertainty_score":0.0244205},"labels":[],"label_agreement":null},{"id":"W3217380447","doi":"10.3390/s21237785","title":"Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Energy, Northern Development and Mines","funders":"State Key Laboratory of Fire Science; University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Smoke; Classifier (UML); RGB color model; Feature (linguistics); Deep learning; Pixel; Pattern recognition (psychology); Test data; Computer vision; Engineering","score_opus":0.02282959069153991,"score_gpt":0.21033028816167462,"score_spread":0.1875006974701347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217380447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.631494,0.00032847203,0.3621667,0.00017451892,0.0001454975,0.00010988311,0.0005830333,0.002096896,0.0029010526],"genre_scores_gemma":[0.9079789,0.00015308858,0.0886402,0.000121712634,0.00002127516,0.00006356638,0.001415904,0.00006856398,0.0015366715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998394,0.000020161042,0.000007870203,0.00005748453,0.00004224813,0.00003283282],"domain_scores_gemma":[0.99978846,0.000055010256,0.000020675485,0.000046116726,0.00007456521,0.000015173637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004492195,0.0005791006,0.00031412605,0.0004977149,0.00014206117,0.00038965364,0.0004902743,0.00047109375,0.00066657393],"category_scores_gemma":[0.00086532155,0.00016856482,0.0005912847,0.00033487412,0.00031921698,0.0005448607,0.0003676905,0.00059306284,0.00027909383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053786644,0.00043834242,0.0126705775,0.00017966887,0.00013994188,0.0003146681,0.00012974268,0.46605614,0.12748219,0.0021111064,0.0036903333,0.38624936],"study_design_scores_gemma":[0.0000065281592,0.000048895356,0.0024373382,0.00000641044,0.000010731352,0.00005338517,0.000024852206,0.97377485,0.022700137,0.00042122827,0.00050557486,0.000010020039],"about_ca_topic_score_codex":0.0034448712,"about_ca_topic_score_gemma":0.004835714,"teacher_disagreement_score":0.0034448712,"about_ca_system_score_codex":0.00034110525,"about_ca_system_score_gemma":0.00033268385,"threshold_uncertainty_score":0.0068496466},"labels":[],"label_agreement":null},{"id":"W3217426506","doi":"10.3390/s21237851","title":"Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and Higher Education of the Russian Federation; Tomsk State University","keywords":"Aptamer; Dielectric spectroscopy; Materials science; Electrode; Electrochemistry; Oligonucleotide; Lung cancer; Nanotechnology; Biomedical engineering; Chemistry; Pathology; Medicine; DNA; Molecular biology; Biology; Biochemistry","score_opus":0.009263913690850426,"score_gpt":0.3037672304794264,"score_spread":0.29450331678857594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217426506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28120747,0.02191146,0.68653995,0.001183234,0.0004908559,0.0006526567,0.0008790613,0.002598404,0.004536834],"genre_scores_gemma":[0.5356368,0.0073018908,0.44830695,0.0008777616,0.00010777152,0.0004204491,0.00069069513,0.00006851459,0.0065892073],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954957,0.0000968985,0.00003445914,0.00013767523,0.0001543721,0.00002696662],"domain_scores_gemma":[0.9997385,0.00008699049,0.00002609802,0.000020666408,0.00009585541,0.000031899755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006966305,0.00049295375,0.00046136964,0.00036135744,0.00013909534,0.0003601285,0.00050023117,0.0008554698,0.0008075671],"category_scores_gemma":[0.0007100966,0.00030583082,0.0002485856,0.00023803824,0.00013489947,0.00034510365,0.00028972022,0.0006895018,0.00090899447],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030859388,0.000026546064,0.00030678266,0.000082342325,0.0000124765265,0.00005888036,0.00003052736,0.00017472566,0.98768985,0.00014641805,0.00011224549,0.01132839],"study_design_scores_gemma":[0.00001058159,0.0002574509,0.0013077841,0.000011187604,0.000022775772,0.00072399026,0.000018709574,0.0065642376,0.9863886,0.00017807593,0.004501514,0.000015083195],"about_ca_topic_score_codex":0.00035443585,"about_ca_topic_score_gemma":0.0007467962,"teacher_disagreement_score":0.0008554698,"about_ca_system_score_codex":0.00019852008,"about_ca_system_score_gemma":0.00024161466,"threshold_uncertainty_score":0.003684163},"labels":[],"label_agreement":null},{"id":"W3217586766","doi":"10.3390/s21238083","title":"Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":186,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Contextual image classification; Land cover; Remote sensing; Residual; Pattern recognition (psychology); Machine learning; Clipping (morphology); Artificial neural network; Land use; Image (mathematics); Geography; Algorithm; Engineering","score_opus":0.06235099648868551,"score_gpt":0.2757454833334606,"score_spread":0.2133944868447751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217586766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8346177,0.018414287,0.12196851,0.0011017614,0.00045968167,0.00025009472,0.0016172209,0.0028557533,0.0187151],"genre_scores_gemma":[0.96767366,0.0017787354,0.023732644,0.00010151427,0.00008020184,0.000059936214,0.0024462347,0.000092166425,0.004035083],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922407,0.00020943882,0.00004970833,0.00015267763,0.00025414512,0.00010993444],"domain_scores_gemma":[0.998863,0.00052101637,0.00006291343,0.00015682398,0.0003302816,0.00006597504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024327035,0.0009830615,0.00072330254,0.0016832832,0.00030536085,0.0007737188,0.00093588856,0.0009380226,0.0018929753],"category_scores_gemma":[0.0031369063,0.00016504011,0.0006071606,0.0015451808,0.00037064144,0.0016623812,0.0008541486,0.00081481686,0.0007194226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010951887,0.000819441,0.018105382,0.0005463271,0.00044362817,0.00017056626,0.00012195914,0.23830612,0.004934061,0.0020501406,0.008392648,0.72501457],"study_design_scores_gemma":[0.000031417756,0.000301203,0.0120697925,0.00003629792,0.000083601655,0.00006279678,0.00012973174,0.9783277,0.0046346406,0.0014919669,0.0028085879,0.000022256098],"about_ca_topic_score_codex":0.01179151,"about_ca_topic_score_gemma":0.0071042953,"teacher_disagreement_score":0.01179151,"about_ca_system_score_codex":0.0009817072,"about_ca_system_score_gemma":0.00066977285,"threshold_uncertainty_score":0.023445725},"labels":[],"label_agreement":null},{"id":"W3217646127","doi":"10.3390/s21227700","title":"Influence of Magnetostriction Induced by the Periodic Permanent Magnet Electromagnetic Acoustic Transducer (PPM EMAT) on Steel","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Mitacs","keywords":"Electromagnetic acoustic transducer; Magnetostriction; Acoustics; Materials science; Transducer; Lorentz force; Magnet; Amplitude; Transmitter; SIGNAL (programming language); Physics; Ultrasonic sensor; Magnetic field; Electrical engineering; Engineering; Optics; Computer science; Ultrasonic testing; Channel (broadcasting)","score_opus":0.005783738206116337,"score_gpt":0.19236115769692674,"score_spread":0.18657741949081041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217646127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99562436,0.00018225149,0.0033165896,0.00003754693,0.00002227414,0.0000075539338,0.000031606018,0.000052042575,0.0007257727],"genre_scores_gemma":[0.9993,0.000058423768,0.00043749428,0.000008847696,0.000003075152,0.0000016789777,0.000013983324,0.0000041909984,0.00017223279],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966705,0.000058854934,0.000017389688,0.00006128308,0.00014879742,0.000046598554],"domain_scores_gemma":[0.99918205,0.00043680985,0.00016710328,0.00006595748,0.00010670738,0.000041344752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002860787,0.00027603787,0.00017101475,0.00017646309,0.00012975004,0.00019529184,0.00020052273,0.00028531492,0.00047562007],"category_scores_gemma":[0.0013543958,0.00022206716,0.00013721849,0.00013085075,0.00034852204,0.0001558432,0.00022224677,0.00018801243,0.00011599619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020203298,0.000013629757,0.0014803495,0.00005987161,0.000010996149,0.00018719365,0.000050566996,0.0034271334,0.9912226,0.000052456395,0.00006274323,0.0032303645],"study_design_scores_gemma":[0.000013081493,0.0007863075,0.018656833,0.000011846004,0.00003226244,0.00024826147,0.0000799701,0.023746658,0.95576346,0.000037038597,0.0006105701,0.000013695762],"about_ca_topic_score_codex":0.00090709457,"about_ca_topic_score_gemma":0.0017178212,"teacher_disagreement_score":0.00090709457,"about_ca_system_score_codex":0.00022069072,"about_ca_system_score_gemma":0.00015267456,"threshold_uncertainty_score":0.0018036962},"labels":[],"label_agreement":null},{"id":"W4200042261","doi":"10.3390/s21248218","title":"Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor Approach","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Université de Sherbrooke","keywords":"Sensor fusion; Road surface; Computer science; Deep learning; Data mining; State (computer science); Microphone; Artificial intelligence; Real-time computing; Engineering; Telecommunications; Civil engineering","score_opus":0.09274216694963608,"score_gpt":0.320363633219294,"score_spread":0.22762146626965793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200042261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1383576,0.00029704013,0.85957044,0.00017200285,0.00005178197,0.00003168878,0.00020463612,0.00038248126,0.0009323978],"genre_scores_gemma":[0.9048876,0.00011255987,0.09422568,0.000039074996,0.000022262606,0.000025273965,0.00021273426,0.000018991852,0.00045586706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.000090374466,0.000027697302,0.00012338419,0.00012498692,0.00004032944],"domain_scores_gemma":[0.99952316,0.00015152976,0.000078897785,0.00008936097,0.00013270642,0.000024376495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007432802,0.000581208,0.00047086077,0.00096597953,0.00020301831,0.0005801959,0.0005691791,0.00059068337,0.00052197726],"category_scores_gemma":[0.0015302007,0.00025825793,0.0005988893,0.0006463493,0.000413103,0.001361623,0.0008399768,0.00085736543,0.00014929559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064563716,0.00033542776,0.012597613,0.00027622658,0.00024728486,0.00031839331,0.00053125585,0.44487408,0.16703771,0.009736572,0.0014990112,0.3619007],"study_design_scores_gemma":[0.000005605185,0.00007407652,0.004711492,0.000007270054,0.000022187105,0.000040960862,0.000046603865,0.97413754,0.01714558,0.0032069783,0.0005772467,0.000024441826],"about_ca_topic_score_codex":0.0009820926,"about_ca_topic_score_gemma":0.0016674958,"teacher_disagreement_score":0.0009820926,"about_ca_system_score_codex":0.0004227867,"about_ca_system_score_gemma":0.00033781966,"threshold_uncertainty_score":0.0039309263},"labels":[],"label_agreement":null},{"id":"W4200090978","doi":"10.3390/s21248399","title":"The Validity and Reliability of Two Commercially Available Load Sensors for Clinical Strength Assessment","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC; WorkSafeBC","keywords":"Reliability engineering; Reliability (semiconductor); Computer science; Engineering","score_opus":0.048318793572909816,"score_gpt":0.33436538588222453,"score_spread":0.28604659230931473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200090978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9650814,0.003244477,0.023929037,0.0003285683,0.00029431842,0.0005497585,0.0005847702,0.00019128993,0.005796344],"genre_scores_gemma":[0.9818563,0.0003559184,0.015940571,0.00013350528,0.00007188387,0.0003122541,0.00036915782,0.000030698455,0.0009297273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97632027,0.008987659,0.0022362121,0.002638163,0.009453168,0.00036452612],"domain_scores_gemma":[0.95728195,0.022253755,0.0063633854,0.0029927127,0.01056583,0.00054238824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019764312,0.000989035,0.0005320637,0.001863667,0.00041791372,0.0011483551,0.0012193844,0.0014892848,0.0017485747],"category_scores_gemma":[0.038718082,0.00050991634,0.0010304954,0.0008144128,0.0010957661,0.0010979526,0.0013798927,0.0006060558,0.0008962546],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035235283,0.0008173061,0.78683454,0.0005233643,0.00083448924,0.000111619454,0.0012523573,0.001816712,0.014261944,0.00061904814,0.0014940117,0.1879111],"study_design_scores_gemma":[0.00047097914,0.00449611,0.96028596,0.00039630078,0.0004650674,0.00092916976,0.00060474285,0.01897447,0.009012469,0.0006224554,0.003624779,0.00011739462],"about_ca_topic_score_codex":0.0009942936,"about_ca_topic_score_gemma":0.0020442593,"teacher_disagreement_score":0.019764312,"about_ca_system_score_codex":0.00058382214,"about_ca_system_score_gemma":0.00063345133,"threshold_uncertainty_score":0.10452497},"labels":[],"label_agreement":null},{"id":"W4200099133","doi":"10.3390/s22010084","title":"Representation of Spatial Variability of the Water Fluxes over the Congo Basin Region","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Structural basin; Spatial variability; Precipitation; Climatology; Spatial ecology; Common spatial pattern; Evapotranspiration; Tropical Atlantic; Image resolution; Environmental science; Geology; Geography; Meteorology; Sea surface temperature; Geomorphology","score_opus":0.019393896745912236,"score_gpt":0.23907411263287068,"score_spread":0.21968021588695844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200099133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975846,0.00008146256,0.0006406451,0.00004451991,0.0000022996892,0.000002777094,0.0010335628,0.000044991502,0.0005652536],"genre_scores_gemma":[0.9989176,0.000036449692,0.00039115426,0.0000029414464,0.0000013095131,0.0000027927035,0.00054118276,0.0000033449887,0.0001033457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999386,0.0000143900725,0.0000051309285,0.000020676025,0.000007881584,0.0000133782105],"domain_scores_gemma":[0.9998945,0.000031607513,0.000026632777,0.000014984954,0.000023334498,0.0000089909845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016655237,0.00016150145,0.00014647066,0.0007664086,0.00015234997,0.0005130331,0.00016906945,0.0001640737,0.0006270763],"category_scores_gemma":[0.00044193887,0.00008985302,0.00020326955,0.0007216893,0.0001263291,0.00024536691,0.0001886902,0.000110292676,0.00006014025],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048724643,0.00020300069,0.5028594,0.00013277239,0.000364794,0.0007032541,0.00042081054,0.38928258,0.036598977,0.0017289715,0.0018342041,0.06538401],"study_design_scores_gemma":[0.000017024522,0.000021491845,0.58113396,0.000018826357,0.000048386548,0.00008925402,0.00021500447,0.41432315,0.002820432,0.00023245355,0.0010545473,0.000025398756],"about_ca_topic_score_codex":0.056019258,"about_ca_topic_score_gemma":0.04715858,"teacher_disagreement_score":0.056019258,"about_ca_system_score_codex":0.00054917077,"about_ca_system_score_gemma":0.00026591725,"threshold_uncertainty_score":0.11138636},"labels":[],"label_agreement":null},{"id":"W4200127458","doi":"10.3390/s21248480","title":"Detecting Teeth Defects on Automotive Gears Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive industry; Process (computing); Scalability; Engineering; Automotive engineering; Visual inspection; Component (thermodynamics); Computer science; Artificial intelligence","score_opus":0.021659974509176082,"score_gpt":0.24000533710431635,"score_spread":0.21834536259514026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200127458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81007063,0.0009269112,0.18049411,0.00025449545,0.00010223603,0.00006340858,0.00044795018,0.0047082896,0.0029318798],"genre_scores_gemma":[0.9618539,0.00016338342,0.035665695,0.00007938953,0.000014499318,0.00001436537,0.000492731,0.000052413514,0.0016635763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978477,0.000015160997,0.000011592799,0.000065634915,0.00007298136,0.000049998023],"domain_scores_gemma":[0.99966466,0.000087046305,0.00005802279,0.000047307665,0.000120151395,0.000022784023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027510413,0.00077534927,0.00053966016,0.0013328786,0.00017872266,0.00044457658,0.0008212419,0.00073615246,0.0010168697],"category_scores_gemma":[0.0009100946,0.0003051918,0.00053951563,0.00040972192,0.0002575456,0.0004823605,0.00055398355,0.0004507983,0.00043060962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047607083,0.00041138934,0.028061522,0.00021752606,0.00014666874,0.0011791781,0.00015275794,0.21189906,0.120003276,0.00070828694,0.0060970723,0.6306473],"study_design_scores_gemma":[0.000005868254,0.00006529367,0.0054848716,0.00001359757,0.000017400398,0.00014285572,0.00003769837,0.9782637,0.015043828,0.0004246863,0.00049205025,0.000008073569],"about_ca_topic_score_codex":0.0047522173,"about_ca_topic_score_gemma":0.007353584,"teacher_disagreement_score":0.0047522173,"about_ca_system_score_codex":0.00044457527,"about_ca_system_score_gemma":0.00031078974,"threshold_uncertainty_score":0.009449124},"labels":[],"label_agreement":null},{"id":"W4200151102","doi":"10.3390/s21248169","title":"An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Wearable computer; Artificial intelligence; Noise (video); Baseline (sea); Wearable technology; Mobile device; Motion sensors; Medical diagnosis; SIGNAL (programming language); Ambulatory ECG; Ambulatory; Computer vision; Noise reduction; Motion (physics); Real-time computing; Medicine; Embedded system","score_opus":0.021905933091655182,"score_gpt":0.3419431074636854,"score_spread":0.3200371743720302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200151102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029497305,0.0005994092,0.9673903,0.00010023527,0.00015150325,0.00009448796,0.00006758793,0.0014718449,0.0006273832],"genre_scores_gemma":[0.1078599,0.00045572416,0.8877835,0.00014314159,0.0001592278,0.00012299395,0.00037731096,0.00018723957,0.0029109102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927694,0.00007140439,0.00005843791,0.0001882377,0.0003691548,0.00003578211],"domain_scores_gemma":[0.999164,0.00021169976,0.00009667875,0.00009568081,0.0003984621,0.000033528395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005358485,0.00097368984,0.00075111986,0.0013118127,0.00040230417,0.0005768267,0.00084282784,0.0010247574,0.0010523038],"category_scores_gemma":[0.0018352641,0.00029960804,0.0007120099,0.000840631,0.00031513002,0.0006309006,0.00043110806,0.00067777856,0.0007732552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022252194,0.0002038522,0.0011837734,0.00016042471,0.00008923106,0.00019178035,0.00008608366,0.0057496037,0.21812545,0.00077036984,0.0024298234,0.77078706],"study_design_scores_gemma":[0.00023568513,0.0013043741,0.02942469,0.00007094725,0.00038905352,0.005051413,0.000108821216,0.63475835,0.3040471,0.0012500108,0.023171853,0.00018759776],"about_ca_topic_score_codex":0.0014758665,"about_ca_topic_score_gemma":0.002268561,"teacher_disagreement_score":0.0014758665,"about_ca_system_score_codex":0.00017949681,"about_ca_system_score_gemma":0.0007318506,"threshold_uncertainty_score":0.0035202503},"labels":[],"label_agreement":null},{"id":"W4200200047","doi":"10.3390/s21248408","title":"Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Codebook; Reproducing kernel Hilbert space; Computer science; Bit error rate; Quantization (signal processing); Algorithm; Multiuser detection; Noma; Computer engineering; Detector; Electronic engineering; Mathematics; Telecommunications; Telecommunications link; Engineering; Hilbert space; Decoding methods","score_opus":0.05721576314701495,"score_gpt":0.3973488942491614,"score_spread":0.3401331311021465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200200047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032470733,0.0040750466,0.9541961,0.00028466902,0.00006207733,0.00006181538,0.000041749547,0.00032501668,0.008482873],"genre_scores_gemma":[0.7404939,0.0044638203,0.24855547,0.00014319569,0.00011872891,0.00011365314,0.000110165434,0.00014060922,0.005860512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99808323,0.00062661024,0.000049798058,0.0001402843,0.0009888352,0.00011120487],"domain_scores_gemma":[0.9884433,0.008658977,0.00055322587,0.0005444256,0.0017184309,0.00008164177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021813512,0.00093600765,0.00074078195,0.0012947416,0.00034803574,0.0011759324,0.00077579456,0.000895123,0.0022119768],"category_scores_gemma":[0.013178881,0.00032862969,0.0005485924,0.0009962627,0.0008450493,0.0016799114,0.0008703301,0.0008398127,0.00054927415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004222767,0.00016636642,0.0020534932,0.0008335729,0.00017098153,0.00024084546,0.00037164267,0.599744,0.036009185,0.10231946,0.001840837,0.25582722],"study_design_scores_gemma":[0.000004270934,0.00007438005,0.00030577328,0.000033449927,0.000014652124,0.00015089053,0.000027715945,0.98768824,0.0068512606,0.0037519098,0.0010799905,0.000017467311],"about_ca_topic_score_codex":0.0010258133,"about_ca_topic_score_gemma":0.001519624,"teacher_disagreement_score":0.0022119768,"about_ca_system_score_codex":0.0011361863,"about_ca_system_score_gemma":0.0008938007,"threshold_uncertainty_score":0.011536241},"labels":[],"label_agreement":null},{"id":"W4200264153","doi":"10.3390/s21248352","title":"A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Displacement (psychology); Landslide; Algorithm; Support vector machine; Computer science; Particle swarm optimization; Artificial intelligence; Residual; Component (thermodynamics); Machine learning; Data mining; Engineering","score_opus":0.016285219150720686,"score_gpt":0.2425459306159293,"score_spread":0.22626071146520862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200264153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01362264,0.00039704208,0.983217,0.00015976452,0.00007086128,0.00004082094,0.00006593511,0.00039429616,0.0020315172],"genre_scores_gemma":[0.789272,0.00079130265,0.20109688,0.00019085385,0.00011775132,0.00045054205,0.0004835121,0.00010570582,0.00749146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959785,0.00007155775,0.000036067388,0.00013027168,0.00012151275,0.000042641444],"domain_scores_gemma":[0.9996973,0.00010247862,0.00004844853,0.000016899616,0.000120364166,0.000014468619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007566178,0.0010336994,0.0012090661,0.00069665414,0.00046311162,0.0010487995,0.0014341201,0.0010477384,0.0013502693],"category_scores_gemma":[0.00095100765,0.000528661,0.001028937,0.00065778784,0.00044233652,0.0010039667,0.00078570825,0.0010419362,0.0003567512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037041307,0.000029622108,0.001246008,0.000056537938,0.00005390095,0.000075716314,0.000042211825,0.95366544,0.0017630329,0.0024290222,0.0007814407,0.039819997],"study_design_scores_gemma":[0.000002393647,0.000007757443,0.00008494309,0.0000017892147,0.0000033812441,0.00000569668,0.0000018570429,0.99936837,0.00014163117,0.0002026775,0.000176962,0.000002564822],"about_ca_topic_score_codex":0.012167602,"about_ca_topic_score_gemma":0.0065486985,"teacher_disagreement_score":0.012167602,"about_ca_system_score_codex":0.00055784127,"about_ca_system_score_gemma":0.0008832583,"threshold_uncertainty_score":0.024193585},"labels":[],"label_agreement":null},{"id":"W4200278843","doi":"10.3390/s21248172","title":"An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"University of Manitoba; Natural Sciences and Engineering Research Council of Canada; CancerCare Manitoba Foundation","keywords":"Artifact (error); Radar; Artificial intelligence; Computer science; Iterative reconstruction; Sensitivity (control systems); Feature (linguistics); Computer vision; Microwave; Microwave imaging; Imaging phantom; Algorithm; Pattern recognition (psychology); Medicine; Nuclear medicine; Electronic engineering; Engineering; Telecommunications","score_opus":0.00556312905979181,"score_gpt":0.19543943468001815,"score_spread":0.18987630562022634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200278843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034073351,0.000078116194,0.9959346,0.00004981427,0.0000049899068,0.000013914492,0.0000062247086,0.000092354894,0.0004125796],"genre_scores_gemma":[0.119135484,0.0002780439,0.87837034,0.00007203747,0.000023724444,0.00008678645,0.00004892411,0.00011814195,0.0018666283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997197,0.00012291777,0.000015742702,0.000041432657,0.000082467304,0.000017772238],"domain_scores_gemma":[0.99959403,0.00024360503,0.000054349668,0.00002733741,0.000071077244,0.000009676516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008855556,0.00053591,0.00058579113,0.00033564057,0.00015053453,0.00040936237,0.00048690476,0.00059582683,0.00091096526],"category_scores_gemma":[0.0016125399,0.00039795673,0.00048652824,0.0003042148,0.00046847097,0.0004355114,0.00041078767,0.00065740704,0.0002687643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080240265,0.000041654355,0.00036642182,0.00011423272,0.00005002118,0.000056085315,0.000053153708,0.88352996,0.027008103,0.011777359,0.00044465877,0.0764781],"study_design_scores_gemma":[0.0000044697194,0.000019760033,0.00008888501,0.0000031565346,0.000003644476,0.00002317844,0.0000030613855,0.9953863,0.0031073987,0.0008663332,0.000488461,0.000005418051],"about_ca_topic_score_codex":0.0017823345,"about_ca_topic_score_gemma":0.0016848762,"teacher_disagreement_score":0.0017823345,"about_ca_system_score_codex":0.00039939658,"about_ca_system_score_gemma":0.00060702406,"threshold_uncertainty_score":0.0046833754},"labels":[],"label_agreement":null},{"id":"W4200280393","doi":"10.3390/s22010186","title":"Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Taif University","keywords":"Cluster analysis; Computer science; Component (thermodynamics); Entropy (arrow of time); Artificial intelligence; Generalization; Categorization; Scheme (mathematics); Model selection; Machine learning; Algorithm; Mathematics","score_opus":0.013011291037890409,"score_gpt":0.25903295338330834,"score_spread":0.24602166234541792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200280393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014828464,0.00006941857,0.9979055,0.000036287965,0.000008885238,0.000011377187,0.000009543838,0.000052524665,0.00042363413],"genre_scores_gemma":[0.23400082,0.00047126773,0.7590754,0.0001631845,0.000074739735,0.000256619,0.00025118585,0.0003073887,0.005399423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949205,0.0002314793,0.000025332012,0.000081217746,0.00012667541,0.000043147666],"domain_scores_gemma":[0.99941874,0.00033347742,0.00003665941,0.00007328482,0.00009358658,0.000044138127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018161122,0.0009621745,0.001021856,0.0007441204,0.00063005846,0.00091946963,0.0022460076,0.0012920051,0.0025362126],"category_scores_gemma":[0.0030460272,0.0006566228,0.001178255,0.00082996365,0.0012502761,0.001627605,0.0022433857,0.0020084535,0.0007793575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008168725,0.000036949514,0.00047757593,0.00009930729,0.00006329837,0.00007370881,0.00021174143,0.7258636,0.007961619,0.18844773,0.0016420033,0.07504084],"study_design_scores_gemma":[0.000002236401,0.000005861514,0.000017298562,0.0000022901038,0.0000025002282,0.000009094791,0.0000033747722,0.9897517,0.00039238873,0.0094087925,0.00039949038,0.0000048809675],"about_ca_topic_score_codex":0.0038778675,"about_ca_topic_score_gemma":0.004068617,"teacher_disagreement_score":0.0038778675,"about_ca_system_score_codex":0.0010782178,"about_ca_system_score_gemma":0.001294106,"threshold_uncertainty_score":0.009604633},"labels":[],"label_agreement":null},{"id":"W4200332722","doi":"10.3390/s22010082","title":"A Review of Data Gathering Methods for Evaluating Socially Assistive Systems","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Office for Philosophy and Social Sciences; Shanghai Jiao Tong University; Technische Universiteit Eindhoven","keywords":"Loneliness; Autism; Usability; Psychology; Sass; Inclusion (mineral); Social isolation; Data collection; Observational study; Web accessibility; Scopus; Applied psychology; Developmental psychology; World Wide Web; Computer science; The Internet; MEDLINE; Medicine; Social psychology; Psychiatry; Human–computer interaction","score_opus":0.5442463531610501,"score_gpt":0.6017458709419242,"score_spread":0.05749951778087414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200332722","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010808888,0.9887284,0.0028315263,0.0006718948,0.00023248182,0.0043005245,0.0011835542,0.00003784972,0.00093276455],"genre_scores_gemma":[0.012332879,0.95770246,0.014589971,0.00081801193,0.00009657799,0.01329799,0.00095562433,0.000034180244,0.00017242537],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.8462699,0.06442079,0.06476384,0.0040393802,0.01952146,0.0009846637],"domain_scores_gemma":[0.61220765,0.31770828,0.027394548,0.0058774366,0.035752226,0.0010598891],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10006667,0.0032728487,0.010528077,0.041122146,0.0025439698,0.0065594064,0.004277703,0.003258027,0.00391211],"category_scores_gemma":[0.27019638,0.0027502875,0.011864106,0.037210412,0.002906818,0.006537323,0.0044143214,0.0026715712,0.00076870923],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010612616,0.00002545896,0.0005426096,0.89787674,0.0024304024,0.00008634959,0.0008193406,0.00015165322,0.0001768087,0.0006831001,0.0013237682,0.095777564],"study_design_scores_gemma":[0.00010018264,0.00019973425,0.001806408,0.96177363,0.009585457,0.00016414562,0.0007791491,0.00011148184,0.0003386311,0.00056912395,0.024510663,0.00006138871],"about_ca_topic_score_codex":0.013489352,"about_ca_topic_score_gemma":0.027669167,"teacher_disagreement_score":0.89993334,"about_ca_system_score_codex":0.012569807,"about_ca_system_score_gemma":0.037992544,"threshold_uncertainty_score":0.5292095},"labels":[],"label_agreement":null},{"id":"W4200349887","doi":"10.3390/s21248445","title":"Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University; New Brunswick Innovation Foundation","keywords":"Wireless sensor network; Cluster (spacecraft); Computer science; Path (computing); Head (geology); Energy (signal processing); Selection (genetic algorithm); Real-time computing; Wireless; Computer network; Telecommunications; Artificial intelligence; Physics","score_opus":0.02896251709596767,"score_gpt":0.24123596428018704,"score_spread":0.21227344718421937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200349887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037545398,0.001313283,0.95436525,0.0004645835,0.00011497313,0.00007693107,0.00009536045,0.00021854385,0.005805744],"genre_scores_gemma":[0.9438746,0.0014129292,0.048037276,0.0000975074,0.00005367648,0.00015728247,0.00018221646,0.000042333253,0.0061421692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997272,0.00010099586,0.000010072638,0.00005370263,0.00006573543,0.000042191816],"domain_scores_gemma":[0.9997764,0.000093246636,0.00003519979,0.000014440442,0.00006453206,0.00001631514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042229102,0.00060489436,0.0005190733,0.0004841607,0.0005833455,0.0005559299,0.001424515,0.0008025499,0.001425689],"category_scores_gemma":[0.0009589987,0.00023096298,0.00059896073,0.000635016,0.0005239487,0.00075400993,0.00057364727,0.00057970785,0.00024740762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023959112,0.000008438035,0.0002764237,0.000020689627,0.000011155578,0.000059575363,0.00003253117,0.98759896,0.0005846359,0.0055664144,0.00045482168,0.0053623915],"study_design_scores_gemma":[0.0000018402078,0.000009142078,0.000056012646,0.000001637914,0.0000028276818,0.000008174822,0.00000740887,0.9988003,0.000082109706,0.00081661844,0.00021205826,0.000001980102],"about_ca_topic_score_codex":0.013455269,"about_ca_topic_score_gemma":0.008402137,"teacher_disagreement_score":0.013455269,"about_ca_system_score_codex":0.0009521155,"about_ca_system_score_gemma":0.0008451202,"threshold_uncertainty_score":0.026753902},"labels":[],"label_agreement":null},{"id":"W4200397974","doi":"10.3390/s22010035","title":"A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Ministry of Energy, Northern Development and Mines; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laser scanning; Identification (biology); Remote sensing; Scanner; Laser; Computer science; Environmental science; Artificial intelligence; Computer vision; Geography; Optics; Biology; Ecology; Physics","score_opus":0.0335524823753098,"score_gpt":0.28340946550997015,"score_spread":0.24985698313466034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200397974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8868891,0.0012670128,0.0905315,0.00023594545,0.00014062959,0.0003813134,0.005292059,0.0021696023,0.013092865],"genre_scores_gemma":[0.79439914,0.0006673049,0.19733714,0.00018209832,0.000044224787,0.0002918043,0.0053259674,0.00021816607,0.0015341273],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99825674,0.00030704626,0.00011819865,0.00025644063,0.00094714557,0.00011450624],"domain_scores_gemma":[0.9968893,0.0010303751,0.0002511572,0.00030456553,0.0014183542,0.00010631826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019546796,0.0004721322,0.00039310183,0.002228817,0.00036151838,0.00079851836,0.00065728865,0.00075395755,0.0012380789],"category_scores_gemma":[0.0030607588,0.0003109874,0.00059210014,0.0015056704,0.00021263986,0.0011634806,0.00051726383,0.00035845133,0.00088628905],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014851759,0.000360863,0.24788073,0.0009721252,0.00061272475,0.00014370518,0.0005816942,0.018528843,0.19566453,0.0016885733,0.0058235894,0.5262575],"study_design_scores_gemma":[0.00020026311,0.0014277648,0.6667387,0.00032392034,0.0006212011,0.0017310458,0.0017348584,0.15322123,0.14427075,0.0026235844,0.026807265,0.00029951363],"about_ca_topic_score_codex":0.002616332,"about_ca_topic_score_gemma":0.008454984,"teacher_disagreement_score":0.002616332,"about_ca_system_score_codex":0.00036932563,"about_ca_system_score_gemma":0.0003990666,"threshold_uncertainty_score":0.010337412},"labels":[],"label_agreement":null},{"id":"W4200437778","doi":"10.3390/s21238139","title":"Medical Range Radiation Dosimeter Based on Polymer-Embedded Fiber Bragg Gratings","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dosimeter; Materials science; Dosimetry; Polymer; Optical fiber; Optics; Fiber Bragg grating; Radiation; Optoelectronics; Composite material; Nuclear medicine; Physics","score_opus":0.006541963485897254,"score_gpt":0.22422104368235224,"score_spread":0.217679080196455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200437778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8215258,0.0050921105,0.16874945,0.0001106817,0.00013798961,0.0001719995,0.00039664924,0.0012748471,0.0025403642],"genre_scores_gemma":[0.90248984,0.0017704882,0.0934899,0.00008357657,0.000024269988,0.00006132053,0.00019247146,0.000052121377,0.0018359557],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957114,0.00009004588,0.00001999054,0.000106772764,0.00018717413,0.000024872747],"domain_scores_gemma":[0.99953604,0.0001971826,0.00014243562,0.000039782608,0.000064527376,0.000020072555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005288525,0.000633161,0.0003639282,0.00046851448,0.00011823818,0.00021711693,0.00045207987,0.0005105396,0.00060634175],"category_scores_gemma":[0.0006306782,0.00035183367,0.00032558636,0.00032818917,0.00038623755,0.00047822183,0.00029930886,0.00030398666,0.00028641082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009152622,0.000022121438,0.00092606776,0.00011798668,0.000023258272,0.000026025582,0.000029824425,0.0013374278,0.9916715,0.00015947408,0.000040398343,0.0055542644],"study_design_scores_gemma":[0.0000047427357,0.00016587443,0.0020687296,0.000009646812,0.00002644534,0.00009606779,0.0000096252625,0.005575097,0.9911034,0.000038325757,0.0008889751,0.000013146918],"about_ca_topic_score_codex":0.00033450205,"about_ca_topic_score_gemma":0.00060970604,"teacher_disagreement_score":0.000633161,"about_ca_system_score_codex":0.00031715148,"about_ca_system_score_gemma":0.00016162716,"threshold_uncertainty_score":0.0027968884},"labels":[],"label_agreement":null},{"id":"W4200466906","doi":"10.3390/s21248245","title":"Efficient Authentication Protocol and Its Application in Resonant Inductive Coupling Wireless Power Transfer Systems","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Authentication protocol; Computer science; Authentication (law); Maximum power transfer theorem; Wireless power transfer; Robustness (evolution); Wireless; Protocol (science); Transmitter; Wireless security; Key (lock); Cryptographic protocol; Electrical engineering; Computer network; Cryptography; Electronic engineering; Power (physics); Engineering; Computer security; Wireless network; Channel (broadcasting); Telecommunications; Physics","score_opus":0.010016497629381125,"score_gpt":0.2293812257117857,"score_spread":0.2193647280824046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200466906","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027358437,0.00048025826,0.9646164,0.00043837412,0.00006596581,0.000102631166,0.00002518222,0.00016352997,0.0067492095],"genre_scores_gemma":[0.9171315,0.0007620995,0.076718666,0.0001064973,0.00008855871,0.00020908927,0.000040185318,0.00003906169,0.0049044155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990482,0.00031126203,0.00006892961,0.00016176206,0.0003195728,0.00009032965],"domain_scores_gemma":[0.99809533,0.0010431163,0.00021573559,0.00028439646,0.00031807637,0.00004342606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009716043,0.0003577757,0.00046289075,0.00060981535,0.0006815049,0.0010486875,0.00058432954,0.0012176138,0.0019151998],"category_scores_gemma":[0.0032365662,0.00022029673,0.0004991324,0.000587606,0.0016043328,0.0016464078,0.0012544158,0.0010937904,0.00046295824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022092702,0.000065295935,0.0006759122,0.0001764718,0.000046655463,0.000501304,0.00047668134,0.17723076,0.058790006,0.7167747,0.001424877,0.043616418],"study_design_scores_gemma":[0.00002486369,0.00014510671,0.00019225845,0.000031777316,0.000023076833,0.00036249586,0.00004878261,0.8944782,0.016077375,0.084447086,0.004125294,0.000043762786],"about_ca_topic_score_codex":0.00043971842,"about_ca_topic_score_gemma":0.00019793643,"teacher_disagreement_score":0.0019151998,"about_ca_system_score_codex":0.0007133652,"about_ca_system_score_gemma":0.0006497323,"threshold_uncertainty_score":0.006406963},"labels":[],"label_agreement":null},{"id":"W4200491387","doi":"10.3390/s21248391","title":"Prioritising Organisational Factors Impacting Cloud ERP Adoption and the Critical Issues Related to Security, Usability, and Vendors: A Systematic Literature Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Usability; Vendor; Enterprise resource planning; Cloud computing; Critical success factor; Knowledge management; Process management; Computer science; Business; Marketing","score_opus":0.020367758562425453,"score_gpt":0.3263508584203646,"score_spread":0.30598309985793914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200491387","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009150295,0.9860757,0.0012641208,0.0009730638,0.00014642112,0.0008626329,0.00055462465,0.000015869166,0.0009573176],"genre_scores_gemma":[0.04350213,0.95024467,0.0038415545,0.00065387995,0.00007343298,0.0010478524,0.00042379263,0.0000131403185,0.00019955864],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9861865,0.0042277374,0.0051401756,0.00095475314,0.003048145,0.00044259342],"domain_scores_gemma":[0.8877402,0.084390774,0.012078942,0.0012073015,0.013637813,0.0009448884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020892316,0.0014986093,0.0041444385,0.03671843,0.0016255041,0.0047687227,0.0016543248,0.0021408491,0.0019099233],"category_scores_gemma":[0.07117522,0.0015565605,0.0055873413,0.026591467,0.0015629914,0.005344361,0.0025005415,0.0016892103,0.00033377766],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012253842,0.0001009612,0.008138664,0.8000717,0.00340275,0.0007192227,0.004024707,0.0003479214,0.00073460117,0.0018003389,0.0021266711,0.17840987],"study_design_scores_gemma":[0.00005961998,0.00021414502,0.013363341,0.9257201,0.019282443,0.0008703191,0.006083332,0.00026429212,0.0006069262,0.0011188005,0.032308597,0.000108196145],"about_ca_topic_score_codex":0.015086935,"about_ca_topic_score_gemma":0.04496212,"teacher_disagreement_score":0.03671843,"about_ca_system_score_codex":0.0060844617,"about_ca_system_score_gemma":0.0435283,"threshold_uncertainty_score":0.1104905},"labels":[],"label_agreement":null},{"id":"W4200499641","doi":"10.3390/s22010211","title":"Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Transfer of learning; Computer science; Generalization; Artificial intelligence; Domain adaptation; Adaptation (eye); Robot; Machine learning; Domain (mathematical analysis); Labeled data; Pattern recognition (psychology); Mathematics","score_opus":0.013572846881825248,"score_gpt":0.22558875854466728,"score_spread":0.21201591166284203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200499641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20902513,0.0011779015,0.78089267,0.0002767612,0.00015358168,0.000114042676,0.00029855725,0.0037540777,0.004307334],"genre_scores_gemma":[0.9351225,0.00026441505,0.05917577,0.00015538838,0.000036663456,0.00012696176,0.00042084054,0.000082730716,0.0046147383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998498,0.000029663366,0.0000057165175,0.00005597539,0.000035475798,0.000023253086],"domain_scores_gemma":[0.9997954,0.000075689204,0.00002571448,0.000037070367,0.00005019764,0.000015968431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044478656,0.00071539264,0.00035897267,0.00020480253,0.00012041851,0.00026903092,0.00057680305,0.00049950223,0.0013247019],"category_scores_gemma":[0.0011180606,0.00021210103,0.00038207878,0.00018086696,0.00021188354,0.00041707992,0.0005471947,0.0007670161,0.00057034916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030810828,0.00030778168,0.004072215,0.00013939435,0.0001355262,0.00021084765,0.00015809029,0.4477802,0.059974276,0.00094525295,0.004122626,0.48184577],"study_design_scores_gemma":[0.000006355591,0.00009811449,0.0020951873,0.00000949929,0.000010620019,0.000051631305,0.000015094751,0.9900803,0.0063210493,0.0005538967,0.00074865966,0.000009528188],"about_ca_topic_score_codex":0.0036839482,"about_ca_topic_score_gemma":0.0049100723,"teacher_disagreement_score":0.0036839482,"about_ca_system_score_codex":0.0002519075,"about_ca_system_score_gemma":0.00036471954,"threshold_uncertainty_score":0.0073249936},"labels":[],"label_agreement":null},{"id":"W4200517475","doi":"10.3390/s22010148","title":"An Open-Source, Durable, and Low-Cost Alternative to Commercially Available Soil Temperature Data Loggers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Environment and Climate Change Canada","funders":"Colgate University; University of Notre Dame; National Science Foundation","keywords":"Data logger; Open source; Environmental science; Computer science; Engineering; Operating system; Software","score_opus":0.06640907567448734,"score_gpt":0.2872062181910813,"score_spread":0.22079714251659394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200517475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10069008,0.00094365655,0.6614913,0.0009961317,0.0009703929,0.0022892407,0.03052888,0.16599917,0.036091197],"genre_scores_gemma":[0.27860236,0.00090704486,0.59916717,0.0019488557,0.00044347034,0.0041270554,0.03785242,0.012697513,0.06425419],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979686,0.00013299209,0.00012079796,0.00057338603,0.0010678784,0.00013640181],"domain_scores_gemma":[0.99673223,0.0007789644,0.000379279,0.000811605,0.0010478063,0.0002502054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013490656,0.00092243194,0.0007704363,0.0020616846,0.00050267274,0.00095026725,0.0028368104,0.0010097177,0.028678233],"category_scores_gemma":[0.0042095194,0.00065902836,0.00059439696,0.0018625833,0.00041629723,0.0026107607,0.002321599,0.0010996746,0.014737259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010624802,0.001242171,0.023174979,0.001119966,0.00019330131,0.00058713736,0.0005859023,0.004041504,0.13560274,0.0031245186,0.103432685,0.7258326],"study_design_scores_gemma":[0.0005984599,0.0012978887,0.063150845,0.0004075149,0.00037425102,0.0020443927,0.0005295744,0.075791836,0.191247,0.008248157,0.6555823,0.00072774565],"about_ca_topic_score_codex":0.0017686615,"about_ca_topic_score_gemma":0.0035041375,"teacher_disagreement_score":0.028678233,"about_ca_system_score_codex":0.0003766526,"about_ca_system_score_gemma":0.0009066184,"threshold_uncertainty_score":0.095938265},"labels":[],"label_agreement":null},{"id":"W4200529091","doi":"10.3390/s21248412","title":"Wearable Technology to Increase Self-Awareness of Low Back Pain: A Survey of Technology Needs among Health Care Workers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wearable computer; Low back pain; Health care; Population; Wearable technology; Medicine; Population ageing; Perception; Nursing; Applied psychology; Physical medicine and rehabilitation; Physical therapy; Environmental health; Psychology; Computer science; Alternative medicine","score_opus":0.010038196940922967,"score_gpt":0.285328173366786,"score_spread":0.27528997642586306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200529091","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99922657,0.00015881483,0.000090565074,0.00011934484,0.000003713054,0.000027166852,0.000052455696,0.0000019812908,0.00031930325],"genre_scores_gemma":[0.9990922,0.00027858707,0.00018923578,0.000119220735,0.0000049943737,0.000038354287,0.000048677466,0.0000010112382,0.00022764386],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993063,0.000204502,0.00011575826,0.000054519012,0.00022254208,0.000096283686],"domain_scores_gemma":[0.99728143,0.00070271903,0.000996676,0.000083225896,0.00058604334,0.00034983418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012571753,0.00015383586,0.00020329744,0.0005688911,0.0004357674,0.0005313562,0.00021180739,0.000555065,0.00086013484],"category_scores_gemma":[0.004220944,0.00028663265,0.00029739767,0.00048581063,0.00022897472,0.00060571043,0.00056171615,0.00045696052,0.000244825],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037664024,0.00025534417,0.9826529,0.0001244968,0.000023703711,0.000097474534,0.0057101734,0.000039564795,0.0008481557,0.000015858197,0.00021675472,0.009978036],"study_design_scores_gemma":[0.000003767937,0.00035982224,0.9905372,0.00004500318,0.000010327352,0.00011425087,0.008226024,0.0001328934,0.00007697878,0.000011781796,0.00047655407,0.0000053737917],"about_ca_topic_score_codex":0.004154312,"about_ca_topic_score_gemma":0.006234564,"teacher_disagreement_score":0.004154312,"about_ca_system_score_codex":0.00026835513,"about_ca_system_score_gemma":0.00043208848,"threshold_uncertainty_score":0.00826031},"labels":[],"label_agreement":null},{"id":"W4200532863","doi":"10.3390/s21248428","title":"The Effect of a Verbal Cognitive Task on Postural Sway Does Not Persist When the Task Is Over","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"North York General Hospital; University of Toronto","funders":"National Institute on Aging","keywords":"Cognition; Balance (ability); Task (project management); Elementary cognitive task; Psychology; Task switching; Cognitive psychology; Physical medicine and rehabilitation; Medicine; Engineering","score_opus":0.01290330933270692,"score_gpt":0.3250314225904263,"score_spread":0.3121281132577194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200532863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980027,0.00017000896,0.00077587913,0.000015648457,0.00001832717,0.000014540174,0.00010279811,0.000014101357,0.00088603486],"genre_scores_gemma":[0.998968,0.000065545595,0.0003904323,0.000028164875,0.000016195938,0.000024751267,0.00014792114,0.0000071541554,0.000351767],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996202,0.00004613016,0.000053849806,0.000059515532,0.00016784351,0.00005241809],"domain_scores_gemma":[0.9969721,0.0014004351,0.0008249473,0.0002909812,0.00029705468,0.00021453788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004522777,0.0004604944,0.0003749105,0.0003792229,0.00020255194,0.0005514002,0.00017870496,0.00034953255,0.001588442],"category_scores_gemma":[0.007001829,0.00014843569,0.00022295056,0.00019605101,0.000350392,0.00028786392,0.0006360909,0.00028916748,0.00030935183],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017685732,0.0019704343,0.32982138,0.0009886088,0.00071242615,0.0008485121,0.0018387395,0.001075692,0.43354136,0.00022549652,0.00089188665,0.21039979],"study_design_scores_gemma":[0.000039666884,0.00279841,0.98797625,0.000020565665,0.00006363632,0.00027116342,0.00019593723,0.00054667686,0.0076069618,0.00014207925,0.00032431178,0.0000141549845],"about_ca_topic_score_codex":0.0009844229,"about_ca_topic_score_gemma":0.002058817,"teacher_disagreement_score":0.001588442,"about_ca_system_score_codex":0.0000998107,"about_ca_system_score_gemma":0.00017693687,"threshold_uncertainty_score":0.0053138137},"labels":[],"label_agreement":null},{"id":"W4200626815","doi":"10.3390/s21238120","title":"Isolating In-Situ Grip and Push Force Distribution from Hand-Handle Contact Pressure with an Industrial Electric Nutrunner","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hand strength; Engineering; Structural engineering; Contact force; Grip strength; Simulation; Computer science; Mechanical engineering; Physics; Physical therapy; Medicine","score_opus":0.02430386608487326,"score_gpt":0.22685375876271427,"score_spread":0.20254989267784101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200626815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30100185,0.000117884454,0.6969912,0.00004798104,0.000021565475,0.00007902901,0.000072051494,0.0009185759,0.00074980565],"genre_scores_gemma":[0.7480757,0.00014141499,0.2504432,0.000037293485,0.000020610234,0.00013389195,0.00012244284,0.000092614755,0.00093283254],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996741,0.000036748952,0.000022875267,0.000121069956,0.00011759608,0.000027564278],"domain_scores_gemma":[0.9992454,0.00033318246,0.00015865025,0.000054847667,0.00016717969,0.000040741255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047422326,0.00079553545,0.00057253917,0.0007668662,0.00024795948,0.00052475266,0.00072361453,0.0006213312,0.0009777695],"category_scores_gemma":[0.00218052,0.0003002709,0.00030280073,0.00040841947,0.0002419337,0.00042778434,0.0005034872,0.00033555672,0.0003281094],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009051077,0.00041123253,0.046660066,0.0005128596,0.00015024877,0.00057564,0.00053707365,0.08121843,0.38466528,0.0005007208,0.000554115,0.48330918],"study_design_scores_gemma":[0.000051534487,0.00058634207,0.06993988,0.00004360872,0.00008178944,0.0007519643,0.00022546721,0.8119632,0.11448204,0.0007083084,0.0011078957,0.000057988767],"about_ca_topic_score_codex":0.0011821362,"about_ca_topic_score_gemma":0.0016890373,"teacher_disagreement_score":0.0011821362,"about_ca_system_score_codex":0.00018912628,"about_ca_system_score_gemma":0.00039869602,"threshold_uncertainty_score":0.0032710433},"labels":[],"label_agreement":null},{"id":"W4205102820","doi":"10.3390/s22010096","title":"Artifacts in EEG-Based BCI Therapies: Friend or Foe?","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"Bundesministerium für Bildung und Forschung","keywords":"Brain–computer interface; Neurofeedback; Electroencephalography; Neurorehabilitation; Computer science; Motor imagery; Brain activity and meditation; Artificial intelligence; Feature extraction; Feature (linguistics); SIGNAL (programming language); Pattern recognition (psychology); Machine learning; Speech recognition; Psychology; Rehabilitation; Neuroscience","score_opus":0.04424915726796002,"score_gpt":0.29117804012283965,"score_spread":0.24692888285487963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205102820","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26379815,0.07307051,0.5914431,0.05273984,0.0033179289,0.00030347175,0.0006511966,0.0024653836,0.012210374],"genre_scores_gemma":[0.866173,0.018423682,0.09975866,0.008703603,0.0021321448,0.00012309225,0.00031323635,0.000636699,0.0037358552],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99412656,0.0030639812,0.0005266728,0.0005876423,0.0015532308,0.00014203096],"domain_scores_gemma":[0.9528728,0.032617807,0.0042555886,0.003557112,0.0061734575,0.0005232155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071338844,0.0007869622,0.00083083677,0.0011805659,0.0007869727,0.0027541665,0.00092221616,0.0020082893,0.0016687162],"category_scores_gemma":[0.055895943,0.0003245197,0.0005754563,0.0009382291,0.0022562172,0.003293006,0.0010251958,0.0018588223,0.0010889948],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008310778,0.00013807393,0.044113226,0.0023044758,0.0006627352,0.002493877,0.0050996966,0.003307086,0.021896377,0.0073360517,0.012932111,0.89888513],"study_design_scores_gemma":[0.00036401552,0.005029185,0.23680858,0.009828832,0.0031538224,0.06696955,0.015402225,0.06314379,0.1530561,0.19379647,0.25156605,0.00088133867],"about_ca_topic_score_codex":0.0012472895,"about_ca_topic_score_gemma":0.002294796,"teacher_disagreement_score":0.0071338844,"about_ca_system_score_codex":0.00045495556,"about_ca_system_score_gemma":0.0005240841,"threshold_uncertainty_score":0.03772807},"labels":[],"label_agreement":null},{"id":"W4205146235","doi":"10.3390/s22020563","title":"How Long Should GPS Recording Lengths Be to Capture the Community Mobility of An Older Clinical Population? A Parkinson’s Example","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Older Adults Driving Studies","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Parkwood Institute; Université du Québec à Montréal; London Health Sciences Centre; Université de Sherbrooke; Ontario Shores Centre for Mental Health Sciences; Western University","funders":"Canadian Institutes of Health Research; Government of Ontario","keywords":"Global Positioning System; Context (archaeology); Inertial measurement unit; Population; Physical medicine and rehabilitation; Wearable computer; Medicine; Computer science; Geography; Simulation; Telecommunications; Artificial intelligence","score_opus":0.20756975375671555,"score_gpt":0.4550396168017736,"score_spread":0.24746986304505808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205146235","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8512721,0.021799508,0.065127075,0.047093958,0.001971897,0.00056999613,0.0037546682,0.000282359,0.008128486],"genre_scores_gemma":[0.9492166,0.0035128398,0.041036338,0.0032055187,0.00045539276,0.00084686687,0.0008709561,0.000058910933,0.00079646724],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.983896,0.009716178,0.0027056853,0.0010811976,0.0021936793,0.00040734938],"domain_scores_gemma":[0.9215747,0.039715245,0.014800186,0.00631636,0.016087912,0.0015056061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026235357,0.0005714829,0.0008008433,0.0009761931,0.0012933478,0.0017434908,0.0012785345,0.0018217389,0.0015382731],"category_scores_gemma":[0.11218666,0.0004232407,0.001243683,0.0017226967,0.0008154499,0.0031538007,0.0011937972,0.0014993687,0.00054168754],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035315913,0.0003201568,0.6629279,0.0027121156,0.0007523205,0.00024388869,0.008083913,0.0021632174,0.0028650148,0.0018631568,0.0158681,0.2986685],"study_design_scores_gemma":[0.00037745808,0.005111354,0.90827113,0.0049081375,0.0012090626,0.0010768773,0.016950585,0.011120619,0.005321918,0.004949051,0.040445052,0.0002588218],"about_ca_topic_score_codex":0.0097874245,"about_ca_topic_score_gemma":0.020686314,"teacher_disagreement_score":0.026235357,"about_ca_system_score_codex":0.0014652349,"about_ca_system_score_gemma":0.0026110031,"threshold_uncertainty_score":0.13874751},"labels":[],"label_agreement":null},{"id":"W4205175281","doi":"10.3390/s22020438","title":"Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Current (fluid); Computer science; Systems engineering; Engineering; Risk analysis (engineering); Medicine; Electrical engineering","score_opus":0.6249967064854097,"score_gpt":0.495899882838799,"score_spread":0.12909682364661074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205175281","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019763643,0.99615365,0.0012407384,0.00049610797,0.00028508488,0.000012353703,0.00002003115,0.000020201831,0.0015742186],"genre_scores_gemma":[0.0016541705,0.99506617,0.0016174907,0.0003427284,0.00034140068,0.000023325783,0.000046858353,0.0000072434887,0.0009006255],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990663,0.00015073626,0.00010660897,0.00020080035,0.00040518015,0.00007038042],"domain_scores_gemma":[0.9973411,0.0015294236,0.00017501907,0.0001077402,0.00075100706,0.00009563978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022612487,0.0011887426,0.0017226811,0.0026682585,0.00043156487,0.0019887346,0.0017916118,0.0023902738,0.0037782104],"category_scores_gemma":[0.0026563155,0.0006670268,0.00096105586,0.0030262608,0.0009839316,0.003424884,0.0011738099,0.0025439276,0.0029266414],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054025815,0.00009155101,0.00026652,0.017530652,0.00006571942,0.00008760077,0.000107815955,0.0005214035,0.0028212178,0.010243786,0.0140363835,0.9541733],"study_design_scores_gemma":[0.000012120142,0.00020762328,0.0010300907,0.006261984,0.00013726712,0.0010036482,0.00021008849,0.00073793903,0.0018095352,0.0058669215,0.982668,0.000054916665],"about_ca_topic_score_codex":0.0012803833,"about_ca_topic_score_gemma":0.0012778407,"teacher_disagreement_score":0.0037782104,"about_ca_system_score_codex":0.00069372344,"about_ca_system_score_gemma":0.0016942434,"threshold_uncertainty_score":0.012639403},"labels":[],"label_agreement":null},{"id":"W4205289949","doi":"10.3390/s22020521","title":"As-Built Inventory and Deformation Analysis of a High Rockfill Dam under Construction with Terrestrial Laser Scanning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; Sichuan University; National Natural Science Foundation of China","keywords":"Point cloud; Deformation monitoring; Laser scanning; Geodetic datum; Computer science; Deformation (meteorology); Pipeline (software); Computer vision; Geology; Artificial intelligence; Geodesy; Laser","score_opus":0.015644693534699286,"score_gpt":0.21078998047980801,"score_spread":0.19514528694510874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205289949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879946,0.000034973087,0.010110846,0.000029952345,0.0000030541162,0.000020253206,0.0005971817,0.00017904262,0.0010301464],"genre_scores_gemma":[0.99205935,0.00002895635,0.0069640838,0.0000035038304,0.000002714083,0.000011383731,0.000642702,0.000012212809,0.00027502904],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997768,0.00002308039,0.0000101035985,0.000043207114,0.00011208857,0.000034720342],"domain_scores_gemma":[0.9998116,0.000017057517,0.00005085149,0.000037298134,0.000060687096,0.000022490854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028827728,0.00023048605,0.0002028614,0.001605842,0.00025174182,0.00036940223,0.00032820358,0.00025568568,0.0007224177],"category_scores_gemma":[0.00029766202,0.00015347428,0.00035073556,0.0015681299,0.00028409672,0.00035105352,0.0004378097,0.00017266769,0.0002387631],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006070814,0.0004308384,0.36144257,0.00023002559,0.00015360053,0.0012903556,0.0010164166,0.25522152,0.17584547,0.0015927932,0.0018739636,0.20029542],"study_design_scores_gemma":[0.000017413515,0.00026527813,0.5260198,0.000020386537,0.000068202484,0.00038226892,0.000651513,0.44613874,0.02424238,0.00039579306,0.0017285931,0.00006967977],"about_ca_topic_score_codex":0.00577245,"about_ca_topic_score_gemma":0.009481951,"teacher_disagreement_score":0.00577245,"about_ca_system_score_codex":0.0003274438,"about_ca_system_score_gemma":0.00040975824,"threshold_uncertainty_score":0.011477709},"labels":[],"label_agreement":null},{"id":"W4205350121","doi":"10.3390/s22020509","title":"Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Terrain; Tracking (education); Computer science; Track (disk drive); Remote sensing; Upper and lower bounds; Algorithm; Computer vision; Geodesy; Mathematics; Geography","score_opus":0.03801057766011327,"score_gpt":0.2627648365090975,"score_spread":0.2247542588489842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205350121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044461217,0.0002657035,0.95341355,0.000058410136,0.000028153027,0.000017645625,0.00006208181,0.0002870059,0.0014062701],"genre_scores_gemma":[0.8044532,0.00040212602,0.19323531,0.000100806945,0.00003524054,0.000046729965,0.00024270194,0.000029931884,0.0014539913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994222,0.00006968246,0.000031043226,0.0001647366,0.00024118078,0.00007119479],"domain_scores_gemma":[0.99940884,0.00022679752,0.00011298505,0.00009093603,0.00013859413,0.000021976384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048198912,0.0005864573,0.0005339714,0.00029068277,0.0003340212,0.0007300795,0.00057032955,0.0005998994,0.00033915916],"category_scores_gemma":[0.0020930516,0.00028173067,0.00038256496,0.0006591125,0.0003271243,0.0011340707,0.00091544015,0.00067868934,0.00019463823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031788292,0.00006315406,0.010704364,0.00020453644,0.000100713565,0.00025264442,0.00027330845,0.6704481,0.05567998,0.01432527,0.0010045586,0.24662547],"study_design_scores_gemma":[0.000019974206,0.000093428185,0.0020490154,0.000021216614,0.000028995608,0.000121079414,0.00003442332,0.97881204,0.012836916,0.0045998874,0.0013646086,0.000018414881],"about_ca_topic_score_codex":0.004745499,"about_ca_topic_score_gemma":0.0038369026,"teacher_disagreement_score":0.004745499,"about_ca_system_score_codex":0.0003822265,"about_ca_system_score_gemma":0.0007626907,"threshold_uncertainty_score":0.009435773},"labels":[],"label_agreement":null},{"id":"W4205361279","doi":"10.3390/s22030779","title":"A Control Method with Reinforcement Learning for Urban Un-Signalized Intersection in Hybrid Traffic Environment","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Horizon 2020 Framework Programme","keywords":"Reinforcement learning; Intersection (aeronautics); Computer science; Scalability; Intelligent transportation system; Wireless; Control (management); Distributed computing; Real-time computing; Engineering; Transport engineering; Artificial intelligence; Telecommunications","score_opus":0.004720424319509744,"score_gpt":0.18211299635559486,"score_spread":0.1773925720360851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205361279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022582026,0.000194988,0.9743197,0.00013783127,0.000055663648,0.000048952068,0.000014459169,0.00034451298,0.0023019908],"genre_scores_gemma":[0.9522922,0.000096015734,0.045482945,0.00007314188,0.000030001998,0.00009708168,0.000029860388,0.000031409934,0.001867308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953926,0.00009629098,0.000019948653,0.00014251475,0.000109241286,0.000092857],"domain_scores_gemma":[0.9994708,0.000196776,0.000075290256,0.00003609253,0.00016510322,0.00005591613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072032405,0.0006779178,0.0006825608,0.0003321942,0.00055368006,0.00063722156,0.000989224,0.00066788675,0.0013075031],"category_scores_gemma":[0.0011014097,0.00027022732,0.00044443968,0.00025556673,0.0006974539,0.0005844613,0.0009458642,0.0008488128,0.00016205493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074880154,0.00005050941,0.00064247736,0.00005162271,0.00002967022,0.00008524557,0.00007441117,0.9573713,0.0027221735,0.005378243,0.0006297324,0.03288978],"study_design_scores_gemma":[0.0000075011135,0.000030107758,0.000049093513,0.0000015471172,0.0000038627672,0.0000068174377,0.000005087084,0.99891806,0.00029485507,0.00049121684,0.00018936886,0.0000025151378],"about_ca_topic_score_codex":0.010675875,"about_ca_topic_score_gemma":0.005778904,"teacher_disagreement_score":0.010675875,"about_ca_system_score_codex":0.00075686147,"about_ca_system_score_gemma":0.0014205032,"threshold_uncertainty_score":0.021227479},"labels":[],"label_agreement":null},{"id":"W4205436213","doi":"10.3390/s22010247","title":"A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Overtaking; Component (thermodynamics); Fuse (electrical); Context (archaeology); Position (finance); Autonomous system (mathematics); Intelligent transportation system","score_opus":0.02173849097151072,"score_gpt":0.2884758166117836,"score_spread":0.2667373256402729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205436213","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054522175,0.12374378,0.85822403,0.0006727985,0.00045580664,0.00011281451,0.0003039211,0.0014503143,0.009584233],"genre_scores_gemma":[0.20200409,0.24428035,0.53020203,0.0008231138,0.0015109025,0.00053444365,0.002281511,0.00076745765,0.01759612],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988495,0.00024601576,0.000095404896,0.0003282862,0.0003908576,0.000089860136],"domain_scores_gemma":[0.9983784,0.0007282433,0.00011984881,0.0001792933,0.0005593714,0.00003480486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010245402,0.00150444,0.0016893543,0.0026153391,0.0006544371,0.0014688701,0.0025806169,0.002006709,0.002749559],"category_scores_gemma":[0.0035103322,0.00087921193,0.0013337898,0.0038869516,0.00065536273,0.0027083328,0.0013072478,0.0010772742,0.002901862],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075084965,0.000060008944,0.0015746539,0.0029120753,0.00010520564,0.00012933838,0.0002597166,0.025146887,0.0037397298,0.01304056,0.012002344,0.9409543],"study_design_scores_gemma":[0.00005509285,0.0005004733,0.0071824957,0.0024735397,0.00036438848,0.0023527453,0.0013725038,0.37979776,0.014307492,0.046843234,0.5444737,0.00027663418],"about_ca_topic_score_codex":0.004656509,"about_ca_topic_score_gemma":0.00300518,"teacher_disagreement_score":0.004656509,"about_ca_system_score_codex":0.000832244,"about_ca_system_score_gemma":0.001250616,"threshold_uncertainty_score":0.009258866},"labels":[],"label_agreement":null},{"id":"W4205470011","doi":"10.3390/s22010400","title":"Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Machine learning; Computer science; Sitting; Artificial intelligence; Random forest; Motion (physics); Decision tree; Support vector machine; Convolutional neural network","score_opus":0.022147866659935723,"score_gpt":0.30980539697865567,"score_spread":0.28765753031871993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205470011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19209196,0.00043821387,0.80007434,0.0002495764,0.00016569703,0.00017665779,0.0004048557,0.0032636584,0.003135059],"genre_scores_gemma":[0.75410175,0.00023723644,0.2429328,0.000108978835,0.000034944424,0.00015497158,0.00047619684,0.00004406508,0.0019091584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997553,0.00004334304,0.000021124033,0.00008680922,0.000053752818,0.000039722858],"domain_scores_gemma":[0.9995011,0.00022748568,0.000054540098,0.000038301467,0.00015617232,0.000022352215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005334833,0.0008485197,0.00043674803,0.00068950676,0.0003016174,0.00054160494,0.00049313094,0.00074598705,0.0014954132],"category_scores_gemma":[0.0018356215,0.00025726066,0.00043109283,0.00044083042,0.0001509996,0.00047574524,0.00023556194,0.0005214451,0.00072571286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032657577,0.00044158255,0.023649842,0.00014499771,0.000116921896,0.00017229977,0.0001756701,0.1959766,0.02691425,0.00081769674,0.0022208372,0.7490428],"study_design_scores_gemma":[0.0000117014815,0.00012004277,0.008399423,0.000025711965,0.000023682725,0.00006679099,0.00004920033,0.9837568,0.006200645,0.0007510036,0.0005766088,0.00001840761],"about_ca_topic_score_codex":0.0096404785,"about_ca_topic_score_gemma":0.011076312,"teacher_disagreement_score":0.0096404785,"about_ca_system_score_codex":0.000357505,"about_ca_system_score_gemma":0.00064870797,"threshold_uncertainty_score":0.019168735},"labels":[],"label_agreement":null},{"id":"W4205723155","doi":"10.3390/s22010333","title":"Design of a SIMO Deep Learning-Based Chaos Shift Keying (DLCSK) Communication System","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Demodulation; Keying; Computer science; Synchronization (alternating current); Channel (broadcasting); Electronic engineering; Bit error rate; Chaotic; Communications system; Algorithm; Real-time computing; Telecommunications; Artificial intelligence; Engineering","score_opus":0.011113498896415509,"score_gpt":0.22361013160930618,"score_spread":0.21249663271289068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205723155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031157935,0.00043428334,0.95473945,0.0005416079,0.00016344298,0.00018289982,0.00011683261,0.0012936022,0.011369947],"genre_scores_gemma":[0.8233357,0.0002595717,0.16791642,0.00045154814,0.00006283266,0.00026090123,0.0001456342,0.000045029523,0.007522395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997141,0.000036958772,0.000020877844,0.00008764764,0.00009830197,0.00004212231],"domain_scores_gemma":[0.99986243,0.000019117258,0.00002118759,0.0000131452525,0.00006541616,0.000018594545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659307,0.00044225535,0.00051567104,0.00021400383,0.0004770235,0.00053941715,0.0010540687,0.0006853522,0.0023516202],"category_scores_gemma":[0.00026720014,0.00029539247,0.00033268004,0.00019947998,0.00041257034,0.000618663,0.0007120703,0.00063160184,0.0008459255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006190818,0.000372516,0.0044666864,0.0006298109,0.0001959417,0.0008654171,0.0003295597,0.37251407,0.21920522,0.036976315,0.0092316335,0.35459378],"study_design_scores_gemma":[0.000027234035,0.00018592087,0.00030961214,0.000016647038,0.000025154333,0.00012582626,0.000014017436,0.9768654,0.016114928,0.0014764975,0.004818373,0.000020440737],"about_ca_topic_score_codex":0.001898413,"about_ca_topic_score_gemma":0.0025281336,"teacher_disagreement_score":0.0023516202,"about_ca_system_score_codex":0.00071749964,"about_ca_system_score_gemma":0.00081339723,"threshold_uncertainty_score":0.007866919},"labels":[],"label_agreement":null},{"id":"W4205774012","doi":"10.3390/s22020627","title":"Contactless Measurement of Vital Signs Using Thermal and RGB Cameras: A Study of COVID 19-Related Health Monitoring","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vital signs; Heart rate; RGB color model; Face masks; Convolutional neural network; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Computer science; Computer vision; Medicine; Blood pressure; Surgery; Pathology; Internal medicine","score_opus":0.049423507204017156,"score_gpt":0.2749011921807502,"score_spread":0.22547768497673304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205774012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792261,0.0010356125,0.018429907,0.000047382084,0.000044829958,0.000050367544,0.00008093925,0.000028977129,0.0010559354],"genre_scores_gemma":[0.99231017,0.00057823135,0.0061331154,0.00007176621,0.00003160788,0.000027072005,0.00012217744,0.0000071358195,0.00071876455],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995067,0.000108680564,0.000024506391,0.00014393758,0.0001753155,0.00004089748],"domain_scores_gemma":[0.99961025,0.00013263739,0.000050908737,0.000036602712,0.00013986685,0.00002973703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035509857,0.0002853816,0.00041131038,0.00034019348,0.0001686796,0.0003237101,0.00034331932,0.00046698668,0.0005791558],"category_scores_gemma":[0.0010549076,0.00012744148,0.00019894833,0.00031689007,0.00022421873,0.00039332182,0.00026851278,0.00020602175,0.00014288702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019180864,0.0009843579,0.21412934,0.0009286372,0.00031711932,0.0019910203,0.001280053,0.0042354777,0.5274553,0.00088566996,0.0010315254,0.24484347],"study_design_scores_gemma":[0.00007851237,0.0060210503,0.760868,0.000104412604,0.0004434657,0.008194264,0.0012723894,0.07004306,0.14803134,0.000293749,0.0045513553,0.00009833458],"about_ca_topic_score_codex":0.0016104754,"about_ca_topic_score_gemma":0.0022267248,"teacher_disagreement_score":0.0016104754,"about_ca_system_score_codex":0.00015979624,"about_ca_system_score_gemma":0.00015922102,"threshold_uncertainty_score":0.0032021403},"labels":[],"label_agreement":null},{"id":"W4205790976","doi":"10.3390/s22020660","title":"ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Distributed computing; Bottleneck; Cloud computing; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Edge computing; Orchestration; Energy consumption; Resource allocation; Workload; Task (project management); Computer network; Embedded system; Engineering","score_opus":0.011128373321079488,"score_gpt":0.20835527982515778,"score_spread":0.1972269065040783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205790976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025237065,0.0006817025,0.9653913,0.00022003012,0.00013835025,0.00009154664,0.00011126206,0.0014192178,0.0067095165],"genre_scores_gemma":[0.84319544,0.0005614832,0.14794002,0.00023768574,0.0000768324,0.00010835249,0.00018799226,0.00015064588,0.007541624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997856,0.00004501858,0.000008045504,0.000055524408,0.000050185758,0.00005556477],"domain_scores_gemma":[0.99976987,0.0000954218,0.000020982108,0.000044654083,0.00003257831,0.00003650874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048659227,0.00050364103,0.0005765765,0.00029030142,0.00033925782,0.0006806078,0.0013313917,0.00047119547,0.0023893747],"category_scores_gemma":[0.00081551244,0.00019560418,0.0002834304,0.00033830662,0.0003655801,0.00084670924,0.0011269496,0.00048206514,0.00042599865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039461284,0.00018163427,0.001261612,0.00015277529,0.00005920553,0.00024323091,0.00010136424,0.7805873,0.014225435,0.024754262,0.009757731,0.16828083],"study_design_scores_gemma":[0.00002017423,0.000034792778,0.00025036137,0.000007240364,0.000008493096,0.000051399118,0.000020703326,0.98715264,0.001988184,0.0065296036,0.0039269244,0.000009456842],"about_ca_topic_score_codex":0.002455129,"about_ca_topic_score_gemma":0.0042160163,"teacher_disagreement_score":0.002455129,"about_ca_system_score_codex":0.00043834237,"about_ca_system_score_gemma":0.00070184725,"threshold_uncertainty_score":0.007993221},"labels":[],"label_agreement":null},{"id":"W4205882101","doi":"10.3390/s22020666","title":"A Mass-Producible Washable Smart Garment with Embedded Textile EMG Electrodes for Control of Myoelectric Prostheses: A Pilot Study","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University Health Network","keywords":"Electromyography; Biomedical engineering; Electrode; Prosthetic hand; Computer science; Materials science; Engineering; Physical medicine and rehabilitation; Artificial intelligence; Medicine","score_opus":0.013310568019478825,"score_gpt":0.2135637124893683,"score_spread":0.20025314446988948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205882101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98774606,0.000884956,0.010216639,0.00007889121,0.00006971273,0.00013771473,0.00008968485,0.00007122191,0.00070515083],"genre_scores_gemma":[0.98106027,0.0008193351,0.014978916,0.00006536615,0.000022817976,0.00007121881,0.00010306298,0.000025923115,0.0028532024],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997857,0.000041810705,0.000019530014,0.000050291983,0.000079362755,0.00002332066],"domain_scores_gemma":[0.99969935,0.00007488658,0.000052392254,0.000058946487,0.00007375208,0.00004066576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003999696,0.00046102365,0.00040426353,0.00032522058,0.00012042838,0.00032308177,0.00044833592,0.00060968456,0.0012610146],"category_scores_gemma":[0.00063606753,0.00014445643,0.000435095,0.00018331624,0.00026778775,0.0004629503,0.00030353718,0.0002456555,0.00033959612],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055073574,0.00082461437,0.0012650673,0.00054146483,0.000044244225,0.00074002094,0.00021854899,0.0015922986,0.9639208,0.00012743085,0.00021289017,0.029961977],"study_design_scores_gemma":[0.00013790467,0.037839796,0.023026546,0.000101025806,0.00021248509,0.0017632693,0.00046126844,0.014108523,0.9140572,0.0001316188,0.008097363,0.00006294666],"about_ca_topic_score_codex":0.00022982665,"about_ca_topic_score_gemma":0.00042241745,"teacher_disagreement_score":0.0012610146,"about_ca_system_score_codex":0.00010685139,"about_ca_system_score_gemma":0.000097835764,"threshold_uncertainty_score":0.0042185783},"labels":[],"label_agreement":null},{"id":"W4205971121","doi":"10.3390/s22020594","title":"A 65 nm Duplex Transconductance Path Up-Conversion Mixer for 24 GHz Automotive Short-Range Radar Sensor Applications","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Transconductance; Frequency mixer; Linearity; Operational transconductance amplifier; Electrical engineering; Materials science; Amplifier; Capacitor; Harmonic mixer; CMOS; Electronic engineering; Optoelectronics; Voltage; Radio frequency; Engineering; Local oscillator; Transistor; Operational amplifier","score_opus":0.022847445671348745,"score_gpt":0.22579123451253139,"score_spread":0.20294378884118264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205971121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5297546,0.0056940806,0.43925664,0.0009155144,0.0006394043,0.00030208574,0.00054158934,0.0020709496,0.020825123],"genre_scores_gemma":[0.89073724,0.00095790747,0.09835473,0.0003044626,0.0001474796,0.00008737819,0.00028721796,0.000039890514,0.00908375],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983215,0.000020912934,0.000011218638,0.000040249197,0.00006845887,0.000027042759],"domain_scores_gemma":[0.99990106,0.000016764865,0.000026383314,0.000011538037,0.000031035746,0.000013291755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002087079,0.0003560236,0.00032739228,0.00031569172,0.00021487729,0.0005589318,0.0005063349,0.00046057516,0.0020379296],"category_scores_gemma":[0.0001764333,0.00019303971,0.000249286,0.00030607788,0.00013177357,0.00043191717,0.00020111268,0.00042660846,0.00073931535],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096097254,0.00003073152,0.0005030958,0.00007050917,0.000022177344,0.00012221643,0.00002975183,0.00040224617,0.9751159,0.0012289931,0.000522457,0.021855788],"study_design_scores_gemma":[0.000072211784,0.000897902,0.0022650433,0.00001949494,0.00010391973,0.001394659,0.00003554461,0.013066802,0.9479849,0.00025569554,0.033866722,0.000037108603],"about_ca_topic_score_codex":0.00022541182,"about_ca_topic_score_gemma":0.00080327224,"teacher_disagreement_score":0.0020379296,"about_ca_system_score_codex":0.00027773107,"about_ca_system_score_gemma":0.0003430633,"threshold_uncertainty_score":0.0068175793},"labels":[],"label_agreement":null},{"id":"W4206149286","doi":"10.3390/s22010276","title":"An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fuzhou University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Angle of arrival; Cluster analysis; Multipath propagation; Computer science; Algorithm; Tracking (education); Channel (broadcasting); Least-squares function approximation; Path (computing); SIGNAL (programming language); Phase angle (astronomy); Artificial intelligence; Mathematics; Antenna (radio); Statistics","score_opus":0.0329187215215515,"score_gpt":0.24744953876558123,"score_spread":0.21453081724402973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206149286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071778907,0.00013226457,0.99104977,0.000033455563,0.000046224453,0.00003086645,0.0000524526,0.0008425713,0.000634485],"genre_scores_gemma":[0.2171113,0.00044577304,0.77813345,0.00008984319,0.00007181343,0.00019756483,0.0006364891,0.00012739855,0.0031863144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993223,0.000055249737,0.00006024876,0.00020835448,0.00028403604,0.00006986993],"domain_scores_gemma":[0.99945194,0.00007563254,0.00006760301,0.00007214024,0.0003073928,0.000025386329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034081563,0.000713943,0.0007300169,0.0013456169,0.00046480683,0.0006466192,0.001208552,0.00052269443,0.0011689467],"category_scores_gemma":[0.001563522,0.0003469644,0.0006157377,0.0016162297,0.00024952306,0.0010305493,0.0006976234,0.0007415437,0.0008897574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020709082,0.00007137557,0.0031148824,0.00007259166,0.000058127956,0.000070955735,0.0000876585,0.07725221,0.031437263,0.0033581764,0.003328294,0.88094145],"study_design_scores_gemma":[0.000033358952,0.00010919771,0.003185832,0.000012261718,0.000037259877,0.0003396045,0.00006513482,0.9620523,0.026518892,0.0016808434,0.005921263,0.00004416086],"about_ca_topic_score_codex":0.0060942746,"about_ca_topic_score_gemma":0.0048886384,"teacher_disagreement_score":0.0060942746,"about_ca_system_score_codex":0.00044822268,"about_ca_system_score_gemma":0.00083923514,"threshold_uncertainty_score":0.012117624},"labels":[],"label_agreement":null},{"id":"W4206324549","doi":"10.3390/s22020535","title":"Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Brain–computer interface; Computer science; Stress (linguistics); Interface (matter); Transfer of learning; Cognition; Electroencephalography; Artificial intelligence; Psychology; Psychiatry","score_opus":0.06946407488833116,"score_gpt":0.31154316715114583,"score_spread":0.24207909226281465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206324549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8113015,0.00047644053,0.18400362,0.00015911224,0.0001424848,0.0003290016,0.0005290496,0.00062097266,0.002437765],"genre_scores_gemma":[0.95651,0.00025318112,0.041325163,0.00010844838,0.00009917051,0.00021410965,0.00036632954,0.00004018124,0.0010833887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995764,0.000084852916,0.000032013035,0.0001301745,0.00013500552,0.000041564945],"domain_scores_gemma":[0.9992336,0.0003076934,0.000079376136,0.00007912747,0.00024649675,0.00005380291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005359233,0.0009552626,0.0005574538,0.00065469474,0.00015586149,0.0005523002,0.00035616316,0.0006001107,0.0013146532],"category_scores_gemma":[0.0023156935,0.00018678006,0.00027453815,0.00040679288,0.00020823443,0.0005562537,0.0004182561,0.0003948392,0.00053677626],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017925503,0.00058940175,0.037370987,0.0005013196,0.00018815753,0.00020495977,0.00047104093,0.0040531885,0.5689634,0.00025861128,0.0012200681,0.38438627],"study_design_scores_gemma":[0.00019539193,0.0029986394,0.5568904,0.00011633286,0.00049973174,0.0024904346,0.00052229536,0.20416671,0.22590704,0.0025430562,0.0035015843,0.0001684191],"about_ca_topic_score_codex":0.0005852654,"about_ca_topic_score_gemma":0.0014072084,"teacher_disagreement_score":0.0013146532,"about_ca_system_score_codex":0.00012497614,"about_ca_system_score_gemma":0.00018458223,"threshold_uncertainty_score":0.0043979287},"labels":[],"label_agreement":null},{"id":"W4206366445","doi":"10.3390/s22030761","title":"Game-Based Dual-Task Exercise Program for Children with Cerebral Palsy: Blending Balance, Visuomotor and Cognitive Training: Feasibility Randomized Control Trial","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Randomized controlled trial; Cerebral palsy; Physical therapy; Balance (ability); Physical medicine and rehabilitation; Psychology; Balance training; Task (project management); Medicine; Surgery","score_opus":0.019440808019769575,"score_gpt":0.28711418555665214,"score_spread":0.2676733775368826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206366445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87051094,0.004504073,0.0034392255,0.0006585879,0.0011035537,0.116215006,0.00095783087,0.00029495868,0.0023158528],"genre_scores_gemma":[0.8030771,0.003263778,0.015756266,0.0009603688,0.0006720917,0.17358668,0.00044771814,0.000032382817,0.0022036082],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9961302,0.002170071,0.000448461,0.00047644935,0.00041308312,0.00036160505],"domain_scores_gemma":[0.9971686,0.0014330975,0.00048377074,0.00020616637,0.00023166383,0.00047671114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004900371,0.0020304353,0.004007976,0.0013143052,0.0009600176,0.0013611634,0.00206659,0.00389189,0.006227782],"category_scores_gemma":[0.0073112003,0.0011758959,0.0018766213,0.0009486828,0.0016747813,0.0014827874,0.0012774147,0.0022192344,0.0006798957],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.9189265,0.047689553,0.0005109644,0.0033104308,0.0009990307,0.00011325883,0.00019476168,0.00036851046,0.0020750174,0.00026894215,0.0003543998,0.025188565],"study_design_scores_gemma":[0.91178805,0.08598253,0.0008559477,0.00011502733,0.0002909388,0.000019179266,0.000042596985,0.00022500369,0.00022034513,0.00008742051,0.00036179787,0.000011209253],"about_ca_topic_score_codex":0.002642026,"about_ca_topic_score_gemma":0.0044589294,"teacher_disagreement_score":0.006227782,"about_ca_system_score_codex":0.0013056408,"about_ca_system_score_gemma":0.0027386046,"threshold_uncertainty_score":0.02591592},"labels":[],"label_agreement":null},{"id":"W4206396078","doi":"10.3390/s22010403","title":"Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Gait; Modular design; Deep learning; Machine learning; Robotics; Inference; Motion capture; Overfitting; Artificial neural network; Domain (mathematical analysis); Motion (physics); Computer vision; Robot","score_opus":0.02605629724070772,"score_gpt":0.22005224969776982,"score_spread":0.1939959524570621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206396078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123924814,0.000429099,0.8693621,0.00031812617,0.00026472742,0.000111013906,0.00039600258,0.0018145593,0.0033795973],"genre_scores_gemma":[0.84277284,0.0002713585,0.1497211,0.00020775558,0.000079245794,0.00013053168,0.00090956624,0.00006724284,0.0058404556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999863,0.000015750127,0.0000062840736,0.00006124677,0.00003198888,0.000021692184],"domain_scores_gemma":[0.9998988,0.000015291269,0.0000149421785,0.000011652528,0.00004547428,0.000013818412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023603183,0.00074607274,0.00042636902,0.00035761698,0.00018506421,0.00028444311,0.00069776474,0.00034585706,0.0014972842],"category_scores_gemma":[0.000503038,0.00026816866,0.00045517483,0.00030671645,0.00024018099,0.00044721246,0.00050218374,0.0005376649,0.00048034897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055954244,0.00039129468,0.005632557,0.00012297896,0.00020585243,0.00022755514,0.00009298861,0.24640219,0.08331809,0.0022975833,0.0071188947,0.65363055],"study_design_scores_gemma":[0.000006682027,0.00006662782,0.001352242,0.0000044347585,0.00001650246,0.000032064465,0.000006379198,0.99371135,0.0036237116,0.0007355269,0.00043822185,0.0000063904504],"about_ca_topic_score_codex":0.0040438194,"about_ca_topic_score_gemma":0.008303208,"teacher_disagreement_score":0.0040438194,"about_ca_system_score_codex":0.00041185514,"about_ca_system_score_gemma":0.00037053597,"threshold_uncertainty_score":0.008040547},"labels":[],"label_agreement":null},{"id":"W4207005730","doi":"10.3390/s22030858","title":"Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Discriminator; Bridge (graph theory); Computer science; Generator (circuit theory); Generative adversarial network; Artificial intelligence; Motion capture; Process (computing); Data mining; Missing data; Pattern recognition (psychology); Motion (physics); Deep learning; Machine learning; Power (physics); Telecommunications","score_opus":0.029245182483337493,"score_gpt":0.2733847781313244,"score_spread":0.2441395956479869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207005730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015371623,0.00018760667,0.98274463,0.00020234805,0.000036592777,0.000025349396,0.000039706236,0.00029839037,0.0010937242],"genre_scores_gemma":[0.8779693,0.0003046217,0.114144646,0.00031501264,0.000055291293,0.00012355058,0.00035772988,0.00012957341,0.006600339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958795,0.00009159114,0.000027842574,0.00011777707,0.000118077805,0.000056688456],"domain_scores_gemma":[0.999398,0.00030593036,0.00006843417,0.00006600943,0.00012971164,0.000031930456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013252918,0.0009309563,0.00076550123,0.00042260034,0.00029105964,0.0006484468,0.0010656314,0.000953638,0.0017393825],"category_scores_gemma":[0.0019265632,0.00040717682,0.00085957645,0.0003427942,0.00060549803,0.0009515686,0.0012540927,0.0016875379,0.00035559825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012141069,0.000030337997,0.0012767027,0.00006195412,0.000046082077,0.00015070905,0.000080917074,0.9224301,0.0041817916,0.005902957,0.0011363213,0.0645807],"study_design_scores_gemma":[0.0000017691408,0.000014359036,0.00010304488,0.000002704201,0.0000040199557,0.000023263712,0.000004027423,0.9981493,0.00069242273,0.00083022134,0.00017133221,0.0000034655543],"about_ca_topic_score_codex":0.002963243,"about_ca_topic_score_gemma":0.001952504,"teacher_disagreement_score":0.002963243,"about_ca_system_score_codex":0.0006638467,"about_ca_system_score_gemma":0.0006070906,"threshold_uncertainty_score":0.00700891},"labels":[],"label_agreement":null},{"id":"W4207066168","doi":"10.3390/s22030830","title":"Stretching Method-Based Damage Detection Using Neural Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Community College","funders":"","keywords":"Artificial neural network; Natural (archaeology); Computer science; Measure (data warehouse); Natural frequency; Pixel; Biological system; Data mining; Remote sensing; Acoustics; Real-time computing; Pattern recognition (psychology); Artificial intelligence; Geology; Physics; Vibration","score_opus":0.024273778263860707,"score_gpt":0.3027012419425891,"score_spread":0.2784274636787284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207066168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2706656,0.0008242429,0.7246912,0.0002360838,0.00006344475,0.0000747907,0.00013527821,0.00090711133,0.0024022402],"genre_scores_gemma":[0.93767977,0.00020872884,0.05973439,0.000054418662,0.000040100134,0.000055763754,0.00014635324,0.000026901733,0.0020535274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997975,0.000035398712,0.000010652916,0.000064812324,0.00006287652,0.000028708477],"domain_scores_gemma":[0.99938405,0.00030986185,0.00013829283,0.000047090733,0.00009697341,0.000023718505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005935438,0.00069423043,0.0005107114,0.0006628642,0.00026699054,0.0004060229,0.00083281833,0.00076308177,0.0009718376],"category_scores_gemma":[0.0019529522,0.0003060595,0.00032231942,0.00039924754,0.00041303114,0.00088537077,0.0006105987,0.00060651026,0.00015304361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008940517,0.000052635583,0.0020577402,0.000035252968,0.000029609044,0.00005872014,0.000029396755,0.90888757,0.0070726024,0.00073464355,0.00027216572,0.08068018],"study_design_scores_gemma":[0.0000010434092,0.000013758202,0.00037293375,0.0000020461796,0.0000021094236,0.000007670587,0.0000021747428,0.9983296,0.00095340825,0.00026596006,0.000046878657,0.0000024596106],"about_ca_topic_score_codex":0.00410213,"about_ca_topic_score_gemma":0.0038950911,"teacher_disagreement_score":0.00410213,"about_ca_system_score_codex":0.00062493735,"about_ca_system_score_gemma":0.0002594015,"threshold_uncertainty_score":0.008156538},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4210249165","doi":"10.3390/s22031152","title":"Optimization of Silicon Nitride Waveguide Platform for On-Chip Virus Detection","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Silicon nitride; Chip; Waveguide; Materials science; Optoelectronics; Nitride; Silicon; Computer science; Nanotechnology; Electronic engineering; Engineering; Telecommunications","score_opus":0.014404331815487304,"score_gpt":0.22017379155722777,"score_spread":0.20576945974174046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210249165","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85428005,0.002113063,0.13449933,0.00022639456,0.00010177443,0.0001687538,0.0003786546,0.00036523823,0.007866772],"genre_scores_gemma":[0.8698644,0.0012025421,0.12557735,0.00005148008,0.000016388936,0.0002024359,0.00038402653,0.000066732086,0.0026346466],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997267,0.00002852219,0.000010192261,0.00005635391,0.00013620197,0.000041936823],"domain_scores_gemma":[0.99986184,0.000032861186,0.00003946066,0.000014016053,0.000041173473,0.000010690893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043666892,0.00062092097,0.00031762535,0.00024363112,0.00016830914,0.00042380916,0.00056464405,0.00039751158,0.00041329858],"category_scores_gemma":[0.00031815632,0.000272233,0.0004069395,0.00019557118,0.000247351,0.0004041323,0.0003375154,0.00034154925,0.00031275355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039403138,0.000046076995,0.00038310717,0.00012934425,0.000019142923,0.000084761494,0.000021914107,0.012688231,0.9819857,0.0012256741,0.000121620586,0.0032548958],"study_design_scores_gemma":[0.0000145108415,0.0003517748,0.0008599599,0.000012303066,0.000019142824,0.00008892733,0.00003057667,0.112412624,0.8835235,0.00028490415,0.0023851593,0.000016572249],"about_ca_topic_score_codex":0.0007459181,"about_ca_topic_score_gemma":0.0018001915,"teacher_disagreement_score":0.0007459181,"about_ca_system_score_codex":0.00056435406,"about_ca_system_score_gemma":0.00041712305,"threshold_uncertainty_score":0.0040946603},"labels":[],"label_agreement":null},{"id":"W4210298736","doi":"10.3390/s22030920","title":"AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Computer science; Particle swarm optimization; Inertia; Cloud computing; Scheduling (production processes); Job shop scheduling; Distributed computing; Heuristics; Swarm intelligence; Mathematical optimization; Algorithm; Mathematics","score_opus":0.023361565950183325,"score_gpt":0.23624742490287565,"score_spread":0.21288585895269233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210298736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01909191,0.0010096658,0.9687752,0.00033270614,0.00026323236,0.00014447597,0.000095488955,0.0005832195,0.009704072],"genre_scores_gemma":[0.67410594,0.0013692196,0.31534803,0.00025824166,0.0001472711,0.00039567702,0.00034983226,0.00012774079,0.007898029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997813,0.00005064509,0.000015825382,0.00003725808,0.0000832614,0.00003166835],"domain_scores_gemma":[0.9998436,0.000047472156,0.000024156163,0.000013439173,0.000053182725,0.000018142651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032271454,0.00063889235,0.0005976628,0.0004483468,0.00047376624,0.0006915705,0.00090720947,0.000571918,0.0014574608],"category_scores_gemma":[0.00072418025,0.00026062017,0.00070188136,0.0006151136,0.00025829833,0.00045922468,0.0005354765,0.00082085264,0.00032685805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008800628,0.00008585357,0.00084928115,0.0001941368,0.00007478647,0.00013429431,0.00007745712,0.8982735,0.006016909,0.007732262,0.0030873055,0.083386295],"study_design_scores_gemma":[0.000016605165,0.000042998905,0.0001574014,0.000006922967,0.0000098075925,0.000030815656,0.000012882643,0.99552464,0.0005683246,0.0011977459,0.0024260187,0.000005776604],"about_ca_topic_score_codex":0.0065340195,"about_ca_topic_score_gemma":0.005206549,"teacher_disagreement_score":0.0065340195,"about_ca_system_score_codex":0.00043984526,"about_ca_system_score_gemma":0.0011323456,"threshold_uncertainty_score":0.012991965},"labels":[],"label_agreement":null},{"id":"W4210304793","doi":"10.3390/s22031022","title":"Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Université Laval","keywords":"Cerebral palsy; Activities of daily living; Physical medicine and rehabilitation; Intraclass correlation; Concurrent validity; Psychology; Physical therapy; Population; Medicine; Developmental psychology; Psychometrics","score_opus":0.02342001663132989,"score_gpt":0.2617136802466739,"score_spread":0.23829366361534401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210304793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96325815,0.0026911737,0.025039727,0.00014194545,0.00012963536,0.00038291188,0.0022377407,0.00033789955,0.005780747],"genre_scores_gemma":[0.9752366,0.0009075066,0.02090585,0.00007023038,0.00007403248,0.00034994338,0.0012950493,0.00003863612,0.0011219835],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985612,0.00042327787,0.00025589135,0.00018967438,0.0005173419,0.000052553467],"domain_scores_gemma":[0.9969212,0.0008702783,0.0010622817,0.00016941773,0.0008433582,0.00013357335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018674085,0.0008880515,0.00055322325,0.0026068229,0.00024338224,0.0005858149,0.00031653952,0.00050376635,0.00138181],"category_scores_gemma":[0.007414705,0.00020106953,0.00035274768,0.0017959432,0.0002640165,0.0005397363,0.00083251763,0.00036373813,0.0004297362],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080626755,0.0002399686,0.82362837,0.00053899334,0.00048750656,0.00012348639,0.00062841934,0.0018400761,0.0139561,0.00025246709,0.0012146182,0.15628372],"study_design_scores_gemma":[0.000024872677,0.00084329356,0.9917745,0.00006237023,0.000109581924,0.00067867804,0.0003676101,0.003091609,0.0018230486,0.00014404011,0.0010561466,0.00002429729],"about_ca_topic_score_codex":0.001690005,"about_ca_topic_score_gemma":0.0047613713,"teacher_disagreement_score":0.0026068229,"about_ca_system_score_codex":0.00020481934,"about_ca_system_score_gemma":0.00030127796,"threshold_uncertainty_score":0.009875953},"labels":[],"label_agreement":null},{"id":"W4210478904","doi":"10.3390/s22031220","title":"Indoor Location Data for Tracking Human Behaviours: A Scoping Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Toronto Rehabilitation Institute; Toronto Metropolitan University; University Health Network","funders":"Toronto Rehabilitation Institute; AGE-WELL","keywords":"Real-time locating system; Location data; Data mining; Computer science; Tracing; Tracking (education); Data science; Real-time computing; Psychology","score_opus":0.3530140727037212,"score_gpt":0.5109633893863862,"score_spread":0.157949316682665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210478904","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000085222935,0.998606,0.0003139316,0.0002272347,0.000113373986,0.000050569393,0.00012299402,0.000008673841,0.0004718954],"genre_scores_gemma":[0.00081832265,0.9981142,0.00056666264,0.00011993062,0.000047118036,0.00009005229,0.00012588892,0.0000042427714,0.00011345429],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9967084,0.0010259869,0.0009085233,0.0003894084,0.00085481047,0.00011298934],"domain_scores_gemma":[0.9713867,0.022836348,0.0019416247,0.00055624894,0.0030664867,0.00021269664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067316433,0.0017497271,0.0041011386,0.015081203,0.00078418385,0.003171843,0.0022996957,0.0028265226,0.0063902186],"category_scores_gemma":[0.027623938,0.0010203861,0.0041343374,0.016026538,0.0012385177,0.0036570514,0.0020894222,0.0019508592,0.0017907277],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005771495,0.00004529554,0.00048786498,0.40876785,0.0007799243,0.00009731623,0.00043305787,0.00043430028,0.00024819354,0.0032817507,0.010496842,0.57486993],"study_design_scores_gemma":[0.000019990322,0.000081311715,0.0022112874,0.6690039,0.0029496066,0.00040455745,0.0005279338,0.0002076403,0.00025317282,0.002218541,0.32206398,0.000058014182],"about_ca_topic_score_codex":0.012667745,"about_ca_topic_score_gemma":0.018018026,"teacher_disagreement_score":0.015081203,"about_ca_system_score_codex":0.0025109737,"about_ca_system_score_gemma":0.0115367165,"threshold_uncertainty_score":0.03560078},"labels":[],"label_agreement":null},{"id":"W4210509436","doi":"10.3390/s22030874","title":"Development of a Smart Clinical Bluetooth Thermometer Based on an Improved Low-Power Resistive Transducer Circuit","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Bluetooth and Wireless Communication Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Bluetooth; Resistive touchscreen; Thermometer; Transducer; Smart transducer; Power consumption; Electrical engineering; Power (physics); Electronic engineering; Computer science; Engineering; Wireless; Telecommunications","score_opus":0.04008591172060108,"score_gpt":0.28312851510246206,"score_spread":0.24304260338186098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210509436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08182116,0.0037611735,0.89769185,0.0017558861,0.0010193831,0.0011602074,0.0003692556,0.0035235859,0.008897465],"genre_scores_gemma":[0.44252098,0.0022119703,0.54068846,0.0017871854,0.00050479395,0.00090021273,0.0003584766,0.00023470064,0.010793208],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908507,0.0001683227,0.00006612118,0.0001767093,0.00045630446,0.000047495894],"domain_scores_gemma":[0.9993375,0.0001818616,0.000110111345,0.00007371224,0.00025179895,0.00004494437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008119897,0.00045177745,0.0007001084,0.00066634914,0.00016809486,0.0005822917,0.0015939714,0.0008812501,0.0022734466],"category_scores_gemma":[0.0014019234,0.00045270278,0.00044675573,0.0004481985,0.0003241786,0.0012220623,0.0005359416,0.0006163747,0.0014151306],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035131336,0.00015228453,0.001673557,0.00080708,0.00006769763,0.0003368741,0.0001383915,0.0016823912,0.8691588,0.0036474415,0.0039621694,0.11802199],"study_design_scores_gemma":[0.00038641493,0.003707412,0.0069414307,0.00013181003,0.0004386253,0.007917401,0.00009595825,0.08830173,0.81246644,0.0012201084,0.0781817,0.00021085069],"about_ca_topic_score_codex":0.00012023115,"about_ca_topic_score_gemma":0.0002053062,"teacher_disagreement_score":0.0022734466,"about_ca_system_score_codex":0.00024598365,"about_ca_system_score_gemma":0.00048405342,"threshold_uncertainty_score":0.0076054335},"labels":[],"label_agreement":null},{"id":"W4210754377","doi":"10.3390/s22031060","title":"Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"Canada Research Chairs","keywords":"Autoregressive integrated moving average; Bike sharing; Computer science; Benchmark (surveying); Demand forecasting; Deep learning; TRIPS architecture; Demand patterns; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Set (abstract data type); Machine learning; Operations research; Time series; Demand management; Transport engineering; Engineering; Geography","score_opus":0.15406279201127987,"score_gpt":0.3563155326095609,"score_spread":0.20225274059828102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210754377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9570468,0.00028665614,0.03387833,0.0005579729,0.000073131436,0.00004972494,0.0033531785,0.0010873233,0.003666788],"genre_scores_gemma":[0.9904165,0.00005872417,0.004645429,0.000037506237,0.000010649552,0.000021752532,0.003354353,0.000013282013,0.0014418631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984944,0.000016523436,0.0000061257706,0.000042937576,0.000022857797,0.0000620624],"domain_scores_gemma":[0.99977857,0.000038884602,0.00002313034,0.000018311901,0.00010167814,0.000039479277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003047164,0.0010274838,0.00044051616,0.0005203615,0.0003061777,0.00053172756,0.0009541379,0.0004960707,0.0015181581],"category_scores_gemma":[0.0007823514,0.0002489879,0.00041014768,0.00064497365,0.00021871827,0.0007863708,0.0007778273,0.00084280217,0.000349355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004432559,0.0004957905,0.06460268,0.000085625004,0.00013391061,0.00023489274,0.00008555678,0.86495,0.002599578,0.0007745013,0.005905619,0.059688535],"study_design_scores_gemma":[0.0000047105136,0.000021178674,0.0034689542,0.0000027087688,0.000005153382,0.000004107604,0.00003375372,0.99575305,0.00036883602,0.00016555436,0.00016767,0.0000042856313],"about_ca_topic_score_codex":0.23969248,"about_ca_topic_score_gemma":0.24191785,"teacher_disagreement_score":0.23969248,"about_ca_system_score_codex":0.0016450619,"about_ca_system_score_gemma":0.0014655,"threshold_uncertainty_score":0.47659463},"labels":[],"label_agreement":null},{"id":"W4210766754","doi":"10.3390/s22030948","title":"Development of a Real-Time Pectic Oligosaccharide-Detecting Biosensor Using the Rapid and Flexible Computational Identification of Non-Disruptive Conjugation Sites (CINC) Biosensor Design Platform","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Agriculture and Agri-Food Canada; University of Lethbridge","funders":"","keywords":"Biosensor; In silico; Chemistry; Rational design; Fluorophore; Ligand (biochemistry); Pipeline (software); Fluorescence; Combinatorial chemistry; Biochemistry; Nanotechnology; Computer science; Materials science","score_opus":0.03951589662292738,"score_gpt":0.30316238645951415,"score_spread":0.2636464898365868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210766754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40727407,0.00016893461,0.5842831,0.00037854427,0.000082010076,0.00028050266,0.0004478057,0.0038449082,0.003240148],"genre_scores_gemma":[0.53335625,0.00016275559,0.46342736,0.00017649843,0.000015967362,0.0006149735,0.00060139835,0.00017672533,0.0014680029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989307,0.000019369209,0.0000053083936,0.000029853207,0.000036817204,0.000015499358],"domain_scores_gemma":[0.9998654,0.000048445083,0.000015260823,0.00001058955,0.00003889332,0.000021412683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040315647,0.0006958751,0.0007210355,0.00018597432,0.00021875852,0.00037771696,0.0009358828,0.0007109918,0.0010282364],"category_scores_gemma":[0.00051781314,0.00031750245,0.0004875761,0.00015999597,0.00024068415,0.00028193404,0.00032685214,0.000854195,0.0001983063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020575209,0.00029412287,0.0028340768,0.00016173373,0.00010517542,0.00019853767,0.000045073983,0.83750397,0.1271607,0.0032165498,0.0009227169,0.027351607],"study_design_scores_gemma":[0.00002143609,0.00005654982,0.000120422024,0.000001410584,0.000007991495,0.000011606795,0.0000034619068,0.98435414,0.014929802,0.00016224224,0.00032550556,0.0000055656565],"about_ca_topic_score_codex":0.0021455858,"about_ca_topic_score_gemma":0.0022053847,"teacher_disagreement_score":0.0021455858,"about_ca_system_score_codex":0.00052231766,"about_ca_system_score_gemma":0.0009714113,"threshold_uncertainty_score":0.0042662024},"labels":[],"label_agreement":null},{"id":"W4210963337","doi":"10.3390/s22041321","title":"User-Independent Hand Gesture Recognition Classification Models Using Sensor Fusion","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Ministero dello Sviluppo Economico; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Gesture; Wearable computer; Inertial measurement unit; Gesture recognition; Computer science; Interface (matter); Artificial intelligence; Sensor fusion; Support vector machine; Mechatronics; Exoskeleton; Human–computer interaction; Speech recognition; Machine learning; Pattern recognition (psychology); Simulation; Embedded system","score_opus":0.04161209160024609,"score_gpt":0.23097848211843341,"score_spread":0.18936639051818732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210963337","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09023577,0.0005456855,0.9066573,0.00013270647,0.00006967025,0.00009901979,0.00009882215,0.00095373916,0.0012073001],"genre_scores_gemma":[0.920338,0.00043008622,0.07591079,0.00006392588,0.000038548616,0.00021121016,0.00025912683,0.00003828618,0.0027101347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931633,0.00014687749,0.000066597124,0.00021201612,0.0001826409,0.00007550791],"domain_scores_gemma":[0.9993292,0.00029027226,0.00010463134,0.00006721897,0.00018628714,0.000022385144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012950345,0.0009252602,0.00088783,0.0007726696,0.00029804028,0.0007639636,0.00070959196,0.00079463574,0.0010354246],"category_scores_gemma":[0.0022447405,0.0003771542,0.0011882422,0.0006307831,0.0003880393,0.00087055075,0.00068798393,0.0010752743,0.0005808394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004238454,0.00029725235,0.004426422,0.00013106992,0.00023637457,0.00014573833,0.00022102709,0.54543483,0.020378822,0.0014702439,0.0009931353,0.42584127],"study_design_scores_gemma":[0.0000028881693,0.000045539684,0.0009506133,0.0000050140707,0.000013057707,0.000015497792,0.0000068868494,0.99704033,0.001495412,0.00028898168,0.00012703215,0.000008661108],"about_ca_topic_score_codex":0.0042738495,"about_ca_topic_score_gemma":0.0028573968,"teacher_disagreement_score":0.0042738495,"about_ca_system_score_codex":0.00039412186,"about_ca_system_score_gemma":0.00044863689,"threshold_uncertainty_score":0.008497953},"labels":[],"label_agreement":null},{"id":"W4212772615","doi":"10.3390/s22041620","title":"Microfluidic Point-of-Care (POC) Devices in Early Diagnosis: A Review of Opportunities and Challenges","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":279,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Microfluidics; Limiting; Point-of-care testing; Risk analysis (engineering); Wearable computer; Computer science; Point of care; Nanotechnology; Medicine; Engineering; Embedded system; Pathology","score_opus":0.08914771008060048,"score_gpt":0.27730545306357174,"score_spread":0.18815774298297128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212772615","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000112447255,0.9983638,0.000307915,0.0002898232,0.00021473269,0.0000054978536,0.000012443221,0.0000058942305,0.00068744406],"genre_scores_gemma":[0.00057931495,0.99853253,0.00034065134,0.00013784805,0.000116801726,0.000007235524,0.000014866819,0.0000011534717,0.000269586],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952734,0.00009305748,0.00007180638,0.000076787255,0.00018734504,0.000043655695],"domain_scores_gemma":[0.9991186,0.0005143367,0.00009175511,0.000018754912,0.00021455387,0.000042121435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012741868,0.0010297323,0.0013088126,0.0030461936,0.00037009222,0.0012669216,0.00081515504,0.0013449833,0.0027858352],"category_scores_gemma":[0.0013373137,0.00047114203,0.0008169984,0.0029532518,0.00057840365,0.002033219,0.00077943,0.0015719285,0.001253101],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005882062,0.00009925332,0.00028083968,0.043345243,0.00009165269,0.00025739663,0.0001352924,0.00077017513,0.004995518,0.010168586,0.026719645,0.9130776],"study_design_scores_gemma":[0.000009309425,0.00017013411,0.00062229764,0.0065350747,0.00013531922,0.0010206507,0.00010696241,0.0002865511,0.0016252511,0.0023282466,0.98712045,0.00003966962],"about_ca_topic_score_codex":0.0012370263,"about_ca_topic_score_gemma":0.0020059156,"teacher_disagreement_score":0.0030461936,"about_ca_system_score_codex":0.0007165396,"about_ca_system_score_gemma":0.0016616991,"threshold_uncertainty_score":0.009319544},"labels":[],"label_agreement":null},{"id":"W4212802944","doi":"10.3390/s22041393","title":"Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Reinforcement learning; Overfitting; Computer science; Kalman filter; Artificial intelligence; Temporal difference learning; Representation (politics); Inefficiency; Machine learning; Artificial neural network","score_opus":0.036404439744145194,"score_gpt":0.27237387310785766,"score_spread":0.23596943336371246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212802944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023883022,0.00023685672,0.97317874,0.00015710792,0.000042470117,0.000032493575,0.000025796355,0.00035789548,0.0020856725],"genre_scores_gemma":[0.9396926,0.000103714534,0.058775526,0.000064734704,0.000020624042,0.00008096314,0.00004359494,0.00002013457,0.0011980005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954444,0.00013393506,0.000027312366,0.000110848996,0.00012541986,0.000057975547],"domain_scores_gemma":[0.99887794,0.00060826266,0.00019607312,0.00008021341,0.00016567037,0.00007177088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010314442,0.0006218022,0.0007878735,0.00028660914,0.00028807396,0.00059846335,0.0012192973,0.00072534574,0.0010527661],"category_scores_gemma":[0.0027449932,0.00025556184,0.00035992736,0.000263154,0.00071261614,0.00076438667,0.0009152505,0.0010242835,0.00015194534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007034266,0.000052503954,0.00075583527,0.00005162921,0.000028620567,0.000063945554,0.000045309433,0.9460191,0.0015460392,0.0105631,0.00055467745,0.040248957],"study_design_scores_gemma":[0.0000050430995,0.000015647884,0.000037510046,0.0000016535773,0.0000020493496,0.000005519162,0.000001708249,0.9985145,0.00017629673,0.0011159302,0.00012222414,0.0000019451854],"about_ca_topic_score_codex":0.0047899247,"about_ca_topic_score_gemma":0.0037186989,"teacher_disagreement_score":0.0047899247,"about_ca_system_score_codex":0.0005932644,"about_ca_system_score_gemma":0.0009511662,"threshold_uncertainty_score":0.009524047},"labels":[],"label_agreement":null},{"id":"W4212916010","doi":"10.3390/s22041531","title":"SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"LPWAN; Computer science; Computer network; Network packet; Header; Channel (broadcasting); Wide area network","score_opus":0.011737554670187937,"score_gpt":0.2640819687704009,"score_spread":0.25234441410021297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212916010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801558,0.0001836364,0.016694259,0.000048387814,0.000032577937,0.00009525613,0.00015419775,0.0008581052,0.0017777039],"genre_scores_gemma":[0.99413353,0.00008005008,0.00512735,0.000014828832,0.0000031356835,0.00003641193,0.00015439316,0.000039356382,0.0004110058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981053,0.0003624623,0.0001162438,0.00024964017,0.0008177985,0.0003485284],"domain_scores_gemma":[0.9936951,0.0025503922,0.00070781587,0.00083236094,0.0019597192,0.00025455162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024192112,0.0009719595,0.0005454453,0.0012734885,0.0005303947,0.0005827536,0.0011686494,0.00062177493,0.00076188135],"category_scores_gemma":[0.007380165,0.0002275457,0.0003882837,0.0010722547,0.0009941226,0.00094857794,0.0005846756,0.0007932153,0.00022621836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004181367,0.0025922335,0.021423059,0.00045012595,0.00022651546,0.00040544636,0.00058032933,0.76261955,0.12705867,0.0026539215,0.0010811087,0.07672759],"study_design_scores_gemma":[0.00007024596,0.0026549336,0.0048575983,0.00002078876,0.00005825689,0.0001404151,0.00015778262,0.83904415,0.15215263,0.0002944247,0.000497455,0.00005127838],"about_ca_topic_score_codex":0.011078292,"about_ca_topic_score_gemma":0.004361545,"teacher_disagreement_score":0.011078292,"about_ca_system_score_codex":0.0016470483,"about_ca_system_score_gemma":0.0008145575,"threshold_uncertainty_score":0.022027612},"labels":[],"label_agreement":null},{"id":"W4212956061","doi":"10.3390/s22041573","title":"A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Forests; University of Northern British Columbia","funders":"","keywords":"Support vector machine; Receiver operating characteristic; Sensitivity (control systems); Landslide; Artificial intelligence; Computer science; Deep learning; Benchmark (surveying); Data mining; Machine learning; Algorithm; Pattern recognition (psychology); Geology; Geography; Cartography; Engineering; Geotechnical engineering","score_opus":0.03104443128496844,"score_gpt":0.24009499502800172,"score_spread":0.20905056374303327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212956061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9547352,0.00022891548,0.0425069,0.00033119885,0.000020086138,0.00005067591,0.0005559917,0.0002893782,0.0012817993],"genre_scores_gemma":[0.9895107,0.00006738576,0.009477502,0.000018308154,0.0000048669704,0.0000270513,0.000350833,0.000007960655,0.00053543085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978405,0.000052635438,0.000012944156,0.00006278064,0.00003597626,0.000051510127],"domain_scores_gemma":[0.9996331,0.00014373478,0.00004772919,0.000025344096,0.00013223031,0.000017832257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000718397,0.0009490439,0.0005007273,0.0008058302,0.0003188544,0.0006282344,0.0013535885,0.00068241055,0.0005412971],"category_scores_gemma":[0.0011108974,0.0002862918,0.000671745,0.000766227,0.00040138423,0.0005191975,0.0004802567,0.000570394,0.000113429334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006865122,0.00009141043,0.018584108,0.00004476406,0.00005029288,0.00034672266,0.000064892316,0.9610351,0.00075135863,0.0004863968,0.00043247416,0.018043833],"study_design_scores_gemma":[0.0000048424436,0.000017460392,0.002847994,0.0000035130452,0.000010886836,0.000025752423,0.000053789896,0.99626046,0.00033793604,0.00031020734,0.0001209254,0.000006238086],"about_ca_topic_score_codex":0.08925691,"about_ca_topic_score_gemma":0.057854652,"teacher_disagreement_score":0.08925691,"about_ca_system_score_codex":0.0016295477,"about_ca_system_score_gemma":0.0012786892,"threshold_uncertainty_score":0.17747474},"labels":[],"label_agreement":null},{"id":"W4212991060","doi":"10.3390/s22041415","title":"Design and Analysis of Electronic Head Protector for Taekwondo Sports","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Accelerometer; Gyroscope; Robustness (evolution); Computer science; Engineering; Simulation; Artificial intelligence","score_opus":0.012822740827439198,"score_gpt":0.22474133984249006,"score_spread":0.21191859901505086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212991060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3049787,0.001076402,0.6671291,0.00043134845,0.00018893364,0.0008478132,0.00047683672,0.0020872012,0.022783652],"genre_scores_gemma":[0.92289853,0.00041799492,0.05943579,0.00007521781,0.000022857215,0.00028263265,0.00032130338,0.00009296525,0.016452694],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940133,0.00008095126,0.000023813953,0.000097869415,0.00033155436,0.00006448312],"domain_scores_gemma":[0.9995834,0.00005020615,0.00006614917,0.00004470643,0.00022565044,0.000029881214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005752211,0.00064516487,0.0004820337,0.0008018383,0.000388534,0.0006608879,0.000988807,0.00052971725,0.005351052],"category_scores_gemma":[0.00078823324,0.00031629697,0.00046984924,0.00024148708,0.0002514957,0.0004999025,0.0005460722,0.0002202287,0.001485535],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012889885,0.00032552864,0.017654277,0.0014209724,0.00021076552,0.0008556898,0.00038489647,0.2232279,0.39879385,0.006470738,0.006247165,0.34311923],"study_design_scores_gemma":[0.00018882075,0.0072756847,0.03666849,0.00019143664,0.00044798487,0.00088450464,0.00058249984,0.6839093,0.21156603,0.0016800098,0.056489006,0.0001161792],"about_ca_topic_score_codex":0.0017690286,"about_ca_topic_score_gemma":0.0018451238,"teacher_disagreement_score":0.005351052,"about_ca_system_score_codex":0.0004905454,"about_ca_system_score_gemma":0.0007975032,"threshold_uncertainty_score":0.017901123},"labels":[],"label_agreement":null},{"id":"W4213059108","doi":"10.3390/s22041454","title":"Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Wearable computer; Machine learning; AdaBoost; Personalization; Test data; Support vector machine","score_opus":0.2832962874225659,"score_gpt":0.35152540339644395,"score_spread":0.06822911597387804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213059108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2719803,0.0010904961,0.7203521,0.0005571034,0.00022325733,0.000223821,0.0010109249,0.0024468817,0.0021151798],"genre_scores_gemma":[0.8732637,0.00045284166,0.119564354,0.0003117348,0.0002054826,0.00024772558,0.0024757574,0.0001409171,0.0033373912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935573,0.00013214258,0.000036609377,0.00030747373,0.00009838103,0.00006967079],"domain_scores_gemma":[0.99935895,0.00017828702,0.000068747046,0.0001435406,0.00019030443,0.000060283044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010255309,0.0011290519,0.0012289534,0.0012461992,0.00030203754,0.0006150552,0.0009919291,0.0007543464,0.00079775444],"category_scores_gemma":[0.0021503565,0.0004240913,0.0008858298,0.00084147527,0.0003147805,0.0008773713,0.00089653255,0.0009617602,0.0007250361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008942583,0.0013888254,0.05975318,0.00023829359,0.00042541095,0.0004441404,0.0007989018,0.2937939,0.022503547,0.0015263106,0.011230899,0.6070024],"study_design_scores_gemma":[0.000014296339,0.00009883759,0.006811848,0.00001559526,0.000042099655,0.00010748351,0.00012369614,0.9881782,0.0019055215,0.001146751,0.0015359734,0.000019759791],"about_ca_topic_score_codex":0.0061237067,"about_ca_topic_score_gemma":0.008479031,"teacher_disagreement_score":0.0061237067,"about_ca_system_score_codex":0.00032974096,"about_ca_system_score_gemma":0.0005606127,"threshold_uncertainty_score":0.012176156},"labels":[],"label_agreement":null},{"id":"W4213195098","doi":"10.3390/s22041583","title":"Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA)","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Dysphagia Assessment and Management","field":"Health Professions","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Hidden Markov model; Fluency; Language model; Pronunciation; Artificial intelligence; Natural language processing; Psychology; Linguistics","score_opus":0.11360810611922964,"score_gpt":0.44804987733633367,"score_spread":0.33444177121710406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213195098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76702887,0.00046668106,0.22538613,0.00010713027,0.00011397054,0.00053100183,0.0010368001,0.0022772967,0.0030522898],"genre_scores_gemma":[0.9017101,0.00022678045,0.09526517,0.000072895986,0.00003409184,0.00036078115,0.0009438963,0.00007012204,0.0013160993],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990552,0.0003160396,0.00008819219,0.00024982318,0.00022378123,0.000066945446],"domain_scores_gemma":[0.99905246,0.0003503605,0.00007790614,0.000059333463,0.0004217104,0.00003816882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011970156,0.00065253786,0.0003810748,0.0010051237,0.00022029171,0.00061405747,0.00034222592,0.0004923428,0.0010721178],"category_scores_gemma":[0.0031831234,0.0001421983,0.00055633776,0.000531945,0.00022776081,0.00049302145,0.00036086948,0.000281912,0.00052997307],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001205671,0.00038000257,0.047761813,0.00045911522,0.0003356728,0.0006989917,0.0007325475,0.024913829,0.23199424,0.00064767303,0.0020592331,0.68881124],"study_design_scores_gemma":[0.00021215307,0.0029812981,0.2261464,0.00008832577,0.00054351153,0.0034739918,0.00079887325,0.5205232,0.23864013,0.0010953983,0.005125519,0.00037119148],"about_ca_topic_score_codex":0.009016274,"about_ca_topic_score_gemma":0.009740036,"teacher_disagreement_score":0.009016274,"about_ca_system_score_codex":0.0002627143,"about_ca_system_score_gemma":0.0005285832,"threshold_uncertainty_score":0.017927587},"labels":[],"label_agreement":null},{"id":"W4213325660","doi":"10.3390/s22041592","title":"Determination of Drugs in Clinical Trials: Current Status and Outlook","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Analytical Methods in Pharmaceuticals","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Clinical trial; Medicine; Drug; Intensive care medicine; Pharmacology; Medical physics; Biochemical engineering; Internal medicine; Engineering","score_opus":0.4575945076163762,"score_gpt":0.6019412473149922,"score_spread":0.144346739698616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213325660","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002031319,0.9965172,0.00043471748,0.0008486638,0.00018200795,0.0000239432,0.00003500934,0.000016053194,0.0017392498],"genre_scores_gemma":[0.0015099725,0.99625045,0.0006546795,0.00063172186,0.00023484683,0.000038732087,0.00006188042,0.0000049367254,0.0006127362],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850804,0.00043048416,0.00020885607,0.00019490151,0.00056126737,0.00009639156],"domain_scores_gemma":[0.99436414,0.003566994,0.00062013,0.000092914306,0.0010976568,0.00025806774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005532819,0.0009363528,0.0021441132,0.0020736775,0.00034327744,0.0021085818,0.0014697606,0.0021031518,0.0070284414],"category_scores_gemma":[0.0048804265,0.0004824886,0.0011288433,0.0022910186,0.0011537798,0.0025738114,0.0008878828,0.0025390452,0.002966894],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018210341,0.00011183322,0.00030933198,0.015094497,0.00010148503,0.00009817728,0.00006862775,0.00029465082,0.0010699151,0.0045897556,0.015475608,0.9626039],"study_design_scores_gemma":[0.000115422205,0.0005840291,0.001337556,0.010705053,0.0003552963,0.0010243604,0.00012590912,0.00030778165,0.0012120648,0.0047289967,0.97946084,0.000042708783],"about_ca_topic_score_codex":0.00077000476,"about_ca_topic_score_gemma":0.0016114379,"teacher_disagreement_score":0.0070284414,"about_ca_system_score_codex":0.00096711505,"about_ca_system_score_gemma":0.0027997938,"threshold_uncertainty_score":0.029260695},"labels":[],"label_agreement":null},{"id":"W4213417251","doi":"10.3390/s22051749","title":"Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Smartphone application; Computer science; Artificial intelligence; Machine learning; Human–computer interaction; Physical medicine and rehabilitation; Simulation; Embedded system; Medicine; Multimedia","score_opus":0.07985555337996017,"score_gpt":0.3612671624545941,"score_spread":0.2814116090746339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213417251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9712326,0.0008783349,0.0222234,0.00031329916,0.000110195775,0.00012832182,0.0030564377,0.0005054818,0.0015519059],"genre_scores_gemma":[0.98640776,0.00035523812,0.008635845,0.00009020709,0.00004775244,0.00009667845,0.0031865474,0.00001023321,0.0011697318],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997558,0.000033255103,0.000026440708,0.000084940126,0.00006245664,0.0000371042],"domain_scores_gemma":[0.9995061,0.0001484757,0.00008306592,0.000040300663,0.00017521776,0.000046841164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003574111,0.00062973966,0.0005392444,0.0013450711,0.00014886346,0.00049385795,0.0003476327,0.00057247374,0.0008536441],"category_scores_gemma":[0.0022153377,0.00015047698,0.0005697349,0.0007294592,0.00012528399,0.0004160106,0.00045481315,0.00039406194,0.0006698712],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015576528,0.0011671155,0.3485378,0.00042747063,0.00046285015,0.0016134189,0.00038107112,0.08765713,0.010876309,0.00039783723,0.008826644,0.5380947],"study_design_scores_gemma":[0.00004159056,0.0006778048,0.2708504,0.00012980656,0.00011079821,0.0008171108,0.00040568403,0.7199212,0.0038831586,0.0010085615,0.002098107,0.00005582815],"about_ca_topic_score_codex":0.0062411316,"about_ca_topic_score_gemma":0.008440194,"teacher_disagreement_score":0.0062411316,"about_ca_system_score_codex":0.000287059,"about_ca_system_score_gemma":0.00030159295,"threshold_uncertainty_score":0.012409627},"labels":[],"label_agreement":null},{"id":"W4213434291","doi":"10.3390/s22051727","title":"Towards the World’s Smallest Gravimetric Particulate Matter Sensor: A Miniaturized Virtual Impactor with a Folded Design","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Particulates; Miniaturization; Gravimetric analysis; Work (physics); Volume (thermodynamics); Computer science; Environmental science; Nanotechnology; Process engineering; Mechanical engineering; Automotive engineering; Engineering; Materials science; Physics; Chemistry","score_opus":0.03606063488745277,"score_gpt":0.2441257363550216,"score_spread":0.20806510146756885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213434291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23728232,0.00449885,0.7494839,0.0013597158,0.0005507438,0.0003518565,0.00028478898,0.002255844,0.003931929],"genre_scores_gemma":[0.5123374,0.0019360695,0.47712177,0.0008503929,0.00014235094,0.00024386853,0.00037542408,0.00014482657,0.0068478193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992267,0.00007735767,0.000037104335,0.0002005212,0.00039093808,0.00006739706],"domain_scores_gemma":[0.9995846,0.000064279666,0.00007062172,0.00006829262,0.00013467012,0.00007753933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007763851,0.000631612,0.0007141696,0.00046168792,0.0002723651,0.0010480828,0.001897003,0.0013468664,0.001183579],"category_scores_gemma":[0.0007011677,0.00052823283,0.0006968852,0.00033252247,0.0010299477,0.0018486583,0.0010638032,0.00091637566,0.00070351],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015871758,0.00004522088,0.00051431067,0.00019463351,0.000038945007,0.00014954495,0.00010614656,0.0014497816,0.96968853,0.0027172524,0.000553678,0.024383217],"study_design_scores_gemma":[0.000050129176,0.0021794923,0.0025180392,0.00004667814,0.00013763882,0.0013092526,0.00010657154,0.03342449,0.9296135,0.0012760528,0.029225621,0.000112538466],"about_ca_topic_score_codex":0.00026456252,"about_ca_topic_score_gemma":0.0003011077,"teacher_disagreement_score":0.001897003,"about_ca_system_score_codex":0.00053596566,"about_ca_system_score_gemma":0.00049941265,"threshold_uncertainty_score":0.0041059256},"labels":[],"label_agreement":null},{"id":"W4213441196","doi":"10.3390/s22051722","title":"Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Running Injury Clinic; Canadian Sport Centre Pacific; University of Calgary","funders":"","keywords":"Inertial measurement unit; Accelerometer; Units of measurement; Biomechanics; Gait; Sports biomechanics; Treadmill; Backpack; Metric (unit); Gait analysis; STRIDE; Simulation; Physical medicine and rehabilitation; Computer science; Engineering; Artificial intelligence; Physical therapy; Medicine","score_opus":0.08987338046356767,"score_gpt":0.34175836128233483,"score_spread":0.25188498081876715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213441196","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024057635,0.99841416,0.00026770975,0.0003455224,0.00017639608,0.00004135571,0.000086533095,0.000005971039,0.00042179882],"genre_scores_gemma":[0.0024990616,0.9961047,0.00054399646,0.0003772792,0.000107369524,0.00009394151,0.000110968234,0.0000055849623,0.00015711148],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9946622,0.0014914531,0.0019560466,0.00052725617,0.0012031562,0.00015981993],"domain_scores_gemma":[0.9745695,0.019059813,0.002676666,0.00057302625,0.0029652417,0.0001557999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077055544,0.0014006803,0.0039581736,0.011452844,0.00071568583,0.003413168,0.001654443,0.0023005754,0.0037953316],"category_scores_gemma":[0.030117875,0.00087160314,0.0035881565,0.009460956,0.0010991482,0.0035871153,0.0014085827,0.0012899092,0.0009898103],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010191518,0.000040307754,0.0009418861,0.6103048,0.0020936332,0.00022316822,0.00063944556,0.00024795652,0.00058810064,0.0023230799,0.009071849,0.37342387],"study_design_scores_gemma":[0.000029316925,0.00013783886,0.0037017681,0.77000654,0.006586374,0.000908303,0.00091034657,0.00011809798,0.00052372064,0.0020204966,0.21500142,0.000055718443],"about_ca_topic_score_codex":0.0055440143,"about_ca_topic_score_gemma":0.010390613,"teacher_disagreement_score":0.011452844,"about_ca_system_score_codex":0.0017226746,"about_ca_system_score_gemma":0.0075846245,"threshold_uncertainty_score":0.040751338},"labels":[],"label_agreement":null},{"id":"W4213454278","doi":"10.3390/s22051726","title":"Indirect-Neural-Approximation-Based Fault-Tolerant Integrated Attitude and Position Control of Spacecraft Proximity Operations","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"King Faisal University; Deanship of Scientific Research, King Faisal University","keywords":"Control theory (sociology); Position (finance); Controller (irrigation); Attitude control; Artificial neural network; Fault tolerance; Bounded function; Spacecraft; Adaptive control; Upper and lower bounds; Actuator; Computer science; Reaction wheel; Control (management); Approximation error; Engineering; Control engineering; Mathematics; Artificial intelligence; Algorithm; Aerospace engineering","score_opus":0.009967060153375831,"score_gpt":0.2140401887853287,"score_spread":0.20407312863195287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213454278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08596244,0.0005117259,0.908945,0.0001256548,0.00012002009,0.000031950327,0.000017232736,0.00037549817,0.0039104084],"genre_scores_gemma":[0.98310006,0.000106325504,0.015474639,0.000032212458,0.000021186113,0.000023861865,0.00001742817,0.00000596496,0.0012182167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998159,0.000023030589,0.000012050944,0.00004544532,0.0000796583,0.00002404423],"domain_scores_gemma":[0.9998072,0.000032659074,0.00005793026,0.000020520527,0.00006933437,0.000012330995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023649947,0.00039312578,0.00031966987,0.00014509918,0.0003154637,0.00040077305,0.00065479835,0.00036430254,0.0005982675],"category_scores_gemma":[0.0004890908,0.00012704449,0.00022890922,0.00014900192,0.0003158049,0.00040311072,0.00053405896,0.0004642303,0.00010268797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034197944,0.00010843644,0.0017423602,0.00024474313,0.00008459081,0.00025515555,0.00028594985,0.684959,0.0625272,0.007510365,0.001298517,0.24064173],"study_design_scores_gemma":[0.000016160919,0.00016827718,0.0005071258,0.000007966773,0.000015836811,0.000055193042,0.00001248225,0.992937,0.0049145077,0.00060207787,0.0007553919,0.00000801162],"about_ca_topic_score_codex":0.0024475092,"about_ca_topic_score_gemma":0.0025435246,"teacher_disagreement_score":0.0024475092,"about_ca_system_score_codex":0.00024956494,"about_ca_system_score_gemma":0.00032176953,"threshold_uncertainty_score":0.004866481},"labels":[],"label_agreement":null},{"id":"W4214481061","doi":"10.3390/s22051808","title":"A Multiband Shared Aperture MIMO Antenna for Millimeter-Wave and Sub-6GHz 5G Applications","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Antenna aperture; Antenna factor; Antenna measurement; Antenna (radio); Optics; Physics; Microstrip antenna; Extremely high frequency; Patch antenna; Dipole antenna; Acoustics; Electrical engineering; Engineering","score_opus":0.015926079159185032,"score_gpt":0.20574925240505726,"score_spread":0.18982317324587222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214481061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13466457,0.0007753015,0.84544003,0.00037830032,0.0003182336,0.00004445071,0.0001887134,0.0009367354,0.017253691],"genre_scores_gemma":[0.83190477,0.00041187136,0.16030772,0.00026621937,0.000117610405,0.000071671995,0.00016723717,0.00003538499,0.00671753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970394,0.00006785176,0.00001627011,0.00006173627,0.00010679795,0.00004331767],"domain_scores_gemma":[0.99971527,0.00003654949,0.000077558485,0.00006337744,0.00008565838,0.000021556181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015837475,0.0004954602,0.00037428876,0.00020128072,0.00012277898,0.00053584104,0.00052928244,0.0005936292,0.0011090264],"category_scores_gemma":[0.00023420481,0.00016104535,0.00046963542,0.00033076043,0.00014730741,0.00037261334,0.0003630968,0.00039510085,0.0010636116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030912738,0.000095985284,0.003205985,0.00031541413,0.00019549446,0.0006416567,0.0001341409,0.051020186,0.7827125,0.014699544,0.005108571,0.1415614],"study_design_scores_gemma":[0.000073559684,0.002357636,0.0063964524,0.000057304187,0.00018228522,0.005494066,0.00015980999,0.4734701,0.44833905,0.0036465982,0.059707325,0.00011574639],"about_ca_topic_score_codex":0.00012937978,"about_ca_topic_score_gemma":0.00021358002,"teacher_disagreement_score":0.0011090264,"about_ca_system_score_codex":0.00028114166,"about_ca_system_score_gemma":0.00015463887,"threshold_uncertainty_score":0.003710091},"labels":[],"label_agreement":null},{"id":"W4214525045","doi":"10.3390/s22051797","title":"Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"National Natural Science Foundation of China","keywords":"Node (physics); Modulation (music); Hamming distance; Sensor fusion; Computer science; Pattern recognition (psychology); Signal-to-noise ratio (imaging); Algorithm; Fusion; Hamming code; Artificial intelligence; Engineering; Telecommunications; Acoustics; Physics","score_opus":0.05232477950174942,"score_gpt":0.28678002396259017,"score_spread":0.23445524446084076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214525045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04368452,0.00042187466,0.9536706,0.00020663561,0.00010089073,0.00007211194,0.000037261812,0.00032776903,0.0014782145],"genre_scores_gemma":[0.87874657,0.00014776748,0.11952469,0.00018980975,0.00006691898,0.00007606526,0.00009259609,0.000019154297,0.0011363432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985967,0.00037549416,0.000078248464,0.00027256084,0.0005106657,0.00016630495],"domain_scores_gemma":[0.99845123,0.0006247378,0.00020307342,0.00023445138,0.00042181066,0.00006466169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012332392,0.00074731937,0.0007273011,0.0007868647,0.0005809731,0.0008937866,0.0012844494,0.00091862713,0.00066032284],"category_scores_gemma":[0.0032738615,0.00024421533,0.0004497101,0.0009621553,0.0007453905,0.0015906306,0.0011535706,0.00091370434,0.00021046425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058039767,0.00022744907,0.002981567,0.0001658073,0.00013681145,0.00017122956,0.00029708975,0.2673602,0.056773014,0.0175031,0.0031920397,0.6506113],"study_design_scores_gemma":[0.000011474571,0.00008770152,0.00035604462,0.0000063383527,0.000015808184,0.000040836756,0.00001971453,0.988765,0.0073125116,0.002826528,0.0005432378,0.000014764304],"about_ca_topic_score_codex":0.001996005,"about_ca_topic_score_gemma":0.0022663635,"teacher_disagreement_score":0.001996005,"about_ca_system_score_codex":0.0005992596,"about_ca_system_score_gemma":0.00080192846,"threshold_uncertainty_score":0.0065220594},"labels":[],"label_agreement":null},{"id":"W4214557247","doi":"10.3390/s22051784","title":"Calibration of Stereo Pairs Using Speckle Metrology","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metrology; Speckle pattern; Calibration; Computer vision; Artificial intelligence; Translation (biology); Computer science; Rotation (mathematics); Speckle noise; Optics; Physics","score_opus":0.059403126746803876,"score_gpt":0.2753034664644331,"score_spread":0.21590033971762923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214557247","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030413417,0.00016073383,0.96387124,0.000045699002,0.00006222934,0.00007066804,0.000083238396,0.0005839646,0.0047088084],"genre_scores_gemma":[0.35513404,0.000306708,0.641856,0.00011735424,0.00004762259,0.00012910888,0.00026797596,0.00015909191,0.001982076],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783844,0.00035310542,0.00007502513,0.00037821403,0.0012759521,0.00007922329],"domain_scores_gemma":[0.99888974,0.00021303934,0.00012373767,0.00038493052,0.00035922404,0.000029326913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133549,0.00050640345,0.00056402385,0.0013575326,0.00043763279,0.00073619693,0.00077648455,0.0006401788,0.002165146],"category_scores_gemma":[0.00252477,0.00040357964,0.0003854574,0.0011718721,0.0006136438,0.0010432841,0.0019592433,0.0007993173,0.0008566656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023131723,0.00010349991,0.004252723,0.00029925938,0.00008869219,0.00020525491,0.0005064599,0.028408332,0.41850075,0.023231018,0.0027721112,0.5214006],"study_design_scores_gemma":[0.0000945072,0.00041155115,0.008274487,0.000111978195,0.00006420453,0.0017454266,0.00029283823,0.30013874,0.63728595,0.018460814,0.032952923,0.00016657723],"about_ca_topic_score_codex":0.00037395727,"about_ca_topic_score_gemma":0.00054132723,"teacher_disagreement_score":0.002165146,"about_ca_system_score_codex":0.0004251062,"about_ca_system_score_gemma":0.0005233969,"threshold_uncertainty_score":0.007243097},"labels":[],"label_agreement":null},{"id":"W4214580916","doi":"10.3390/s22051853","title":"Detection of E. coli Bacteria in Milk by an Acoustic Wave Aptasensor with an Anti-Fouling Coating","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Fouling; Coating; Bacteria; Materials science; Microbiology; Chemistry; Nanotechnology; Biology; Biochemistry; Genetics","score_opus":0.010086715390580937,"score_gpt":0.19990084770719244,"score_spread":0.1898141323166115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214580916","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8956516,0.0035475409,0.09669854,0.0004046671,0.00015068043,0.00014952323,0.00024367328,0.00062439847,0.0025293396],"genre_scores_gemma":[0.84756374,0.002173334,0.14269757,0.0003616697,0.000057844136,0.00014575184,0.0002596361,0.000035580437,0.0067048254],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996419,0.000072963696,0.000020951671,0.00009394437,0.00013807876,0.00003215644],"domain_scores_gemma":[0.99981254,0.000049714843,0.000037676837,0.00001476623,0.00005983153,0.000025554182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043108768,0.000608333,0.00036660233,0.00029788716,0.00011801268,0.00026328175,0.00046066736,0.0006892843,0.00048046943],"category_scores_gemma":[0.0004449055,0.00031142443,0.00027655522,0.00022688581,0.00018654193,0.0003307147,0.00030698732,0.00044035472,0.0004136267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011683384,0.000008483784,0.000059652324,0.000014007419,0.0000021655892,0.000011807641,0.000005158868,0.000024697614,0.9992078,0.000015186028,0.000008809935,0.00063064956],"study_design_scores_gemma":[0.0000035614394,0.00017441496,0.0007003223,0.0000022896063,0.0000067943492,0.00014824883,0.000008627841,0.0017924574,0.9964725,0.000017385424,0.0006693722,0.000004154221],"about_ca_topic_score_codex":0.0003065457,"about_ca_topic_score_gemma":0.0005173205,"teacher_disagreement_score":0.0006892843,"about_ca_system_score_codex":0.00017686754,"about_ca_system_score_gemma":0.00017194315,"threshold_uncertainty_score":0.0022798777},"labels":[],"label_agreement":null},{"id":"W4214583847","doi":"10.3390/s22051858","title":"E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Distracted driving; Distraction; Overfitting; Computer science; Deep learning; Machine learning; Artificial intelligence; Ensemble learning; Generalization; Scalability; Phone; Artificial neural network","score_opus":0.024697261340166702,"score_gpt":0.31714667063333724,"score_spread":0.29244940929317054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214583847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26747027,0.0047363746,0.70855427,0.0010665826,0.00056153035,0.0002395063,0.0026241369,0.009919931,0.0048273965],"genre_scores_gemma":[0.86177063,0.00094657036,0.1246055,0.00054713717,0.00015869713,0.00020161393,0.0040227454,0.0001429605,0.007604148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996197,0.000067529356,0.000018675804,0.00014427945,0.000073724324,0.00007599471],"domain_scores_gemma":[0.9995407,0.00014868318,0.0000363108,0.0000572289,0.00017960733,0.000037545524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000930278,0.0013906294,0.0011089778,0.00072498294,0.00029563028,0.00045822552,0.0022671097,0.0009494835,0.0011772772],"category_scores_gemma":[0.0017273716,0.00055131456,0.0010202125,0.0006416325,0.00015105635,0.0008825605,0.00075445056,0.0019292955,0.0006106581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000430331,0.0009679766,0.012569988,0.00010747497,0.00063972233,0.00017737142,0.00011609495,0.5428801,0.004812493,0.0010250704,0.012599554,0.42367384],"study_design_scores_gemma":[0.000010227298,0.00007375201,0.0007160128,0.000007611772,0.000034974633,0.000016045116,0.0000075939574,0.9977203,0.0005902314,0.00028575718,0.00053018465,0.0000072286866],"about_ca_topic_score_codex":0.02871235,"about_ca_topic_score_gemma":0.037382346,"teacher_disagreement_score":0.02871235,"about_ca_system_score_codex":0.0006287606,"about_ca_system_score_gemma":0.00089026784,"threshold_uncertainty_score":0.05709046},"labels":[],"label_agreement":null},{"id":"W4214595570","doi":"10.3390/s22051843","title":"Machine Learning and Smart Devices for Diabetes Management: Systematic Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Chicoutimi; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Diabetes mellitus; Scopus; Diabetes management; Wearable technology; Wearable computer; Medicine; Blood sugar; Computer science; Intensive care medicine; Artificial intelligence; MEDLINE; Risk analysis (engineering); Type 2 diabetes; Embedded system","score_opus":0.20599350209804215,"score_gpt":0.4991403695490511,"score_spread":0.29314686745100893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214595570","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038477048,0.9988249,0.000075777796,0.00019753771,0.000062098625,0.00011244266,0.00015117817,0.000004131695,0.00018710754],"genre_scores_gemma":[0.0069156643,0.9917454,0.0003689663,0.0003786092,0.00007115207,0.0002819217,0.0001339121,0.000002407587,0.000101896716],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.996228,0.0013586064,0.001200066,0.0002563792,0.00080945145,0.00014749769],"domain_scores_gemma":[0.97817093,0.017416641,0.002870055,0.00018753539,0.0011730343,0.00018183714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050563198,0.0010512918,0.005314012,0.0058015916,0.00043858978,0.0017963108,0.0015916267,0.0018202697,0.0064987903],"category_scores_gemma":[0.025876561,0.0005751292,0.004879727,0.0076900376,0.0005594368,0.0018693784,0.0009635349,0.0010803331,0.00039776746],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013572928,0.000023976567,0.0006493837,0.94518226,0.00427777,0.00007163059,0.00009202191,0.00012935737,0.00005108385,0.00022740653,0.0026929262,0.04646639],"study_design_scores_gemma":[0.00020163211,0.00015577192,0.0034819955,0.9463594,0.028344875,0.00030640786,0.00013694407,0.00016734455,0.00008965163,0.00032153097,0.020407736,0.000026792955],"about_ca_topic_score_codex":0.0064465385,"about_ca_topic_score_gemma":0.017099729,"teacher_disagreement_score":0.0064987903,"about_ca_system_score_codex":0.0026579988,"about_ca_system_score_gemma":0.007877045,"threshold_uncertainty_score":0.02674073},"labels":[],"label_agreement":null},{"id":"W4214611281","doi":"10.3390/s22051870","title":"Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF)","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Homography; Computer vision; Artificial intelligence; Computer science; Pixel; Camera resectioning; Image sensor; Sensor fusion; Calibration; Photogrammetry; Frame (networking); Mathematics","score_opus":0.00944108413409425,"score_gpt":0.1775730754981939,"score_spread":0.16813199136409965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214611281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02745509,0.00025231426,0.96477693,0.00012136168,0.00007755852,0.00014812069,0.000079091995,0.0034903998,0.0035989978],"genre_scores_gemma":[0.5546185,0.00031661807,0.43983,0.00019179613,0.00005096988,0.0001977235,0.000605818,0.00012012625,0.004068548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934226,0.00005165084,0.000035063047,0.00017616178,0.00030990771,0.00008508316],"domain_scores_gemma":[0.9996393,0.00003332221,0.00003161473,0.000068508685,0.00020045962,0.000026836191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006432082,0.0005881691,0.000622666,0.0006638875,0.00038854196,0.00068358023,0.00077461853,0.0006070111,0.0017178891],"category_scores_gemma":[0.0007133282,0.00025999377,0.0005011138,0.00038338552,0.0003142216,0.0009847179,0.0011047429,0.0006914697,0.0007870387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022029878,0.00010032206,0.0034436844,0.00017601479,0.00006231117,0.00047675794,0.0003394486,0.070962556,0.11793391,0.003776948,0.0053759357,0.7971318],"study_design_scores_gemma":[0.00006657526,0.0005226531,0.0053710165,0.000031706815,0.000062322084,0.00073705695,0.00013827786,0.8910336,0.08610557,0.0022263466,0.01361309,0.00009187604],"about_ca_topic_score_codex":0.0033920717,"about_ca_topic_score_gemma":0.001720001,"teacher_disagreement_score":0.0033920717,"about_ca_system_score_codex":0.00034546212,"about_ca_system_score_gemma":0.0008737552,"threshold_uncertainty_score":0.006744623},"labels":[],"label_agreement":null},{"id":"W4214647988","doi":"10.3390/s22051817","title":"Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Computer science; Principal component analysis; Computer vision; Sorting; Support vector machine; Data set; Dynamic range; High dynamic range; Remote sensing; Pattern recognition (psychology); Algorithm; Geology","score_opus":0.01144463708852683,"score_gpt":0.24229891369728643,"score_spread":0.2308542766087596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214647988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16065136,0.0031256932,0.8285756,0.00029639364,0.00011121262,0.00009951177,0.000114607494,0.0012902004,0.005735517],"genre_scores_gemma":[0.5348005,0.0016412555,0.45866662,0.00036935203,0.00010629581,0.00010909616,0.00023344602,0.000117601405,0.0039557805],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936765,0.00011016152,0.000027314567,0.00015475141,0.0002937447,0.000046358215],"domain_scores_gemma":[0.99929166,0.00025228254,0.00011752676,0.00010003632,0.00021226764,0.000026139473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005424351,0.0005905694,0.00028357687,0.00070769497,0.00018260465,0.0004897663,0.0005825884,0.00060102023,0.001609531],"category_scores_gemma":[0.00078296,0.00029444855,0.00037811956,0.0004102918,0.00031570438,0.0012074974,0.00053984637,0.0006697949,0.00070524967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014594829,0.00011216334,0.0016090816,0.0002727422,0.000043459208,0.000114713985,0.0001277113,0.0046849526,0.7494456,0.0011382377,0.00086991355,0.24143542],"study_design_scores_gemma":[0.0000214276,0.00035746468,0.0070458828,0.000036814923,0.00006160919,0.0010226793,0.00009992113,0.12332154,0.8520293,0.00084915146,0.015076653,0.00007746162],"about_ca_topic_score_codex":0.00027164497,"about_ca_topic_score_gemma":0.00069976883,"teacher_disagreement_score":0.001609531,"about_ca_system_score_codex":0.00024969107,"about_ca_system_score_gemma":0.00016744697,"threshold_uncertainty_score":0.005384445},"labels":[],"label_agreement":null},{"id":"W4214698899","doi":"10.3390/s22051818","title":"CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Major Scientific and Technological Innovation Project of Shandong Province; Texas Space Grant Consortium","keywords":"Computer network; Computer science; Active queue management; Network congestion; Wireless network; Packet loss; Router; Network packet; Bottleneck; Wireless; Distributed computing; Telecommunications; Embedded system","score_opus":0.019103426273181203,"score_gpt":0.22916080234603786,"score_spread":0.21005737607285666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214698899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014481747,0.000597669,0.9814586,0.00013202812,0.00018464154,0.000084197454,0.000017324728,0.0011012856,0.0019425161],"genre_scores_gemma":[0.9148213,0.00050245924,0.081352346,0.00017454264,0.00008742647,0.00020530603,0.000069195194,0.000050067003,0.002737278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994491,0.00009846513,0.000031527583,0.00012057632,0.0002191123,0.00008116617],"domain_scores_gemma":[0.9994875,0.00014253196,0.00006794884,0.000034813864,0.00022305203,0.00004414201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007357162,0.00076230185,0.00059095933,0.0005982179,0.0006036015,0.0007437203,0.0019474734,0.000656375,0.0014790721],"category_scores_gemma":[0.0014810693,0.00020807385,0.00041619007,0.000496289,0.00060520374,0.00073169614,0.0007775801,0.0010909236,0.00020979787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040446242,0.00029968866,0.0018222294,0.0003296635,0.00011847406,0.0003329496,0.00028216338,0.68601817,0.032659225,0.020045092,0.006160041,0.2515278],"study_design_scores_gemma":[0.000016610702,0.00007258447,0.000077140445,0.000004039959,0.000007430437,0.000022363505,0.0000047714157,0.99702066,0.001432701,0.0007335488,0.00059973804,0.000008275595],"about_ca_topic_score_codex":0.008618904,"about_ca_topic_score_gemma":0.0038070993,"teacher_disagreement_score":0.008618904,"about_ca_system_score_codex":0.00080480723,"about_ca_system_score_gemma":0.0010820605,"threshold_uncertainty_score":0.017137468},"labels":[],"label_agreement":null},{"id":"W4214700465","doi":"10.3390/s22051813","title":"Development of a Coaching System for Functional Electrical Stimulation Rowing: A Feasibility Study in Able-Bodied Individuals","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Coaching; Rowing; Functional electrical stimulation; Physical medicine and rehabilitation; Physical therapy; Work (physics); Medicine; Stimulation; Psychology; Engineering; Internal medicine; Mechanical engineering","score_opus":0.04355168480737324,"score_gpt":0.2623974015579682,"score_spread":0.21884571675059497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214700465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968945,0.000028063936,0.0025164606,0.000022834909,0.000003394543,0.00033290364,0.000020889802,0.000011737545,0.00016920717],"genre_scores_gemma":[0.98717296,0.00008118163,0.011384715,0.00004381639,0.0000096971235,0.0006670172,0.000119556185,0.000004114546,0.00051700725],"study_design_codex":"bench_or_experimental","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99960333,0.00014698246,0.000027079612,0.00007176303,0.00007770423,0.0000732588],"domain_scores_gemma":[0.9994253,0.00016130018,0.000043975644,0.000052360912,0.00015649233,0.00016050991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011021243,0.0004606182,0.00028130203,0.0003674382,0.00042561532,0.00022758172,0.00041279517,0.0005345194,0.0016799594],"category_scores_gemma":[0.0010959974,0.00017010159,0.00032659454,0.00009729337,0.00041255244,0.0003579644,0.0005230677,0.00030008433,0.0003132429],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009382384,0.06164642,0.16481718,0.0013563279,0.00021603823,0.0021832387,0.0041103666,0.0041140215,0.57301736,0.00040037115,0.0006466055,0.17810972],"study_design_scores_gemma":[0.0017866016,0.39778867,0.523872,0.00012088699,0.00029697202,0.0016124808,0.0036772257,0.014412251,0.05374471,0.00017179309,0.0024378996,0.000078444966],"about_ca_topic_score_codex":0.0012109384,"about_ca_topic_score_gemma":0.0015638854,"teacher_disagreement_score":0.0016799594,"about_ca_system_score_codex":0.00021624097,"about_ca_system_score_gemma":0.00065016345,"threshold_uncertainty_score":0.005828619},"labels":[],"label_agreement":null},{"id":"W4214879618","doi":"10.3390/s22051932","title":"Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Brain–computer interface; Support vector machine; Computer science; Artificial intelligence; Linear discriminant analysis; Convolutional neural network; Functional near-infrared spectroscopy; Gait; Exoskeleton; Pattern recognition (psychology); Motor imagery; Artificial neural network; Primary motor cortex; Machine learning; Electroencephalography; Motor cortex; Physical medicine and rehabilitation; Prefrontal cortex; Cognition; Simulation; Psychology; Neuroscience","score_opus":0.04288942214659797,"score_gpt":0.29089892679287044,"score_spread":0.24800950464627247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214879618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92731506,0.00081593165,0.06771905,0.00014103932,0.00007994762,0.000059915008,0.0003431388,0.00047540522,0.0030506009],"genre_scores_gemma":[0.9890295,0.00015444626,0.009655373,0.000026212518,0.000008831421,0.000031714604,0.0002607724,0.000012021378,0.0008211337],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997317,0.000032715896,0.000020761681,0.000050628376,0.00010361534,0.000060626488],"domain_scores_gemma":[0.9997545,0.000071428636,0.000022860184,0.000018443186,0.00011923948,0.0000135786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046402967,0.0006673946,0.00032067898,0.0006558373,0.00019004548,0.00027527832,0.0002468633,0.0003326877,0.0007312577],"category_scores_gemma":[0.0013955522,0.00008093136,0.00021736571,0.00038145552,0.00013364614,0.00030346197,0.00020400873,0.00022295417,0.00024245559],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011769005,0.000605737,0.04298005,0.0003226373,0.00021669612,0.00029251957,0.00019412125,0.069167875,0.14235051,0.0006091049,0.0024127667,0.73967105],"study_design_scores_gemma":[0.000020018331,0.0005402927,0.0824219,0.000029147954,0.00009243062,0.0002657529,0.00013851101,0.8484097,0.06685069,0.0004753619,0.0007240997,0.000032012762],"about_ca_topic_score_codex":0.008015246,"about_ca_topic_score_gemma":0.010009024,"teacher_disagreement_score":0.008015246,"about_ca_system_score_codex":0.00030005656,"about_ca_system_score_gemma":0.0003213382,"threshold_uncertainty_score":0.01593721},"labels":[],"label_agreement":null},{"id":"W4214895504","doi":"10.3390/s22051977","title":"Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Deep learning; Computer science; Artificial intelligence; Segmentation; Machine learning; Transformer; Random forest; Architecture; Engineering; Geography","score_opus":0.012736050181385055,"score_gpt":0.19446363794009508,"score_spread":0.18172758775871004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214895504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08666422,0.0013433599,0.90501004,0.00022637,0.00009423771,0.000077048186,0.00029050492,0.0035894762,0.0027047156],"genre_scores_gemma":[0.8016551,0.0009020834,0.19130176,0.00025691398,0.00006079065,0.00006766306,0.0011606173,0.00016276726,0.004432309],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974424,0.000027250588,0.000015079288,0.00009309473,0.000058091748,0.000062206374],"domain_scores_gemma":[0.99982136,0.000047361988,0.000025649042,0.000026832122,0.000059364127,0.000019488698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044080516,0.0010229041,0.00078299624,0.0012142672,0.00029939532,0.0006956845,0.0011716154,0.0008266045,0.0014822545],"category_scores_gemma":[0.0006611985,0.00043525387,0.00097642804,0.000893971,0.00040010977,0.0010404077,0.0007075051,0.0009867112,0.00051996013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033281345,0.0002158444,0.003465004,0.00013574037,0.00013645148,0.00018910458,0.00010000985,0.36259538,0.026033321,0.0048799794,0.0036704554,0.5982459],"study_design_scores_gemma":[0.000003811832,0.000024578447,0.00033684904,0.000005384539,0.000011947159,0.00002922902,0.000011019966,0.99416745,0.0036280483,0.001326892,0.00045044976,0.0000043577766],"about_ca_topic_score_codex":0.011975346,"about_ca_topic_score_gemma":0.01393982,"teacher_disagreement_score":0.011975346,"about_ca_system_score_codex":0.0008337624,"about_ca_system_score_gemma":0.0009220286,"threshold_uncertainty_score":0.02381128},"labels":[],"label_agreement":null},{"id":"W4214915154","doi":"10.3390/s22051955","title":"Heterogeneous Skin Phantoms for Experimental Validation of Microwave-Based Diagnostic Tools","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microwave; Microwave imaging; Biomedical engineering; Basal cell carcinoma; Computer science; Imaging phantom; Medical imaging; Dielectric; Medical physics; Materials science; Radiology; Medicine; Artificial intelligence; Basal cell; Optoelectronics; Pathology","score_opus":0.014541110994493308,"score_gpt":0.23287223337124713,"score_spread":0.2183311223767538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214915154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48890075,0.0033595068,0.49870303,0.00026582667,0.00028071055,0.0018878008,0.0015553703,0.0011638714,0.0038832023],"genre_scores_gemma":[0.740974,0.002688366,0.24939452,0.00024750628,0.000047324105,0.0022145489,0.0015164365,0.0001853722,0.002731888],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913234,0.0003641849,0.000070836366,0.00015952205,0.00019886532,0.00007416357],"domain_scores_gemma":[0.9984603,0.00069319026,0.00022031358,0.00033666936,0.00021352546,0.0000760216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020229118,0.0009570696,0.00037253843,0.0009370039,0.00026017427,0.00039683256,0.00067830394,0.0006718737,0.0026417507],"category_scores_gemma":[0.0020354989,0.0003164001,0.00028260527,0.0006510423,0.00053784205,0.0003765013,0.00051765493,0.00053135236,0.0004993289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002364098,0.000156959,0.00034008877,0.00014746143,0.000012663781,0.00013879576,0.00007276613,0.0024700065,0.9919525,0.0006962689,0.00018175364,0.0035944057],"study_design_scores_gemma":[0.000040197006,0.0010473372,0.0016555875,0.000038386916,0.00004546728,0.0005167025,0.00007104757,0.008110878,0.9831055,0.0002830235,0.0050629866,0.000022973281],"about_ca_topic_score_codex":0.00042836362,"about_ca_topic_score_gemma":0.0005336089,"teacher_disagreement_score":0.0026417507,"about_ca_system_score_codex":0.00029459345,"about_ca_system_score_gemma":0.00032944436,"threshold_uncertainty_score":0.010698259},"labels":[],"label_agreement":null},{"id":"W4214916010","doi":"10.3390/s22051972","title":"Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Authentication (law); Head (geology); Blockchain; Cluster (spacecraft); Computer science; Selection (genetic algorithm); The Internet; Internet of Things; Computer security; Computer network; Operating system; Artificial intelligence; Geology","score_opus":0.040728458613043875,"score_gpt":0.2779609986657108,"score_spread":0.23723254005266692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214916010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054907672,0.0012368897,0.93411887,0.00063638424,0.0001668639,0.00022622375,0.0001603665,0.00079346605,0.0077533307],"genre_scores_gemma":[0.9336422,0.00092139427,0.06013207,0.00010809407,0.000045793746,0.00020546933,0.00021396729,0.00003217215,0.004698929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851054,0.0004753044,0.00009833881,0.0002776853,0.0004705922,0.000167479],"domain_scores_gemma":[0.99930775,0.00020255895,0.000105180945,0.00014366703,0.00018597838,0.00005491631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012842854,0.000462169,0.0008105082,0.00065857155,0.0010744162,0.0008132801,0.0014756852,0.00074650865,0.0012461614],"category_scores_gemma":[0.0015129513,0.00032794368,0.0005824377,0.0009459998,0.00078520447,0.0022954326,0.0015626707,0.0005274797,0.00033244895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035342702,0.000103502265,0.0028374365,0.00033299078,0.00012449254,0.00054341555,0.0003749372,0.80673796,0.014300935,0.0535778,0.003102768,0.1176104],"study_design_scores_gemma":[0.000037340054,0.0001347066,0.00034103007,0.00001736815,0.000026375312,0.00014765405,0.00006865791,0.9749357,0.004489586,0.015368945,0.0044013034,0.000031317577],"about_ca_topic_score_codex":0.0049985503,"about_ca_topic_score_gemma":0.004497071,"teacher_disagreement_score":0.0049985503,"about_ca_system_score_codex":0.0010154224,"about_ca_system_score_gemma":0.0014372317,"threshold_uncertainty_score":0.009938955},"labels":[],"label_agreement":null},{"id":"W4214916328","doi":"10.3390/s22051958","title":"Privacy Preserving Multi-Party Key Exchange Protocol for Wireless Mesh Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Authentication Protocols Security","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer network; Computer science; Wireless mesh network; Ticket; Handover; Authentication (law); Computer security; Key exchange; Authentication protocol; Plaintext; Encryption; Wireless; Wireless network; Public-key cryptography; Telecommunications","score_opus":0.05185548019668659,"score_gpt":0.3360295361436454,"score_spread":0.28417405594695877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214916328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010335939,0.0014972317,0.98121923,0.0005546416,0.00020212472,0.0003899617,0.00012302455,0.00059830147,0.005079533],"genre_scores_gemma":[0.6750433,0.002658565,0.30988577,0.0004857579,0.00025343092,0.0017908745,0.0007071387,0.000106813895,0.009068339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613106,0.0018203125,0.00036400591,0.00028611723,0.0011645,0.00023405785],"domain_scores_gemma":[0.9977068,0.00090526167,0.0003321451,0.00068905205,0.0002832117,0.00008356718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030108127,0.0005541986,0.0007620993,0.0005970599,0.0012742627,0.0014553914,0.0011418719,0.001263012,0.0022267068],"category_scores_gemma":[0.0049094222,0.0003011499,0.00047706623,0.0011268514,0.0010513915,0.0037198777,0.0023893462,0.0018860409,0.0008196306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001600734,0.00038543157,0.0014223686,0.0018503407,0.0002798203,0.0024366588,0.0014325676,0.05382007,0.09871861,0.4714483,0.017924005,0.34868106],"study_design_scores_gemma":[0.0005014576,0.0011001049,0.0010659583,0.00025200774,0.00021006142,0.0032236185,0.00040292618,0.49710357,0.0707718,0.30458087,0.12056632,0.00022121696],"about_ca_topic_score_codex":0.00022346196,"about_ca_topic_score_gemma":0.00017181679,"teacher_disagreement_score":0.0030108127,"about_ca_system_score_codex":0.0006204953,"about_ca_system_score_gemma":0.0010094503,"threshold_uncertainty_score":0.015922844},"labels":[],"label_agreement":null},{"id":"W4220660138","doi":"10.3390/s22062287","title":"Monitoring Respiratory Motion during VMAT Treatment Delivery Using Ultra-Wideband Radar","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Breathing; Radar; Computer science; Imaging phantom; SIGNAL (programming language); Artificial intelligence; Computer vision; Interference (communication); Respiratory monitoring; Artificial neural network; Physics; Telecommunications; Medicine; Respiratory system; Optics; Channel (broadcasting)","score_opus":0.021127123331177285,"score_gpt":0.27437532337266307,"score_spread":0.2532482000414858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220660138","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83363366,0.0017811838,0.16204627,0.0002033849,0.000056448436,0.000081692655,0.00012520958,0.0006607568,0.0014114762],"genre_scores_gemma":[0.97302973,0.0003077043,0.025657922,0.000119892706,0.000013603261,0.000029329209,0.00006821769,0.000023561826,0.00075013953],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980825,0.000050704446,0.000008865494,0.00003457776,0.0000832084,0.000014493966],"domain_scores_gemma":[0.9998066,0.000083143124,0.000054413817,0.000017225528,0.000028376298,0.000010234911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026644234,0.0001582075,0.00022915227,0.00013831408,0.00007162464,0.00024094041,0.0002364289,0.00031446642,0.00042524523],"category_scores_gemma":[0.00070679176,0.00008728374,0.00012202067,0.00009425368,0.000113916234,0.0003081876,0.00019866192,0.00017612084,0.00012985476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048256473,0.000055437264,0.0064886794,0.00018048783,0.00003066229,0.00012007521,0.00011887566,0.012802143,0.9155608,0.00020526808,0.00025604223,0.063699044],"study_design_scores_gemma":[0.00004802207,0.0016346076,0.040813666,0.000037149974,0.00011767501,0.0011184906,0.000130732,0.16079941,0.7912951,0.0002156714,0.003731943,0.000057540103],"about_ca_topic_score_codex":0.00025306732,"about_ca_topic_score_gemma":0.00038894016,"teacher_disagreement_score":0.00042524523,"about_ca_system_score_codex":0.00018273214,"about_ca_system_score_gemma":0.00011611161,"threshold_uncertainty_score":0.001422584},"labels":[],"label_agreement":null},{"id":"W4220704048","doi":"10.3390/s22072575","title":"LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Centre of Robotics and Automation","keywords":"Lasso (programming language); Brain–computer interface; Representation (politics); Homotopy; Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning; Mathematics; Psychology; Neuroscience; Electroencephalography; Pure mathematics; Programming language","score_opus":0.057535775061085474,"score_gpt":0.3687424682145611,"score_spread":0.3112066931534756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220704048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035506282,0.00018381825,0.9625141,0.00015458217,0.000040038765,0.000068312365,0.00010616692,0.0004940317,0.00093273143],"genre_scores_gemma":[0.48632404,0.00037127908,0.50754493,0.00014159559,0.00009161816,0.0003790103,0.0010100513,0.00011056046,0.004026988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999569,0.00013942987,0.000027309756,0.00008428033,0.00013594251,0.00004401957],"domain_scores_gemma":[0.99927276,0.0003257741,0.00006808029,0.000056455076,0.000255587,0.000021211315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008384523,0.0005872275,0.0005454703,0.000563428,0.0003034206,0.00035334256,0.00047128845,0.0005787255,0.0014984094],"category_scores_gemma":[0.0029742392,0.00016024942,0.00061462773,0.00058767933,0.00030331212,0.00044978593,0.0004458833,0.0008041786,0.00053874817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038385933,0.00024232642,0.0024012085,0.00017598698,0.0001100809,0.00019857733,0.00018745975,0.20055263,0.051315665,0.0056418264,0.004915422,0.7338749],"study_design_scores_gemma":[0.0000057959865,0.00005385727,0.00076394196,0.000005411338,0.000009164309,0.000037441212,0.000016164347,0.99343276,0.0041745338,0.00090436765,0.0005879949,0.00000851811],"about_ca_topic_score_codex":0.0029521314,"about_ca_topic_score_gemma":0.002686301,"teacher_disagreement_score":0.0029521314,"about_ca_system_score_codex":0.00025335388,"about_ca_system_score_gemma":0.0005259194,"threshold_uncertainty_score":0.0058698654},"labels":[],"label_agreement":null},{"id":"W4220721561","doi":"10.3390/s22072556","title":"Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Loam; Remote sensing; Soil water; Partial least squares regression; Multispectral image; Calibration; Environmental science; Soil texture; Soil test; Near-infrared spectroscopy; Soil science; Geology; Mathematics; Optics; Statistics","score_opus":0.02656623806664271,"score_gpt":0.24449034692135763,"score_spread":0.21792410885471492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220721561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861902,0.00033224892,0.009715512,0.000058834936,0.000017768434,0.00032156575,0.001270061,0.00026236873,0.0018313819],"genre_scores_gemma":[0.93156856,0.0008338687,0.06093725,0.000104909595,0.000010103137,0.00025986455,0.0024814133,0.00006722677,0.003736795],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99874216,0.00007962415,0.000040062325,0.00032003783,0.00071439624,0.000103866],"domain_scores_gemma":[0.99856585,0.00015517102,0.00017058838,0.0000682251,0.0009109128,0.00012935963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017371104,0.0015088114,0.0006435326,0.0012356055,0.0009831191,0.0008172574,0.0017660805,0.0007042593,0.0007498497],"category_scores_gemma":[0.0018604704,0.0005007748,0.00065986486,0.0022889986,0.00051337405,0.00063321745,0.0005877718,0.00060089334,0.0002603167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001826977,0.0017656859,0.3408379,0.0007771386,0.00052581594,0.00026374345,0.0009336893,0.03243849,0.4261251,0.0006041474,0.0015687137,0.19233257],"study_design_scores_gemma":[0.00024296237,0.0020178116,0.66614634,0.00007048222,0.0004946051,0.00029268715,0.0013149274,0.13164374,0.19060655,0.00019880076,0.0067163664,0.0002546734],"about_ca_topic_score_codex":0.51461816,"about_ca_topic_score_gemma":0.72044474,"teacher_disagreement_score":0.48538184,"about_ca_system_score_codex":0.0045686113,"about_ca_system_score_gemma":0.0041904715,"threshold_uncertainty_score":0.9764807},"labels":[],"label_agreement":null},{"id":"W4220723969","doi":"10.3390/s22072467","title":"Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Spectrogram; Computer science; Artificial intelligence; Pattern recognition (psychology); Speech recognition","score_opus":0.004517238409663837,"score_gpt":0.2114838822746569,"score_spread":0.20696664386499306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220723969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31504542,0.0013309737,0.67678934,0.00023043147,0.00025148995,0.00011775462,0.00089311006,0.003209547,0.0021319692],"genre_scores_gemma":[0.83905196,0.0007158481,0.15608491,0.00009962941,0.00011998682,0.000039249953,0.0013075566,0.00009167409,0.0024892038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978656,0.000024777526,0.000020081494,0.00007121871,0.0000714922,0.000025890065],"domain_scores_gemma":[0.9996915,0.000086633474,0.00006873025,0.00004733741,0.00008709805,0.00001869534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020935207,0.000646948,0.00041999706,0.0013705584,0.000103714374,0.00028413575,0.00028992194,0.00049294555,0.0006999209],"category_scores_gemma":[0.0010044082,0.00017130002,0.00045065806,0.00052234455,0.0001453551,0.00040425462,0.00032047482,0.0003848941,0.0004243222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048322856,0.00026620706,0.028586911,0.0001778465,0.0002043632,0.00093252916,0.000069346184,0.027843542,0.22267474,0.00071096484,0.0039756065,0.71407473],"study_design_scores_gemma":[0.000038507394,0.00036787018,0.08555493,0.000042986296,0.00014766841,0.002563979,0.00006402247,0.8265631,0.07850047,0.0014494442,0.0046585556,0.000048480222],"about_ca_topic_score_codex":0.0014878275,"about_ca_topic_score_gemma":0.0030274678,"teacher_disagreement_score":0.0014878275,"about_ca_system_score_codex":0.0001498524,"about_ca_system_score_gemma":0.00020843129,"threshold_uncertainty_score":0.0029583573},"labels":[],"label_agreement":null},{"id":"W4220777925","doi":"10.3390/s22062383","title":"Drone-Mountable Gas Sensing Platform Using Graphene Chemiresistors for Remote In-Field Monitoring","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministère de la Défense Nationale","keywords":"Microcontroller; Drone; Wireless; Electrical engineering; Computer science; Embedded system; Engineering; Real-time computing; Telecommunications","score_opus":0.023396406481110566,"score_gpt":0.23792250753902133,"score_spread":0.21452610105791076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220777925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9435027,0.0007438277,0.048764266,0.00025018372,0.00017407455,0.0001299566,0.00027854383,0.00089431275,0.005262091],"genre_scores_gemma":[0.9550785,0.00029412648,0.04106603,0.00008073951,0.000018810337,0.000064569926,0.0001246427,0.00003036227,0.0032423134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999143,0.0000071392205,0.0000027432063,0.00002195303,0.00004323981,0.000010589109],"domain_scores_gemma":[0.99990964,0.000015187132,0.000024455649,0.000018669822,0.00002094042,0.000011031143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006354418,0.0004259256,0.0001413829,0.00020083137,0.00014913965,0.00020100744,0.00054047344,0.0004486546,0.0007416356],"category_scores_gemma":[0.00015578869,0.00015546444,0.00013815143,0.0001096139,0.00015653901,0.0003633304,0.00026558785,0.0002465053,0.00027470101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019631721,0.000017369088,0.00017830626,0.000045818215,0.0000041583644,0.00008356386,0.00001799681,0.00055132795,0.9948348,0.00011797559,0.00013394612,0.003995033],"study_design_scores_gemma":[0.0000072303096,0.00029859235,0.0015353393,0.000005812898,0.000009393605,0.00015069841,0.0000241423,0.008371403,0.9867337,0.00006598146,0.0027837737,0.000013888517],"about_ca_topic_score_codex":0.00050687336,"about_ca_topic_score_gemma":0.0012987043,"teacher_disagreement_score":0.0007416356,"about_ca_system_score_codex":0.00018440315,"about_ca_system_score_gemma":0.0001304093,"threshold_uncertainty_score":0.0024809837},"labels":[],"label_agreement":null},{"id":"W4220799614","doi":"10.3390/s22072644","title":"Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Microgrid; Converters; Mode (computer interface); Control theory (sociology); Computer science; Engineering; Voltage; Control (management); Artificial intelligence","score_opus":0.05552465189172685,"score_gpt":0.27648250338540664,"score_spread":0.2209578514936798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220799614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025871165,0.00015614937,0.9715317,0.000104987506,0.00006225817,0.000057405647,0.000012766272,0.00028686377,0.001916726],"genre_scores_gemma":[0.9748994,0.00013561519,0.023639483,0.000038798415,0.00002291236,0.000100222926,0.000021150414,0.000010277871,0.0011321261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963415,0.00006622304,0.000030133077,0.0001227071,0.000105619954,0.000041215953],"domain_scores_gemma":[0.9994005,0.00015224202,0.00013058165,0.00006187447,0.00022530126,0.000029546865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007997703,0.0008353183,0.00055754755,0.00030944357,0.00038694605,0.0008226144,0.00084566354,0.00057056255,0.00079986145],"category_scores_gemma":[0.0012652219,0.0002509589,0.00041804323,0.00021995505,0.00070249033,0.0004903453,0.00071118184,0.000827285,0.00015737621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022714779,0.0000759009,0.0009765672,0.00019009884,0.000078710975,0.00014930566,0.00026812401,0.898786,0.018552236,0.007872719,0.00078057195,0.07204257],"study_design_scores_gemma":[0.000018717044,0.0001240128,0.00014097539,0.000006163479,0.000013597461,0.000013100479,0.000013119108,0.99669915,0.002010427,0.00059905625,0.00035653525,0.000005093959],"about_ca_topic_score_codex":0.0036166816,"about_ca_topic_score_gemma":0.002090216,"teacher_disagreement_score":0.0036166816,"about_ca_system_score_codex":0.00048465986,"about_ca_system_score_gemma":0.00081231666,"threshold_uncertainty_score":0.0071913004},"labels":[],"label_agreement":null},{"id":"W4220803734","doi":"10.3390/s22052029","title":"Fear of Falling Does Not Influence Dual-Task Gait Costs in People with Parkinson’s Disease: A Cross-Sectional Study","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gait; STRIDE; Cadence; Physical medicine and rehabilitation; Fear of falling; Cognition; Montreal Cognitive Assessment; Parkinson's disease; Rating scale; Poison control; Gait analysis; Psychology; Medicine; Physical therapy; Injury prevention; Cognitive impairment; Disease; Developmental psychology; Internal medicine; Psychiatry","score_opus":0.017753033371938415,"score_gpt":0.33679562858665196,"score_spread":0.31904259521471356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220803734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99931264,0.00020058111,0.000069024965,0.00001558891,0.0000061163078,0.000011442338,0.00015890076,0.0000016594823,0.00022410246],"genre_scores_gemma":[0.99953365,0.00006690389,0.00006591987,0.000022908313,0.000008491813,0.000012281458,0.00018146551,0.0000011550037,0.0001072973],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99952424,0.000102674996,0.000071019706,0.0001471825,0.000096220225,0.00005858871],"domain_scores_gemma":[0.9985928,0.00027487436,0.0005177321,0.00013625472,0.00025416678,0.00022423039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008865754,0.00041739017,0.0006029331,0.0007828835,0.0006588058,0.00079190906,0.0003284117,0.0009970232,0.0010754875],"category_scores_gemma":[0.002395345,0.0005326777,0.0009847118,0.00075176294,0.00032997745,0.0007102726,0.00047702467,0.0007248855,0.0002740507],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018263987,0.000120649216,0.9982438,0.000017414552,0.00018270973,0.000053262,0.00018736695,0.000022642676,0.00014167377,0.000007604263,0.00004432041,0.00079582253],"study_design_scores_gemma":[0.0000063163466,0.00018350338,0.99934286,0.000003564617,0.000058198213,0.0001067095,0.00012398472,0.000082897415,0.000013819741,0.000012193971,0.00006221965,0.0000036358936],"about_ca_topic_score_codex":0.0056464793,"about_ca_topic_score_gemma":0.0071048094,"teacher_disagreement_score":0.0056464793,"about_ca_system_score_codex":0.00025773008,"about_ca_system_score_gemma":0.00021666964,"threshold_uncertainty_score":0.0112271905},"labels":[],"label_agreement":null},{"id":"W4220839778","doi":"10.3390/s22062393","title":"Local Trust in Internet of Things Based on Contract Theory","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"The Internet; Internet privacy; Contract theory; Computer security; Business; Computer science; World Wide Web; Economics; Microeconomics","score_opus":0.006515042064305441,"score_gpt":0.21505711432208777,"score_spread":0.20854207225778232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220839778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038330004,0.0007155202,0.948893,0.00095578027,0.00009745779,0.00010956199,0.00008929679,0.00015748484,0.010651951],"genre_scores_gemma":[0.94373643,0.0007075687,0.05004795,0.00011586752,0.00007029926,0.0001989072,0.00009246481,0.000044805263,0.004985668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99786747,0.00090695696,0.00010239333,0.00024642984,0.00067559944,0.00020116761],"domain_scores_gemma":[0.99589276,0.0023562247,0.0004722376,0.00050313695,0.0005061436,0.00026953386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027370683,0.000490537,0.0010122885,0.000635007,0.0010481228,0.0019121225,0.0014239813,0.0011978699,0.0027115224],"category_scores_gemma":[0.008788961,0.0004045218,0.00083297194,0.00096279534,0.0024320395,0.0035204012,0.0019494783,0.0017238624,0.00038999904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008382266,0.000046534,0.0008949922,0.00011046646,0.000036282217,0.00019523031,0.00020405723,0.47295582,0.001195661,0.5012972,0.0012584405,0.021721423],"study_design_scores_gemma":[0.000013236704,0.00003357221,0.00013413794,0.000014902765,0.00000677577,0.00004144176,0.000031949767,0.83406276,0.00026192173,0.1639367,0.0014490117,0.000013645988],"about_ca_topic_score_codex":0.00445653,"about_ca_topic_score_gemma":0.0025666421,"teacher_disagreement_score":0.00445653,"about_ca_system_score_codex":0.0022193908,"about_ca_system_score_gemma":0.0021711334,"threshold_uncertainty_score":0.01610291},"labels":[],"label_agreement":null},{"id":"W4220842428","doi":"10.3390/s22062346","title":"Automated Feature Extraction on AsMap for Emotion Classification Using EEG","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Feature extraction; Artificial intelligence; Pattern recognition (psychology); Computer science; Electroencephalography; Support vector machine; Convolutional neural network; Differential entropy; Entropy (arrow of time); Feature (linguistics); Speech recognition; Principle of maximum entropy; Rényi entropy; Psychology","score_opus":0.07217421529605517,"score_gpt":0.3344619873014697,"score_spread":0.26228777200541453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220842428","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1106094,0.0006598794,0.8794569,0.00021374518,0.00021221695,0.00035576732,0.0017528023,0.0035393906,0.0031998944],"genre_scores_gemma":[0.59644836,0.0007112084,0.39417893,0.00011805182,0.00013775054,0.0007073939,0.0038576217,0.00018087724,0.0036598456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996088,0.000058712867,0.00003690811,0.000087777764,0.00015317797,0.000054695887],"domain_scores_gemma":[0.99966586,0.00008966655,0.00004152833,0.00004771233,0.00014192864,0.000013317744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041762262,0.0009063702,0.0005970963,0.0012702256,0.00023398786,0.0006679619,0.0004330924,0.00040597047,0.0027110402],"category_scores_gemma":[0.001419122,0.00014182444,0.00077148067,0.0011205198,0.00018512917,0.000746847,0.00060058845,0.0005246251,0.0012021839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035932238,0.00014586063,0.0037114893,0.0002315042,0.00008690155,0.00019323318,0.000078977304,0.007555446,0.12606189,0.0009917662,0.005115736,0.8554678],"study_design_scores_gemma":[0.000084924,0.0007932319,0.09047221,0.000117625204,0.00016232046,0.0013890263,0.00035173024,0.6951938,0.18993287,0.0057493374,0.015630191,0.00012270354],"about_ca_topic_score_codex":0.001088045,"about_ca_topic_score_gemma":0.0012181973,"teacher_disagreement_score":0.0027110402,"about_ca_system_score_codex":0.00016719697,"about_ca_system_score_gemma":0.00033754215,"threshold_uncertainty_score":0.009069264},"labels":[],"label_agreement":null},{"id":"W4220884671","doi":"10.3390/s22062324","title":"The Effect of Breathing Laterality on Hip Roll Kinematics in Submaximal Front Crawl Swimming","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Front crawl; Kinematics; Breathing; Medicine; Laterality; Physical medicine and rehabilitation; Diaphragmatic breathing; Anesthesia; Audiology; Physics","score_opus":0.011183059578391401,"score_gpt":0.2629522006117684,"score_spread":0.251769141033377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220884671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99945205,0.00010936717,0.00021186704,0.000008759621,0.0000034213779,0.00000930814,0.00002524683,0.0000053717476,0.00017458448],"genre_scores_gemma":[0.99895895,0.0001260247,0.00049935817,0.000026107282,0.000008244051,0.000023372793,0.000058710524,0.0000059510517,0.00029328847],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99975604,0.00006115921,0.000029689223,0.00004377618,0.00006980352,0.000039461414],"domain_scores_gemma":[0.9991351,0.00028971868,0.00029067902,0.00005831777,0.00010398359,0.0001222411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033225692,0.00032360796,0.0003496317,0.00024697962,0.00016655016,0.00029445137,0.00011046699,0.00024156236,0.0010452968],"category_scores_gemma":[0.0017978384,0.00020266729,0.00020765173,0.00007774445,0.00032495652,0.00017760943,0.00037293375,0.00019273293,0.00017608273],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006411778,0.00028471104,0.061389726,0.00022885688,0.00008247809,0.00036136815,0.00022621974,0.00039593107,0.9071943,0.000021166597,0.00007128489,0.023332193],"study_design_scores_gemma":[0.000053663545,0.0037285087,0.96925306,0.00002020689,0.000044588673,0.00020327623,0.000154354,0.00044292002,0.025880685,0.000024513725,0.00018102882,0.000013151883],"about_ca_topic_score_codex":0.00083732925,"about_ca_topic_score_gemma":0.002150016,"teacher_disagreement_score":0.0010452968,"about_ca_system_score_codex":0.00009787235,"about_ca_system_score_gemma":0.00018252022,"threshold_uncertainty_score":0.0034968257},"labels":[],"label_agreement":null},{"id":"W4220889384","doi":"10.3390/s22052062","title":"Deductive Reasoning and Working Memory Skills in Individuals with Blindness","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"Ariel University; Université de Montréal","keywords":"Working memory; Deductive reasoning; Psychology; Cognitive psychology; Blindness; Visual reasoning; Verbal reasoning; Everyday life; Visual memory; Developmental psychology; Cognition; Computer science; Cognitive science; Medicine; Artificial intelligence; Neuroscience","score_opus":0.023825407359201765,"score_gpt":0.26999611539534935,"score_spread":0.24617070803614757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220889384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997223,0.000067072986,0.00004536594,0.000006943703,0.0000012485268,0.00000242024,0.000012101558,0.0000020923112,0.00014050584],"genre_scores_gemma":[0.9997769,0.000024007686,0.000060300084,0.0000042147403,0.0000011165904,0.00000193355,0.000016314307,4.467403e-7,0.0001148127],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997625,0.000029322317,0.000043087,0.000053726857,0.00006083866,0.000050507155],"domain_scores_gemma":[0.99897575,0.00022207228,0.0004428556,0.00007461801,0.00014091468,0.00014386949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047246087,0.00042856598,0.00030469027,0.0014579841,0.0004184986,0.00050497864,0.00020649335,0.0003391506,0.0015370942],"category_scores_gemma":[0.0036894544,0.00017292587,0.00022574478,0.00035312067,0.0005699342,0.00048098856,0.0006309118,0.0003203068,0.00016150791],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005778282,0.00032556444,0.9565556,0.00008472122,0.000075851254,0.0010049698,0.0057961424,0.00017742833,0.008138654,0.00013929988,0.00013009686,0.026993847],"study_design_scores_gemma":[0.000011149544,0.0004063304,0.9952828,0.000013122435,0.00002824023,0.0016440937,0.0013095876,0.000198938,0.0006583026,0.00028715757,0.00014980983,0.0000104618875],"about_ca_topic_score_codex":0.004946152,"about_ca_topic_score_gemma":0.0051469086,"teacher_disagreement_score":0.004946152,"about_ca_system_score_codex":0.00022624554,"about_ca_system_score_gemma":0.00018074007,"threshold_uncertainty_score":0.009834707},"labels":[],"label_agreement":null},{"id":"W4220932531","doi":"10.3390/s22052035","title":"Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Umm Al-Qura University","keywords":"Cloud computing; Computer science; Dependability; Job scheduler; Distributed computing; Scheduling (production processes); Machine learning; Software; Automation; Artificial intelligence; Software engineering; Operating system; Engineering","score_opus":0.018141315352686562,"score_gpt":0.23888474490924447,"score_spread":0.2207434295565579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220932531","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8714874,0.00069051055,0.12362532,0.00048591685,0.000067985246,0.00012773422,0.0011137114,0.0012231832,0.0011783639],"genre_scores_gemma":[0.99051803,0.00011744285,0.008136996,0.0000236321,0.000017666654,0.000051290444,0.00067169947,0.000017610573,0.00044565526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927706,0.00015866163,0.00006647718,0.00017938948,0.00016667302,0.00015182381],"domain_scores_gemma":[0.9955747,0.0028069192,0.0005539017,0.00028233227,0.0006109743,0.00017115813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023222752,0.0010921875,0.000895987,0.0019019047,0.0005171531,0.0007770023,0.0010928884,0.00068612106,0.0006612769],"category_scores_gemma":[0.005924138,0.00026301705,0.0007051004,0.0010543957,0.00035215565,0.0006850616,0.00038825168,0.0008444436,0.0002711554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001830043,0.0001774461,0.030310867,0.00006333531,0.000050303825,0.00012605418,0.00006293312,0.9468344,0.00077943195,0.00057144015,0.0007270785,0.020113647],"study_design_scores_gemma":[0.0000010389635,0.000008864326,0.0014219774,0.0000017965879,0.0000025054992,0.000007069906,0.0000050996205,0.998237,0.0001263367,0.00016390992,0.000022318665,0.000002128463],"about_ca_topic_score_codex":0.03711835,"about_ca_topic_score_gemma":0.016779818,"teacher_disagreement_score":0.03711835,"about_ca_system_score_codex":0.0013834488,"about_ca_system_score_gemma":0.0012952201,"threshold_uncertainty_score":0.07380456},"labels":[],"label_agreement":null},{"id":"W4221001776","doi":"10.3390/s22062091","title":"Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Human skeleton; Skeleton (computer programming); Action recognition; Artificial intelligence; Graph; Artificial neural network; Pattern recognition (psychology); Machine learning; Theoretical computer science","score_opus":0.07627594509854449,"score_gpt":0.2865009319069265,"score_spread":0.210224986808382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221001776","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038846586,0.1385328,0.7927858,0.0010974597,0.0011489204,0.000297474,0.0018399406,0.0048523154,0.020598575],"genre_scores_gemma":[0.5078177,0.13789771,0.31500003,0.0010097526,0.0012249532,0.00045251576,0.009494301,0.0005753633,0.026527714],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996045,0.000060478687,0.000031293155,0.00016629697,0.000109221335,0.000028140656],"domain_scores_gemma":[0.99959356,0.0001676672,0.000038275502,0.000046842088,0.0001290988,0.000024608526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005513314,0.0014224484,0.00113321,0.0017907915,0.00020488282,0.0005436598,0.0010867101,0.0008194303,0.0027389275],"category_scores_gemma":[0.0016880352,0.0003181439,0.0007716277,0.0020024306,0.00038611642,0.0010519561,0.00049795007,0.00057207746,0.0015578261],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011099255,0.00008650177,0.0018086269,0.00076854473,0.00011112791,0.00006022676,0.00003222904,0.025136894,0.0031900106,0.0016481829,0.008970788,0.9580758],"study_design_scores_gemma":[0.000025364612,0.000384529,0.01389301,0.00053233735,0.00027933944,0.00065929955,0.00016253126,0.9086538,0.013962098,0.01308586,0.048246533,0.00011527249],"about_ca_topic_score_codex":0.0069507365,"about_ca_topic_score_gemma":0.008983455,"teacher_disagreement_score":0.0069507365,"about_ca_system_score_codex":0.00042581093,"about_ca_system_score_gemma":0.0005320946,"threshold_uncertainty_score":0.013820529},"labels":[],"label_agreement":null},{"id":"W4221008669","doi":"10.3390/s22051942","title":"Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Viral Infections and Vectors","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Iran National Science Foundation; National Science Foundation","keywords":"Malaria; Anopheles; Abundance (ecology); Outbreak; Time series; Habitat; Ecology; Environmental science; Geography; Biology; Statistics; Mathematics","score_opus":0.04086595965181079,"score_gpt":0.2956596306903945,"score_spread":0.25479367103858375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221008669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9476898,0.0004797871,0.04341687,0.0001501819,0.00006921441,0.00006991357,0.005087088,0.0011854104,0.0018516079],"genre_scores_gemma":[0.98335546,0.00022191278,0.014158008,0.000010013773,0.000012080449,0.000026161122,0.0019677894,0.000011551643,0.00023701947],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982244,0.000029640722,0.000020707444,0.00004159398,0.000060762348,0.000024840167],"domain_scores_gemma":[0.9997863,0.000052183477,0.000067093155,0.000018579143,0.000058467806,0.000017343778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031737625,0.00054347125,0.00025015528,0.0022350482,0.00011853825,0.00034481456,0.00026330433,0.00021025944,0.00040245376],"category_scores_gemma":[0.0007081456,0.000099055105,0.0003807799,0.0013903208,0.00008923294,0.0004038833,0.00024378099,0.00018330538,0.0001476739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007694074,0.0005338709,0.4445004,0.0005506975,0.0004684668,0.0010935749,0.0005703294,0.20110758,0.03890116,0.0019526472,0.006830159,0.30272165],"study_design_scores_gemma":[0.000014817783,0.00017116031,0.22231688,0.000033802928,0.000098934805,0.00028619188,0.000446124,0.764668,0.009155609,0.000695838,0.0020669387,0.000045816647],"about_ca_topic_score_codex":0.014308366,"about_ca_topic_score_gemma":0.014961856,"teacher_disagreement_score":0.014308366,"about_ca_system_score_codex":0.0001865398,"about_ca_system_score_gemma":0.00023474873,"threshold_uncertainty_score":0.028450131},"labels":[],"label_agreement":null},{"id":"W4221048763","doi":"10.3390/s22062090","title":"Compact Finite Field Multiplication Processor Structure for Cryptographic Algorithms in IoT Devices with Limited Resources","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Prince Sattam bin Abdulaziz University","keywords":"Computer science; Cryptography; Encryption; Cryptographic primitive; Cryptographic protocol; Embedded system; Public-key cryptography; Multiplier (economics); Algorithm; Energy consumption; Distributed computing; Computer network; Engineering","score_opus":0.01715402475444999,"score_gpt":0.27147067212295234,"score_spread":0.25431664736850235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221048763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043497123,0.0006353518,0.9462307,0.00015674255,0.00007917128,0.000118002,0.00006550173,0.000652622,0.0085647395],"genre_scores_gemma":[0.4063617,0.00055552414,0.5855948,0.00013127428,0.000069893,0.00021304161,0.00016573827,0.000055579625,0.0068524457],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998673,0.000027242017,0.000014062777,0.000026439522,0.000054033775,0.000010904701],"domain_scores_gemma":[0.9997986,0.000058805897,0.000041867224,0.000044850924,0.000049644314,0.0000062540876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018710822,0.0003117943,0.00021091402,0.00035568388,0.00025701625,0.000483044,0.0005634924,0.00023548536,0.0026410425],"category_scores_gemma":[0.00069688016,0.0001383154,0.00018623057,0.0004955423,0.00028367285,0.0008058072,0.00021949041,0.00037945528,0.00068482565],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041094504,0.000113975555,0.0010457418,0.00066119805,0.000050288923,0.00036824474,0.0002953632,0.051856253,0.3866568,0.23302671,0.003820194,0.3216943],"study_design_scores_gemma":[0.00015950999,0.002139537,0.0013957474,0.00025666546,0.00011779422,0.0019689184,0.0001324871,0.42722303,0.40593877,0.057089046,0.103498854,0.00007961331],"about_ca_topic_score_codex":0.00021308941,"about_ca_topic_score_gemma":0.0005093509,"teacher_disagreement_score":0.0026410425,"about_ca_system_score_codex":0.0003539459,"about_ca_system_score_gemma":0.00061550795,"threshold_uncertainty_score":0.0088351965},"labels":[],"label_agreement":null},{"id":"W4221079563","doi":"10.3390/s22062087","title":"Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":580,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Wireless sensor network; Internet of Things; Automation; Computer science; Industrial Revolution; Industry 4.0; Industrial Internet; The Internet; Telecommunications; Computer security; Data science; Engineering; World Wide Web; Computer network; Embedded system","score_opus":0.019335485875822355,"score_gpt":0.2831437044229557,"score_spread":0.2638082185471334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221079563","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000602039,0.9970681,0.00042410233,0.0005591265,0.00012079155,0.00011817721,0.00021274108,0.00000752138,0.00088748446],"genre_scores_gemma":[0.0037143389,0.9945469,0.00085910194,0.00037790174,0.00005625457,0.00014907306,0.00015822746,0.000004081972,0.00013415789],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99639016,0.0011409606,0.0011016895,0.00031068327,0.0009218318,0.00013467367],"domain_scores_gemma":[0.97938764,0.016319403,0.0018854699,0.00023571891,0.0019877995,0.00018401591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004692596,0.0011992959,0.0022909052,0.016744398,0.0008389182,0.0023804768,0.0011048565,0.0015754737,0.0050706924],"category_scores_gemma":[0.017779632,0.00078803307,0.003694192,0.016762847,0.0008217519,0.0031540738,0.0014353202,0.0013894598,0.0006240767],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008470014,0.00007177653,0.0025857177,0.6110634,0.0013225484,0.0003946648,0.0010648858,0.0006411176,0.00036859847,0.0065092063,0.010051784,0.3658416],"study_design_scores_gemma":[0.000027070371,0.00013636856,0.005173988,0.7796503,0.005923806,0.0009786948,0.0013668085,0.00035273892,0.00026201122,0.0027797227,0.20328641,0.00006207512],"about_ca_topic_score_codex":0.008547458,"about_ca_topic_score_gemma":0.02409939,"teacher_disagreement_score":0.016744398,"about_ca_system_score_codex":0.0022754895,"about_ca_system_score_gemma":0.013267625,"threshold_uncertainty_score":0.02481711},"labels":[],"label_agreement":null},{"id":"W4221105034","doi":"10.3390/s22072631","title":"KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Convolutional neural network; Artificial intelligence; Computer science; Deep learning; Inference; Representation (politics); Machine learning; Pattern recognition (psychology); Computer vision; Physical medicine and rehabilitation","score_opus":0.02219075222480557,"score_gpt":0.2114265206684704,"score_spread":0.18923576844366483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221105034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050930247,0.0028132873,0.86084217,0.00040228534,0.0004994785,0.00058573496,0.025696762,0.049551163,0.008678974],"genre_scores_gemma":[0.44044325,0.0019498165,0.48701578,0.00076101144,0.00007919729,0.0012229937,0.049116686,0.0013437852,0.018067539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969697,0.000023177407,0.000022545912,0.00009248391,0.00013125161,0.000033660173],"domain_scores_gemma":[0.99984944,0.00002633404,0.00002548367,0.000022358341,0.00005700779,0.000019375531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003145018,0.0013014855,0.0006846717,0.0010464749,0.00021342328,0.00050018926,0.0012541285,0.00064789696,0.0064840596],"category_scores_gemma":[0.0009718589,0.00053884985,0.0006014041,0.0007467328,0.00022894917,0.00075034256,0.00091852696,0.00073874707,0.002431083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012598977,0.0004068153,0.008363336,0.0010130823,0.00042167932,0.0005268328,0.00011335013,0.06166435,0.0558963,0.0040424718,0.07195741,0.7943345],"study_design_scores_gemma":[0.00012539355,0.0002639357,0.012703918,0.00017244247,0.000091980364,0.0006756258,0.00004237006,0.9200354,0.03967942,0.003966054,0.02212525,0.0001182406],"about_ca_topic_score_codex":0.009094297,"about_ca_topic_score_gemma":0.01643902,"teacher_disagreement_score":0.009094297,"about_ca_system_score_codex":0.0005388996,"about_ca_system_score_gemma":0.0007501799,"threshold_uncertainty_score":0.021691322},"labels":[],"label_agreement":null},{"id":"W4221130546","doi":"10.3390/s22062370","title":"Development and Evaluation of a Slip Detection Algorithm for Walking on Level and Inclined Ice Surfaces","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Slip (aerodynamics); Kinematics; Heel; Algorithm; Artificial intelligence; Support vector machine; Computer science; Simulation; Geodesy; Computer vision; Geology; Mathematics; Engineering; Structural engineering; Physics; Aerospace engineering","score_opus":0.09417438155665105,"score_gpt":0.3902809002971654,"score_spread":0.29610651874051436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221130546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3456081,0.0004942534,0.6487067,0.00014372035,0.000105795225,0.00036591725,0.00029618107,0.0028573936,0.0014220468],"genre_scores_gemma":[0.5895646,0.0002507546,0.40635714,0.00010290486,0.000036402886,0.00030268938,0.0010328343,0.00009787799,0.0022548186],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993711,0.00007631834,0.00007012273,0.00020440547,0.00020627247,0.0000718166],"domain_scores_gemma":[0.99882704,0.00026374598,0.00011001124,0.00005113666,0.0006935448,0.000054464606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007612711,0.00072918466,0.0008175522,0.001184175,0.00041482455,0.00065444486,0.00096711365,0.0009542811,0.0010602827],"category_scores_gemma":[0.0023744497,0.00029671172,0.0005161581,0.0005429079,0.00017715007,0.00061331084,0.00039647843,0.0005015908,0.00061179756],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004951637,0.0005369193,0.02519623,0.0001693933,0.00017366606,0.00029338797,0.00013159255,0.06896907,0.053731736,0.0005983679,0.0032015897,0.84650284],"study_design_scores_gemma":[0.00003969381,0.00031404625,0.0133716,0.000027063119,0.000036529484,0.00015415081,0.00007470785,0.9698039,0.014479113,0.00023546202,0.0014450673,0.000018624369],"about_ca_topic_score_codex":0.0069509437,"about_ca_topic_score_gemma":0.00507591,"teacher_disagreement_score":0.0069509437,"about_ca_system_score_codex":0.00039587452,"about_ca_system_score_gemma":0.00099053,"threshold_uncertainty_score":0.013820946},"labels":[],"label_agreement":null},{"id":"W4221133369","doi":"10.3390/s22072535","title":"Fast Constant-Time Modular Inversion over Fp Resistant to Simple Power Analysis Attacks for IoT Applications","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Field-programmable gate array; Power analysis; Cryptography; Virtex; Modular design; Embedded system; Modular exponentiation; Elliptic curve cryptography; Byte; Computer engineering; Public-key cryptography; Computer hardware; Algorithm; Computer network; Operating system","score_opus":0.011015464205982935,"score_gpt":0.2748922386962183,"score_spread":0.2638767744902354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221133369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21197048,0.0020804717,0.76831853,0.0003987213,0.00018397089,0.00014856136,0.00009974908,0.002058742,0.014740677],"genre_scores_gemma":[0.8708573,0.0006541827,0.12419458,0.00013065561,0.00006137843,0.000065828215,0.00011182709,0.000076450815,0.003847737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996848,0.00005391886,0.000021563892,0.00005600753,0.00014299965,0.000040700674],"domain_scores_gemma":[0.9996019,0.00011168764,0.000087312954,0.00009101155,0.00009221189,0.00001588847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033038633,0.00047763233,0.0002632057,0.00061356195,0.0002854526,0.00066178606,0.0006043029,0.00028513192,0.0021979036],"category_scores_gemma":[0.00089817896,0.00015208857,0.00026022663,0.0004949244,0.00036337366,0.0011776565,0.00039328999,0.00061097275,0.0005052374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008207498,0.00016893631,0.0025493372,0.0005382629,0.00011972331,0.0006264958,0.00027155108,0.03092247,0.4076733,0.060296424,0.0046620998,0.49135062],"study_design_scores_gemma":[0.00022559981,0.0027533066,0.0029156082,0.00015439797,0.00020701857,0.0027948362,0.00013718671,0.36672425,0.5417131,0.021495707,0.06077961,0.00009939474],"about_ca_topic_score_codex":0.00027916674,"about_ca_topic_score_gemma":0.0004767042,"teacher_disagreement_score":0.0021979036,"about_ca_system_score_codex":0.00044982156,"about_ca_system_score_gemma":0.00055819046,"threshold_uncertainty_score":0.00735265},"labels":[],"label_agreement":null},{"id":"W4221133782","doi":"10.3390/s22062206","title":"Tool Condition Monitoring for High-Performance Machining Systems—A Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":144,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Machining; Engineering; Computer science; Systems engineering; Mechanical engineering","score_opus":0.03409289144050574,"score_gpt":0.3079308721806382,"score_spread":0.27383798074013244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221133782","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024374494,0.9969313,0.0010064367,0.00011464598,0.00019610314,0.000014011271,0.000032840733,0.000016044603,0.0014447685],"genre_scores_gemma":[0.002143087,0.9955434,0.0012499056,0.00011378019,0.00018182056,0.000017112949,0.00006560251,0.0000048425172,0.0006804637],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995628,0.000057006175,0.00006082854,0.00009540577,0.00019754795,0.000026407308],"domain_scores_gemma":[0.99913836,0.0004807139,0.00010740933,0.000028318038,0.00022299476,0.000022130738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007543956,0.0011699783,0.0012820179,0.0025606735,0.00031325038,0.0010862139,0.0011976698,0.0012431081,0.0034045018],"category_scores_gemma":[0.0012241644,0.00052056886,0.00088806933,0.002997699,0.00036575246,0.0019657575,0.00060848927,0.00092818495,0.0017381485],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005023454,0.00008474601,0.0002753596,0.04020221,0.000114715935,0.00012017827,0.00006719016,0.0010980266,0.0035193698,0.0041744253,0.012184924,0.93810856],"study_design_scores_gemma":[0.000010883063,0.00027017304,0.0016989437,0.008668464,0.00033006052,0.0011579454,0.00011836566,0.0010347955,0.0033610722,0.0025061916,0.9807751,0.000068079105],"about_ca_topic_score_codex":0.00140834,"about_ca_topic_score_gemma":0.0014159482,"teacher_disagreement_score":0.0034045018,"about_ca_system_score_codex":0.0004065601,"about_ca_system_score_gemma":0.0011630091,"threshold_uncertainty_score":0.011389196},"labels":[],"label_agreement":null},{"id":"W4221136209","doi":"10.3390/s22072568","title":"DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Canonical correlation; Computer science; Artificial intelligence; Motion (physics); Pattern recognition (psychology); Speech recognition; Computer vision","score_opus":0.02038780068716544,"score_gpt":0.2542792319152997,"score_spread":0.23389143122813427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221136209","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008765532,0.00012800473,0.9898197,0.000055827128,0.00001626088,0.00005738795,0.00007272971,0.00025090348,0.0008337298],"genre_scores_gemma":[0.47627783,0.00045193837,0.5197141,0.00015523432,0.00005020661,0.0004308443,0.00061948755,0.00011244691,0.0021879305],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995554,0.0001509148,0.000025924894,0.000102866776,0.00011752315,0.00004732361],"domain_scores_gemma":[0.9995703,0.0001158729,0.00004916583,0.000049715833,0.00017546165,0.000039378716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006945619,0.00072801183,0.00040867532,0.00051172497,0.00028282948,0.0005433298,0.000809504,0.00058597844,0.0018488931],"category_scores_gemma":[0.0016386635,0.00021516104,0.00051947345,0.00045720828,0.00039249202,0.00058246794,0.00056030287,0.0006303447,0.0006074613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038333776,0.0002473738,0.0025747865,0.00031753094,0.00014711026,0.00035399903,0.00016305812,0.21081538,0.074031286,0.049611617,0.0072441944,0.6541104],"study_design_scores_gemma":[0.000009619034,0.00013769903,0.00072398706,0.00001562162,0.000015135908,0.00014358429,0.000012808236,0.9858078,0.006974568,0.0040921774,0.00204812,0.000018883928],"about_ca_topic_score_codex":0.0030945542,"about_ca_topic_score_gemma":0.004230368,"teacher_disagreement_score":0.0030945542,"about_ca_system_score_codex":0.00045457995,"about_ca_system_score_gemma":0.000938509,"threshold_uncertainty_score":0.0061852336},"labels":[],"label_agreement":null},{"id":"W4223558580","doi":"10.3390/s22072714","title":"A Systematic Review of Industrial Exoskeletons for Injury Prevention: Efficacy Evaluation Metrics, Target Tasks, and Supported Body Postures","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Government of Alberta","keywords":"Exoskeleton; Wearable computer; Work-related musculoskeletal disorders; Engineering; Risk analysis (engineering); Work (physics); Software deployment; Computer science; Human factors and ergonomics; Simulation; Poison control; Medicine; Mechanical engineering; Embedded system","score_opus":0.0819365652914858,"score_gpt":0.4061403264365688,"score_spread":0.324203761145083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223558580","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008610172,0.9971102,0.0004044117,0.00029238436,0.00013289684,0.00044574597,0.000302842,0.000009741674,0.00044077515],"genre_scores_gemma":[0.010348313,0.98600984,0.0017094609,0.00045520221,0.00007794372,0.00097209914,0.00026465437,0.000009365647,0.0001532399],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98115176,0.006375467,0.007490928,0.0009883397,0.0036722356,0.0003211502],"domain_scores_gemma":[0.9348155,0.048247114,0.008321492,0.0008833565,0.0071625174,0.0005701318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01836372,0.0019916005,0.0073742433,0.013535943,0.0010234417,0.004017806,0.0023616145,0.0021578027,0.0034866484],"category_scores_gemma":[0.08036242,0.0010603099,0.008505804,0.01146357,0.0012931796,0.0033772464,0.0019720963,0.0015022748,0.00038294995],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001881081,0.000035371475,0.0007235158,0.9066367,0.0045948997,0.00007394489,0.00037685916,0.0001480479,0.00019015493,0.00057933916,0.0019164064,0.0845365],"study_design_scores_gemma":[0.00012084587,0.00023444062,0.0033318426,0.94573563,0.025343219,0.00023301961,0.00034163616,0.000102976504,0.0001939857,0.0004507753,0.023869487,0.000042172294],"about_ca_topic_score_codex":0.010607827,"about_ca_topic_score_gemma":0.032816358,"teacher_disagreement_score":0.01836372,"about_ca_system_score_codex":0.005667013,"about_ca_system_score_gemma":0.021081967,"threshold_uncertainty_score":0.09711778},"labels":[],"label_agreement":null},{"id":"W4223560587","doi":"10.3390/s22082892","title":"Cost-Effective Bull’s Eye Aperture-Style Multi-Band Metamaterial Absorber at Sub-THz Band: Design, Numerical Analysis, and Physical Interpretation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Terahertz radiation; Metamaterial; Semiconductor; Optoelectronics; Optics; Absorption (acoustics); Frequency band; Materials science; Aperture (computer memory); Dielectric; Metamaterial absorber; Physics; Computer science; Telecommunications; Bandwidth (computing)","score_opus":0.02294770220199655,"score_gpt":0.29068225295026034,"score_spread":0.2677345507482638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223560587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5301274,0.004491495,0.4488807,0.00089477253,0.00013317031,0.00016518717,0.00023565,0.0006364025,0.014435278],"genre_scores_gemma":[0.70341986,0.0013268538,0.29175264,0.00007738724,0.000023918139,0.00013077725,0.00009160805,0.000056145243,0.0031208764],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986887,0.000022072214,0.000006677017,0.000024694238,0.00006366763,0.000013998038],"domain_scores_gemma":[0.99986696,0.000032264998,0.000043242053,0.00002048433,0.00002794626,0.000009053116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003405389,0.0003425303,0.00033677218,0.0002766106,0.00014870257,0.00046033532,0.0005096605,0.0006822467,0.00054671714],"category_scores_gemma":[0.0002650824,0.0002462737,0.00048597946,0.00027967984,0.00026630337,0.00048113806,0.0002704851,0.00029511153,0.00031832018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007865444,0.000111114285,0.0008460537,0.00040229212,0.000035912508,0.00021663301,0.000081294886,0.045879662,0.9218634,0.0134980185,0.000501994,0.016485076],"study_design_scores_gemma":[0.0000411971,0.00044271754,0.0011949671,0.000022950902,0.000045106102,0.0003482441,0.00004108195,0.6784454,0.31140164,0.0014440278,0.006522529,0.00005023434],"about_ca_topic_score_codex":0.00029396274,"about_ca_topic_score_gemma":0.0003087131,"teacher_disagreement_score":0.0006822467,"about_ca_system_score_codex":0.0005113113,"about_ca_system_score_gemma":0.0002045902,"threshold_uncertainty_score":0.0037099123},"labels":[],"label_agreement":null},{"id":"W4223588161","doi":"10.3390/s22072795","title":"Design of a Planner-Based Intervention to Facilitate Diet Behaviour Change in Type 2 Diabetes","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Irish Research Council","keywords":"Behaviour change; Workbook; Behavior change; Planner; Mindfulness; Intervention (counseling); Psychology; Type 2 diabetes; Applied psychology; Action (physics); Developmental psychology; Social psychology; Medicine; Computer science; Clinical psychology; Diabetes mellitus; Artificial intelligence","score_opus":0.2097880530985559,"score_gpt":0.3953982826887718,"score_spread":0.18561022959021592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223588161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71366346,0.00037220458,0.20091589,0.00147161,0.00053075433,0.067190014,0.0013091584,0.0046895314,0.00985734],"genre_scores_gemma":[0.49130434,0.000339617,0.44372758,0.00049108523,0.000048254326,0.05923272,0.000538539,0.0001003904,0.0042174156],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99805766,0.0011168586,0.000112512724,0.00030538897,0.00024502352,0.00016254358],"domain_scores_gemma":[0.998103,0.0010645993,0.00019289015,0.00014684781,0.00012975349,0.00036284977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028957385,0.0009693275,0.0006271964,0.0008773471,0.0006231492,0.0008280953,0.0014665275,0.0012306067,0.005511278],"category_scores_gemma":[0.0051182825,0.0006219865,0.0008117101,0.00030720254,0.00053982326,0.00057888404,0.0013960699,0.0009447293,0.00057920197],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03942987,0.08421351,0.013321214,0.009949104,0.00075703044,0.0012771741,0.009486365,0.04122839,0.05505977,0.0070645316,0.009361635,0.7288514],"study_design_scores_gemma":[0.09567885,0.3107033,0.11029955,0.0042961016,0.0029960878,0.0016793777,0.007211141,0.20737782,0.08372411,0.018778887,0.15636778,0.00088701927],"about_ca_topic_score_codex":0.00064915075,"about_ca_topic_score_gemma":0.0010570559,"teacher_disagreement_score":0.005511278,"about_ca_system_score_codex":0.00093679945,"about_ca_system_score_gemma":0.002253424,"threshold_uncertainty_score":0.018437088},"labels":[],"label_agreement":null},{"id":"W4223638673","doi":"10.3390/s22072771","title":"Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orthogonal frequency-division multiplexing; Computer science; Non-line-of-sight propagation; Bit error rate; Fading; Multiplexing; Electronic engineering; Communications system; Visible light communication; Single antenna interference cancellation; Interference (communication); Spectral efficiency; Deep learning; MIMO; Detector; Decoding methods; Algorithm; Artificial intelligence; Telecommunications; Wireless; Engineering; Electrical engineering; Channel (broadcasting)","score_opus":0.03899990862825667,"score_gpt":0.23190380536720398,"score_spread":0.1929038967389473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223638673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038783226,0.0008810677,0.9494494,0.00048089758,0.00011311887,0.000039860985,0.00010846804,0.00085132476,0.00929269],"genre_scores_gemma":[0.8398583,0.00056770036,0.15439343,0.000307621,0.000038753606,0.00005758674,0.00019517753,0.000045546476,0.004535924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979335,0.000043464235,0.000009573026,0.00002574316,0.0000941282,0.000033779354],"domain_scores_gemma":[0.9997745,0.00006132803,0.000028612643,0.000030474097,0.000088735906,0.000016318345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038858273,0.00033817394,0.0002455483,0.0002481823,0.00023971315,0.00057170703,0.00062191923,0.0004604786,0.0018591824],"category_scores_gemma":[0.0007588508,0.00013745448,0.00023256995,0.00031069756,0.00027989617,0.0008139993,0.0005058981,0.0009134967,0.00050381676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028269095,0.00024075659,0.0024447998,0.00022398657,0.00009997705,0.00018676088,0.00012484028,0.47917494,0.054580327,0.041967183,0.005227377,0.41544637],"study_design_scores_gemma":[0.0000062449903,0.00005385128,0.00016276946,0.0000097470465,0.000008778012,0.000036471254,0.000007579723,0.986304,0.008380045,0.0029171829,0.0021050088,0.000008340757],"about_ca_topic_score_codex":0.0020778428,"about_ca_topic_score_gemma":0.003692946,"teacher_disagreement_score":0.0020778428,"about_ca_system_score_codex":0.0005879746,"about_ca_system_score_gemma":0.0007164184,"threshold_uncertainty_score":0.0062196255},"labels":[],"label_agreement":null},{"id":"W4223654159","doi":"10.3390/s22082894","title":"Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; U.S. Geological Survey; Tertiary Education Trust Fund; National Aeronautics and Space Administration","keywords":"Environmental science; Impervious surface; Daytime; Climatology; Population; Geography; Climate change; Global warming; Physical geography; Atmospheric sciences; Oceanography; Demography","score_opus":0.010093432345257926,"score_gpt":0.19789024932208998,"score_spread":0.18779681697683206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223654159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99006546,0.0009222018,0.0002788037,0.00023587637,0.00001177093,0.000037179107,0.0038055123,0.00002625672,0.0046167644],"genre_scores_gemma":[0.99549484,0.00041793543,0.00051943655,0.000060143946,0.0000053224167,0.000018987428,0.0018555043,0.000006056547,0.0016216317],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996537,0.00001925401,0.000014114922,0.0000641932,0.000131601,0.000117276955],"domain_scores_gemma":[0.99947697,0.000027501632,0.00007137552,0.000015257775,0.0002988597,0.00011016537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027243161,0.0004928274,0.00022369089,0.0017262026,0.0022990578,0.0012926635,0.0006710514,0.00022872617,0.0010672493],"category_scores_gemma":[0.00067223434,0.00017208404,0.00037286067,0.0040722084,0.0005147939,0.00023779825,0.0006977983,0.00026339045,0.00011222501],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010422856,0.000035877907,0.975155,0.00007824269,0.000076296485,0.0002788144,0.0011178163,0.0014517208,0.0011041288,0.00032703445,0.0025039078,0.017766979],"study_design_scores_gemma":[0.0000025366398,0.0000038074058,0.996932,0.000013269258,0.000018499532,0.000029232277,0.0011536439,0.0006127495,0.00007775384,0.00002082461,0.0011271563,0.000008538076],"about_ca_topic_score_codex":0.996765,"about_ca_topic_score_gemma":0.9988883,"teacher_disagreement_score":0.022825167,"about_ca_system_score_codex":0.022825167,"about_ca_system_score_gemma":0.021743892,"threshold_uncertainty_score":0.16560894},"labels":[],"label_agreement":null},{"id":"W4223919817","doi":"10.3390/s22082948","title":"Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Ministry of Education, India","keywords":"Electroencephalography; Computer science; Artificial intelligence; Pattern recognition (psychology); Artifact (error); Wavelet; Brain–computer interface; Filter (signal processing); Noise reduction; Wavelet packet decomposition; Speech recognition; Wavelet transform; Computer vision","score_opus":0.052430140055836925,"score_gpt":0.25337849988767147,"score_spread":0.20094835983183454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223919817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046073526,0.0002204107,0.95251226,0.000051903226,0.000017930144,0.000034455777,0.00001834397,0.00028142525,0.0007896814],"genre_scores_gemma":[0.62127656,0.00018498497,0.3768501,0.00008657891,0.000020721312,0.00016983174,0.000115874835,0.00007173826,0.0012236768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973077,0.00006305077,0.000022665397,0.0000670561,0.000077500095,0.00003902171],"domain_scores_gemma":[0.99960226,0.0001926684,0.000066301305,0.0000306899,0.000092504866,0.000015685888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007365921,0.0008720422,0.00093662494,0.00061834935,0.0003295279,0.00071307976,0.00075086433,0.0009280571,0.00059548137],"category_scores_gemma":[0.001765852,0.0003867906,0.0008922755,0.00041140194,0.0003491973,0.0005575258,0.00050130615,0.00056439545,0.00018590217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015939667,0.00013854778,0.0011643224,0.00014302853,0.00015908974,0.00011434635,0.00011907319,0.8070586,0.026261277,0.0021232974,0.0005147243,0.1620444],"study_design_scores_gemma":[0.000009501905,0.00005250096,0.00024994073,0.0000050865174,0.00001874425,0.000018878853,0.0000118237895,0.99605775,0.002867311,0.0005537623,0.00014950085,0.0000052861005],"about_ca_topic_score_codex":0.0027277365,"about_ca_topic_score_gemma":0.0028029135,"teacher_disagreement_score":0.0027277365,"about_ca_system_score_codex":0.00038190527,"about_ca_system_score_gemma":0.00082993193,"threshold_uncertainty_score":0.0054237247},"labels":[],"label_agreement":null},{"id":"W4223984911","doi":"10.3390/s22082994","title":"Simple and Robust Microfabrication of Polymeric Piezoelectric Resonating MEMS Mass Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Microfabrication; Microelectromechanical systems; Surface micromachining; Materials science; Nanotechnology; Fabrication; Accelerometer; Microtechnology; Microsystem; Optoelectronics; Computer science","score_opus":0.01082222792507555,"score_gpt":0.2233349491936006,"score_spread":0.21251272126852505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223984911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6230666,0.005954285,0.35396746,0.0009481007,0.0009362389,0.000758261,0.00059961993,0.0033979723,0.010371403],"genre_scores_gemma":[0.6824719,0.0021023022,0.3069463,0.00029588985,0.00014441797,0.0003798609,0.0003653933,0.00024241766,0.007051541],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993709,0.00004285298,0.000045950906,0.00013876395,0.00033449402,0.000066981156],"domain_scores_gemma":[0.9997272,0.000055999433,0.00010539422,0.000052412102,0.00004149141,0.000017584509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038306694,0.00068563904,0.00035511956,0.00036999592,0.0002470034,0.0002936062,0.00072055066,0.00060777436,0.00092748133],"category_scores_gemma":[0.00057890615,0.00043612815,0.0005510542,0.00021417427,0.00030158117,0.00038766843,0.00044734168,0.00079294026,0.0007731203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064951496,0.000012569171,0.000029392406,0.000041440842,0.0000030547108,0.00005408107,0.000016774557,0.00017756733,0.9960437,0.00012355598,0.000074223884,0.0034171636],"study_design_scores_gemma":[0.000005522443,0.00012136024,0.00052819465,0.0000042927777,0.0000059401787,0.00017634955,0.000010212487,0.0011301504,0.99467963,0.000038634076,0.0032896767,0.000009967775],"about_ca_topic_score_codex":0.00022310301,"about_ca_topic_score_gemma":0.00050625746,"teacher_disagreement_score":0.00092748133,"about_ca_system_score_codex":0.00022674138,"about_ca_system_score_gemma":0.00021496377,"threshold_uncertainty_score":0.0031027198},"labels":[],"label_agreement":null},{"id":"W4224031880","doi":"10.3390/s22082967","title":"On Slip Detection for Quadruped Robots","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Robot; Slip (aerodynamics); Slippage; Robot locomotion; Legged robot; Inertial measurement unit; Artificial intelligence; Search and rescue; Computer science; Terrain; Computer vision; Engineering; Inertial frame of reference; Rescue robot; Simulation; Mobile robot; Control engineering; Robot control; Aerospace engineering; Geography","score_opus":0.007566760680092184,"score_gpt":0.19427629041375746,"score_spread":0.18670952973366528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224031880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07785444,0.0027177788,0.9150898,0.00021486987,0.00016964764,0.000076946206,0.00009217374,0.001065663,0.0027186447],"genre_scores_gemma":[0.80722064,0.0020535192,0.18666829,0.00019804205,0.00019196651,0.00009174123,0.0002714905,0.00013049234,0.0031737897],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966,0.000060428025,0.000019433368,0.000055382272,0.00017373331,0.000031004656],"domain_scores_gemma":[0.9993544,0.0002856359,0.00009820963,0.00006380587,0.00017012628,0.000027885462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031486966,0.00053330814,0.00053108024,0.00083730737,0.00033683315,0.000460719,0.0004164721,0.0006337986,0.001025503],"category_scores_gemma":[0.0019431555,0.0001994996,0.00026576617,0.0005055669,0.0005450521,0.00061606755,0.0005713187,0.00035402385,0.00039588302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028243393,0.00010493324,0.0043798555,0.00045003594,0.00007087041,0.0006550908,0.00032698325,0.25360435,0.08853768,0.0072158566,0.0036223931,0.6407496],"study_design_scores_gemma":[0.000009252941,0.00023205536,0.003559229,0.000059562197,0.000014219409,0.0003411575,0.00006617223,0.97043884,0.013412229,0.0072055487,0.0046331445,0.00002857673],"about_ca_topic_score_codex":0.0017882481,"about_ca_topic_score_gemma":0.00091515284,"teacher_disagreement_score":0.0017882481,"about_ca_system_score_codex":0.00024831673,"about_ca_system_score_gemma":0.00025575343,"threshold_uncertainty_score":0.003555715},"labels":[],"label_agreement":null},{"id":"W4224049172","doi":"10.3390/s22082935","title":"Validity and Reliability of Smartphone App for Evaluating Postural Adjustments during Step Initiation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Kinematics; Physical medicine and rehabilitation; Simulation; Reliability (semiconductor); Linear correlation; Physical therapy; Smartphone app; Computer science; Validity; Correlation; Psychology; Medicine; Statistics; Mathematics; Human–computer interaction; Physics; Psychometrics","score_opus":0.0606525854168078,"score_gpt":0.3898214528826028,"score_spread":0.329168867465795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224049172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99138474,0.0005371132,0.0039208615,0.000066885084,0.0001289721,0.00034345523,0.0006993909,0.00010668369,0.0028118682],"genre_scores_gemma":[0.99253196,0.00029088146,0.0051702056,0.00007133172,0.00004474254,0.0003225853,0.0006637238,0.00002491376,0.0008796628],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953347,0.0012518432,0.0006300656,0.00085637206,0.0017214501,0.00020559516],"domain_scores_gemma":[0.98904455,0.004255127,0.0017701719,0.000689958,0.003990798,0.00024932672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004400414,0.0006872529,0.0004857303,0.0010742598,0.00031260122,0.0009977035,0.0005635365,0.0007868535,0.0011175632],"category_scores_gemma":[0.017238649,0.00034219457,0.0010919013,0.00050776167,0.00035729393,0.0006924067,0.0008602869,0.0004217618,0.00080813444],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011084042,0.00037743917,0.9297809,0.000394827,0.00049209956,0.00012538898,0.0011408888,0.00054554496,0.0069393297,0.00015964598,0.00082803733,0.05810746],"study_design_scores_gemma":[0.00004358389,0.0014302544,0.9900081,0.00012634891,0.00018380818,0.00038060453,0.0006143097,0.0037678555,0.0018164623,0.0001256074,0.0014727938,0.000030328132],"about_ca_topic_score_codex":0.001154064,"about_ca_topic_score_gemma":0.0022306608,"teacher_disagreement_score":0.004400414,"about_ca_system_score_codex":0.00019017456,"about_ca_system_score_gemma":0.0003270426,"threshold_uncertainty_score":0.023271918},"labels":[],"label_agreement":null},{"id":"W4224213717","doi":"10.3390/s22093135","title":"FPGA-Based Autonomous GPS-Disciplined Oscillatorsfor Wireless Sensor Network Nodes","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Field-programmable gate array; Global Positioning System; Computer science; Gate array; Context (archaeology); Embedded system; SIGNAL (programming language); Wireless sensor network; GPS signals; Assisted GPS; Precision Lightweight GPS Receiver; Real-time computing; Wireless; Computer hardware; Telecommunications; Computer network; Gps receiver","score_opus":0.008303739136767572,"score_gpt":0.20407918331306577,"score_spread":0.1957754441762982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224213717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3096646,0.0023297307,0.6585961,0.0002834802,0.0006239724,0.0003337013,0.00047876718,0.006750554,0.020939017],"genre_scores_gemma":[0.9028412,0.00029412392,0.09039475,0.000107532665,0.00004560851,0.00007864018,0.00018348653,0.000044720306,0.0060098837],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998616,0.000020661386,0.000008707977,0.000035164034,0.000052530395,0.000021350126],"domain_scores_gemma":[0.99989307,0.000022982274,0.000021811142,0.000018058563,0.000035713463,0.000008292498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001121821,0.00027506793,0.00014809934,0.00025789425,0.00013480471,0.00020074924,0.0005893624,0.00016215633,0.002704769],"category_scores_gemma":[0.00025004463,0.00010292766,0.00008673812,0.00016280361,0.00010882031,0.00028963268,0.00016618466,0.00018726582,0.0004966245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081181404,0.00018199107,0.006053966,0.00080773025,0.00010714642,0.00047824226,0.0002415495,0.034201477,0.5355848,0.0093752835,0.008456609,0.4036994],"study_design_scores_gemma":[0.00042053388,0.002572601,0.011077711,0.00012267586,0.00019534685,0.0019235559,0.000098066026,0.3002829,0.58619434,0.0017968816,0.09520863,0.00010680089],"about_ca_topic_score_codex":0.00077240204,"about_ca_topic_score_gemma":0.001799078,"teacher_disagreement_score":0.002704769,"about_ca_system_score_codex":0.0002046694,"about_ca_system_score_gemma":0.0002466892,"threshold_uncertainty_score":0.009048343},"labels":[],"label_agreement":null},{"id":"W4224230193","doi":"10.3390/s22082830","title":"Towards Development of Specular Reflection Vascular Imaging","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Toronto Metropolitan University","funders":"","keywords":"Specular reflection; Specular highlight; Reflection (computer programming); Computer vision; SIGNAL (programming language); Computer science; Pixel; Blood flow; Waveform; Artificial intelligence; Visualization; Ray tracing (physics); Medical imaging; Biomedical engineering; Optics; Acoustics; Medicine; Radiology; Physics; Telecommunications","score_opus":0.015504908297177562,"score_gpt":0.3111572747466534,"score_spread":0.29565236644947585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224230193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067744856,0.0035637547,0.9850557,0.000311936,0.00009676717,0.000068823065,0.000033063137,0.0004955549,0.0035998533],"genre_scores_gemma":[0.09515296,0.004840822,0.89513135,0.00027978094,0.0001639526,0.00011213356,0.00012088438,0.00012901695,0.004069069],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985031,0.00043399303,0.000053874952,0.00032535061,0.00060961605,0.000074127456],"domain_scores_gemma":[0.99717367,0.0009731232,0.0002068802,0.0003710847,0.0011666798,0.000108541935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002450687,0.00089225674,0.00068210234,0.00091929216,0.00020162646,0.0017554759,0.0018064271,0.001404855,0.0017767393],"category_scores_gemma":[0.0034911362,0.00071627536,0.0007082472,0.00076880486,0.0009308126,0.0018216668,0.0010077093,0.0015800566,0.0012164095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020997364,0.00015473983,0.0022860146,0.00085156196,0.00008082461,0.0004943177,0.00059440697,0.014629922,0.3703346,0.045123518,0.003187058,0.56205297],"study_design_scores_gemma":[0.000065632885,0.0015537408,0.003912382,0.0004898322,0.00013848036,0.0033545985,0.00036114722,0.3600704,0.48787257,0.019241005,0.12268363,0.00025655937],"about_ca_topic_score_codex":0.00050251366,"about_ca_topic_score_gemma":0.00045250304,"teacher_disagreement_score":0.002450687,"about_ca_system_score_codex":0.0006288056,"about_ca_system_score_gemma":0.000654453,"threshold_uncertainty_score":0.012960613},"labels":[],"label_agreement":null},{"id":"W4224239230","doi":"10.3390/s22083029","title":"Improving Data Security with Blockchain and Internet of Things in the Gourmet Cocoa Bean Fermentation Process","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Blockchain; Process (computing); Computer science; Simple Network Management Protocol; The Internet; Database; Agricultural engineering; Engineering; Computer security; Computer network; World Wide Web; Operating system","score_opus":0.012379944987483226,"score_gpt":0.2469976651068049,"score_spread":0.23461772011932167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224239230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67254627,0.0015178105,0.31021693,0.0012415004,0.00014982387,0.00048598569,0.00017937383,0.0016321471,0.012030177],"genre_scores_gemma":[0.98701453,0.00020544599,0.011581713,0.00003436498,0.0000065570143,0.000047335066,0.000037556918,0.000013349763,0.001059077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990421,0.00034203206,0.000050560895,0.0001065572,0.00031385306,0.00014481705],"domain_scores_gemma":[0.99882406,0.0005069733,0.00010808928,0.00021047263,0.00026501593,0.000085363885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012819427,0.0003584324,0.0003971368,0.000455531,0.00074145605,0.0009964914,0.00066480966,0.0007283909,0.0014871818],"category_scores_gemma":[0.0018985547,0.0001745475,0.00022106431,0.00040852884,0.0006769571,0.0018768223,0.0012570346,0.00055439584,0.00026287566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026580405,0.00078531675,0.0113836005,0.0007689314,0.00013479899,0.0020083545,0.000984866,0.61451143,0.14590007,0.06207398,0.0025411937,0.15624946],"study_design_scores_gemma":[0.00012724548,0.0005671545,0.0014297559,0.0000486024,0.000041847972,0.00026202644,0.00013764252,0.9282132,0.05152424,0.011745272,0.0058641126,0.000038915045],"about_ca_topic_score_codex":0.002455358,"about_ca_topic_score_gemma":0.0018790102,"teacher_disagreement_score":0.002455358,"about_ca_system_score_codex":0.0006659631,"about_ca_system_score_gemma":0.0011659036,"threshold_uncertainty_score":0.0067796707},"labels":[],"label_agreement":null},{"id":"W4224249136","doi":"10.3390/s22083043","title":"Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":172,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Austrian Science Fund","keywords":"Artificial intelligence; Computer science; Big data; Leverage (statistics); Data science; Engineering; Data mining","score_opus":0.01956298547597833,"score_gpt":0.21240663813081037,"score_spread":0.19284365265483205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224249136","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010937204,0.2587221,0.0831522,0.56373054,0.004223491,0.00015488305,0.00032577137,0.0010740388,0.07767982],"genre_scores_gemma":[0.3572829,0.34132206,0.18649344,0.0632906,0.008464311,0.00056224,0.00097591244,0.00037113857,0.041237455],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99758613,0.0010063987,0.00011165082,0.00031457035,0.0006359589,0.0003452255],"domain_scores_gemma":[0.98726314,0.0070843226,0.0004201998,0.0008684307,0.0025612551,0.0018026661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007007159,0.00074318214,0.00071608904,0.0010295989,0.0018880788,0.0069554313,0.0022301823,0.007914626,0.013334241],"category_scores_gemma":[0.00632813,0.000321481,0.000740518,0.0013667387,0.006591192,0.014426289,0.004232361,0.0058297487,0.003959324],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001977856,0.00046274494,0.0017587173,0.002736301,0.000067026594,0.00029676728,0.0018494808,0.0054894127,0.001972043,0.30281296,0.09829532,0.58406144],"study_design_scores_gemma":[0.00004042751,0.0002687697,0.001332676,0.002138919,0.000039083778,0.00038834385,0.0077119665,0.010803876,0.0010136919,0.46051922,0.5156325,0.00011047624],"about_ca_topic_score_codex":0.0034373656,"about_ca_topic_score_gemma":0.0037222444,"teacher_disagreement_score":0.013334241,"about_ca_system_score_codex":0.0023303009,"about_ca_system_score_gemma":0.006807677,"threshold_uncertainty_score":0.04460752},"labels":[],"label_agreement":null},{"id":"W4224252765","doi":"10.3390/s22083059","title":"Calibration and Localization of Optically Pumped Magnetometers Using Electromagnetic Coils","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Coquitlam College","funders":"National Institutes of Health; European Research Council; National Nuclear Security Administration; National Institute of Biomedical Imaging and Bioengineering; Sandia National Laboratories; U.S. Department of Energy","keywords":"Magnetometer; Calibration; Physics; Position (finance); Imaging phantom; Orientation (vector space); Acoustics; Electromagnetic coil; Magnetic field; Optics; Mathematics; Geometry","score_opus":0.013930869018301402,"score_gpt":0.259338710749576,"score_spread":0.24540784173127458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224252765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013392552,0.00018161078,0.98529667,0.00007110116,0.000050295905,0.000031243795,0.000028166774,0.00047871255,0.0004696912],"genre_scores_gemma":[0.28587767,0.00040042875,0.71192575,0.00020861473,0.000101874335,0.00012951269,0.00010598418,0.0001204716,0.0011297181],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897563,0.0002968811,0.000049522714,0.00024396706,0.00038486964,0.000049156126],"domain_scores_gemma":[0.99858224,0.0005069417,0.00034616125,0.00026773312,0.00026635505,0.000030460435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083422853,0.0008220707,0.00053903426,0.0006438015,0.00026475833,0.0007077692,0.0009603817,0.0007667,0.00059553393],"category_scores_gemma":[0.0049292278,0.0005474866,0.00034313445,0.0004913803,0.00070800155,0.0011898782,0.00082440797,0.0008139306,0.0005059531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038416317,0.000111440095,0.008867456,0.000332731,0.0001795449,0.00020213284,0.0004882063,0.10652524,0.4087083,0.018368723,0.0021479132,0.45368415],"study_design_scores_gemma":[0.00010117835,0.00036262916,0.0066112243,0.00008288921,0.00013276504,0.0009973445,0.00014092187,0.482082,0.4743643,0.013285747,0.021631869,0.0002071198],"about_ca_topic_score_codex":0.000713625,"about_ca_topic_score_gemma":0.0010164876,"teacher_disagreement_score":0.0009603817,"about_ca_system_score_codex":0.0004908508,"about_ca_system_score_gemma":0.0006617375,"threshold_uncertainty_score":0.004411876},"labels":[],"label_agreement":null},{"id":"W4224255587","doi":"10.3390/s22083044","title":"Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Drone; Computer science; Systems engineering; Data science; Engineering; Artificial intelligence","score_opus":0.07052316807765835,"score_gpt":0.3342369705640771,"score_spread":0.26371380248641874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224255587","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012984226,0.9986104,0.00022210731,0.0001809293,0.0001132137,0.000010548225,0.00002606012,0.0000072848825,0.00069959054],"genre_scores_gemma":[0.00089700223,0.9983765,0.00033067752,0.00010265681,0.00008080711,0.000010708394,0.000026702535,0.000001493867,0.00017347268],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994873,0.000093568466,0.00009628799,0.00009901534,0.000190785,0.00003303747],"domain_scores_gemma":[0.99832743,0.0011063161,0.0001784061,0.000036217272,0.00030984907,0.000041722575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010234149,0.0011859693,0.0015700579,0.0039150524,0.00034151264,0.0014062097,0.0010766822,0.0014192072,0.0035814117],"category_scores_gemma":[0.0023018967,0.00045846277,0.0012077879,0.004429324,0.0004487346,0.0018154624,0.000743145,0.0011241403,0.0011807014],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050091767,0.0000928359,0.00042811755,0.09219136,0.0002578775,0.00016068041,0.00014571441,0.00072083564,0.001021178,0.0040284796,0.0160391,0.88486373],"study_design_scores_gemma":[0.000015834235,0.00021451004,0.002335472,0.03599301,0.0008935463,0.0011081167,0.00022280277,0.00045993808,0.0008027688,0.003154925,0.9547347,0.00006449764],"about_ca_topic_score_codex":0.002153701,"about_ca_topic_score_gemma":0.0027640932,"teacher_disagreement_score":0.0039150524,"about_ca_system_score_codex":0.0005802363,"about_ca_system_score_gemma":0.0019517636,"threshold_uncertainty_score":0.01198101},"labels":[],"label_agreement":null},{"id":"W4224260518","doi":"10.3390/s22082829","title":"LIPSHOK: LIARA Portable Smart Home Kit","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Home automation; Variety (cybernetics); Modular design; Context (archaeology); Embedded system; Implementation; Computer science; Architecture; Cover (algebra); Systems engineering; Engineering; Software engineering; Telecommunications; Operating system; Artificial intelligence","score_opus":0.01962379691736156,"score_gpt":0.2225571053647789,"score_spread":0.20293330844741736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224260518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088497005,0.0027926685,0.4351745,0.0013506503,0.00073338195,0.0018900934,0.0037145277,0.15085837,0.31498885],"genre_scores_gemma":[0.37415975,0.0018613146,0.2285263,0.00180194,0.00019401904,0.0016779309,0.008105416,0.0056455084,0.37802777],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930906,0.00007907958,0.00003972172,0.0001349921,0.00031228,0.00012491492],"domain_scores_gemma":[0.99945766,0.000066160224,0.00006148606,0.000113666705,0.00018827854,0.000112843896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037971855,0.0014158619,0.0007030295,0.0010260444,0.00047624655,0.0012893885,0.0018346878,0.0011473546,0.04472388],"category_scores_gemma":[0.0009783068,0.0006042445,0.0005421297,0.0005761939,0.0005316746,0.0029399279,0.0027127566,0.0010357748,0.03518454],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012239198,0.0005241854,0.0034983687,0.0022298337,0.00009348006,0.0018548432,0.0017663424,0.0022020852,0.08141485,0.026545316,0.20565993,0.6729869],"study_design_scores_gemma":[0.00014983317,0.0009775653,0.0043970244,0.00038024905,0.00012631266,0.0031817995,0.00064735796,0.012614763,0.039634265,0.0045687403,0.9331113,0.00021079242],"about_ca_topic_score_codex":0.0006627527,"about_ca_topic_score_gemma":0.0008574348,"teacher_disagreement_score":0.04472388,"about_ca_system_score_codex":0.00046812696,"about_ca_system_score_gemma":0.0005737219,"threshold_uncertainty_score":0.14961624},"labels":[],"label_agreement":null},{"id":"W4224280689","doi":"10.3390/s22083040","title":"Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Agencia Nacional de Investigación y Desarrollo","keywords":"Advanced driver assistance systems; Computer science; Principal (computer security); Architecture; Systems engineering; The Internet; Human–computer interaction; Work (physics); State (computer science); Engineering; Artificial intelligence; Computer security; World Wide Web","score_opus":0.033728685314047886,"score_gpt":0.28465542878017613,"score_spread":0.25092674346612825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224280689","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056365365,0.9935389,0.0013879235,0.00044412003,0.00016905935,0.000021885593,0.000059476068,0.000024678664,0.0037902272],"genre_scores_gemma":[0.0038133946,0.9931631,0.0014107274,0.0002532884,0.00018316795,0.000024908166,0.00011872804,0.000008186362,0.0010245491],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992724,0.00011886699,0.00010082262,0.00016558729,0.00028572662,0.00005657751],"domain_scores_gemma":[0.9979353,0.0010811718,0.00017691783,0.000071851086,0.00065417326,0.00008064987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012821612,0.00111668,0.0012330561,0.0034627258,0.0004271391,0.0017425399,0.0016171496,0.0015000049,0.0035231991],"category_scores_gemma":[0.0024515607,0.0006217497,0.0006999439,0.004178802,0.00054137217,0.0030283013,0.0010086894,0.0011935681,0.002300793],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005365111,0.00011187857,0.00075209816,0.021576041,0.00009412319,0.000094914256,0.00014031779,0.0015944192,0.00085614284,0.011745971,0.015552382,0.9474281],"study_design_scores_gemma":[0.000008522192,0.00026656472,0.0017684506,0.008979182,0.00014598368,0.00067525473,0.00025671071,0.0008799712,0.0008744148,0.0042373654,0.981862,0.000045520985],"about_ca_topic_score_codex":0.0027197667,"about_ca_topic_score_gemma":0.0024149881,"teacher_disagreement_score":0.0035231991,"about_ca_system_score_codex":0.00086631527,"about_ca_system_score_gemma":0.0022776101,"threshold_uncertainty_score":0.011786282},"labels":[],"label_agreement":null},{"id":"W4224308015","doi":"10.3390/s22093186","title":"Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Occupancy; Training (meteorology); Computer science; Machine learning; Training set; Artificial intelligence; Engineering; Geography","score_opus":0.07356807976357183,"score_gpt":0.2831314134841884,"score_spread":0.20956333372061658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224308015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3020912,0.00024151616,0.6916725,0.00019120376,0.00007084552,0.00008566742,0.00035438774,0.0027700304,0.0025226937],"genre_scores_gemma":[0.9641761,0.00004245484,0.03448023,0.00005368546,0.000026824215,0.00006521639,0.0004413677,0.000035485817,0.0006786067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942183,0.00014328059,0.000029576224,0.00020158917,0.00010515564,0.00009858591],"domain_scores_gemma":[0.998027,0.0010443006,0.00020481618,0.00033700405,0.0002989363,0.0000877347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084803393,0.0009669058,0.0009817736,0.00041629066,0.00031508194,0.00061350554,0.0016365461,0.0006801776,0.0009572488],"category_scores_gemma":[0.0037892647,0.00040379784,0.00059281895,0.00052477163,0.0005577517,0.0011138995,0.0012567946,0.0011892748,0.00033058264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005095371,0.00035598964,0.012694068,0.000096362724,0.00007300025,0.00013192126,0.00017000509,0.8364303,0.004325062,0.0009912892,0.001213523,0.14300892],"study_design_scores_gemma":[0.000005001369,0.000024900828,0.00075000554,0.000002066231,0.000003853361,0.0000061828437,0.000010062974,0.99778265,0.00095866574,0.0003680397,0.00008490818,0.000003693734],"about_ca_topic_score_codex":0.0077681956,"about_ca_topic_score_gemma":0.010274148,"teacher_disagreement_score":0.0077681956,"about_ca_system_score_codex":0.00054184604,"about_ca_system_score_gemma":0.0008239025,"threshold_uncertainty_score":0.015445948},"labels":[],"label_agreement":null},{"id":"W4224309512","doi":"10.3390/s22093183","title":"Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ultrasonic sensor; Mean squared error; Computer science; Artificial intelligence; Artificial neural network; Feature (linguistics); SIGNAL (programming language); Standard deviation; Pattern recognition (psychology); Computer vision; Acoustics; Mathematics","score_opus":0.013366000401658727,"score_gpt":0.21360338818983193,"score_spread":0.2002373877881732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224309512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1522736,0.0004412103,0.84346324,0.00015862664,0.00006387264,0.000040278897,0.00009859276,0.0014552284,0.0020053377],"genre_scores_gemma":[0.92024446,0.00018600901,0.07636147,0.00006782259,0.000014636812,0.00004955642,0.00013099355,0.000039570303,0.0029055777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998888,0.0000229071,0.0000058511923,0.000034989167,0.000030407466,0.00001710438],"domain_scores_gemma":[0.99973947,0.00014238297,0.000023798191,0.00002045708,0.00006289805,0.000010916126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029425605,0.0005571101,0.00027344987,0.0002141312,0.0001342069,0.00031366816,0.00038372446,0.0004473005,0.0013144029],"category_scores_gemma":[0.0009914332,0.00016148851,0.0002046575,0.00026006243,0.00014292271,0.00034171145,0.00029215976,0.0004698047,0.0003421701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025891495,0.00010422204,0.0019377451,0.00014368563,0.000049004986,0.00014992422,0.00006066245,0.56536406,0.058022674,0.0011350778,0.0011632936,0.37161067],"study_design_scores_gemma":[0.000001745763,0.000026955835,0.000287632,0.000003054055,0.0000023082057,0.000007421987,0.0000048452043,0.9962212,0.003181306,0.00016336753,0.00009797692,0.0000021873186],"about_ca_topic_score_codex":0.0036095167,"about_ca_topic_score_gemma":0.0039496724,"teacher_disagreement_score":0.0036095167,"about_ca_system_score_codex":0.00035859694,"about_ca_system_score_gemma":0.0002801426,"threshold_uncertainty_score":0.0071769953},"labels":[],"label_agreement":null},{"id":"W4224312324","doi":"10.3390/s22093268","title":"Developing Broadband Microstrip Patch Antennas Fed by SIW Feeding Network for Spatially Low Cross-Polarization Situation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Microstrip antenna; Patch antenna; Broadband; Bandwidth (computing); Microstrip; Optoelectronics; Materials science; Optics; Acoustics; Physics; Engineering; Electrical engineering; Telecommunications; Antenna (radio)","score_opus":0.011896334962985686,"score_gpt":0.22755891966359876,"score_spread":0.21566258470061306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224312324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52601624,0.0006291582,0.46300697,0.0001847504,0.00012766276,0.00002944757,0.00009356179,0.00093774527,0.008974512],"genre_scores_gemma":[0.8021738,0.0004452508,0.19314668,0.000053370684,0.00003244789,0.000039890845,0.00012495187,0.000069977774,0.003913681],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989426,0.000014722061,0.0000053453214,0.000027510072,0.00003750318,0.000020565829],"domain_scores_gemma":[0.9998331,0.000022426311,0.000053493215,0.000030449648,0.00004797736,0.0000124482185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011027744,0.00039970796,0.00027603575,0.00017318988,0.00006144482,0.00038342664,0.00035899557,0.0004343925,0.00046758648],"category_scores_gemma":[0.00020226614,0.00019758081,0.0003205176,0.0002852706,0.000127342,0.0003780383,0.00024540178,0.00030240085,0.00069060037],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003954851,0.000018516437,0.00069294224,0.00006514077,0.000018431238,0.00017374233,0.000041936964,0.0036365066,0.9666494,0.0018191778,0.00038311596,0.026461555],"study_design_scores_gemma":[0.000015575324,0.0003977902,0.0025083576,0.000012688918,0.000036254216,0.0008537151,0.000051839233,0.082249664,0.90409505,0.00041781928,0.009337819,0.000023461118],"about_ca_topic_score_codex":0.00012119712,"about_ca_topic_score_gemma":0.00014590847,"teacher_disagreement_score":0.00046758648,"about_ca_system_score_codex":0.00016181034,"about_ca_system_score_gemma":0.0000980936,"threshold_uncertainty_score":0.0015642643},"labels":[],"label_agreement":null},{"id":"W4224316852","doi":"10.3390/s22093191","title":"Surface Optimization and Design Adaptation toward Spheroid Formation On-Chip","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Spheroid; Biochip; Polydimethylsiloxane; Microfluidics; Adhesion; Materials science; Nanotechnology; Cell adhesion; Surface modification; Chip; Cell culture; Chemistry; Composite material; Computer science; Biology","score_opus":0.05007457185765749,"score_gpt":0.2559771310617537,"score_spread":0.2059025592040962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224316852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69145674,0.002377338,0.29311273,0.00041877636,0.0003260446,0.0010195368,0.0017127799,0.0017878929,0.0077881142],"genre_scores_gemma":[0.6834469,0.0019129964,0.3065325,0.00021199095,0.000036950707,0.0010049836,0.0018000166,0.00033235506,0.0047212928],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969125,0.00003976959,0.000029568093,0.00007868681,0.00010935381,0.000051354866],"domain_scores_gemma":[0.99981517,0.000051097068,0.000029023055,0.000030352672,0.000057459347,0.000016899656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035867814,0.0006049336,0.00035450188,0.00033097778,0.00019602652,0.00039206512,0.000339713,0.00047099136,0.00094863673],"category_scores_gemma":[0.0004225828,0.00023279023,0.00043281814,0.00028158512,0.00017915979,0.00021746977,0.00025638798,0.0004721111,0.0008136445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015494456,0.000017851246,0.00008135673,0.000047697842,0.000005483946,0.000026657726,0.000016288268,0.00067647576,0.99586403,0.000077275035,0.00015839681,0.0030128777],"study_design_scores_gemma":[0.0000080731015,0.00012129388,0.0010113132,0.0000036189047,0.000012321743,0.00007274077,0.000015163361,0.0057257484,0.9866788,0.000052407653,0.006287017,0.000011416113],"about_ca_topic_score_codex":0.000546226,"about_ca_topic_score_gemma":0.0013846476,"teacher_disagreement_score":0.00094863673,"about_ca_system_score_codex":0.0003465188,"about_ca_system_score_gemma":0.0002866843,"threshold_uncertainty_score":0.00317353},"labels":[],"label_agreement":null},{"id":"W4224437668","doi":"10.3390/s22093283","title":"Inter-Patient Congestive Heart Failure Detection Using ECG-Convolution-Vision Transformer Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Heart failure; Cardiology; Transformer; Convolution (computer science); Internal medicine; Medicine; Computer science; Artificial intelligence; Engineering; Electrical engineering; Voltage; Artificial neural network","score_opus":0.011841525967326329,"score_gpt":0.26222433565062203,"score_spread":0.2503828096832957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224437668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33524296,0.0006844654,0.6584328,0.00031009174,0.00016388223,0.00011804941,0.00021750368,0.0015883761,0.0032419686],"genre_scores_gemma":[0.97492707,0.00015680901,0.023491945,0.00008029072,0.0000179085,0.00002606335,0.00014843348,0.000014474006,0.0011369367],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997714,0.000029044018,0.000012628262,0.000074868265,0.00006825439,0.00004375552],"domain_scores_gemma":[0.99980444,0.000061639694,0.000025306315,0.000019052704,0.000070253096,0.00001946203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047946494,0.0005604174,0.0004203604,0.000516625,0.00019857954,0.0003594048,0.00054550567,0.00047914716,0.00067934097],"category_scores_gemma":[0.00093623553,0.0002083953,0.0004395818,0.00030342,0.000217845,0.0005237073,0.0004461095,0.00040023052,0.00013587326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009887794,0.00049204024,0.024189316,0.000121072124,0.00020003613,0.000603478,0.00011226585,0.2978165,0.05886258,0.002195728,0.0027485474,0.6116697],"study_design_scores_gemma":[0.0000078864405,0.00007052186,0.0025617536,0.0000031821494,0.000019357849,0.00011835467,0.0000072461553,0.9903562,0.0063072066,0.0003679089,0.0001731711,0.000007261568],"about_ca_topic_score_codex":0.0063771377,"about_ca_topic_score_gemma":0.00570844,"teacher_disagreement_score":0.0063771377,"about_ca_system_score_codex":0.0005997862,"about_ca_system_score_gemma":0.0005142493,"threshold_uncertainty_score":0.012679994},"labels":[],"label_agreement":null},{"id":"W4224491618","doi":"10.3390/s22093278","title":"Design of a Linear Wavenumber Spectrometer for Line Scanning Optical Coherence Tomography with 50 mm Focal Length Cylindrical Optics","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optics; Optical coherence tomography; Spectrometer; Wavenumber; Line (geometry); Physics; Coherence (philosophical gambling strategy); Tomography; Materials science; Mathematics; Geometry","score_opus":0.022638991029295848,"score_gpt":0.2436418249329332,"score_spread":0.22100283390363737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224491618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18937662,0.001118488,0.79522073,0.0006469483,0.0001756779,0.0014077646,0.0008311505,0.0038787646,0.007343743],"genre_scores_gemma":[0.24239719,0.00029757235,0.75268584,0.00020733876,0.000027093556,0.00074265705,0.0003592481,0.00012558917,0.0031574029],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991259,0.00008750323,0.000048172576,0.00025157814,0.0004049254,0.00008198543],"domain_scores_gemma":[0.99942917,0.00008843834,0.00018466933,0.000049128987,0.00018193152,0.00006669808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080763054,0.0011499911,0.00069162884,0.00072830956,0.000468724,0.0006086624,0.0015095507,0.0010307304,0.0024669818],"category_scores_gemma":[0.0006204729,0.0005888226,0.0005359912,0.0004170265,0.00043204275,0.0006319128,0.00060547295,0.000397351,0.0014155147],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025130596,0.000121914934,0.0015419475,0.00032496298,0.000036945414,0.00032365284,0.00014651126,0.0036688752,0.96156317,0.003057889,0.0014088647,0.027554063],"study_design_scores_gemma":[0.00031556995,0.0026120683,0.010076959,0.0000840218,0.0001573619,0.0024847586,0.00017209566,0.13818164,0.7914194,0.00094999437,0.053209856,0.00033628146],"about_ca_topic_score_codex":0.0015853264,"about_ca_topic_score_gemma":0.00228436,"teacher_disagreement_score":0.0024669818,"about_ca_system_score_codex":0.0012848949,"about_ca_system_score_gemma":0.0019405098,"threshold_uncertainty_score":0.009322643},"labels":[],"label_agreement":null},{"id":"W4224951819","doi":"10.3390/s22093321","title":"Research Trends in Collaborative Drones","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Drone; Open research; Computer science; Anonymity; Cloud computing; Data science; Perspective (graphical); Computer security; World Wide Web; Artificial intelligence","score_opus":0.019330074648672885,"score_gpt":0.2982051947021483,"score_spread":0.2788751200534754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224951819","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020974707,0.6946212,0.05856592,0.025590636,0.003572961,0.00012831301,0.00028036884,0.00014874637,0.19611715],"genre_scores_gemma":[0.2923588,0.63025385,0.03599963,0.0039741364,0.0048976666,0.00017472605,0.00056051597,0.000100059806,0.03168063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809045,0.0005254956,0.000101755264,0.0005098952,0.0006224272,0.00015005202],"domain_scores_gemma":[0.9952846,0.0024854909,0.00031799145,0.00038160282,0.0012088417,0.00032140862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022407295,0.00069768494,0.0007457521,0.0018487709,0.0008906857,0.0041425023,0.0015420582,0.002450333,0.009165876],"category_scores_gemma":[0.0048919474,0.00039339685,0.000712756,0.0035921072,0.0018994018,0.0067460313,0.0019167718,0.0025374226,0.0023914955],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103713464,0.0002362211,0.003701772,0.00357864,0.00007853836,0.00030657527,0.0014632584,0.01005189,0.0014759892,0.5591946,0.025536004,0.39427274],"study_design_scores_gemma":[0.00002710017,0.00035281686,0.0032390845,0.0023770246,0.000058792724,0.0007735789,0.0034774635,0.014659061,0.0015581708,0.11944797,0.8539571,0.000071769384],"about_ca_topic_score_codex":0.0027578976,"about_ca_topic_score_gemma":0.0018493825,"teacher_disagreement_score":0.009165876,"about_ca_system_score_codex":0.001587503,"about_ca_system_score_gemma":0.0016654854,"threshold_uncertainty_score":0.030662954},"labels":[],"label_agreement":null},{"id":"W4224982626","doi":"10.3390/s22093338","title":"Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Mitacs","keywords":"Ground reaction force; Biomechanics; Inverse dynamics; Simulation; Treadmill; Force platform; Plantar pressure; Engineering; Computer science; Physical medicine and rehabilitation; Pressure sensor; Physical therapy; Medicine; Physics; Mechanical engineering; Kinematics","score_opus":0.012009485714000977,"score_gpt":0.2005867265275615,"score_spread":0.1885772408135605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224982626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9698176,0.000054755033,0.029422458,0.000014094411,0.000007630919,0.000034003428,0.00021559554,0.000120590325,0.00031324336],"genre_scores_gemma":[0.992906,0.000044873606,0.00659046,0.000005916143,0.0000051272173,0.000018928373,0.00023518458,0.0000084958465,0.00018497914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988294,0.000024861292,0.000009321989,0.000038401628,0.000026316418,0.00001818655],"domain_scores_gemma":[0.99981457,0.00007107217,0.000030959218,0.00001648573,0.0000491357,0.000017806924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021912657,0.00049401517,0.00029379802,0.00037652208,0.00010501537,0.00032311995,0.00020824932,0.00038274517,0.00052711606],"category_scores_gemma":[0.0012074877,0.00018766067,0.0002333429,0.0002336,0.00008932012,0.0002452497,0.00018237391,0.00021021234,0.00019242665],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013884094,0.0007242455,0.33828777,0.00036701266,0.00038717157,0.0005327164,0.0005690092,0.16441886,0.21386401,0.00020958649,0.0007906834,0.2784605],"study_design_scores_gemma":[0.00003829637,0.00086167,0.40250242,0.000030035153,0.00011237363,0.00027380473,0.00020078821,0.5761822,0.019200433,0.00026044162,0.00029003256,0.000047494806],"about_ca_topic_score_codex":0.0051703253,"about_ca_topic_score_gemma":0.007910071,"teacher_disagreement_score":0.0051703253,"about_ca_system_score_codex":0.000082251296,"about_ca_system_score_gemma":0.00020420566,"threshold_uncertainty_score":0.01028043},"labels":[],"label_agreement":null},{"id":"W4224991288","doi":"10.3390/s22093348","title":"Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Higher Education Commision, Pakistan","keywords":"Computer science; Convolutional neural network; Mean squared error; Geospatial analysis; Pipeline (software); Feature (linguistics); Data mining; Mean absolute percentage error; Data pre-processing; Deep learning; Variety (cybernetics); Feature engineering; Artificial intelligence; Preprocessor; Intelligent transportation system; Machine learning; Artificial neural network; Engineering; Geography; Cartography","score_opus":0.023802451260926807,"score_gpt":0.22603591440351847,"score_spread":0.20223346314259166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224991288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5729072,0.0014149646,0.41303197,0.00089128636,0.00034991727,0.000096710675,0.0011400172,0.004604376,0.0055634263],"genre_scores_gemma":[0.96460474,0.0001854005,0.03267161,0.00009718363,0.000033047352,0.000044097746,0.00083289825,0.00004730348,0.0014838767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997845,0.000033239096,0.000013269638,0.00007485247,0.00004041035,0.000053737374],"domain_scores_gemma":[0.9997509,0.00008363848,0.000027793176,0.000021095713,0.00009872047,0.00001787628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054311415,0.0011583323,0.0006809151,0.0009631416,0.00034786543,0.0006834083,0.0010988043,0.0008389901,0.0010645022],"category_scores_gemma":[0.0012718727,0.0003241304,0.0006274882,0.0012043167,0.0002242585,0.001299961,0.0005499193,0.0010778414,0.00035820177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000253307,0.00030016847,0.0063236714,0.00008435265,0.00014188234,0.00022524588,0.00008517938,0.8150572,0.004903496,0.0013947628,0.003300808,0.16792984],"study_design_scores_gemma":[0.0000017478793,0.000012610345,0.00032174244,0.0000027102064,0.0000060420216,0.0000055671217,0.000008493925,0.99880815,0.00046889033,0.00028139714,0.00007973868,0.0000028611507],"about_ca_topic_score_codex":0.026868366,"about_ca_topic_score_gemma":0.02486249,"teacher_disagreement_score":0.026868366,"about_ca_system_score_codex":0.0007148311,"about_ca_system_score_gemma":0.0009064867,"threshold_uncertainty_score":0.05342394},"labels":[],"label_agreement":null},{"id":"W4225278593","doi":"10.3390/s22093419","title":"Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre de réadaptation Lethbridge-Layton-Mackay; Centre for Interdisciplinary Research in Rehabilitation; McGill University Health Centre","funders":"","keywords":"Inertial measurement unit; Artificial intelligence; Algorithm; Ice hockey; Motion capture; Computer science; Kinematics; Wearable computer; Computer vision; Simulation; Engineering; Motion (physics)","score_opus":0.04864701381755628,"score_gpt":0.3097995993732432,"score_spread":0.26115258555568693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225278593","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8872621,0.00014236395,0.11050495,0.00008101009,0.00008273693,0.00037129267,0.00012408647,0.0005232168,0.00090824603],"genre_scores_gemma":[0.9514874,0.00006229754,0.04744261,0.00004524983,0.000023329674,0.00011280112,0.00010104506,0.00003057986,0.0006946663],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927074,0.00025430004,0.00005670609,0.00015254212,0.00019449055,0.00007114219],"domain_scores_gemma":[0.99756974,0.0011984216,0.00014335496,0.00021589854,0.0007532855,0.00011939902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001679143,0.00067629194,0.00057428866,0.00037884695,0.00018184223,0.00063384266,0.0008258432,0.0005715343,0.001526079],"category_scores_gemma":[0.0060800626,0.00030602273,0.00023571515,0.00024194522,0.0003243378,0.0007470217,0.0004428538,0.00029688008,0.00041899414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0075396574,0.004334521,0.08392436,0.0011405739,0.00044076805,0.0015076589,0.0016418304,0.08736891,0.33356488,0.0011423281,0.0021918009,0.4752027],"study_design_scores_gemma":[0.0004661632,0.022437526,0.08153241,0.0001311635,0.0002453691,0.0010073035,0.00084196666,0.7384735,0.15035057,0.0005004016,0.003926422,0.000087223205],"about_ca_topic_score_codex":0.0015853264,"about_ca_topic_score_gemma":0.0014570908,"teacher_disagreement_score":0.001679143,"about_ca_system_score_codex":0.00024390958,"about_ca_system_score_gemma":0.00035343354,"threshold_uncertainty_score":0.008880258},"labels":[],"label_agreement":null},{"id":"W4225281030","doi":"10.3390/s22093435","title":"Free Space Ground to Satellite Optical Communications Using Kramers–Kronig Transceiver in the Presence of Atmospheric Turbulence","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Coral Reef Conservation Program; Natural Sciences and Engineering Research Council of Canada","keywords":"Transceiver; Satellite; Kramers–Kronig relations; Turbulence; Free-space optical communication; Atmospheric turbulence; Communications satellite; Space (punctuation); Physics; Telecommunications; Remote sensing; Optical communication; Meteorology; Optics; Computer science; Geography; Refractive index; Astronomy","score_opus":0.02616620620693024,"score_gpt":0.25240192721672766,"score_spread":0.2262357210097974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225281030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9471243,0.00070771243,0.048084337,0.0001219525,0.000030059424,0.000022251941,0.00003735943,0.00027751908,0.0035946344],"genre_scores_gemma":[0.99270564,0.00020022091,0.0061412468,0.000026012602,0.0000073582414,0.000005325671,0.000029640229,0.000005883613,0.0008786587],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996828,0.000050289542,0.000014379862,0.000042249772,0.00014216514,0.00006806744],"domain_scores_gemma":[0.999645,0.00010457142,0.0000982842,0.00004127863,0.00009379731,0.000017102264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035454144,0.0003161863,0.00029128423,0.00022257568,0.00037302403,0.00045447037,0.00032030194,0.00031199888,0.0006634792],"category_scores_gemma":[0.00054470083,0.00011490871,0.00010283446,0.00033708342,0.0003701136,0.0007846802,0.0002416921,0.00025175023,0.00024739912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078141736,0.0001456318,0.011758489,0.0002472291,0.0000859876,0.00078348554,0.00030348322,0.029978367,0.87179786,0.0059598917,0.0010756248,0.07708264],"study_design_scores_gemma":[0.00006975263,0.002561503,0.022516338,0.00007346095,0.00019271187,0.0018716719,0.00058768614,0.34395617,0.6184676,0.0034215578,0.0061867107,0.00009478064],"about_ca_topic_score_codex":0.001039026,"about_ca_topic_score_gemma":0.0023164141,"teacher_disagreement_score":0.001039026,"about_ca_system_score_codex":0.00030664945,"about_ca_system_score_gemma":0.00039741347,"threshold_uncertainty_score":0.0022249222},"labels":[],"label_agreement":null},{"id":"W4225322540","doi":"10.3390/s22093445","title":"A Universal Detection Method for Adversarial Examples and Fake Images","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Adversarial system; Computer science; Artificial intelligence; Computer vision; Computer security","score_opus":0.01314637095750225,"score_gpt":0.2676303055536461,"score_spread":0.2544839345961438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225322540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0127322,0.00031182752,0.9841151,0.00022340272,0.00008578946,0.00008223043,0.000065962,0.0014704376,0.0009131366],"genre_scores_gemma":[0.5540267,0.00052733463,0.43926865,0.00065090274,0.00019173262,0.00020468346,0.0004205944,0.00021284241,0.004496546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978542,0.00031875525,0.00011251618,0.0007543092,0.0006744944,0.00028572057],"domain_scores_gemma":[0.9978362,0.0005423958,0.00030773992,0.00071998313,0.00045128842,0.00014247015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002140358,0.0014718545,0.0020674486,0.0016098145,0.00064934196,0.0009883826,0.0021161574,0.0020377093,0.0015199301],"category_scores_gemma":[0.005818602,0.00059907435,0.0013418985,0.0007739895,0.0020454212,0.002627417,0.0031839982,0.0024998805,0.000660904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039451872,0.00027415782,0.0052419463,0.00027717874,0.00031363918,0.00059956836,0.00024568953,0.16247234,0.043035064,0.040111944,0.013105596,0.73392844],"study_design_scores_gemma":[0.000014644863,0.00006585749,0.00068521104,0.00002057823,0.00003542423,0.00059591385,0.000016516711,0.9663335,0.022017134,0.008173041,0.0020062898,0.000035829325],"about_ca_topic_score_codex":0.0016081678,"about_ca_topic_score_gemma":0.001695649,"teacher_disagreement_score":0.002140358,"about_ca_system_score_codex":0.00097533554,"about_ca_system_score_gemma":0.0013995004,"threshold_uncertainty_score":0.0113194585},"labels":[],"label_agreement":null},{"id":"W4229368211","doi":"10.3390/s22093533","title":"Single Image Video Prediction with Auto-Regressive GANs","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Computer vision; Autoregressive model; Computer graphics (images); Mathematics; Econometrics","score_opus":0.009194348238214457,"score_gpt":0.23160646083763653,"score_spread":0.22241211259942206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229368211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02415626,0.0008370781,0.9691113,0.0002456491,0.00018622866,0.00005891172,0.00036854838,0.0028542958,0.0021817638],"genre_scores_gemma":[0.7538492,0.0006974547,0.23522052,0.00040071402,0.00021252879,0.00014339304,0.0016920116,0.00034451752,0.007439603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.00003634599,0.000007737666,0.00009183673,0.00005826547,0.00002813356],"domain_scores_gemma":[0.9995914,0.0001928794,0.000038928,0.000056474804,0.00009638862,0.000023844916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000513752,0.0009965084,0.00069519435,0.0003847244,0.00014629493,0.00047179122,0.0013774682,0.00057841034,0.0015957762],"category_scores_gemma":[0.0014314293,0.0004763204,0.00056237297,0.00039809788,0.00029119925,0.00063393876,0.00037599285,0.0012820276,0.0007067908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018833997,0.000091930604,0.001372836,0.00006527927,0.00008678303,0.00013889847,0.000044232467,0.8142552,0.0085879285,0.0033887408,0.0061213477,0.16565858],"study_design_scores_gemma":[0.0000016352635,0.0000057382267,0.00006631523,0.0000019634954,0.0000022777012,0.000007870966,0.0000011607505,0.99873537,0.0005834119,0.00042414217,0.00016854002,0.0000015794759],"about_ca_topic_score_codex":0.012064334,"about_ca_topic_score_gemma":0.0150486985,"teacher_disagreement_score":0.012064334,"about_ca_system_score_codex":0.0007463625,"about_ca_system_score_gemma":0.000448537,"threshold_uncertainty_score":0.023988247},"labels":[],"label_agreement":null},{"id":"W4229375743","doi":"10.3390/s22093532","title":"The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Frailty in Older Adults","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Toronto Rehabilitation Institute","keywords":"Concurrent validity; Bivariate analysis; Scale (ratio); Computer science; Identification (biology); Vital signs; Pearson product-moment correlation coefficient; Assisted living; Activities of daily living; Correlation; Gerontology; Real-time computing; Psychology; Medicine; Machine learning; Psychometrics; Clinical psychology; Physical therapy; Statistics; Mathematics","score_opus":0.09889750495987537,"score_gpt":0.3557287518014398,"score_spread":0.25683124684156444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229375743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8944933,0.00043669282,0.09449078,0.00034530554,0.00018566029,0.004137945,0.0010022172,0.00081644,0.004091689],"genre_scores_gemma":[0.880373,0.00022274972,0.114685446,0.00012804597,0.00003658276,0.0028827882,0.0008455348,0.00006034066,0.00076543103],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99320424,0.003043153,0.0008645701,0.00073347474,0.0019033052,0.00025131908],"domain_scores_gemma":[0.9893889,0.0047541005,0.0008618585,0.00085151376,0.0037189224,0.0004247167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012633904,0.00061465206,0.00058421906,0.0011925413,0.0005180254,0.0013503361,0.0008010913,0.00049489585,0.001084182],"category_scores_gemma":[0.024972301,0.00039612027,0.0011577581,0.00075529155,0.00046155,0.0012641209,0.0021579918,0.0008062695,0.00032873967],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016351639,0.0016805436,0.54827714,0.0013947138,0.0010775407,0.0002764035,0.005385777,0.0050679003,0.018210448,0.002020067,0.0029510807,0.41202325],"study_design_scores_gemma":[0.00040081618,0.0051328545,0.87632316,0.0007259159,0.00081452075,0.0009816748,0.0039131204,0.078718506,0.016733777,0.0036433963,0.01231137,0.00030083282],"about_ca_topic_score_codex":0.0026592414,"about_ca_topic_score_gemma":0.0071850717,"teacher_disagreement_score":0.012633904,"about_ca_system_score_codex":0.0007656227,"about_ca_system_score_gemma":0.0017529874,"threshold_uncertainty_score":0.06681526},"labels":[],"label_agreement":null},{"id":"W4229450111","doi":"10.3390/s22093592","title":"Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Smoothing; Quality of service; Artificial intelligence; Exponential smoothing; Artificial neural network; Time series; Data mining; Deep learning; Process (computing); Machine learning; Real-time computing; Computer network","score_opus":0.008634188150996518,"score_gpt":0.2216526330960097,"score_spread":0.21301844494501318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229450111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013749754,0.000268749,0.9836128,0.00014142411,0.000059227037,0.000019331776,0.000073602256,0.0007772606,0.0012978197],"genre_scores_gemma":[0.8637458,0.0003468409,0.13194285,0.00011476846,0.00011110467,0.000145431,0.00036786008,0.000113324015,0.003111911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997745,0.000045424764,0.000014588272,0.000067130764,0.000055809618,0.000042580417],"domain_scores_gemma":[0.99966824,0.00014488102,0.000030067677,0.000019279792,0.00011984879,0.000017719049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007274848,0.0006944782,0.00060295034,0.00052831165,0.00031388717,0.0005975723,0.00095044327,0.0007250306,0.0015381661],"category_scores_gemma":[0.0016402815,0.00032451627,0.00052965764,0.0005035246,0.00033205078,0.0006279536,0.00063656457,0.0011725611,0.0003647114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003247868,0.000021613792,0.0003705187,0.000020502694,0.000020803272,0.000029276445,0.000023629014,0.94928366,0.0011486358,0.0033782218,0.00057885685,0.045091856],"study_design_scores_gemma":[6.422282e-7,0.0000027225688,0.000022235705,6.6277266e-7,0.0000010371568,0.000001250982,8.787952e-7,0.99942195,0.00008502528,0.000382316,0.00008038863,9.2171547e-7],"about_ca_topic_score_codex":0.020908078,"about_ca_topic_score_gemma":0.012931576,"teacher_disagreement_score":0.020908078,"about_ca_system_score_codex":0.0007042959,"about_ca_system_score_gemma":0.0011409493,"threshold_uncertainty_score":0.04157275},"labels":[],"label_agreement":null},{"id":"W4280496176","doi":"10.3390/s22103877","title":"Recent Advances in the Use of Surface-Enhanced Raman Scattering for Illicit Drug Detection","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Nanotechnology; Raman scattering; Drug detection; Drug; Illicit drug; Materials science; Raman spectroscopy; Chemistry; Medicine; Pharmacology; Chromatography; Physics; Optics","score_opus":0.092782502715145,"score_gpt":0.31784389349802744,"score_spread":0.22506139078288245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280496176","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006542337,0.9941105,0.0014727082,0.00035396285,0.00026528476,0.000010459945,0.000024629151,0.000023002609,0.0030851602],"genre_scores_gemma":[0.0028613587,0.9943613,0.0012862523,0.0001839212,0.00021740394,0.000011157167,0.0000384838,0.000004752947,0.0010353879],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953926,0.00007641298,0.000042013573,0.000097474636,0.0002026619,0.000042163854],"domain_scores_gemma":[0.99917716,0.00047000436,0.00008034027,0.000028770588,0.00021157821,0.000032240772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011819206,0.0010904139,0.00082708476,0.0025070594,0.0002751592,0.00076861953,0.00076463405,0.0010785547,0.002757604],"category_scores_gemma":[0.0010470988,0.00045113065,0.0007376106,0.0020779397,0.0004880292,0.0012245668,0.0006895363,0.0014708177,0.0014981462],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058291876,0.00010586012,0.00039995756,0.025463568,0.00012320306,0.0003018281,0.00014301787,0.0008862097,0.02250966,0.0070286007,0.0127332825,0.9302465],"study_design_scores_gemma":[0.000010251021,0.00023366735,0.0011963499,0.0020836298,0.00015183781,0.0016779791,0.00010983764,0.0005400174,0.01463824,0.0030479657,0.9762496,0.00006058907],"about_ca_topic_score_codex":0.0008072451,"about_ca_topic_score_gemma":0.0011841885,"teacher_disagreement_score":0.002757604,"about_ca_system_score_codex":0.00047912548,"about_ca_system_score_gemma":0.0008226811,"threshold_uncertainty_score":0.00922513},"labels":[],"label_agreement":null},{"id":"W4280514173","doi":"10.3390/s22103650","title":"A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs; Compute Canada; Polytechnique Montréal","keywords":"Artificial intelligence; Computer science; Machine learning; Robot; Deep learning; Electromyography; Feature (linguistics); Gesture recognition; Gesture; Physical medicine and rehabilitation","score_opus":0.03198505694666148,"score_gpt":0.23493945529121757,"score_spread":0.2029543983445561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280514173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18495518,0.18084644,0.6229433,0.0008569567,0.0003950794,0.0002849607,0.00037733524,0.0005476985,0.008793092],"genre_scores_gemma":[0.6971534,0.12696664,0.16358142,0.0006176412,0.00042239344,0.00033575718,0.0010787685,0.00015928515,0.009684679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989681,0.00025655946,0.00012460523,0.0002694673,0.00034945435,0.00003184239],"domain_scores_gemma":[0.9974232,0.001169953,0.00019218828,0.00023397733,0.0009504329,0.000030399931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015249322,0.0005678058,0.00062202447,0.0008948428,0.00014624848,0.0006804616,0.00044793138,0.00047603872,0.0006243334],"category_scores_gemma":[0.0045031016,0.0002584614,0.000743665,0.0013042117,0.0002775426,0.0010270595,0.00036706115,0.00052454363,0.00031887935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012598604,0.00025882432,0.0077082915,0.0024426235,0.0003634169,0.00011369567,0.00012122848,0.024896352,0.028233266,0.0032309734,0.0016702294,0.9308351],"study_design_scores_gemma":[0.00007753665,0.004887075,0.06791594,0.0032451286,0.0015758771,0.0015668982,0.00054710597,0.6763462,0.1441079,0.0068040243,0.09270691,0.00021937973],"about_ca_topic_score_codex":0.0014913263,"about_ca_topic_score_gemma":0.0014137587,"teacher_disagreement_score":0.0015249322,"about_ca_system_score_codex":0.00025369035,"about_ca_system_score_gemma":0.0006411496,"threshold_uncertainty_score":0.008064687},"labels":[],"label_agreement":null},{"id":"W4280517983","doi":"10.3390/s22103620","title":"RETRACTED: Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":19,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Qassim University","keywords":"Biometrics; Artificial intelligence; Iris recognition; Pattern recognition (psychology); Computer science; Classifier (UML); Feature extraction; Fuzzy logic; Artificial neural network; Computer vision","score_opus":0.10446551961661214,"score_gpt":0.3190579954491928,"score_spread":0.21459247583258062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280517983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1750081,0.0028589116,0.762137,0.0034621824,0.006454843,0.0008562501,0.0018077806,0.012875904,0.034538962],"genre_scores_gemma":[0.72832,0.0008013166,0.19815579,0.00077554426,0.00039995217,0.0001875307,0.0032490825,0.00038543335,0.067725256],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991198,0.000081155296,0.00007282204,0.00021127936,0.00042983258,0.00008507931],"domain_scores_gemma":[0.9986577,0.00013075092,0.000026856826,0.00016102789,0.00096836186,0.000055235796],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0012287684,0.00057504536,0.00078126113,0.00059621397,0.00080449553,0.0009762806,0.0017526344,0.0017013395,0.009877411],"category_scores_gemma":[0.0025527573,0.00018568514,0.0006820237,0.00034982423,0.00036208818,0.000987392,0.00071907684,0.0010910928,0.004409025],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015056987,0.00038788174,0.0076141614,0.00052942015,0.00023452473,0.0014484983,0.00037035733,0.040299773,0.090559795,0.004674434,0.071956895,0.78041846],"study_design_scores_gemma":[0.000056128643,0.0003725198,0.008067266,0.00009805687,0.00010766196,0.0009975276,0.0002455572,0.8251897,0.12042641,0.0019500187,0.042405177,0.00008389921],"about_ca_topic_score_codex":0.01411792,"about_ca_topic_score_gemma":0.009038012,"teacher_disagreement_score":0.99829865,"about_ca_system_score_codex":0.00066467153,"about_ca_system_score_gemma":0.0010160045,"threshold_uncertainty_score":0.033043265},"labels":[],"label_agreement":null},{"id":"W4280549986","doi":"10.3390/s22103870","title":"Feasibility of Overground Gait Training Using a Joint-Torque-Assisting Wearable Exoskeletal Robot in Children with Static Brain Injury","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gait training; Physical medicine and rehabilitation; Gait; Cerebral palsy; Rehabilitation; Physical therapy; Medicine; Gross motor skill; Motor skill","score_opus":0.054152314256388896,"score_gpt":0.3014571348665552,"score_spread":0.2473048206101663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280549986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993432,0.00008619288,0.00028182106,0.000009270691,0.0000016692934,0.000017691511,0.000053249314,0.000005139124,0.00020163886],"genre_scores_gemma":[0.9990632,0.000121724195,0.00047627004,0.000009075936,0.0000026180105,0.00003236913,0.00012799798,0.0000024745848,0.00016419662],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995609,0.00010031532,0.000041720574,0.00006261597,0.00012104516,0.0001134072],"domain_scores_gemma":[0.9994153,0.00011321314,0.00020590225,0.00002211185,0.00014931885,0.00009430633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046476675,0.0003366858,0.00035739847,0.00047623477,0.00030152197,0.00025459076,0.00024981992,0.00025139906,0.00067430525],"category_scores_gemma":[0.0013145794,0.00011339257,0.00042646023,0.00027310857,0.00030442575,0.00018742982,0.0003254197,0.00020067957,0.000121799334],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003770116,0.0003927145,0.95591587,0.00020366066,0.000055864944,0.0016147917,0.0006146067,0.00042582085,0.004773502,0.000026901069,0.00013165633,0.035467718],"study_design_scores_gemma":[0.0000132276755,0.0015931501,0.99334925,0.000019836783,0.00006664681,0.0014710809,0.00064283656,0.0005200455,0.0019115567,0.000006886659,0.00039719374,0.00000829151],"about_ca_topic_score_codex":0.009467192,"about_ca_topic_score_gemma":0.018105786,"teacher_disagreement_score":0.009467192,"about_ca_system_score_codex":0.00044538907,"about_ca_system_score_gemma":0.0007286331,"threshold_uncertainty_score":0.01882416},"labels":[],"label_agreement":null},{"id":"W4280561000","doi":"10.3390/s22103747","title":"Robustness and Tracking Performance Evaluation of PID Motion Control of 7 DoF Anthropomorphic Exoskeleton Robot Assisted Upper Limb Rehabilitation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College Ahuntsic","funders":"","keywords":"Exoskeleton; PID controller; Robot; Robustness (evolution); Control theory (sociology); Trajectory; Powered exoskeleton; Engineering; Computer science; Controller (irrigation); Simulation; Control engineering; Artificial intelligence; Control (management)","score_opus":0.02746867462193849,"score_gpt":0.2934634456368662,"score_spread":0.26599477101492774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280561000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.640379,0.0009864495,0.34656465,0.00020465779,0.00017281246,0.00023186806,0.00012987394,0.0017200622,0.009610573],"genre_scores_gemma":[0.9961021,0.00007376643,0.003171493,0.0000146896455,0.0000032306773,0.000043569038,0.00003842989,0.000008096007,0.0005447281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933225,0.00013664566,0.000073140865,0.00013286632,0.00023994115,0.00008516023],"domain_scores_gemma":[0.99837625,0.0005670136,0.00025788255,0.00019819342,0.0005562492,0.000044303935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013591633,0.0007222776,0.0005240863,0.00041877915,0.0002997792,0.0004857738,0.00043539974,0.00071417977,0.0009799807],"category_scores_gemma":[0.0027085221,0.00022621061,0.00042987053,0.00013412707,0.00037018873,0.00028232165,0.00042477224,0.00035703156,0.00023728616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002451114,0.00047628712,0.0046834345,0.0012379711,0.0002737015,0.00038431986,0.00046229744,0.71701294,0.17747876,0.0014403304,0.0008523399,0.09324649],"study_design_scores_gemma":[0.00012080996,0.0021562115,0.009299732,0.00004753196,0.000074944466,0.00010143032,0.00008313036,0.94361615,0.043118592,0.00021677506,0.0011237367,0.00004097734],"about_ca_topic_score_codex":0.002522805,"about_ca_topic_score_gemma":0.0009969083,"teacher_disagreement_score":0.002522805,"about_ca_system_score_codex":0.00031286685,"about_ca_system_score_gemma":0.00031914032,"threshold_uncertainty_score":0.007188022},"labels":[],"label_agreement":null},{"id":"W4280583324","doi":"10.3390/s22103840","title":"Intraoperative Optical Monitoring of Spinal Cord Hemodynamics Using Multiwavelength Imaging System","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Spinal cord; Medicine; Spinal cord injury; Photoplethysmogram; Multiple sclerosis; Biomedical engineering; Neuroscience; Computer science","score_opus":0.022945015440713458,"score_gpt":0.33915457644619534,"score_spread":0.31620956100548187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280583324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8231863,0.0023656706,0.17110798,0.00024775066,0.000112181486,0.00011928703,0.00016860523,0.0005079952,0.0021843466],"genre_scores_gemma":[0.9110593,0.001100013,0.0862906,0.0001313193,0.000068240515,0.000072764786,0.00005312696,0.000026674405,0.0011979222],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998171,0.000033975037,0.0000108544145,0.000045679186,0.00007255526,0.00001981875],"domain_scores_gemma":[0.999764,0.000084348,0.000064621796,0.000018206201,0.000045133205,0.000023708819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021487483,0.00029055064,0.00020320248,0.00042635357,0.0001323565,0.00023927633,0.00027248773,0.00047948505,0.0006976763],"category_scores_gemma":[0.00035740275,0.00013603763,0.00014375288,0.00018216587,0.00020183978,0.00044699342,0.00029161488,0.00035107107,0.00013653537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098063014,0.000029866884,0.00087471405,0.00006104616,0.000005290443,0.00007921552,0.00003953151,0.00021835467,0.98509765,0.00005632708,0.000053868833,0.013386061],"study_design_scores_gemma":[0.000020845484,0.0011885263,0.01655605,0.00002504812,0.000052551684,0.0015818428,0.000095745665,0.011837155,0.9668787,0.0001665281,0.0015570185,0.00003999069],"about_ca_topic_score_codex":0.00011944176,"about_ca_topic_score_gemma":0.00025870572,"teacher_disagreement_score":0.0006976763,"about_ca_system_score_codex":0.000111511465,"about_ca_system_score_gemma":0.000160695,"threshold_uncertainty_score":0.002333939},"labels":[],"label_agreement":null},{"id":"W4280645238","doi":"10.3390/s22103611","title":"Safety Monitoring System of CAVs Considering the Trade-Off between Sampling Interval and Data Reliability","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Interval (graph theory); Sampling (signal processing); Computer science; Statistics; Data mining; Real-time computing; Engineering; Mathematics; Telecommunications; Physics","score_opus":0.03468217509482023,"score_gpt":0.2581740796031003,"score_spread":0.22349190450828005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280645238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3899153,0.00071912457,0.5912022,0.0007306295,0.00019261432,0.00035974797,0.00028022853,0.008155797,0.008444365],"genre_scores_gemma":[0.979199,0.00008930045,0.019319918,0.00006637765,0.000030199391,0.00008970093,0.00013331143,0.000037657697,0.0010345152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843234,0.00023748145,0.00013693805,0.0004590375,0.0005360748,0.00019803904],"domain_scores_gemma":[0.9969081,0.00048373963,0.0004003044,0.00032003073,0.0017176149,0.0001702209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012063857,0.0006044236,0.0005526988,0.0010258714,0.0008510853,0.0012076434,0.0013785022,0.00061723834,0.0014548164],"category_scores_gemma":[0.0038251306,0.0002552577,0.0003707077,0.00049242144,0.00064214855,0.0015267564,0.0012463387,0.00046905922,0.0005326795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002685823,0.00046235393,0.07007623,0.0010598405,0.00019815814,0.0014145055,0.0020846485,0.1851344,0.28190285,0.013928925,0.010487986,0.43056428],"study_design_scores_gemma":[0.00014671806,0.0010570008,0.014808293,0.000083429375,0.00022193583,0.00082657696,0.00059654634,0.86353004,0.10055825,0.0047697746,0.013293489,0.00010793701],"about_ca_topic_score_codex":0.0030001383,"about_ca_topic_score_gemma":0.001790489,"teacher_disagreement_score":0.0030001383,"about_ca_system_score_codex":0.00087185024,"about_ca_system_score_gemma":0.0016483158,"threshold_uncertainty_score":0.0063800216},"labels":[],"label_agreement":null},{"id":"W4280651687","doi":"10.3390/s22103755","title":"Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Optics; Adaptive optics; Gradient descent; Microscope; Confocal; Optical path; Refractive index; Laser; Field of view; Computer science; Optical aberration; Interference (communication); Materials science; Wavefront; Physics; Artificial intelligence; Artificial neural network","score_opus":0.007714274595076558,"score_gpt":0.26108730254254536,"score_spread":0.2533730279474688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280651687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057481737,0.00011243328,0.9928669,0.000081767306,0.000024691317,0.000041238334,0.00003009463,0.0005631419,0.0005315996],"genre_scores_gemma":[0.035059784,0.000088157925,0.96355915,0.000030233798,0.0000075714133,0.00011681634,0.00010378908,0.000069411944,0.0009650069],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996443,0.000062772466,0.000018304207,0.00006613151,0.00018059884,0.000027781438],"domain_scores_gemma":[0.9996013,0.00008134826,0.000046965506,0.00004238766,0.00020759736,0.000020394651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005293596,0.00082108966,0.00048226738,0.0006618037,0.0004441864,0.00041294395,0.001146142,0.00085741194,0.0020621242],"category_scores_gemma":[0.0011295838,0.00033793447,0.00047858641,0.00070453825,0.000405742,0.0005508736,0.00072351284,0.0010667898,0.0007364398],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026790483,0.00013009862,0.0020462254,0.00036749584,0.00013509653,0.00018577067,0.00031392585,0.26674655,0.14847562,0.018188365,0.007820981,0.55532205],"study_design_scores_gemma":[0.000017730305,0.00004384097,0.0003292243,0.0000075328435,0.000007444884,0.00008151866,0.000015139292,0.98175496,0.012910474,0.0011294918,0.0036843887,0.000018357578],"about_ca_topic_score_codex":0.006770062,"about_ca_topic_score_gemma":0.010638444,"teacher_disagreement_score":0.006770062,"about_ca_system_score_codex":0.00077172555,"about_ca_system_score_gemma":0.002182971,"threshold_uncertainty_score":0.013461351},"labels":[],"label_agreement":null},{"id":"W4281250019","doi":"10.3390/s22103892","title":"Preliminary Evaluation of the Effect of Mechanotactile Feedback Location on Myoelectric Prosthesis Performance Using a Sensorized Prosthetic Hand","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; Alberta Health Services; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Task (project management); Haptic technology; Computer science; Hand strength; Prosthesis; Physical medicine and rehabilitation; GRASP; Prosthetic hand; Neuroprosthetics; Simulation; Artificial intelligence; Engineering; Grip strength; Medicine; Physical therapy","score_opus":0.012822687950671506,"score_gpt":0.22036706878257017,"score_spread":0.20754438083189866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281250019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99396294,0.00009506074,0.005411429,0.000016863405,0.000014470894,0.00016983961,0.00006924455,0.000021400223,0.00023870068],"genre_scores_gemma":[0.98652995,0.00011755677,0.012534266,0.00002887699,0.0000151935255,0.0002751629,0.00008472329,0.000011895889,0.00040242507],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990427,0.00034740573,0.00017339982,0.00014735857,0.0001810456,0.000108120985],"domain_scores_gemma":[0.99706763,0.0019666902,0.00022542902,0.00027250417,0.0003126759,0.00015495697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012976688,0.00081513607,0.0003813103,0.00022522407,0.000158222,0.00027903874,0.00044855327,0.00044533404,0.0019679843],"category_scores_gemma":[0.0045228098,0.00024269587,0.00029397788,0.00013799172,0.00053614913,0.0004013163,0.00049980415,0.0003193062,0.00018695771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01007194,0.0032918528,0.0018724161,0.0004523935,0.000056691144,0.00011453379,0.00028541475,0.0013307857,0.953716,0.000075000346,0.00004956762,0.028683355],"study_design_scores_gemma":[0.0012006675,0.3364887,0.08983848,0.00005385564,0.00031644024,0.0005054233,0.00039695384,0.0066999383,0.5629875,0.00026076476,0.0011909223,0.000060409235],"about_ca_topic_score_codex":0.0002640034,"about_ca_topic_score_gemma":0.0004433359,"teacher_disagreement_score":0.0019679843,"about_ca_system_score_codex":0.0000948929,"about_ca_system_score_gemma":0.00025700888,"threshold_uncertainty_score":0.006862819},"labels":[],"label_agreement":null},{"id":"W4281631995","doi":"10.3390/s22114025","title":"Dual Mode pHRI-teleHRI Control System with a Hybrid Admittance-Force Controller for Ultrasound Imaging","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Alberta","keywords":"Controller (irrigation); Admittance; Ultrasound; Dual mode; Mode (computer interface); Computer science; Engineering; Dual (grammatical number); Control theory (sociology); Acoustics; Simulation; Control (management); Physics; Electrical engineering; Electronic engineering; Human–computer interaction; Electrical impedance; Artificial intelligence; Biology","score_opus":0.004598729870670695,"score_gpt":0.19546860787181655,"score_spread":0.19086987800114585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281631995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08204895,0.0008486905,0.8955138,0.0004683295,0.00056354026,0.00055891625,0.00017187178,0.0067643053,0.01306162],"genre_scores_gemma":[0.87994045,0.0002533758,0.10293497,0.0005275676,0.00016539668,0.00061114284,0.00017803223,0.0000895473,0.015299494],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992712,0.000068413945,0.00005370466,0.00019550862,0.00035183565,0.00005927055],"domain_scores_gemma":[0.99946815,0.00008107195,0.000108650056,0.00007452937,0.0002155148,0.000052053565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005102122,0.000705883,0.0004958145,0.00043742135,0.00042753585,0.0005126768,0.001723425,0.0007125509,0.0045878883],"category_scores_gemma":[0.00064302277,0.0002791806,0.0003145117,0.00016710802,0.00032767645,0.00056051946,0.0008222034,0.0005032188,0.0011537331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011534473,0.00051525905,0.0024633876,0.00084944285,0.000084815256,0.00084346556,0.0006363505,0.014542926,0.5751194,0.0027898334,0.009358488,0.3916432],"study_design_scores_gemma":[0.00071786396,0.0045869197,0.013769432,0.0001673272,0.00021162552,0.0032387723,0.00022787944,0.6386338,0.27672094,0.001345397,0.060021024,0.00035911996],"about_ca_topic_score_codex":0.0015939904,"about_ca_topic_score_gemma":0.0018034384,"teacher_disagreement_score":0.0045878883,"about_ca_system_score_codex":0.0002829072,"about_ca_system_score_gemma":0.0004673202,"threshold_uncertainty_score":0.015348017},"labels":[],"label_agreement":null},{"id":"W4281698938","doi":"10.3390/s22114102","title":"Detection of Volatile Organic Compounds by Using MEMS Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"King Abdulaziz City for Science and Technology; CMC Microsystems","keywords":"Formaldehyde; Hydrogen sulfide; Benzene; Volatile organic compound; Aniline; Polymer; Polyaniline; Selectivity; Detector; Hydrogen sulfide sensor; Parts-per notation; Materials science; Microelectromechanical systems; Chemistry; Nanotechnology; Organic chemistry; Computer science; Catalysis; Sulfur; Polymerization; Telecommunications","score_opus":0.008683623586944222,"score_gpt":0.20385681885069593,"score_spread":0.1951731952637517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281698938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9315929,0.0065613566,0.054513633,0.0003753216,0.00015620401,0.00008160006,0.00028087033,0.00035974322,0.0060783983],"genre_scores_gemma":[0.96602905,0.0024107185,0.02891004,0.00013996153,0.00008743729,0.00004156035,0.0001492871,0.000013458178,0.0022183908],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980885,0.000021702866,0.000008181312,0.00003600403,0.000103957704,0.000021302569],"domain_scores_gemma":[0.9999144,0.000028285142,0.00001807456,0.0000056003805,0.00002624837,0.0000073372544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013226793,0.0002954891,0.00018823575,0.000259032,0.00015734082,0.00023441951,0.00027007202,0.00023633245,0.0005711611],"category_scores_gemma":[0.00022908475,0.00011789548,0.00013917919,0.00016910072,0.0002062167,0.0002781569,0.00026293355,0.0001826816,0.0002775685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016020262,0.000006370384,0.00046678033,0.00003741534,0.000003077216,0.000023136308,0.000011285192,0.0001298942,0.9931753,0.0001462526,0.00006284334,0.005921585],"study_design_scores_gemma":[0.000002391905,0.00013468317,0.0014158201,0.000004642611,0.000008713187,0.000093146424,0.000018307279,0.0021722496,0.99357575,0.000056704463,0.002512665,0.0000049677637],"about_ca_topic_score_codex":0.00041847545,"about_ca_topic_score_gemma":0.0008403587,"teacher_disagreement_score":0.0005711611,"about_ca_system_score_codex":0.00023086654,"about_ca_system_score_gemma":0.0001097821,"threshold_uncertainty_score":0.0019107461},"labels":[],"label_agreement":null},{"id":"W4281745630","doi":"10.3390/s22114303","title":"Identifying A(s) and β(s) in Single-Loop Feedback Circuits Using the Intermediate Transfer Function Approach","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Transfer function; Control theory (sociology); Feedback loop; Electronic circuit; Loop (graph theory); Equivalent circuit; Closed-loop transfer function; Function (biology); Describing function; Stability (learning theory); Electrical impedance; Computer science; Minor loop feedback; Feedback control; Loop gain; Control (management); Mathematics; Engineering; Control engineering; Output feedback; Nonlinear system; Physics; Voltage","score_opus":0.03962868400506344,"score_gpt":0.21203429756638817,"score_spread":0.17240561356132472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281745630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012410629,0.00013722426,0.98450977,0.00003579422,0.000015987294,0.000030372335,0.000018472509,0.0003953975,0.0024464643],"genre_scores_gemma":[0.7662632,0.0005123699,0.2285913,0.00010072021,0.000029678153,0.00023248034,0.000062386636,0.00012803129,0.004079822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995516,0.000089625355,0.000026794074,0.00012166065,0.00016279185,0.000047544498],"domain_scores_gemma":[0.9995535,0.00022680937,0.00008195031,0.00005865657,0.00006702292,0.000012106914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060859515,0.001222334,0.0005845878,0.0009508101,0.00043942302,0.0013513756,0.0012665228,0.0012989289,0.0022980825],"category_scores_gemma":[0.0019243034,0.0003351686,0.00069015723,0.0003662069,0.0010467632,0.0016480592,0.0005966972,0.0010823981,0.00079406874],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018022886,0.00014808886,0.0018397487,0.0005240311,0.00007740509,0.0004909925,0.0007095119,0.5417876,0.15943396,0.1612069,0.00069713464,0.13290441],"study_design_scores_gemma":[0.000018189301,0.00023982886,0.00049467344,0.000075170065,0.00004449752,0.00034916995,0.000072764,0.8963914,0.037270542,0.060199805,0.0047983546,0.000045536897],"about_ca_topic_score_codex":0.00075123686,"about_ca_topic_score_gemma":0.0006728198,"teacher_disagreement_score":0.0022980825,"about_ca_system_score_codex":0.00061117305,"about_ca_system_score_gemma":0.0005051093,"threshold_uncertainty_score":0.0076878667},"labels":[],"label_agreement":null},{"id":"W4281760522","doi":"10.3390/s22114175","title":"A Wavelength Modulation Spectroscopy-Based Methane Flux Sensor for Quantification of Venting Sources at Oil and Gas Sites","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Methane; Allan variance; Optics; Laser; Materials science; Multi-mode optical fiber; Volume (thermodynamics); Absorption (acoustics); Wavelength; Standard deviation; Analytical Chemistry (journal); Optical fiber; Optoelectronics; Chemistry; Physics","score_opus":0.02099051538367582,"score_gpt":0.2717788972251096,"score_spread":0.2507883818414338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281760522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82834166,0.0016296195,0.16600412,0.0001746231,0.00012354311,0.0002465252,0.00061891874,0.0014681262,0.0013928403],"genre_scores_gemma":[0.8310044,0.000574446,0.16615544,0.00013616859,0.00003190172,0.0001616837,0.0002817787,0.00004605929,0.0016081692],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994593,0.00006749084,0.000019613963,0.00014382963,0.00027276063,0.00003696236],"domain_scores_gemma":[0.99966943,0.000085799,0.00008248699,0.000024661189,0.00011339323,0.000024244518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046397437,0.000703509,0.00055434677,0.000668586,0.00030760784,0.00026523788,0.000895561,0.00082618714,0.0005922449],"category_scores_gemma":[0.00050177274,0.00033169703,0.00028335795,0.00045095972,0.00026128147,0.00045375348,0.00037167923,0.00037230054,0.00027740106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007374885,0.000041283005,0.0011354733,0.00006143517,0.0000067922388,0.00002316732,0.000014533704,0.0002548222,0.98919255,0.000047826008,0.000056455654,0.009091972],"study_design_scores_gemma":[0.0000140241245,0.00030060287,0.0048900857,0.0000074845566,0.000018844476,0.000156192,0.0000140535,0.010937115,0.9824938,0.000027285569,0.0011180736,0.00002239235],"about_ca_topic_score_codex":0.0011187494,"about_ca_topic_score_gemma":0.002335175,"teacher_disagreement_score":0.0011187494,"about_ca_system_score_codex":0.0006634368,"about_ca_system_score_gemma":0.000488735,"threshold_uncertainty_score":0.004813552},"labels":[],"label_agreement":null},{"id":"W4281776186","doi":"10.3390/s22114129","title":"Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Percentile; Treadmill; Intraclass correlation; Reliability (semiconductor); Biomechanics; Physical medicine and rehabilitation; Physical therapy; Simulation; Medicine; Mathematics; Computer science; Statistics; Artificial intelligence; Reproducibility","score_opus":0.009715345145878022,"score_gpt":0.25878611864812257,"score_spread":0.24907077350224455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281776186","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99637514,0.00029398527,0.0025639946,0.000007922152,0.00003272362,0.000027996108,0.00017614229,0.000035148758,0.00048683104],"genre_scores_gemma":[0.9980293,0.000056426838,0.0012108813,0.000010964827,0.000026815516,0.00004106193,0.00031353344,0.000023459814,0.00028756383],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99704903,0.0009557416,0.0003086883,0.0007565533,0.00075098407,0.00017906821],"domain_scores_gemma":[0.9878001,0.0058208294,0.0022894794,0.0015342587,0.0023014802,0.00025375586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029233498,0.00057444826,0.0007161822,0.0005945626,0.00031689185,0.0005702046,0.00038626534,0.0005480865,0.00070847775],"category_scores_gemma":[0.015338239,0.00030728366,0.00043108297,0.00044365658,0.00038208076,0.00046817074,0.0006156426,0.00033987418,0.00038210282],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0068308716,0.0003684216,0.8755812,0.00039529847,0.0011856557,0.00014957236,0.0015979547,0.0015964551,0.05967116,0.00005390564,0.0003648244,0.05220476],"study_design_scores_gemma":[0.000009580997,0.00091620826,0.99578434,0.000007129812,0.00006487754,0.000084956024,0.00011300178,0.0005928964,0.0022064748,0.000019365578,0.00018928497,0.000011813546],"about_ca_topic_score_codex":0.00094603316,"about_ca_topic_score_gemma":0.0022998552,"teacher_disagreement_score":0.0029233498,"about_ca_system_score_codex":0.00011932337,"about_ca_system_score_gemma":0.00011314522,"threshold_uncertainty_score":0.015460372},"labels":[],"label_agreement":null},{"id":"W4281849368","doi":"10.3390/s22114107","title":"Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université du Québec en Outaouais","funders":"","keywords":"Beluga Whale; Artificial intelligence; Computer science; Beluga; Computer vision; Object detection; Pattern recognition (psychology); Fishery; Oceanography; Geology; Biology","score_opus":0.013843959970185489,"score_gpt":0.216820152486684,"score_spread":0.2029761925164985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281849368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3323023,0.005186992,0.6148391,0.000585699,0.0006882814,0.00090572936,0.004558619,0.035977587,0.0049557155],"genre_scores_gemma":[0.57706267,0.00089007866,0.40375495,0.0004243328,0.0001805512,0.00044765676,0.010519303,0.00020017507,0.006520207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992231,0.000062958985,0.00006205026,0.0003865281,0.00014973957,0.00011565163],"domain_scores_gemma":[0.9994554,0.00013113495,0.000059920407,0.00008906012,0.0002173652,0.000047208217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005776409,0.0017086358,0.0009543284,0.001980449,0.00041769666,0.0006069538,0.0017935708,0.0010816132,0.0016380114],"category_scores_gemma":[0.0011697921,0.00042097474,0.0007846663,0.0009054982,0.00021795883,0.000857517,0.00067829265,0.0007697718,0.0014064622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004499542,0.00064569217,0.008938046,0.00037242196,0.00023112675,0.0005590188,0.00011363267,0.019778522,0.064378396,0.00046716412,0.018534895,0.8855311],"study_design_scores_gemma":[0.0000484095,0.00019762541,0.013033846,0.00003030045,0.000081236205,0.00034012285,0.0001167393,0.9562651,0.02466824,0.0006716768,0.004508752,0.00003796902],"about_ca_topic_score_codex":0.014464605,"about_ca_topic_score_gemma":0.022237342,"teacher_disagreement_score":0.014464605,"about_ca_system_score_codex":0.00071267347,"about_ca_system_score_gemma":0.000714932,"threshold_uncertainty_score":0.02876085},"labels":[],"label_agreement":null},{"id":"W4281865645","doi":"10.3390/s22114069","title":"Wearable Sensor Based on Flexible Sinusoidal Antenna for Strain Sensing Applications","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Canada First Research Excellence Fund; Université Laval","keywords":"Antenna (radio); Dipole antenna; Antenna measurement; Coaxial antenna; Antenna factor; Electrical engineering; Acoustics; Monopole antenna; Loop antenna; Materials science; Electronic engineering; Computer science; Optoelectronics; Engineering; Physics","score_opus":0.017155313264635173,"score_gpt":0.2364592519219843,"score_spread":0.21930393865734912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281865645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6199329,0.0039154342,0.3667293,0.00043830526,0.00053790933,0.00013455976,0.0004498796,0.0014072018,0.006454454],"genre_scores_gemma":[0.9141681,0.0011222749,0.08073664,0.0002144059,0.00006722074,0.000046052814,0.00018873622,0.000036303867,0.0034201995],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998486,0.000023180346,0.000011131411,0.000045125573,0.000054403496,0.000017643626],"domain_scores_gemma":[0.9997961,0.00005053564,0.000048406437,0.000037916136,0.000051017167,0.000016098491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015589003,0.00027296928,0.00034102442,0.00020820663,0.00008509612,0.00031364962,0.00040022744,0.0005069018,0.00065714173],"category_scores_gemma":[0.00028005766,0.00012414294,0.00027808134,0.00029928776,0.00020366568,0.00042390436,0.00027401044,0.00018693222,0.00031661254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086380765,0.000019540688,0.0008762918,0.00021236548,0.000019084388,0.00015570024,0.000037904607,0.0009964791,0.9607357,0.0006307634,0.000580361,0.03564943],"study_design_scores_gemma":[0.000019737157,0.0013508665,0.0072897067,0.000030144894,0.000078622776,0.0017534449,0.00009723683,0.036434554,0.93930596,0.0004883366,0.013089939,0.00006146046],"about_ca_topic_score_codex":0.00007612244,"about_ca_topic_score_gemma":0.0001526792,"teacher_disagreement_score":0.00065714173,"about_ca_system_score_codex":0.00011819388,"about_ca_system_score_gemma":0.00008757312,"threshold_uncertainty_score":0.0021983385},"labels":[],"label_agreement":null},{"id":"W4282548290","doi":"10.3390/s22124390","title":"The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algonquin College; Concordia University","funders":"Zayed University","keywords":"Smart city; Computer science; Process (computing); Deep learning; Zoning; Population; Computer security; Data science; Risk analysis (engineering); Artificial intelligence; Engineering; Internet of Things; Civil engineering","score_opus":0.023141262226850125,"score_gpt":0.24490835535744543,"score_spread":0.2217670931305953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282548290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002931408,0.00028790248,0.9929096,0.00033692003,0.00004360649,0.000050393162,0.00026233878,0.0016568631,0.0015209591],"genre_scores_gemma":[0.19657628,0.0012037088,0.79094255,0.0005302295,0.000107363994,0.00036888308,0.0020249235,0.0003380187,0.007908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997975,0.00003894765,0.00001049395,0.000059308382,0.000052764863,0.000041000778],"domain_scores_gemma":[0.9998777,0.00002991208,0.000016293312,0.000022844148,0.00003532985,0.000017962993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050739344,0.00079414976,0.000574215,0.00088734826,0.0003623264,0.0014763239,0.0020022125,0.001409063,0.0026376934],"category_scores_gemma":[0.0008504947,0.00045108353,0.0009254691,0.0008820972,0.00059016934,0.0017979369,0.001421918,0.0018140302,0.001023763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010095546,0.00018779245,0.0018884355,0.00020529417,0.00014702219,0.00016193894,0.00014317772,0.5507789,0.005663815,0.071646854,0.017332435,0.3517434],"study_design_scores_gemma":[0.0000028384802,0.000008885631,0.00009468097,0.000009797985,0.0000042720862,0.000010430426,0.000007185579,0.98673254,0.00057913805,0.010317589,0.002227268,0.0000054222737],"about_ca_topic_score_codex":0.012979327,"about_ca_topic_score_gemma":0.021673197,"teacher_disagreement_score":0.012979327,"about_ca_system_score_codex":0.001212972,"about_ca_system_score_gemma":0.0010424685,"threshold_uncertainty_score":0.02580756},"labels":[],"label_agreement":null},{"id":"W4282581695","doi":"10.3390/s22124359","title":"An Effective Color Image Encryption Based on Henon Map, Tent Chaotic Map, and Orthogonal Matrices","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Encryption; Key space; Chaotic; Hénon map; Algorithm; Cipher; Theoretical computer science; Computer science; Pixel; Probabilistic encryption; Mathematics; Key (lock); Computer vision; Artificial intelligence; Computer network; Computer security","score_opus":0.005728873672159069,"score_gpt":0.22786512436542514,"score_spread":0.22213625069326606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282581695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07885568,0.003500952,0.8990071,0.0004741929,0.00046837432,0.00022821604,0.00013205539,0.00075915514,0.016574338],"genre_scores_gemma":[0.7065973,0.003497386,0.26712292,0.00017301209,0.0001201772,0.00017175086,0.0001958671,0.000057140784,0.022064365],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997044,0.000040224368,0.00001672537,0.000034885667,0.0001792541,0.000024550352],"domain_scores_gemma":[0.99985623,0.000023209612,0.000020346888,0.000020670459,0.00007003845,0.000009408014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001906664,0.00032603144,0.00032477782,0.000488615,0.0003256025,0.0003948153,0.00032758774,0.000356147,0.0010984009],"category_scores_gemma":[0.00040533414,0.00012754215,0.0003305171,0.0005299748,0.00027763998,0.0011947517,0.0003497259,0.00035852243,0.00027638467],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055350934,0.00014183973,0.0014429924,0.0006678425,0.000105380204,0.0010980848,0.00034807305,0.040132783,0.3380979,0.114077225,0.007479421,0.49585494],"study_design_scores_gemma":[0.00011036513,0.00075608696,0.0026724874,0.0000770874,0.00009693781,0.0048202244,0.00016122664,0.6154109,0.28974602,0.018592201,0.067385375,0.00017099113],"about_ca_topic_score_codex":0.0008528994,"about_ca_topic_score_gemma":0.00072112284,"teacher_disagreement_score":0.0010984009,"about_ca_system_score_codex":0.0003498486,"about_ca_system_score_gemma":0.00050430396,"threshold_uncertainty_score":0.0036745071},"labels":[],"label_agreement":null},{"id":"W4282585228","doi":"10.3390/s22124320","title":"Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trilateration; Bluetooth Low Energy; Received signal strength indication; Computer science; Real-time computing; Indoor positioning system; Artificial neural network; Wearable computer; Path loss; Artificial intelligence; Bluetooth; Computer vision; Embedded system; Wireless; Engineering; Telecommunications; Accelerometer","score_opus":0.006134418416638544,"score_gpt":0.21197757547668084,"score_spread":0.2058431570600423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282585228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049187943,0.00059981394,0.9448813,0.00012598842,0.00013967142,0.00003280779,0.00004828982,0.0022148767,0.0027693685],"genre_scores_gemma":[0.6390376,0.0005988164,0.35459527,0.00017336002,0.0000902987,0.00005823565,0.00017578906,0.0001421784,0.0051283976],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996201,0.000055514578,0.000024128776,0.00011594069,0.00013950947,0.000044881464],"domain_scores_gemma":[0.99947673,0.00012414333,0.000076076634,0.00006435216,0.0002399493,0.00001870179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005779567,0.0009868898,0.00056633266,0.0012627466,0.00022455443,0.0007673761,0.0007400196,0.0007459387,0.0011912856],"category_scores_gemma":[0.0012874937,0.00031472227,0.00047794404,0.0008811174,0.00026720727,0.0010776503,0.0005031264,0.00059068337,0.0007143561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023454339,0.00021940134,0.0022204474,0.00014775056,0.000094387,0.00010875737,0.00008107443,0.10749083,0.06391885,0.001059591,0.0012383001,0.82318604],"study_design_scores_gemma":[0.000012733029,0.00015502953,0.0022306375,0.000016296326,0.000042662363,0.0001256193,0.0000235706,0.96608967,0.029040152,0.00064704707,0.0015854205,0.00003112058],"about_ca_topic_score_codex":0.002638568,"about_ca_topic_score_gemma":0.004537624,"teacher_disagreement_score":0.002638568,"about_ca_system_score_codex":0.00037085934,"about_ca_system_score_gemma":0.00035923903,"threshold_uncertainty_score":0.0052464604},"labels":[],"label_agreement":null},{"id":"W4282590599","doi":"10.3390/s22124341","title":"Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Machine learning; Respiratory rate; Artificial intelligence; Computer science; Algorithm; Decision tree; Coronavirus disease 2019 (COVID-19); Oxygen saturation; Support vector machine; Inference; Heart rate; Medicine; Infectious disease (medical specialty); Internal medicine; Disease","score_opus":0.06665898170698173,"score_gpt":0.3644744155449904,"score_spread":0.2978154338380087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282590599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061055914,0.0038621547,0.9234854,0.001521984,0.000342458,0.0002641275,0.0007697586,0.004808789,0.003889464],"genre_scores_gemma":[0.44715166,0.0023637395,0.5413008,0.0011370636,0.0003165717,0.0003714273,0.0018616407,0.00013351988,0.0053636623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993082,0.00020203814,0.00005610327,0.00019590463,0.00017725029,0.000060507424],"domain_scores_gemma":[0.99905604,0.0003952767,0.00007728459,0.00006402231,0.00035646508,0.00005091507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187613,0.0012132024,0.00074299553,0.0011832532,0.0001896805,0.0010157983,0.0008951852,0.0014216779,0.0027133075],"category_scores_gemma":[0.0035852597,0.0002521408,0.00073128456,0.0005945023,0.00019167805,0.0009972223,0.00084380613,0.0010234485,0.0018730647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005303022,0.00031495956,0.01750343,0.00055719167,0.00018112273,0.00053758937,0.00015878388,0.029517243,0.04178601,0.0022085013,0.0098982025,0.8968066],"study_design_scores_gemma":[0.000064750646,0.000718467,0.01223571,0.00028536105,0.0001626487,0.0011704724,0.00022134047,0.9158447,0.043773327,0.0057766107,0.01966684,0.00007978088],"about_ca_topic_score_codex":0.0014934302,"about_ca_topic_score_gemma":0.0014120075,"teacher_disagreement_score":0.0027133075,"about_ca_system_score_codex":0.00036584958,"about_ca_system_score_gemma":0.00043903635,"threshold_uncertainty_score":0.009076893},"labels":[],"label_agreement":null},{"id":"W4282601779","doi":"10.3390/s22124327","title":"An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"GNSS applications; Lidar; Extended Kalman filter; Mean squared error; Computer science; Kalman filter; Inertial navigation system; Simultaneous localization and mapping; Navigation system; Real-time computing; Trajectory; Remote sensing; Global Positioning System; Artificial intelligence; Geography; Telecommunications; Inertial frame of reference; Mobile robot; Mathematics; Robot","score_opus":0.009340088308167203,"score_gpt":0.20582384065392909,"score_spread":0.19648375234576188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282601779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13337803,0.00032170906,0.8552928,0.00016183277,0.00019519936,0.00014160822,0.0005197558,0.0069250725,0.0030639404],"genre_scores_gemma":[0.71172106,0.0001608658,0.28308785,0.0001617189,0.00006264177,0.00016028083,0.0014510904,0.00013521606,0.0030592994],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995708,0.00004646334,0.000020449104,0.00011339242,0.00018687372,0.00006202658],"domain_scores_gemma":[0.9997004,0.000016324919,0.000035297322,0.00006071833,0.00016436608,0.000022961922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004061919,0.00078538747,0.00053086434,0.00055245735,0.00040555757,0.00058375095,0.0010508707,0.00056733296,0.0010372289],"category_scores_gemma":[0.0007255127,0.0003443381,0.00040322545,0.0006697544,0.00024140376,0.00083207834,0.0013431106,0.00065913174,0.0009523795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057957956,0.00027559936,0.013906983,0.0003660697,0.0003233741,0.0006093988,0.0005001184,0.14778328,0.22840029,0.0028193425,0.008375708,0.5960602],"study_design_scores_gemma":[0.00013114889,0.0005050243,0.014916293,0.00005033282,0.0001419722,0.0004069017,0.00022228497,0.92377454,0.041555166,0.0016757491,0.016520545,0.00010006516],"about_ca_topic_score_codex":0.010966107,"about_ca_topic_score_gemma":0.014202691,"teacher_disagreement_score":0.010966107,"about_ca_system_score_codex":0.00030359274,"about_ca_system_score_gemma":0.0013869168,"threshold_uncertainty_score":0.021804571},"labels":[],"label_agreement":null},{"id":"W4282930545","doi":"10.3390/s22114051","title":"Design of Dust-Filtering Algorithms for LiDAR Sensors Using Intensity and Range Information in Off-Road Vehicles","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Lidar; Point cloud; Filter (signal processing); Computer science; Range (aeronautics); Intensity (physics); Algorithm; Remote sensing; Point (geometry); Artificial intelligence; Computer vision; Engineering; Mathematics; Geology; Optics","score_opus":0.030744382160779293,"score_gpt":0.24833891363752417,"score_spread":0.21759453147674487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282930545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057260938,0.00022771076,0.9415331,0.00006613351,0.000034236982,0.000055783657,0.000027993292,0.00040397065,0.00039021732],"genre_scores_gemma":[0.4745418,0.0002765654,0.5228445,0.00011011404,0.000048759975,0.00016401906,0.00020645732,0.00005812843,0.0017496784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995846,0.000031573443,0.000027749622,0.00012743224,0.00014837793,0.00008030125],"domain_scores_gemma":[0.999326,0.00016188205,0.000069423644,0.000039377577,0.0003675352,0.000035837144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077379553,0.00068055565,0.0006955386,0.0006901484,0.00045121642,0.00067303557,0.0014679849,0.0010741543,0.00051114726],"category_scores_gemma":[0.0015696005,0.000475092,0.0006336645,0.00038633935,0.00038254712,0.0007716854,0.00050599815,0.0005612347,0.00035258548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056586316,0.00040215906,0.011192103,0.0002234479,0.00016413565,0.00027110343,0.00030844987,0.18295495,0.1663887,0.0025754103,0.0018850047,0.6330687],"study_design_scores_gemma":[0.000027755794,0.00013450457,0.0038998309,0.000008923282,0.000035023593,0.000100587116,0.00008111722,0.95710444,0.036552835,0.0006576341,0.001379926,0.000017462808],"about_ca_topic_score_codex":0.0073768375,"about_ca_topic_score_gemma":0.007856213,"teacher_disagreement_score":0.0073768375,"about_ca_system_score_codex":0.00064122723,"about_ca_system_score_gemma":0.0010588206,"threshold_uncertainty_score":0.014667809},"labels":[],"label_agreement":null},{"id":"W4282978072","doi":"10.3390/s22124494","title":"An Edge-Fog Architecture for Distributed 3D Reconstruction and Remote Monitoring of a Power Plant Site in the Context of 5G","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universidade Federal de Juiz de Fora; Agência Nacional de Energia Elétrica; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Nacional de Energia Elétrica; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Scalability; Computer science; Context (archaeology); Robot; Enhanced Data Rates for GSM Evolution; Real-time computing; Edge computing; Distributed computing; Data processing; Embedded system; Artificial intelligence; Database","score_opus":0.012859843719546122,"score_gpt":0.2342337675641777,"score_spread":0.22137392384463156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282978072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16701458,0.00048694207,0.8176192,0.00039242156,0.00010366254,0.0001039662,0.00012072326,0.0018533953,0.012305057],"genre_scores_gemma":[0.82102525,0.00023714952,0.1747528,0.000114477625,0.000017152961,0.00004211525,0.00015486607,0.00004415442,0.0036120454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999013,0.000013395878,0.0000037534733,0.000024419485,0.000030567327,0.000026667525],"domain_scores_gemma":[0.9999435,0.00000975221,0.000004307826,0.000015198353,0.000015639342,0.000011636502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011923749,0.00028782577,0.00022016214,0.00018472342,0.0005007507,0.00058214826,0.00088111026,0.00047231928,0.0012568197],"category_scores_gemma":[0.00019003589,0.00014680008,0.0002765278,0.0002523361,0.0003128949,0.0005109919,0.0006662313,0.000347697,0.0002561009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077906117,0.00026231539,0.005147689,0.00020122634,0.00013050527,0.0012490246,0.0005693048,0.5992064,0.111973524,0.03219951,0.011060808,0.2372206],"study_design_scores_gemma":[0.000009684737,0.00008287876,0.0010009313,0.000009164333,0.00001947673,0.00014285573,0.00007073815,0.9818942,0.008234704,0.0037598715,0.0047613834,0.000014134547],"about_ca_topic_score_codex":0.00683635,"about_ca_topic_score_gemma":0.011341422,"teacher_disagreement_score":0.00683635,"about_ca_system_score_codex":0.00043728252,"about_ca_system_score_gemma":0.0005426839,"threshold_uncertainty_score":0.013593137},"labels":[],"label_agreement":null},{"id":"W4282981448","doi":"10.3390/s22124482","title":"Real-Time Megapixel Electro-Optical Imaging of THz Beams with Probe Power Normalization","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Terahertz technology and applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministère des relations internationales et de la Francophonie","keywords":"Terahertz radiation; Optics; Optical power; Electric field; Magnification; Normalization (sociology); Calibration; Materials science; Image resolution; Physics; Optoelectronics; Laser","score_opus":0.0022957871023404973,"score_gpt":0.18124819249284224,"score_spread":0.17895240539050175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282981448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40701082,0.001183435,0.5845233,0.00042650654,0.0002578543,0.00014196282,0.00038437292,0.0028318823,0.003239898],"genre_scores_gemma":[0.418296,0.000512423,0.5772115,0.0001378626,0.00004779289,0.00013772146,0.00025096457,0.00022347878,0.003182315],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973315,0.000024879055,0.000013453973,0.000065075896,0.00014085995,0.000022604621],"domain_scores_gemma":[0.9997012,0.00009299186,0.000063835745,0.00006592086,0.00005679434,0.0000190878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029719018,0.00040173065,0.0004037191,0.00033971123,0.0001270886,0.00051430764,0.00083555514,0.00038983888,0.0016329297],"category_scores_gemma":[0.00068551255,0.00030863157,0.00010883359,0.00047075978,0.00029449686,0.0008658791,0.0004763034,0.0004523702,0.00040971758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052662617,0.00002175665,0.00019059944,0.000034331515,0.0000044994813,0.0000255907,0.000030175162,0.0003976846,0.9804627,0.00037430474,0.00016988617,0.018235944],"study_design_scores_gemma":[0.00000686169,0.00004855481,0.0013279155,0.0000029005998,0.0000040538175,0.0001435452,0.00001410824,0.01783714,0.97877675,0.00012629051,0.0017017599,0.000010092074],"about_ca_topic_score_codex":0.0003620029,"about_ca_topic_score_gemma":0.0011059996,"teacher_disagreement_score":0.0016329297,"about_ca_system_score_codex":0.00041894903,"about_ca_system_score_gemma":0.0002467473,"threshold_uncertainty_score":0.005462706},"labels":[],"label_agreement":null},{"id":"W4283158218","doi":"10.3390/s22124598","title":"Synchrotron Radiation Study of Gain, Noise, and Collection Efficiency of GaAs SAM-APDs with Staircase Structure","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Elettra-Sincrotrone Trieste","keywords":"Avalanche photodiode; APDS; Optoelectronics; Materials science; Synchrotron radiation; Gallium arsenide; Absorption (acoustics); Semiconductor; Photodetector; Noise (video); Molecular beam epitaxy; Optics; Detector; Physics; Nanotechnology; Epitaxy; Computer science","score_opus":0.0044834486847830975,"score_gpt":0.2202989957527897,"score_spread":0.2158155470680066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283158218","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983736,0.00027866717,0.0010314051,0.000010535792,0.0000046744894,0.0000050038066,0.00009694505,0.000026238931,0.00017295744],"genre_scores_gemma":[0.99795747,0.00013279633,0.0012916441,0.000007203295,0.0000022594909,0.0000101912365,0.00010508502,0.000010314181,0.00048300237],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976975,0.000027134403,0.000013146178,0.00006464034,0.00009157844,0.00003382604],"domain_scores_gemma":[0.9995376,0.00017133802,0.00008996493,0.000044689674,0.00012248562,0.000033969165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002117979,0.00034283672,0.00025801625,0.00035836385,0.00016302156,0.000359587,0.00046262497,0.00040884715,0.0007221296],"category_scores_gemma":[0.00050060253,0.0001761662,0.0002084897,0.00041140057,0.00019422242,0.00022901509,0.00017079557,0.00025518908,0.00018622949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010216325,0.000018380606,0.0012825499,0.000043213528,0.000019833486,0.000062215084,0.00006574557,0.00029279318,0.99663025,0.00004634844,0.000016596485,0.0014198849],"study_design_scores_gemma":[0.000004545346,0.00024052919,0.00765803,0.0000037784441,0.000020539863,0.00011953423,0.000053754506,0.0029548167,0.98844796,0.000014652352,0.0004757299,0.0000060709863],"about_ca_topic_score_codex":0.0005178958,"about_ca_topic_score_gemma":0.000475231,"teacher_disagreement_score":0.0007221296,"about_ca_system_score_codex":0.00029562294,"about_ca_system_score_gemma":0.00008396817,"threshold_uncertainty_score":0.0024157763},"labels":[],"label_agreement":null},{"id":"W4283159488","doi":"10.3390/s22124610","title":"LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universities Space Research Association","keywords":"Lidar; Structural health monitoring; Computer science; Construction engineering; Ranging; Remote sensing; Deformation monitoring; Civil engineering; Engineering; Geology; Telecommunications; Deformation (meteorology); Structural engineering","score_opus":0.05601801705192361,"score_gpt":0.30556600197494715,"score_spread":0.24954798492302355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283159488","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023274585,0.9970784,0.00063434563,0.0001648092,0.00017337475,0.000010351803,0.000027418631,0.000011326539,0.0016672419],"genre_scores_gemma":[0.0017488628,0.99648213,0.0007895668,0.00013724106,0.00012289184,0.000009474801,0.000042524225,0.0000034159705,0.00066384027],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996277,0.000048891747,0.00003212453,0.000076796576,0.00018248695,0.000031985044],"domain_scores_gemma":[0.9992465,0.00036003478,0.000066285356,0.000022310918,0.00027038323,0.0000345408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010377177,0.00086587877,0.001240644,0.003190218,0.00026773923,0.0009871498,0.0009802516,0.0011863534,0.0032663546],"category_scores_gemma":[0.0012302517,0.00037629533,0.0007392559,0.0030937835,0.0004357101,0.0016469855,0.00071937393,0.0012426795,0.0017932465],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031474832,0.00004869862,0.0002524427,0.014008167,0.000071595925,0.00010309705,0.000054637687,0.00064352877,0.0027361058,0.004053364,0.00959753,0.9683993],"study_design_scores_gemma":[0.000009563375,0.00015289949,0.0013693979,0.004345859,0.00015856436,0.0010593993,0.00011137346,0.00044639633,0.0016780789,0.0023721964,0.988257,0.000039303166],"about_ca_topic_score_codex":0.0015499323,"about_ca_topic_score_gemma":0.0020321086,"teacher_disagreement_score":0.0032663546,"about_ca_system_score_codex":0.0005485383,"about_ca_system_score_gemma":0.001147466,"threshold_uncertainty_score":0.010927081},"labels":[],"label_agreement":null},{"id":"W4283160981","doi":"10.3390/s22124579","title":"Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software portability; Wearable computer; Wearable technology; Benchmark (surveying); Noise (video); Context (archaeology); Usability; Representation (politics); Human–computer interaction; Artificial intelligence; Embedded system","score_opus":0.2180605987224319,"score_gpt":0.3878639290134978,"score_spread":0.16980333029106592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283160981","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016466094,0.92681086,0.06040687,0.0017712732,0.0013742731,0.00007144701,0.0002070442,0.00022444536,0.0074871723],"genre_scores_gemma":[0.017747851,0.9406658,0.032098707,0.0011052785,0.0018728691,0.00012632522,0.0005797055,0.00008444666,0.005718933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994505,0.00009987243,0.0000576566,0.00012909005,0.00023151227,0.00003135542],"domain_scores_gemma":[0.998765,0.00070943456,0.00009548056,0.00006653639,0.00033671685,0.000026846343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011671749,0.0009749635,0.0008951771,0.0022756022,0.00018639506,0.0014680882,0.00083421223,0.0012830152,0.0034380644],"category_scores_gemma":[0.0019934305,0.00039178453,0.0007277951,0.0022470923,0.0006337327,0.0019779322,0.00060821127,0.001862634,0.002803604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045060395,0.000049058825,0.0002330912,0.0052885823,0.00006615104,0.00011960428,0.00006715997,0.0011583763,0.008307826,0.010153501,0.01439013,0.96012145],"study_design_scores_gemma":[0.000019481062,0.0003632092,0.0021830783,0.0042017,0.00023027763,0.00280987,0.00012064597,0.008121652,0.022277705,0.015545483,0.9439804,0.00014651023],"about_ca_topic_score_codex":0.00083573547,"about_ca_topic_score_gemma":0.0006569007,"teacher_disagreement_score":0.0034380644,"about_ca_system_score_codex":0.0004307987,"about_ca_system_score_gemma":0.0006077625,"threshold_uncertainty_score":0.011501431},"labels":[],"label_agreement":null},{"id":"W4283163889","doi":"10.3390/s22124609","title":"Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Qassim University; New Brunswick Innovation Foundation","keywords":"Neuroimaging; Convolutional neural network; Artificial intelligence; Deep learning; Categorization; Computer science; Alzheimer's disease; Modality (human–computer interaction); Pattern recognition (psychology); Machine learning; Psychology; Neuroscience; Medicine; Disease; Pathology","score_opus":0.0402851368638024,"score_gpt":0.3131666423535685,"score_spread":0.2728815054897661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283163889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9092346,0.0015615661,0.08340142,0.00030074795,0.00015246806,0.00015087552,0.00089714566,0.0012521859,0.003049063],"genre_scores_gemma":[0.96667963,0.00036833057,0.029638398,0.000095090945,0.000026637308,0.00005913184,0.0015703705,0.00002332552,0.0015391055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996668,0.000056009725,0.000020010957,0.000116348405,0.00006604369,0.00007477431],"domain_scores_gemma":[0.9997819,0.00004971474,0.000028435466,0.000037168134,0.00007771092,0.000025020967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006909032,0.00091780914,0.0004503729,0.0009624651,0.00021999134,0.0006371511,0.0005118008,0.00069890445,0.0005859296],"category_scores_gemma":[0.0012209278,0.0002557805,0.00083673635,0.00043723747,0.00025736372,0.00055160234,0.0006300771,0.00063068117,0.0003135991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013398875,0.0006948925,0.059129164,0.0002905746,0.00051310455,0.00081812154,0.00031267566,0.27497923,0.079557784,0.0015912603,0.0066396194,0.57413363],"study_design_scores_gemma":[0.000014793068,0.00016315148,0.01719095,0.00003599376,0.0000771223,0.00018170217,0.00008785092,0.9639728,0.016315458,0.0008408312,0.0010869076,0.000032419557],"about_ca_topic_score_codex":0.011637111,"about_ca_topic_score_gemma":0.011767563,"teacher_disagreement_score":0.011637111,"about_ca_system_score_codex":0.000506208,"about_ca_system_score_gemma":0.00057912635,"threshold_uncertainty_score":0.023138762},"labels":[],"label_agreement":null},{"id":"W4283263407","doi":"10.3390/s22124642","title":"Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Botulinum Toxin and Related Neurological Disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Cervical dystonia; Spasmodic Torticollis; Rating scale; Scale (ratio); Reliability (semiconductor); Artificial intelligence; Correlation; Protractor; Physical medicine and rehabilitation; Physical therapy; Dystonia; Computer science; Psychology; Medicine; Statistics; Mathematics","score_opus":0.09237479380094231,"score_gpt":0.38698857871274556,"score_spread":0.29461378491180323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283263407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9142402,0.00027544802,0.078073084,0.00030583117,0.00012927028,0.005296476,0.00025863614,0.00020892185,0.0012122667],"genre_scores_gemma":[0.8995177,0.00019190219,0.09643557,0.0002329958,0.00008453538,0.0023679084,0.00024618578,0.000036578538,0.0008867376],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9959992,0.002382498,0.0002061824,0.00044500394,0.00073866546,0.00022840907],"domain_scores_gemma":[0.99697304,0.0012436712,0.00012707336,0.00023253911,0.0011180307,0.0003057357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007077839,0.00092131953,0.00051864126,0.00068960764,0.00036591635,0.00039388842,0.0012545789,0.0009649999,0.0025822795],"category_scores_gemma":[0.006255169,0.0004581546,0.0004237169,0.00021913303,0.00052856957,0.00094661245,0.0007820723,0.00049183576,0.0005008727],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017943839,0.033332635,0.15677197,0.0015267303,0.0003593617,0.0027805483,0.0038285682,0.0029769554,0.4716609,0.00089912256,0.0028495276,0.3050698],"study_design_scores_gemma":[0.0057525486,0.45897785,0.3549446,0.00022622563,0.0009085303,0.008179012,0.0029947741,0.07129315,0.08957793,0.0005892928,0.0061725117,0.0003835682],"about_ca_topic_score_codex":0.0016685677,"about_ca_topic_score_gemma":0.00232113,"teacher_disagreement_score":0.007077839,"about_ca_system_score_codex":0.00031607298,"about_ca_system_score_gemma":0.0009350548,"threshold_uncertainty_score":0.037431657},"labels":[],"label_agreement":null},{"id":"W4283312962","doi":"10.3390/s22134714","title":"Self-Sovereignty Identity Management Model for Smart Healthcare System","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Authentication Protocols Security","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Identity management; Identity (music); Computer security; Health care; Computer science; Digital identity; Identity theft; Internet privacy; Business; Access control; Law; Political science","score_opus":0.023210273227174557,"score_gpt":0.29002367507828075,"score_spread":0.2668134018511062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283312962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041458383,0.0005328881,0.92442214,0.002135453,0.00021022167,0.00031061814,0.0001539573,0.0007042226,0.030072019],"genre_scores_gemma":[0.9030447,0.00046678146,0.07798028,0.0003087021,0.00015111976,0.0003160836,0.00022782091,0.000037225942,0.017467162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738854,0.0006715622,0.0002292168,0.0005266366,0.00084527984,0.00033884484],"domain_scores_gemma":[0.9985378,0.00029959256,0.00020978936,0.0003361449,0.00046431815,0.00015235365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024639878,0.00034861205,0.00048308267,0.0006422991,0.001285194,0.0027180847,0.001674605,0.0014771431,0.0045109447],"category_scores_gemma":[0.0024817383,0.00022030326,0.0007031728,0.0006613778,0.001639471,0.005213939,0.0024028632,0.0014180817,0.0011930481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015735497,0.00012513898,0.0018501373,0.00014064199,0.00004553805,0.0005550356,0.0015294371,0.110438175,0.0061871945,0.8229537,0.0062655415,0.049751982],"study_design_scores_gemma":[0.000046090776,0.0001670725,0.0005532136,0.000041605326,0.000034910096,0.00038556496,0.00027916452,0.83766866,0.0026475242,0.1310693,0.027055832,0.00005103589],"about_ca_topic_score_codex":0.0035889102,"about_ca_topic_score_gemma":0.0019810842,"teacher_disagreement_score":0.0045109447,"about_ca_system_score_codex":0.0016674966,"about_ca_system_score_gemma":0.001942549,"threshold_uncertainty_score":0.015090585},"labels":[],"label_agreement":null},{"id":"W4283320436","doi":"10.3390/s22134701","title":"A Novel 65 nm Active-Inductor-Based VCO with Improved Q-Factor for 24 GHz Automotive Radar Applications","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Voltage-controlled oscillator; Inductor; Q factor; Automotive industry; Radar; Electrical engineering; Materials science; Electronic engineering; Engineering; Computer science; Automotive engineering; Aerospace engineering; Voltage; Resonator","score_opus":0.017887085700994853,"score_gpt":0.2163124327595505,"score_spread":0.19842534705855566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283320436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73083794,0.0035128575,0.23664162,0.0008700423,0.0007854029,0.00020687893,0.0004537916,0.0026868912,0.024004688],"genre_scores_gemma":[0.97249043,0.00032473737,0.02332061,0.00015299725,0.000067098656,0.000027355474,0.000085868625,0.000036173336,0.0034946536],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998708,0.0000097358525,0.0000069096154,0.000032854274,0.00005073606,0.000028974913],"domain_scores_gemma":[0.9999019,0.000014871626,0.000023367787,0.000008069008,0.000036975627,0.000014844972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013219706,0.00024688567,0.00024691413,0.00027017118,0.00020407993,0.0005180054,0.00069469534,0.00026109992,0.0010670506],"category_scores_gemma":[0.00020159167,0.00010981749,0.00018603144,0.00030938574,0.0001658815,0.00051869516,0.00021223698,0.00018681391,0.00029003693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012978946,0.00005252853,0.0012094293,0.00017214737,0.00003089893,0.000282537,0.00009282953,0.0022387607,0.9558593,0.002878466,0.001434372,0.035618898],"study_design_scores_gemma":[0.000077312216,0.000869983,0.002607439,0.00004009092,0.00013369057,0.001063932,0.00007268159,0.057376932,0.88684565,0.00068640057,0.050168075,0.000057854435],"about_ca_topic_score_codex":0.00058847666,"about_ca_topic_score_gemma":0.0016885037,"teacher_disagreement_score":0.0010670506,"about_ca_system_score_codex":0.00048886915,"about_ca_system_score_gemma":0.00027612524,"threshold_uncertainty_score":0.003569603},"labels":[],"label_agreement":null},{"id":"W4283364792","doi":"10.3390/s22134678","title":"Evaluation of a High-Sensitivity Organ-Targeted PET Camera","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sinai Health System; University of Toronto; University Health Network; Women's College Hospital; Thunder Bay Regional Research Institute; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society Research Institute","keywords":"Imaging phantom; Nuclear medicine; Image resolution; Positron emission tomography; Cardiac imaging; Medical imaging; Magnetic resonance imaging; Physics; Materials science; Biomedical engineering; Medicine; Optics; Radiology","score_opus":0.03026564754698066,"score_gpt":0.3226164853833163,"score_spread":0.29235083783633564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283364792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.852546,0.006024368,0.1352358,0.00016206798,0.00008840893,0.00047087084,0.00025212084,0.0009598681,0.0042605363],"genre_scores_gemma":[0.91246367,0.001960214,0.08182288,0.00019590011,0.000051186027,0.0001712877,0.00048035182,0.00012696143,0.0027275584],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984493,0.00067640643,0.00007632106,0.00022493533,0.00049750047,0.00007551474],"domain_scores_gemma":[0.9987913,0.00042110792,0.00013210511,0.00010305334,0.0004663685,0.000086009415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001904304,0.0006665981,0.0005358666,0.0004712458,0.000110601155,0.00087253883,0.0006326154,0.0007481834,0.0012088191],"category_scores_gemma":[0.0028726524,0.0002619812,0.00022533233,0.00029749208,0.00022997832,0.0004645178,0.00031089282,0.00020791065,0.00063322403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092767004,0.00014026689,0.00674699,0.00039287945,0.00008874406,0.00058212655,0.00006746221,0.0021522464,0.95041585,0.00014977454,0.0002722279,0.03806371],"study_design_scores_gemma":[0.00008416946,0.0066851075,0.046302523,0.00005923394,0.00043703304,0.009433655,0.00011027734,0.020918582,0.91024524,0.000053405933,0.0055873347,0.000083340994],"about_ca_topic_score_codex":0.00050965225,"about_ca_topic_score_gemma":0.00049553736,"teacher_disagreement_score":0.001904304,"about_ca_system_score_codex":0.00036210404,"about_ca_system_score_gemma":0.0003532449,"threshold_uncertainty_score":0.010071039},"labels":[],"label_agreement":null},{"id":"W4283639857","doi":"10.3390/s22134859","title":"Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Artificial intelligence; Computer science; Reliability (semiconductor); Process (computing); Residual neural network; Deep learning; Power grid; Grid; Pattern recognition (psychology); Power (physics); Layer (electronics); Mathematics","score_opus":0.007911045808173865,"score_gpt":0.21754762394959903,"score_spread":0.20963657814142517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283639857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22113113,0.0020588904,0.75608593,0.0010350363,0.0002737856,0.00014965146,0.002292829,0.0076326104,0.009340132],"genre_scores_gemma":[0.90909314,0.0004305791,0.07872671,0.00035697466,0.00004823269,0.00008556393,0.0031660607,0.000118154596,0.007974607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997398,0.0000496844,0.000014365234,0.000070557406,0.000071755785,0.00005370812],"domain_scores_gemma":[0.9996642,0.00011227771,0.000043404812,0.00004240229,0.00011690176,0.000020808287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052524207,0.00091855845,0.00048157692,0.0006312919,0.00025282736,0.00061606406,0.0012561195,0.0009255758,0.0021337962],"category_scores_gemma":[0.0013796292,0.00035988653,0.00057383126,0.0006398884,0.00043166257,0.0012186007,0.0007390826,0.0011969501,0.00046793118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046906303,0.00023426584,0.00277007,0.00014071206,0.00010269406,0.00022166374,0.00004576406,0.7061703,0.0064376555,0.0036337269,0.010881727,0.26889238],"study_design_scores_gemma":[0.0000033658107,0.000016348435,0.00020961491,0.000004310861,0.000004059718,0.000014571862,0.0000048485053,0.99745435,0.0009787169,0.0010510725,0.00025596557,0.000002751237],"about_ca_topic_score_codex":0.009516919,"about_ca_topic_score_gemma":0.010695171,"teacher_disagreement_score":0.009516919,"about_ca_system_score_codex":0.000865087,"about_ca_system_score_gemma":0.00093317684,"threshold_uncertainty_score":0.018923044},"labels":[],"label_agreement":null},{"id":"W4283643625","doi":"10.3390/s22134849","title":"Enabling Fog–Blockchain Computing for Autonomous-Vehicle-Parking System: A Solution to Reinforce IoT–Cloud Platform for Future Smart Parking","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Cloud computing; Computer science; Internet of Things; Computer security; Edge computing; Automation; Middleware (distributed applications); Cryptography; Embedded system; Computer network; Distributed computing; Engineering; Operating system","score_opus":0.021655753343219477,"score_gpt":0.25140582240553583,"score_spread":0.22975006906231635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283643625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37913844,0.0015932635,0.5627152,0.0016914271,0.0006705807,0.0008051529,0.0005370313,0.0047631427,0.048085857],"genre_scores_gemma":[0.9739509,0.00020173531,0.022139193,0.00016966672,0.000021408123,0.00007632585,0.00014081232,0.000026817408,0.0032732275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996722,0.000040814164,0.000013459261,0.000041725616,0.000110716406,0.00012106949],"domain_scores_gemma":[0.99973994,0.000024361165,0.000017594664,0.000060841117,0.00010218562,0.00005511376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000331435,0.00030870427,0.0003009734,0.000271264,0.00065015565,0.0007851263,0.00082799705,0.000632233,0.002823762],"category_scores_gemma":[0.00039725134,0.00013330518,0.00025006323,0.00031362483,0.00039980322,0.001655081,0.0013016205,0.00050844165,0.00069730176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00160801,0.000884007,0.016030785,0.0011510624,0.0002257459,0.0038827008,0.0014242268,0.13771713,0.30601436,0.1337792,0.041623633,0.35565916],"study_design_scores_gemma":[0.00012873944,0.0006764688,0.0032128145,0.000093948795,0.00007931165,0.0009023339,0.00048608184,0.7641278,0.11104336,0.02785284,0.09128578,0.00011051942],"about_ca_topic_score_codex":0.0027154246,"about_ca_topic_score_gemma":0.0037535392,"teacher_disagreement_score":0.002823762,"about_ca_system_score_codex":0.0005104571,"about_ca_system_score_gemma":0.0013642176,"threshold_uncertainty_score":0.009446442},"labels":[],"label_agreement":null},{"id":"W4283822379","doi":"10.3390/s22135029","title":"Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ciena (Canada); University of Ottawa","funders":"Ontario Centres of Excellence","keywords":"C-RAN; Computer science; Distributed computing; Heuristic; Solver; Energy consumption; Relocation; Radio access network; Architecture; Computer network; Base station; Artificial intelligence; Engineering; Operating system","score_opus":0.008205039265974374,"score_gpt":0.21579722835935322,"score_spread":0.20759218909337884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283822379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033123177,0.00027199584,0.96138686,0.00021871242,0.00005674632,0.00003860246,0.00001618719,0.0002334556,0.0046542506],"genre_scores_gemma":[0.92555237,0.00013911207,0.070790045,0.00015387147,0.000029996227,0.00008442581,0.00004218938,0.000045614983,0.0031623016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995784,0.00014412594,0.000016070384,0.000101604965,0.00007636075,0.00008334801],"domain_scores_gemma":[0.99915826,0.0005010907,0.00011913541,0.000040161583,0.000109604254,0.00007175215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008741361,0.00093190273,0.00085761177,0.0002923406,0.00037643543,0.00085153437,0.0010550945,0.00089220155,0.0012287678],"category_scores_gemma":[0.0017397013,0.00037662173,0.00037147733,0.00026417917,0.0007370808,0.00065883715,0.00079105946,0.00097471173,0.00018153022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006356784,0.000029625093,0.00043751285,0.000024393725,0.000020843003,0.000067225345,0.000024847179,0.9812268,0.001345386,0.003284075,0.00043328071,0.013042503],"study_design_scores_gemma":[0.0000042677075,0.000008931054,0.000027705917,0.0000019703732,0.000003005171,0.0000060702123,0.0000039959405,0.999094,0.00016792418,0.0005596474,0.0001208869,0.000001593078],"about_ca_topic_score_codex":0.0037501457,"about_ca_topic_score_gemma":0.004826246,"teacher_disagreement_score":0.0037501457,"about_ca_system_score_codex":0.00063573086,"about_ca_system_score_gemma":0.0010297941,"threshold_uncertainty_score":0.0074566603},"labels":[],"label_agreement":null},{"id":"W4284711554","doi":"10.3390/s22145103","title":"Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Qassim University","keywords":"Support vector machine; Computer science; Artificial intelligence; Machine learning; Deep learning; Algorithm; Pattern recognition (psychology)","score_opus":0.10604883473495169,"score_gpt":0.3702079943709413,"score_spread":0.2641591596359896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284711554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41861472,0.0010771316,0.5665634,0.00094765343,0.00020177053,0.0001557527,0.0014239468,0.00773654,0.0032790888],"genre_scores_gemma":[0.88111216,0.00029566744,0.113483414,0.00019265841,0.00005475688,0.00012801349,0.0020841425,0.00010786799,0.0025413514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997062,0.000050073304,0.000024744362,0.00011195386,0.00006415679,0.00004283483],"domain_scores_gemma":[0.99956185,0.0001927163,0.000041878993,0.00006470405,0.00011184016,0.000027053784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069554785,0.0008255824,0.00043346806,0.0008826904,0.00024986136,0.00060994324,0.0007069939,0.00072026055,0.0012827354],"category_scores_gemma":[0.0026723503,0.00021213136,0.0004403386,0.0006013497,0.00022786007,0.0006649801,0.00067116716,0.0007668209,0.0004647574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029995444,0.00027753768,0.015837101,0.00011740153,0.00010623878,0.00032249466,0.00012289819,0.27579993,0.012834526,0.0020145501,0.007886755,0.68438065],"study_design_scores_gemma":[0.000005783385,0.000029181714,0.0013172468,0.0000067464284,0.0000073321658,0.000025858079,0.000012250702,0.9938684,0.0033870467,0.0009338068,0.0004011562,0.0000052472433],"about_ca_topic_score_codex":0.0067576263,"about_ca_topic_score_gemma":0.007939154,"teacher_disagreement_score":0.0067576263,"about_ca_system_score_codex":0.00059363886,"about_ca_system_score_gemma":0.00083104504,"threshold_uncertainty_score":0.013436556},"labels":[],"label_agreement":null},{"id":"W4285005191","doi":"10.3390/s22145155","title":"Characterizing Seasonal Radial Growth Dynamics of Balsam Fir in a Cold Environment Using Continuous Dendrometric Data: A Case Study in a 12-Year Soil Warming Experiment","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; Ministère des Ressources naturelles et des Forêts; Université du Québec à Montréal","funders":"Ouranos; Mitacs; Ministère des Forêts, de la Faune et des Parcs","keywords":"Growing season; Evergreen; Phenology; Environmental science; Balsam; Seasonality; Boreal; Growth rate; Ecology; Atmospheric sciences; Biology; Horticulture; Mathematics; Geology","score_opus":0.039082128161422855,"score_gpt":0.2552340614521376,"score_spread":0.21615193329071475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285005191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99969137,0.000010605229,0.00018644994,0.000001705744,8.686635e-7,0.0000029511907,0.000062213876,0.0000041210624,0.000039662023],"genre_scores_gemma":[0.99900144,0.000016338961,0.00071135064,0.0000029021662,0.000001709731,0.000007888041,0.00020350995,0.0000023435382,0.000052652602],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998903,0.000028406293,0.0000067633337,0.000037883674,0.000019506666,0.000016991444],"domain_scores_gemma":[0.99960464,0.00012544809,0.0000627944,0.00006260115,0.00008627918,0.000058303405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005736989,0.00024860716,0.0003084442,0.00036053092,0.00032702295,0.0002251371,0.00024165315,0.00024638715,0.0001737299],"category_scores_gemma":[0.00042125533,0.000089591216,0.00029143895,0.0004047179,0.00023787624,0.00015600349,0.00015319881,0.00024919404,0.000048797665],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014895732,0.0011010487,0.6370216,0.00011801352,0.00019049132,0.000656924,0.00095467194,0.011640094,0.3239334,0.00016408793,0.00020790366,0.022522107],"study_design_scores_gemma":[0.000008176193,0.00041591,0.984185,0.0000028929003,0.000028671964,0.000071603696,0.00020701342,0.0067144386,0.008111183,0.000029906247,0.00021151737,0.000013705939],"about_ca_topic_score_codex":0.009729772,"about_ca_topic_score_gemma":0.024542376,"teacher_disagreement_score":0.009729772,"about_ca_system_score_codex":0.0002720187,"about_ca_system_score_gemma":0.00013596877,"threshold_uncertainty_score":0.019346297},"labels":[],"label_agreement":null},{"id":"W4285011763","doi":"10.3390/s22145157","title":"Design of an Integrated Micro-Viscometer for Monitoring Engine Oil","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"","keywords":"Viscometer; Engineering; Petroleum engineering; Automotive engineering; Environmental science; Materials science; Viscosity; Composite material","score_opus":0.0252979419997506,"score_gpt":0.24950675510138282,"score_spread":0.2242088131016322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285011763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09394661,0.0012109681,0.8930501,0.0003790772,0.00048466102,0.00048661805,0.00037054,0.0051195584,0.004951734],"genre_scores_gemma":[0.45580626,0.0005833242,0.5350309,0.00042589285,0.00015862162,0.00043051655,0.00034921378,0.00015903059,0.007056294],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991696,0.000058428624,0.000036080826,0.0002499972,0.00040585516,0.00008006217],"domain_scores_gemma":[0.9996215,0.00005116279,0.00006012925,0.000028403045,0.00020450779,0.000034227567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004681036,0.0007540711,0.0008016022,0.00061838323,0.000313792,0.0008406991,0.0027065573,0.0009777802,0.0015838637],"category_scores_gemma":[0.00051528093,0.00059952703,0.00035385665,0.0004092289,0.00025464466,0.00079654885,0.00047527088,0.00054286106,0.0011573882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026381598,0.00016858637,0.0021355567,0.00035347856,0.00007783087,0.00018751418,0.00010653722,0.0037543434,0.9092873,0.0024182613,0.0015142325,0.079732545],"study_design_scores_gemma":[0.00009828642,0.0012033161,0.0032784475,0.00003277594,0.00015411936,0.0008291119,0.000051237323,0.12279112,0.84707934,0.0003798352,0.02401405,0.0000884996],"about_ca_topic_score_codex":0.00080016785,"about_ca_topic_score_gemma":0.0013446792,"teacher_disagreement_score":0.0027065573,"about_ca_system_score_codex":0.00091663457,"about_ca_system_score_gemma":0.0010491862,"threshold_uncertainty_score":0.0066506267},"labels":[],"label_agreement":null},{"id":"W4285594658","doi":"10.3390/s22145264","title":"Built-In Packaging for Single Terminal Devices","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"CMC Microsystems; Natural Sciences and Engineering Research Council of Canada; Mitacs; Discovery Eye Foundation","keywords":"Terminal (telecommunication); Wire bonding; Interconnection; Electronic packaging; Actuator; Integrated circuit packaging; Chip; Electrical engineering; Electronic engineering; Engineering; Materials science; Telecommunications","score_opus":0.01739806563788683,"score_gpt":0.24300676679407937,"score_spread":0.22560870115619253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285594658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22225827,0.007468484,0.7304744,0.0006485174,0.0028967364,0.00031882443,0.000571964,0.00503074,0.03033213],"genre_scores_gemma":[0.6727114,0.0026695088,0.30834007,0.00033602637,0.00029568936,0.00027597786,0.0006650401,0.0006153537,0.014091015],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965715,0.000048399295,0.000020923197,0.00008002024,0.00015815008,0.000035308884],"domain_scores_gemma":[0.9995358,0.000056684145,0.00009529017,0.00018139495,0.000102386846,0.000028483502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025295347,0.0008421081,0.000470734,0.00032160236,0.000315683,0.0007556976,0.001183822,0.0006409248,0.0019737405],"category_scores_gemma":[0.00047248136,0.00035894496,0.0004274568,0.0002612461,0.00030539202,0.00087958394,0.00053553976,0.000765075,0.0016370041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008769296,0.000073434676,0.00038695123,0.0004625316,0.000035963214,0.0005300297,0.0001441763,0.00064811285,0.94559824,0.0061625787,0.0032980717,0.04257221],"study_design_scores_gemma":[0.000021162465,0.0006031179,0.0012053543,0.00004156129,0.000061076054,0.001994983,0.000029653072,0.005331402,0.94635445,0.0007871683,0.043528955,0.000041076615],"about_ca_topic_score_codex":0.000045302033,"about_ca_topic_score_gemma":0.00010020274,"teacher_disagreement_score":0.0019737405,"about_ca_system_score_codex":0.00018061223,"about_ca_system_score_gemma":0.00020235215,"threshold_uncertainty_score":0.0066028237},"labels":[],"label_agreement":null},{"id":"W4285794073","doi":"10.3390/s22145375","title":"Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Ran; Radio access network; Orchestration; C-RAN; Computer science; Virtualization; Cloud computing; Software deployment; Computer network; Base station; Operating system","score_opus":0.022107348654444994,"score_gpt":0.2785335052848206,"score_spread":0.25642615663037566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285794073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06258612,0.000938953,0.9270609,0.0010868325,0.0001505592,0.0000987559,0.00004254403,0.00020226075,0.007833131],"genre_scores_gemma":[0.91406256,0.00055733335,0.080966465,0.0003668717,0.00013008008,0.00024840346,0.00006296372,0.000038767263,0.003566578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888307,0.0005338154,0.000046445544,0.00023374976,0.00012658798,0.00017634736],"domain_scores_gemma":[0.9968227,0.0021111176,0.0003927815,0.00013704976,0.00022207368,0.00031424544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018976849,0.0008796355,0.0013163465,0.00031489154,0.0007682164,0.0016952334,0.0012604815,0.0015802333,0.0015278741],"category_scores_gemma":[0.0052932743,0.00040041617,0.0007234845,0.00029986896,0.0015208494,0.001210355,0.0022353842,0.0016677156,0.00022980166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006945868,0.000070687056,0.0009787979,0.00007556779,0.00007096175,0.00016471997,0.00014585798,0.95641786,0.00058018323,0.02757998,0.0006849875,0.013160969],"study_design_scores_gemma":[0.000021183288,0.000053513155,0.00010352843,0.000011396216,0.000008740057,0.00001642881,0.00003482941,0.984884,0.00013702452,0.014100157,0.00062237045,0.0000067753012],"about_ca_topic_score_codex":0.003954513,"about_ca_topic_score_gemma":0.0021181905,"teacher_disagreement_score":0.003954513,"about_ca_system_score_codex":0.0007533808,"about_ca_system_score_gemma":0.0012393213,"threshold_uncertainty_score":0.010035992},"labels":[],"label_agreement":null},{"id":"W4285802183","doi":"10.3390/s22145362","title":"Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Microwave; Resonator; Permittivity; Materials science; Methanol; Acetone; Convolutional neural network; Selectivity; Resonance (particle physics); Analytical Chemistry (journal); Optoelectronics; Nanotechnology; Computer science; Chemistry; Chromatography; Artificial intelligence; Telecommunications; Dielectric; Physics; Organic chemistry","score_opus":0.008538074657649799,"score_gpt":0.1952461417071325,"score_spread":0.1867080670494827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285802183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2179319,0.0009236312,0.77309966,0.0002082511,0.0001381299,0.00006538287,0.00017811796,0.0024027105,0.00505224],"genre_scores_gemma":[0.7868799,0.00035429248,0.20855111,0.00010723661,0.000026426183,0.000051880033,0.00023348631,0.000066083194,0.0037295197],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976546,0.000031129264,0.0000091726415,0.00007663529,0.00009073887,0.000026949047],"domain_scores_gemma":[0.999795,0.000068466914,0.000039015733,0.000028217994,0.000061410225,0.000007918179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002952962,0.0004725422,0.00030206487,0.00023849464,0.00008041439,0.00026927318,0.00055635767,0.00040618298,0.0009425167],"category_scores_gemma":[0.0005241553,0.00016990044,0.00024556863,0.00018403007,0.00019517439,0.0005133944,0.00026680253,0.0003878213,0.00050238386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016594122,0.00005809601,0.001405406,0.00015736882,0.00003185579,0.000085891865,0.00003462299,0.0143779265,0.8473713,0.0011772962,0.00084733916,0.13428694],"study_design_scores_gemma":[0.000010774188,0.00017273311,0.0021538537,0.000008512833,0.000026460242,0.00016434387,0.000014750156,0.49871075,0.49579713,0.0004284761,0.0024918062,0.000020391451],"about_ca_topic_score_codex":0.00060449156,"about_ca_topic_score_gemma":0.001368523,"teacher_disagreement_score":0.0009425167,"about_ca_system_score_codex":0.00029367817,"about_ca_system_score_gemma":0.00022103502,"threshold_uncertainty_score":0.0031530857},"labels":[],"label_agreement":null},{"id":"W4285803809","doi":"10.3390/s22145336","title":"Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Suite; Visualization; Robot; Bridging (networking); Computer science; Robotics; Rapid prototyping; Software; Middleware (distributed applications); Embedded system; Open source; Artificial intelligence; Computer hardware; Engineering; Operating system","score_opus":0.02926988209124496,"score_gpt":0.277258764789555,"score_spread":0.24798888269831007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285803809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012605169,0.0003895197,0.91085726,0.0003007877,0.0001261911,0.00028627858,0.00086373737,0.06957265,0.0049984064],"genre_scores_gemma":[0.16486332,0.00096904737,0.79393226,0.0010678192,0.00017013046,0.0010116762,0.004192799,0.023897706,0.009895205],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986456,0.00022180313,0.00010161727,0.0002931646,0.00061100605,0.00012685405],"domain_scores_gemma":[0.998331,0.0005110662,0.00018995954,0.0005090072,0.00028855941,0.00017039687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001767915,0.0013686403,0.00076628075,0.0011851997,0.00032022805,0.0011612782,0.0026217215,0.0010897751,0.011482986],"category_scores_gemma":[0.0036654356,0.001028861,0.0013817633,0.00041514597,0.00056267105,0.0014449746,0.0036005022,0.0011985461,0.006040035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014356319,0.00030284192,0.005850659,0.002137874,0.00037442768,0.0018697636,0.002423617,0.025229843,0.25139037,0.022385953,0.06713686,0.6194622],"study_design_scores_gemma":[0.00020034637,0.00084852584,0.010593982,0.00045671407,0.0002222083,0.0037801145,0.00038852548,0.14792188,0.38462207,0.010298856,0.44010475,0.0005620517],"about_ca_topic_score_codex":0.0008213569,"about_ca_topic_score_gemma":0.00095924555,"teacher_disagreement_score":0.011482986,"about_ca_system_score_codex":0.00038763497,"about_ca_system_score_gemma":0.00092544267,"threshold_uncertainty_score":0.03841442},"labels":[],"label_agreement":null},{"id":"W4285803959","doi":"10.3390/s22145320","title":"Power Line Communication and Sensing Using Time Series Forecasting","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Bergische Universität Wuppertal","keywords":"Power-line communication; Smart grid; Computer science; Electronic engineering; Gaussian; Real-time computing; Telecommunications network; Engineering; Power (physics); Data mining; Electrical engineering; Telecommunications","score_opus":0.022436341742302645,"score_gpt":0.22482951259095138,"score_spread":0.20239317084864875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285803959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071701735,0.00036040915,0.92368686,0.00040004656,0.000076001255,0.000043803513,0.00015343749,0.0010448361,0.0025329322],"genre_scores_gemma":[0.9043356,0.00043718398,0.09379379,0.000062660845,0.000073847375,0.00004520442,0.00025999654,0.000041993542,0.00094980694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996208,0.00009529317,0.000029470491,0.00009804301,0.0001291687,0.00002728264],"domain_scores_gemma":[0.9988863,0.000694061,0.00015755786,0.0000846908,0.0001508634,0.00002651595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078683096,0.00073024514,0.00046244528,0.00061716564,0.00031527967,0.00060904166,0.0005854724,0.0006037819,0.0006788394],"category_scores_gemma":[0.0033289618,0.00021533281,0.00038808628,0.0008223476,0.0002410484,0.0009411393,0.00032842017,0.000765266,0.00022105001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006803078,0.000056727906,0.0034474614,0.000036680925,0.000035394685,0.000085572,0.00003778713,0.88604414,0.0030576477,0.0030839515,0.00079367077,0.10325296],"study_design_scores_gemma":[0.0000010901207,0.0000060987245,0.0002087047,0.0000018443876,0.0000025674103,0.0000077388795,0.0000034271447,0.9985526,0.00044232263,0.00065164577,0.00011963754,0.0000021447427],"about_ca_topic_score_codex":0.0077206013,"about_ca_topic_score_gemma":0.0061183893,"teacher_disagreement_score":0.0077206013,"about_ca_system_score_codex":0.00048431396,"about_ca_system_score_gemma":0.0004139691,"threshold_uncertainty_score":0.0153512955},"labels":[],"label_agreement":null},{"id":"W4285803960","doi":"10.3390/s22145328","title":"Microsphere Coupled Off-Core Fiber Sensor for Ultrasound Sensing","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Ottawa","keywords":"Core (optical fiber); Microsphere; Materials science; Fiber; Acoustic sensor; Computer science; Engineering; Acoustics; Composite material; Physics","score_opus":0.012911192919720602,"score_gpt":0.22850761227915325,"score_spread":0.21559641935943266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285803960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83538043,0.0041493815,0.15416317,0.00032832182,0.0003748652,0.0001824821,0.0005901024,0.0016874841,0.0031438277],"genre_scores_gemma":[0.84734064,0.0006741998,0.1467249,0.00017943022,0.0000778372,0.000065742825,0.00021552789,0.000034517823,0.004687163],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995609,0.000036917674,0.000013715815,0.00011125284,0.00024068069,0.00003653928],"domain_scores_gemma":[0.99969196,0.000045401528,0.0001113839,0.000018191298,0.0000973316,0.000035640314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023835391,0.00050706207,0.00036149682,0.00032422374,0.00022945442,0.00032418573,0.00091642805,0.000904642,0.0007018904],"category_scores_gemma":[0.0002573377,0.0002589152,0.00018965844,0.00020527645,0.00031540083,0.0005541375,0.0003775888,0.00036818717,0.0004180954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025750838,0.0000122613765,0.00012005548,0.000028944023,0.0000030735607,0.000025467752,0.000010747442,0.00008242437,0.9969405,0.000108800275,0.000072623494,0.002569269],"study_design_scores_gemma":[0.000007692516,0.00026953075,0.0013703346,0.0000034189482,0.000012385438,0.00022236936,0.000012958171,0.00552024,0.99096805,0.000052935957,0.0015411106,0.00001904421],"about_ca_topic_score_codex":0.0017793026,"about_ca_topic_score_gemma":0.004943196,"teacher_disagreement_score":0.0017793026,"about_ca_system_score_codex":0.00081713276,"about_ca_system_score_gemma":0.00049782195,"threshold_uncertainty_score":0.005928755},"labels":[],"label_agreement":null},{"id":"W4285803968","doi":"10.3390/s22145330","title":"Torsional Low-Strain Test for Nondestructive Integrity Examination of Existing High-Pile Foundation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"China University of Geosciences; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Pile; Foundation (evidence); Structural engineering; Nondestructive testing; Superstructure; Engineering; Finite element method; Head (geology); Structural integrity; Geotechnical engineering; Geology; Physics","score_opus":0.02088746280442158,"score_gpt":0.2411483096402198,"score_spread":0.22026084683579822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285803968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15932804,0.0003475318,0.833888,0.00012787724,0.0000660299,0.0000914595,0.0001459222,0.0004922986,0.0055128057],"genre_scores_gemma":[0.8700491,0.0002829282,0.12645133,0.00005315835,0.00001412659,0.000070394504,0.00016556795,0.000021558237,0.0028918197],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994165,0.00013511068,0.00002470652,0.00004473722,0.00035023523,0.000028781922],"domain_scores_gemma":[0.99944204,0.00018993567,0.0000995178,0.00008020148,0.0001562112,0.000032170494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004097483,0.00039588398,0.00022586102,0.00062181376,0.00021230416,0.0003387331,0.00067252875,0.00047521156,0.0024721997],"category_scores_gemma":[0.0009927762,0.0001686645,0.00023600683,0.00034300788,0.00043690312,0.00046314806,0.00036071384,0.00032851755,0.0005970286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028051966,0.00015954213,0.00990716,0.00036572063,0.00002208085,0.00049393735,0.0001903898,0.03044344,0.7620689,0.0068732603,0.0012499888,0.187945],"study_design_scores_gemma":[0.000042830714,0.0009523389,0.018422836,0.000092146016,0.000056195524,0.001777915,0.00033669567,0.4478449,0.5209256,0.0026947202,0.0067343335,0.00011949272],"about_ca_topic_score_codex":0.00052156945,"about_ca_topic_score_gemma":0.0020912923,"teacher_disagreement_score":0.0024721997,"about_ca_system_score_codex":0.00022829826,"about_ca_system_score_gemma":0.0006683249,"threshold_uncertainty_score":0.008270323},"labels":[],"label_agreement":null},{"id":"W4286208732","doi":"10.3390/s22145413","title":"Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downtime; Fault (geology); Bearing (navigation); Computer science; Data mining; Fault coverage; Real-time computing; Reliability engineering; Engineering; Artificial intelligence","score_opus":0.02109747711845792,"score_gpt":0.27639029860493536,"score_spread":0.2552928214864774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286208732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015409905,0.00024601168,0.98241675,0.00017097918,0.000054443637,0.00005157629,0.000075678676,0.0005670496,0.0010075868],"genre_scores_gemma":[0.6953376,0.00032081065,0.29889628,0.0003381787,0.0001243938,0.0002202784,0.00057750184,0.00017057057,0.004014409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995493,0.00010722002,0.000025296336,0.00015177656,0.00011897275,0.00004749391],"domain_scores_gemma":[0.9991359,0.00048928155,0.00009353057,0.00006609642,0.00017351078,0.00004173116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000993921,0.0007873403,0.00063731667,0.0007090836,0.000319711,0.00060567696,0.001375475,0.00089203997,0.0015804041],"category_scores_gemma":[0.002596833,0.00035684847,0.00064565416,0.0003974267,0.000701554,0.00079207926,0.0010628201,0.0012001706,0.0003374749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010103432,0.000037279744,0.000864295,0.00005310073,0.00003645316,0.00009749857,0.000058534384,0.9065206,0.0036464985,0.004904449,0.00075490726,0.082925394],"study_design_scores_gemma":[0.0000015327585,0.000008950675,0.00005643336,0.0000017630354,0.000002658907,0.000009981029,0.0000021375229,0.99851876,0.0004060382,0.0008525398,0.00013734188,0.0000019364466],"about_ca_topic_score_codex":0.0063366,"about_ca_topic_score_gemma":0.0064016916,"teacher_disagreement_score":0.0063366,"about_ca_system_score_codex":0.00071742036,"about_ca_system_score_gemma":0.00082629104,"threshold_uncertainty_score":0.012599409},"labels":[],"label_agreement":null},{"id":"W4286208857","doi":"10.3390/s22145400","title":"Experimental Study and FEM Simulations for Detection of Rebars in Concrete Slabs by Coplanar Capacitive Sensing Technique","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Rebar; Capacitive sensing; Finite element method; Multiphysics; Slab; Materials science; Structural engineering; Composite material; Dielectric; Acoustics; Engineering; Electrical engineering; Optoelectronics; Physics","score_opus":0.009187409628856785,"score_gpt":0.23472080183557462,"score_spread":0.22553339220671784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286208857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92068666,0.00026616972,0.0750697,0.00008395506,0.000028810826,0.00003510937,0.00018506675,0.00033110578,0.0033133258],"genre_scores_gemma":[0.9753811,0.00014988045,0.023736184,0.000008912893,0.0000027682754,0.00002363643,0.0000480056,0.000010599889,0.00063892204],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984443,0.000020479141,0.000006472447,0.000030346557,0.00007932202,0.000018919782],"domain_scores_gemma":[0.99966717,0.00015966439,0.000051140738,0.000041923162,0.00006918681,0.000010891098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030050433,0.0004048248,0.00021779681,0.0003406904,0.00014379389,0.00017211423,0.00039226012,0.00052019785,0.0014555175],"category_scores_gemma":[0.000563721,0.00019586079,0.00025884117,0.0002793904,0.0003705457,0.00030219997,0.00015750428,0.00020059884,0.00018315637],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016366881,0.00012595828,0.003505337,0.00031633885,0.0000151469485,0.00039687086,0.00026190482,0.23552129,0.7408083,0.0011849212,0.00026090277,0.017439352],"study_design_scores_gemma":[0.00001665381,0.00029616474,0.005495658,0.000021635033,0.000015031465,0.00017054037,0.00014965804,0.78374887,0.20864294,0.00029899442,0.001117809,0.000026135434],"about_ca_topic_score_codex":0.0013028334,"about_ca_topic_score_gemma":0.0017227081,"teacher_disagreement_score":0.0014555175,"about_ca_system_score_codex":0.00023771844,"about_ca_system_score_gemma":0.00022309495,"threshold_uncertainty_score":0.004869163},"labels":[],"label_agreement":null},{"id":"W4286433333","doi":"10.3390/s22145462","title":"Gaze Estimation Approach Using Deep Differential Residual Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Residual; Gaze; Artificial intelligence; Computer science; Differential (mechanical device); Estimation; Computer vision; Machine learning; Engineering; Algorithm; Systems engineering","score_opus":0.02133739245612412,"score_gpt":0.24022423250689479,"score_spread":0.21888684005077066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286433333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14197132,0.0037174907,0.83611023,0.0009917881,0.0002525821,0.00013953565,0.0013535233,0.0070230393,0.0084405],"genre_scores_gemma":[0.8634295,0.00090833416,0.11835218,0.00036479428,0.00012368915,0.00009480357,0.0017869928,0.00020147275,0.0147382505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997466,0.000040303785,0.0000100417965,0.000105940846,0.000054304077,0.000042821608],"domain_scores_gemma":[0.99971,0.00006581107,0.0000323034,0.000042959848,0.00012875443,0.000020168143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038138643,0.0010903432,0.0006047215,0.0010009515,0.0003006086,0.000421177,0.0014052297,0.0007610335,0.0025901564],"category_scores_gemma":[0.0011704275,0.0003620716,0.0007519084,0.00050575536,0.00023715274,0.0009045545,0.0009099654,0.0009856513,0.000791716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006799965,0.00033864577,0.010144458,0.00021656559,0.00040445544,0.0004081549,0.0002653421,0.24337542,0.039530214,0.0046834433,0.017337656,0.6826157],"study_design_scores_gemma":[0.000013969017,0.000063072766,0.0016071541,0.000012177523,0.00003081523,0.00008046675,0.00002082245,0.99223435,0.0031990316,0.0016784273,0.0010503635,0.000009436463],"about_ca_topic_score_codex":0.023748843,"about_ca_topic_score_gemma":0.028628698,"teacher_disagreement_score":0.023748843,"about_ca_system_score_codex":0.0008812677,"about_ca_system_score_gemma":0.00069213624,"threshold_uncertainty_score":0.047221243},"labels":[],"label_agreement":null},{"id":"W4286434174","doi":"10.3390/s22145440","title":"Non-Destructive Testing Using Eddy Current Sensors for Defect Detection in Additively Manufactured Titanium and Stainless-Steel Parts","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Surface roughness; Titanium; SIGNAL (programming language); Surface finish; Coating; Eddy current; RADIUS; Layer (electronics); Composite material; Eddy-current testing; Metallurgy; Optics","score_opus":0.027938401211366204,"score_gpt":0.2612954156990532,"score_spread":0.23335701448768698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286434174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8689044,0.0015446686,0.12811336,0.0000863907,0.00007197131,0.00006379608,0.000069296424,0.00031112207,0.00083506695],"genre_scores_gemma":[0.94311076,0.000311858,0.055577595,0.00005355842,0.000012998731,0.000033270055,0.000049326824,0.00001701378,0.0008336848],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991159,0.00012956165,0.000036335965,0.00010985767,0.00057652715,0.00003177496],"domain_scores_gemma":[0.999032,0.00036102242,0.00022051438,0.00007654322,0.0002790076,0.000030867413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049434963,0.00048415727,0.00029474686,0.00053281605,0.000104716484,0.00027985562,0.0006058545,0.0005091257,0.00043358956],"category_scores_gemma":[0.001094201,0.0002120351,0.0002543644,0.00027665863,0.0003716041,0.00048109726,0.00027027912,0.00020948885,0.0001691088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077466066,0.000020968673,0.0012669944,0.00008346384,0.000009555128,0.000032477972,0.000052885032,0.00019454714,0.985371,0.00005845466,0.00003239216,0.012799937],"study_design_scores_gemma":[0.00001148577,0.0005399255,0.007953872,0.0000071152367,0.00002852902,0.00037299294,0.0000933138,0.005955717,0.9842763,0.00007974842,0.0006637939,0.000017225944],"about_ca_topic_score_codex":0.00021430604,"about_ca_topic_score_gemma":0.00061037036,"teacher_disagreement_score":0.0006058545,"about_ca_system_score_codex":0.00017899639,"about_ca_system_score_gemma":0.00013928473,"threshold_uncertainty_score":0.002614379},"labels":[],"label_agreement":null},{"id":"W4288053335","doi":"10.3390/s22155568","title":"Correction: Elsheikh et al. Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles. Sensors 2019, 19, 4896","year":2022,"lang":"en","type":"erratum","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Trusted Positioning (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Real-time computing","score_opus":0.009438835593064717,"score_gpt":0.2457548886690329,"score_spread":0.23631605307596817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288053335","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022979247,0.0010433245,0.0022398252,0.029516922,0.9504178,0.00003577845,0.008142686,0.001320311,0.0070536355],"genre_scores_gemma":[0.025782187,0.012487579,0.027039336,0.075981945,0.19472823,0.00049918075,0.037598792,0.011266083,0.6146166],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950984,0.00040258034,0.0006301937,0.00062296394,0.0029298344,0.00031609982],"domain_scores_gemma":[0.96212655,0.0032703187,0.0015065337,0.002311225,0.029890781,0.00089465413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030560177,0.003076114,0.0016303819,0.0044837,0.0035769953,0.0051644375,0.0029241478,0.0049603786,0.06271562],"category_scores_gemma":[0.05286674,0.0013289839,0.0015793414,0.004429614,0.0019639286,0.0033331143,0.0028761183,0.008849082,0.06695392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012917146,0.0000025330796,0.000038245274,0.000075245414,0.0000047234425,0.00004916653,0.000019992658,0.00003832085,0.000043255324,0.00036731188,0.9952366,0.0041117133],"study_design_scores_gemma":[0.000015833615,0.0000074438044,0.00047582688,0.00018697926,0.000019260844,0.0001508321,0.000069379945,0.00016337997,0.00036105185,0.0007622514,0.99775946,0.000028352975],"about_ca_topic_score_codex":0.045184754,"about_ca_topic_score_gemma":0.044763792,"teacher_disagreement_score":0.06271562,"about_ca_system_score_codex":0.0031444498,"about_ca_system_score_gemma":0.007166021,"threshold_uncertainty_score":0.20980465},"labels":[],"label_agreement":null},{"id":"W4288068997","doi":"10.3390/s22155588","title":"A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Kinematics; Artificial intelligence; Computer science; Engineering; Pattern recognition (psychology); Geodesy; Geology; Physics; Classical mechanics","score_opus":0.04168418981708233,"score_gpt":0.24500516247581408,"score_spread":0.20332097265873175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288068997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64444005,0.0022410525,0.3436125,0.0007214485,0.00021503611,0.00020012571,0.0012491721,0.0004519413,0.0068687247],"genre_scores_gemma":[0.9675838,0.00049927423,0.029628113,0.000029588986,0.000051314175,0.00004088365,0.0014046588,0.00003784597,0.00072449073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802446,0.0005652694,0.00016009886,0.0005208144,0.00055555016,0.00017378069],"domain_scores_gemma":[0.98444474,0.009958899,0.0014701043,0.0016066311,0.0020776545,0.0004419744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046134307,0.000857499,0.0006161815,0.004209865,0.0006152701,0.0023893076,0.0007189747,0.0009302261,0.0011951107],"category_scores_gemma":[0.022199191,0.00022025139,0.00083527487,0.0051064203,0.0013239552,0.0040663085,0.001019124,0.0015777105,0.00069081364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060398807,0.00036257482,0.21951278,0.00033328644,0.0002258379,0.0002967663,0.001007234,0.22849113,0.008395695,0.022957472,0.0039260876,0.5138871],"study_design_scores_gemma":[0.000014990508,0.0004358154,0.0839194,0.00010802671,0.00007972945,0.00033860802,0.00082454446,0.8896449,0.0050426787,0.015563511,0.0039503304,0.00007750862],"about_ca_topic_score_codex":0.0075909365,"about_ca_topic_score_gemma":0.0035961594,"teacher_disagreement_score":0.0075909365,"about_ca_system_score_codex":0.0012252079,"about_ca_system_score_gemma":0.0010468988,"threshold_uncertainty_score":0.024398446},"labels":[],"label_agreement":null},{"id":"W4288081273","doi":"10.3390/s22155627","title":"Usability Evaluation of the SmartWheeler through Qualitative and Quantitative Studies","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Usability; System usability scale; Qualitative research; Computer science; Engineering; Medical education; Applied psychology; Psychology; Human–computer interaction; Usability engineering; Medicine","score_opus":0.19544682755458279,"score_gpt":0.4282649563538975,"score_spread":0.2328181287993147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288081273","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975319,0.00068350387,0.0114983795,0.00052169623,0.000053500687,0.004826388,0.0005128308,0.00005805595,0.0065266653],"genre_scores_gemma":[0.97230566,0.0006707411,0.01561637,0.00028024236,0.000026322452,0.008702626,0.00026103554,0.000055882,0.0020811749],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9486392,0.036712404,0.003496991,0.001909498,0.007631396,0.0016105154],"domain_scores_gemma":[0.8378149,0.11665314,0.0058955615,0.0024923522,0.03569626,0.0014477461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06982157,0.0008081005,0.0012997908,0.004452254,0.0023230761,0.003563381,0.0011018562,0.000941647,0.0023178107],"category_scores_gemma":[0.089416735,0.00038765365,0.0009459168,0.002911788,0.0037430339,0.0025977956,0.0023696914,0.0009021762,0.00038951938],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008484698,0.0015702472,0.048993614,0.008909616,0.00016391292,0.0009297544,0.79053634,0.0008532736,0.010866402,0.0028292877,0.0022395384,0.13125953],"study_design_scores_gemma":[0.00019148385,0.005279081,0.074223995,0.004560452,0.00022464726,0.0005256882,0.8772475,0.0026822207,0.009882803,0.001959542,0.022996143,0.00022636495],"about_ca_topic_score_codex":0.0022444627,"about_ca_topic_score_gemma":0.0033804337,"teacher_disagreement_score":0.06982157,"about_ca_system_score_codex":0.004597903,"about_ca_system_score_gemma":0.0042786817,"threshold_uncertainty_score":0.36925614},"labels":[],"label_agreement":null},{"id":"W4288720071","doi":"10.3390/s22155659","title":"Plant Tissue Modelling Using Power-Law Filters","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Academy of Scientific Research and Technology","keywords":"Electrical impedance; Filter (signal processing); Power (physics); Dielectric spectroscopy; Equivalent circuit; Heuristic; Electronic engineering; Biological system; Computer science; Engineering; Electrical engineering; Voltage; Chemistry; Electrode; Electrochemistry; Physics; Artificial intelligence; Biology","score_opus":0.030473856898595057,"score_gpt":0.24876777781564605,"score_spread":0.218293920917051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288720071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04941055,0.0001587319,0.94694984,0.00004918185,0.000011252733,0.000036951897,0.000097235854,0.0004349903,0.0028512704],"genre_scores_gemma":[0.7738249,0.0005228615,0.21690843,0.000059815706,0.000010935832,0.00022751867,0.00030183414,0.00018794824,0.007955851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999882,0.00002896093,0.0000057389334,0.000034775563,0.000034055876,0.0000144950345],"domain_scores_gemma":[0.99977237,0.00012760467,0.000025484063,0.000022006527,0.000046871966,0.0000056583985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027415497,0.0005528643,0.00036353618,0.0004855826,0.00017693752,0.00068222097,0.00078830414,0.0010981696,0.0016419736],"category_scores_gemma":[0.0008041948,0.00024961637,0.0007833104,0.00045027287,0.00024718855,0.0006299669,0.0002245022,0.0004395005,0.00062012905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025511292,0.00002029876,0.00032446248,0.000050430484,0.000017889024,0.000048720947,0.00006962487,0.9580546,0.023405975,0.002290728,0.00015427297,0.015537527],"study_design_scores_gemma":[0.0000014776255,0.0000120409795,0.00010677035,0.0000026285184,0.0000040973623,0.000015768495,0.0000056738763,0.9962149,0.0025321515,0.0005367762,0.00056446076,0.0000031984937],"about_ca_topic_score_codex":0.0042162025,"about_ca_topic_score_gemma":0.0025850688,"teacher_disagreement_score":0.0042162025,"about_ca_system_score_codex":0.0005751306,"about_ca_system_score_gemma":0.00040518405,"threshold_uncertainty_score":0.008383334},"labels":[],"label_agreement":null},{"id":"W4289529023","doi":"10.3390/s22155733","title":"BEST—Blockchain-Enabled Secure and Trusted Public Emergency Services for Smart Cities Environment","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Jawaharlal Nehru University; Nottingham Trent University","keywords":"Computer security; Computer science; Smart city; Service (business); Service provider; Smart grid; Transparency (behavior); Context (archaeology); Message queue; Single point of failure; Computer network; Internet of Things; Engineering; Business","score_opus":0.012190972126094666,"score_gpt":0.21234816071895976,"score_spread":0.2001571885928651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07320661,0.0012976605,0.87826854,0.0025992135,0.00038979595,0.00060908124,0.000724861,0.0044053267,0.038498953],"genre_scores_gemma":[0.92723787,0.000703605,0.05726201,0.00022359015,0.000069664464,0.00021073452,0.00046915025,0.00013211776,0.013691243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981919,0.0005201469,0.00012798286,0.00022480827,0.0005509067,0.00038420883],"domain_scores_gemma":[0.998423,0.0003563201,0.00016957072,0.00043658877,0.00037636698,0.00023814147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013511286,0.00049673504,0.0006754562,0.00072727585,0.0017117604,0.0020179697,0.001326338,0.0011735595,0.0071939104],"category_scores_gemma":[0.0028135544,0.0003405798,0.00053091167,0.0008795035,0.0016565409,0.0037398234,0.0033785305,0.0012190709,0.0016317485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012263134,0.00028260172,0.0029537089,0.0005530612,0.00011286058,0.0022352417,0.0009742974,0.43159074,0.016092328,0.3718035,0.023408236,0.14876719],"study_design_scores_gemma":[0.00012373464,0.00012746167,0.00034014357,0.000072345574,0.000032162847,0.00037818193,0.00016818172,0.8336461,0.007559489,0.114161015,0.04333357,0.00005768486],"about_ca_topic_score_codex":0.006107239,"about_ca_topic_score_gemma":0.006810789,"teacher_disagreement_score":0.0071939104,"about_ca_system_score_codex":0.0015779997,"about_ca_system_score_gemma":0.003318407,"threshold_uncertainty_score":0.02406609},"labels":[],"label_agreement":null},{"id":"W4289529353","doi":"10.3390/s22155707","title":"UAV-Based Smart Educational Mechatronics System Using a MoCap Laboratory and Hardware-in-the-Loop","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Mechatronics Education and Applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Drone; Mechatronics; Waypoint; Process (computing); Engineering; Control (management); Computer science; Systems engineering; Simulation; Human–computer interaction; Artificial intelligence; Engineering management; Embedded system; Real-time computing","score_opus":0.01229242114594595,"score_gpt":0.23163970244938262,"score_spread":0.21934728130343667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22292407,0.0001452555,0.7118862,0.00023851282,0.000101244856,0.0011742106,0.0007606991,0.030818425,0.031951398],"genre_scores_gemma":[0.802927,0.00019157873,0.1743571,0.00023010044,0.00002808412,0.0008616255,0.0011942754,0.00033126312,0.01987909],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999814,0.0000380507,0.000014475911,0.00004788739,0.00005617772,0.000029584895],"domain_scores_gemma":[0.9996892,0.00007666336,0.000030348328,0.00005605266,0.000060917046,0.000086774344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032210528,0.00054636085,0.00033116434,0.00053439505,0.0002760026,0.00073472026,0.00089098583,0.000557164,0.01083942],"category_scores_gemma":[0.0005976426,0.0001904716,0.00036291635,0.00017778022,0.0003360491,0.0005439436,0.0011373889,0.00042992856,0.0019731065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018338165,0.0026143,0.013440648,0.0012842314,0.000200142,0.0022279692,0.001693048,0.15697964,0.4073029,0.023664024,0.010974605,0.37778473],"study_design_scores_gemma":[0.00059365213,0.0041796914,0.0207462,0.00025828433,0.00021726066,0.0014745615,0.00034405955,0.57301444,0.26920533,0.0043189153,0.12539007,0.0002574994],"about_ca_topic_score_codex":0.0012370534,"about_ca_topic_score_gemma":0.00093714095,"teacher_disagreement_score":0.01083942,"about_ca_system_score_codex":0.00038874222,"about_ca_system_score_gemma":0.0007354236,"threshold_uncertainty_score":0.0362615},"labels":[],"label_agreement":null},{"id":"W4289529413","doi":"10.3390/s22155722","title":"Optical Fiber Sensors for High-Temperature Monitoring: A Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Shandong University","keywords":"Optical fiber; Aerospace; Fiber optic sensor; Temperature measurement; Materials science; Electromagnetic interference; Multiplexing; Fiber; Interference (communication); Optoelectronics; Electrical engineering; Electronic engineering; Engineering; Aerospace engineering; Telecommunications; Composite material; Physics","score_opus":0.03787850395390171,"score_gpt":0.30732605999266255,"score_spread":0.26944755603876086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529413","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021495073,0.9969273,0.00045925676,0.00018906665,0.0003458393,0.000010925315,0.00003224167,0.000017059543,0.0018034165],"genre_scores_gemma":[0.0008764507,0.99698037,0.0005845677,0.00013160979,0.00018045955,0.000011448319,0.00004197465,0.000002882935,0.0011902105],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972993,0.000026594844,0.000026170994,0.00005553053,0.00013656751,0.000025191068],"domain_scores_gemma":[0.9996444,0.00013724844,0.00005998194,0.000013497654,0.00011812806,0.00002684575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064389146,0.0012480897,0.0011111781,0.0027148565,0.00038597308,0.0010446755,0.00095585716,0.0011594583,0.0053054797],"category_scores_gemma":[0.00064292474,0.0005049735,0.0006437878,0.00315676,0.0003374043,0.002181539,0.0006979027,0.0015529445,0.0037289646],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042392072,0.00014037664,0.00022623502,0.026192822,0.00007972625,0.00017406094,0.00007516751,0.00062066125,0.0084060235,0.0049547674,0.030709581,0.9283783],"study_design_scores_gemma":[0.0000054306133,0.0001019183,0.0004708676,0.0022087356,0.00008012524,0.00070634385,0.000043543856,0.00015947515,0.0016629616,0.0013085564,0.99322575,0.000026232912],"about_ca_topic_score_codex":0.0008944495,"about_ca_topic_score_gemma":0.0014833297,"teacher_disagreement_score":0.0053054797,"about_ca_system_score_codex":0.0003920773,"about_ca_system_score_gemma":0.0010490692,"threshold_uncertainty_score":0.017748535},"labels":[],"label_agreement":null},{"id":"W4289529569","doi":"10.3390/s22155710","title":"Gait Characteristics and Cognitive Function in Middle-Aged Adults with and without Type 2 Diabetes Mellitus: Data from ENBIND","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Meath Foundation","keywords":"Gait; Montreal Cognitive Assessment; Cognition; Physical medicine and rehabilitation; Dementia; Bayesian multivariate linear regression; Cognitive decline; Preferred walking speed; Psychology; Gait analysis; Effects of sleep deprivation on cognitive performance; Neuropsychology; Medicine; Linear regression; Cognitive impairment; Internal medicine; Psychiatry; Statistics; Mathematics; Disease","score_opus":0.038424432899046256,"score_gpt":0.30352098383497406,"score_spread":0.2650965509359278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99314606,0.00045743823,0.00010574302,0.000015916676,0.000005796016,0.000023287814,0.005546573,0.0000056630365,0.00069349393],"genre_scores_gemma":[0.985691,0.0004350337,0.00034450315,0.000042015366,0.000012078842,0.00006776033,0.012446873,0.0000053295644,0.00095532695],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99960476,0.0000642306,0.00009979919,0.00008299152,0.00011376131,0.000034398523],"domain_scores_gemma":[0.9992557,0.00011046509,0.0002415033,0.00007669072,0.00019928138,0.00011633506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007934758,0.0003768135,0.0004714226,0.0015918018,0.00024321616,0.00055793114,0.00036033953,0.0003251688,0.0010491128],"category_scores_gemma":[0.0015249664,0.00017784041,0.00047435617,0.0014011186,0.00011910623,0.00030601016,0.00064294005,0.00024541205,0.00047585976],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045335965,0.000061587416,0.9957178,0.000029285968,0.00010048947,0.000044570672,0.000065886765,0.00004273202,0.0001210905,0.0000073814977,0.00027295688,0.0030828277],"study_design_scores_gemma":[0.000011539566,0.00005480238,0.9994511,0.000005269668,0.000021013379,0.000050772207,0.00006631923,0.000044043474,0.000019802541,0.0000038718854,0.0002696084,0.0000018910273],"about_ca_topic_score_codex":0.017099136,"about_ca_topic_score_gemma":0.021297123,"teacher_disagreement_score":0.017099136,"about_ca_system_score_codex":0.00032271454,"about_ca_system_score_gemma":0.00019410133,"threshold_uncertainty_score":0.033999205},"labels":[],"label_agreement":null},{"id":"W4289529943","doi":"10.3390/s22155688","title":"RSOnet: An Image-Processing Framework for a Dual-Purpose Star Tracker as an Opportunistic Space Surveillance Sensor","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Dual (grammatical number); Computer vision; Star (game theory); Computer science; Artificial intelligence; Space (punctuation); Image sensor; Image processing; Image (mathematics); Embedded system; Computer graphics (images); Physics; Art; Operating system; Astrophysics","score_opus":0.0177578242151662,"score_gpt":0.27032248529718794,"score_spread":0.25256466108202175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0086817555,0.00007574294,0.98243827,0.00007627747,0.000036030444,0.00007674694,0.00035805104,0.0064820386,0.0017750297],"genre_scores_gemma":[0.21926245,0.00019614854,0.7713978,0.0001851048,0.00004655214,0.00021651389,0.0021439702,0.0007526108,0.005798798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983823,0.000014342679,0.0000063931648,0.00005035678,0.00006549117,0.00002512278],"domain_scores_gemma":[0.9998747,0.000018495695,0.000015214565,0.000022873335,0.000049514423,0.000019168374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031204292,0.0005390787,0.00036990936,0.00050729216,0.00025684794,0.00076840236,0.0014616478,0.0005524094,0.002720227],"category_scores_gemma":[0.0006617627,0.00030019335,0.00061155827,0.0003139901,0.00037172658,0.0010673307,0.000901845,0.0006826567,0.001118987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007372861,0.00043170256,0.005374847,0.00028495645,0.00031665736,0.0005971454,0.00037828257,0.2556486,0.13378437,0.03849247,0.03962523,0.5243284],"study_design_scores_gemma":[0.000011246705,0.000054826414,0.0007825937,0.0000073039932,0.000011724283,0.00007863549,0.00002247318,0.9748677,0.010362868,0.004622092,0.009160763,0.000017841083],"about_ca_topic_score_codex":0.0069478834,"about_ca_topic_score_gemma":0.0116832,"teacher_disagreement_score":0.0069478834,"about_ca_system_score_codex":0.0006258574,"about_ca_system_score_gemma":0.0006876811,"threshold_uncertainty_score":0.013814867},"labels":[],"label_agreement":null},{"id":"W4289529959","doi":"10.3390/s22155690","title":"Towards an Explainable Universal Feature Set for IoT Intrusion Detection","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Intrusion detection system; Internet of Things; Computer science; Feature selection; Classifier (UML); Artificial intelligence; Machine learning; Set (abstract data type); Intrusion; Data mining; Computer security","score_opus":0.013910506344800762,"score_gpt":0.23488535403467897,"score_spread":0.2209748476898782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289529959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06323793,0.00031057204,0.932358,0.0005620969,0.000046183854,0.00016297698,0.0011397778,0.001400551,0.0007818121],"genre_scores_gemma":[0.6047303,0.00022830158,0.3889493,0.00022284802,0.00008230944,0.00045271663,0.0043818755,0.000088437875,0.0008638802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981255,0.0006005916,0.00020566046,0.00047090446,0.00044348073,0.00015391434],"domain_scores_gemma":[0.99566793,0.0024054637,0.00047139492,0.00059250835,0.00075342564,0.00010913455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024943382,0.0012153479,0.00096957874,0.0030136195,0.0006282919,0.0013814904,0.0016430528,0.0010630949,0.0017773405],"category_scores_gemma":[0.009573197,0.0003661905,0.0016528307,0.0015093344,0.0007813598,0.0021522637,0.0016022093,0.0018404532,0.00034736932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006038535,0.0006319371,0.02498259,0.00051522075,0.0005750027,0.00076748704,0.0007697747,0.26489922,0.012653909,0.06413481,0.007959521,0.62150663],"study_design_scores_gemma":[0.000032131764,0.00011775784,0.003035157,0.000055500444,0.00007712972,0.000099283636,0.000100094294,0.95186585,0.0027770635,0.039371554,0.0024350036,0.000033460696],"about_ca_topic_score_codex":0.003918086,"about_ca_topic_score_gemma":0.0036080074,"teacher_disagreement_score":0.003918086,"about_ca_system_score_codex":0.0012578119,"about_ca_system_score_gemma":0.001449649,"threshold_uncertainty_score":0.013191462},"labels":[],"label_agreement":null},{"id":"W4289731515","doi":"10.3390/s22155808","title":"Sensor-Based Automated Detection of Electrosurgical Cautery States","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Thyroid and Parathyroid Surgery","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre; Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Engineering","score_opus":0.01014265705932743,"score_gpt":0.24932728041080904,"score_spread":0.2391846233514816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289731515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37030578,0.0012046682,0.61565894,0.00019770948,0.0002125712,0.00024981072,0.0010501038,0.0065129776,0.004607452],"genre_scores_gemma":[0.8756237,0.00038169872,0.12133484,0.00009504749,0.00003299497,0.00009717461,0.0004905904,0.00013665266,0.0018073384],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997067,0.000035292695,0.000024522376,0.000098179444,0.0001172086,0.000018030387],"domain_scores_gemma":[0.99917275,0.00028191006,0.00021285516,0.00008089956,0.00021609664,0.000035503814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025682125,0.00042408606,0.0002521093,0.0011432976,0.00012978469,0.0005026739,0.00038626458,0.00038759195,0.0012165084],"category_scores_gemma":[0.0015218729,0.00016651284,0.00018994258,0.0005015547,0.00016495751,0.00053532724,0.0003098696,0.00030344285,0.00051111],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056142674,0.00018259576,0.050040208,0.000500295,0.00012402475,0.00029154046,0.0002964653,0.011402969,0.42731014,0.00074974756,0.002545217,0.50599545],"study_design_scores_gemma":[0.00004707112,0.0005712537,0.15952219,0.00009115233,0.00010839225,0.0015333142,0.00031143444,0.44634572,0.3785907,0.0024153139,0.010343675,0.000119706114],"about_ca_topic_score_codex":0.00056397496,"about_ca_topic_score_gemma":0.0014500762,"teacher_disagreement_score":0.0012165084,"about_ca_system_score_codex":0.00022572627,"about_ca_system_score_gemma":0.00021126584,"threshold_uncertainty_score":0.0040696263},"labels":[],"label_agreement":null},{"id":"W4289861074","doi":"10.3390/s22155817","title":"SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Pooling; Feature (linguistics); Object detection; Artificial intelligence; FLOPS; Pyramid (geometry); Mode (computer interface); Algorithm; Pattern recognition (psychology); Focus (optics); Computer vision; Mathematics","score_opus":0.010425799147298265,"score_gpt":0.22884592122204844,"score_spread":0.21842012207475017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289861074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03462412,0.00037365739,0.9597425,0.000097210424,0.00007352973,0.00007109164,0.00010339394,0.0034952313,0.0014192042],"genre_scores_gemma":[0.43698126,0.0003054587,0.55682844,0.00021238675,0.00004769855,0.000117520685,0.0007582191,0.00022729646,0.0045216624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.000039598348,0.000019882837,0.0001231546,0.00018199698,0.00006308407],"domain_scores_gemma":[0.99970776,0.000056917033,0.000031631553,0.000049349324,0.00012818296,0.000026185446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007588859,0.00091842766,0.0008428636,0.0010433567,0.00041091398,0.00057552924,0.0017953815,0.0007384388,0.0020235076],"category_scores_gemma":[0.0011931225,0.000355348,0.00065640255,0.0006116947,0.0004407344,0.0018259656,0.0012337303,0.00079949945,0.0005639032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055354385,0.000149318,0.0028423555,0.00011248554,0.00010783061,0.000181907,0.00012667899,0.06795606,0.07918095,0.0055541373,0.0062807254,0.83695394],"study_design_scores_gemma":[0.00002306599,0.00013790451,0.0011571964,0.000007202048,0.000024239156,0.00014131721,0.000017088632,0.9694417,0.024726614,0.0014564072,0.002841669,0.000025574616],"about_ca_topic_score_codex":0.0074981228,"about_ca_topic_score_gemma":0.0066156466,"teacher_disagreement_score":0.0074981228,"about_ca_system_score_codex":0.0008215115,"about_ca_system_score_gemma":0.00093837525,"threshold_uncertainty_score":0.014908969},"labels":[],"label_agreement":null},{"id":"W4289861182","doi":"10.3390/s22155829","title":"Dark Current Modeling for a Polyimide—Amorphous Lead Oxide-Based Direct Conversion X-ray Detector","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"Mitacs","keywords":"Electric field; Amorphous solid; X-ray detector; Polyimide; Optoelectronics; Materials science; Direct current; Detector; Electrode; Photoconductivity; Analytical Chemistry (journal); Physics; Layer (electronics); Nanotechnology; Optics; Voltage; Chemistry; Crystallography","score_opus":0.021240307342539982,"score_gpt":0.2474960543689505,"score_spread":0.22625574702641052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289861182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33983213,0.0031847167,0.59835386,0.0012090168,0.00015101227,0.0004837852,0.0010757188,0.0019521735,0.05375758],"genre_scores_gemma":[0.9590385,0.0013878312,0.018265205,0.00015140256,0.000021477425,0.0002873834,0.00022713786,0.00010162633,0.02051942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998703,0.00001446083,0.0000056740296,0.00003561932,0.000055750326,0.000018072522],"domain_scores_gemma":[0.99985385,0.000061334824,0.000022201928,0.000009930851,0.00004593709,0.0000068549175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022802677,0.0005214178,0.00037964465,0.00039608649,0.0002854461,0.000508837,0.001402511,0.0010029642,0.0032460808],"category_scores_gemma":[0.00046343842,0.00029201858,0.00081029814,0.00028318135,0.000351418,0.0006672464,0.0002265877,0.0005530076,0.0009153133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015378655,0.000164542,0.0011805396,0.000364941,0.000046316756,0.00036722075,0.0002815434,0.87286335,0.09803185,0.014246548,0.0010206308,0.011278778],"study_design_scores_gemma":[0.0000039630922,0.000023569826,0.0001390431,0.0000065589325,0.000005941221,0.000026012056,0.00000848318,0.9952565,0.0037775687,0.000292859,0.00045610522,0.0000033741826],"about_ca_topic_score_codex":0.009315199,"about_ca_topic_score_gemma":0.005581881,"teacher_disagreement_score":0.009315199,"about_ca_system_score_codex":0.0014902565,"about_ca_system_score_gemma":0.0007316676,"threshold_uncertainty_score":0.018521965},"labels":[],"label_agreement":null},{"id":"W4290755266","doi":"10.3390/s22155890","title":"Direct Conversion X-ray Detector with Micron-Scale Pixel Pitch for Edge-Illumination and Propagation-Based X-ray Phase-Contrast Imaging","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Phase contrast microscopy; Optics; Detector; X-ray; Pixel; X-Ray Phase-Contrast Imaging; Phase-contrast imaging; Contrast (vision); X-ray detector; Phase (matter); Enhanced Data Rates for GSM Evolution; Scale (ratio); Materials science; Physics; Computer science; Computer vision","score_opus":0.005377762964220944,"score_gpt":0.23852516968550172,"score_spread":0.2331474067212808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290755266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45897302,0.0023476747,0.5234526,0.0002802292,0.0001461528,0.00021467588,0.00030011783,0.0018861074,0.012399405],"genre_scores_gemma":[0.619945,0.00039567644,0.3759456,0.00010111431,0.000009696163,0.00006246213,0.00013809727,0.00009020866,0.0033122506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974793,0.000021938913,0.000009039477,0.000053112508,0.00014956597,0.000018334513],"domain_scores_gemma":[0.9996257,0.0001576984,0.000067883724,0.000059161943,0.0000735023,0.00001598536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003979537,0.0002872145,0.00023289303,0.0001769043,0.00010603724,0.00069066125,0.0007775638,0.000398014,0.0022250323],"category_scores_gemma":[0.00079093064,0.000260737,0.00017990582,0.00022289359,0.00024059537,0.0007982879,0.00041008336,0.00047283462,0.00046931504],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030270143,0.00020354144,0.0047278,0.0004272733,0.00004929947,0.00022096427,0.00013839042,0.012040064,0.9147172,0.008295013,0.00090507686,0.05797264],"study_design_scores_gemma":[0.000028043654,0.00040298168,0.0031056923,0.000024105522,0.000040021394,0.00072550535,0.00006086063,0.09872231,0.8867848,0.0006169581,0.009456132,0.00003258952],"about_ca_topic_score_codex":0.00034031284,"about_ca_topic_score_gemma":0.0008410961,"teacher_disagreement_score":0.0022250323,"about_ca_system_score_codex":0.00047887553,"about_ca_system_score_gemma":0.00036443316,"threshold_uncertainty_score":0.0074434876},"labels":[],"label_agreement":null},{"id":"W4290755476","doi":"10.3390/s22155904","title":"Towards Real-Time Monitoring of Thermal Peaks in Systems-on-Chip (SoC)","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"3D IC and TSV technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chip; Field-programmable gate array; Thermal; Integrated circuit; System on a chip; Gate array; Computer science; Electronic circuit; Electronic engineering; Computer hardware; Materials science; Embedded system; Electrical engineering; Engineering; Optoelectronics; Physics; Telecommunications","score_opus":0.013689441356872464,"score_gpt":0.2168812680898269,"score_spread":0.20319182673295444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290755476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18110763,0.0021598523,0.80682904,0.00019084338,0.00021481524,0.0000784575,0.00026860106,0.00479072,0.0043600034],"genre_scores_gemma":[0.76691943,0.0005939802,0.22964165,0.000103656166,0.000047957717,0.00006174706,0.0001341147,0.00018085078,0.0023166004],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954754,0.00006576581,0.000013768919,0.00008989464,0.00025346558,0.000029470242],"domain_scores_gemma":[0.99949884,0.00012257084,0.000102142665,0.00007189883,0.0001842888,0.000020238149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034550033,0.0006020462,0.00026983445,0.00069630926,0.00012016066,0.0005311506,0.00045432674,0.00042393446,0.0009619091],"category_scores_gemma":[0.0007680422,0.00018417893,0.00012582874,0.00045148513,0.00025228556,0.000573381,0.0002854499,0.00042709368,0.00036393185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020878346,0.00005986015,0.002859807,0.00032563764,0.00003656077,0.00013249689,0.0001868313,0.0071522635,0.8014546,0.0016345951,0.0014458052,0.18450277],"study_design_scores_gemma":[0.000020117819,0.0005207527,0.0063774963,0.00003693943,0.0000422103,0.00047397934,0.00010474364,0.13521758,0.8463315,0.00095564144,0.009866785,0.000052423067],"about_ca_topic_score_codex":0.00032740415,"about_ca_topic_score_gemma":0.0006878853,"teacher_disagreement_score":0.0009619091,"about_ca_system_score_codex":0.00022687585,"about_ca_system_score_gemma":0.00019677465,"threshold_uncertainty_score":0.003217876},"labels":[],"label_agreement":null},{"id":"W4291163808","doi":"10.3390/s22165998","title":"Comparative Analysis of Multilayer Lead Oxide-Based X-ray Detector Prototypes","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Innovációs és Technológiai Minisztérium","keywords":"Lead oxide; Detector; X-ray detector; Materials science; Crystallite; Oxide; Amorphous solid; Optoelectronics; X-ray; Bilayer; Optics; Physics; Chemistry; Crystallography","score_opus":0.022818329244008614,"score_gpt":0.2600331168039566,"score_spread":0.23721478755994796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291163808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99469614,0.0005179073,0.0034038625,0.000029654913,0.000019269346,0.000041266274,0.00024462203,0.0001164189,0.00093069376],"genre_scores_gemma":[0.98805493,0.0005531741,0.009565931,0.00001628647,0.0000049998475,0.000044889333,0.00037959288,0.000036984584,0.0013432477],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969196,0.000031755735,0.000025178824,0.000058003126,0.00014214515,0.000050936505],"domain_scores_gemma":[0.9995208,0.00010925073,0.00007858835,0.00006919423,0.0001775931,0.000044579396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003120551,0.00037325115,0.00036768417,0.00039922338,0.00020328678,0.0004568911,0.00073117967,0.00046306947,0.001299521],"category_scores_gemma":[0.0009092725,0.0002385492,0.00024222683,0.00031797166,0.0001507208,0.00046219825,0.00029509692,0.00021614677,0.0002604448],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027587477,0.00006119747,0.0013394663,0.00017230264,0.00002675825,0.00012304181,0.00009798475,0.0010396502,0.99118596,0.00013522319,0.00014479576,0.005397795],"study_design_scores_gemma":[0.000022120177,0.0011855989,0.0071779448,0.000017001814,0.00006808383,0.00027737286,0.00011510549,0.006585501,0.9820515,0.00003063191,0.0024511337,0.000017971217],"about_ca_topic_score_codex":0.0010270175,"about_ca_topic_score_gemma":0.0014568474,"teacher_disagreement_score":0.001299521,"about_ca_system_score_codex":0.00038371203,"about_ca_system_score_gemma":0.00019580123,"threshold_uncertainty_score":0.004347384},"labels":[],"label_agreement":null},{"id":"W4291825014","doi":"10.3390/s22166063","title":"A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Ocean Frontier Institute; Dalhousie University","keywords":"Trajectory; Leverage (statistics); Computer science; Benchmark (surveying); Artificial neural network; Data stream mining; Artificial intelligence; Data mining; Pattern recognition (psychology); Geography","score_opus":0.06424361309320115,"score_gpt":0.2821787112488314,"score_spread":0.21793509815563028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291825014","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02965115,0.00006756779,0.96753734,0.000073201445,0.000029455357,0.000119038625,0.00038665553,0.0015161625,0.0006194432],"genre_scores_gemma":[0.4379237,0.00008232822,0.55567425,0.000088198016,0.00007503079,0.00041272285,0.0029866896,0.00019166032,0.0025654237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987826,0.00027535693,0.00009973013,0.00052965747,0.0002230642,0.00008959818],"domain_scores_gemma":[0.9977422,0.0006721807,0.00042039086,0.00043399574,0.0006510837,0.00008028926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010642288,0.001105654,0.0007751993,0.001333784,0.000541883,0.0006710104,0.0016776419,0.0007333401,0.0009447136],"category_scores_gemma":[0.0028321499,0.00038020682,0.0010400089,0.0009562169,0.0005962381,0.0008210032,0.0009314575,0.0012042967,0.0009165508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003290404,0.00064716965,0.01367056,0.00030488867,0.000290055,0.00021463938,0.0004792343,0.20721813,0.044727385,0.004437547,0.0065739024,0.7211075],"study_design_scores_gemma":[0.000008371121,0.00008161238,0.0028319182,0.000013772194,0.000016616714,0.00007554767,0.000059739472,0.98496675,0.008053689,0.0024226373,0.0014503126,0.00001907781],"about_ca_topic_score_codex":0.0043683755,"about_ca_topic_score_gemma":0.009748952,"teacher_disagreement_score":0.0043683755,"about_ca_system_score_codex":0.0005489366,"about_ca_system_score_gemma":0.0013146488,"threshold_uncertainty_score":0.008685887},"labels":[],"label_agreement":null},{"id":"W4291825156","doi":"10.3390/s22166050","title":"Dual Stream Transmission and Downlink Power Control for Multiple LEO Satellites-Assisted IoT Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Satellite Communication Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Key Research and Development Program of China","keywords":"Telecommunications link; Computer science; Offset (computer science); Data transmission; Transmission (telecommunications); Real-time computing; Internet of Things; Power control; Frequency offset; Data stream; Computer network; Synchronization (alternating current); Power (physics); Telecommunications; Orthogonal frequency-division multiplexing; Embedded system; Channel (broadcasting)","score_opus":0.015993128425327966,"score_gpt":0.2242983752684406,"score_spread":0.20830524684311263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291825156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16707483,0.0010187194,0.82427055,0.0002890969,0.00013699761,0.00004857962,0.000025651028,0.00018390389,0.0069516287],"genre_scores_gemma":[0.99083406,0.00019166204,0.008155096,0.000024966055,0.000017857381,0.000016047941,0.000009712076,0.0000040745626,0.0007466742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984,0.00003561491,0.000009091581,0.000034769197,0.00006014744,0.00002031821],"domain_scores_gemma":[0.99985266,0.0000432038,0.000034101224,0.000012327734,0.000047504152,0.000010164829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002350495,0.0002619819,0.00018143124,0.00017324158,0.00040755,0.0003833018,0.0003605287,0.0001874014,0.000634275],"category_scores_gemma":[0.00038253455,0.0000994641,0.00014933916,0.00020773252,0.0002858516,0.00050063117,0.00032890897,0.00031432498,0.00007608513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006341108,0.00020159553,0.005745171,0.00032751437,0.00008773859,0.000833668,0.0005556774,0.5186619,0.12583117,0.033311903,0.0036922086,0.3101173],"study_design_scores_gemma":[0.000012850492,0.000108572494,0.00038675097,0.000006349956,0.000014282511,0.00010906659,0.000044767366,0.989646,0.007150334,0.0014102748,0.0011027958,0.000007870999],"about_ca_topic_score_codex":0.0013213728,"about_ca_topic_score_gemma":0.0018643794,"teacher_disagreement_score":0.0013213728,"about_ca_system_score_codex":0.0003322842,"about_ca_system_score_gemma":0.00027125934,"threshold_uncertainty_score":0.0026273727},"labels":[],"label_agreement":null},{"id":"W4292261075","doi":"10.3390/s22166139","title":"Reduction of Signal Drift in a Wavelength Modulation Spectroscopy-Based Methane Flux Sensor","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Methane; SIGNAL (programming language); Wavelength; Modulation (music); Casing; Flux (metallurgy); Materials science; Environmental science; Chemistry; Mechanics; Physics; Acoustics; Optics; Computer science","score_opus":0.011663470885493679,"score_gpt":0.25598227407608953,"score_spread":0.24431880319059585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292261075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76102084,0.0005888465,0.23598239,0.00029633837,0.00014542624,0.00006691958,0.00010465224,0.0007070108,0.0010875735],"genre_scores_gemma":[0.826291,0.00030541627,0.17135896,0.00029848702,0.00005320038,0.00005016408,0.00014897936,0.000075371805,0.0014184292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990496,0.00011801669,0.00004271624,0.00020414803,0.00053556997,0.00004989412],"domain_scores_gemma":[0.99929786,0.00018950464,0.00014325204,0.000073238436,0.00027214497,0.000023845529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009886294,0.00045616302,0.00033337125,0.00041770897,0.00019622192,0.00036015426,0.0009075034,0.0007304811,0.00029015366],"category_scores_gemma":[0.0015823632,0.00026920848,0.00026190325,0.00027784257,0.00034793472,0.00070545333,0.0004464639,0.0005172848,0.00015615097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011058249,0.000034615307,0.0009332013,0.000048881702,0.000010273822,0.000041538213,0.000058676786,0.0015411264,0.9796647,0.00025674468,0.00010816557,0.017191565],"study_design_scores_gemma":[0.000011883249,0.00031620215,0.0021310514,0.000005497654,0.000015404134,0.00013488086,0.000017836437,0.036906265,0.95885617,0.000091466754,0.0014865216,0.00002676328],"about_ca_topic_score_codex":0.0007775449,"about_ca_topic_score_gemma":0.0012207888,"teacher_disagreement_score":0.0009886294,"about_ca_system_score_codex":0.0006364577,"about_ca_system_score_gemma":0.0003649359,"threshold_uncertainty_score":0.00522846},"labels":[],"label_agreement":null},{"id":"W4292263417","doi":"10.3390/s22166117","title":"Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Particle swarm optimization; Denial-of-service attack; Computer science; Computer security; The Internet; Hacker; Internet of Things; Ant colony optimization algorithms; Internet Protocol; Computer network; Artificial intelligence; World Wide Web; Machine learning","score_opus":0.013233826107215208,"score_gpt":0.22643984516318538,"score_spread":0.21320601905597017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292263417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14971788,0.00072910124,0.8414316,0.0005642198,0.00012112477,0.000118503885,0.000023901202,0.00044641554,0.006847225],"genre_scores_gemma":[0.92413133,0.00033403328,0.07430349,0.000057631452,0.000023684448,0.000058387923,0.000023357117,0.000015183742,0.0010529534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974495,0.000092038455,0.0000148592835,0.000036512673,0.00008018624,0.000031405907],"domain_scores_gemma":[0.99939907,0.0003434541,0.00012286581,0.000039480332,0.000074795425,0.000020322306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000778876,0.000619813,0.0006996478,0.0006942122,0.00043920943,0.0007117725,0.0003620395,0.00063291367,0.00035067287],"category_scores_gemma":[0.0015271504,0.0002541891,0.0006014096,0.00036156818,0.0005342507,0.00069426576,0.00048419854,0.00052583264,0.000059712063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003740651,0.000067638386,0.0014797609,0.00004156491,0.00006785676,0.00005558001,0.000036713307,0.96471524,0.0022549033,0.0036468988,0.00035828014,0.027238164],"study_design_scores_gemma":[0.0000055954797,0.000036029163,0.00030499193,0.000004370241,0.0000093019635,0.000013147365,0.000011378107,0.9979862,0.0006337025,0.00081241026,0.00017973465,0.000003118059],"about_ca_topic_score_codex":0.0027659435,"about_ca_topic_score_gemma":0.0018845415,"teacher_disagreement_score":0.0027659435,"about_ca_system_score_codex":0.0003704989,"about_ca_system_score_gemma":0.000571225,"threshold_uncertainty_score":0.0054997206},"labels":[],"label_agreement":null},{"id":"W4292506131","doi":"10.3390/s22165986","title":"A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":256,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; Computer science; Particle swarm optimization; Intrusion detection system; Random forest; Support vector machine; Data mining; Artificial intelligence; Benchmark (surveying); Machine learning; Fitness function; Genetic algorithm; Artificial neural network","score_opus":0.015044665010429718,"score_gpt":0.24725175567039961,"score_spread":0.2322070906599699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292506131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037954472,0.000842533,0.9569662,0.00028398726,0.000105016996,0.000068378824,0.00013876236,0.0007147519,0.0029258833],"genre_scores_gemma":[0.77021736,0.0008181033,0.22168595,0.00021786147,0.00009759141,0.00027215006,0.00054951845,0.000078209196,0.006063236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947184,0.00011350886,0.000032344746,0.0001503206,0.00014579942,0.000086173146],"domain_scores_gemma":[0.99954057,0.00020072178,0.00005866062,0.000024034285,0.00015477804,0.000021317415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008446501,0.0008602906,0.0013736698,0.0011160696,0.00041067041,0.00092785654,0.0016414133,0.0010386014,0.0013155544],"category_scores_gemma":[0.0012553403,0.00043999078,0.0014597867,0.0009496685,0.00033910666,0.0010926812,0.00047478548,0.0007521764,0.00033737728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006794809,0.00005837744,0.0021560986,0.00005426539,0.000074293755,0.00009731564,0.00003289448,0.9293695,0.0012714118,0.0022518048,0.001155415,0.06341068],"study_design_scores_gemma":[0.0000037800608,0.000010701559,0.00011188863,0.0000022982,0.0000061793044,0.00001582003,0.0000019134504,0.9992286,0.00010235177,0.00036476954,0.00014904328,0.000002677692],"about_ca_topic_score_codex":0.013955494,"about_ca_topic_score_gemma":0.008144442,"teacher_disagreement_score":0.013955494,"about_ca_system_score_codex":0.0005557026,"about_ca_system_score_gemma":0.0009621699,"threshold_uncertainty_score":0.027748525},"labels":[],"label_agreement":null},{"id":"W4292865274","doi":"10.3390/s22176350","title":"Hydration Assessment Using the Bio-Impedance Analysis Method","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Qatar National Library; Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Wearable computer; Electrical impedance; Computer science; Wearable technology; Electronics; Biomedical engineering; Electronic engineering; Engineering; Embedded system; Electrical engineering","score_opus":0.05027310803553311,"score_gpt":0.39117314665738956,"score_spread":0.34090003862185647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292865274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13655274,0.0032247033,0.8534996,0.00027928964,0.00024587032,0.0002850693,0.0005483328,0.001093152,0.0042712046],"genre_scores_gemma":[0.8027367,0.0034309658,0.18942927,0.00016739883,0.00008726687,0.00027443652,0.00031492402,0.000058336973,0.0035007948],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992379,0.00018803729,0.000049750943,0.00014683949,0.00035033902,0.000027074619],"domain_scores_gemma":[0.99972147,0.00008926565,0.000053982683,0.000030833708,0.00009317654,0.000011283466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038128227,0.0005833049,0.0005507573,0.00079936034,0.00015933659,0.0007332143,0.0005109008,0.00081483624,0.0013286498],"category_scores_gemma":[0.0012663997,0.00016949736,0.0004440702,0.0006816783,0.00024323633,0.00085624494,0.00049159676,0.00034799596,0.00066185894],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005676152,0.00016585884,0.010838118,0.0010981745,0.00017134685,0.00032885262,0.0003060046,0.011209291,0.7096075,0.0015565411,0.0016218516,0.26252872],"study_design_scores_gemma":[0.00011027552,0.0020642802,0.04895055,0.00028152106,0.00036978276,0.0034750733,0.0006310456,0.32923567,0.5896042,0.0036280055,0.021360202,0.00028940503],"about_ca_topic_score_codex":0.00040936682,"about_ca_topic_score_gemma":0.0005285129,"teacher_disagreement_score":0.0013286498,"about_ca_system_score_codex":0.00018107885,"about_ca_system_score_gemma":0.00020820287,"threshold_uncertainty_score":0.004444778},"labels":[],"label_agreement":null},{"id":"W4292972440","doi":"10.3390/s22166272","title":"Towards the Objective Identification of the Presence of Pain Based on Electroencephalography Signals’ Analysis: A Proof-of-Concept","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Electroencephalography; Fibromyalgia; Physical medicine and rehabilitation; Physical therapy; Medicine; Chronic pain; Audiology; Psychology; Neuroscience","score_opus":0.012029043153304953,"score_gpt":0.24349464653038816,"score_spread":0.23146560337708322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292972440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.514064,0.009737044,0.46262383,0.0018011935,0.0011647182,0.0030069905,0.0010660727,0.0010921328,0.005443902],"genre_scores_gemma":[0.74211115,0.006039583,0.2399189,0.0014762369,0.0003694924,0.003343394,0.00068231934,0.000099123565,0.005959684],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991173,0.00019344782,0.000045172066,0.00018394494,0.0003697,0.00009050613],"domain_scores_gemma":[0.99900347,0.00037134363,0.00021529262,0.00005322456,0.00024960452,0.00010699736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016432357,0.0009667967,0.0008140509,0.0003114084,0.0001568809,0.00089926814,0.0010646741,0.0012760989,0.0014828305],"category_scores_gemma":[0.0019966708,0.00038909275,0.0005041348,0.00015704884,0.0005023451,0.0010602362,0.00072333426,0.001260203,0.0007949224],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041813642,0.0002766052,0.00023844198,0.0007894928,0.000051168376,0.00027041134,0.00006587924,0.00030017138,0.97978276,0.00029243794,0.0006664707,0.016847966],"study_design_scores_gemma":[0.00036362628,0.009594573,0.004116261,0.00013976285,0.00017012082,0.0024937592,0.00014194295,0.008825863,0.95855474,0.0005448109,0.014969619,0.000084871404],"about_ca_topic_score_codex":0.00011626862,"about_ca_topic_score_gemma":0.00016185587,"teacher_disagreement_score":0.0016432357,"about_ca_system_score_codex":0.00015338445,"about_ca_system_score_gemma":0.0003940409,"threshold_uncertainty_score":0.008690417},"labels":[],"label_agreement":null},{"id":"W4293065158","doi":"10.3390/s22166135","title":"Circular Optical Phased Array with Large Steering Range and High Resolution","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Optiwave Systems (Canada); Carleton University","funders":"Agentúra na Podporu Výskumu a Vývoja","keywords":"Beam steering; Phased array; Phased-array optics; Broadband; Photonics; Optics; Wavelength; Optical communication; Beam (structure); Free-space optical communication; Materials science; Optoelectronics; Physics; Engineering; Telecommunications; Antenna (radio)","score_opus":0.006762710693312983,"score_gpt":0.19089989097323903,"score_spread":0.18413718027992604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293065158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63112485,0.0013645322,0.34226885,0.0003139784,0.00012379704,0.00007242177,0.0001594514,0.0007099318,0.023862183],"genre_scores_gemma":[0.82066524,0.0003352515,0.17629214,0.00012629005,0.000023655719,0.00006507541,0.00011586507,0.0000342016,0.0023421722],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998066,0.00002776799,0.000009963354,0.00003916717,0.00009301373,0.000023541766],"domain_scores_gemma":[0.9997534,0.000047522455,0.000069135735,0.000035409306,0.0000746496,0.000019963738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017027848,0.00024903016,0.00018858587,0.00017374614,0.00010930421,0.00039675424,0.00030426093,0.00027870294,0.0006010311],"category_scores_gemma":[0.00032832028,0.00020659203,0.00015037874,0.00028259473,0.00023121659,0.00033056847,0.00030007228,0.00014508191,0.00037358847],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013338798,0.000094259,0.0012697133,0.00012503495,0.000022291155,0.00012469082,0.00006223904,0.020107849,0.92583925,0.007371549,0.00060522114,0.04424444],"study_design_scores_gemma":[0.00009893056,0.00054290553,0.00198182,0.000011081573,0.000033662927,0.0006427554,0.000046900168,0.26146498,0.7155953,0.0026370932,0.016882638,0.00006189694],"about_ca_topic_score_codex":0.00020066541,"about_ca_topic_score_gemma":0.00037576462,"teacher_disagreement_score":0.0006010311,"about_ca_system_score_codex":0.00023821623,"about_ca_system_score_gemma":0.00029629702,"threshold_uncertainty_score":0.0020106435},"labels":[],"label_agreement":null},{"id":"W4293222331","doi":"10.3390/s22176389","title":"Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Qassim University","keywords":"Cheating; Computer science; Task (project management); Convolutional neural network; Artificial intelligence; Deep learning; Machine learning; Frame (networking); Algorithm; Identification (biology); Engineering; Psychology","score_opus":0.018220676159771382,"score_gpt":0.277963696062659,"score_spread":0.2597430199028876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293222331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08710258,0.00028808977,0.8828678,0.00043631386,0.00022707772,0.00021745662,0.0003614372,0.022483848,0.006015512],"genre_scores_gemma":[0.8206694,0.00017280667,0.1702949,0.0003100238,0.00004794094,0.00018630535,0.0006718554,0.00013006978,0.0075166463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997316,0.00002246854,0.000021225995,0.00008664637,0.00008675808,0.000051356452],"domain_scores_gemma":[0.9996731,0.000040929066,0.000039457504,0.000045899757,0.00016386372,0.000036710248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035407481,0.0005404266,0.00040266887,0.0004143477,0.00027087994,0.0005027621,0.0011567016,0.0004794567,0.0031526198],"category_scores_gemma":[0.0007262356,0.00030864042,0.00033204444,0.00018622684,0.00017247944,0.00059025176,0.0006114286,0.00065458333,0.00088242564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063270685,0.0006208821,0.009120316,0.00023121748,0.00011844873,0.00042592385,0.00015759935,0.13361444,0.07576803,0.0025717735,0.013491557,0.76324713],"study_design_scores_gemma":[0.000021465014,0.000109516004,0.0015502381,0.000010618476,0.000018676947,0.00006415411,0.000013108005,0.9704174,0.024510458,0.0005367424,0.002732055,0.000015679718],"about_ca_topic_score_codex":0.006865309,"about_ca_topic_score_gemma":0.006589544,"teacher_disagreement_score":0.006865309,"about_ca_system_score_codex":0.0006890548,"about_ca_system_score_gemma":0.0010816942,"threshold_uncertainty_score":0.013650715},"labels":[],"label_agreement":null},{"id":"W4293660955","doi":"10.3390/s22176454","title":"Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Mitacs; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Kinematics; Motion capture; Computer science; Cumulative trauma disorder; Motion analysis; Motion (physics); Inertial measurement unit; Simulation; Accelerometer; Poison control; Human factors and ergonomics; Physics; Artificial intelligence","score_opus":0.007921117997578138,"score_gpt":0.2640830627178027,"score_spread":0.2561619447202245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293660955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84629554,0.0013540648,0.14641093,0.00007665588,0.00007530812,0.00026429925,0.0013811842,0.00043150666,0.0037106085],"genre_scores_gemma":[0.9676781,0.00060038123,0.029750492,0.00007501162,0.00006584318,0.00014443489,0.0006321587,0.00003300324,0.0010205196],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996773,0.00005753894,0.000019897161,0.000071413866,0.00014319536,0.000030654614],"domain_scores_gemma":[0.9995684,0.0001296583,0.000102989434,0.000029002558,0.00014944287,0.00002053432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038423712,0.0006326567,0.00040026905,0.0017736009,0.00015839755,0.00042894445,0.00031686106,0.00044634513,0.0010493937],"category_scores_gemma":[0.0011695871,0.00016977557,0.00019933455,0.000886561,0.00018041808,0.00026483156,0.00035055788,0.00016672812,0.00040740726],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012400578,0.00023682335,0.1126227,0.0017882563,0.00014978528,0.00021849944,0.00091009145,0.006768539,0.49284318,0.0002581951,0.0015077281,0.38145614],"study_design_scores_gemma":[0.00006441839,0.0009922616,0.8835814,0.00013968538,0.00015610755,0.00088446186,0.0005545975,0.035499964,0.07584645,0.000177063,0.0019960236,0.00010761487],"about_ca_topic_score_codex":0.002326633,"about_ca_topic_score_gemma":0.005239413,"teacher_disagreement_score":0.002326633,"about_ca_system_score_codex":0.00014753513,"about_ca_system_score_gemma":0.00022339549,"threshold_uncertainty_score":0.004626155},"labels":[],"label_agreement":null},{"id":"W4293660989","doi":"10.3390/s22176430","title":"Minimize Tracking Occlusion in Collaborative Pick-and-Place Tasks: An Analytical Approach for Non-Wrist-Partitioned Manipulators","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kinova (Canada); École de Technologie Supérieure","funders":"","keywords":"Computer science; Inverse kinematics; Workstation; Computer vision; Set (abstract data type); Object (grammar); Kinematics; Tracking (education); Operator (biology); Artificial intelligence; Simulation; Robot","score_opus":0.02545976411283755,"score_gpt":0.2681962240309553,"score_spread":0.24273645991811774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293660989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044558635,0.00007838367,0.9939367,0.000034760196,0.0000060015313,0.000017742666,0.00000516345,0.000058753652,0.0014065608],"genre_scores_gemma":[0.57770914,0.00090796856,0.41331753,0.000085890686,0.00007728478,0.00031875307,0.00005320349,0.00024461353,0.007285543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966943,0.00005705806,0.000013914066,0.000054278345,0.00015651503,0.000048777492],"domain_scores_gemma":[0.99943846,0.00028739125,0.00010844755,0.0000445447,0.00009481976,0.000026267693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077780837,0.0009184176,0.00080652046,0.000614872,0.0004617832,0.00082655495,0.001070286,0.00091261376,0.0018423954],"category_scores_gemma":[0.0025465945,0.00072640344,0.0008088224,0.000508725,0.0009981893,0.00096299296,0.00093239744,0.00073040073,0.0005057047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014419619,0.000032455435,0.00014206376,0.000054924294,0.000010109935,0.000037397473,0.00008366979,0.97250473,0.0035135811,0.007974361,0.00023694291,0.015395294],"study_design_scores_gemma":[0.000002153412,0.000017078672,0.000036747235,0.000004669664,0.0000035141268,0.000010630543,0.000009698014,0.9976217,0.00052848115,0.001474252,0.00028796884,0.0000031270733],"about_ca_topic_score_codex":0.0033984156,"about_ca_topic_score_gemma":0.0030881604,"teacher_disagreement_score":0.0033984156,"about_ca_system_score_codex":0.0010320403,"about_ca_system_score_gemma":0.001664352,"threshold_uncertainty_score":0.0074880123},"labels":[],"label_agreement":null},{"id":"W4293661004","doi":"10.3390/s22176432","title":"Less Vibrotactile Feedback Is Effective to Improve Human Balance Control during Sensory Cues Alteration","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Balance (ability); Sensory system; Control (management); Sensory cue; Psychology; Computer science; Physical medicine and rehabilitation; Audiology; Cognitive psychology; Medicine; Neuroscience; Artificial intelligence","score_opus":0.014718134091415178,"score_gpt":0.32588310246367663,"score_spread":0.31116496837226143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293661004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99613684,0.0005874402,0.002738698,0.000057912835,0.000026455476,0.000024456816,0.00004453529,0.000039296632,0.00034442457],"genre_scores_gemma":[0.9967632,0.00023727944,0.0024374826,0.00006740912,0.000023209795,0.000037667178,0.000046615427,0.00000689562,0.00038026547],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998105,0.000031455565,0.00001708071,0.00003820248,0.000071253904,0.000031491585],"domain_scores_gemma":[0.999718,0.00010753141,0.00007252966,0.000020621334,0.000039482537,0.000041764837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024722816,0.00037723905,0.0004060169,0.00019507094,0.00011689024,0.00022284918,0.00015670584,0.00036331982,0.0023952173],"category_scores_gemma":[0.0011042294,0.000108004126,0.00013410942,0.000082292245,0.00019059588,0.00022968317,0.00024577853,0.0002449086,0.0001460389],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023080115,0.0003419219,0.0042306907,0.00028499242,0.000035283152,0.00006720214,0.0000953165,0.00028085778,0.9418035,0.000051493884,0.00013239772,0.05036844],"study_design_scores_gemma":[0.0004626644,0.024602804,0.71585006,0.00014586384,0.0002258606,0.0010322292,0.0003688176,0.005321749,0.2484961,0.0006674597,0.0027713864,0.000055033976],"about_ca_topic_score_codex":0.0004485347,"about_ca_topic_score_gemma":0.0008087534,"teacher_disagreement_score":0.0023952173,"about_ca_system_score_codex":0.000102133075,"about_ca_system_score_gemma":0.00014046296,"threshold_uncertainty_score":0.008012772},"labels":[],"label_agreement":null},{"id":"W4293661077","doi":"10.3390/s22176428","title":"A Study of Optimizing Lamb Wave Acoustic Mass Sensors’ Performance through Adjustment of the Transduction Electrode Metallization Ratio","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Total harmonic distortion; Sensitivity (control systems); CMOS; Materials science; Transmission (telecommunications); Optoelectronics; Electrode; Distortion (music); Acoustics; Electronic engineering; Electrical engineering; Physics; Engineering; Voltage; Amplifier","score_opus":0.01783407497409793,"score_gpt":0.21426796856588634,"score_spread":0.19643389359178842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293661077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9430659,0.0009993428,0.05270307,0.000121768324,0.00003895418,0.000061519684,0.00009111203,0.00044138407,0.0024769546],"genre_scores_gemma":[0.98168945,0.00030792825,0.017355423,0.000015978312,0.0000063226953,0.000020755928,0.000032531192,0.000039761693,0.00053192413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997625,0.000033551198,0.000012599135,0.00006797906,0.00008478433,0.000038662078],"domain_scores_gemma":[0.9997032,0.00014897405,0.00006361641,0.000028898456,0.000046854377,0.0000083971545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032083158,0.00060328515,0.0003743466,0.00018034533,0.000107412634,0.00034154698,0.00047715654,0.00044345384,0.0004930532],"category_scores_gemma":[0.0008088975,0.00023162743,0.00027937602,0.00016178873,0.00015231379,0.00032346422,0.000121471785,0.00019678795,0.00021496828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012010708,0.00004807782,0.0006322131,0.00014349306,0.00002906835,0.00008937047,0.000042668824,0.020530801,0.96965885,0.00022261763,0.00009165025,0.008391197],"study_design_scores_gemma":[0.000019805953,0.0007866635,0.0020862336,0.000005695993,0.000055481414,0.00011734501,0.000021529793,0.07994595,0.91558766,0.00005309829,0.0013040092,0.000016490549],"about_ca_topic_score_codex":0.00036244388,"about_ca_topic_score_gemma":0.00041476978,"teacher_disagreement_score":0.00060328515,"about_ca_system_score_codex":0.00026647103,"about_ca_system_score_gemma":0.00013970213,"threshold_uncertainty_score":0.0019334555},"labels":[],"label_agreement":null},{"id":"W4293661603","doi":"10.3390/s22176445","title":"Cross-Language Speech Emotion Recognition Using Bag-of-Word Representations, Domain Adaptation, and Data Augmentation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Normalization (sociology); Artificial intelligence; Speech recognition; Test data; Natural language processing; Domain adaptation; Language model; Sentiment analysis; Classifier (UML)","score_opus":0.12607604168743033,"score_gpt":0.40277199348422393,"score_spread":0.2766959517967936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293661603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13538577,0.0014513234,0.8519732,0.00026185467,0.0004690157,0.00023470329,0.00087325525,0.006269977,0.0030809534],"genre_scores_gemma":[0.62498313,0.00083442003,0.3614053,0.00043597425,0.00019422793,0.0006665386,0.005555285,0.0004458094,0.0054794066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885833,0.0003321792,0.00008446042,0.00042931194,0.00019056788,0.00010527892],"domain_scores_gemma":[0.99880326,0.00039139736,0.00010380284,0.0003226135,0.00032282434,0.000056145123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016970211,0.0013695635,0.00092991727,0.0010834747,0.0002925422,0.000951847,0.0008529242,0.000565339,0.0020483872],"category_scores_gemma":[0.0041156695,0.00025999994,0.0010951881,0.0010139777,0.00043215687,0.0019183616,0.0020735709,0.0013368719,0.002536625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060507143,0.00044977793,0.0033905203,0.00026031618,0.00021192359,0.00019467798,0.0003197028,0.014679601,0.10978923,0.0015273945,0.0059595522,0.8626123],"study_design_scores_gemma":[0.00009073885,0.00078320503,0.021044122,0.00008861965,0.00021865178,0.00084355305,0.00085110526,0.80311567,0.14506689,0.01019664,0.017535316,0.00016558557],"about_ca_topic_score_codex":0.0008251329,"about_ca_topic_score_gemma":0.0011919797,"teacher_disagreement_score":0.0020483872,"about_ca_system_score_codex":0.00024725078,"about_ca_system_score_gemma":0.00041206862,"threshold_uncertainty_score":0.008974791},"labels":[],"label_agreement":null},{"id":"W4293680188","doi":"10.3390/s22166193","title":"Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Government of Ontario","keywords":"Structural health monitoring; Computer science; Identification (biology); Deep learning; Convolutional neural network; Field (mathematics); Scarcity; Machine learning; Artificial intelligence; Risk analysis (engineering); Data science; Engineering; Business","score_opus":0.018246234209579652,"score_gpt":0.250722101997159,"score_spread":0.23247586778757934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293680188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09854812,0.00217238,0.8912282,0.00065171433,0.00015692771,0.000082917875,0.0007650002,0.0035179318,0.0028768755],"genre_scores_gemma":[0.8893864,0.0006681867,0.10256733,0.00047129588,0.000086345266,0.00018107782,0.0025222825,0.00013316235,0.003983986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999671,0.000071744216,0.00002318485,0.000105230916,0.000074779535,0.000053949076],"domain_scores_gemma":[0.99924785,0.0003709746,0.00008648659,0.00011520085,0.00013855277,0.0000409569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084310333,0.0009711332,0.00074983755,0.00064438436,0.00023563631,0.0005144113,0.0014263021,0.000875418,0.0016959058],"category_scores_gemma":[0.0025868558,0.000437739,0.0007188209,0.00059738546,0.0006375946,0.0009750187,0.0011997472,0.0016559458,0.0005574748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002406239,0.00024307957,0.003230985,0.00019592242,0.000097319215,0.0001360994,0.000109478744,0.69884825,0.007862871,0.0027565781,0.00509005,0.2811887],"study_design_scores_gemma":[0.000002331929,0.000020684025,0.0002470776,0.0000072483017,0.0000043377368,0.000011659701,0.000005600231,0.99708813,0.0010834151,0.0012237222,0.00030278735,0.0000031354527],"about_ca_topic_score_codex":0.0039233565,"about_ca_topic_score_gemma":0.0055812453,"teacher_disagreement_score":0.0039233565,"about_ca_system_score_codex":0.00070870796,"about_ca_system_score_gemma":0.00069750176,"threshold_uncertainty_score":0.007801056},"labels":[],"label_agreement":null},{"id":"W4293717400","doi":"10.3390/s22176555","title":"Teaching Essential EMG Theory to Kinesiologists and Physical Therapists Using Analogies Visual Descriptions, and Qualitative Analysis of Biophysical Concepts","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multidisciplinary approach; Action (physics); Kinesiology; Presentation (obstetrics); Electromyography; Computer science; Physical education; Psychology; Physical medicine and rehabilitation; Mathematics education; Neuroscience; Medical education; Medicine; Physics","score_opus":0.02225225818304281,"score_gpt":0.34497273345942997,"score_spread":0.32272047527638714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293717400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02522418,0.001166928,0.8267602,0.023670029,0.0013185438,0.0011346164,0.00013103786,0.0007587641,0.11983571],"genre_scores_gemma":[0.2960916,0.004204551,0.62883985,0.0073739053,0.0004921702,0.0023033514,0.00017662064,0.0003435702,0.060174394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9971008,0.0016951307,0.0001214017,0.00023774378,0.0006777032,0.00016722547],"domain_scores_gemma":[0.99351305,0.004481692,0.00033346072,0.00037085955,0.0008561778,0.00044474486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046665333,0.00086247094,0.00042434665,0.0012291102,0.0011258111,0.0037822688,0.0016242516,0.0013027976,0.011787749],"category_scores_gemma":[0.011903856,0.0002859808,0.0005926207,0.0005178346,0.00583783,0.004218163,0.0037306335,0.003737227,0.0020570788],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007687724,0.0009460233,0.0015021955,0.0013095491,0.000020246196,0.00067982427,0.06008719,0.0044817342,0.01040928,0.5630449,0.048332177,0.30911002],"study_design_scores_gemma":[0.000097206226,0.00043530294,0.0019682797,0.0016738204,0.000024109635,0.0013381384,0.030080294,0.012462469,0.0070917592,0.55805945,0.38668644,0.000082763094],"about_ca_topic_score_codex":0.0007129072,"about_ca_topic_score_gemma":0.0010843865,"teacher_disagreement_score":0.011787749,"about_ca_system_score_codex":0.0026104455,"about_ca_system_score_gemma":0.0031763387,"threshold_uncertainty_score":0.039433956},"labels":[],"label_agreement":null},{"id":"W4293778742","doi":"10.3390/s22166215","title":"Trust and Mobility-Based Protocol for Secure Routing in Internet of Things","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"King Saud University","keywords":"Computer science; Routing protocol; Computer network; Routing (electronic design automation); Protocol (science); Network packet; Throughput; Computer security; Distributed computing; Wireless","score_opus":0.01805223179302284,"score_gpt":0.27345838637743447,"score_spread":0.2554061545844116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293778742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02472963,0.0022092871,0.95497614,0.0015539695,0.000677851,0.00088983285,0.00019964926,0.0014694739,0.013294114],"genre_scores_gemma":[0.8185675,0.0015057273,0.16982432,0.00061059376,0.00019100447,0.0013133256,0.00061480585,0.0001314645,0.0072413166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760145,0.0008667381,0.0003237148,0.00018491688,0.0008466148,0.00017662553],"domain_scores_gemma":[0.9979305,0.0006458703,0.00035481763,0.0003928539,0.00057012204,0.00010584789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022109519,0.0004841725,0.0006698964,0.00072547747,0.0011357523,0.0013187118,0.0010073729,0.00096972095,0.0011260395],"category_scores_gemma":[0.0056091053,0.00018100221,0.0005580274,0.0008568746,0.0010442551,0.0020998968,0.0018872134,0.0014622693,0.00040649035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009032477,0.00023542263,0.0029013315,0.0011431533,0.00023947928,0.0022915192,0.001960768,0.11705373,0.059270963,0.48724592,0.032714933,0.29403958],"study_design_scores_gemma":[0.0001663307,0.00097596645,0.0015619983,0.00026133002,0.00018860631,0.0026311532,0.000492792,0.76114225,0.028247926,0.097847156,0.10623842,0.00024612344],"about_ca_topic_score_codex":0.001537289,"about_ca_topic_score_gemma":0.0018591896,"teacher_disagreement_score":0.0022109519,"about_ca_system_score_codex":0.0013543229,"about_ca_system_score_gemma":0.0016978887,"threshold_uncertainty_score":0.011692762},"labels":[],"label_agreement":null},{"id":"W4294168966","doi":"10.3390/s22176623","title":"Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Qassim University","keywords":"Orchestration; Slicing; Computer science; Scalability; Automation; Construct (python library); Core network; Service (business); Software engineering; Service provider; Telecommunications; Computer network; Engineering; World Wide Web; Operating system","score_opus":0.170359289104957,"score_gpt":0.3348325553624418,"score_spread":0.16447326625748476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294168966","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008082711,0.9831193,0.0039026923,0.0016781501,0.00034812474,0.000035105713,0.000059148053,0.00003074736,0.010018453],"genre_scores_gemma":[0.006336001,0.98746395,0.0036728077,0.0005515167,0.00026981614,0.000043188986,0.00010688011,0.000014654708,0.0015411277],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993155,0.00012821554,0.000058447742,0.00008868349,0.00035748072,0.00005159924],"domain_scores_gemma":[0.9979797,0.001161113,0.00018546748,0.00007004327,0.00053693925,0.00006681816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022935006,0.0007836344,0.0006567724,0.0029091297,0.0004176186,0.0018097156,0.00085375435,0.0009955064,0.003211707],"category_scores_gemma":[0.003473152,0.00036355125,0.00042508068,0.003141453,0.0004588016,0.0031425476,0.00072287704,0.0012712629,0.0013954093],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002380199,0.000039096638,0.0003052803,0.009939083,0.00004643071,0.00011914565,0.00013610494,0.0015321879,0.0012770164,0.04741075,0.01775296,0.92141813],"study_design_scores_gemma":[0.000004911357,0.00008588819,0.0007223708,0.0064961403,0.000077726596,0.0004678912,0.00019905789,0.0010101905,0.0015501728,0.0112645095,0.978096,0.000025169646],"about_ca_topic_score_codex":0.002046577,"about_ca_topic_score_gemma":0.0035297764,"teacher_disagreement_score":0.003211707,"about_ca_system_score_codex":0.0011341284,"about_ca_system_score_gemma":0.0032139535,"threshold_uncertainty_score":0.012129307},"labels":[],"label_agreement":null},{"id":"W4294952288","doi":"10.3390/s22186733","title":"Low-Cost, Open-Source, Emoncms-Based SCADA System for a Large Grid-Connected PV System","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SCADA; Arduino; Dashboard; Embedded system; JSON; Open source hardware; Data logger; Operating system; Photovoltaic system; Modbus; Computer science; Open source; Computer hardware; Data acquisition; Database; Software; Real-time computing; Engineering; Electrical engineering","score_opus":0.010403424005711249,"score_gpt":0.2077582614921857,"score_spread":0.19735483748647445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294952288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17053266,0.00032037188,0.72464883,0.00030951988,0.00015768301,0.0008440224,0.0013106791,0.0728626,0.02901364],"genre_scores_gemma":[0.8253763,0.00013511242,0.15267201,0.00018100276,0.00007959046,0.00055518793,0.0014844519,0.0009875797,0.01852866],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994624,0.00008701716,0.000038036666,0.00012256276,0.00025758936,0.000032461467],"domain_scores_gemma":[0.99957913,0.00004734717,0.00004635936,0.00010793367,0.00018165783,0.000037598205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036196306,0.0006023834,0.00041676773,0.0005447758,0.00034829832,0.00048756003,0.0008519625,0.0003511434,0.0070114173],"category_scores_gemma":[0.0007928518,0.00021021315,0.0001796564,0.00037117483,0.00018030406,0.0006611014,0.0004767974,0.00033287643,0.0030569998],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001092969,0.0005985921,0.009612047,0.0005653838,0.00009210829,0.0015702008,0.000779338,0.020009292,0.31996223,0.0051464187,0.060424548,0.58014697],"study_design_scores_gemma":[0.000415376,0.0015070991,0.03882303,0.00018486807,0.0002372591,0.0018982042,0.00019849553,0.43144196,0.25570965,0.002394158,0.26697063,0.00021934134],"about_ca_topic_score_codex":0.0013796831,"about_ca_topic_score_gemma":0.0018802269,"teacher_disagreement_score":0.0070114173,"about_ca_system_score_codex":0.00053546985,"about_ca_system_score_gemma":0.000506691,"threshold_uncertainty_score":0.0234555},"labels":[],"label_agreement":null},{"id":"W4295084061","doi":"10.3390/s22176640","title":"Can We Quantify Aging-Associated Postural Changes Using Photogrammetry? A Systematic Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Photogrammetry; Medicine; Physical medicine and rehabilitation; Physical therapy; Sagittal plane; Lumbar lordosis; MEDLINE; Kyphosis; Artificial intelligence; Computer science; Surgery","score_opus":0.17034668764152652,"score_gpt":0.40098345996442036,"score_spread":0.23063677232289384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295084061","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005018877,0.9982596,0.00021360087,0.0002110105,0.00011523002,0.0002604822,0.0002660629,0.000007520163,0.00016461198],"genre_scores_gemma":[0.00856049,0.9892903,0.0007752659,0.00035801312,0.000084894906,0.000640718,0.00020290147,0.0000052023624,0.000082173254],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98930055,0.003427056,0.004237493,0.0008323026,0.0019718437,0.00023074955],"domain_scores_gemma":[0.95466053,0.033134867,0.0072659436,0.0008238411,0.0038356988,0.00027917293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012011916,0.0019317083,0.009207455,0.017163545,0.0007663008,0.004119183,0.0027975768,0.002625265,0.0051142825],"category_scores_gemma":[0.06359236,0.001356812,0.0075971214,0.014153171,0.001167717,0.0041614436,0.001858111,0.0010607156,0.00050742173],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064749576,0.000008869012,0.0005637854,0.9577724,0.004875386,0.00005719741,0.00015273165,0.000052431456,0.00006846064,0.00014228649,0.00088479905,0.03535694],"study_design_scores_gemma":[0.000096500626,0.00010523104,0.0029317548,0.9377305,0.045133766,0.0002988799,0.00028367198,0.000075670636,0.000117512565,0.00029310686,0.012899168,0.000034174045],"about_ca_topic_score_codex":0.006516171,"about_ca_topic_score_gemma":0.01658715,"teacher_disagreement_score":0.017163545,"about_ca_system_score_codex":0.0033651507,"about_ca_system_score_gemma":0.011537609,"threshold_uncertainty_score":0.063525856},"labels":[],"label_agreement":null},{"id":"W4295414598","doi":"10.3390/s22186876","title":"Sustainable Earthquake Resilience with the Versatile Shape Memory Alloy (SMA)-Based Superelasticity-Assisted Slider","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"University of Bonab","keywords":"Pseudoelasticity; Shape-memory alloy; Resilience (materials science); Slider; Earthquake shaking table; Engineering; Earthquake engineering; SMA*; Computer science; Structural engineering; Mechanical engineering; Materials science; Artificial intelligence","score_opus":0.011108018559887836,"score_gpt":0.2161101753047287,"score_spread":0.20500215674484087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295414598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9189145,0.0024543942,0.065551616,0.0002191568,0.00015848264,0.00006689,0.00017559067,0.0012509407,0.011208327],"genre_scores_gemma":[0.9870494,0.00033336936,0.009306471,0.000046104473,0.000020180989,0.000014682514,0.00007181175,0.00002748483,0.0031304674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998838,0.000009020226,0.000006364244,0.000023624441,0.000058077087,0.000019211444],"domain_scores_gemma":[0.9998907,0.000013658822,0.00004662258,0.000017207061,0.000018555053,0.000013270452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010231088,0.00028905284,0.0002693181,0.00029211715,0.000116838215,0.00025972642,0.00041386276,0.00020652013,0.0014004402],"category_scores_gemma":[0.0001885873,0.00011634512,0.00017998424,0.00016994061,0.00023590244,0.0003813886,0.00041136687,0.00021192213,0.0006009716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001379983,0.00002437532,0.00057920656,0.00009461502,0.000009364324,0.00010225699,0.00005372432,0.0015969734,0.97068125,0.0008017461,0.00036492964,0.025553506],"study_design_scores_gemma":[0.00002354618,0.00087813975,0.004648217,0.000014506311,0.000026784315,0.0003795085,0.00010148573,0.018052952,0.96370006,0.00034330742,0.011809936,0.000021524838],"about_ca_topic_score_codex":0.00013658484,"about_ca_topic_score_gemma":0.00046842135,"teacher_disagreement_score":0.0014004402,"about_ca_system_score_codex":0.00012972146,"about_ca_system_score_gemma":0.000100887206,"threshold_uncertainty_score":0.004684925},"labels":[],"label_agreement":null},{"id":"W4295414632","doi":"10.3390/s22186866","title":"Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and a Nonlinear Optimization Tool","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Control theory (sociology); Nonlinear system; Observer (physics); Fuzzy logic; Actuator; Continuous stirred-tank reactor; Lyapunov function; Fuzzy control system; State observer; Fault (geology); Mathematics; Computer science; Engineering; Artificial intelligence; Control (management)","score_opus":0.009698032385475496,"score_gpt":0.21677585902540597,"score_spread":0.20707782663993046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295414632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077959057,0.00008421071,0.9914995,0.000028848963,0.000015002844,0.000013018409,0.000005585154,0.00013673429,0.00042124878],"genre_scores_gemma":[0.79044336,0.00028616274,0.20694993,0.00003941291,0.000035658693,0.0001262602,0.00006675754,0.000032036543,0.0020204675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964285,0.00005245723,0.000034831493,0.00009656287,0.00014270523,0.00003051082],"domain_scores_gemma":[0.9997061,0.00009757955,0.00007128881,0.000040761126,0.00007208251,0.000012235337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005402533,0.00083354913,0.0007716248,0.000327853,0.00028679037,0.00050009723,0.0006193423,0.0006344703,0.00051179813],"category_scores_gemma":[0.00089276314,0.00035473585,0.00089225674,0.00019818533,0.00042383364,0.0009825306,0.00064807985,0.00082636625,0.00014396112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035967518,0.00008672472,0.0022394154,0.00041706127,0.00018102757,0.00029749496,0.00046008377,0.6430205,0.100315124,0.014434696,0.0006665347,0.2375216],"study_design_scores_gemma":[0.000009445032,0.0000620737,0.0002466635,0.000008252997,0.000016691572,0.000032663003,0.0000135807695,0.99114555,0.0072240136,0.00068610645,0.00054625067,0.0000086111],"about_ca_topic_score_codex":0.0033171675,"about_ca_topic_score_gemma":0.003303903,"teacher_disagreement_score":0.0033171675,"about_ca_system_score_codex":0.00033530346,"about_ca_system_score_gemma":0.0007928201,"threshold_uncertainty_score":0.006595731},"labels":[],"label_agreement":null},{"id":"W4295414691","doi":"10.3390/s22186863","title":"Evaluating the Reliability of a Shape Capturing Process for Transradial Residual Limb Using a Non-Contact Scanner","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scanner; Residual; Reliability (semiconductor); Process (computing); 3d scanning; Computer science; Elbow; Biomedical engineering; Computer vision; Artificial intelligence; Engineering; Medicine; Surgery; Algorithm","score_opus":0.04805237705065843,"score_gpt":0.31102211304686295,"score_spread":0.26296973599620455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295414691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7853522,0.00092235714,0.20907246,0.000115921925,0.00020648217,0.00085126114,0.00036333554,0.0006070811,0.0025089094],"genre_scores_gemma":[0.8964323,0.00041168294,0.100967236,0.00008946761,0.00005226155,0.0003754177,0.00027450183,0.00016399824,0.0012332192],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99463165,0.0019519372,0.00050826365,0.0010090101,0.0017330269,0.00016607548],"domain_scores_gemma":[0.98088926,0.007614903,0.0019523463,0.0029835978,0.006234845,0.0003249239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007856346,0.0007584283,0.00043661526,0.0010571964,0.00037289603,0.00082826824,0.000807337,0.0009595645,0.0016648212],"category_scores_gemma":[0.019313766,0.00039722482,0.0005820248,0.0005716016,0.0010082778,0.0005878662,0.0011354629,0.00041432263,0.00087294506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047497414,0.00056504225,0.16740967,0.0020914662,0.0005685273,0.00066530553,0.005921612,0.006648197,0.52605295,0.0009182823,0.0015271959,0.28288198],"study_design_scores_gemma":[0.0003194156,0.011987302,0.59431934,0.0005120424,0.0009334853,0.0072893184,0.004670795,0.07160287,0.294341,0.0015253028,0.012094625,0.00040450378],"about_ca_topic_score_codex":0.0009790522,"about_ca_topic_score_gemma":0.002125298,"teacher_disagreement_score":0.007856346,"about_ca_system_score_codex":0.00019776777,"about_ca_system_score_gemma":0.00044247461,"threshold_uncertainty_score":0.04154879},"labels":[],"label_agreement":null},{"id":"W4295745583","doi":"10.3390/s22186850","title":"Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Face recognition and analysis","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Canada Research Chairs","keywords":"Pose; Orientation (vector space); Artificial intelligence; Computer science; Computer vision; Motion capture; RGB color model; Face (sociological concept); Head (geology); Human head; Algorithm; Motion (physics); Mathematics; Engineering","score_opus":0.1004720361416903,"score_gpt":0.3834542486952571,"score_spread":0.2829822125535668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295745583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06490906,0.010833975,0.90631866,0.00053622876,0.0007454643,0.00065969065,0.0013726664,0.007835779,0.00678842],"genre_scores_gemma":[0.3527384,0.010955523,0.6200512,0.00060676644,0.00037597274,0.00097149593,0.0069771255,0.0010086773,0.006314724],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973449,0.0005646556,0.0002484265,0.0005209692,0.0011649443,0.00015614902],"domain_scores_gemma":[0.9971058,0.0014519815,0.00021439136,0.00025206967,0.0008827276,0.000093002694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024711322,0.0020550087,0.0009819238,0.0021856283,0.00030198283,0.001412296,0.0014320293,0.0013187747,0.004324043],"category_scores_gemma":[0.011830986,0.00040152238,0.00092835125,0.0008865796,0.00036931262,0.0011516344,0.0011140911,0.00078598905,0.002680208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073311315,0.00012706727,0.0046803113,0.000707448,0.00025203146,0.00011287486,0.000104986626,0.026677543,0.013699376,0.0010169639,0.0058701625,0.94601816],"study_design_scores_gemma":[0.000281242,0.0028735218,0.031939555,0.0007929835,0.0004908467,0.0040058857,0.0005375751,0.8228643,0.09292273,0.004292513,0.03873021,0.00026866136],"about_ca_topic_score_codex":0.002555002,"about_ca_topic_score_gemma":0.0034340005,"teacher_disagreement_score":0.004324043,"about_ca_system_score_codex":0.00043374643,"about_ca_system_score_gemma":0.0010728207,"threshold_uncertainty_score":0.014465392},"labels":[],"label_agreement":null},{"id":"W4295926833","doi":"10.3390/s22186946","title":"Techniques to Improve the Performance of Planar Microwave Sensors: A Review and Recent Developments","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Institució Catalana de Recerca i Estudis Avançats","keywords":"Robustness (evolution); Planar; Resonator; Microwave; Analyte; Computer science; Electronic engineering; Embedding; Sensitivity (control systems); Materials science; Engineering; Optoelectronics; Telecommunications; Artificial intelligence; Chemistry","score_opus":0.03654609109292754,"score_gpt":0.26804478788833097,"score_spread":0.2314986967954034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295926833","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008358568,0.99471647,0.0017464259,0.00019902115,0.00019502158,0.000014522289,0.00004161847,0.00003421725,0.002216964],"genre_scores_gemma":[0.0027503052,0.9934157,0.0020016024,0.00015601204,0.00016885776,0.00001928077,0.00007525229,0.0000072254356,0.0014058214],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997305,0.000024155412,0.000030553216,0.000067199355,0.0001232587,0.00002441282],"domain_scores_gemma":[0.9996618,0.00016130558,0.00005535227,0.00001272772,0.000093011615,0.000015808982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051272195,0.0011693481,0.0010643799,0.0025355865,0.0002606378,0.0008022808,0.0007392281,0.0009952674,0.0031904397],"category_scores_gemma":[0.00061337603,0.0005277681,0.0005866231,0.002141978,0.00031003536,0.0013395414,0.00045063312,0.0011577377,0.0022456131],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006145455,0.00016951613,0.00026804794,0.028853819,0.00009394689,0.00022044289,0.000083999366,0.00096214545,0.028056446,0.004991638,0.012532478,0.92370605],"study_design_scores_gemma":[0.000009482603,0.0003152107,0.00087993103,0.0021398808,0.00017396451,0.0015064795,0.000079315134,0.0005649918,0.015918305,0.0016565896,0.976699,0.00005691223],"about_ca_topic_score_codex":0.00049627764,"about_ca_topic_score_gemma":0.00072481233,"teacher_disagreement_score":0.0031904397,"about_ca_system_score_codex":0.0002935004,"about_ca_system_score_gemma":0.0005104951,"threshold_uncertainty_score":0.010673046},"labels":[],"label_agreement":null},{"id":"W4295942055","doi":"10.3390/s22186945","title":"Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer network; Wireless sensor network; Computer science; Routing protocol; Energy consumption; Network packet; Sink (geography); Key distribution in wireless sensor networks; Efficient energy use; Base station; Wireless; Engineering; Wireless network; Telecommunications; Electrical engineering","score_opus":0.022665903872838553,"score_gpt":0.24801094236895357,"score_spread":0.22534503849611504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295942055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019553006,0.003170933,0.96694183,0.00053338386,0.00033450613,0.0003102303,0.00011000177,0.0010044985,0.008041738],"genre_scores_gemma":[0.67814755,0.0031736728,0.30300963,0.0004434723,0.00012850188,0.0011122858,0.0007684637,0.00011219127,0.013104261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946564,0.00016341307,0.000045695542,0.00006750922,0.00021599223,0.000041639567],"domain_scores_gemma":[0.99955577,0.0001295306,0.000058035283,0.000057794805,0.00018077964,0.000018153054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005885008,0.00041992724,0.00048703217,0.0005593416,0.00056315796,0.00047251445,0.0010191178,0.0004727501,0.0006933381],"category_scores_gemma":[0.0013259326,0.00018567187,0.0003054957,0.00079238956,0.00033905136,0.0007972551,0.00077001686,0.00057706423,0.00025613044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028599313,0.00020768776,0.0012213858,0.0008787472,0.00024067839,0.0010548688,0.0011091325,0.3615187,0.08858924,0.11258466,0.02843055,0.40387833],"study_design_scores_gemma":[0.000085997985,0.0003721281,0.00060497125,0.00008248439,0.0000998524,0.0006255744,0.00020075322,0.88508725,0.018112041,0.02960057,0.0650483,0.00008012874],"about_ca_topic_score_codex":0.0018626757,"about_ca_topic_score_gemma":0.0028452736,"teacher_disagreement_score":0.0018626757,"about_ca_system_score_codex":0.00046974962,"about_ca_system_score_gemma":0.0007930789,"threshold_uncertainty_score":0.0037037134},"labels":[],"label_agreement":null},{"id":"W4296020990","doi":"10.3390/s22186931","title":"Case-Study-Based Overview of Methods and Technical Solutions of Analog and Digital Transmission in Measurement and Control Ship Systems","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Federation for the Humanities and Social Sciences","keywords":"Reliability (semiconductor); Transmitter; Transmission (telecommunications); Field (mathematics); Wireless; Computer science; Engineering; Electronic engineering; Systems engineering; Control engineering; Electrical engineering; Power (physics); Telecommunications","score_opus":0.18214017001560653,"score_gpt":0.37165939112169716,"score_spread":0.18951922110609062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296020990","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009380909,0.9698364,0.013635144,0.00080231595,0.00052858505,0.00015134801,0.00006622083,0.000037908296,0.014003912],"genre_scores_gemma":[0.01264348,0.96603715,0.016033283,0.0006410524,0.0005231219,0.00031037518,0.00011977148,0.000020323856,0.0036714883],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976986,0.00078108156,0.0003130352,0.0003352691,0.00075373496,0.00011834839],"domain_scores_gemma":[0.99582726,0.0032065809,0.0003049187,0.00018931279,0.00040783724,0.000063970285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029899478,0.0017841926,0.0014023795,0.0044568637,0.00057773455,0.0028125234,0.0021395313,0.0031033694,0.005795626],"category_scores_gemma":[0.0052776253,0.0007285967,0.0011836946,0.0057227043,0.001451764,0.0026931302,0.0014263204,0.0019367362,0.0020720107],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007485174,0.00031253882,0.00088530243,0.042902503,0.00016730896,0.0010037858,0.00049108826,0.003646228,0.0020397757,0.083325036,0.016955452,0.84819615],"study_design_scores_gemma":[0.000020356865,0.0003080546,0.0012821637,0.016274398,0.0001745084,0.0034893893,0.0003982639,0.0018691059,0.0030327996,0.01882535,0.95425147,0.00007415681],"about_ca_topic_score_codex":0.0021485852,"about_ca_topic_score_gemma":0.0017439381,"teacher_disagreement_score":0.005795626,"about_ca_system_score_codex":0.0013498289,"about_ca_system_score_gemma":0.0018866237,"threshold_uncertainty_score":0.019388318},"labels":[],"label_agreement":null},{"id":"W4296022518","doi":"10.3390/s22186928","title":"PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Relay; Computer network; Energy consumption; Incentive; The Internet; Encryption; Protocol (science); Distributed computing; Computer security; Engineering","score_opus":0.011179635302868862,"score_gpt":0.24301349862434,"score_spread":0.23183386332147113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296022518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0737285,0.0003824457,0.9146917,0.0008725326,0.0002913697,0.00048888574,0.00009184852,0.00059930066,0.008853501],"genre_scores_gemma":[0.8980636,0.00020026782,0.09561387,0.0002990754,0.000044054133,0.00023563698,0.0000577189,0.000040195147,0.005445732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992059,0.0002524,0.000052070187,0.00011795571,0.00025666508,0.000115090486],"domain_scores_gemma":[0.99810755,0.0007770544,0.00024983005,0.0004299177,0.00031902597,0.0001166465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015068243,0.00031554519,0.00051962695,0.000397364,0.0009216874,0.00068322395,0.0014477348,0.0007624508,0.0017362424],"category_scores_gemma":[0.003804263,0.00018263128,0.00034354077,0.0003020505,0.0011657203,0.0022233191,0.0023693205,0.0012636778,0.00023507664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011278905,0.00039620348,0.0024057615,0.0005519764,0.00014542163,0.001297253,0.0015844023,0.078751415,0.14549932,0.60665643,0.007953404,0.15363057],"study_design_scores_gemma":[0.00016953835,0.001267089,0.0010514751,0.00009039631,0.0000990341,0.001189925,0.00043907564,0.7612831,0.06757968,0.14674635,0.019941289,0.00014304745],"about_ca_topic_score_codex":0.0005691064,"about_ca_topic_score_gemma":0.0011566294,"teacher_disagreement_score":0.0017362424,"about_ca_system_score_codex":0.00056860707,"about_ca_system_score_gemma":0.0012090775,"threshold_uncertainty_score":0.007968903},"labels":[],"label_agreement":null},{"id":"W4296100806","doi":"10.3390/s22186747","title":"Automated Fluid Intake Detection Using RGB Videos","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Thermoregulation and physiological responses","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Convolutional neural network; Artificial intelligence; Deep learning; Computer science; RGB color model; Machine learning; Artificial neural network; Computer vision","score_opus":0.038977067048687926,"score_gpt":0.3095041609326323,"score_spread":0.27052709388394436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296100806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69819874,0.0029681209,0.27549303,0.00046222087,0.00034844503,0.00038067583,0.0075295717,0.0043106573,0.010308561],"genre_scores_gemma":[0.91077584,0.0012582983,0.079378076,0.00023627012,0.00008229781,0.00015298977,0.003381742,0.00010331213,0.0046311286],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998487,0.000018998117,0.000006622699,0.000048704842,0.00004664108,0.000030272344],"domain_scores_gemma":[0.9998784,0.000021082837,0.000020554799,0.00000835359,0.0000595405,0.000012029226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017407115,0.0006484356,0.00035028462,0.00079548993,0.00011651889,0.0003613645,0.000319654,0.00043468582,0.0018201191],"category_scores_gemma":[0.000663679,0.00018267307,0.0003219748,0.00037197594,0.00010938689,0.00034861223,0.00035593283,0.00022558813,0.0006305391],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016170644,0.00044059925,0.04704862,0.00069094694,0.000203201,0.0006816005,0.00018595824,0.022338526,0.24208662,0.0006325167,0.013297315,0.6707771],"study_design_scores_gemma":[0.00007578866,0.00074263156,0.1786313,0.0002479714,0.00015518942,0.0010425316,0.00040112968,0.63965,0.16337071,0.0015307586,0.014056243,0.000095716074],"about_ca_topic_score_codex":0.0066347243,"about_ca_topic_score_gemma":0.009678216,"teacher_disagreement_score":0.0066347243,"about_ca_system_score_codex":0.00026459002,"about_ca_system_score_gemma":0.0003141659,"threshold_uncertainty_score":0.013192177},"labels":[],"label_agreement":null},{"id":"W4296105182","doi":"10.3390/s22186766","title":"Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Malware; Artificial intelligence; Machine learning; Encoder; Android (operating system); Transfer of learning; Pattern recognition (psychology); Data mining","score_opus":0.02455415237552018,"score_gpt":0.295160512104081,"score_spread":0.27060635972856084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296105182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1261588,0.00023978582,0.86058235,0.00023265497,0.000070613845,0.0001745291,0.00026902877,0.009539214,0.0027330602],"genre_scores_gemma":[0.8641399,0.00018963913,0.1304503,0.00013352033,0.00003077749,0.0001500053,0.00069887034,0.00013942631,0.00406751],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997861,0.00002208266,0.000009994326,0.00007587648,0.00006673297,0.00003917257],"domain_scores_gemma":[0.99974877,0.00006934314,0.000033559027,0.000045560864,0.00008653563,0.000016219405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003297573,0.0006991676,0.00042560897,0.0008312596,0.00023786967,0.0005692917,0.000744716,0.00046552622,0.0017313996],"category_scores_gemma":[0.0011585365,0.00020581481,0.00070326135,0.00036447245,0.00026737343,0.0012095349,0.00081428053,0.0006972515,0.00055264507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038459178,0.00028095307,0.0034999955,0.00007741742,0.000078688114,0.00026281,0.00013911848,0.16784373,0.037317127,0.0036319853,0.003308115,0.7831754],"study_design_scores_gemma":[0.0000055540227,0.000050783496,0.0006420111,0.0000029886994,0.000010455838,0.00004303557,0.000014796087,0.9890478,0.008404311,0.0012885412,0.00048056876,0.00000921141],"about_ca_topic_score_codex":0.005064069,"about_ca_topic_score_gemma":0.0041233203,"teacher_disagreement_score":0.005064069,"about_ca_system_score_codex":0.0006882961,"about_ca_system_score_gemma":0.0005375883,"threshold_uncertainty_score":0.010069191},"labels":[],"label_agreement":null},{"id":"W4296350785","doi":"10.3390/s22187013","title":"Measuring Repositioning in Home Care for Pressure Injury Prevention and Management","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; University Health Network Foundation","keywords":"Pressure injury; Computer science; Medicine; Engineering; Medical emergency; Risk analysis (engineering)","score_opus":0.03991813457760056,"score_gpt":0.35908070031139433,"score_spread":0.31916256573379376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296350785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9585078,0.001322811,0.02606959,0.00068102713,0.0002149146,0.0004780936,0.0021206397,0.0015680743,0.009037006],"genre_scores_gemma":[0.968491,0.0006902287,0.02776071,0.00023018957,0.00007180471,0.00018891193,0.0006907364,0.000041993462,0.0018344275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887246,0.00040096542,0.00011486625,0.00014457086,0.00038887892,0.00007822933],"domain_scores_gemma":[0.99734384,0.0008382628,0.0006053561,0.0001414729,0.0008574217,0.00021356333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009181332,0.0005216614,0.00044906687,0.00093669636,0.00029606605,0.0006343642,0.0005145079,0.00046663987,0.0027144537],"category_scores_gemma":[0.0051979073,0.00020355075,0.00026875918,0.0006074029,0.00018402025,0.00034662863,0.0004121521,0.00041720897,0.0011445906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007505225,0.0006226899,0.6261525,0.00070094684,0.00013926286,0.0004554487,0.0018679103,0.0011013359,0.023150658,0.00016813581,0.009676785,0.33521372],"study_design_scores_gemma":[0.00006365149,0.0014013938,0.9468359,0.00033096178,0.00013694142,0.0015226905,0.0033601376,0.017411217,0.021722054,0.00040771955,0.006731805,0.00007559956],"about_ca_topic_score_codex":0.0044246376,"about_ca_topic_score_gemma":0.010466222,"teacher_disagreement_score":0.0044246376,"about_ca_system_score_codex":0.00032422034,"about_ca_system_score_gemma":0.0004274332,"threshold_uncertainty_score":0.009080768},"labels":[],"label_agreement":null},{"id":"W4296744480","doi":"10.3390/s22197134","title":"Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Ilam University","keywords":"Ripeness; Linear discriminant analysis; Artificial intelligence; Machine vision; Pattern recognition (psychology); Computer science; Artificial neural network; Image processing; Computer vision; Mathematics; Food science; Image (mathematics); Ripening; Biology","score_opus":0.018964245284748216,"score_gpt":0.2855045582277232,"score_spread":0.266540312942975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296744480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.327723,0.00043692382,0.66023046,0.00016667187,0.00012625345,0.0004279378,0.0004713261,0.005655455,0.0047620595],"genre_scores_gemma":[0.70112896,0.00020790291,0.29418042,0.00014265347,0.000029326178,0.00031856698,0.0004832569,0.000063463965,0.0034453615],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998491,0.000016034528,0.000009908233,0.00004964611,0.000063568565,0.000011806445],"domain_scores_gemma":[0.9998344,0.000024181962,0.00002214018,0.000018508537,0.00008931082,0.000011426433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031393292,0.00038538518,0.00031506483,0.000558096,0.00018601741,0.0002946783,0.0005125517,0.0003867594,0.0011277002],"category_scores_gemma":[0.0003250374,0.00019564018,0.00020743563,0.00028660995,0.00014315677,0.0004310766,0.00024228434,0.00027650304,0.00038675778],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023839249,0.0001647544,0.00947015,0.00021905905,0.000047988397,0.00025487735,0.00012935002,0.0044791726,0.70657426,0.00053355895,0.0017115264,0.2761769],"study_design_scores_gemma":[0.00008388962,0.001283466,0.085659966,0.00005541414,0.00023233106,0.0014091076,0.00017701858,0.46875882,0.43065208,0.0007008031,0.010845218,0.00014189848],"about_ca_topic_score_codex":0.0010670628,"about_ca_topic_score_gemma":0.0016084289,"teacher_disagreement_score":0.0011277002,"about_ca_system_score_codex":0.00022181765,"about_ca_system_score_gemma":0.000300248,"threshold_uncertainty_score":0.0037724972},"labels":[],"label_agreement":null},{"id":"W4296746238","doi":"10.3390/s22187094","title":"Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tangled Art + Disability; Israel Innovation Authority; National Institute on Aging; National Institutes of Health; Innovative Medicines Initiative","keywords":"Wrist; Accelerometer; Gait; Receiver operating characteristic; Convolutional neural network; Artificial intelligence; Physical medicine and rehabilitation; Computer science; Medicine; Machine learning","score_opus":0.01964437590871296,"score_gpt":0.3372286264433754,"score_spread":0.31758425053466244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296746238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99921834,0.0001358645,0.00043674948,0.00002419529,0.0000056173785,0.000017170534,0.00006912278,0.000004284862,0.0000885961],"genre_scores_gemma":[0.9983504,0.00013945073,0.0010001545,0.000042529657,0.000019269699,0.000039287017,0.0002472918,0.000002318258,0.00015921338],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995258,0.0001322102,0.00007739942,0.00012720606,0.000095066825,0.00004226264],"domain_scores_gemma":[0.9990765,0.00021671495,0.00023635474,0.00006664048,0.0002852821,0.00011852089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011505893,0.0004981912,0.00056994864,0.0008843973,0.00028870854,0.0005371343,0.0002542325,0.00069735124,0.0003154745],"category_scores_gemma":[0.0029269534,0.00021664401,0.00045683424,0.00054275733,0.00024782572,0.0005247944,0.0004674695,0.00042856127,0.00017780498],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008883039,0.0007097775,0.9717349,0.000095486546,0.00021026835,0.00019288002,0.0005417741,0.000344571,0.0017940982,0.00003520047,0.00018592906,0.023266856],"study_design_scores_gemma":[0.000045367615,0.0016866399,0.99022573,0.00002707286,0.00010745209,0.00051145896,0.0005055535,0.0059285276,0.0005593967,0.00009714461,0.00028829093,0.00001744721],"about_ca_topic_score_codex":0.0026890018,"about_ca_topic_score_gemma":0.004795852,"teacher_disagreement_score":0.0026890018,"about_ca_system_score_codex":0.00020633628,"about_ca_system_score_gemma":0.0001476756,"threshold_uncertainty_score":0.0060849786},"labels":[],"label_agreement":null},{"id":"W4296795461","doi":"10.3390/s22187087","title":"Generation of Prior Information in a Dual-Mode Microwave-Ultrasound Breast Imaging System","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microwave imaging; Microwave; Breast imaging; Computer science; Dual mode; Ultrasound; Breast ultrasound; Computer vision; Inversion (geology); Artificial intelligence; Acoustics; Mammography; Medical physics; Electronic engineering; Telecommunications; Physics; Breast cancer; Medicine; Engineering; Geology","score_opus":0.0072022039261839905,"score_gpt":0.2008420173713149,"score_spread":0.19363981344513093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296795461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079186216,0.00013899534,0.91905123,0.00012405575,0.000021373173,0.000049432725,0.000094517774,0.00026255165,0.0010715115],"genre_scores_gemma":[0.27235922,0.0002592036,0.7253857,0.00009006561,0.00001710106,0.00009151806,0.00020517565,0.000046256384,0.0015457354],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999561,0.000084502506,0.000016708942,0.00007403663,0.00023067543,0.0000330274],"domain_scores_gemma":[0.9992447,0.00036174798,0.00010005967,0.0001377329,0.000111629306,0.000044143235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008206837,0.00033196973,0.0004141582,0.00050218013,0.00017735374,0.00062748883,0.00053319836,0.00060731085,0.0013449481],"category_scores_gemma":[0.0024325044,0.0004905905,0.00023357393,0.00036289665,0.00042464814,0.00085716246,0.0012882021,0.00077052147,0.0005651076],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066720403,0.00016533927,0.0020569155,0.000295504,0.00004016143,0.00024462328,0.00026254935,0.06939264,0.7768329,0.01121419,0.00070080964,0.13812721],"study_design_scores_gemma":[0.000060277558,0.00037631535,0.0041893264,0.000058168163,0.00004762303,0.00089716265,0.00007149589,0.47383413,0.50961745,0.0037366364,0.0070095183,0.000101946454],"about_ca_topic_score_codex":0.00033385787,"about_ca_topic_score_gemma":0.0005995801,"teacher_disagreement_score":0.0013449481,"about_ca_system_score_codex":0.00027353907,"about_ca_system_score_gemma":0.0004983133,"threshold_uncertainty_score":0.004499316},"labels":[],"label_agreement":null},{"id":"W4296829682","doi":"10.3390/s22197167","title":"An Efficient and Uncertainty-Aware Decision Support System for Disaster Response Using Aerial Imagery","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Decision support system; Uncertainty quantification; Overhead (engineering); Cloud computing; Artificial intelligence; Machine learning","score_opus":0.012552392919505252,"score_gpt":0.2439856061524387,"score_spread":0.23143321323293345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296829682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02993069,0.0002843632,0.96051943,0.00039737878,0.00009963978,0.00013916528,0.00074727094,0.0063938904,0.0014881602],"genre_scores_gemma":[0.5249891,0.00025378182,0.4699434,0.00026349953,0.000108995104,0.00026109588,0.0022949646,0.00008698395,0.0017980995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965274,0.000051143124,0.000034882898,0.00012859366,0.000096107775,0.00003658661],"domain_scores_gemma":[0.9996006,0.000101149904,0.000060916696,0.000068347006,0.00012831856,0.000040647206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065318996,0.00074020185,0.00063196104,0.0008959997,0.0004711052,0.0009293561,0.0010045182,0.0007272093,0.0019474345],"category_scores_gemma":[0.0015420561,0.0002944146,0.0005262646,0.0005458505,0.00024675883,0.0011070225,0.0013191818,0.00086460944,0.0007331305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006354898,0.00025651415,0.0049332813,0.00027809766,0.00014943085,0.0004795456,0.0002941244,0.17633083,0.03647376,0.0061841104,0.02123275,0.75275207],"study_design_scores_gemma":[0.000020419953,0.00006852563,0.0008880473,0.000017110424,0.000028290366,0.000058331472,0.000091035035,0.9842354,0.0068597035,0.0039059832,0.0038073736,0.000019711906],"about_ca_topic_score_codex":0.0033064568,"about_ca_topic_score_gemma":0.004175103,"teacher_disagreement_score":0.0033064568,"about_ca_system_score_codex":0.00048174616,"about_ca_system_score_gemma":0.00090731017,"threshold_uncertainty_score":0.0065743923},"labels":[],"label_agreement":null},{"id":"W4296830594","doi":"10.3390/s22197180","title":"Detecting Gait Events from Accelerations Using Reservoir Computing","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut interdisciplinaire d'innovation technologique; Centre for Interdisciplinary Research in Rehabilitation; Université de Sherbrooke","funders":"","keywords":"Computer science; Inertial measurement unit; Wearable computer; Gait; Ranging; Artificial intelligence; Echo state network; Recurrent neural network; Real-time computing; Algorithm; Artificial neural network; Embedded system","score_opus":0.044590935184042946,"score_gpt":0.276399883833775,"score_spread":0.23180894864973206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296830594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17733842,0.00039133147,0.81946546,0.00012903695,0.0000700766,0.00006424271,0.00023236514,0.0010323505,0.0012767438],"genre_scores_gemma":[0.89581233,0.00021002267,0.10283294,0.000036199188,0.00002072707,0.000065331464,0.00022852795,0.00003504807,0.00075883133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998524,0.000021983262,0.000013107129,0.000044311422,0.000048120888,0.000019945921],"domain_scores_gemma":[0.99957615,0.00024286765,0.00005205144,0.000035280817,0.00007646661,0.000017164122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003208331,0.00043124388,0.00040169427,0.00055615936,0.0001436278,0.00047928307,0.0004944762,0.00031642424,0.00078752707],"category_scores_gemma":[0.0019044933,0.00026491567,0.00030898515,0.0005615395,0.00024398098,0.0007228695,0.00053067086,0.00044466957,0.0001958244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043891903,0.00018794689,0.014035664,0.00021013753,0.00015787716,0.00027472418,0.00019515902,0.57353276,0.033392347,0.0039734403,0.0011913474,0.37240967],"study_design_scores_gemma":[0.0000039504876,0.000030211733,0.0014827672,0.0000055413225,0.0000056396216,0.000029664223,0.000009552393,0.99511,0.0023175543,0.00080526905,0.00019266712,0.000007157967],"about_ca_topic_score_codex":0.0023731082,"about_ca_topic_score_gemma":0.002708875,"teacher_disagreement_score":0.0023731082,"about_ca_system_score_codex":0.00020314127,"about_ca_system_score_gemma":0.00039725268,"threshold_uncertainty_score":0.0047186017},"labels":[],"label_agreement":null},{"id":"W4296831324","doi":"10.3390/s22197155","title":"A Meta-Analysis on Remote HRI and In-Person HRI: What Is a Socially Assistive Robot to Do?","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robot; Human–computer interaction; Human–robot interaction; Computer science; Psychology; Artificial intelligence","score_opus":0.3300878792826564,"score_gpt":0.4697558010594311,"score_spread":0.13966792177677467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296831324","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009983363,0.9853978,0.0017177078,0.00057714724,0.00032852698,0.0002496657,0.0009835119,0.000034444056,0.00072789507],"genre_scores_gemma":[0.46739057,0.5185334,0.005290041,0.003236128,0.0005806508,0.0016215985,0.0023602096,0.0001055162,0.00088188966],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.983973,0.008980856,0.0035205195,0.0018092828,0.0013099709,0.00040641386],"domain_scores_gemma":[0.9547334,0.0377761,0.0037393046,0.0014226562,0.0018967824,0.00043169933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01748327,0.0020079843,0.009265303,0.002954874,0.0008914236,0.0036813286,0.0017185606,0.0022714487,0.00450389],"category_scores_gemma":[0.050906878,0.0008551951,0.041682146,0.0042613517,0.0007648315,0.0021207388,0.0014888445,0.0026309146,0.00040854403],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038171667,0.000047303165,0.013883068,0.19561917,0.7647925,0.00016031155,0.00019487999,0.00042053632,0.00027750357,0.000308291,0.0010628607,0.019416397],"study_design_scores_gemma":[0.000578425,0.00029263753,0.0087402435,0.016159678,0.97071517,0.0001151903,0.00008354515,0.0001484345,0.00013519137,0.00040785904,0.0026018652,0.00002171977],"about_ca_topic_score_codex":0.006725499,"about_ca_topic_score_gemma":0.01335902,"teacher_disagreement_score":0.01748327,"about_ca_system_score_codex":0.0020081133,"about_ca_system_score_gemma":0.0026652163,"threshold_uncertainty_score":0.09246147},"labels":[],"label_agreement":null},{"id":"W4296831502","doi":"10.3390/s22197145","title":"Coaxial Mach–Zehnder Digital Strain Sensor Made from a Tapered Depressed Cladding Fiber","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Financiadora de Estudos e Projetos; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ministério da Ciência, Tecnologia e Inovação; Fundo para o Desenvolvimento Tecnológico das Telecomunicações; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Materials science; Optical fiber; Optics; Extinction ratio; Cladding (metalworking); Free spectral range; Mach–Zehnder interferometer; Interferometry; Fiber optic sensor; Insertion loss; Coaxial; Optoelectronics; Electrical engineering; Physics; Wavelength; Engineering; Composite material","score_opus":0.010727930335857767,"score_gpt":0.20691450148131488,"score_spread":0.19618657114545712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296831502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7743027,0.0008344702,0.22007768,0.00016473648,0.00015708477,0.0001507483,0.00039433452,0.00096691784,0.0029512385],"genre_scores_gemma":[0.8783097,0.00029364647,0.11962993,0.000048792277,0.000027653923,0.000043309057,0.00013424948,0.0000102990725,0.0015024142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996985,0.00002295115,0.000014003958,0.00009396054,0.00015371622,0.000016772485],"domain_scores_gemma":[0.99960893,0.00005651728,0.000111416404,0.000054827764,0.00013845977,0.000029910772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019998982,0.00039431392,0.0003094306,0.00031906302,0.00014210072,0.00032484657,0.00084021373,0.0004127161,0.000451998],"category_scores_gemma":[0.00038052953,0.00025832516,0.000119365126,0.00021829283,0.00029613182,0.00047043993,0.00018336378,0.00030171,0.00017305669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008938715,0.000039225964,0.0011951213,0.00010633811,0.000008020222,0.000073173964,0.000021939735,0.0012548408,0.9816126,0.00040747318,0.00014173,0.015050078],"study_design_scores_gemma":[0.000019872292,0.00035403477,0.0043529975,0.000009692687,0.000027544223,0.0005247173,0.00002247957,0.051064216,0.94126415,0.000096354306,0.0022330151,0.00003107881],"about_ca_topic_score_codex":0.0006021053,"about_ca_topic_score_gemma":0.0012032842,"teacher_disagreement_score":0.00084021373,"about_ca_system_score_codex":0.00040086915,"about_ca_system_score_gemma":0.00031186923,"threshold_uncertainty_score":0.0029085279},"labels":[],"label_agreement":null},{"id":"W4297005985","doi":"10.3390/s22197143","title":"Compact Quad Band MIMO Antenna Design with Enhanced Gain for Wireless Communications","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Diversity gain; MIMO; Antenna (radio); Electronic engineering; Antenna gain; Microstrip; Physics; Computer science; Topology (electrical circuits); Engineering; Electrical engineering; Microstrip antenna; Telecommunications; Antenna factor; Channel (broadcasting)","score_opus":0.029228787146070793,"score_gpt":0.24417562787430927,"score_spread":0.21494684072823847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297005985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10005962,0.00089879904,0.89012873,0.00022543728,0.00019436411,0.00003258979,0.000081995764,0.0005579893,0.007820435],"genre_scores_gemma":[0.76822865,0.00052517944,0.22649364,0.00020659252,0.000101873666,0.0000668409,0.00014839404,0.00004082872,0.0041880542],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998301,0.000036291334,0.000007757431,0.000041825886,0.00005893452,0.00002505835],"domain_scores_gemma":[0.9998318,0.000024820602,0.000044186578,0.000026887577,0.00005908116,0.000013158585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011308633,0.0005080485,0.00035428314,0.00022474292,0.00012821464,0.00040446027,0.00057142135,0.0005199497,0.0009857105],"category_scores_gemma":[0.00021158677,0.00020595694,0.0003858018,0.00028909443,0.00015153617,0.0004384508,0.00029712505,0.0003636211,0.00096778694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000272876,0.000080795944,0.0015641053,0.00024624658,0.00013705424,0.00037936127,0.00009535191,0.046228275,0.8136973,0.012263025,0.0022954997,0.12274008],"study_design_scores_gemma":[0.00008895898,0.0019320817,0.0042197187,0.000044311804,0.00014575817,0.004002513,0.00013470223,0.6228554,0.32489684,0.0053009363,0.03627198,0.00010680016],"about_ca_topic_score_codex":0.000120944176,"about_ca_topic_score_gemma":0.000180957,"teacher_disagreement_score":0.0009857105,"about_ca_system_score_codex":0.00024391492,"about_ca_system_score_gemma":0.000119854536,"threshold_uncertainty_score":0.0032975078},"labels":[],"label_agreement":null},{"id":"W4297006247","doi":"10.3390/s22197169","title":"Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Obfuscation; Cloud computing; Computer science; Energy consumption; Distributed computing; Computer security; Operating system; Engineering","score_opus":0.039289098708354155,"score_gpt":0.29945222345710154,"score_spread":0.26016312474874737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297006247","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30774295,0.0024852336,0.6605018,0.0024279968,0.00052231573,0.0003398,0.00033233064,0.0029418296,0.022705618],"genre_scores_gemma":[0.9559284,0.00046503765,0.040750735,0.00014694284,0.000044475313,0.0000635646,0.00010838347,0.000061031846,0.002431498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880767,0.00022319067,0.00004676203,0.00022263476,0.00037352246,0.00032617227],"domain_scores_gemma":[0.99928707,0.00022206799,0.00008615114,0.00016820444,0.0001614373,0.000075178265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009011088,0.0006244676,0.0006086772,0.00077090564,0.0013003784,0.001254095,0.00076110096,0.0006086778,0.0019196732],"category_scores_gemma":[0.0021426016,0.00019593663,0.00059273426,0.0008554795,0.00077766535,0.0018533433,0.0018151643,0.00080931693,0.0003355423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019159438,0.00046622974,0.027451377,0.0006088227,0.00026008286,0.0025212592,0.0011291215,0.3199072,0.0656572,0.07046705,0.015151307,0.49446437],"study_design_scores_gemma":[0.000035117097,0.00011804599,0.0049055973,0.000049438342,0.000064551066,0.00038056754,0.0005235335,0.93239254,0.028768525,0.019406263,0.013295698,0.00006014979],"about_ca_topic_score_codex":0.0059969407,"about_ca_topic_score_gemma":0.0055262055,"teacher_disagreement_score":0.0059969407,"about_ca_system_score_codex":0.0012742019,"about_ca_system_score_gemma":0.0016219112,"threshold_uncertainty_score":0.011924088},"labels":[],"label_agreement":null},{"id":"W4297237151","doi":"10.3390/s22197274","title":"Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"","keywords":"Breathing; Respiratory monitoring; Medicine; Respiratory system; Respiratory rate; Ventilation (architecture); Exhalation; Continuous monitoring; Biomedical engineering; Cardiology; Audiology; Anesthesia; Internal medicine; Heart rate","score_opus":0.014139625672354297,"score_gpt":0.25449582817137056,"score_spread":0.24035620249901626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297237151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87923205,0.008667295,0.106484264,0.0012316124,0.00025398217,0.00018303648,0.00024927122,0.00046941784,0.0032290164],"genre_scores_gemma":[0.92664146,0.002467491,0.06934294,0.00041325952,0.00011042772,0.00005191343,0.00008403783,0.000016864708,0.00087153184],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997727,0.00007908641,0.000010378015,0.00006257813,0.00005875185,0.000016470853],"domain_scores_gemma":[0.9995944,0.00020032622,0.000072038725,0.000023539846,0.00006388355,0.000045823803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066676363,0.0003963541,0.00029965377,0.00055529753,0.00021008991,0.00035369105,0.00036646362,0.00084819715,0.00094227336],"category_scores_gemma":[0.0010007237,0.00021225783,0.00021313898,0.00035180582,0.0003120376,0.00038616848,0.0003139117,0.00026075862,0.00014227656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079956366,0.0004656459,0.046397652,0.00045423495,0.000069184105,0.00021737766,0.00019681781,0.0023392686,0.7743939,0.00037136,0.0006595807,0.17363544],"study_design_scores_gemma":[0.00018601416,0.008247035,0.32616374,0.00023441334,0.00047315733,0.00434708,0.0009089253,0.13711366,0.50747305,0.0027419697,0.011937569,0.00017341865],"about_ca_topic_score_codex":0.0006745054,"about_ca_topic_score_gemma":0.0018254747,"teacher_disagreement_score":0.00094227336,"about_ca_system_score_codex":0.00017069447,"about_ca_system_score_gemma":0.00021003083,"threshold_uncertainty_score":0.0035262704},"labels":[],"label_agreement":null},{"id":"W4297474248","doi":"10.3390/s22197332","title":"Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Cryptography; Crossover; Computer science; Key space; Redundancy (engineering); Population; Theoretical computer science; Key (lock); ENCODE; DNA computing; Algorithm; Biology; Computer security; Genetics; Artificial intelligence; Gene","score_opus":0.019149735593029616,"score_gpt":0.2314581414857692,"score_spread":0.21230840589273958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297474248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019427529,0.0007001979,0.9765736,0.00023016118,0.000057863035,0.000058970414,0.000023881894,0.00021888551,0.002708945],"genre_scores_gemma":[0.33572435,0.0011297307,0.65831643,0.00017704432,0.000052267365,0.00022214596,0.00008934907,0.00009523848,0.00419334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994435,0.00019925552,0.000022626946,0.00009073084,0.0002001875,0.000043700682],"domain_scores_gemma":[0.9994797,0.0002710901,0.000058419006,0.00005849538,0.00011354407,0.000018748064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000823525,0.0005992833,0.00070394465,0.00114471,0.0004339021,0.00086223835,0.0011535392,0.0012593331,0.0013616801],"category_scores_gemma":[0.002596639,0.0003147118,0.0006742956,0.00085452467,0.0009020082,0.0013878834,0.0007951046,0.0009310955,0.00043379914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007991533,0.00006413703,0.0010635204,0.0001430409,0.00010103035,0.0001279977,0.00013666629,0.75652665,0.014383437,0.08304202,0.00084875064,0.14348279],"study_design_scores_gemma":[0.00002572982,0.000097996315,0.00018432105,0.000027580018,0.000032273376,0.0001774843,0.000033631957,0.95985603,0.0048020193,0.031561557,0.0031772014,0.000024193436],"about_ca_topic_score_codex":0.0011035469,"about_ca_topic_score_gemma":0.0010123164,"teacher_disagreement_score":0.0013616801,"about_ca_system_score_codex":0.00074855564,"about_ca_system_score_gemma":0.0009372726,"threshold_uncertainty_score":0.005431235},"labels":[],"label_agreement":null},{"id":"W4297474739","doi":"10.3390/s22197328","title":"Multi-Modal Deep Learning for Assessing Surgeon Technical Skill","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Victoria Regional Health Centre; University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Knot tying; Artificial intelligence; Computer science; Machine learning; Replicate; Deep learning; Task (project management); Modal; Tying; Intraclass correlation; Engineering; Statistics; Mathematics; Medicine; Surgery; Psychometrics","score_opus":0.040661083474357436,"score_gpt":0.342800608090413,"score_spread":0.3021395246160556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297474739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4425195,0.0021485335,0.53999144,0.00069612975,0.00031977886,0.00035232145,0.004717213,0.004249008,0.005006096],"genre_scores_gemma":[0.88867974,0.0003695314,0.101792954,0.00030283156,0.000056530273,0.00025674314,0.0048956894,0.000092713475,0.003553298],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934167,0.00018839806,0.00003767997,0.0001807447,0.00017275078,0.000078798876],"domain_scores_gemma":[0.99899274,0.00042534506,0.00010953626,0.00013016563,0.0002769541,0.000065162836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013005678,0.0010714362,0.00038333217,0.0007124304,0.00021996618,0.00057198096,0.0008082153,0.000980181,0.0014076346],"category_scores_gemma":[0.003748883,0.00021688043,0.00061717094,0.00048652245,0.00029938103,0.0006921095,0.0009641709,0.0011303675,0.00062759884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088224025,0.00095244014,0.023173515,0.00045606386,0.00035139758,0.00026587464,0.0002996183,0.27834216,0.042946357,0.0016346188,0.014897185,0.6357985],"study_design_scores_gemma":[0.000024259587,0.00023527475,0.012087017,0.000044724715,0.000044782333,0.00011731277,0.000076410295,0.9694587,0.013072671,0.0025712997,0.0022280593,0.000039511226],"about_ca_topic_score_codex":0.0059586563,"about_ca_topic_score_gemma":0.010562318,"teacher_disagreement_score":0.0059586563,"about_ca_system_score_codex":0.0007376025,"about_ca_system_score_gemma":0.00065893633,"threshold_uncertainty_score":0.011847913},"labels":[],"label_agreement":null},{"id":"W4297491420","doi":"10.3390/s22197322","title":"Using Deep Learning for Task and Tremor Type Classification in People with Parkinson’s Disease","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada; Ministero dello Sviluppo Economico; California HIV/AIDS Research Program","keywords":"Task (project management); Wearable computer; Motion (physics); Computer science; Artificial intelligence; Physical medicine and rehabilitation; Identification (biology); Parkinson's disease; Electromyography; Machine learning; Disease; Medicine; Engineering","score_opus":0.0394549625770126,"score_gpt":0.2842871322531323,"score_spread":0.2448321696761197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297491420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9703946,0.00049392343,0.027574059,0.00018220003,0.000030211786,0.00003231588,0.00018343845,0.00017429178,0.0009349059],"genre_scores_gemma":[0.9941906,0.00009605333,0.0049408902,0.000041399464,0.0000073013925,0.000013937909,0.00018063858,0.0000036841654,0.0005254622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997403,0.00007666019,0.000022861057,0.000071479146,0.000032348747,0.000056355588],"domain_scores_gemma":[0.9997174,0.00015035295,0.000026592026,0.00002385959,0.000055403198,0.000026357773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077180204,0.00059976586,0.0005143571,0.0005543238,0.00020702444,0.0003882543,0.00028250934,0.0006266724,0.0004190067],"category_scores_gemma":[0.0017051728,0.00018526602,0.00040829604,0.0002687812,0.00014254272,0.0003735762,0.00053182745,0.00044191946,0.00018182436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020286439,0.00089548394,0.21275398,0.00017775754,0.00039521419,0.0006394858,0.00058529316,0.09760028,0.019431524,0.00034062477,0.001826113,0.66332567],"study_design_scores_gemma":[0.000030453775,0.0003563337,0.05906353,0.00003363578,0.00009705892,0.00016808127,0.00017622276,0.9347134,0.004139723,0.0008203467,0.00038076806,0.000020418534],"about_ca_topic_score_codex":0.006649133,"about_ca_topic_score_gemma":0.007811878,"teacher_disagreement_score":0.006649133,"about_ca_system_score_codex":0.00027979026,"about_ca_system_score_gemma":0.00025400595,"threshold_uncertainty_score":0.013220847},"labels":[],"label_agreement":null},{"id":"W4297504217","doi":"10.3390/s22197393","title":"Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Innovations in Aquaponics and Hydroponics Systems","field":"Agricultural and Biological Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Aquaponics; Computer science; Automation; The Internet; Real-time computing; Embedded system; Engineering; Operating system","score_opus":0.016148606825158732,"score_gpt":0.21392633758728982,"score_spread":0.1977777307621311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297504217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15480678,0.00032513659,0.83186525,0.00023961313,0.00010611242,0.00014703757,0.00009773661,0.0018269033,0.010585418],"genre_scores_gemma":[0.8351513,0.00020315775,0.16007137,0.000078157296,0.000019142493,0.0000753399,0.00013802077,0.00006310813,0.004200463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994468,0.000087339045,0.000050296807,0.00015704769,0.0002098202,0.00004868039],"domain_scores_gemma":[0.99964404,0.00007719685,0.00004038572,0.00008919339,0.00009909588,0.00005005615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005992885,0.00032268313,0.0004566801,0.00033400368,0.00039223317,0.0014011853,0.0009472648,0.00039203,0.0019174827],"category_scores_gemma":[0.00095809874,0.00026658335,0.00033004195,0.00040267344,0.00087279826,0.0020379063,0.0016014864,0.00057622057,0.00028862432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010513352,0.00033692448,0.0067789853,0.00041076675,0.00008637345,0.0010387321,0.0010966365,0.32263458,0.25958413,0.07875385,0.0027229025,0.32550487],"study_design_scores_gemma":[0.000056968114,0.00047418114,0.0024600734,0.000035325076,0.00005532376,0.00034687697,0.00021679098,0.8638851,0.091394514,0.014996044,0.026030337,0.000048505786],"about_ca_topic_score_codex":0.002527328,"about_ca_topic_score_gemma":0.0023056704,"teacher_disagreement_score":0.002527328,"about_ca_system_score_codex":0.0008128091,"about_ca_system_score_gemma":0.00072841096,"threshold_uncertainty_score":0.0064145923},"labels":[],"label_agreement":null},{"id":"W4297880094","doi":"10.3390/s22197128","title":"The Effect of Fractionation during the Vacuum Deposition of Stabilized Amorphous Selenium Alloy Photoconductors on the Overall Charge Collection Efficiency","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deposition (geology); Materials science; Fractionation; Substrate (aquarium); Alloy; Amorphous solid; Selenium; Analytical Chemistry (journal); Range (aeronautics); Chemistry; Metallurgy; Composite material; Crystallography","score_opus":0.00571552263909065,"score_gpt":0.20615761958470874,"score_spread":0.2004420969456181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297880094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99739957,0.00065652997,0.0012849723,0.000025646352,0.000010934807,0.000008497369,0.00008239048,0.000052440344,0.00047898712],"genre_scores_gemma":[0.99829084,0.00035191307,0.00084177335,0.000023610435,0.00000411704,0.00000768659,0.000058735415,0.000026682324,0.00039460615],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997522,0.000018798512,0.000023871937,0.00007118544,0.00008111541,0.000052761363],"domain_scores_gemma":[0.99930453,0.00033543655,0.00010219451,0.0000901571,0.00014372727,0.000023830318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029240528,0.0002826058,0.00030669887,0.0001882781,0.0002435949,0.00040287484,0.00032012488,0.0002433456,0.0007797134],"category_scores_gemma":[0.0013243877,0.00020301773,0.0002164359,0.0003534649,0.0002010009,0.00042631087,0.00025141155,0.00026892527,0.00018340784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030130075,0.000026811955,0.0021614365,0.00007764047,0.000018834799,0.00014783327,0.00012672061,0.0012401016,0.9875848,0.00012438615,0.00006406595,0.008126021],"study_design_scores_gemma":[0.000006351549,0.00029473545,0.0072973925,0.0000066396533,0.000022571712,0.00007530724,0.000046051515,0.0025652635,0.9891704,0.000023486338,0.00048487302,0.0000069224498],"about_ca_topic_score_codex":0.0022175235,"about_ca_topic_score_gemma":0.0021727993,"teacher_disagreement_score":0.0022175235,"about_ca_system_score_codex":0.00069521856,"about_ca_system_score_gemma":0.00020330248,"threshold_uncertainty_score":0.0050441623},"labels":[],"label_agreement":null},{"id":"W4303980470","doi":"10.3390/s22197440","title":"Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet++","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Multispectral image; Remote sensing; Satellite imagery; Vegetation (pathology); Environmental science; Segmentation; Normalized Difference Vegetation Index; Image segmentation; RGB color model; Computer science; Artificial intelligence; Geography; Ecology; Leaf area index; Biology","score_opus":0.009353029388970778,"score_gpt":0.22715173825869578,"score_spread":0.217798708869725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303980470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.406786,0.0010604779,0.5558707,0.00043508975,0.00023808189,0.00029840125,0.0036513829,0.023940038,0.0077197333],"genre_scores_gemma":[0.5470329,0.00038182872,0.43577653,0.00035426332,0.00006199722,0.00018937627,0.009484964,0.00048053433,0.0062374817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982905,0.000014797488,0.000011213896,0.00006644081,0.000049253467,0.000029218416],"domain_scores_gemma":[0.99983335,0.00002908332,0.000019628938,0.000030786734,0.00007346704,0.000013646114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025842298,0.0008067399,0.00041111987,0.0008453075,0.00019367189,0.00046512837,0.00079354685,0.00047776164,0.0017809286],"category_scores_gemma":[0.0005742892,0.00027465963,0.00060501724,0.00055150344,0.00015589016,0.0006501631,0.00045605196,0.0004639976,0.00071009964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005112502,0.00026959355,0.007890142,0.00024714784,0.00014258751,0.00028180305,0.00011388272,0.092251085,0.11034339,0.0006948124,0.008934531,0.7783197],"study_design_scores_gemma":[0.00002179263,0.00012277091,0.00735697,0.000011134906,0.00004059214,0.00013215572,0.000031317195,0.957266,0.031224905,0.00040837243,0.0033654792,0.00001842768],"about_ca_topic_score_codex":0.009455626,"about_ca_topic_score_gemma":0.01733394,"teacher_disagreement_score":0.009455626,"about_ca_system_score_codex":0.00034999519,"about_ca_system_score_gemma":0.00032974628,"threshold_uncertainty_score":0.018801153},"labels":[],"label_agreement":null},{"id":"W4303980478","doi":"10.3390/s22197472","title":"Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Chicoutimi; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Work (physics); Wearable technology; Risk analysis (engineering); Computer science; Field (mathematics); Occupational safety and health; Computer security; Engineering; Business; Medicine; Embedded system","score_opus":0.10233526403842723,"score_gpt":0.4075338743597866,"score_spread":0.30519861032135936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303980478","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001222202,0.99892515,0.00019192595,0.00016411336,0.00010248798,0.000008121214,0.000021157579,0.000006948947,0.00045781574],"genre_scores_gemma":[0.00092509633,0.99829227,0.0003115585,0.00012704835,0.000108118096,0.000011987076,0.000026594767,0.0000017974758,0.00019551128],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.999458,0.00010166367,0.0001087578,0.000121502235,0.00017991116,0.000030190238],"domain_scores_gemma":[0.9974158,0.0018383636,0.00024183483,0.00004283723,0.0004123101,0.00004879454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013087734,0.0011548585,0.0018139577,0.004010912,0.00032683872,0.0012856813,0.0012329392,0.0014953968,0.00437078],"category_scores_gemma":[0.003420249,0.00046909586,0.001589405,0.0037678347,0.00051095,0.0018283955,0.00074683345,0.0011474218,0.0014778284],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075338285,0.00008507428,0.00051942235,0.07084123,0.00023074367,0.00011938366,0.00014390273,0.00024923487,0.0009794768,0.0015614795,0.010977091,0.91421753],"study_design_scores_gemma":[0.0000350808,0.00043017225,0.006778964,0.08744278,0.0022001013,0.0032454357,0.00048424664,0.0005594187,0.001834725,0.0035152757,0.89336103,0.00011284751],"about_ca_topic_score_codex":0.001633246,"about_ca_topic_score_gemma":0.0021785237,"teacher_disagreement_score":0.00437078,"about_ca_system_score_codex":0.0004925822,"about_ca_system_score_gemma":0.0013762317,"threshold_uncertainty_score":0.014621735},"labels":[],"label_agreement":null},{"id":"W4303980543","doi":"10.3390/s22197428","title":"An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; University of Guelph","keywords":"Random forest; Feature selection; Support vector machine; Computer science; Artificial intelligence; Feature (linguistics); Data mining; Classifier (UML); Pattern recognition (psychology); Decision tree; Machine learning","score_opus":0.012708954255724115,"score_gpt":0.23536673140665243,"score_spread":0.22265777715092833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303980543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11641017,0.00022677594,0.8789174,0.0001463913,0.000046667767,0.000076130345,0.00013804303,0.001195784,0.0028425853],"genre_scores_gemma":[0.8021727,0.00008611326,0.19697528,0.000040095718,0.000018454068,0.00005062212,0.00013809976,0.00003458848,0.00048406917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990909,0.00019679709,0.000043401687,0.0002443214,0.00028525686,0.00013942234],"domain_scores_gemma":[0.99947673,0.00010667953,0.000068069705,0.00009854235,0.00022235469,0.000027714152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016042596,0.0006579859,0.00064763863,0.00091067713,0.00042556782,0.00074543274,0.0008373911,0.0007566596,0.0007249387],"category_scores_gemma":[0.0015158918,0.00024541278,0.000656665,0.0007883268,0.00039271693,0.0016430786,0.0013054606,0.00054047373,0.00030944918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078372314,0.00023397124,0.010586069,0.00025726558,0.00018725825,0.0005367738,0.00058061676,0.24642038,0.28492543,0.012431591,0.0022817424,0.44077522],"study_design_scores_gemma":[0.000029785891,0.00030567494,0.006035338,0.00002328669,0.00009348859,0.00031758,0.0001170172,0.905518,0.07758526,0.0061015356,0.0038096176,0.000063441934],"about_ca_topic_score_codex":0.001365255,"about_ca_topic_score_gemma":0.0010357146,"teacher_disagreement_score":0.0016042596,"about_ca_system_score_codex":0.00030905675,"about_ca_system_score_gemma":0.0003921466,"threshold_uncertainty_score":0.008484244},"labels":[],"label_agreement":null},{"id":"W4303981168","doi":"10.3390/s22197571","title":"SUDOSCAN, an Innovative, Simple and Non-Invasive Medical Device for Assessing Sudomotor Function","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sudomotor; Medicine; Glycated hemoglobin; Peripheral neuropathy; Diabetes mellitus; Internal medicine; Diabetic neuropathy; Skin conductance; Cardiology; Physical therapy; Type 2 diabetes; Endocrinology","score_opus":0.024347074509455568,"score_gpt":0.3084258811766402,"score_spread":0.28407880666718466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303981168","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9129312,0.015719678,0.052568994,0.00049816776,0.0006320002,0.00074166886,0.0046440903,0.00092129107,0.011342942],"genre_scores_gemma":[0.9331017,0.00495788,0.05246043,0.0006569197,0.00038382728,0.0004725589,0.0017667306,0.00004858289,0.0061512943],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994831,0.00015375184,0.000042162934,0.00008218605,0.0002130094,0.000025727295],"domain_scores_gemma":[0.99955016,0.00015790664,0.00011685577,0.000029634035,0.00009526918,0.00005008626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005323584,0.0005037438,0.0005479405,0.000996904,0.00017693653,0.00036585864,0.0003391055,0.00032402505,0.0013194977],"category_scores_gemma":[0.0009158668,0.00013813602,0.0002734603,0.0006071279,0.00018840577,0.00024888298,0.0004984473,0.00032181462,0.00023192423],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003655148,0.0009806819,0.29100916,0.0017194276,0.0005495752,0.0011402617,0.00048783713,0.0012083941,0.24556404,0.00092461536,0.007351643,0.4454093],"study_design_scores_gemma":[0.00071476016,0.009971663,0.86456597,0.00036934446,0.0008285351,0.0149218,0.0005363327,0.015921148,0.067435354,0.0010823467,0.023418013,0.00023460534],"about_ca_topic_score_codex":0.000868936,"about_ca_topic_score_gemma":0.0019911323,"teacher_disagreement_score":0.0013194977,"about_ca_system_score_codex":0.00017205122,"about_ca_system_score_gemma":0.0002922063,"threshold_uncertainty_score":0.004414141},"labels":[],"label_agreement":null},{"id":"W4303981198","doi":"10.3390/s22197500","title":"Caregivers’ Profiles Based on the Canadian Occupational Performance Measure for the Adoption of Assistive Technologies","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perception; Identification (biology); Psychological intervention; Psychology; Hierarchy; Applied psychology; Measure (data warehouse); Sample (material); Cluster (spacecraft); Assistive technology; Hierarchical clustering; Multilevel model; Computer science; Human–computer interaction; Cluster analysis; Data mining; Artificial intelligence; Psychiatry; Machine learning","score_opus":0.08721200286311406,"score_gpt":0.36301507507973546,"score_spread":0.27580307221662137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303981198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98760104,0.00018853789,0.0013826938,0.00014301368,0.000019403979,0.0006181872,0.0035396535,0.00002642033,0.006481071],"genre_scores_gemma":[0.99030834,0.00028612316,0.0036395425,0.000025491569,0.0000067881474,0.00055977574,0.003144737,0.0000059569747,0.0020230405],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99860674,0.00019680511,0.0001932376,0.00007292151,0.00074273295,0.00018768388],"domain_scores_gemma":[0.9969553,0.00023817382,0.00059689116,0.00010923585,0.0017820287,0.0003184657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017139136,0.00044112466,0.0004008231,0.0024488461,0.0013514542,0.0006542307,0.0003992664,0.00020030298,0.002559872],"category_scores_gemma":[0.0061080786,0.00015475864,0.0005716654,0.0020821076,0.0002510898,0.00028366558,0.0008153326,0.00035062156,0.0004913524],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018384127,0.00012729551,0.9509025,0.00012048399,0.000042154887,0.00015158698,0.003868068,0.00034726894,0.0009089197,0.00033524007,0.00330165,0.039711002],"study_design_scores_gemma":[0.000012884601,0.00013704714,0.9901025,0.000049188562,0.000017018268,0.00024386696,0.0035714083,0.0007312852,0.000613598,0.00016279441,0.0043317922,0.000026490587],"about_ca_topic_score_codex":0.15036587,"about_ca_topic_score_gemma":0.21203326,"teacher_disagreement_score":0.8496341,"about_ca_system_score_codex":0.001881927,"about_ca_system_score_gemma":0.0032079802,"threshold_uncertainty_score":0.2989813},"labels":[],"label_agreement":null},{"id":"W4303982392","doi":"10.3390/s22197655","title":"Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; University of Victoria","funders":"","keywords":"Computer science; Authentication (law); Biometrics; Computer security; Context (archaeology); Counterfeit; Human–computer interaction; Artificial intelligence; Machine learning; Multimedia","score_opus":0.03581303710179541,"score_gpt":0.3035662580561431,"score_spread":0.2677532209543477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303982392","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002509793,0.8024809,0.122486,0.00728066,0.008704602,0.00014539948,0.00028659034,0.00054641697,0.055559635],"genre_scores_gemma":[0.024039345,0.7312465,0.10786807,0.007394925,0.025087537,0.00042156558,0.0007235564,0.00050708384,0.10271143],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999527,0.00014037648,0.00003469783,0.000111709414,0.0001492262,0.000037147995],"domain_scores_gemma":[0.9993418,0.0004264337,0.000039333834,0.000027836857,0.00012421906,0.000040323714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009068892,0.0022500502,0.0009922378,0.0021271969,0.00044269275,0.0018644638,0.00090110535,0.0020786407,0.008112115],"category_scores_gemma":[0.0013555611,0.000685942,0.0008852033,0.0022970976,0.00091089105,0.0039795986,0.0009884033,0.003877934,0.006668773],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012062431,0.00052627403,0.0010606749,0.0026884833,0.0001748798,0.00051039056,0.00047399706,0.008197316,0.0038533485,0.09548783,0.30905083,0.5778552],"study_design_scores_gemma":[0.000017113009,0.00027808547,0.002488588,0.0016893356,0.00004542713,0.0009787582,0.00013867322,0.013732225,0.0011488978,0.05818521,0.92119235,0.000105198436],"about_ca_topic_score_codex":0.0011152682,"about_ca_topic_score_gemma":0.0012021231,"teacher_disagreement_score":0.008112115,"about_ca_system_score_codex":0.0008757943,"about_ca_system_score_gemma":0.00041119312,"threshold_uncertainty_score":0.027137697},"labels":[],"label_agreement":null},{"id":"W4303982406","doi":"10.3390/s22197596","title":"Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Independent component analysis; Support vector machine; Artificial intelligence; Classifier (UML); Computer science; Component (thermodynamics); Pattern recognition (psychology); Electroencephalography; Component analysis; Machine learning; Speech recognition; Psychology; Neuroscience","score_opus":0.06329387871427461,"score_gpt":0.288903879493987,"score_spread":0.22561000077971238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303982406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15031669,0.00069429376,0.843181,0.00021678656,0.00021812039,0.00028403994,0.0005127597,0.0027230764,0.0018532387],"genre_scores_gemma":[0.6887751,0.0003487765,0.306172,0.00008271671,0.0001066165,0.00037777354,0.0016176678,0.00009040886,0.002429037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931645,0.00012645872,0.00007715389,0.00017899854,0.00020411321,0.00009684338],"domain_scores_gemma":[0.99917275,0.00023971463,0.00006496886,0.00008004403,0.00041260663,0.000029928216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008582343,0.0009273642,0.0008345032,0.0012610051,0.00023539132,0.0006590217,0.0006461057,0.00074460026,0.0011219489],"category_scores_gemma":[0.002048392,0.00017438448,0.00086267886,0.0012161522,0.000204035,0.0006478531,0.00046225844,0.00074668013,0.0006141265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005705144,0.00037213232,0.0041684355,0.00014737269,0.00011385831,0.00015544222,0.000053944048,0.04238718,0.042238127,0.0010137073,0.0045535835,0.90422565],"study_design_scores_gemma":[0.000023268729,0.00020527189,0.004399144,0.000009527962,0.000030417163,0.000090234964,0.000026712813,0.982444,0.011381695,0.0005098585,0.0008636206,0.000016266515],"about_ca_topic_score_codex":0.0023568,"about_ca_topic_score_gemma":0.0015886736,"teacher_disagreement_score":0.0023568,"about_ca_system_score_codex":0.00024085153,"about_ca_system_score_gemma":0.00060517003,"threshold_uncertainty_score":0.0046862364},"labels":[],"label_agreement":null},{"id":"W4304187267","doi":"10.3390/s22207696","title":"Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Detector; Autoencoder; Microchannel; Computer science; Natural gas; Microfluidics; Hydrogen; Compressed natural gas; Modular design; Materials science; Artificial intelligence; Environmental science; Process engineering; Nanotechnology; Deep learning; Chemistry; Engineering; Mechanical engineering; Telecommunications","score_opus":0.0328877423752223,"score_gpt":0.24246540955017407,"score_spread":0.20957766717495177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304187267","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03077835,0.00033073607,0.96724427,0.00022272162,0.00003234015,0.000037890106,0.00004772172,0.00043082386,0.0008751069],"genre_scores_gemma":[0.7735137,0.0004832065,0.2214172,0.0003374054,0.000093557304,0.0002452236,0.00037449374,0.00005831441,0.003476898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996679,0.00008212054,0.000022229662,0.00010441417,0.000080077436,0.000043241896],"domain_scores_gemma":[0.9994072,0.00031229042,0.000055974837,0.000032527474,0.00016310895,0.000028879042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010186456,0.00072298385,0.00075544417,0.00032579346,0.00021246982,0.00047028792,0.0010590375,0.0010609814,0.0007924213],"category_scores_gemma":[0.0018298927,0.00036473764,0.00066319184,0.00031308198,0.0005099829,0.000776667,0.0007422829,0.0013245606,0.00027178536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001131413,0.00012016008,0.0013578918,0.00007643202,0.000073464915,0.00010436732,0.000067856134,0.8844095,0.012399737,0.0039563794,0.0007165954,0.09660449],"study_design_scores_gemma":[0.0000017607416,0.000012568719,0.000053473454,0.0000012391484,0.0000024683936,0.000004048391,0.0000013856497,0.9991035,0.0005202071,0.00023608538,0.000061319195,0.0000018566534],"about_ca_topic_score_codex":0.0064933,"about_ca_topic_score_gemma":0.0040892116,"teacher_disagreement_score":0.0064933,"about_ca_system_score_codex":0.00051519444,"about_ca_system_score_gemma":0.0010055514,"threshold_uncertainty_score":0.012911022},"labels":[],"label_agreement":null},{"id":"W4304689290","doi":"10.3390/s22207730","title":"A Formal Energy Consumption Analysis to Secure Cluster-Based WSN: A Case Study of Multi-Hop Clustering Algorithm Based on Spectral Classification Using Lightweight Blockchain","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Wireless sensor network; Energy consumption; Cluster analysis; Distributed computing; Computer network; Node (physics); Efficient energy use; Load balancing (electrical power); Routing protocol; Algorithm; Routing (electronic design automation); Mathematics; Artificial intelligence; Engineering","score_opus":0.0322359161602107,"score_gpt":0.2723842988274946,"score_spread":0.2401483826672839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304689290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04120859,0.00047986695,0.94113433,0.00078743254,0.000056195433,0.00014485525,0.00007206002,0.00009630758,0.016020387],"genre_scores_gemma":[0.86774075,0.00066147477,0.12168425,0.000098748504,0.00006272002,0.00018898724,0.000073568095,0.00007728347,0.009412226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884427,0.00046899804,0.000056186913,0.00013335935,0.00037077314,0.00012640523],"domain_scores_gemma":[0.9977787,0.0013927912,0.00018868121,0.0003094488,0.00026215208,0.000068306545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016424685,0.00053230056,0.0003743996,0.0006986204,0.00084926107,0.0018763292,0.0011206613,0.0010364382,0.0030534053],"category_scores_gemma":[0.0036118736,0.00027254564,0.00084967504,0.00097354193,0.0025609084,0.0028624376,0.0013420787,0.0014243617,0.00035087368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055551893,0.00008297275,0.0006779014,0.00009726174,0.00001674866,0.00036135985,0.00028529268,0.19805777,0.0035504387,0.78423345,0.000626404,0.011954813],"study_design_scores_gemma":[0.0000134948605,0.0000954474,0.0002556681,0.000040525585,0.000016279491,0.00023853603,0.00015336671,0.80494606,0.002323571,0.18575896,0.0061383233,0.000019770041],"about_ca_topic_score_codex":0.002207185,"about_ca_topic_score_gemma":0.0019235298,"teacher_disagreement_score":0.0030534053,"about_ca_system_score_codex":0.0020255751,"about_ca_system_score_gemma":0.0010600989,"threshold_uncertainty_score":0.014696658},"labels":[],"label_agreement":null},{"id":"W4304782139","doi":"10.3390/s22207726","title":"Towards Developing a Robust Intrusion Detection Model Using Hadoop–Spark and Data Augmentation for IoT Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Cistel Technology (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SPARK (programming language); Computer science; Robustness (evolution); Intrusion detection system; Big data; Data mining; Machine learning; Anomaly detection; Artificial intelligence; Key (lock); Internet of Things","score_opus":0.07873728330986736,"score_gpt":0.2934666326543262,"score_spread":0.21472934934445886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304782139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037417784,0.00042013812,0.95606726,0.000833815,0.00013057175,0.000142203,0.00030817368,0.0036315355,0.0010485637],"genre_scores_gemma":[0.7266856,0.00043991144,0.26820132,0.0005548188,0.00015834214,0.00032898426,0.0009494434,0.00029593776,0.0023855441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990765,0.00018507412,0.000049132075,0.000310852,0.00024566156,0.00013267786],"domain_scores_gemma":[0.99824715,0.0007448069,0.00019991814,0.00021361899,0.0004868193,0.0001077022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030745848,0.0011328085,0.0013578009,0.0009146128,0.00055722374,0.0014363704,0.0024617994,0.0009311221,0.000765973],"category_scores_gemma":[0.004555011,0.000653605,0.0012569202,0.0007023236,0.0010193135,0.0024576788,0.0015238393,0.0021264923,0.00037289035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015316615,0.00014430913,0.0020385345,0.0000676762,0.00008992511,0.000065539745,0.00006360514,0.94941646,0.0028433027,0.0065463944,0.0026036652,0.035967413],"study_design_scores_gemma":[0.0000021468984,0.0000076215556,0.00004441582,0.000001539173,0.0000027058654,0.0000061833357,0.000002675512,0.9975889,0.00029311536,0.0019145064,0.00013361668,0.000002621732],"about_ca_topic_score_codex":0.0069115777,"about_ca_topic_score_gemma":0.006007566,"teacher_disagreement_score":0.0069115777,"about_ca_system_score_codex":0.0013079229,"about_ca_system_score_gemma":0.0018207202,"threshold_uncertainty_score":0.016260147},"labels":[],"label_agreement":null},{"id":"W4306398587","doi":"10.3390/s22207837","title":"Design and Analysis of Multi-User Faster-Than-Nyquist-DCSK Communication Systems over Multi-Path Fading Channels","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Nyquist–Shannon sampling theorem; Spectral efficiency; Communications system; Fading; Nyquist frequency; Electronic engineering; Sampling (signal processing); Systems design; Keying; Wireless; Telecommunications; Channel (broadcasting); Bandwidth (computing); Engineering; Detector","score_opus":0.03503951358191109,"score_gpt":0.25961076570316427,"score_spread":0.22457125212125317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306398587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06223802,0.00068304944,0.9316507,0.00018191528,0.00003301131,0.00006054178,0.000043889064,0.00012905472,0.0049798205],"genre_scores_gemma":[0.90690136,0.0007724902,0.089281045,0.00004922486,0.00003086047,0.00006828221,0.000044875087,0.000022148735,0.0028298558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996669,0.000076222284,0.00001360581,0.000047484005,0.0001612123,0.000034494817],"domain_scores_gemma":[0.99958616,0.00017713345,0.000076290446,0.000036641715,0.00010993841,0.000013800134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033724523,0.00031998393,0.00032772426,0.00023536348,0.00031436692,0.000537501,0.00032887197,0.00037459863,0.0012248637],"category_scores_gemma":[0.00091007166,0.00017532613,0.00018671916,0.00023693754,0.00041429538,0.00059817056,0.00029186718,0.00045477433,0.00025028887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002874587,0.000064297674,0.0021593315,0.0003964538,0.00007273704,0.00037635263,0.00040872607,0.6135908,0.17055324,0.090840384,0.0010290768,0.12022113],"study_design_scores_gemma":[0.000010742727,0.00010455538,0.0004758833,0.000012783976,0.000010916345,0.00013847467,0.00003002678,0.973499,0.01991178,0.0032880288,0.0024995487,0.00001818658],"about_ca_topic_score_codex":0.0010975316,"about_ca_topic_score_gemma":0.0012707888,"teacher_disagreement_score":0.0012248637,"about_ca_system_score_codex":0.00077862374,"about_ca_system_score_gemma":0.00058246375,"threshold_uncertainty_score":0.005649388},"labels":[],"label_agreement":null},{"id":"W4306403150","doi":"10.3390/s22207868","title":"Deep Learning for LiDAR Point Cloud Classification in Remote Sensing","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Metropolitan University","keywords":"Point cloud; Computer science; Deep learning; Benchmarking; Lidar; Convolutional neural network; Artificial intelligence; Benchmark (surveying); Segmentation; Cloud computing; Feature extraction; Data mining; Feature (linguistics); Remote sensing; Machine learning; Cartography; Geography","score_opus":0.046194464674596036,"score_gpt":0.30292143592543797,"score_spread":0.25672697125084193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306403150","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035203353,0.94975626,0.03678075,0.0011337877,0.0005233153,0.000051843697,0.0003590661,0.00021600346,0.0076586823],"genre_scores_gemma":[0.039281845,0.93281627,0.018750569,0.00068642653,0.00041275364,0.00007278385,0.0010569576,0.000050960734,0.006871428],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978346,0.000032180073,0.000019302328,0.000053236912,0.0000876671,0.000024097004],"domain_scores_gemma":[0.99974674,0.00009574017,0.000022380982,0.000011810517,0.00011135973,0.000012041931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006187143,0.00087866094,0.0006807947,0.0013226514,0.00015908974,0.0007449821,0.0009786591,0.00076870044,0.002348625],"category_scores_gemma":[0.0010358306,0.00033735152,0.00075898605,0.0018874122,0.00024981063,0.0013017247,0.0006065091,0.0012478366,0.0014804003],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030340872,0.00005049831,0.0005687533,0.0037976396,0.00012744342,0.000060675113,0.000026922338,0.007111176,0.001158154,0.0049905227,0.015461327,0.9666166],"study_design_scores_gemma":[0.000036010468,0.00034239562,0.0044371826,0.0052200835,0.0005929758,0.0009846283,0.00014706295,0.08073381,0.010585387,0.020613104,0.87619734,0.000110072186],"about_ca_topic_score_codex":0.0038869865,"about_ca_topic_score_gemma":0.004968693,"teacher_disagreement_score":0.0038869865,"about_ca_system_score_codex":0.0006047875,"about_ca_system_score_gemma":0.0009506312,"threshold_uncertainty_score":0.007856965},"labels":[],"label_agreement":null},{"id":"W4306673999","doi":"10.3390/s22207889","title":"A Robust Angular Rate Sensor Utilizing 2:1 Auto-Parametric Resonance Excitation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Bandwidth (computing); Gyroscope; Parametric statistics; Voltage; Resonance (particle physics); Parametric oscillator; Inertial frame of reference; Excitation; Acoustics; Physics; Control theory (sociology); Engineering; Electrical engineering; Computer science; Optics; Telecommunications","score_opus":0.025539656067727658,"score_gpt":0.22371091684877215,"score_spread":0.1981712607810445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306673999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56511635,0.002562279,0.422733,0.00034831592,0.00049272476,0.00022804069,0.00042077195,0.001968506,0.0061299456],"genre_scores_gemma":[0.83172435,0.0005855084,0.16421527,0.00014229646,0.00008528783,0.0000861902,0.00018497695,0.00008236477,0.0028936479],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993181,0.00006400735,0.00003062695,0.00019920754,0.00034343105,0.000044589917],"domain_scores_gemma":[0.9995528,0.0000973122,0.00015143312,0.000055007884,0.00010866734,0.000034802724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004625324,0.00047457396,0.00056931964,0.0003277386,0.00014376071,0.00053130754,0.00071394606,0.00061165384,0.0007746765],"category_scores_gemma":[0.0006858171,0.00028095438,0.00019048214,0.00020798143,0.00030906883,0.00081869576,0.00055497827,0.00041251746,0.00038979147],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056209286,0.000021297072,0.0004093392,0.000112132206,0.0000066214907,0.000060963248,0.00004072787,0.00061847706,0.97630787,0.00040785302,0.00018460625,0.021773877],"study_design_scores_gemma":[0.00001545899,0.00045059682,0.0027915468,0.000013522745,0.0000164415,0.0008902099,0.00003733842,0.022510448,0.96751034,0.00012530749,0.005581917,0.00005677223],"about_ca_topic_score_codex":0.00017069837,"about_ca_topic_score_gemma":0.00035456207,"teacher_disagreement_score":0.0007746765,"about_ca_system_score_codex":0.00017334006,"about_ca_system_score_gemma":0.0002438196,"threshold_uncertainty_score":0.0025915504},"labels":[],"label_agreement":null},{"id":"W4306761536","doi":"10.3390/s22207888","title":"Review of Navigation Assistive Tools and Technologies for the Visually Impaired","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visually impaired; Assistive technology; Human–computer interaction; Field (mathematics); Computer science; Assistive device; Face (sociological concept); Multimedia; Physical medicine and rehabilitation; Medicine","score_opus":0.15701933145526284,"score_gpt":0.40143341387409254,"score_spread":0.2444140824188297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306761536","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015709917,0.99752873,0.00024370175,0.00023002978,0.0002137531,0.000008913533,0.00005553565,0.000012811787,0.0015494113],"genre_scores_gemma":[0.0005745457,0.99830854,0.00030858343,0.00011527381,0.000119208475,0.000008963113,0.000046846297,0.000001776489,0.00051618484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997603,0.00003603268,0.000050602313,0.000043479005,0.000091724076,0.000017732786],"domain_scores_gemma":[0.99944514,0.00029391798,0.00006298348,0.000013687192,0.00015836689,0.000025827916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052621245,0.0009824716,0.0011039785,0.0032036325,0.00032610667,0.0009882884,0.0008951618,0.0010625452,0.0055873017],"category_scores_gemma":[0.0010894497,0.0003010661,0.00072679983,0.002528403,0.0003358132,0.0015658686,0.00062771223,0.00095939945,0.0020712132],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052803196,0.00006444649,0.00019498292,0.055750728,0.00011376594,0.00019154121,0.00011130407,0.0002523649,0.0017924558,0.002542174,0.027770666,0.91116285],"study_design_scores_gemma":[0.00000827172,0.0001366924,0.0013937659,0.015532034,0.00029543642,0.00163739,0.000117895455,0.00010710125,0.0007206657,0.0018699225,0.9781506,0.000030207768],"about_ca_topic_score_codex":0.0018040385,"about_ca_topic_score_gemma":0.0029201298,"teacher_disagreement_score":0.0055873017,"about_ca_system_score_codex":0.00045517852,"about_ca_system_score_gemma":0.0016590283,"threshold_uncertainty_score":0.01869142},"labels":[],"label_agreement":null},{"id":"W4306769507","doi":"10.3390/s22207923","title":"Towards Online Ageing Detection in Transformer Oil: A Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Dissolved gas analysis; Transformer; Reliability engineering; Robustness (evolution); Internet of Things; Transformer oil; Computer science; Engineering; Computer security; Voltage; Electrical engineering","score_opus":0.03529483609999484,"score_gpt":0.28929040483759993,"score_spread":0.2539955687376051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306769507","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023039986,0.99812895,0.0004112041,0.00014171436,0.00017792855,0.000009138765,0.000031570216,0.000010868089,0.0008582437],"genre_scores_gemma":[0.0011373548,0.99779785,0.00038936257,0.00010101035,0.00010651353,0.000008209558,0.00004276872,0.000002493608,0.00041454704],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999658,0.00004670544,0.00005957502,0.00007658913,0.00013244273,0.000026740992],"domain_scores_gemma":[0.9989518,0.0005368398,0.00015038459,0.000031069892,0.00028761092,0.000042230597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083746854,0.001083146,0.0014307922,0.0037131892,0.0003057378,0.0011320156,0.0009842906,0.0011966309,0.0037488649],"category_scores_gemma":[0.0013300185,0.00047997685,0.00096766383,0.0029881827,0.0004005299,0.0019978362,0.00067639025,0.0010259212,0.0016312157],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000661608,0.000117677424,0.00035760354,0.05955343,0.0001652184,0.00021887549,0.00010654796,0.0007389912,0.0042135674,0.0033220388,0.016045291,0.9150947],"study_design_scores_gemma":[0.000010469724,0.00024513638,0.0013206451,0.009874084,0.00041894813,0.0013187886,0.000117843156,0.0003530869,0.0029684112,0.0014521335,0.981873,0.000047512338],"about_ca_topic_score_codex":0.0013111244,"about_ca_topic_score_gemma":0.0018556763,"teacher_disagreement_score":0.0037488649,"about_ca_system_score_codex":0.000411916,"about_ca_system_score_gemma":0.0012252803,"threshold_uncertainty_score":0.012541175},"labels":[],"label_agreement":null},{"id":"W4306964787","doi":"10.3390/s22208008","title":"Power Spectrum of Acceleration and Angular Velocity Signals as Indicators of Muscle Fatigue during Upper Limb Low-Load Repetitive Tasks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University; Centre Hospitalier Universitaire Sainte-Justine; Centre for Interdisciplinary Research in Rehabilitation; Université de Montréal","funders":"","keywords":"Angular acceleration; Angular velocity; Acceleration; Accelerometer; Gyroscope; Pelvis; Acoustics; Work (physics); Physics; Anatomy; Medicine","score_opus":0.009032460345862341,"score_gpt":0.26578812207950536,"score_spread":0.256755661733643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306964787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99650204,0.00038327082,0.0025969958,0.000009933304,0.0000070977394,0.000026570176,0.00007909236,0.000020620044,0.00037438536],"genre_scores_gemma":[0.99750745,0.00016334446,0.0019256276,0.000015535023,0.000014838277,0.000031435826,0.00013557715,0.0000073376455,0.00019878782],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999561,0.00013426275,0.00003723694,0.00008234279,0.00014747355,0.000037505844],"domain_scores_gemma":[0.9970772,0.0015551763,0.00076402526,0.00012159454,0.00035444915,0.00012754086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007827145,0.00043417022,0.00031421141,0.00089585764,0.00012096735,0.0002826354,0.00017398753,0.00042539442,0.0013129214],"category_scores_gemma":[0.005663194,0.00017155829,0.00017749025,0.0003759787,0.00018525912,0.00027648668,0.0002965604,0.0001846435,0.00019957835],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0075716325,0.00057362573,0.4370506,0.0010314408,0.0004920637,0.00047619457,0.0020703867,0.0024584115,0.38282707,0.00009525381,0.0003373212,0.16501598],"study_design_scores_gemma":[0.00001608868,0.0006115074,0.99520963,0.000012768283,0.000039501803,0.00018706433,0.00012904244,0.0008079341,0.002846279,0.00004268435,0.00008714976,0.000010230117],"about_ca_topic_score_codex":0.00063178036,"about_ca_topic_score_gemma":0.0009240219,"teacher_disagreement_score":0.0013129214,"about_ca_system_score_codex":0.000072523144,"about_ca_system_score_gemma":0.00006521359,"threshold_uncertainty_score":0.004392147},"labels":[],"label_agreement":null},{"id":"W4307189197","doi":"10.3390/s22218088","title":"Radiofrequency Energy Harvesting Systems for Internet of Things Applications: A Comprehensive Overview of Design Issues","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de l'Abitibi Témiscamingue","funders":"","keywords":"Rectenna; Energy harvesting; Computer science; Radio frequency; Wireless; Power (physics); Power management; Telecommunications; Electrical engineering; Energy (signal processing); Systems engineering; Engineering; Physics","score_opus":0.12138693365561662,"score_gpt":0.31719160115482603,"score_spread":0.1958046674992094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307189197","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00077343814,0.9894049,0.003506388,0.00048115276,0.00034986288,0.000024577268,0.00005268583,0.000031695316,0.005375431],"genre_scores_gemma":[0.0036044589,0.9907671,0.0026800514,0.00028065636,0.00022129262,0.000035432287,0.000077803204,0.000008504916,0.0023247823],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977213,0.000027635602,0.00003030006,0.00004782925,0.000104511935,0.000017638946],"domain_scores_gemma":[0.99969935,0.00015465639,0.000031941152,0.000011558217,0.00009344794,0.000009086229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047530647,0.00070245215,0.00075836247,0.0014333526,0.0002683224,0.0008556984,0.00058139,0.00095529895,0.0026976268],"category_scores_gemma":[0.000550667,0.00041185552,0.0005607522,0.0016432545,0.00025906906,0.0015097269,0.00045094895,0.0010354769,0.0017111878],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046728444,0.000102100625,0.00026494658,0.029803025,0.00007027319,0.00023567826,0.00014404223,0.0014326498,0.016166179,0.015181963,0.017084073,0.91946816],"study_design_scores_gemma":[0.0000061966784,0.00018028327,0.0005953971,0.00349812,0.00010815856,0.0012316272,0.00010262077,0.0007319334,0.0048997845,0.0045773503,0.9840337,0.00003475576],"about_ca_topic_score_codex":0.00038606673,"about_ca_topic_score_gemma":0.00051556266,"teacher_disagreement_score":0.0026976268,"about_ca_system_score_codex":0.00029867832,"about_ca_system_score_gemma":0.00065490976,"threshold_uncertainty_score":0.009024441},"labels":[],"label_agreement":null},{"id":"W4307280399","doi":"10.3390/s22218165","title":"Analysis of Wind-Induced Vibrations on HVTL Conductors Using Wireless Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Vibration and Dynamic Analysis","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electrical conductor; Accelerometer; Vibration; Wind power; Wireless; Conductor; Electric power transmission; Engineering; Renewable energy; Wireless sensor network; Energy harvesting; Transmission (telecommunications); Condition monitoring; Electrical engineering; Computer science; Energy (signal processing); Acoustics; Telecommunications; Materials science; Physics; Computer network","score_opus":0.022834254686335716,"score_gpt":0.24552617839571897,"score_spread":0.22269192370938326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307280399","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9629868,0.00026645747,0.035521634,0.00003491237,0.000021161757,0.00002505502,0.00015997996,0.000164677,0.0008192708],"genre_scores_gemma":[0.99570423,0.000111152345,0.0036592842,0.000006687005,0.000006789823,0.000011367084,0.00007030323,0.0000050526733,0.0004252064],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990284,0.000012453445,0.000005226469,0.000027063117,0.000040288527,0.000012052774],"domain_scores_gemma":[0.9998808,0.000031046013,0.000032430115,0.000008441752,0.000040976043,0.0000063102916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010527818,0.00029032162,0.00015375228,0.00037806475,0.00009229695,0.00017314103,0.00018010972,0.00022976792,0.00050293485],"category_scores_gemma":[0.00023511678,0.00007619725,0.00012888454,0.00028770533,0.000124923,0.00022548449,0.000104289786,0.00009332499,0.000108005064],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000440042,0.00012125669,0.053477958,0.00026588354,0.00006990393,0.00064243533,0.00036472536,0.023986926,0.8170339,0.00025825156,0.00060339563,0.102735244],"study_design_scores_gemma":[0.000043448665,0.0021990696,0.28345382,0.000057284346,0.00014564247,0.001149102,0.0010145617,0.39495102,0.31341296,0.0003284914,0.0031965517,0.00004809916],"about_ca_topic_score_codex":0.00073942705,"about_ca_topic_score_gemma":0.0009650522,"teacher_disagreement_score":0.00073942705,"about_ca_system_score_codex":0.00010109156,"about_ca_system_score_gemma":0.000061936815,"threshold_uncertainty_score":0.0016825199},"labels":[],"label_agreement":null},{"id":"W4307436727","doi":"10.3390/s22218245","title":"FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"3v Geomatics (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Multispectral image; Convolutional neural network; Artificial intelligence; Satellite; Deep learning; Remote sensing; Computer vision; Data mining; Engineering","score_opus":0.006460682955909413,"score_gpt":0.19051752585781304,"score_spread":0.18405684290190363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307436727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26895544,0.0038205667,0.6466685,0.0010479355,0.0010548536,0.0006066675,0.012886797,0.05688958,0.008069583],"genre_scores_gemma":[0.6976169,0.0006904266,0.26930287,0.0005470393,0.00016955055,0.00035127535,0.02252709,0.0006978447,0.008097016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970585,0.00003212196,0.000012374,0.00012478279,0.00006489018,0.000059975217],"domain_scores_gemma":[0.99977165,0.00006325119,0.000024119963,0.000048742073,0.00007139847,0.000020837333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056212937,0.0015488163,0.0009004059,0.0011126598,0.0004098987,0.0005486149,0.0019827518,0.0010251567,0.0030051959],"category_scores_gemma":[0.0013866812,0.00037758265,0.00093719503,0.0010218803,0.00034046927,0.0015058159,0.001190939,0.0012584188,0.0010688987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011073007,0.0005029412,0.0050155832,0.0002882595,0.00035081615,0.00034724904,0.00009128891,0.18747146,0.024397185,0.0015955004,0.051101606,0.7277307],"study_design_scores_gemma":[0.00003753495,0.00015336554,0.0018970547,0.000016874048,0.000042949247,0.00009620244,0.000029011677,0.9806686,0.011474069,0.0017049891,0.0038606634,0.000018668377],"about_ca_topic_score_codex":0.015500227,"about_ca_topic_score_gemma":0.014030653,"teacher_disagreement_score":0.015500227,"about_ca_system_score_codex":0.0007861539,"about_ca_system_score_gemma":0.00063812925,"threshold_uncertainty_score":0.030820012},"labels":[],"label_agreement":null},{"id":"W4307551435","doi":"10.3390/s22218203","title":"Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grating; Fiber Bragg grating; Bandwidth (computing); Computer science; Sorting; Electronic engineering; Genetic algorithm; MATLAB; Materials science; Optical fiber; Engineering; Optoelectronics; Algorithm; Telecommunications","score_opus":0.015437086932837749,"score_gpt":0.21822106051926282,"score_spread":0.20278397358642508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307551435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3688945,0.0010187178,0.62272656,0.00019091099,0.00004700563,0.00009163382,0.000046124955,0.0003825989,0.006601987],"genre_scores_gemma":[0.72443056,0.00045749685,0.2739152,0.000052524352,0.0000052542664,0.00005138083,0.00003533731,0.000030465339,0.0010217607],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967647,0.00005893879,0.000017094746,0.000053801094,0.00015439569,0.00003926776],"domain_scores_gemma":[0.99983644,0.00006432923,0.000045267698,0.00001182632,0.00003588867,0.0000062134163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045506642,0.0005814997,0.00027794563,0.00047562033,0.00025307407,0.00043818614,0.00024385283,0.0004381335,0.00027960786],"category_scores_gemma":[0.0005998619,0.000191237,0.00029500044,0.00045187798,0.00033775187,0.00034893342,0.00022429253,0.00025516524,0.000104452105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001299941,0.00022392762,0.004447821,0.0003098044,0.000042736312,0.00011999362,0.00018032065,0.340984,0.5123529,0.0048690643,0.00041798217,0.13592146],"study_design_scores_gemma":[0.000019184543,0.0002351691,0.0025341285,0.000027183725,0.000038931947,0.00015479761,0.00008939104,0.7247523,0.26723984,0.0018464435,0.0030319686,0.000030656985],"about_ca_topic_score_codex":0.0022281269,"about_ca_topic_score_gemma":0.0044333716,"teacher_disagreement_score":0.0022281269,"about_ca_system_score_codex":0.00052619714,"about_ca_system_score_gemma":0.00061108073,"threshold_uncertainty_score":0.0044303536},"labels":[],"label_agreement":null},{"id":"W4308118708","doi":"10.3390/s22218372","title":"Fleet’s Geode: A Breakthrough Sensor for Real-Time Ambient Seismic Noise Tomography over DtS-IoT","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Geophone; Noise (video); Ambient noise level; Sensitivity (control systems); Tomography; Passive seismic; Geology; Seismic noise; Acoustics; Computer science; Seismology; Electronic engineering; Engineering; Optics; Physics; Artificial intelligence","score_opus":0.009726578696386897,"score_gpt":0.21457594629744595,"score_spread":0.20484936760105904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308118708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40528136,0.0018478676,0.5076774,0.0014673502,0.0008791747,0.00037135364,0.00536952,0.012502585,0.06460345],"genre_scores_gemma":[0.86790174,0.0006672604,0.100045666,0.0005481432,0.00007351651,0.00013575416,0.004450601,0.0002937153,0.025883557],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997074,0.00004054322,0.000010742751,0.000049431736,0.00015668108,0.00003521704],"domain_scores_gemma":[0.99979323,0.00003137407,0.000025170619,0.000033253367,0.00007961214,0.00003742672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036132854,0.0005325627,0.00029385125,0.00066591013,0.0001842476,0.000470734,0.0005831595,0.00059026695,0.0031998493],"category_scores_gemma":[0.0005463215,0.00014666717,0.00016882364,0.00042645476,0.00024338224,0.0012038512,0.0009145758,0.0005778452,0.0010827178],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012255737,0.00029480693,0.030697713,0.00056624395,0.00015613493,0.001610478,0.0007534982,0.023939205,0.43176755,0.017548814,0.06643056,0.42500946],"study_design_scores_gemma":[0.00019566694,0.002638402,0.038125463,0.00025761419,0.00017002205,0.0032343061,0.0006955053,0.3010721,0.22377518,0.006022933,0.4235031,0.00030975413],"about_ca_topic_score_codex":0.0012945271,"about_ca_topic_score_gemma":0.0038955922,"teacher_disagreement_score":0.0031998493,"about_ca_system_score_codex":0.00031342782,"about_ca_system_score_gemma":0.00031672273,"threshold_uncertainty_score":0.010704577},"labels":[],"label_agreement":null},{"id":"W4308331372","doi":"10.3390/s22218455","title":"A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Hamilton Health Sciences; St. Joseph’s Healthcare Hamilton; University of Guelph","funders":"National Institutes of Health","keywords":"Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Computer science; Spinal cord injury; Electromyography; Classifier (UML); Deep learning; Artificial neural network; Support vector machine; Spinal cord; Machine learning; Speech recognition; Medicine; Physical medicine and rehabilitation; Neuroscience; Psychology","score_opus":0.035443857282922854,"score_gpt":0.281309578202603,"score_spread":0.24586572091968015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308331372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15685421,0.0014542104,0.8351514,0.00029555784,0.00017420857,0.0001368082,0.00027691128,0.0017104346,0.003946281],"genre_scores_gemma":[0.83944684,0.00067379914,0.15470894,0.00018370207,0.00005440512,0.00008922562,0.00040984253,0.0000351007,0.004398168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998154,0.000017738937,0.000010922246,0.00006210232,0.000058549133,0.000035242512],"domain_scores_gemma":[0.99980766,0.000041346106,0.000029952402,0.000023695206,0.00008473877,0.000012624072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003487414,0.00051466684,0.00030966403,0.00051640545,0.00018042549,0.00032374344,0.0005425528,0.000536972,0.0007090478],"category_scores_gemma":[0.00052578805,0.00014892992,0.00032338497,0.0004702498,0.00022095285,0.00044493226,0.00032911316,0.00032147946,0.00025028505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002742682,0.00024869115,0.012921495,0.00021784662,0.00013989065,0.00032143094,0.00008209,0.08093995,0.13984832,0.002455346,0.002718515,0.75983226],"study_design_scores_gemma":[0.000010429667,0.0002808569,0.010097142,0.000027201433,0.0000657027,0.00035280938,0.000031964617,0.93385047,0.050784513,0.0011148322,0.003357619,0.000026510841],"about_ca_topic_score_codex":0.005184605,"about_ca_topic_score_gemma":0.0070120543,"teacher_disagreement_score":0.005184605,"about_ca_system_score_codex":0.00044443228,"about_ca_system_score_gemma":0.0005340007,"threshold_uncertainty_score":0.010308802},"labels":[],"label_agreement":null},{"id":"W4308719908","doi":"10.3390/s22218492","title":"Molecularly Imprinted Polymer-Modified Microneedle Sensor for the Detection of Imidacloprid Pesticides in Food Samples","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Insect and Pesticide Research","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imidacloprid; Molecularly imprinted polymer; Pesticide; Chromatography; Environmental chemistry; Nanotechnology; Materials science; Chemistry; Organic chemistry; Selectivity; Biology","score_opus":0.03756685151657242,"score_gpt":0.25042921599886847,"score_spread":0.21286236448229606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308719908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8215452,0.01143259,0.1598439,0.00042965272,0.00022433921,0.00029708896,0.0011931777,0.0019341914,0.0030999007],"genre_scores_gemma":[0.78767,0.003851523,0.20259498,0.00029245424,0.000045059853,0.00018741541,0.00063142774,0.00004858708,0.004678509],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969876,0.00003251035,0.000015753516,0.000092247654,0.00013933869,0.000021464293],"domain_scores_gemma":[0.9998679,0.000033967954,0.000031907715,0.000009186908,0.000040149498,0.00001683763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019161537,0.0005921605,0.00043970539,0.00034643887,0.00014829096,0.00018188661,0.00047221145,0.00060603814,0.00045695886],"category_scores_gemma":[0.000374015,0.00023746607,0.00021340762,0.00021924987,0.00016001992,0.00032405378,0.00022314748,0.00044574076,0.00031866203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010034155,0.0000050034478,0.000050817926,0.000028187065,0.0000027488838,0.000022905368,0.0000033179822,0.000046910325,0.99779,0.000011621277,0.000020084211,0.0020083906],"study_design_scores_gemma":[0.0000025763582,0.000082957806,0.0008466321,0.0000024180406,0.0000068592435,0.00013874321,0.0000042881375,0.0016782176,0.9964998,0.000006736229,0.0007249492,0.0000057270317],"about_ca_topic_score_codex":0.0008467021,"about_ca_topic_score_gemma":0.0027709734,"teacher_disagreement_score":0.0008467021,"about_ca_system_score_codex":0.00043588894,"about_ca_system_score_gemma":0.0002687866,"threshold_uncertainty_score":0.0031626225},"labels":[],"label_agreement":null},{"id":"W4308871358","doi":"10.3390/s22228666","title":"An Adaptive and Spectrally Efficient Multi-Channel Medium Access Control Protocol for Dynamic Ad Hoc Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wireless ad hoc network; Computer network; Computer science; Throughput; Multiple Access with Collision Avoidance for Wireless; Vehicular ad hoc network; Channel (broadcasting); Ad hoc wireless distribution service; Optimized Link State Routing Protocol; Mobile ad hoc network; Access control; Media access control; Protocol (science); Wireless network; Distributed computing; Wireless; Network packet; Telecommunications","score_opus":0.02807606670021668,"score_gpt":0.3215213867298455,"score_spread":0.2934453200296288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308871358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049687706,0.0014172972,0.94437176,0.00013472587,0.00020697278,0.00036049323,0.000048489615,0.00080479996,0.0029676862],"genre_scores_gemma":[0.74621964,0.00070576125,0.2507871,0.00015780618,0.000107187436,0.00035372618,0.000089245725,0.00003148082,0.0015480537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992575,0.0002173748,0.00004328474,0.0000905373,0.00034134256,0.000049905695],"domain_scores_gemma":[0.9989579,0.00035085395,0.00015259051,0.00016515845,0.00033112545,0.000042420448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001007427,0.00040422266,0.00031261283,0.0005531863,0.0005945286,0.00054168,0.0011216791,0.0004087151,0.00034191675],"category_scores_gemma":[0.0024287724,0.00012591011,0.00027088073,0.00051024236,0.00049215584,0.00091563904,0.00053950533,0.0007445428,0.0001072175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064036064,0.0006006136,0.0037323833,0.0006235588,0.00024730497,0.0005857486,0.00033331927,0.20399597,0.24853693,0.046679243,0.0034893705,0.49053538],"study_design_scores_gemma":[0.000056087407,0.0011155905,0.0018088454,0.00003523591,0.00010167913,0.0007899371,0.00008151041,0.9182398,0.055846736,0.0065384055,0.015304112,0.00008211555],"about_ca_topic_score_codex":0.00080232014,"about_ca_topic_score_gemma":0.001336026,"teacher_disagreement_score":0.0011216791,"about_ca_system_score_codex":0.00040011265,"about_ca_system_score_gemma":0.00069953717,"threshold_uncertainty_score":0.005327821},"labels":[],"label_agreement":null},{"id":"W4308872194","doi":"10.3390/s22228694","title":"Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"MNIST database; Computer science; Field-programmable gate array; Convolutional neural network; Artificial intelligence; Artificial neural network; Deep learning; Computation; Computer engineering; Coprocessor; Convolution (computer science); Machine learning; Embedded system; Computer hardware; Algorithm","score_opus":0.047036598868040014,"score_gpt":0.30004488978155136,"score_spread":0.25300829091351135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308872194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21827602,0.0026563352,0.7508899,0.0005663283,0.00030159988,0.00016940404,0.0005934945,0.0077318074,0.018815085],"genre_scores_gemma":[0.86620474,0.0005963034,0.12833509,0.00017613872,0.000022911108,0.00008551534,0.0004010457,0.00006429257,0.004114122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981576,0.00002540681,0.00001270483,0.00003367705,0.000089120535,0.000023222687],"domain_scores_gemma":[0.9998134,0.00006053645,0.000026256532,0.000028988286,0.00006383858,0.0000070384995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018321349,0.00038142607,0.00022851735,0.00035794938,0.00013636309,0.00050129514,0.0006620513,0.00019448982,0.0038747229],"category_scores_gemma":[0.0006557101,0.00014295896,0.00016442421,0.00039748693,0.00013864935,0.0005793303,0.00017709282,0.00025558402,0.0004264322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058535475,0.00016780326,0.0056684157,0.00055052695,0.00017143597,0.0005883916,0.00009595506,0.2865119,0.06533863,0.009379576,0.008862476,0.62207955],"study_design_scores_gemma":[0.00003212776,0.00017548195,0.0025046286,0.00003287318,0.000040068113,0.00019093583,0.000043251755,0.9666345,0.023087643,0.0018577715,0.005382817,0.000017867369],"about_ca_topic_score_codex":0.00538691,"about_ca_topic_score_gemma":0.007755386,"teacher_disagreement_score":0.00538691,"about_ca_system_score_codex":0.00055823487,"about_ca_system_score_gemma":0.00042948476,"threshold_uncertainty_score":0.012962222},"labels":[],"label_agreement":null},{"id":"W4308974975","doi":"10.3390/s22228708","title":"Rule-Driven Forwarding for Resilient WSN Infrastructures","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Resilience (materials science); Footprint; Computer science; Wireless sensor network; Wireless ad hoc network; Computer security; Computer network; Simple (philosophy); Function (biology); Scheme (mathematics); Class (philosophy); Wireless; Telecommunications; Geography","score_opus":0.008983187301641427,"score_gpt":0.22691860046551712,"score_spread":0.21793541316387569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308974975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014440611,0.00037674,0.9799027,0.0002704344,0.00014044167,0.0001667829,0.00006738684,0.0007261805,0.003908665],"genre_scores_gemma":[0.5356506,0.0009927143,0.4558363,0.00029021973,0.00013968209,0.0003127628,0.00029383952,0.00012096088,0.006362931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924904,0.0001495813,0.00007510268,0.00012639197,0.00031842128,0.00008146085],"domain_scores_gemma":[0.99871826,0.00057382765,0.00011984457,0.00037027127,0.00015415976,0.000063563675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015345936,0.0005174887,0.0006034735,0.00074623805,0.0008379279,0.0010338052,0.0016193365,0.0011015135,0.0016811194],"category_scores_gemma":[0.0035784454,0.0003033711,0.00062842335,0.00055155053,0.001030952,0.0017988477,0.0012675612,0.0009248564,0.00048506726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019776612,0.00017093691,0.0008774697,0.00030926563,0.00008766796,0.00093993254,0.0003366273,0.57051325,0.029839544,0.2142904,0.0047856183,0.17765142],"study_design_scores_gemma":[0.000029706138,0.00015352496,0.000152532,0.000030109262,0.000027701157,0.00027358189,0.00003246835,0.8904839,0.005881073,0.08891301,0.013988851,0.00003351872],"about_ca_topic_score_codex":0.0010459094,"about_ca_topic_score_gemma":0.0012852926,"teacher_disagreement_score":0.0016811194,"about_ca_system_score_codex":0.0005349893,"about_ca_system_score_gemma":0.00067495526,"threshold_uncertainty_score":0.008115828},"labels":[],"label_agreement":null},{"id":"W4309012905","doi":"10.3390/s22228714","title":"Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Deep learning; Artificial neural network; Spall; Process (computing); Machine learning; Engineering; Structural engineering","score_opus":0.01892164853725569,"score_gpt":0.23670341743936946,"score_spread":0.21778176890211376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309012905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9296126,0.013162163,0.03897674,0.0009718813,0.0007346612,0.00016710554,0.0021235696,0.0047087334,0.009542604],"genre_scores_gemma":[0.9742766,0.0014675816,0.016177012,0.0001941094,0.0000711728,0.00006663099,0.0035501644,0.000087986315,0.004108648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991491,0.00013719418,0.00008404951,0.00024215854,0.00019508357,0.00019236558],"domain_scores_gemma":[0.9988599,0.00038758555,0.000101374506,0.00010720739,0.0004258366,0.000118251795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019273142,0.0023659563,0.0010728515,0.0019421182,0.00037944262,0.0009314296,0.0017334536,0.0014123498,0.0013215552],"category_scores_gemma":[0.0034376988,0.00041211888,0.0009003776,0.00093251123,0.00034726466,0.0012451255,0.00090657006,0.0009792127,0.00055642636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034242775,0.00092121353,0.024896739,0.0005709944,0.00081103673,0.00034814572,0.000099965735,0.5110711,0.009410474,0.0011049556,0.010748802,0.43659237],"study_design_scores_gemma":[0.000028671497,0.00021987507,0.0030502898,0.00003180141,0.0000817007,0.000043538857,0.000030504814,0.99192566,0.0038679936,0.00024248801,0.000458922,0.000018518984],"about_ca_topic_score_codex":0.03820841,"about_ca_topic_score_gemma":0.026233545,"teacher_disagreement_score":0.03820841,"about_ca_system_score_codex":0.001870512,"about_ca_system_score_gemma":0.0015468583,"threshold_uncertainty_score":0.07597208},"labels":[],"label_agreement":null},{"id":"W4309026232","doi":"10.3390/s22228803","title":"A Novel Paper-Based Reagentless Dual Functional Soil Test to Instantly Detect Phosphate Infield","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Guelph","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Phosphate; Soil test; Phosphorus; Environmental science; Correlation coefficient; Extraction (chemistry); Fertilizer; Chemistry; Soil water; Soil science; Chromatography; Mathematics; Statistics","score_opus":0.012909375141096212,"score_gpt":0.1958815829986543,"score_spread":0.18297220785755808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309026232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28669268,0.0049562664,0.69749075,0.0006327289,0.0007869256,0.0006077002,0.0010294956,0.0029029655,0.0049005076],"genre_scores_gemma":[0.57859415,0.0022225184,0.40625408,0.00081252743,0.000185023,0.00048615749,0.0006553257,0.00008874393,0.01070144],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865437,0.00014887482,0.000058904585,0.00029616954,0.0007852911,0.00005633836],"domain_scores_gemma":[0.9993957,0.00017551583,0.00012059312,0.000050907034,0.0002061735,0.000051083865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062412425,0.00090965774,0.00055619754,0.0009322948,0.00018806728,0.0004909678,0.0021380608,0.0016379678,0.0012787324],"category_scores_gemma":[0.0009968869,0.0004986963,0.00043184636,0.0004647991,0.0003398105,0.0008723439,0.00073434715,0.0007047343,0.0007838506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008978124,0.000073066716,0.0008437764,0.00017477268,0.000020828935,0.00007519697,0.000018212564,0.0001430967,0.9714726,0.00017593811,0.0002772111,0.026635442],"study_design_scores_gemma":[0.000030087289,0.0006069803,0.0024031284,0.000011934205,0.000037886697,0.0009426214,0.000021099071,0.006532312,0.9839611,0.000107583764,0.005309694,0.000035581856],"about_ca_topic_score_codex":0.00031964638,"about_ca_topic_score_gemma":0.00085535896,"teacher_disagreement_score":0.0021380608,"about_ca_system_score_codex":0.00034843298,"about_ca_system_score_gemma":0.00036011028,"threshold_uncertainty_score":0.0042777658},"labels":[],"label_agreement":null},{"id":"W4309118524","doi":"10.3390/s22228835","title":"Robust Synchronization of Ambient Vibration Time Histories Based on Phase Angle Compensations and Kernel Density Function","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Robustness (evolution); Computer science; Modal; Synchronization (alternating current); Vibration; Real-time computing; Structural health monitoring; Ambient vibration; Algorithm; Noise (video); Engineering; Telecommunications; Structural engineering; Artificial intelligence; Acoustics","score_opus":0.01707244850454352,"score_gpt":0.23223467090631103,"score_spread":0.21516222240176752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309118524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037643604,0.00013372149,0.9613292,0.000046565016,0.000033835895,0.000026005524,0.00003295159,0.00036642922,0.00038754864],"genre_scores_gemma":[0.64169395,0.00025861396,0.3553699,0.000063228355,0.00006605548,0.00011470498,0.00043221225,0.00012791409,0.0018734201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930155,0.00011555982,0.000041807118,0.00021761817,0.00026047573,0.00006286055],"domain_scores_gemma":[0.99894756,0.00038083026,0.0002265651,0.00013125569,0.0002627607,0.00005100032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083100545,0.0007293675,0.0006027734,0.0006717959,0.00028023502,0.0005100447,0.00068798225,0.0005294714,0.00071047165],"category_scores_gemma":[0.004073042,0.00025194854,0.00045031193,0.000639476,0.0004451259,0.00081901654,0.0008027488,0.0008283483,0.00042970688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006840681,0.0001833501,0.0049035205,0.0002160289,0.00011999386,0.00024735284,0.00031731793,0.31213403,0.093783915,0.008921376,0.001798266,0.5766909],"study_design_scores_gemma":[0.000011279885,0.00006747785,0.002059035,0.000008396316,0.000010553086,0.00006487616,0.000021852515,0.9855232,0.01054234,0.00096520584,0.0007085805,0.000017166538],"about_ca_topic_score_codex":0.0022940277,"about_ca_topic_score_gemma":0.0018081762,"teacher_disagreement_score":0.0022940277,"about_ca_system_score_codex":0.00030073445,"about_ca_system_score_gemma":0.000731958,"threshold_uncertainty_score":0.004561305},"labels":[],"label_agreement":null},{"id":"W4309210524","doi":"10.3390/s22228812","title":"Fatigue Performance of Type I and Type II Fibre Bragg Gratings Fabricated by Femtosecond Laser Inscription through the Coating","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Office of Naval Research Global; Office of Naval Research; Monash University","keywords":"Materials science; Fiber Bragg grating; Coating; Femtosecond; Laser; Durability; Composite material; Optical coating; Strain gauge; Optical fiber; Optics; Optoelectronics; Wavelength","score_opus":0.01609556843798318,"score_gpt":0.22251443983443336,"score_spread":0.20641887139645018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309210524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99816066,0.00034501968,0.0011359788,0.000010363712,0.000018604791,0.000009002328,0.00005728008,0.00003532509,0.00022786342],"genre_scores_gemma":[0.9954151,0.00033443794,0.0029759132,0.000024626124,0.000006835702,0.000013936961,0.00014061893,0.000014495326,0.0010739091],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972624,0.000015065836,0.000025535972,0.00006763569,0.00012134815,0.000044273816],"domain_scores_gemma":[0.9994678,0.00006130681,0.00020123039,0.00004808302,0.00016543134,0.000056265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024248079,0.0004837734,0.0002676999,0.00030762338,0.00009928423,0.00019483245,0.00033303737,0.0005840865,0.000335957],"category_scores_gemma":[0.00045371673,0.0002813028,0.00031748213,0.00021465408,0.00018688053,0.0002462986,0.00014293513,0.00024372849,0.00013364998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047563586,0.000011263691,0.00052940514,0.000018046163,0.0000052590553,0.00001989716,0.000026153373,0.00013454845,0.99756587,0.000006842911,0.000018873605,0.0016163955],"study_design_scores_gemma":[0.0000036881186,0.0004337658,0.010880464,0.0000046568325,0.000013539942,0.00007425778,0.000025459063,0.0018588797,0.9863977,0.0000031141478,0.0002962687,0.0000082760425],"about_ca_topic_score_codex":0.0018260891,"about_ca_topic_score_gemma":0.0029925471,"teacher_disagreement_score":0.0018260891,"about_ca_system_score_codex":0.0002902588,"about_ca_system_score_gemma":0.00012697086,"threshold_uncertainty_score":0.0036309361},"labels":[],"label_agreement":null},{"id":"W4309287908","doi":"10.3390/s22228909","title":"A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Inverse kinematics; Artificial neural network; Kinematics; Artificial intelligence; Robot; Forward kinematics; Robotics; Piecewise; Set (abstract data type); Mathematics","score_opus":0.016857938159535926,"score_gpt":0.20668254125455632,"score_spread":0.1898246030950204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309287908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021096838,0.00018160457,0.9961176,0.0000619576,0.000019280842,0.000013035375,0.000011862337,0.000053824617,0.0014312197],"genre_scores_gemma":[0.38406837,0.0011265957,0.6051315,0.00013778868,0.000116229385,0.00027658985,0.00017517323,0.00007646703,0.008891196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978703,0.000045491488,0.00001604571,0.00006158533,0.00007076661,0.000019017283],"domain_scores_gemma":[0.9998405,0.000063513355,0.000025252983,0.000013853642,0.000049187936,0.000007639515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048583286,0.00074256555,0.00053282164,0.00045112526,0.0003695366,0.00064873794,0.0007512949,0.0012043226,0.0021305103],"category_scores_gemma":[0.000866061,0.0004170002,0.00058825716,0.0004957724,0.0005698867,0.00089488283,0.00074119365,0.0013816007,0.0004266607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028105416,0.000019773648,0.000335859,0.00014085327,0.00002313278,0.00007908125,0.00006449833,0.91428614,0.0052763037,0.013223797,0.00058645447,0.06593601],"study_design_scores_gemma":[0.000002141753,0.000015606056,0.00008276699,0.000007653078,0.0000036115846,0.000025011575,0.000007632043,0.99473685,0.00076093763,0.0035300262,0.00082295336,0.000004809679],"about_ca_topic_score_codex":0.0036787605,"about_ca_topic_score_gemma":0.003219133,"teacher_disagreement_score":0.0036787605,"about_ca_system_score_codex":0.00040782787,"about_ca_system_score_gemma":0.00077676843,"threshold_uncertainty_score":0.0073147416},"labels":[],"label_agreement":null},{"id":"W4309456570","doi":"10.3390/s22228927","title":"Formulation of the Alpha Sliding Innovation Filter: A Robust Linear Estimation Strategy","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Robustness (evolution); Control theory (sociology); Filter (signal processing); Actuator; Hyperplane; Damper; Computer science; Engineering; Control engineering; Mathematics; Artificial intelligence; Control (management)","score_opus":0.037130715510323084,"score_gpt":0.25367148832566644,"score_spread":0.21654077281534334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309456570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006479345,0.00009559285,0.99860066,0.000032451146,0.000027363505,0.000008848353,0.000010125106,0.000051647225,0.00052541564],"genre_scores_gemma":[0.3624873,0.0015121363,0.6231585,0.00024592274,0.0005294013,0.000363487,0.0002571106,0.00011981339,0.011326366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993955,0.0001131319,0.000042765074,0.00015630572,0.00023985567,0.000052528747],"domain_scores_gemma":[0.9994686,0.00021744023,0.00006364457,0.00003849937,0.00019060886,0.000021230086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012554175,0.00086108444,0.00085504074,0.00047608907,0.00025406797,0.0008550313,0.000984038,0.0011891152,0.0025054715],"category_scores_gemma":[0.0015617656,0.0003284092,0.00064864825,0.00047612426,0.00062142865,0.0011103927,0.00061166025,0.0012444047,0.0007203045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020604901,0.000066996225,0.0007448619,0.0004015934,0.0001268722,0.00018768053,0.00018487668,0.5514335,0.024659218,0.10155201,0.0034150952,0.31702125],"study_design_scores_gemma":[0.0000113992755,0.00007683164,0.00012824428,0.000011286418,0.000014179391,0.000047768583,0.0000062785552,0.9898741,0.0026256028,0.0039713727,0.0032191041,0.000013882425],"about_ca_topic_score_codex":0.0028361792,"about_ca_topic_score_gemma":0.0015049487,"teacher_disagreement_score":0.0028361792,"about_ca_system_score_codex":0.0005961799,"about_ca_system_score_gemma":0.0009848427,"threshold_uncertainty_score":0.008381665},"labels":[],"label_agreement":null},{"id":"W4309457658","doi":"10.3390/s22228888","title":"Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ontario Council on Graduate Studies, Council of Ontario Universities; Council of Ontario Universities","keywords":"Gait; Sensitivity (control systems); Exoskeleton; Simulation; Gait cycle; Computer science; Motion capture; Ground reaction force; Gait analysis; Acceleration; Physical medicine and rehabilitation; Algorithm; Artificial intelligence; Engineering; Motion (physics); Kinematics; Medicine; Physics","score_opus":0.00862334875762858,"score_gpt":0.21044947956773186,"score_spread":0.20182613081010328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309457658","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5235716,0.0002714491,0.47223756,0.00008173085,0.000045096822,0.0002448041,0.00039230543,0.001958588,0.0011968424],"genre_scores_gemma":[0.8633282,0.0000999076,0.13494658,0.000054306078,0.000011739854,0.00016409701,0.00045851443,0.00004343252,0.00089322124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996376,0.00005287267,0.000049454124,0.00013773581,0.00009200874,0.0000302937],"domain_scores_gemma":[0.9991097,0.00039476203,0.00010891156,0.000055862623,0.0002886223,0.000042227402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006947685,0.0004229812,0.00049015414,0.0006295891,0.00014209557,0.0004348499,0.0004961073,0.00046499868,0.00073712395],"category_scores_gemma":[0.0029921248,0.0001840134,0.00022267501,0.00024238993,0.00012252892,0.0003094241,0.000347058,0.00027166438,0.00037177705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002028757,0.00048398186,0.058077108,0.0002481532,0.00019903628,0.0005207801,0.00042027183,0.039409824,0.084944606,0.0004058516,0.0018135211,0.81144804],"study_design_scores_gemma":[0.00016083676,0.0007626832,0.11818667,0.000047590343,0.00015491674,0.0011394745,0.00019058643,0.8307325,0.046108875,0.0006088114,0.001837051,0.00007002943],"about_ca_topic_score_codex":0.0018959724,"about_ca_topic_score_gemma":0.0032222024,"teacher_disagreement_score":0.0018959724,"about_ca_system_score_codex":0.00014928763,"about_ca_system_score_gemma":0.000271888,"threshold_uncertainty_score":0.0037698746},"labels":[],"label_agreement":null},{"id":"W4309570861","doi":"10.3390/s22239031","title":"Automated Impact Damage Detection Technique for Composites Based on Thermographic Image Processing and Machine Learning Classification","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Université Laval","funders":"","keywords":"Support vector machine; Aerospace; Composite number; Computer science; Thermography; Confusion matrix; Structural health monitoring; Artificial intelligence; Pattern recognition (psychology); Machine learning; Structural engineering; Engineering; Algorithm; Aerospace engineering","score_opus":0.0101831854583401,"score_gpt":0.23852555038123055,"score_spread":0.22834236492289045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309570861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21014342,0.00052362564,0.78395647,0.000112453024,0.000069101734,0.00015975544,0.00015330657,0.0028169993,0.00206492],"genre_scores_gemma":[0.6164774,0.00034263392,0.38075465,0.000054479624,0.0000417306,0.00012236566,0.00029052768,0.0000628948,0.0018532526],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964464,0.00003823864,0.000023124221,0.00007878961,0.00018057186,0.00003471105],"domain_scores_gemma":[0.9994892,0.00011248107,0.00010338246,0.00006591983,0.00021208395,0.000016860138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038347946,0.00056846376,0.00048804667,0.0020798827,0.00023796245,0.00041876035,0.00050486356,0.00053895125,0.00097650243],"category_scores_gemma":[0.00063059706,0.00023320409,0.0005013797,0.00097163726,0.0002917158,0.00055890833,0.0002505594,0.0004529367,0.0004041059],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028126827,0.00026252362,0.005823058,0.00022273525,0.00005604278,0.00018664116,0.00015599436,0.0199392,0.3607988,0.0009836026,0.0013293625,0.60996073],"study_design_scores_gemma":[0.000016361457,0.00035283485,0.027989168,0.00002330165,0.00005042405,0.0004648549,0.00010522573,0.7337763,0.23363139,0.0009038383,0.0026241885,0.00006211664],"about_ca_topic_score_codex":0.0010563638,"about_ca_topic_score_gemma":0.0016242748,"teacher_disagreement_score":0.0020798827,"about_ca_system_score_codex":0.00028298,"about_ca_system_score_gemma":0.00030734547,"threshold_uncertainty_score":0.0032667518},"labels":[],"label_agreement":null},{"id":"W4309572416","doi":"10.3390/s22239034","title":"Heuristic Resource Reservation Policies for Public Clouds in the IoT Era","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reservation; Cloud computing; Computer science; Heuristic; Service (business); Integer programming; Term (time); Service provider; Operations research; Distributed computing; Computer network; Mathematical optimization; Engineering; Algorithm; Artificial intelligence; Operating system; Business; Mathematics; Marketing","score_opus":0.05255571061160056,"score_gpt":0.2697507236677772,"score_spread":0.21719501305617664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309572416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18319046,0.0015117209,0.80163693,0.0010687634,0.00016745174,0.00023757125,0.00016255741,0.00049610715,0.011528421],"genre_scores_gemma":[0.9487454,0.00028684264,0.04921958,0.000092716014,0.000028515145,0.00006310862,0.000057685407,0.000041938903,0.0014641291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990829,0.00033367326,0.00003561799,0.000095469884,0.0001475763,0.0003047436],"domain_scores_gemma":[0.9981616,0.0011821929,0.00021221046,0.00011119675,0.0001416228,0.00019128772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016809922,0.00064296485,0.0010564488,0.00046849326,0.0007460404,0.0014316782,0.0012999709,0.0009920374,0.002152536],"category_scores_gemma":[0.002893883,0.00046217119,0.00044331988,0.00069906167,0.00090369605,0.0013908652,0.00068889523,0.001003479,0.00019389713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017559578,0.00010715994,0.0003403419,0.00005735262,0.000021156377,0.00008239472,0.00006309769,0.9620343,0.0011134754,0.021316301,0.0014967849,0.013191991],"study_design_scores_gemma":[0.000012544473,0.000015891294,0.00005604655,0.000005569895,0.000004086596,0.000013008869,0.0000342689,0.9952608,0.00022799478,0.004016567,0.00034945214,0.0000039320357],"about_ca_topic_score_codex":0.007639393,"about_ca_topic_score_gemma":0.0072498624,"teacher_disagreement_score":0.007639393,"about_ca_system_score_codex":0.0023814905,"about_ca_system_score_gemma":0.002814099,"threshold_uncertainty_score":0.01727897},"labels":[],"label_agreement":null},{"id":"W4309738721","doi":"10.3390/s22228942","title":"An Advanced Data Fusion Method to Improve Wetland Classification Using Multi-Source Remotely Sensed Data","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; European Space Agency","keywords":"Remote sensing; Random forest; Computer science; Synthetic aperture radar; Wetland; Sensor fusion; Artificial intelligence; Data mining; Pattern recognition (psychology); Geography","score_opus":0.08153233792812759,"score_gpt":0.3433394832141234,"score_spread":0.2618071452859958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309738721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04502686,0.00041422536,0.952485,0.00010580637,0.00008757596,0.000070429494,0.000116750314,0.00085771555,0.0008356434],"genre_scores_gemma":[0.41274813,0.00031666495,0.5851569,0.000087603425,0.00008122563,0.00010074874,0.00038875177,0.000040043025,0.0010799154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993191,0.00008416264,0.000048879963,0.00015688916,0.00033808264,0.000052925127],"domain_scores_gemma":[0.9995029,0.00008340397,0.0000536658,0.000057217025,0.00028804695,0.0000146987795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013827579,0.0006928695,0.00060033065,0.001972332,0.00032063984,0.00048046274,0.0006096012,0.00052606926,0.0007283078],"category_scores_gemma":[0.0014809319,0.00025667658,0.0008900677,0.001193812,0.00021489203,0.0010938894,0.0006362958,0.0005355005,0.0003447416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016880057,0.0001503421,0.0041606794,0.00017135582,0.00014836046,0.00013149889,0.00014992,0.052340556,0.101453364,0.0018788561,0.001679107,0.83756715],"study_design_scores_gemma":[0.000028316892,0.000174326,0.008795629,0.000029954415,0.00010162675,0.00021725513,0.00006345481,0.94321275,0.04066684,0.0015585949,0.005090493,0.00006082072],"about_ca_topic_score_codex":0.0023095335,"about_ca_topic_score_gemma":0.002666986,"teacher_disagreement_score":0.0023095335,"about_ca_system_score_codex":0.00030957372,"about_ca_system_score_gemma":0.00044834233,"threshold_uncertainty_score":0.0073127747},"labels":[],"label_agreement":null},{"id":"W4309740957","doi":"10.3390/s22228967","title":"A Generic Image Processing Pipeline for Enhancing Accuracy and Robustness of Visual Odometry","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Robustness (evolution); Computer science; Computer vision; Outlier; Feature extraction; Pipeline (software); Visual odometry; Monocular; Odometry; Feature (linguistics); Histogram; Pattern recognition (psychology); Mobile robot; Robot; Image (mathematics)","score_opus":0.01151528692475873,"score_gpt":0.247688176695216,"score_spread":0.23617288977045728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309740957","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058593405,0.00007373582,0.99086714,0.000024010193,0.000020194195,0.000051458865,0.00007311659,0.0023513662,0.00067963736],"genre_scores_gemma":[0.120439745,0.00016797683,0.8771013,0.000053799482,0.00002816896,0.00010596106,0.00044956745,0.00017114177,0.0014823339],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994578,0.000038931816,0.000026655849,0.00016219704,0.00024109139,0.00007328188],"domain_scores_gemma":[0.9995437,0.000071865456,0.000056843313,0.000112723355,0.0001963824,0.000018459252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053516217,0.0008783809,0.00060064456,0.0011310757,0.00036501852,0.0007119244,0.001092061,0.0006592017,0.0028676495],"category_scores_gemma":[0.0016447628,0.0004491268,0.0006114105,0.0009340086,0.0004288878,0.0010073833,0.0012380164,0.0006318026,0.001617302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021769674,0.00007105194,0.0014611449,0.0002374271,0.000058605976,0.000102099286,0.00012068236,0.030267034,0.26300997,0.004532716,0.002975285,0.6969463],"study_design_scores_gemma":[0.00005386762,0.00041630815,0.0072843353,0.000047738587,0.00006517131,0.0006533476,0.00009316637,0.66699004,0.28746152,0.004828333,0.032003745,0.0001023821],"about_ca_topic_score_codex":0.0025529175,"about_ca_topic_score_gemma":0.0023302066,"teacher_disagreement_score":0.0028676495,"about_ca_system_score_codex":0.0004098803,"about_ca_system_score_gemma":0.0007500552,"threshold_uncertainty_score":0.009593189},"labels":[],"label_agreement":null},{"id":"W4309785599","doi":"10.3390/s22228986","title":"A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Locality; Robustness (evolution); Feature extraction; Wavelet; Wavelet transform; Classifier (UML); Computer vision","score_opus":0.010001606232035155,"score_gpt":0.216596902570368,"score_spread":0.20659529633833285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309785599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025757963,0.0003786567,0.9702746,0.00010938415,0.000068554924,0.000066319466,0.00010258329,0.0011151369,0.0021267717],"genre_scores_gemma":[0.35065305,0.00056099327,0.64275223,0.00017528958,0.000064713255,0.00009914191,0.00050116197,0.0001153292,0.0050780745],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977964,0.000019463405,0.000008976451,0.00006634533,0.00009333734,0.00003228043],"domain_scores_gemma":[0.99979836,0.000040134684,0.00002832491,0.00003422722,0.00008434848,0.000014608516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035355453,0.0007474903,0.0005098201,0.0016092599,0.00023076181,0.00057673734,0.0010375535,0.00063129846,0.0016268782],"category_scores_gemma":[0.0005556433,0.00033560456,0.00067232974,0.00087053323,0.00035198565,0.0012949526,0.0006286846,0.00061203405,0.0006400049],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019533813,0.00018366601,0.0027390975,0.00020124536,0.0001020938,0.00016013005,0.00007695025,0.09387925,0.08193833,0.007956331,0.004340973,0.8082266],"study_design_scores_gemma":[0.000008268317,0.00006164509,0.0011325734,0.000009975235,0.000016972104,0.00014379038,0.000015065732,0.98108554,0.013530136,0.0015462404,0.002435305,0.00001453617],"about_ca_topic_score_codex":0.0043190597,"about_ca_topic_score_gemma":0.0066056014,"teacher_disagreement_score":0.0043190597,"about_ca_system_score_codex":0.00049410964,"about_ca_system_score_gemma":0.00075684057,"threshold_uncertainty_score":0.008587837},"labels":[],"label_agreement":null},{"id":"W4310041601","doi":"10.3390/s22239144","title":"Towards an Optimized Ensemble Feature Selection for DDoS Detection Using Both Supervised and Unsupervised Method","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer science; Feature selection; Machine learning; Denial-of-service attack; Feature (linguistics); Ensemble learning; Unsupervised learning; Feature learning; Autoencoder; Generalization; Supervised learning; Data mining; Pattern recognition (psychology); Deep learning; Artificial neural network; The Internet","score_opus":0.02469834112788495,"score_gpt":0.2786693424216744,"score_spread":0.25397100129378947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310041601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06870281,0.00032390375,0.92900234,0.00008700342,0.00003934063,0.000064240216,0.000110884146,0.0010582719,0.0006112533],"genre_scores_gemma":[0.6906247,0.00023144268,0.30586725,0.000116202464,0.00008201385,0.00020159202,0.0008885823,0.00011019007,0.0018779857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993036,0.00016073037,0.00004939985,0.00019481141,0.0001938758,0.00009755887],"domain_scores_gemma":[0.99905676,0.0002901566,0.00008177892,0.00009663704,0.00043513946,0.000039417533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012518532,0.0013286056,0.0013739903,0.0015124573,0.00043069996,0.00058442674,0.0009679436,0.0006723042,0.00065824337],"category_scores_gemma":[0.0022715167,0.00029985324,0.0010816405,0.0010716624,0.0002714099,0.0008620548,0.00059323356,0.0006901822,0.00035269826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035534924,0.0003645624,0.0075084777,0.00007211466,0.00024089382,0.00013129905,0.000100864214,0.24792768,0.023228602,0.0012470246,0.0034902398,0.71533275],"study_design_scores_gemma":[0.000011435456,0.000078173245,0.0016355186,0.0000052572877,0.00003300903,0.00004626888,0.00001772155,0.9928181,0.004365711,0.00052356586,0.00045565495,0.000009629785],"about_ca_topic_score_codex":0.0041801035,"about_ca_topic_score_gemma":0.0034018203,"teacher_disagreement_score":0.0041801035,"about_ca_system_score_codex":0.00032255118,"about_ca_system_score_gemma":0.0009258704,"threshold_uncertainty_score":0.00831157},"labels":[],"label_agreement":null},{"id":"W4310153807","doi":"10.3390/s22239205","title":"SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Remote sensing; Python (programming language); Radiance; Software; Preprocessor; Terrain; Computer science; Reflectivity; Environmental science; Geography; Artificial intelligence; Cartography","score_opus":0.04108185071829067,"score_gpt":0.2952864060721814,"score_spread":0.2542045553538907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310153807","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008504668,0.0002698797,0.2346421,0.0002699913,0.00013547963,0.00038952968,0.04805694,0.69436854,0.013362936],"genre_scores_gemma":[0.09415851,0.0007399131,0.46554103,0.001555629,0.00016205644,0.0026355064,0.14771098,0.25912306,0.02837338],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950206,0.00005326946,0.000037156493,0.00013790942,0.00019659533,0.00007299141],"domain_scores_gemma":[0.99938405,0.00021081326,0.00006706846,0.00011730471,0.0001678032,0.000052827407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007722637,0.0016616653,0.00074191974,0.00086136756,0.00061008363,0.0012689233,0.0024311373,0.00054415024,0.042859677],"category_scores_gemma":[0.0023899158,0.0009417533,0.0013171216,0.00090555387,0.00066645234,0.0017900562,0.0021971213,0.0019627728,0.02942031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010186796,0.00028920712,0.007122583,0.0023617658,0.0004508367,0.0005793555,0.00069397094,0.016458098,0.035091773,0.00909834,0.7192701,0.20756525],"study_design_scores_gemma":[0.000567726,0.00025005912,0.019387173,0.00030826408,0.00015839083,0.000704464,0.00026544125,0.2613962,0.084511705,0.036507703,0.5955017,0.000441094],"about_ca_topic_score_codex":0.004133436,"about_ca_topic_score_gemma":0.006350456,"teacher_disagreement_score":0.042859677,"about_ca_system_score_codex":0.00050736254,"about_ca_system_score_gemma":0.0016492468,"threshold_uncertainty_score":0.14337987},"labels":[],"label_agreement":null},{"id":"W4310153860","doi":"10.3390/s22239226","title":"Perception of Recycled Plastics for Improved Consumer Acceptance through Self-Reported and Physiological Measures","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Color perception and design","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universitat Politècnica de Catalunya; University of Victoria","keywords":"Perception; Consumption (sociology); Psychology; Sensory system; Cognition; Natural (archaeology); Natural materials; Applied psychology; Cognitive psychology; Aesthetics; Materials science","score_opus":0.08905657214192672,"score_gpt":0.3438237510773791,"score_spread":0.2547671789354524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310153860","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99706584,0.00023989864,0.0014023436,0.00004651419,0.00000794919,0.000022475895,0.000083614155,0.000014147993,0.0011171297],"genre_scores_gemma":[0.9973302,0.00017889918,0.0017813087,0.000036392117,0.000010268188,0.000030720526,0.00006764039,0.0000048398174,0.00055976654],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991837,0.00034211055,0.000046758625,0.000113002265,0.0002733199,0.000040967632],"domain_scores_gemma":[0.99692374,0.0010060288,0.0012261299,0.00012862169,0.00059151533,0.00012395879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017089962,0.00039976207,0.00023100284,0.00045612213,0.000121870085,0.00080199726,0.00022037595,0.00042241285,0.002474367],"category_scores_gemma":[0.0041903285,0.00014015258,0.00046803217,0.00024491161,0.00021066281,0.0005220487,0.000374424,0.00042491438,0.00018461356],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023194144,0.0018871276,0.7988435,0.0011210624,0.00066231395,0.00031176992,0.004605664,0.00068599754,0.056536462,0.00037240065,0.00045368506,0.13220057],"study_design_scores_gemma":[0.000017845034,0.0015601505,0.98759705,0.000056849156,0.0001476649,0.00021729546,0.0017715319,0.00081963715,0.007078731,0.00017336832,0.0005204828,0.00003949756],"about_ca_topic_score_codex":0.0004460349,"about_ca_topic_score_gemma":0.00074814307,"teacher_disagreement_score":0.002474367,"about_ca_system_score_codex":0.00014305906,"about_ca_system_score_gemma":0.00012446499,"threshold_uncertainty_score":0.00903815},"labels":[],"label_agreement":null},{"id":"W4310153890","doi":"10.3390/s22239192","title":"Very Long-Length FFT Using Multi-Resolution Piecewise-Constant Windows for Hardware-Accelerated Time–Frequency Distribution Calculations in an Ultra-Wideband Digital Receiver","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Electrical Measurement Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Fast Fourier transform; Wideband; Computer science; Main lobe; Frame (networking); Gaussian; Digital subscriber line; Electronic engineering; Acoustics; Algorithm; Telecommunications; Physics; Engineering","score_opus":0.03921227761930313,"score_gpt":0.2702086952992172,"score_spread":0.23099641767991408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310153890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015049,0.00019124146,0.98238707,0.00004355808,0.000049581016,0.000022685233,0.000031081854,0.0011741155,0.0010517314],"genre_scores_gemma":[0.15652362,0.0003284446,0.8406912,0.000031196505,0.000029559198,0.00006253804,0.0001260712,0.0002799213,0.0019273713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998388,0.000024984549,0.000011078857,0.000019805162,0.00008854261,0.000016741693],"domain_scores_gemma":[0.9996871,0.00011979848,0.00003626749,0.000058920414,0.00008210168,0.000015862737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035687487,0.0005187209,0.00021235013,0.00048013218,0.0002197905,0.00048711197,0.0004209952,0.00034606204,0.0033466665],"category_scores_gemma":[0.001201215,0.00020132949,0.00027105046,0.00067633996,0.00023732756,0.00089980004,0.00033643216,0.0006372131,0.0012689504],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057128863,0.000105119354,0.0017302855,0.00031185246,0.000064084976,0.0007206598,0.00042528618,0.052295215,0.39982593,0.029500395,0.0033560763,0.51109385],"study_design_scores_gemma":[0.000043370892,0.0002061672,0.0014340911,0.00004512871,0.000031722622,0.0006783486,0.00010111866,0.7962066,0.17780375,0.0046491986,0.018758047,0.000042445314],"about_ca_topic_score_codex":0.00080422196,"about_ca_topic_score_gemma":0.0010359254,"teacher_disagreement_score":0.0033466665,"about_ca_system_score_codex":0.0002220807,"about_ca_system_score_gemma":0.00039836724,"threshold_uncertainty_score":0.01119566},"labels":[],"label_agreement":null},{"id":"W4310153922","doi":"10.3390/s22239229","title":"IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Wireless sensor network; Scheduling (production processes); Computer science; Base station; Wireless; Computer network; Transmitter power output; Real-time computing; Sensor node; Transmission (telecommunications); Key distribution in wireless sensor networks; Wireless network; Telecommunications; Engineering; Transmitter; Channel (broadcasting)","score_opus":0.006821519246657221,"score_gpt":0.2080151598677681,"score_spread":0.2011936406211109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310153922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11386528,0.0022097758,0.8777582,0.00025057897,0.00017510826,0.00014023938,0.00007518968,0.00056706194,0.0049584443],"genre_scores_gemma":[0.9284718,0.0007565563,0.06941091,0.00007642003,0.000080371065,0.00006928723,0.000036894522,0.00003146848,0.0010662684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889106,0.00044611664,0.000060213224,0.00017213376,0.00026149274,0.00016896898],"domain_scores_gemma":[0.9974322,0.0010122908,0.0006060977,0.00039601335,0.00042090038,0.000132574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019766826,0.0007052446,0.0006528738,0.0005385448,0.0006348856,0.0008275636,0.0014364367,0.00045234562,0.00075126125],"category_scores_gemma":[0.003806025,0.00020584425,0.0002976819,0.00090478506,0.0007502607,0.0011388292,0.0007067803,0.0005885712,0.00023116061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011622395,0.0004128942,0.002343224,0.00055624184,0.00011282562,0.00022315176,0.000355041,0.712826,0.057536237,0.03806616,0.002236244,0.18416978],"study_design_scores_gemma":[0.000032087464,0.0004030106,0.0006660592,0.000028852963,0.00003721877,0.00023722679,0.000106867876,0.9737748,0.014075015,0.0079575125,0.0026516353,0.00002960719],"about_ca_topic_score_codex":0.0014176408,"about_ca_topic_score_gemma":0.0016886387,"teacher_disagreement_score":0.0019766826,"about_ca_system_score_codex":0.0010352723,"about_ca_system_score_gemma":0.0010323328,"threshold_uncertainty_score":0.01045382},"labels":[],"label_agreement":null},{"id":"W4310153928","doi":"10.3390/s22239209","title":"3D Object Recognition Using Fast Overlapped Block Processing Technique","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computational complexity theory; Cognitive neuroscience of visual object recognition; Block (permutation group theory); Artificial intelligence; Benchmark (surveying); Object (grammar); Feature extraction; Support vector machine; 3D single-object recognition; Object detection; Computation; Noise (video); Feature (linguistics); Pattern recognition (psychology); Image processing; Computer vision; Image (mathematics); Algorithm; Mathematics","score_opus":0.030507202054973023,"score_gpt":0.2650796569095968,"score_spread":0.23457245485462375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310153928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009460757,0.00033550872,0.98807275,0.00005682915,0.00004634175,0.000047759244,0.00009041298,0.0010334308,0.000856217],"genre_scores_gemma":[0.13993263,0.0007785458,0.8554064,0.000091114845,0.00006864604,0.00013396464,0.00068323856,0.00014197065,0.0027635877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992686,0.00007344794,0.000038052072,0.00014071932,0.00040947736,0.00006976876],"domain_scores_gemma":[0.999522,0.000108107466,0.000069230424,0.000102456834,0.0001744148,0.000023769471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042478056,0.0008079642,0.00085182977,0.0017595858,0.00035378666,0.0008627636,0.0008712947,0.0007298552,0.0026676117],"category_scores_gemma":[0.0010923289,0.00036783406,0.001014426,0.0015212948,0.00031822425,0.0012518138,0.0009072713,0.00067367574,0.0019274967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028498456,0.0000903739,0.0007314176,0.00016607852,0.00007160327,0.00023012445,0.000101992904,0.03674443,0.16227536,0.004032196,0.0034810193,0.79179037],"study_design_scores_gemma":[0.00002314912,0.00021765665,0.0017313239,0.000019458763,0.000044836044,0.0007488562,0.0000612371,0.8919861,0.092139624,0.0030428933,0.009936593,0.0000482447],"about_ca_topic_score_codex":0.002105619,"about_ca_topic_score_gemma":0.0021888013,"teacher_disagreement_score":0.0026676117,"about_ca_system_score_codex":0.00033131274,"about_ca_system_score_gemma":0.0007274148,"threshold_uncertainty_score":0.008924007},"labels":[],"label_agreement":null},{"id":"W4310171752","doi":"10.3390/s22239151","title":"Piezoelectric Micromachined Ultrasonic Transducers (PMUTs): Performance Metrics, Advancements, and Applications","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Ferroelectric and Piezoelectric Materials","field":"Materials Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Windsor Cancer Centre Foundation; University of Windsor; CMC Microsystems","keywords":"Materials science; Ultrasonic sensor; Microfabrication; Miniaturization; PMUT; Piezoelectricity; Electronics; Capacitive micromachined ultrasonic transducers; Microelectromechanical systems; Transducer; Computer science; Electronic engineering; Optoelectronics; Acoustics; Electrical engineering; Nanotechnology; Engineering; Fabrication","score_opus":0.02654832953815453,"score_gpt":0.28138067055489047,"score_spread":0.2548323410167359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310171752","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062416747,0.65485615,0.24332136,0.004422083,0.0013612356,0.00035517,0.0006777844,0.001769029,0.03082046],"genre_scores_gemma":[0.3973798,0.3426905,0.23925394,0.0010318231,0.0015467276,0.00034856825,0.0008812904,0.0002915184,0.01657582],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99838996,0.00020573143,0.000089221525,0.00021876513,0.0010265795,0.00006980414],"domain_scores_gemma":[0.9990527,0.00036997302,0.00019246519,0.00003583397,0.00030529834,0.000043748634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014070106,0.0011197559,0.0006122636,0.0016174274,0.00030273924,0.0016002732,0.00085925666,0.0014539853,0.0012438453],"category_scores_gemma":[0.0019477598,0.00053966144,0.00020627156,0.0023313165,0.0007684782,0.001988516,0.00065091636,0.0009434237,0.0008536441],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016118669,0.000087063556,0.003239337,0.0030635912,0.0000465696,0.00025653618,0.0002513455,0.0052976157,0.4161668,0.017459897,0.007915548,0.5460544],"study_design_scores_gemma":[0.000018130244,0.0009532907,0.007001658,0.0005219962,0.00013577339,0.0027667135,0.00038170975,0.030522663,0.60694015,0.007850517,0.34275076,0.00015661096],"about_ca_topic_score_codex":0.000525393,"about_ca_topic_score_gemma":0.0008870216,"teacher_disagreement_score":0.0016174274,"about_ca_system_score_codex":0.00089446735,"about_ca_system_score_gemma":0.00054240826,"threshold_uncertainty_score":0.0074411035},"labels":[],"label_agreement":null},{"id":"W4310191140","doi":"10.3390/s22239282","title":"Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Modalities; Electroencephalography; Feature selection; Computer science; Emotion recognition; Feature (linguistics); Selection (genetic algorithm); Affective computing; Emotion classification; Artificial intelligence; Feature engineering; Machine learning; Human–computer interaction; Psychology; Deep learning","score_opus":0.023319695282187476,"score_gpt":0.2726814638179166,"score_spread":0.2493617685357291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310191140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4721499,0.0013825262,0.51681346,0.0002733627,0.00027152963,0.0003080234,0.0019743457,0.005366165,0.0014607359],"genre_scores_gemma":[0.9047316,0.0002269234,0.08860486,0.000103062186,0.000057559253,0.0003852005,0.004472601,0.00018866187,0.0012295706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990534,0.00031716737,0.000065059125,0.00024014199,0.00019337567,0.00013082854],"domain_scores_gemma":[0.99888676,0.0005246305,0.00006973192,0.00018620769,0.00028607648,0.00004666469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00180501,0.0010526868,0.0007058417,0.00058649504,0.00029242368,0.0005368801,0.00067331374,0.00050817634,0.0014809396],"category_scores_gemma":[0.004793322,0.00015401904,0.00082918815,0.0004840239,0.00023148858,0.00053847657,0.0007034661,0.00069508736,0.0008185593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002625249,0.001262821,0.017598646,0.0002651902,0.00037718256,0.000398589,0.00024158396,0.043761667,0.0836618,0.000715733,0.013485071,0.8356065],"study_design_scores_gemma":[0.0001441407,0.0010487244,0.057939146,0.0000385409,0.00016757632,0.00059891725,0.00018328115,0.8768729,0.05712301,0.0019432243,0.0038636548,0.00007686638],"about_ca_topic_score_codex":0.0022280314,"about_ca_topic_score_gemma":0.0025102894,"teacher_disagreement_score":0.0022280314,"about_ca_system_score_codex":0.00027550964,"about_ca_system_score_gemma":0.00031608655,"threshold_uncertainty_score":0.009545922},"labels":[],"label_agreement":null},{"id":"W4310387008","doi":"10.3390/s22239302","title":"A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Support vector machine; Computer science; Electroencephalography; Artificial intelligence; Epileptic seizure; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Spectral density; Classifier (UML); Telecommunications","score_opus":0.049471197205866284,"score_gpt":0.29678615348514964,"score_spread":0.24731495627928335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310387008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053215172,0.00031044072,0.9438438,0.00010723194,0.000056558158,0.00008111632,0.000058224094,0.000674562,0.0016529456],"genre_scores_gemma":[0.58733815,0.00028225515,0.40961933,0.000085087326,0.000046500958,0.000117709766,0.00023890166,0.000050806026,0.0022212875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978226,0.000022617634,0.000017356682,0.000057614114,0.00008610305,0.000034053064],"domain_scores_gemma":[0.99988365,0.000025526015,0.000017831913,0.000013096081,0.000049897455,0.000009893617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024083615,0.00041063162,0.0004855655,0.0012854977,0.0003564534,0.00052863307,0.00046596155,0.00051796,0.0007079121],"category_scores_gemma":[0.00049191946,0.00013451734,0.0007145992,0.0006227274,0.00021591154,0.00058653863,0.00031303693,0.00030125643,0.00019074822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025456824,0.00013458525,0.0076225526,0.00011449901,0.00013057569,0.00045807316,0.00017507919,0.07506005,0.097101755,0.0066021313,0.0025584355,0.8097878],"study_design_scores_gemma":[0.000015827074,0.00016508352,0.008256838,0.000012161908,0.000038224654,0.00058546895,0.000098590535,0.963713,0.02072407,0.0025529675,0.0037987428,0.000039057046],"about_ca_topic_score_codex":0.0024553405,"about_ca_topic_score_gemma":0.0024238313,"teacher_disagreement_score":0.0024553405,"about_ca_system_score_codex":0.00028910217,"about_ca_system_score_gemma":0.00037471202,"threshold_uncertainty_score":0.0048820972},"labels":[],"label_agreement":null},{"id":"W4310535409","doi":"10.3390/s22239369","title":"An AVMD-DBN-ELM Model for Bearing Fault Diagnosis","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Aero Engine Corporation of China; National Key Research and Development Program of China; China Scholarship Council; State Key Laboratory of Mechanics and Control of Mechanical Structures; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Fault (geology); Bearing (navigation); Hilbert–Huang transform; Computer science; Deep belief network; Artificial intelligence; Vibration; Sorting; Pattern recognition (psychology); Set (abstract data type); sort; Similarity (geometry); Engineering; Data mining; Algorithm; Deep learning; Computer vision","score_opus":0.01744041231277173,"score_gpt":0.285604289123188,"score_spread":0.2681638768104163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310535409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031804483,0.00078616204,0.96364546,0.0003600766,0.00010847602,0.00003883248,0.00019829973,0.0004089445,0.0026493096],"genre_scores_gemma":[0.8671938,0.00040597928,0.12275449,0.0002958027,0.00006301728,0.00016491258,0.00061554613,0.00006064099,0.0084457835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982387,0.000033447377,0.000011862894,0.00006231351,0.000042500084,0.000025962912],"domain_scores_gemma":[0.99979144,0.000087089975,0.00002098532,0.000011061146,0.00007661514,0.000012766535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005536083,0.0006083281,0.000608153,0.00043976284,0.00026200115,0.00053397624,0.0011678259,0.00097149046,0.0017801378],"category_scores_gemma":[0.0011624143,0.0003048643,0.0006514489,0.00033690012,0.00034030734,0.0006838265,0.0006094798,0.0012971386,0.00032604762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006777098,0.000038793205,0.0012024785,0.0000534508,0.000039850343,0.000059953814,0.000046606747,0.9310766,0.002008317,0.003899049,0.001196828,0.060310416],"study_design_scores_gemma":[0.0000016475183,0.000004134382,0.00005250015,0.0000014036495,0.0000017671548,0.00000401431,0.0000016352054,0.9993211,0.000110556204,0.000396726,0.00010310831,0.0000013123215],"about_ca_topic_score_codex":0.012330669,"about_ca_topic_score_gemma":0.0118614845,"teacher_disagreement_score":0.012330669,"about_ca_system_score_codex":0.0007064699,"about_ca_system_score_gemma":0.00066550536,"threshold_uncertainty_score":0.024517834},"labels":[],"label_agreement":null},{"id":"W4311104044","doi":"10.3390/s22239383","title":"Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Process (computing); Artificial intelligence; Visualization; Scale (ratio); Deep learning; Control (management); Machine learning; Dimension (graph theory); Spatial analysis; Data mining; Data science","score_opus":0.005442953973494518,"score_gpt":0.249585506539645,"score_spread":0.2441425525661505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311104044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059280902,0.00062724814,0.935422,0.0003590179,0.00010603499,0.0000612836,0.00031182572,0.0025785298,0.0012531786],"genre_scores_gemma":[0.821353,0.0004371386,0.17573035,0.00012829428,0.00006944462,0.00006322848,0.0006826436,0.00012060872,0.0014153164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995221,0.00007547378,0.000028860777,0.0001264903,0.00015288958,0.00009422686],"domain_scores_gemma":[0.99955267,0.00009475132,0.00007802531,0.00007644308,0.00015474536,0.000043294553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007284866,0.001088345,0.00073864707,0.001715917,0.00032990164,0.0011001088,0.0008502059,0.00060670363,0.0013522885],"category_scores_gemma":[0.0019404729,0.00026895784,0.00095597643,0.0010036372,0.00041132548,0.0014084942,0.0012729391,0.0011726203,0.00029464628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033701182,0.00027052558,0.005117716,0.00013281663,0.00014215523,0.00028018883,0.00023037265,0.24108836,0.031604722,0.0057623,0.0071941703,0.70783955],"study_design_scores_gemma":[0.000004741045,0.000035676385,0.0016079404,0.000009503332,0.000016684036,0.00003633323,0.000037945996,0.98727536,0.007357982,0.002755031,0.0008507277,0.0000119977785],"about_ca_topic_score_codex":0.0056258193,"about_ca_topic_score_gemma":0.004454768,"teacher_disagreement_score":0.0056258193,"about_ca_system_score_codex":0.00073183316,"about_ca_system_score_gemma":0.000740633,"threshold_uncertainty_score":0.011186123},"labels":[],"label_agreement":null},{"id":"W4311104334","doi":"10.3390/s22239397","title":"Measurement of Restrained and Unrestrained Shrinkage of Reinforced Concrete Using Distributed Fibre Optic Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Concrete Properties and Behavior","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Transport Canada","keywords":"Shrinkage; Materials science; Composite material; Reinforcement; Fiber-reinforced concrete; Reinforced concrete; Structural engineering; Engineering","score_opus":0.026713851889095374,"score_gpt":0.22021727008080358,"score_spread":0.19350341819170822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311104334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95964676,0.00034399316,0.038838264,0.000021084215,0.000018786612,0.000031159765,0.000109040135,0.0001609311,0.000830025],"genre_scores_gemma":[0.97102654,0.00030147523,0.027324826,0.000017804985,0.000007744882,0.000037218928,0.000072502975,0.000017860886,0.0011939752],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995073,0.00004075998,0.000015515512,0.00011129378,0.00029843042,0.000026705618],"domain_scores_gemma":[0.999511,0.00013143434,0.00015735118,0.000045717316,0.00012441057,0.000030077803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031918017,0.0005408311,0.0002597423,0.0004240828,0.00012243513,0.00016568342,0.00043209628,0.00037281678,0.0003675852],"category_scores_gemma":[0.00063279667,0.00024964515,0.00016168019,0.0002980923,0.0003438609,0.0004491827,0.00031222412,0.00031933998,0.00012888318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047538924,0.000025462987,0.0014474776,0.000044696815,0.0000073704086,0.00003216362,0.00006337722,0.001420842,0.9874421,0.000047824582,0.000021028502,0.009400049],"study_design_scores_gemma":[0.000013086192,0.00039219437,0.019495806,0.000012978982,0.0000218596,0.0002997527,0.0000971427,0.018638585,0.9598168,0.00010814255,0.001062141,0.000041593466],"about_ca_topic_score_codex":0.0008142312,"about_ca_topic_score_gemma":0.0030190311,"teacher_disagreement_score":0.0008142312,"about_ca_system_score_codex":0.0002629694,"about_ca_system_score_gemma":0.00018601688,"threshold_uncertainty_score":0.0019080043},"labels":[],"label_agreement":null},{"id":"W4311132172","doi":"10.3390/s22249631","title":"Design and Implementation of an Open-Source SCADA System for a Community Solar-Powered Reverse Osmosis System","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Open source; Reverse osmosis; SCADA; Embedded system; Computer science; Engineering; Photovoltaic system; Environmental science; Operating system; Systems engineering; Process engineering; Electrical engineering; Chemistry; Software; Membrane","score_opus":0.023660585038036086,"score_gpt":0.24762801943340762,"score_spread":0.22396743439537153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311132172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066469535,0.00019190078,0.8941877,0.00032070433,0.00020311776,0.0014067014,0.00028279988,0.021515964,0.0154216755],"genre_scores_gemma":[0.7057083,0.00017623123,0.2739799,0.00018567525,0.00008469136,0.0011210375,0.0007871186,0.0005224722,0.017434446],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899966,0.00013925506,0.00009013218,0.00020773732,0.00046794262,0.00009526912],"domain_scores_gemma":[0.9989667,0.00008486038,0.0000977709,0.00014297912,0.0005827717,0.00012493764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007245091,0.00062498916,0.0005675448,0.00068852864,0.0004932513,0.0010511841,0.0017757152,0.0006815951,0.0059535913],"category_scores_gemma":[0.00096933806,0.00030757207,0.0003408845,0.0002952244,0.0003065694,0.0007941274,0.000680664,0.0006157541,0.0024482661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081614533,0.0011949545,0.015731275,0.00094067253,0.00023191996,0.002130523,0.0014890196,0.091662906,0.25062248,0.01591591,0.025207313,0.59405696],"study_design_scores_gemma":[0.0005225595,0.002135734,0.012901528,0.00017472362,0.00022416735,0.0013132371,0.0002830408,0.6479466,0.14186342,0.0033872258,0.1890022,0.0002457078],"about_ca_topic_score_codex":0.0015457128,"about_ca_topic_score_gemma":0.00092970027,"teacher_disagreement_score":0.0059535913,"about_ca_system_score_codex":0.0005225836,"about_ca_system_score_gemma":0.001145604,"threshold_uncertainty_score":0.019916713},"labels":[],"label_agreement":null},{"id":"W4311153788","doi":"10.3390/s22249688","title":"Detection Methods for Multi-Modal Inertial Gas Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Sharjah Research Academy; American University of Sharjah","keywords":"Modal; Bifurcation; Asymmetry; Sensitivity (control systems); Inertial frame of reference; Beam (structure); Physics; Acoustics; Transducer; Modal testing; Noise (video); Modal analysis; Capacitance; Nonlinear system; Control theory (sociology); Engineering; Optics; Computer science; Electronic engineering; Materials science; Vibration; Electrode; Classical mechanics","score_opus":0.028746736857142224,"score_gpt":0.32015725937258044,"score_spread":0.2914105225154382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311153788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1660786,0.0017974656,0.8269368,0.00034909506,0.00008231975,0.00011517836,0.00011725239,0.0004848621,0.004038501],"genre_scores_gemma":[0.7458755,0.00085636176,0.2500798,0.00015057746,0.000029782832,0.00013864742,0.00009532705,0.000033221917,0.0027407394],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997445,0.000027238124,0.000008645134,0.000057240188,0.00014387134,0.000018433559],"domain_scores_gemma":[0.999814,0.00007516012,0.000046516412,0.000019777188,0.00003688514,0.000007860912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024159186,0.00032260965,0.00022117775,0.0002803796,0.00013674748,0.00021205877,0.00056762155,0.0005016761,0.0010791447],"category_scores_gemma":[0.00047379604,0.00019702244,0.00022507294,0.00015794208,0.00030421017,0.00057136227,0.00034914422,0.00041289986,0.00030834452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039851446,0.000025262509,0.0004354422,0.00015352551,0.000007751149,0.000046749596,0.000062763436,0.008485434,0.9595013,0.005311371,0.00017235035,0.025758298],"study_design_scores_gemma":[0.000012909464,0.0001817909,0.0010133135,0.000015115552,0.000010646617,0.00017743363,0.000041313637,0.3301734,0.6609618,0.0019074631,0.0054716947,0.00003308222],"about_ca_topic_score_codex":0.00046456142,"about_ca_topic_score_gemma":0.00088038563,"teacher_disagreement_score":0.0010791447,"about_ca_system_score_codex":0.00046436925,"about_ca_system_score_gemma":0.0001626894,"threshold_uncertainty_score":0.0036100745},"labels":[],"label_agreement":null},{"id":"W4311378100","doi":"10.3390/s22249780","title":"Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Mural; Principal component analysis; Artificial intelligence; Computer vision; Smoothing; Computer science; Hyperspectral imaging; Translation (biology); Image restoration; Gaussian blur; Pattern recognition (psychology); Image processing; Image (mathematics); Art","score_opus":0.01547480963197056,"score_gpt":0.2712914240699328,"score_spread":0.2558166144379622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311378100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922339,0.0001937864,0.0059918207,0.00010812543,0.000016199097,0.000043680124,0.00006957978,0.00006954655,0.001273285],"genre_scores_gemma":[0.9901659,0.00011420227,0.008029063,0.000018662753,0.000009987109,0.000008620656,0.00009147009,0.000016813843,0.0015451853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976295,0.000032009542,0.000009342888,0.00004731922,0.000099883735,0.000048556158],"domain_scores_gemma":[0.99980694,0.00003544844,0.00002557886,0.00002604406,0.00007482274,0.000031147003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003436707,0.000544658,0.00039787876,0.0009496215,0.00057642494,0.0006148386,0.00065621146,0.00071379024,0.00047344435],"category_scores_gemma":[0.00038040854,0.0001996897,0.00039500615,0.00053385337,0.0006243019,0.00043233548,0.0005150921,0.00028472036,0.00014519745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010023087,0.0008890046,0.1622506,0.0012738922,0.00029773283,0.04709079,0.0062126573,0.120127626,0.3581176,0.0015903249,0.0046597356,0.2964877],"study_design_scores_gemma":[0.00005641188,0.0006406921,0.4839225,0.00010966161,0.00024684006,0.004888508,0.009491128,0.41275465,0.07827287,0.0008156684,0.008644833,0.00015625647],"about_ca_topic_score_codex":0.01463421,"about_ca_topic_score_gemma":0.042152647,"teacher_disagreement_score":0.01463421,"about_ca_system_score_codex":0.00045576598,"about_ca_system_score_gemma":0.0003737942,"threshold_uncertainty_score":0.029098094},"labels":[],"label_agreement":null},{"id":"W4311387106","doi":"10.3390/s22249747","title":"Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Photoplethysmogram; Computer science; Artificial intelligence; Fuse (electrical); SIGNAL (programming language); Sensor fusion; Computer vision; Machine learning; Real-time computing; Human–computer interaction; Engineering","score_opus":0.012588419984660355,"score_gpt":0.22555860609068362,"score_spread":0.21297018610602328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311387106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041055838,0.00010802307,0.99484986,0.00007891893,0.000034677447,0.000019355837,0.000031279622,0.00036150104,0.00041077],"genre_scores_gemma":[0.31748936,0.00043334265,0.67788404,0.0003267363,0.000095033014,0.00011567529,0.0003845942,0.00012334021,0.0031479406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993894,0.00012661809,0.000040863833,0.00019793361,0.00020769355,0.000037550974],"domain_scores_gemma":[0.9994444,0.0001315136,0.00006304979,0.00012939432,0.00018386642,0.000047812387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013554585,0.00067619875,0.0006868198,0.00059745833,0.00027218548,0.0011474601,0.0012306138,0.0010013966,0.0017493789],"category_scores_gemma":[0.002483225,0.00031718597,0.0005628851,0.00066788675,0.00065619644,0.001992373,0.0019422678,0.0011284782,0.0008737169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065713207,0.00024227065,0.003117661,0.0002718221,0.00018752852,0.00025080622,0.0002467015,0.18588106,0.15504175,0.031842407,0.0040141875,0.61824673],"study_design_scores_gemma":[0.000009828384,0.00016574883,0.0006266861,0.000017229653,0.000024093197,0.0001901246,0.000025036816,0.95950043,0.02730436,0.008122492,0.0039890665,0.00002485231],"about_ca_topic_score_codex":0.0007971522,"about_ca_topic_score_gemma":0.0006988351,"teacher_disagreement_score":0.0017493789,"about_ca_system_score_codex":0.00045992978,"about_ca_system_score_gemma":0.0005333633,"threshold_uncertainty_score":0.007168412},"labels":[],"label_agreement":null},{"id":"W4311387296","doi":"10.3390/s22249755","title":"An Adaptive Refinement Scheme for Depth Estimation Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Benchmark (surveying); Computer science; Inference; Generalization; Particle swarm optimization; Scheme (mathematics); Artificial intelligence; Segmentation; Feature (linguistics); Deep learning; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.023954523147152143,"score_gpt":0.3012783051188913,"score_spread":0.2773237819717392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311387296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011481292,0.00014718522,0.98675936,0.0000751451,0.000026455478,0.000039765993,0.00004894399,0.0008586052,0.0005633288],"genre_scores_gemma":[0.33900347,0.00016311325,0.6575086,0.00012792007,0.000036682304,0.00011917413,0.00021879567,0.00016856425,0.0026537287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994711,0.000092375405,0.00003573709,0.00016605103,0.00018202212,0.000052680072],"domain_scores_gemma":[0.9991998,0.00022799015,0.000106177555,0.00019744212,0.00022968237,0.000039006936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010720673,0.00096484955,0.0006089206,0.00062765955,0.00036123086,0.00037192486,0.0020044958,0.00092749414,0.0018721758],"category_scores_gemma":[0.0032539184,0.0005231555,0.00070889515,0.0004638435,0.0006398009,0.0013044918,0.0013646894,0.0015879915,0.000433721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020228773,0.00006397835,0.0012722175,0.00009650497,0.00005240573,0.00009032474,0.00016949236,0.5677338,0.051161814,0.013036763,0.0023974613,0.36372292],"study_design_scores_gemma":[0.000011143728,0.000040053943,0.00012953577,0.000004923166,0.000005918768,0.000020255287,0.0000045350266,0.99160016,0.00564807,0.0017334603,0.00079515536,0.000006784807],"about_ca_topic_score_codex":0.008959751,"about_ca_topic_score_gemma":0.013213866,"teacher_disagreement_score":0.008959751,"about_ca_system_score_codex":0.0008940971,"about_ca_system_score_gemma":0.0009071286,"threshold_uncertainty_score":0.017815173},"labels":[],"label_agreement":null},{"id":"W4311494423","doi":"10.3390/s22249812","title":"Transcutaneous Functional Electrical Stimulation Controlled by a System of Sensors for the Lower Limbs: A Systematic Review","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Functional electrical stimulation; Nonlinear system; Computer science; Physical medicine and rehabilitation; Functional movement; Inertial measurement unit; Control engineering; Engineering; Control theory (sociology); Control (management); Medicine; Stimulation; Artificial intelligence","score_opus":0.022773156358492943,"score_gpt":0.24893933885145125,"score_spread":0.2261661824929583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311494423","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014847098,0.99927706,0.00009918437,0.000075962416,0.000051029503,0.000029750969,0.000076515134,0.0000040471173,0.00023790711],"genre_scores_gemma":[0.0010193393,0.9984413,0.00023008436,0.00009666127,0.000025333487,0.000038994687,0.00005804714,0.0000015687548,0.00008871042],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9991954,0.00014351682,0.0003126086,0.00011276308,0.00020506085,0.00003063364],"domain_scores_gemma":[0.9966336,0.0024189546,0.0004938556,0.000049958122,0.00035228409,0.000051308456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014117009,0.0011105874,0.0034898543,0.0063612494,0.00036109038,0.0013928409,0.0011742986,0.0012022353,0.0059061563],"category_scores_gemma":[0.0045394204,0.00046754078,0.0025825384,0.0070330827,0.00048640996,0.0015090333,0.00073591643,0.00073872803,0.000783904],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001031518,0.000035365156,0.00022383101,0.7519714,0.0012551545,0.00013398447,0.000099605,0.00016045159,0.00049580954,0.0007395809,0.004524584,0.24025704],"study_design_scores_gemma":[0.00013517585,0.00041615774,0.004109459,0.68997246,0.017687071,0.0019227631,0.00029823766,0.00018589427,0.0007629294,0.0012387133,0.2831996,0.000071539296],"about_ca_topic_score_codex":0.0031920657,"about_ca_topic_score_gemma":0.0072470177,"teacher_disagreement_score":0.0063612494,"about_ca_system_score_codex":0.00094410084,"about_ca_system_score_gemma":0.0047067124,"threshold_uncertainty_score":0.019758105},"labels":[],"label_agreement":null},{"id":"W4311628423","doi":"10.3390/s22239455","title":"Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Age of Information Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université du Québec à Montréal","funders":"Université du Québec à Montréal","keywords":"Computer science; Network packet; Computer network; Quality of service; End-to-end principle; End-to-end delay; Relay; Metric (unit); Performance metric; Base station; Network performance; Queuing delay; Engineering","score_opus":0.027642644488812453,"score_gpt":0.29194933028327075,"score_spread":0.2643066857944583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311628423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56496835,0.0018231339,0.42821124,0.000563679,0.000087114226,0.000068970425,0.0001525487,0.00033348994,0.0037915618],"genre_scores_gemma":[0.9917481,0.00021406478,0.007348299,0.000031242645,0.000007266221,0.000016039494,0.000019586494,0.000011900605,0.0006034828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935466,0.0002549095,0.000028078084,0.00010564431,0.00011394834,0.00014277556],"domain_scores_gemma":[0.9959455,0.003028751,0.00043069237,0.0001348244,0.0003273252,0.00013284762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026365472,0.0005873263,0.0004214393,0.00060586206,0.0005482976,0.0010360226,0.0007651931,0.00068757194,0.0007190011],"category_scores_gemma":[0.0063226665,0.00034446712,0.0003000969,0.0006484749,0.0011146314,0.0015138809,0.0007654925,0.0006881639,0.00005783226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006237761,0.00002401456,0.0012674514,0.0000221613,0.0000114879795,0.00005090267,0.00005618534,0.98733145,0.0015106614,0.0071409754,0.000092105074,0.002430263],"study_design_scores_gemma":[0.0000017001477,0.000027479426,0.00020125009,0.0000019548247,0.0000041634776,0.000008664284,0.000015378117,0.99799395,0.00036744436,0.0013266745,0.000047149348,0.0000041741882],"about_ca_topic_score_codex":0.00821775,"about_ca_topic_score_gemma":0.005276634,"teacher_disagreement_score":0.00821775,"about_ca_system_score_codex":0.0024930371,"about_ca_system_score_gemma":0.00093322573,"threshold_uncertainty_score":0.01808834},"labels":[],"label_agreement":null},{"id":"W4311705741","doi":"10.3390/s22239525","title":"Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Dalhousie University","funders":"","keywords":"Computer network; Routing protocol; Computer science; Link-state routing protocol; Dynamic Source Routing; Static routing; Geographic routing; Network packet; Policy-based routing; Routing domain; Multipath routing; Interior gateway protocol; Wireless sensor network; The Void; Distributed computing","score_opus":0.1861407872339132,"score_gpt":0.3416529783608748,"score_spread":0.15551219112696163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311705741","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010274568,0.6314381,0.31679988,0.0017243715,0.0015443802,0.0005648432,0.00030130253,0.0007655326,0.03658708],"genre_scores_gemma":[0.0879525,0.776122,0.121189594,0.00084931357,0.0012672656,0.00064141344,0.0008442236,0.0001539156,0.010979818],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940825,0.000110960806,0.000079972015,0.00007650008,0.0002833059,0.00004106431],"domain_scores_gemma":[0.9993773,0.00029668908,0.00006833148,0.000049875896,0.00018252511,0.000025214675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007667303,0.0007301247,0.00076463295,0.0014048277,0.0005118911,0.0013219184,0.0014607155,0.0009143949,0.0014088597],"category_scores_gemma":[0.0013682109,0.00041480694,0.0005339098,0.0029970862,0.00045874232,0.0024680968,0.0008518939,0.0010083555,0.00078049465],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057779354,0.00012448091,0.0010407125,0.0054069622,0.000090635665,0.0002887018,0.0002750008,0.015833417,0.006095684,0.036842722,0.015539517,0.9184043],"study_design_scores_gemma":[0.000028196204,0.00042266768,0.0016356745,0.0019214997,0.00024497416,0.0024132838,0.0007044394,0.07480624,0.008304466,0.034098513,0.8752719,0.00014823786],"about_ca_topic_score_codex":0.0014499644,"about_ca_topic_score_gemma":0.001370193,"teacher_disagreement_score":0.0014607155,"about_ca_system_score_codex":0.0005099978,"about_ca_system_score_gemma":0.0011097884,"threshold_uncertainty_score":0.0047130585},"labels":[],"label_agreement":null},{"id":"W4311728780","doi":"10.3390/s22249586","title":"Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"FortisBC; Mitacs","keywords":"High-density polyethylene; Ultrasonic sensor; SIGNAL (programming language); Channel (broadcasting); Acoustics; Signal processing; Computer science; Artificial neural network; Polyethylene; Materials science; Artificial intelligence; Computer hardware; Physics; Telecommunications; Composite material","score_opus":0.013362071916909921,"score_gpt":0.1955664499296703,"score_spread":0.18220437801276038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311728780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28926033,0.0005360499,0.706862,0.00021137237,0.0000355524,0.000041272117,0.00006824716,0.0011490983,0.001836055],"genre_scores_gemma":[0.89919865,0.00024726652,0.09840274,0.00008596994,0.000016609278,0.000022201433,0.00008708758,0.00002917984,0.001910263],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987197,0.000016402273,0.000005768694,0.000036229743,0.000049584174,0.000020064008],"domain_scores_gemma":[0.99971694,0.00010204985,0.000074567266,0.000020225585,0.00006777872,0.000018319543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026764403,0.00062939135,0.00028432757,0.000685317,0.00016406667,0.0003113063,0.00042230674,0.00050768716,0.0004284871],"category_scores_gemma":[0.00066264847,0.0002494503,0.0002343328,0.00031783222,0.0003746004,0.000805302,0.0004558683,0.00041298053,0.00013907519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005988682,0.00016249818,0.01982372,0.00023433885,0.00007595964,0.0007448466,0.0002845298,0.19620554,0.3105401,0.0017748413,0.001224547,0.46833014],"study_design_scores_gemma":[0.000006461035,0.00014129523,0.0047137416,0.000014594032,0.000017809469,0.0001516922,0.00006018239,0.9440179,0.048958384,0.0011785601,0.00072477746,0.000014621782],"about_ca_topic_score_codex":0.0013032716,"about_ca_topic_score_gemma":0.0023899386,"teacher_disagreement_score":0.0013032716,"about_ca_system_score_codex":0.00034736286,"about_ca_system_score_gemma":0.000264471,"threshold_uncertainty_score":0.002591312},"labels":[],"label_agreement":null},{"id":"W4311788265","doi":"10.3390/s22249857","title":"A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Tracing; Computer science; Motor skill; Measure (data warehouse); Function (biology); Machine learning; Physical medicine and rehabilitation; Artificial intelligence; Psychology; Data mining; Neuroscience; Medicine","score_opus":0.1394100191838111,"score_gpt":0.3404440720088749,"score_spread":0.20103405282506379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311788265","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04567204,0.00010078465,0.95220476,0.00005480821,0.000019110788,0.00022366473,0.0004997152,0.0006635542,0.00056154706],"genre_scores_gemma":[0.3374116,0.00015240708,0.65946764,0.000060716964,0.000033766566,0.0011238625,0.00073492795,0.00017160871,0.00084349146],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980368,0.00071644323,0.00014677367,0.0005354047,0.0005184248,0.000046177778],"domain_scores_gemma":[0.9951728,0.0020780826,0.0010831148,0.0009582633,0.0006423207,0.00006543265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032955816,0.00075609627,0.0006272108,0.001883358,0.00035245437,0.0007032362,0.00068844366,0.0007021988,0.0010711735],"category_scores_gemma":[0.0140657965,0.00029753716,0.00049696944,0.0015842016,0.00042248028,0.0008836376,0.00052261667,0.00086919183,0.00035350365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003120837,0.00029814214,0.09922603,0.000542518,0.0010594733,0.0002245703,0.0008841574,0.039712105,0.0886235,0.0060111955,0.0028504774,0.7602557],"study_design_scores_gemma":[0.00007448202,0.0014198966,0.45038703,0.0001647837,0.00041821727,0.0021344558,0.00045533315,0.45848647,0.06084398,0.015920911,0.00923174,0.000462669],"about_ca_topic_score_codex":0.003136468,"about_ca_topic_score_gemma":0.008862924,"teacher_disagreement_score":0.0032955816,"about_ca_system_score_codex":0.00037583825,"about_ca_system_score_gemma":0.00066344946,"threshold_uncertainty_score":0.017428935},"labels":[],"label_agreement":null},{"id":"W4311969913","doi":"10.3390/s22249628","title":"Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Institutes of Health","keywords":"Mean squared error; Translation (biology); Image translation; Computer science; Artificial intelligence; Image (mathematics); Image quality; Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.030704156902123907,"score_gpt":0.3086819293091927,"score_spread":0.2779777724070688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311969913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026289692,0.00035863003,0.9669175,0.0002165252,0.000076963755,0.00006959784,0.00008980948,0.0007886152,0.005192751],"genre_scores_gemma":[0.8229708,0.00044957854,0.16653918,0.00050456804,0.000063028616,0.00019446683,0.00040398506,0.0002844158,0.008590055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998167,0.0000498226,0.0000065242557,0.00004778989,0.00005632336,0.000022719807],"domain_scores_gemma":[0.9998165,0.000077291064,0.000023046994,0.00003792566,0.000031126132,0.000014198015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038215402,0.0007086327,0.00035294297,0.00023355655,0.00011671438,0.00036523922,0.00078990625,0.00045209148,0.0017716518],"category_scores_gemma":[0.0007723807,0.00024577265,0.00047008673,0.00018041379,0.0005144817,0.00043215955,0.0006498784,0.00080097496,0.00035594724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015167992,0.000056026278,0.0012459888,0.00011657507,0.00007674495,0.00023853636,0.00007623339,0.80136484,0.039949954,0.017018024,0.0038409457,0.13586447],"study_design_scores_gemma":[0.000006406325,0.000040909883,0.00015423233,0.0000063114207,0.000007923079,0.00008344971,0.000005118032,0.9917739,0.0038477748,0.0027652257,0.0013023926,0.000006381313],"about_ca_topic_score_codex":0.001314307,"about_ca_topic_score_gemma":0.0017060295,"teacher_disagreement_score":0.0017716518,"about_ca_system_score_codex":0.0003895335,"about_ca_system_score_gemma":0.00033762117,"threshold_uncertainty_score":0.005926788},"labels":[],"label_agreement":null},{"id":"W4311970732","doi":"10.3390/s22249648","title":"Identifying the Optimal Parameters to Express the Capacity–Activity Interrelationship of Community-Dwelling Older Adults Using Wearable Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Physical Activity and Health","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Canadian Institutes of Health Research","keywords":"Correlation; Physical activity; Gerontology; Wearable computer; Medicine; Psychology; Physical therapy; Computer science; Mathematics; Embedded system","score_opus":0.1450327806714478,"score_gpt":0.3436982616897325,"score_spread":0.1986654810182847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311970732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9600967,0.0004263766,0.037213303,0.00008097548,0.000013143322,0.00013021051,0.0005869017,0.00011907437,0.0013333117],"genre_scores_gemma":[0.97088087,0.00026787835,0.02797138,0.000026726706,0.000008642371,0.00014046527,0.00052682165,0.000010907242,0.00016631946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995371,0.00015810033,0.00006663573,0.000097086915,0.00010651964,0.000034630084],"domain_scores_gemma":[0.9990392,0.00041622616,0.00018442149,0.00009897681,0.00023016352,0.000030967294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011055493,0.00068954256,0.0005689169,0.0010650966,0.00020209841,0.0009668128,0.00029832852,0.00050697144,0.00047038682],"category_scores_gemma":[0.004898739,0.000232925,0.0004856289,0.0009865424,0.0001972183,0.00080173055,0.00048112823,0.0003264507,0.0002298056],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005524787,0.00045810067,0.75268143,0.00041572438,0.00034678623,0.00013259078,0.0015133067,0.012836661,0.032118175,0.0008983412,0.0012041717,0.19684228],"study_design_scores_gemma":[0.000037064874,0.00060833595,0.88692147,0.00017507983,0.00016751446,0.0003552564,0.002664573,0.09568616,0.009236196,0.002185925,0.0018665709,0.000095898686],"about_ca_topic_score_codex":0.0030531585,"about_ca_topic_score_gemma":0.0062832413,"teacher_disagreement_score":0.0030531585,"about_ca_system_score_codex":0.00019687072,"about_ca_system_score_gemma":0.00029457186,"threshold_uncertainty_score":0.0060707927},"labels":[],"label_agreement":null},{"id":"W4312174351","doi":"10.3390/s23010084","title":"A Study on the Effects of Lateral-Wedge Insoles on Plantar-Pressure Pattern for Medial Knee Osteoarthritis Using the Wearable Sensing Insole","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Asia University; Industrial Technology Research Institute; Asia University Hospital","keywords":"Forefoot; Heel; Medicine; Osteoarthritis; WOMAC; Plantar pressure; Center of pressure (fluid mechanics); Wedge (geometry); Orthodontics; Surgery; Pressure sensor; Mathematics; Anatomy; Geometry","score_opus":0.019004220550293324,"score_gpt":0.2231703471819096,"score_spread":0.20416612663161626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312174351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99874413,0.0006001992,0.00022648074,0.000030772797,0.000024368694,0.0001420261,0.000029143099,0.0000030979663,0.00019967536],"genre_scores_gemma":[0.9966247,0.0012074491,0.0011502644,0.000090632304,0.000048961047,0.00023188595,0.00007159329,0.0000020891132,0.000572509],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99971765,0.000108077395,0.000043808494,0.000036057336,0.00006753379,0.000026724132],"domain_scores_gemma":[0.999708,0.00008063079,0.00006200771,0.00002214663,0.000043616223,0.00008363807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005517101,0.00035929793,0.00060152455,0.00020105898,0.00018835638,0.0001732553,0.00019685346,0.0002507176,0.0013470876],"category_scores_gemma":[0.0006381697,0.00014941308,0.00061075616,0.00027172113,0.0001958862,0.0002349879,0.0001598351,0.00041474862,0.0001294622],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.246236,0.18237007,0.022412607,0.0052950843,0.0016276289,0.0007648584,0.0008932614,0.0006340887,0.25083947,0.00020176554,0.0005069445,0.28821832],"study_design_scores_gemma":[0.011184627,0.9346355,0.046285316,0.000060477498,0.00041731904,0.00026409165,0.00023963244,0.00035471373,0.0056787897,0.000038717964,0.0008219197,0.000018846808],"about_ca_topic_score_codex":0.00034909084,"about_ca_topic_score_gemma":0.000906238,"teacher_disagreement_score":0.0013470876,"about_ca_system_score_codex":0.00007075118,"about_ca_system_score_gemma":0.0002469862,"threshold_uncertainty_score":0.004506469},"labels":[],"label_agreement":null},{"id":"W4312196629","doi":"10.3390/s22249958","title":"Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fastener; Spiral (railway); Eddy current; Eddy-current testing; Materials science; Sizing; Eddy-current sensor; Electromagnetic coil; Structural health monitoring; Paris' law; Structural engineering; Electrical conductor; Groove (engineering); Mechanical engineering; Composite material; Engineering; Fracture mechanics; Electrical engineering; Crack closure; Metallurgy","score_opus":0.010349331770323853,"score_gpt":0.21542808266271488,"score_spread":0.20507875089239103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312196629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8448301,0.00038454754,0.1533175,0.000060312534,0.000025172496,0.000055758,0.00006869021,0.00022985521,0.0010280325],"genre_scores_gemma":[0.96605486,0.000082393155,0.033489194,0.000010527692,0.0000054331167,0.000014936022,0.000024059435,0.000004550731,0.00031401828],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998994,0.000015568683,0.0000051602583,0.000025703937,0.00004548289,0.000008648995],"domain_scores_gemma":[0.9996494,0.00010884982,0.00010729704,0.000049614282,0.00006490342,0.00001995283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016052256,0.0002949768,0.00022277079,0.0002821126,0.000081355465,0.00015081052,0.00025770182,0.00035072066,0.00024902754],"category_scores_gemma":[0.0004915794,0.00014618672,0.00013572059,0.00013869893,0.00030067455,0.00027633912,0.00021037055,0.00013304765,0.000061386614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079454796,0.000017261289,0.0015726486,0.000057967227,0.000010332479,0.000051172832,0.000049680355,0.00724834,0.9773482,0.00024650467,0.000074785086,0.013243635],"study_design_scores_gemma":[0.00003295859,0.00087062136,0.010218156,0.00001533319,0.000038463393,0.00040445084,0.000046507612,0.20130834,0.78540355,0.00036989164,0.0012579199,0.00003386264],"about_ca_topic_score_codex":0.0002695555,"about_ca_topic_score_gemma":0.00064685487,"teacher_disagreement_score":0.00035072066,"about_ca_system_score_codex":0.00013510583,"about_ca_system_score_gemma":0.00011014691,"threshold_uncertainty_score":0.000980258},"labels":[],"label_agreement":null},{"id":"W4312210179","doi":"10.3390/s22249908","title":"A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Global Positioning System; Computer science; GNSS augmentation; Lidar; Inertial measurement unit; Remote sensing; Air navigation; Satellite system; Compass; Real-time computing; Computer vision; Geography; Telecommunications","score_opus":0.012844466363878078,"score_gpt":0.2146527753968496,"score_spread":0.20180830903297153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312210179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106076404,0.00021185548,0.8862577,0.00007449384,0.00013110897,0.00032286672,0.00023263601,0.0029110317,0.003781944],"genre_scores_gemma":[0.5383425,0.00012562925,0.45741826,0.000055281904,0.000034762834,0.00025153146,0.0006975324,0.00006293932,0.00301156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973637,0.000027042257,0.000018995765,0.0000613962,0.00012536273,0.000030882078],"domain_scores_gemma":[0.9997925,0.0000067188685,0.00002142293,0.000040247447,0.00012457337,0.000014505311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002803959,0.0004730131,0.00033097903,0.00075418607,0.00040525515,0.00032350342,0.000653043,0.00028246004,0.0010744644],"category_scores_gemma":[0.00039318012,0.00019247051,0.00025869955,0.0006040012,0.00016229755,0.0004528342,0.0007446439,0.00036091713,0.0008185585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000311437,0.00019972782,0.008146812,0.00015736297,0.000064602755,0.0004169622,0.00032789004,0.058168408,0.25744438,0.0047539086,0.0042899908,0.66571856],"study_design_scores_gemma":[0.000084195766,0.00062873535,0.017927999,0.00004783921,0.000075278076,0.0004756076,0.00020558039,0.8585951,0.09928812,0.002004238,0.020578632,0.00008874376],"about_ca_topic_score_codex":0.0039535365,"about_ca_topic_score_gemma":0.005986964,"teacher_disagreement_score":0.0039535365,"about_ca_system_score_codex":0.00023251276,"about_ca_system_score_gemma":0.0006677741,"threshold_uncertainty_score":0.007861078},"labels":[],"label_agreement":null},{"id":"W4312210290","doi":"10.3390/s22249887","title":"Phases of Match-Play in Professional Australian Football: Positional Demands and Match-Related Fatigue","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Football; League; Position (finance); Football team; Energy expenditure; Power (physics); Quarter (Canadian coin); Match play; Competition (biology); Aeronautics; Simulation; Statistics; Psychology; Computer science; Mathematics; Engineering; Geography; Business; Physical therapy; Medicine; Ecology; Biology","score_opus":0.023583055994585187,"score_gpt":0.3070878928019376,"score_spread":0.2835048368073524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312210290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99938405,0.00010833104,0.00014536582,0.0000126053965,0.000001987925,0.000010197748,0.000072685856,0.0000013196031,0.00026337442],"genre_scores_gemma":[0.998982,0.00008694838,0.00022310129,0.00001230825,0.000003391726,0.000015934487,0.00014969372,0.0000014467126,0.0005252632],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99964416,0.00008726103,0.000028601082,0.00006440462,0.00010720687,0.00006833344],"domain_scores_gemma":[0.99918824,0.00016882979,0.0003693368,0.00002917386,0.00011692927,0.00012736107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005340793,0.00021645612,0.00030327972,0.0005360656,0.0003919082,0.0005530598,0.00027474476,0.00035117034,0.0011480318],"category_scores_gemma":[0.0018686117,0.0002543964,0.00018521851,0.00041124783,0.00023269726,0.0002207206,0.0005033507,0.00021246841,0.00023670001],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011354275,0.00019988467,0.97349167,0.000090100584,0.00008026439,0.00019393544,0.0017626367,0.00031010754,0.008045464,0.000045044686,0.00010432033,0.014541034],"study_design_scores_gemma":[5.9209583e-7,0.00008975056,0.9995371,0.0000030861895,0.0000032169,0.000023674294,0.00015879209,0.00007780141,0.000044877484,0.00000605868,0.00005368822,0.000001260898],"about_ca_topic_score_codex":0.02503821,"about_ca_topic_score_gemma":0.056581534,"teacher_disagreement_score":0.02503821,"about_ca_system_score_codex":0.00044901596,"about_ca_system_score_gemma":0.00034400355,"threshold_uncertainty_score":0.04978496},"labels":[],"label_agreement":null},{"id":"W4312211566","doi":"10.3390/s22249877","title":"Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Mandibular canal; Visibility; Computer vision; Deep learning; Residual; Orthodontics; Medicine; Algorithm","score_opus":0.014215765573847877,"score_gpt":0.2567410511132834,"score_spread":0.2425252855394355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312211566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18430617,0.0017484179,0.80505705,0.00032083018,0.0001027255,0.0001263492,0.00040731838,0.0050787474,0.002852459],"genre_scores_gemma":[0.7869005,0.00057873654,0.20510504,0.00033003255,0.00006640042,0.00010675982,0.0010654127,0.00016564217,0.0056814253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974185,0.000028256401,0.00001506393,0.000082398146,0.00007165767,0.000060851504],"domain_scores_gemma":[0.99965644,0.00011263634,0.000043583044,0.00005066074,0.00011351694,0.000023069779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047201748,0.0008243215,0.00056535244,0.00060991966,0.0002406369,0.00040560917,0.0012969361,0.000890783,0.0013550726],"category_scores_gemma":[0.00112928,0.00036444637,0.00064981123,0.00045090815,0.00032599355,0.00061694393,0.00073770405,0.0007232081,0.00042461656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046792906,0.00020498464,0.0034680676,0.00017135488,0.00011313689,0.00025127435,0.00011506077,0.18972735,0.07670223,0.0012255231,0.0035680728,0.7239851],"study_design_scores_gemma":[0.0000080630225,0.00006583118,0.0012057705,0.000010529753,0.000030757645,0.00008391613,0.000013159757,0.98355895,0.013710925,0.0006434306,0.00065916777,0.000009486232],"about_ca_topic_score_codex":0.009951206,"about_ca_topic_score_gemma":0.019813148,"teacher_disagreement_score":0.009951206,"about_ca_system_score_codex":0.000640302,"about_ca_system_score_gemma":0.00085838477,"threshold_uncertainty_score":0.019786537},"labels":[],"label_agreement":null},{"id":"W4313259693","doi":"10.3390/s23010209","title":"Wireless Capacitive Liquid-Level Detection Sensor Based on Zero-Power RFID-Sensing Architecture","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"RFID technology advancements","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Alberta Innovates","keywords":"Capacitive sensing; Capacitance; Sensitivity (control systems); Wireless sensor network; Linearity; Sensor node; Wireless; SIGNAL (programming language); Repeatability; Electro-optical sensor; Polyethylene naphthalate; Node (physics); Electronic engineering; Electrical engineering; Materials science; Computer science; Key distribution in wireless sensor networks; Acoustics; Engineering; Wireless network; Telecommunications; Physics; Nanotechnology","score_opus":0.00951638789769928,"score_gpt":0.20380377405720979,"score_spread":0.1942873861595105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313259693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14150046,0.0030210405,0.8448637,0.00054039434,0.00046555966,0.00020551684,0.0002100533,0.002254803,0.00693839],"genre_scores_gemma":[0.7389984,0.0014710934,0.2521456,0.00076238863,0.00014905955,0.00017836076,0.00026255043,0.00007220398,0.0059604],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993094,0.00008947948,0.000036838548,0.00020913177,0.00031751773,0.000037678357],"domain_scores_gemma":[0.99971706,0.000070224385,0.00006364006,0.00003126127,0.00009932622,0.000018482728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030943,0.00055587664,0.00052351493,0.00045454965,0.0002639103,0.0005567247,0.002189342,0.0009277645,0.0009205132],"category_scores_gemma":[0.0005283537,0.00036907734,0.00034223372,0.0005952782,0.000433807,0.0019442599,0.0008028514,0.0004714008,0.000733933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012848122,0.00005875784,0.0009247067,0.0003318943,0.000022152139,0.00015893693,0.000065383036,0.0018439293,0.9559396,0.0022566875,0.0006417901,0.0376278],"study_design_scores_gemma":[0.00004189451,0.0005888874,0.001807147,0.000029754128,0.00008699805,0.0012143505,0.000038319704,0.07303431,0.9074901,0.00074766355,0.014828126,0.00009251679],"about_ca_topic_score_codex":0.00027381876,"about_ca_topic_score_gemma":0.0004119056,"teacher_disagreement_score":0.002189342,"about_ca_system_score_codex":0.00042742115,"about_ca_system_score_gemma":0.00026680838,"threshold_uncertainty_score":0.00310117},"labels":[],"label_agreement":null},{"id":"W4313294819","doi":"10.3390/s23010363","title":"Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Robustness (evolution); Machine learning; Physical medicine and rehabilitation; Medicine","score_opus":0.017464738908623923,"score_gpt":0.25320429759388574,"score_spread":0.2357395586852618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313294819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5584751,0.0010552724,0.4323166,0.0002046619,0.0002085278,0.00018895522,0.00055800093,0.0025773614,0.0044155507],"genre_scores_gemma":[0.9428858,0.00027529453,0.05301569,0.00009307256,0.00005548148,0.00009012896,0.00048823538,0.00003425078,0.0030620755],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997646,0.00003462756,0.00000981791,0.000105049556,0.000053517313,0.000032426717],"domain_scores_gemma":[0.9997595,0.00007315574,0.000043242562,0.000030258903,0.000076969336,0.000016862543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028625986,0.0007125934,0.00046546877,0.0005005961,0.000114211325,0.00033542878,0.00040807523,0.000424772,0.0011222974],"category_scores_gemma":[0.0010097192,0.00019479883,0.00038465147,0.00033955777,0.00014527168,0.00032634087,0.00031041304,0.0003681182,0.0005540282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050696,0.00069117715,0.03135651,0.00016234457,0.00021183096,0.00021784788,0.00008924758,0.11711662,0.077669576,0.0003836977,0.002305149,0.7692891],"study_design_scores_gemma":[0.00001273074,0.00024698715,0.032631896,0.000020721536,0.000031896114,0.00015914663,0.000034073513,0.9495997,0.016088337,0.0003348282,0.00082042813,0.000019199228],"about_ca_topic_score_codex":0.004882002,"about_ca_topic_score_gemma":0.008327631,"teacher_disagreement_score":0.004882002,"about_ca_system_score_codex":0.00035189302,"about_ca_system_score_gemma":0.00034208564,"threshold_uncertainty_score":0.009707212},"labels":[],"label_agreement":null},{"id":"W4313327993","doi":"10.3390/s23010243","title":"Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Royal Society of Edinburgh; National Natural Science Foundation of China; National Key Research and Development Program of China; Engineering and Physical Sciences Research Council; Université de Strasbourg; Université Laval","keywords":"Pattern recognition (psychology); Feature (linguistics); Graph; Computer science; Artificial intelligence; Data mining; Theoretical computer science","score_opus":0.013123274073549332,"score_gpt":0.21682709578502438,"score_spread":0.20370382171147505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313327993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13512929,0.00014321916,0.86243284,0.00015527505,0.000034143704,0.000042178373,0.00018951538,0.0012347247,0.00063888164],"genre_scores_gemma":[0.8530791,0.00008334596,0.1444739,0.00006504505,0.000036870337,0.0000827634,0.0007388203,0.00011031747,0.0013298695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996032,0.00009733206,0.00002269378,0.00012576187,0.000097201635,0.000053805048],"domain_scores_gemma":[0.9989962,0.0005423846,0.0001370985,0.00008300803,0.00019974214,0.000041647607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000647605,0.0008403595,0.0006199391,0.000950869,0.00018585591,0.0005973604,0.0006463009,0.00067291147,0.0007897252],"category_scores_gemma":[0.0026873145,0.0002230877,0.00065661845,0.00072467345,0.0004268218,0.00061647873,0.0004699335,0.00069943117,0.00029935222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045890364,0.00017628244,0.0029540777,0.00013279101,0.00008465677,0.00017363088,0.00009688903,0.5993272,0.036452934,0.0027315267,0.0023349274,0.35507613],"study_design_scores_gemma":[0.000004117374,0.0000281953,0.00075610843,0.0000018542571,0.0000052139894,0.000013051697,0.0000074245795,0.9961777,0.001845667,0.0010183114,0.00013825609,0.000004173185],"about_ca_topic_score_codex":0.0022686955,"about_ca_topic_score_gemma":0.0019579402,"teacher_disagreement_score":0.0022686955,"about_ca_system_score_codex":0.0004356748,"about_ca_system_score_gemma":0.0003623152,"threshold_uncertainty_score":0.004510939},"labels":[],"label_agreement":null},{"id":"W4313436112","doi":"10.3390/s23010006","title":"Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"","keywords":"Computer science; Distillation; Activity recognition; Deep learning; Artificial intelligence; Human–computer interaction; Machine learning; Embedded system; Chemistry; Chromatography","score_opus":0.04525981913289185,"score_gpt":0.2785962715836858,"score_spread":0.23333645245079393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313436112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1032726,0.00059615436,0.88894004,0.000379613,0.000084485444,0.00007735303,0.00023747578,0.0045988034,0.0018134537],"genre_scores_gemma":[0.91092014,0.00010716506,0.0864339,0.0002678405,0.000025112706,0.00010726762,0.00048258255,0.00006331832,0.0015927231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928457,0.00017430703,0.000040018764,0.00025239942,0.00012634523,0.00012231513],"domain_scores_gemma":[0.99916744,0.00028238024,0.000069765025,0.00029267708,0.00012273007,0.00006502809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014088389,0.0010084641,0.0009943472,0.0004600403,0.00048077313,0.0008882826,0.0022516246,0.0011199281,0.0014155674],"category_scores_gemma":[0.002786882,0.00040139037,0.00054982625,0.0005835835,0.00087249145,0.0023250177,0.0023462041,0.0017603801,0.0003901999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000503748,0.0005491912,0.0020590362,0.00008934496,0.00012542328,0.00017119574,0.000121198056,0.7123135,0.007234303,0.0044679744,0.0029175892,0.2694475],"study_design_scores_gemma":[0.000016912347,0.000046821253,0.00017358737,0.0000033405925,0.000007102496,0.000017666374,0.000013909183,0.99347466,0.0021520767,0.0037397898,0.0003482606,0.0000058407772],"about_ca_topic_score_codex":0.0058676354,"about_ca_topic_score_gemma":0.006906124,"teacher_disagreement_score":0.0058676354,"about_ca_system_score_codex":0.000880048,"about_ca_system_score_gemma":0.0011937284,"threshold_uncertainty_score":0.011666954},"labels":[],"label_agreement":null},{"id":"W4313471539","doi":"10.3390/s23010432","title":"SoftSAR: The New Softer Side of Socially Assistive Robots—Soft Robotics with Social Human–Robot Interaction Skills","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences North; Baycrest Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Soft robotics; Robot; Robotics; Artificial intelligence; Human–computer interaction; Field (mathematics); Soft materials; Perspective (graphical); Computer science; Human–robot interaction; Engineering; Nanotechnology; Mathematics","score_opus":0.012628615063844762,"score_gpt":0.2394072148267864,"score_spread":0.22677859976294162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313471539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047015265,0.012681971,0.5860982,0.0253841,0.0021187204,0.00024090779,0.00013468375,0.0015882343,0.32473788],"genre_scores_gemma":[0.6230515,0.011304239,0.2652625,0.0077077663,0.0018288161,0.0006048346,0.00017525026,0.00041170872,0.08965331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988128,0.0004204713,0.000050144485,0.00019342315,0.00040472634,0.00011853289],"domain_scores_gemma":[0.9979651,0.00085061777,0.00022891557,0.00036232537,0.00020260188,0.00039043857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014176052,0.00090924744,0.0005206272,0.0008540766,0.0014633344,0.0032095767,0.0012488449,0.0022166872,0.013301653],"category_scores_gemma":[0.002356771,0.0003966903,0.0005992137,0.00037490303,0.009950252,0.0068583717,0.0053912625,0.0023444213,0.0030046413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006926178,0.00013721304,0.0008240842,0.0010549932,0.00004357684,0.000286066,0.002391482,0.0035469118,0.022356737,0.8070711,0.011203222,0.15101536],"study_design_scores_gemma":[0.00003910743,0.0005695402,0.0017471522,0.00068357954,0.00003714695,0.0013525083,0.0027236543,0.021412252,0.018161753,0.5979311,0.35518324,0.00015892737],"about_ca_topic_score_codex":0.00030934947,"about_ca_topic_score_gemma":0.0005227697,"teacher_disagreement_score":0.013301653,"about_ca_system_score_codex":0.0007025556,"about_ca_system_score_gemma":0.0010491671,"threshold_uncertainty_score":0.044498444},"labels":[],"label_agreement":null},{"id":"W4313472466","doi":"10.3390/s23010404","title":"Submillimeter Sized 2D Electrothermal Optical Fiber Scanner","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Cantilever; Actuator; Scanner; Optical fiber; Optics; Fabrication; Materials science; Fiber; Displacement (psychology); Acoustics; Optoelectronics; Physics; Electrical engineering; Engineering; Composite material","score_opus":0.004708025654924049,"score_gpt":0.18271424020699928,"score_spread":0.17800621455207524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313472466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33836684,0.0026481943,0.6203523,0.0013964549,0.00080559973,0.0004757113,0.002447246,0.0051812846,0.028326321],"genre_scores_gemma":[0.45289493,0.00061726524,0.5262877,0.00046811864,0.00009066572,0.00040068806,0.0008392493,0.00012514197,0.018276313],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974495,0.000021659278,0.000011927045,0.00007973868,0.00011693898,0.000024774958],"domain_scores_gemma":[0.9996302,0.00015533656,0.000052890184,0.000059699516,0.00006673804,0.00003504276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026539926,0.00036263312,0.00034987443,0.00037866062,0.00019329922,0.00052572216,0.0008196122,0.00095815625,0.0058802348],"category_scores_gemma":[0.0004050182,0.00039932688,0.00019762828,0.00027365776,0.00026962615,0.00087434234,0.0007655078,0.00043637765,0.0015142441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017941918,0.000052808235,0.0012954836,0.000138843,0.000012864235,0.0002604141,0.000099959856,0.0019240417,0.94304955,0.0021477484,0.0031086253,0.047730237],"study_design_scores_gemma":[0.00010069226,0.0008640906,0.013472535,0.000082913895,0.000051853545,0.0035203893,0.00021294893,0.12781002,0.77155393,0.002166979,0.079922795,0.00024088038],"about_ca_topic_score_codex":0.0003727473,"about_ca_topic_score_gemma":0.0010876666,"teacher_disagreement_score":0.0058802348,"about_ca_system_score_codex":0.0002897854,"about_ca_system_score_gemma":0.0003746779,"threshold_uncertainty_score":0.01967138},"labels":[],"label_agreement":null},{"id":"W4313472511","doi":"10.3390/s23010425","title":"A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; University of Lethbridge","keywords":"Wearable computer; Load cell; Hammer; Units of measurement; Simulation; Computer science; Arduino; Microcontroller; Training system; Artificial intelligence; Inertial measurement unit; Engineering; Computer hardware; Embedded system; Electrical engineering; Mechanical engineering","score_opus":0.02030846317598878,"score_gpt":0.2640744760108366,"score_spread":0.24376601283484783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313472511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16769804,0.0019918687,0.80503964,0.0005332953,0.00096993987,0.0007717365,0.0010160124,0.009761823,0.012217703],"genre_scores_gemma":[0.86663854,0.00072119344,0.11888073,0.00050542207,0.0001178301,0.00071208534,0.0005733481,0.00010622245,0.011744552],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956435,0.000058958034,0.000052280324,0.00013084803,0.00015508832,0.000038350092],"domain_scores_gemma":[0.9998165,0.000029721728,0.000029274082,0.000026004176,0.00006897499,0.00002943564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004553608,0.00070239994,0.0006705205,0.00042831886,0.00032839106,0.00038960387,0.00080946164,0.0006248596,0.0052221417],"category_scores_gemma":[0.00052928645,0.00025845674,0.00038151862,0.0005283522,0.00019485457,0.00061564404,0.00062610913,0.00041174423,0.0011965819],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000839772,0.00045101548,0.0076340907,0.0012010694,0.00017287715,0.00094126724,0.00046891434,0.0101577295,0.54657394,0.0019587986,0.010576071,0.4190245],"study_design_scores_gemma":[0.00054008927,0.00769088,0.062843725,0.0003496321,0.0007387265,0.003386085,0.0004911886,0.4319584,0.3939232,0.0038802007,0.093832,0.00036593407],"about_ca_topic_score_codex":0.000713322,"about_ca_topic_score_gemma":0.0011491396,"teacher_disagreement_score":0.0052221417,"about_ca_system_score_codex":0.0002482921,"about_ca_system_score_gemma":0.00045814537,"threshold_uncertainty_score":0.017469823},"labels":[],"label_agreement":null},{"id":"W4313472989","doi":"10.3390/s23010405","title":"Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Control theory (sociology); Sliding mode control; Controller (irrigation); A priori and a posteriori; Bounded function; Adaptive control; Stability (learning theory); Path (computing); Computer science; Control engineering; Tracking error; Robust control; Tracking (education); Control (management); Engineering; Control system; Artificial intelligence; Mathematics; Nonlinear system","score_opus":0.015377674197036445,"score_gpt":0.2119399700727537,"score_spread":0.19656229587571725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313472989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034928932,0.00010598453,0.995272,0.000024735178,0.00003198474,0.000025259167,0.0000056700087,0.00027977422,0.00076166756],"genre_scores_gemma":[0.43748802,0.0003530725,0.55807006,0.000089851004,0.00005527309,0.00030940794,0.00010392467,0.00006280476,0.003467651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976593,0.000024969866,0.000016007667,0.00007147975,0.00010253072,0.000019146228],"domain_scores_gemma":[0.99978906,0.000051582054,0.000023304952,0.000019495876,0.0001033836,0.000013137057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038224508,0.0005526098,0.00053262134,0.00037453024,0.0004001893,0.00046816494,0.000970662,0.000718838,0.0010869105],"category_scores_gemma":[0.0006871743,0.00032092,0.00043759777,0.00034633136,0.00038873666,0.00062423164,0.00059273676,0.0008566611,0.0002864791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110658446,0.0000863245,0.0010184776,0.00022597368,0.00009041624,0.0001805083,0.00027628662,0.44537973,0.038356714,0.022507977,0.0022229925,0.48954397],"study_design_scores_gemma":[0.000014956083,0.00007167734,0.00012627592,0.000007216333,0.0000087749295,0.000034973717,0.000007606086,0.9943817,0.0021525098,0.0011395754,0.0020467502,0.000007987683],"about_ca_topic_score_codex":0.0046250466,"about_ca_topic_score_gemma":0.0021895824,"teacher_disagreement_score":0.0046250466,"about_ca_system_score_codex":0.0003067375,"about_ca_system_score_gemma":0.0010170954,"threshold_uncertainty_score":0.009196281},"labels":[],"label_agreement":null},{"id":"W4313494518","doi":"10.3390/s23020577","title":"3-D Data Interpolation and Denoising by an Adaptive Weighting Rank-Reduction Method Using Multichannel Singular Spectrum Analysis Algorithm","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Singular value decomposition; Rank (graph theory); Algorithm; Singular value; Noise reduction; Weighting; Singular spectrum analysis; Projection (relational algebra); Reduction (mathematics); Residual; Interpolation (computer graphics); Mathematics; Noise (video); Low-rank approximation; Computer science; Artificial intelligence; Hankel matrix; Geometry; Image (mathematics); Mathematical analysis; Eigenvalues and eigenvectors","score_opus":0.10633538013993757,"score_gpt":0.3833767301240067,"score_spread":0.27704134998406915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313494518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059207347,0.000039108552,0.99350256,0.000028977547,0.000012072498,0.000015385865,0.000017774995,0.0002380576,0.00022533188],"genre_scores_gemma":[0.04528258,0.00007630061,0.953545,0.000023555904,0.000014674157,0.000053607135,0.000113659924,0.00007273854,0.0008178931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995408,0.000102273276,0.000033019867,0.00008495004,0.0002022504,0.000036831796],"domain_scores_gemma":[0.9994943,0.00014715409,0.00005930318,0.00009073087,0.00018188232,0.000026472368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008212039,0.00074844615,0.0007552555,0.00089803175,0.00044530927,0.0006041705,0.0008508717,0.0009033809,0.0016571664],"category_scores_gemma":[0.0015680918,0.0004058387,0.0012050535,0.0009749909,0.00054406107,0.00084211736,0.0008292035,0.0009406974,0.00075499795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025332865,0.00017106997,0.0012634672,0.00020158969,0.00012816167,0.00021203877,0.0003116022,0.37545672,0.088658735,0.017995935,0.0029135335,0.5124339],"study_design_scores_gemma":[0.0000066339408,0.000028035613,0.00014704258,0.0000032506052,0.0000057638713,0.000046100835,0.000010095622,0.9912266,0.0060064187,0.0013501387,0.0011579955,0.000012040306],"about_ca_topic_score_codex":0.0027014923,"about_ca_topic_score_gemma":0.0036521156,"teacher_disagreement_score":0.0027014923,"about_ca_system_score_codex":0.0003447513,"about_ca_system_score_gemma":0.0010300443,"threshold_uncertainty_score":0.0055437684},"labels":[],"label_agreement":null},{"id":"W4313500201","doi":"10.3390/s23020587","title":"Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"","keywords":"Sitting; Torso; Sedentary behavior; Medicine; Physical therapy; Lying; Physical medicine and rehabilitation; Orthodontics; Physical activity; Anatomy","score_opus":0.028506839612444216,"score_gpt":0.304127172265848,"score_spread":0.2756203326534038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313500201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98333687,0.00024199644,0.012433795,0.00003379946,0.00002776582,0.00017538584,0.002082401,0.0001348957,0.0015330095],"genre_scores_gemma":[0.97204775,0.0003482776,0.022183387,0.00008874708,0.00005008336,0.0007216325,0.0027561237,0.000048225324,0.0017558018],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99974114,0.00006277041,0.000025215244,0.00007884353,0.000061069535,0.000030961077],"domain_scores_gemma":[0.9996289,0.000055112,0.000115678624,0.00002900633,0.000114015165,0.00005716593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026152498,0.0006385926,0.00040259975,0.0007715869,0.00022349968,0.00045711847,0.00030809944,0.00043733732,0.0017093102],"category_scores_gemma":[0.00066232047,0.00021463034,0.0003038264,0.0006279004,0.00015901802,0.00025367222,0.00044483494,0.00027803518,0.00046636927],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003537496,0.0010176534,0.5832215,0.001050157,0.00042565097,0.00052690814,0.0022907252,0.0015591545,0.2763509,0.00020186245,0.002364216,0.12745379],"study_design_scores_gemma":[0.000033405537,0.0012771643,0.98896027,0.000037848164,0.00008413166,0.00050110195,0.00039822664,0.0021658663,0.0055669923,0.000053327094,0.00089175574,0.00002993919],"about_ca_topic_score_codex":0.0013049392,"about_ca_topic_score_gemma":0.005510609,"teacher_disagreement_score":0.0017093102,"about_ca_system_score_codex":0.00009076777,"about_ca_system_score_gemma":0.0001155146,"threshold_uncertainty_score":0.005718231},"labels":[],"label_agreement":null},{"id":"W4313515614","doi":"10.3390/s23010406","title":"A Smart Technology Intervention in the Homes of People with Mental Illness and Physical Comorbidities","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University; Lawson Health Research Institute; Western University","funders":"","keywords":"Mental health; Psychological intervention; Health care; Population; Descriptive statistics; Intervention (counseling); Medicine; Psychology; Nursing; Gerontology; Applied psychology; Psychiatry; Environmental health","score_opus":0.014296715576756326,"score_gpt":0.32083206270146897,"score_spread":0.30653534712471264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313515614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944102,0.00032013978,0.0009790482,0.00048543792,0.000086369146,0.0015925528,0.00007586074,0.00003379511,0.0020164927],"genre_scores_gemma":[0.98694825,0.0005579922,0.0067422185,0.00052878156,0.00006968154,0.0038829606,0.000062178515,0.0000053404556,0.0012026688],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99901116,0.00060656475,0.00006894862,0.00009805464,0.0000795394,0.00013564758],"domain_scores_gemma":[0.99909365,0.00046566114,0.000102581056,0.00005727886,0.00005163735,0.00022922864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016440831,0.0003382655,0.0004606164,0.00037317292,0.0010533329,0.0006617873,0.000396213,0.00059845735,0.0042165266],"category_scores_gemma":[0.0030101356,0.0001884692,0.0007179371,0.0002024749,0.00042065702,0.0006226072,0.0010724155,0.0005613375,0.00023985373],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01152374,0.10712347,0.055019204,0.0036467283,0.00052192574,0.00080762425,0.023152001,0.0008194334,0.0065718116,0.0018092316,0.0053066285,0.78369814],"study_design_scores_gemma":[0.041796494,0.38634107,0.42001536,0.003825461,0.0023214833,0.00097460306,0.0729135,0.005887825,0.0118851615,0.00788179,0.045872778,0.00028446133],"about_ca_topic_score_codex":0.0014107628,"about_ca_topic_score_gemma":0.0049668737,"teacher_disagreement_score":0.0042165266,"about_ca_system_score_codex":0.00066361384,"about_ca_system_score_gemma":0.0017416102,"threshold_uncertainty_score":0.014105678},"labels":[],"label_agreement":null},{"id":"W4313518939","doi":"10.3390/s23010504","title":"Improving Concrete Crack Segmentation Networks through CutMix Data Synthesis and Temporal Data Fusion","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Artificial neural network; Pattern recognition (psychology); Transfer of learning; Pixel; Key (lock); Deep learning; Data mining; Computer vision","score_opus":0.030709527637631222,"score_gpt":0.26222070324008173,"score_spread":0.23151117560245052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313518939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23575525,0.00084704143,0.75306296,0.00036094929,0.00015782246,0.00014814813,0.0009262747,0.0061078337,0.0026337118],"genre_scores_gemma":[0.695125,0.00022939131,0.29669815,0.00022949219,0.000054994252,0.00019718894,0.004325833,0.00020242574,0.002937507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955803,0.000051712766,0.000023991492,0.00018971386,0.0001182239,0.000058385816],"domain_scores_gemma":[0.9993356,0.00019633552,0.000064850625,0.000121593876,0.0002473478,0.000034307763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096649455,0.0014333957,0.0007525051,0.0010210507,0.00036161486,0.00064039323,0.00094605563,0.0010493676,0.0011395474],"category_scores_gemma":[0.0022638622,0.0003704032,0.0008538923,0.00072105345,0.00046987523,0.0017406836,0.0012594884,0.0012244945,0.00065518694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005606677,0.00039864684,0.0043796552,0.00012081416,0.00015325332,0.00015300709,0.00014919227,0.2977832,0.0705588,0.0016510545,0.0037419705,0.62034976],"study_design_scores_gemma":[0.000008418227,0.00006660626,0.00081482646,0.0000049624973,0.000018042794,0.000022548567,0.000027069795,0.98286855,0.014715803,0.00082282466,0.0006208668,0.000009480703],"about_ca_topic_score_codex":0.006625183,"about_ca_topic_score_gemma":0.0090777,"teacher_disagreement_score":0.006625183,"about_ca_system_score_codex":0.0007137759,"about_ca_system_score_gemma":0.0007738021,"threshold_uncertainty_score":0.013173223},"labels":[],"label_agreement":null},{"id":"W4313559813","doi":"10.3390/s23020615","title":"Remote Photoplethysmography with a High-Speed Camera Reveals Temporal and Amplitude Differences between Glabrous and Non-Glabrous Skin","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photoplethysmogram; RGB color model; SIGNAL (programming language); Dorsum; Medicine; Channel (broadcasting); Biomedical engineering; Palm; Artificial intelligence; Computer vision; Computer science; Anatomy; Telecommunications; Physics","score_opus":0.01161640478167936,"score_gpt":0.2171406657511045,"score_spread":0.20552426096942514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313559813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9868197,0.00066283747,0.011177498,0.000053848362,0.000025426754,0.000038233142,0.00011869126,0.00007932834,0.0010243518],"genre_scores_gemma":[0.9903779,0.00042850286,0.007980557,0.00012169801,0.000035170233,0.00003414581,0.00009239438,0.000022908329,0.0009066281],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982196,0.000031396226,0.00000703854,0.00006408508,0.0000540015,0.000021555377],"domain_scores_gemma":[0.99980396,0.0000805033,0.000042261945,0.00001858897,0.0000316891,0.000022900482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018538353,0.00027084275,0.00022743446,0.00031825036,0.00010339621,0.00032219346,0.00013680434,0.00043332818,0.0017161759],"category_scores_gemma":[0.0005237709,0.00013655021,0.0001432021,0.00019142492,0.00023492923,0.000313148,0.00022124671,0.00037026728,0.00026269228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003986141,0.000048498056,0.005862241,0.00013385099,0.000024596353,0.00017689541,0.00011344456,0.000064154825,0.97467417,0.00004022141,0.00011422968,0.018349133],"study_design_scores_gemma":[0.00009098659,0.002187113,0.560548,0.00004157851,0.00015305255,0.005480887,0.0004564647,0.0051001785,0.4243419,0.00027667667,0.0012797114,0.000043368527],"about_ca_topic_score_codex":0.00024706556,"about_ca_topic_score_gemma":0.0005203397,"teacher_disagreement_score":0.0017161759,"about_ca_system_score_codex":0.0000714372,"about_ca_system_score_gemma":0.000091517126,"threshold_uncertainty_score":0.005741179},"labels":[],"label_agreement":null},{"id":"W4313574328","doi":"10.3390/s23010071","title":"Hallway Gait Monitoring System Using an In-Package Integrated Dielectric Lens Paired with a mm-Wave Radar","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gait; Radar; Computer science; Real-time computing; Gait analysis; Simulation; Engineering; Artificial intelligence; Telecommunications; Physical medicine and rehabilitation","score_opus":0.02710849309458693,"score_gpt":0.23146634316545805,"score_spread":0.20435785007087112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313574328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31246227,0.0008756801,0.6755691,0.0002824538,0.00029467288,0.00022886637,0.0006231161,0.0050819847,0.004581861],"genre_scores_gemma":[0.6656198,0.0004401393,0.3249981,0.0004574266,0.00013756177,0.00021621947,0.0004960533,0.00013084742,0.007503773],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997292,0.00004784636,0.000019342611,0.000080841244,0.000097529984,0.000025297364],"domain_scores_gemma":[0.99971265,0.00004320533,0.00006578081,0.000036017354,0.00011070685,0.00003155461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002616038,0.00046569825,0.00041382152,0.000427712,0.00011333559,0.00032929803,0.0005843137,0.00043314832,0.0015853959],"category_scores_gemma":[0.00039407492,0.00021244187,0.00028526463,0.00026164495,0.00011355098,0.00055819977,0.00041825368,0.00023286395,0.00081217196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058279757,0.0002326698,0.010148223,0.0004099375,0.000107343716,0.00036060292,0.00014241795,0.0012983748,0.7473061,0.0006681794,0.0043577584,0.23438562],"study_design_scores_gemma":[0.00021824692,0.0027411864,0.0524566,0.000087090586,0.00040589683,0.0050464794,0.00026805254,0.09057695,0.8109148,0.0005083759,0.03655757,0.00021872178],"about_ca_topic_score_codex":0.00034734316,"about_ca_topic_score_gemma":0.00067061663,"teacher_disagreement_score":0.0015853959,"about_ca_system_score_codex":0.00020135328,"about_ca_system_score_gemma":0.00017471335,"threshold_uncertainty_score":0.005303681},"labels":[],"label_agreement":null},{"id":"W4313576996","doi":"10.3390/s23020599","title":"Infrared and Visible Image Fusion Technology and Application: A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":168,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; Chongqing Municipal Education Commission; Beijing Academy of Agricultural and Forestry Sciences; Natural Science Foundation of Chongqing","keywords":"Artificial intelligence; Computer vision; Image fusion; Computer science; Pixel; Image sensor; Night vision; Infrared; Fusion; Visible spectrum; Noise (video); Interference (communication); Image (mathematics); Materials science; Optics; Telecommunications; Optoelectronics; Physics","score_opus":0.014659769362532712,"score_gpt":0.31092699105458127,"score_spread":0.29626722169204855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313576996","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008407077,0.9895247,0.006239919,0.0002655525,0.00032365526,0.00002298186,0.00003783796,0.00004351162,0.0027010413],"genre_scores_gemma":[0.0070028165,0.9861189,0.0050737667,0.00022440695,0.00038741366,0.00003133617,0.00010505972,0.000011556718,0.0010447238],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936813,0.00007871,0.00010682042,0.00011193948,0.00029559873,0.00003879313],"domain_scores_gemma":[0.99898666,0.00044360332,0.00012084775,0.000039271363,0.00037739737,0.00003219812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011889922,0.00089231884,0.0011887243,0.0035830834,0.000427797,0.0013144847,0.0011627177,0.0012702274,0.0023332692],"category_scores_gemma":[0.0013349329,0.0005255205,0.0011108324,0.0037261262,0.000588032,0.0024546797,0.00075541245,0.0009549492,0.0010808721],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006683347,0.00009631447,0.0006936117,0.017904397,0.00015607341,0.0003282197,0.00014221926,0.0018550197,0.0069460096,0.0061316798,0.012674825,0.95300484],"study_design_scores_gemma":[0.000014162411,0.00033588815,0.0033161899,0.004697734,0.0004462025,0.004401461,0.00023061436,0.0051155738,0.010874742,0.0054776496,0.96494997,0.00013975933],"about_ca_topic_score_codex":0.0019822952,"about_ca_topic_score_gemma":0.0010929519,"teacher_disagreement_score":0.0035830834,"about_ca_system_score_codex":0.0006143472,"about_ca_system_score_gemma":0.0011962462,"threshold_uncertainty_score":0.007805586},"labels":[],"label_agreement":null},{"id":"W4313582599","doi":"10.3390/s23010334","title":"Assessment of Three-Dimensional Kinematics of High- and Low-Calibre Hockey Skaters on Synthetic Ice Using Wearable Sensors","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Winter Sports Injuries and Performance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ice hockey; Kinematics; Speed skating; Wearable computer; Joint (building); Engineering; Simulation; Physical medicine and rehabilitation; Structural engineering; Medicine; Physics","score_opus":0.01518600843100193,"score_gpt":0.2693595548729944,"score_spread":0.25417354644199247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313582599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921182,0.00006485356,0.0069486406,0.000009848531,0.000013206042,0.000037365287,0.00016426716,0.00002368012,0.00061999034],"genre_scores_gemma":[0.9953908,0.00010092025,0.0037472744,0.000017940292,0.000009250922,0.00003808627,0.00020182676,0.0000075026287,0.00048640106],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997477,0.000054349937,0.000018738321,0.00006873517,0.00006797105,0.00004259682],"domain_scores_gemma":[0.99963593,0.000073274256,0.00009916455,0.00002854368,0.00010401667,0.000059073136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025191877,0.00065254536,0.0003518018,0.0007287029,0.0001479393,0.00042876595,0.00019080135,0.000310274,0.0013475253],"category_scores_gemma":[0.0007836415,0.00019258966,0.0002524357,0.00027265353,0.00030259698,0.0002844363,0.00050153607,0.00013418258,0.00030882642],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023781199,0.0005103851,0.29565474,0.0006782937,0.0002441885,0.00037613325,0.0011678805,0.005043115,0.6152025,0.00019889812,0.00046245244,0.07808332],"study_design_scores_gemma":[0.000040382893,0.0020374674,0.9428518,0.00006647304,0.00011978882,0.00060007564,0.0016697667,0.008361408,0.043160956,0.00011863465,0.0009394337,0.000033850687],"about_ca_topic_score_codex":0.00072497776,"about_ca_topic_score_gemma":0.002421211,"teacher_disagreement_score":0.0013475253,"about_ca_system_score_codex":0.000102838,"about_ca_system_score_gemma":0.00017119585,"threshold_uncertainty_score":0.0045078993},"labels":[],"label_agreement":null},{"id":"W4313584746","doi":"10.3390/s23020562","title":"Aptamer-Based Technologies for Parasite Detection","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Aptamer; Nanotechnology; Oligonucleotide; Computational biology; Biosensor; Biology; Computer science; DNA; Materials science; Molecular biology","score_opus":0.04365829084184842,"score_gpt":0.3644657196796033,"score_spread":0.32080742883775487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313584746","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006543418,0.9887932,0.004629024,0.00025262337,0.0004547732,0.00004077686,0.00006235966,0.00007770412,0.0050352104],"genre_scores_gemma":[0.004599828,0.9863327,0.0036810467,0.00039334153,0.00020127189,0.00006186555,0.00012032864,0.000010111667,0.004599534],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994796,0.000062189014,0.000034812852,0.000109543565,0.00026859262,0.000045366738],"domain_scores_gemma":[0.99979955,0.0000870051,0.000032232623,0.000010160762,0.000056380053,0.000014727785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006063083,0.0013382318,0.0012680027,0.0024474289,0.00026265797,0.00091639446,0.0010983973,0.0014009378,0.003397799],"category_scores_gemma":[0.00061426935,0.0005845928,0.00076778495,0.0020338402,0.0005352127,0.0013532109,0.0007596651,0.0023629991,0.003671778],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000710509,0.00016914973,0.00020615844,0.01684621,0.00011469729,0.00036387393,0.00010009001,0.0010321272,0.07195342,0.010063692,0.019451525,0.87962794],"study_design_scores_gemma":[0.000012515979,0.0001772768,0.00043625876,0.001021543,0.000075771946,0.001480365,0.00003355956,0.00041695446,0.024776796,0.002224016,0.9693028,0.000042186435],"about_ca_topic_score_codex":0.00059528335,"about_ca_topic_score_gemma":0.0007134539,"teacher_disagreement_score":0.003397799,"about_ca_system_score_codex":0.0006319959,"about_ca_system_score_gemma":0.000559867,"threshold_uncertainty_score":0.011366725},"labels":[],"label_agreement":null},{"id":"W4313640632","doi":"10.3390/s23020626","title":"An Integrated Optical Circuit Architecture for Inverse-Designed Silicon Photonic Components","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Air Force Office of Scientific Research","keywords":"Photonics; Photonic integrated circuit; Electronic circuit; Splitter; Silicon photonics; Silicon on insulator; Computer science; Optical switch; Resonator; Insertion loss; Waveguide; Optoelectronics; Electronic engineering; Materials science; Electrical engineering; Silicon; Optics; Engineering; Physics","score_opus":0.034360860931387995,"score_gpt":0.26256566083487726,"score_spread":0.22820479990348927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313640632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1041247,0.00065496337,0.8655868,0.00034325907,0.00021646095,0.00009962537,0.00013894464,0.0020204233,0.02681469],"genre_scores_gemma":[0.5416873,0.00031586556,0.4518735,0.00013443452,0.00003069597,0.000115435796,0.00013201626,0.000066298744,0.0056444025],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988747,0.000009459895,0.0000051346306,0.000026223042,0.000056104534,0.000015605809],"domain_scores_gemma":[0.9999498,0.000008046625,0.000008608238,0.000012428129,0.000015695232,0.0000053526223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011441758,0.00033582444,0.00019365361,0.00019304577,0.00020619389,0.00055742374,0.0010060875,0.0003442076,0.0016640692],"category_scores_gemma":[0.00015667969,0.00020936562,0.0002669595,0.00016581287,0.00036564053,0.0006234364,0.00034787753,0.000547561,0.0004902049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009444401,0.00011054214,0.00038747975,0.00020045844,0.000051186584,0.00020493934,0.000081862956,0.03155459,0.8128791,0.09972508,0.0017555582,0.052954827],"study_design_scores_gemma":[0.00005305851,0.0006382363,0.0006420457,0.000036408095,0.0000886956,0.0005834186,0.000038655522,0.41037226,0.51894116,0.011716993,0.056827296,0.00006170775],"about_ca_topic_score_codex":0.00033740333,"about_ca_topic_score_gemma":0.0010172296,"teacher_disagreement_score":0.0016640692,"about_ca_system_score_codex":0.00053968694,"about_ca_system_score_gemma":0.0004575684,"threshold_uncertainty_score":0.005566895},"labels":[],"label_agreement":null},{"id":"W4313648826","doi":"10.3390/s23020634","title":"Survey of Explainable AI Techniques in Healthcare","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":530,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Interpretability; Deep learning; Categorization; Health care; Computer science; Field (mathematics); Artificial intelligence; Data science; Medical imaging","score_opus":0.17247013805736797,"score_gpt":0.4127461832334518,"score_spread":0.24027604517608384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313648826","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003039836,0.9913995,0.0039373026,0.0007418128,0.00019197747,0.00001711857,0.00004090478,0.000029137942,0.0033382229],"genre_scores_gemma":[0.003383144,0.9924677,0.0026677737,0.00033100048,0.0003443256,0.000023009141,0.00007262114,0.000010347341,0.00070014794],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99941933,0.00014749117,0.00007855655,0.00011255239,0.00020386477,0.00003823418],"domain_scores_gemma":[0.9969644,0.0024362628,0.00012246225,0.00009444272,0.00033142476,0.00005099644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012943508,0.0011430182,0.0009602106,0.0032549796,0.00028880022,0.0013340628,0.0012073254,0.0013931652,0.0059727267],"category_scores_gemma":[0.004085529,0.00053751405,0.0010561942,0.0040933965,0.00071511045,0.0022043618,0.00090050895,0.0016441663,0.0017824128],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005380871,0.000078209356,0.00042997018,0.023111638,0.0001689509,0.00011419791,0.0001505031,0.0021762697,0.00063458935,0.025993273,0.019457163,0.9276315],"study_design_scores_gemma":[0.000022181952,0.00017439571,0.001801429,0.015245805,0.00027956304,0.00093200663,0.00017719256,0.0034872692,0.0015940832,0.030175755,0.946038,0.000072461255],"about_ca_topic_score_codex":0.0019057556,"about_ca_topic_score_gemma":0.0016628315,"teacher_disagreement_score":0.0059727267,"about_ca_system_score_codex":0.0010378535,"about_ca_system_score_gemma":0.0015074715,"threshold_uncertainty_score":0.019980729},"labels":[],"label_agreement":null},{"id":"W4313889909","doi":"10.3390/s23020734","title":"Convolutional Neural Networks or Vision Transformers: Who Will Win the Race for Action Recognitions in Visual Data?","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Convolutional neural network; Transformer; Computer science; Artificial intelligence; Action recognition; Deep learning; Machine learning; Computer vision; Engineering","score_opus":0.2209152286585424,"score_gpt":0.43199432283938,"score_spread":0.2110790941808376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313889909","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027636412,0.9937323,0.0012734061,0.0015217781,0.00047115984,0.000009495103,0.000036023077,0.000021250391,0.0026582112],"genre_scores_gemma":[0.0034597726,0.9917064,0.0011067414,0.0010841299,0.00046904717,0.00001793618,0.00006329806,0.00000914526,0.0020835658],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997738,0.000045265075,0.000019985062,0.000055908877,0.000078903344,0.000026160416],"domain_scores_gemma":[0.99931335,0.00038017612,0.00005204208,0.000023225455,0.00019533237,0.000035818175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001063021,0.0008039519,0.00077951356,0.0014306533,0.0001948711,0.0009865443,0.0009842011,0.001500244,0.0033564493],"category_scores_gemma":[0.002138315,0.00033770967,0.00040500605,0.0014882705,0.00068322785,0.0027855174,0.00044323344,0.0019153211,0.0022079675],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061888735,0.000036488596,0.00024749973,0.0059969695,0.00008103266,0.000058612346,0.00004909159,0.00070714654,0.0008267495,0.020839624,0.033626646,0.93746823],"study_design_scores_gemma":[0.000017049691,0.00014030816,0.00070022367,0.0035434004,0.00013535307,0.00048113486,0.00007110803,0.00079655304,0.0010897607,0.01387308,0.9791214,0.000030686755],"about_ca_topic_score_codex":0.0018510522,"about_ca_topic_score_gemma":0.0023399221,"teacher_disagreement_score":0.0033564493,"about_ca_system_score_codex":0.00087697635,"about_ca_system_score_gemma":0.001156007,"threshold_uncertainty_score":0.011228442},"labels":[],"label_agreement":null},{"id":"W4313889918","doi":"10.3390/s23020731","title":"Wideband Dual-Polarized Octagonal Cavity-Backed Antenna with Low Cross-Polarization and High Aperture Efficiency","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Optics; Wideband; Antenna measurement; Antenna factor; Computer science; Acoustics; Electronic engineering; Engineering; Antenna (radio); Physics; Electrical engineering","score_opus":0.005795048765753792,"score_gpt":0.20608115236193708,"score_spread":0.2002861035961833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313889918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58180434,0.00086994463,0.40416017,0.00026279525,0.00014188001,0.000037035727,0.000086809036,0.00077187497,0.011865122],"genre_scores_gemma":[0.8841975,0.00027669547,0.112652406,0.00012230221,0.0000245933,0.000037238005,0.00006490485,0.000050729388,0.002573664],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998222,0.000031378182,0.00000758375,0.00004502169,0.000060497052,0.00003330797],"domain_scores_gemma":[0.9997212,0.00004986633,0.0000760449,0.000052777697,0.00007722541,0.000022813276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001897639,0.00033305294,0.0003920887,0.00020513752,0.000086994645,0.0005019885,0.00041781415,0.00039968832,0.0005928684],"category_scores_gemma":[0.0002975423,0.00017817428,0.000293714,0.00027599523,0.00025953312,0.00035913015,0.00043294134,0.00029713608,0.0006855727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013072276,0.000043808443,0.0012530508,0.00013617343,0.00003535172,0.00014579868,0.00006574507,0.004147036,0.94909954,0.0030104138,0.0006920182,0.041240387],"study_design_scores_gemma":[0.000053778145,0.00063520577,0.003452067,0.000040110837,0.0000691749,0.0021398468,0.000115596406,0.13082893,0.84247106,0.0013302497,0.018771349,0.000092604736],"about_ca_topic_score_codex":0.000085031555,"about_ca_topic_score_gemma":0.00016158124,"teacher_disagreement_score":0.0005928684,"about_ca_system_score_codex":0.00020070809,"about_ca_system_score_gemma":0.00017212062,"threshold_uncertainty_score":0.001983285},"labels":[],"label_agreement":null},{"id":"W4315572261","doi":"10.3390/s23020828","title":"Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Chicoutimi; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Wearable technology; Context (archaeology); Scopus; Smartwatch; Computer science; Systematic review; Health care; Data science; MEDLINE; Embedded system","score_opus":0.03795654200364713,"score_gpt":0.3243267331405984,"score_spread":0.2863701911369513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315572261","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012620492,0.99689615,0.00021179797,0.00026694304,0.000097824464,0.00045758658,0.00047628555,0.000009809683,0.00032163074],"genre_scores_gemma":[0.011830518,0.9854753,0.0008094601,0.00044686627,0.000101760335,0.0008202501,0.00036274648,0.000004947005,0.00014814516],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9929287,0.001788818,0.0028921396,0.00055823824,0.0015957068,0.00023642411],"domain_scores_gemma":[0.9690672,0.021383189,0.00550019,0.00044407145,0.003271573,0.0003337496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00871199,0.0014998442,0.0065638237,0.015334264,0.0007980266,0.002571784,0.0020765027,0.0019780141,0.006342737],"category_scores_gemma":[0.034986947,0.0007970751,0.0071951095,0.014485264,0.0008044273,0.0027719329,0.0015060324,0.0009774528,0.00063648564],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013411173,0.000021876836,0.0010463167,0.9608422,0.0038777,0.000106442574,0.00018801952,0.000067164634,0.0001248167,0.00013904127,0.0013085608,0.03214388],"study_design_scores_gemma":[0.000113742244,0.00018463103,0.0040503615,0.94342697,0.03759884,0.00039125793,0.00033506894,0.00008950484,0.00015213877,0.0001746009,0.013453297,0.000029605495],"about_ca_topic_score_codex":0.0061830725,"about_ca_topic_score_gemma":0.021423716,"teacher_disagreement_score":0.015334264,"about_ca_system_score_codex":0.0032673874,"about_ca_system_score_gemma":0.013131531,"threshold_uncertainty_score":0.046073973},"labels":[],"label_agreement":null},{"id":"W4315572629","doi":"10.3390/s23020787","title":"Natural Nitrogen-Doped Carbon Dots Obtained from Hydrothermal Carbonization of Chebulic Myrobalan and Their Sensing Ability toward Heavy Metal Ions","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Carbonization; Hydrothermal carbonization; Metal ions in aqueous solution; Carbon fibers; Materials science; Aqueous solution; Hydrothermal circulation; Fluorescence; Ferric; Metal; Ion; Nuclear chemistry; Inorganic chemistry; Chemical engineering; Chemistry; Scanning electron microscope; Metallurgy; Physical chemistry; Organic chemistry; Composite material","score_opus":0.016067179638251595,"score_gpt":0.23960649990345462,"score_spread":0.223539320265203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315572629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99368036,0.00076802826,0.0036279417,0.000055043118,0.000037391655,0.000022199103,0.00023424633,0.000057611953,0.0015172266],"genre_scores_gemma":[0.99510515,0.00031140435,0.0032885226,0.000023937655,0.0000035568273,0.000021584781,0.00022067939,0.000010275446,0.0010149949],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99993694,0.000006293313,0.00000483208,0.00002530361,0.000017017843,0.000009585189],"domain_scores_gemma":[0.9999422,0.000011677152,0.000012133496,0.0000049563187,0.000018739678,0.000010263543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000071643706,0.00024855527,0.000095249285,0.00016785573,0.00010515567,0.00011042265,0.00015319545,0.000266244,0.0006754552],"category_scores_gemma":[0.00014402029,0.00008363812,0.00009421954,0.00012004487,0.00014458982,0.00014491944,0.00012377946,0.00019330734,0.00011222918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023971266,0.000005176329,0.00008518261,0.000037679238,0.0000018319095,0.0000364854,0.000008222083,0.00007589597,0.998563,0.00007119528,0.00003306814,0.0010584424],"study_design_scores_gemma":[0.0000031748239,0.000032126994,0.00085244206,0.0000027203062,0.0000030951128,0.000049467948,0.00000909,0.0007457572,0.9974759,0.000014276009,0.0008083765,0.0000034751267],"about_ca_topic_score_codex":0.0009185125,"about_ca_topic_score_gemma":0.002466869,"teacher_disagreement_score":0.0009185125,"about_ca_system_score_codex":0.0002508302,"about_ca_system_score_gemma":0.00007977785,"threshold_uncertainty_score":0.0022596717},"labels":[],"label_agreement":null},{"id":"W4315647459","doi":"10.3390/s23020846","title":"Split Ring Antennas and Their Application for Antenna Miniaturization","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Miniaturization; Materials science; Permittivity; Dielectric; Split-ring resonator; Optoelectronics; Electronic engineering; Electrical engineering; Resonator; Engineering; Nanotechnology","score_opus":0.011260001369740898,"score_gpt":0.2123202295404073,"score_spread":0.2010602281706664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315647459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47731197,0.0036893296,0.5089984,0.00029790818,0.00019970571,0.00007363007,0.00010649376,0.0009584586,0.008364151],"genre_scores_gemma":[0.8112614,0.00084553263,0.18582265,0.000058136135,0.000042298125,0.000040005034,0.00005196421,0.00004116796,0.0018368729],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996978,0.00007641825,0.000018574718,0.00007678929,0.00009842822,0.000032002907],"domain_scores_gemma":[0.99946505,0.00020260939,0.00011891689,0.00012057848,0.00007298481,0.000019811694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035951106,0.00040268805,0.0003013492,0.00022201127,0.00008448631,0.00043353665,0.00046092368,0.00032242268,0.0009883285],"category_scores_gemma":[0.0007981171,0.00023042037,0.00032470754,0.00025949426,0.00027846656,0.0006047284,0.0003470929,0.00026048181,0.00046659872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018523485,0.000026074298,0.00076195406,0.00014559508,0.000045932124,0.00016398038,0.00007596848,0.008531162,0.9379551,0.0029882418,0.00032338648,0.04879729],"study_design_scores_gemma":[0.000019891628,0.0005722008,0.001350846,0.000009198633,0.000059022448,0.0008301769,0.000047663823,0.052955,0.9334858,0.0007540837,0.009887836,0.000028196015],"about_ca_topic_score_codex":0.00005201294,"about_ca_topic_score_gemma":0.00008173689,"teacher_disagreement_score":0.0009883285,"about_ca_system_score_codex":0.00016459583,"about_ca_system_score_gemma":0.00006553847,"threshold_uncertainty_score":0.0033063293},"labels":[],"label_agreement":null},{"id":"W4315647993","doi":"10.3390/s23020777","title":"GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung; Killam Trusts","keywords":"GNSS applications; Android (operating system); Computer science; Tracing; Real-time computing; Embedded system; Database; Global Positioning System; Operating system","score_opus":0.046368633628374414,"score_gpt":0.24601279729074266,"score_spread":0.19964416366236826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315647993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83985114,0.0049907994,0.05077391,0.0008270215,0.00073539914,0.0008888374,0.002501298,0.08798943,0.011442192],"genre_scores_gemma":[0.89728767,0.0013890035,0.087013416,0.00030707679,0.00011949528,0.00026280878,0.0047672875,0.0026033784,0.006249824],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99686086,0.00041739768,0.00032123862,0.0005874681,0.001379525,0.00043350575],"domain_scores_gemma":[0.9932394,0.002076039,0.00037402735,0.0013335301,0.002627338,0.00034957167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020878988,0.0019417825,0.000936013,0.001603851,0.00055382756,0.0016210682,0.0018961835,0.0008772936,0.002412734],"category_scores_gemma":[0.012439715,0.0005032125,0.0006076455,0.001026129,0.0004281731,0.0019956725,0.0012119204,0.0012463839,0.002417895],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062949974,0.0012433156,0.08564897,0.002158175,0.00060216204,0.0017804045,0.0014087067,0.029485676,0.064902075,0.0014469188,0.032577153,0.7724515],"study_design_scores_gemma":[0.00064602244,0.0047580935,0.1486776,0.00047407142,0.00075472565,0.0022982846,0.0013543783,0.61406803,0.17710128,0.0012080095,0.04809561,0.0005639974],"about_ca_topic_score_codex":0.012334595,"about_ca_topic_score_gemma":0.008727409,"teacher_disagreement_score":0.012334595,"about_ca_system_score_codex":0.00054530834,"about_ca_system_score_gemma":0.0008830227,"threshold_uncertainty_score":0.024525583},"labels":[],"label_agreement":null},{"id":"W4315700887","doi":"10.3390/s23020886","title":"Real-Time Temperature Correction of Medical Range Fiber Bragg Gratings Dosimeters","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dosimetry; Dosimeter; Materials science; Optics; Temperature measurement; Fiber Bragg grating; Irradiation; Atmospheric temperature range; Optical fiber; Bragg peak; Radiation; Optoelectronics; Nuclear medicine; Physics; Medicine; Beam (structure)","score_opus":0.006825888550506333,"score_gpt":0.23108744100673873,"score_spread":0.2242615524562324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315700887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5898702,0.0032335997,0.39959133,0.0003650046,0.00045379662,0.000098883145,0.00033507147,0.004294862,0.0017572162],"genre_scores_gemma":[0.83999354,0.00056490704,0.15723719,0.00009867589,0.000047476602,0.000043427102,0.00013764917,0.00027956846,0.0015976662],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990403,0.00016112108,0.000045954504,0.00025240463,0.00043674913,0.000063501095],"domain_scores_gemma":[0.9990181,0.0002470071,0.00026476907,0.00018905057,0.00024515644,0.000035920184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089340995,0.0006474538,0.0004442887,0.0005228606,0.00015053841,0.00036461547,0.0007699981,0.00051902997,0.0007824189],"category_scores_gemma":[0.00194925,0.00031293012,0.00029173977,0.00043744428,0.00030129752,0.0005695642,0.00042852882,0.00056302483,0.00035029848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055359,0.000058918075,0.0025448436,0.00016770886,0.000029856124,0.000058492264,0.00015062056,0.0058785435,0.9270284,0.0003072504,0.00039938936,0.062822476],"study_design_scores_gemma":[0.000013755877,0.00014744798,0.0043696295,0.000011258131,0.000023637707,0.00019915361,0.000024073459,0.028622895,0.96484506,0.00009330751,0.0016136806,0.000035922647],"about_ca_topic_score_codex":0.00069082185,"about_ca_topic_score_gemma":0.00094205746,"teacher_disagreement_score":0.00089340995,"about_ca_system_score_codex":0.0005345609,"about_ca_system_score_gemma":0.00032592114,"threshold_uncertainty_score":0.00472486},"labels":[],"label_agreement":null},{"id":"W4315786962","doi":"10.3390/s23020849","title":"Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Gait; Pattern recognition (psychology); Feature (linguistics); Convolutional neural network; Benchmark (surveying); Gyroscope; Inertial measurement unit; Deep learning; Wearable computer; Activity recognition; Computer vision; Engineering","score_opus":0.0328583358889877,"score_gpt":0.2488271803388547,"score_spread":0.215968844449867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315786962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13662206,0.0041512777,0.83665055,0.0005888126,0.00069958076,0.0001945827,0.0029194825,0.011638045,0.006535671],"genre_scores_gemma":[0.74473673,0.0013100342,0.22865824,0.00069095596,0.00014782605,0.0002825359,0.008547723,0.00017106079,0.015454902],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979824,0.000018333687,0.000014639867,0.000078799916,0.00004766002,0.00004231046],"domain_scores_gemma":[0.9998827,0.000019491938,0.000017312183,0.000015246583,0.00005341038,0.0000118225025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030657757,0.0012332946,0.00070897787,0.0007113883,0.00021316177,0.00032247094,0.0009257072,0.00066787243,0.0023463971],"category_scores_gemma":[0.00062629,0.00034824634,0.00058096787,0.000704165,0.00016523457,0.0006996582,0.00062688923,0.00068109384,0.00086071447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002933749,0.00032866193,0.003922873,0.00016895485,0.00016007463,0.00022031832,0.00003501512,0.10491697,0.022820782,0.0013486149,0.013682846,0.8521015],"study_design_scores_gemma":[0.000013502755,0.000079378486,0.0014551267,0.000015953952,0.000023126511,0.000055482484,0.000007873909,0.9917687,0.0041767256,0.00077949953,0.0016139474,0.000010693544],"about_ca_topic_score_codex":0.009591751,"about_ca_topic_score_gemma":0.01444194,"teacher_disagreement_score":0.009591751,"about_ca_system_score_codex":0.0005916965,"about_ca_system_score_gemma":0.0005380341,"threshold_uncertainty_score":0.019071877},"labels":[],"label_agreement":null},{"id":"W4316039193","doi":"10.3390/s23020919","title":"Feasibility of Skin Water Content Imaging Using CMOS Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stage (stratigraphy); Edema; Water content; Biomedical engineering; Modality (human–computer interaction); Medicine; Materials science; Surgery; Computer science; Artificial intelligence; Biology; Engineering","score_opus":0.2086791462296089,"score_gpt":0.4404766972628999,"score_spread":0.231797551033291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316039193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6109791,0.0021987618,0.37814707,0.0005535943,0.00025225597,0.000162474,0.0003405428,0.0006199466,0.006746253],"genre_scores_gemma":[0.92182606,0.0008339841,0.07588687,0.00010370853,0.00003093731,0.000046458885,0.00006626689,0.000014849449,0.0011908013],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997081,0.00004537637,0.000010008903,0.00006522506,0.00014537828,0.000025864987],"domain_scores_gemma":[0.9996754,0.00013738759,0.000044104727,0.000022068365,0.0001091591,0.000011859345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029774292,0.00032707409,0.00018300852,0.00023487725,0.00013554624,0.00037836132,0.00045750014,0.0005373227,0.0008548053],"category_scores_gemma":[0.0008195813,0.00019112545,0.00032070314,0.00016727112,0.00022061204,0.0005860804,0.00021819108,0.00016040841,0.00028845982],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019896073,0.0000829815,0.005105779,0.00025861544,0.000026807826,0.0001853192,0.00008411835,0.008885355,0.94499284,0.0016079198,0.0003407508,0.038230512],"study_design_scores_gemma":[0.000023611437,0.0008439296,0.0070303227,0.000043481126,0.0000799202,0.0004202971,0.000191642,0.24661009,0.7396884,0.0010473516,0.0039638416,0.000057131478],"about_ca_topic_score_codex":0.0006944193,"about_ca_topic_score_gemma":0.001033177,"teacher_disagreement_score":0.0008548053,"about_ca_system_score_codex":0.00026778493,"about_ca_system_score_gemma":0.0002939179,"threshold_uncertainty_score":0.002859652},"labels":[],"label_agreement":null},{"id":"W4316039214","doi":"10.3390/s23020915","title":"Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Ministry of Education, India; Nanyang Technological University; Ministry of Education - Singapore","keywords":"Electroencephalography; Pattern recognition (psychology); Support vector machine; Artificial intelligence; Feature extraction; Computer science; Random forest; Emotion classification; Speech recognition; Valence (chemistry); Decision tree; Arousal; Psychology","score_opus":0.09390747083917547,"score_gpt":0.3846114742050371,"score_spread":0.29070400336586166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316039214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15041302,0.019416941,0.8234232,0.0006987414,0.00025660134,0.00032702676,0.0012085432,0.0015782126,0.0026777207],"genre_scores_gemma":[0.54142433,0.014426718,0.4364326,0.00021082537,0.00026634996,0.0006704721,0.0037745973,0.00019927222,0.002594791],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989889,0.00018907226,0.00010856834,0.00020280508,0.00046441055,0.000046090045],"domain_scores_gemma":[0.9978351,0.0010270525,0.00021750636,0.00019100771,0.00069867517,0.000030559542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023100565,0.00092309393,0.0006826073,0.0021839817,0.00024025382,0.00067233044,0.00039978133,0.0004276787,0.0008896624],"category_scores_gemma":[0.004921665,0.00015754618,0.00097669,0.0014069692,0.00016985444,0.0009437415,0.00033429865,0.00041745525,0.00042296847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001654958,0.00014436839,0.0056760325,0.00066772365,0.00030983862,0.000095869174,0.000093662034,0.007982728,0.05101066,0.00088970613,0.0020381866,0.9309257],"study_design_scores_gemma":[0.0001086853,0.0015123546,0.25150156,0.0006943601,0.00091322366,0.0019465595,0.00036644508,0.516617,0.18483086,0.0057427557,0.03547401,0.00029212848],"about_ca_topic_score_codex":0.0010394165,"about_ca_topic_score_gemma":0.0010068418,"teacher_disagreement_score":0.0023100565,"about_ca_system_score_codex":0.00026052454,"about_ca_system_score_gemma":0.00036737497,"threshold_uncertainty_score":0.012216866},"labels":[],"label_agreement":null},{"id":"W4316466964","doi":"10.3390/s23020942","title":"Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wrist; Ulnar deviation; Convolutional neural network; Computer science; Artificial neural network; Artificial intelligence; Standard deviation; Wearable computer; Inertial measurement unit; Reliability (semiconductor); Computer vision; Simulation; Physical medicine and rehabilitation; Mathematics; Medicine; Embedded system; Statistics","score_opus":0.01617122834252534,"score_gpt":0.28603196940013487,"score_spread":0.26986074105760954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316466964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7049103,0.0006978344,0.29069722,0.00013437476,0.00008595064,0.00007216273,0.00032396222,0.0008985131,0.0021796203],"genre_scores_gemma":[0.9783628,0.00020963157,0.02019925,0.00003596032,0.000016275031,0.0000284514,0.00016193667,0.000010667555,0.00097497593],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999795,0.000038302645,0.000015516396,0.0000652428,0.000052608462,0.000033403292],"domain_scores_gemma":[0.9996923,0.00012660332,0.000053855336,0.000026557196,0.00008450619,0.000016226595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003976738,0.00072710874,0.00031935732,0.0005527586,0.00016935487,0.00035216135,0.0002665165,0.00044857722,0.0007051431],"category_scores_gemma":[0.0013899317,0.0002938543,0.00032167352,0.00030828195,0.00015060932,0.00029045035,0.00036210215,0.000294002,0.00030107872],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097566476,0.00046755347,0.08536193,0.00024323106,0.00024620135,0.0003540186,0.00031033863,0.20861438,0.09797881,0.00040344684,0.0015333625,0.6035111],"study_design_scores_gemma":[0.000013608973,0.0002208176,0.068072066,0.000037717142,0.0000453299,0.00013939655,0.000056182085,0.918743,0.011901498,0.00040297973,0.00034620523,0.000021166117],"about_ca_topic_score_codex":0.006701041,"about_ca_topic_score_gemma":0.013170271,"teacher_disagreement_score":0.006701041,"about_ca_system_score_codex":0.00035548912,"about_ca_system_score_gemma":0.0003117857,"threshold_uncertainty_score":0.013324082},"labels":[],"label_agreement":null},{"id":"W4317399161","doi":"10.3390/s23031111","title":"Gait Characteristics Associated with Fear of Falling in Hospitalized People with Parkinson’s Disease","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fear of falling; Parkinson's disease; Falling (accident); Gait; Physical medicine and rehabilitation; Disease; Medicine; Psychology; Physical therapy; Injury prevention; Poison control; Psychiatry; Medical emergency; Internal medicine","score_opus":0.01785267830451601,"score_gpt":0.30255369642388685,"score_spread":0.2847010181193708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317399161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995272,0.00015020599,0.000027890468,0.000029711202,0.0000034603113,0.000004736881,0.00010416467,0.0000012335169,0.00015146451],"genre_scores_gemma":[0.99972886,0.000050089628,0.00004234773,0.000015001629,0.000004185702,0.0000037645082,0.00012682643,3.278079e-7,0.000028481913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997434,0.000053950724,0.000055793404,0.00003611884,0.00006952466,0.000041229338],"domain_scores_gemma":[0.99896085,0.00012784946,0.0006188705,0.00003162167,0.00012372747,0.00013701078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367821,0.00023682081,0.00039770527,0.00072577654,0.00036078226,0.0004399656,0.00018990636,0.0003884179,0.0009293132],"category_scores_gemma":[0.0021548448,0.00012253417,0.00037940376,0.0006275707,0.00019147346,0.0002572425,0.0003145069,0.0003644139,0.000089414025],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005108332,0.000027142336,0.9985744,0.000010724662,0.00002160837,0.000065247084,0.00006640073,0.000017317354,0.000075592005,0.0000042667243,0.000042997766,0.0010432041],"study_design_scores_gemma":[0.0000023423358,0.00007304048,0.99934345,0.0000059449453,0.000010742661,0.00026412783,0.00017374173,0.00006701599,0.000013798085,0.000010938307,0.000032374075,0.000002417577],"about_ca_topic_score_codex":0.003689216,"about_ca_topic_score_gemma":0.0055838823,"teacher_disagreement_score":0.003689216,"about_ca_system_score_codex":0.00028449408,"about_ca_system_score_gemma":0.00021088164,"threshold_uncertainty_score":0.007335484},"labels":[],"label_agreement":null},{"id":"W4317510952","doi":"10.3390/s23031132","title":"Sensorimotor Time Delay Estimation by EMG Signal Processing in People Living with Spinal Cord Injury","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Spinal cord injury; Spinal cord; Physical medicine and rehabilitation; SIGNAL (programming language); Estimation; Computer science; Medicine; Psychology; Neuroscience; Engineering","score_opus":0.007376484028161476,"score_gpt":0.23131788577509318,"score_spread":0.22394140174693172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317510952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854417,0.00038405604,0.013490138,0.000041072693,0.000013736849,0.000043608386,0.00009648204,0.000030658462,0.000458465],"genre_scores_gemma":[0.9922976,0.0002634302,0.0070349863,0.000020931851,0.000014603621,0.000028035036,0.00009343051,0.0000035856551,0.00024343758],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998727,0.000027024156,0.000016761673,0.0000353534,0.00003076881,0.000017371733],"domain_scores_gemma":[0.99980074,0.000071899565,0.000041607196,0.0000093499975,0.000059830596,0.000016568867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027598866,0.000391215,0.00027326014,0.0009793713,0.00014251051,0.00026557723,0.00012840294,0.00040766541,0.00050429365],"category_scores_gemma":[0.0013261371,0.00011152179,0.00020466081,0.0004516584,0.00012670115,0.0002665094,0.00025323484,0.00013428331,0.00013356308],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020511264,0.00043861437,0.35281125,0.000776667,0.0002978659,0.0016277699,0.0022844826,0.005785827,0.21781808,0.00025275428,0.0007300524,0.41512552],"study_design_scores_gemma":[0.00003362556,0.0009108622,0.9459464,0.000054925233,0.00018949612,0.0011660926,0.0015021517,0.037530523,0.011630617,0.0003624471,0.000628902,0.000044023993],"about_ca_topic_score_codex":0.0017749092,"about_ca_topic_score_gemma":0.0030568473,"teacher_disagreement_score":0.0017749092,"about_ca_system_score_codex":0.0000763848,"about_ca_system_score_gemma":0.00012502955,"threshold_uncertainty_score":0.0035291314},"labels":[],"label_agreement":null},{"id":"W4317538404","doi":"10.3390/s23031206","title":"Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Biofeedback; Rehabilitation; Physical medicine and rehabilitation; Computer science; Human–computer interaction; Physical therapy; Medicine","score_opus":0.0200736623141516,"score_gpt":0.30225148968927457,"score_spread":0.282177827375123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317538404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33981827,0.00069816894,0.6480201,0.00036277654,0.00030309858,0.0016738262,0.0006976292,0.005393072,0.0030330694],"genre_scores_gemma":[0.80859923,0.0002309016,0.18587643,0.0002555287,0.000027710741,0.0012752954,0.0008653621,0.00008303044,0.002786641],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999418,0.000083327715,0.000040926898,0.00016840626,0.00021670097,0.00007255936],"domain_scores_gemma":[0.99954283,0.00008842866,0.000036298417,0.000045124896,0.0002493133,0.00003801818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001128237,0.00048967154,0.00040978013,0.0004292199,0.00017332801,0.00040353037,0.0010772865,0.00080805627,0.0015944799],"category_scores_gemma":[0.0015086835,0.00024888394,0.00039048566,0.00015250825,0.00030534368,0.0003842454,0.0005102042,0.0003777236,0.00057336164],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087626546,0.0011566264,0.011701894,0.0007417983,0.0001673979,0.0005236378,0.0001801379,0.07278371,0.43445238,0.001128275,0.0038004946,0.47248742],"study_design_scores_gemma":[0.00011546853,0.0014524834,0.022423783,0.00008776369,0.0001037597,0.00043792298,0.000053057676,0.77210265,0.19818373,0.0004905094,0.0044883466,0.000060501956],"about_ca_topic_score_codex":0.004366968,"about_ca_topic_score_gemma":0.0034357326,"teacher_disagreement_score":0.004366968,"about_ca_system_score_codex":0.00061251316,"about_ca_system_score_gemma":0.000902224,"threshold_uncertainty_score":0.008683085},"labels":[],"label_agreement":null},{"id":"W4317624338","doi":"10.3390/s23031188","title":"Differential Sensing with Replicated Plasmonic Gratings Interrogated in the Optical Switch Configuration","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Région Auvergne-Rhône-Alpes; Agence Nationale de la Recherche","keywords":"Grating; Optics; Refractive index; Optical switch; Materials science; Optoelectronics; Limit (mathematics); Diffraction; Diffraction grating; Beam splitter; Physics","score_opus":0.011888240808868416,"score_gpt":0.22255547608664156,"score_spread":0.21066723527777315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317624338","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9639435,0.00041528273,0.033507887,0.00010063538,0.000105957944,0.00004915656,0.000097609314,0.00037380203,0.0014062277],"genre_scores_gemma":[0.9638209,0.00019773563,0.034667116,0.00008857573,0.000025780426,0.000032200558,0.00007204907,0.000014430429,0.0010812965],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997552,0.00003031345,0.000014011565,0.00007931622,0.00009365077,0.000027454616],"domain_scores_gemma":[0.9997303,0.000090480506,0.00006963531,0.000045178836,0.000033503627,0.00003096219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019671443,0.00035407126,0.00028866038,0.000257749,0.00013768286,0.0004435181,0.00075390795,0.00041165398,0.00047158726],"category_scores_gemma":[0.00030331127,0.00027417432,0.00015752472,0.00023968685,0.0004735265,0.0004104344,0.00038239942,0.0003596948,0.00012209866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082171406,0.000022473003,0.00030301602,0.000019083562,0.000005647167,0.00006596836,0.00001862882,0.00022423433,0.9952068,0.00020600362,0.000039964998,0.003805943],"study_design_scores_gemma":[0.000020623515,0.00020947658,0.0012339822,0.000002075396,0.000012643105,0.00031768868,0.000018902436,0.007788497,0.9894692,0.00012003558,0.00079039345,0.000016461327],"about_ca_topic_score_codex":0.0003953017,"about_ca_topic_score_gemma":0.0008009777,"teacher_disagreement_score":0.00075390795,"about_ca_system_score_codex":0.00036420274,"about_ca_system_score_gemma":0.00019525907,"threshold_uncertainty_score":0.0026424527},"labels":[],"label_agreement":null},{"id":"W4317930694","doi":"10.3390/s23031328","title":"Enabling Artificial Intelligent Virtual Sensors in an IoT Environment","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overhead (engineering); Internet of Things; Computer science; Orchestration; Embedded system; Virtual machine; Pace; Real-time computing; Operating system","score_opus":0.019889410628780396,"score_gpt":0.23134014997008567,"score_spread":0.2114507393413053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317930694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06042591,0.000216401,0.9333337,0.00014807623,0.000072205425,0.00004931454,0.00002888485,0.0011647298,0.004560861],"genre_scores_gemma":[0.8608986,0.00024726355,0.13665293,0.00008151934,0.000022187622,0.000059376755,0.000049916103,0.000053382166,0.0019348939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958915,0.00014422028,0.000022459104,0.000058312402,0.00014647466,0.000039463896],"domain_scores_gemma":[0.9996573,0.00013009146,0.0000477141,0.000088523586,0.000053603348,0.000022835757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004291919,0.0003928044,0.00031656303,0.00023039992,0.00021622673,0.0008685891,0.00079056586,0.0004665006,0.0007153609],"category_scores_gemma":[0.00090863596,0.00020290374,0.00029119314,0.00019619668,0.0006251789,0.0017073066,0.0011874315,0.0005047722,0.00019228653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041910235,0.00029313387,0.0047145174,0.00039088115,0.00010005373,0.00067494897,0.0005463936,0.560427,0.1289761,0.11374909,0.0023771417,0.18733159],"study_design_scores_gemma":[0.000011732957,0.00013156446,0.00047446165,0.00001836097,0.000022420361,0.00013974027,0.000060412247,0.9513687,0.027051672,0.0126767205,0.008029041,0.000015235851],"about_ca_topic_score_codex":0.0003789163,"about_ca_topic_score_gemma":0.00052179914,"teacher_disagreement_score":0.0008685891,"about_ca_system_score_codex":0.00020953317,"about_ca_system_score_gemma":0.0003246834,"threshold_uncertainty_score":0.002393186},"labels":[],"label_agreement":null},{"id":"W4318065776","doi":"10.3390/s23031369","title":"A PVDF Receiver for Acoustic Monitoring of Microbubble-Mediated Ultrasound Brain Therapy","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Hydrophone; Materials science; Acoustics; Broadband; Ultrasound; Wideband; Acoustic sensor; Biomedical engineering; Optics; Engineering; Physics","score_opus":0.02059175789459496,"score_gpt":0.24765637355721734,"score_spread":0.22706461566262237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318065776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3214215,0.0061062435,0.662874,0.0008157618,0.00053119723,0.00025183294,0.0005394456,0.0028333452,0.004626674],"genre_scores_gemma":[0.65364987,0.003082439,0.33265764,0.00032120393,0.00023722975,0.00036259147,0.00039931206,0.00015055586,0.009139151],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994598,0.00006858352,0.00003430234,0.00015760833,0.00023689323,0.00004284355],"domain_scores_gemma":[0.99937385,0.0002033076,0.00015827136,0.000058035017,0.00016209715,0.000044342072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005462637,0.00072486204,0.0005284328,0.0005033058,0.00025217255,0.0003688842,0.00091163913,0.0012937974,0.0015064799],"category_scores_gemma":[0.0012220087,0.0003566711,0.0003490419,0.000292704,0.0002678153,0.0007813508,0.00029970176,0.0006546582,0.0011271789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023263305,0.000012769591,0.0000932789,0.00004719181,0.0000031792938,0.000022815551,0.0000144278565,0.00012119739,0.9931043,0.00010826818,0.000112227775,0.006336934],"study_design_scores_gemma":[0.000009008506,0.0002272099,0.00063752616,0.000007733026,0.000017846864,0.00039339342,0.000010173066,0.0035291836,0.9901826,0.00003753666,0.00492827,0.000019642976],"about_ca_topic_score_codex":0.00048015622,"about_ca_topic_score_gemma":0.00059982133,"teacher_disagreement_score":0.0015064799,"about_ca_system_score_codex":0.0004793194,"about_ca_system_score_gemma":0.0004111792,"threshold_uncertainty_score":0.005039692},"labels":[],"label_agreement":null},{"id":"W4318200332","doi":"10.3390/s23031390","title":"Human Activity Recognition with an HMM-Based Generative Model","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Generative grammar; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition","score_opus":0.09214994951530925,"score_gpt":0.300073390858856,"score_spread":0.20792344134354673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318200332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04663107,0.000885109,0.9446126,0.00041238932,0.00014353049,0.00013320915,0.0021254339,0.0033661798,0.0016904036],"genre_scores_gemma":[0.79859,0.00085910596,0.18775062,0.00034797675,0.00014811382,0.00028853735,0.0061427145,0.00024564657,0.005627209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992818,0.00020332242,0.000043063745,0.00031415452,0.000093254486,0.00006432285],"domain_scores_gemma":[0.99899334,0.00064996985,0.000062100866,0.00014073799,0.0001088723,0.000045078377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093761337,0.0006782279,0.0010884524,0.0009338978,0.00023773251,0.00061618997,0.0014764717,0.00092811073,0.0022044447],"category_scores_gemma":[0.002943553,0.0005490439,0.0014759451,0.0011281735,0.00044057542,0.0008737856,0.0007655268,0.0017324713,0.0015087676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004876027,0.00041630812,0.00993947,0.00019414593,0.00031877967,0.00031891835,0.00027600754,0.66155773,0.0073996563,0.007598791,0.008725655,0.302767],"study_design_scores_gemma":[0.0000075749626,0.00001614764,0.00097473746,0.0000072770763,0.000014563254,0.000037681668,0.000007078204,0.99549234,0.0005298811,0.0024750142,0.00042923394,0.000008414759],"about_ca_topic_score_codex":0.017007058,"about_ca_topic_score_gemma":0.020001553,"teacher_disagreement_score":0.017007058,"about_ca_system_score_codex":0.0006522242,"about_ca_system_score_gemma":0.00068135455,"threshold_uncertainty_score":0.0338161},"labels":[],"label_agreement":null},{"id":"W4318200432","doi":"10.3390/s23031398","title":"Extremely Efficient DFB Lasers with Flat-Top Intra-Cavity Power Distribution in Highly Erbium-Doped Fibers","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Materials science; Laser; Fiber laser; Erbium; Optics; Coupling (piping); Slope efficiency; Fiber; Optical fiber; Power (physics); SIGNAL (programming language); Optoelectronics; Doping; Physics; Computer science","score_opus":0.007777215229593507,"score_gpt":0.20921487890522994,"score_spread":0.20143766367563642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318200432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9147037,0.0007446788,0.077825345,0.0001239616,0.000012640586,0.0000637647,0.00021434358,0.00034148127,0.00596996],"genre_scores_gemma":[0.94813806,0.00036433327,0.049568333,0.00001669977,0.0000049620016,0.00009152512,0.00007947467,0.00008106466,0.0016555503],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974185,0.000025504367,0.000008511494,0.00004211195,0.00013354044,0.00004848318],"domain_scores_gemma":[0.9998005,0.00005380551,0.00007060201,0.0000152222,0.000045355384,0.000014554634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033602797,0.00045280808,0.0003650361,0.0002452067,0.00029533502,0.0006917333,0.000288727,0.00048505823,0.0005956645],"category_scores_gemma":[0.00033987837,0.0003797084,0.00022186694,0.00032128548,0.0004417883,0.00036846107,0.00025031893,0.0002668366,0.0004234435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010744134,0.00004011192,0.0005852154,0.00011812846,0.000011531945,0.000033591336,0.00007415216,0.022006083,0.96935207,0.0018881448,0.00019623621,0.005587334],"study_design_scores_gemma":[0.00010344725,0.00038052147,0.0032714356,0.000046290337,0.000023718378,0.00011489301,0.000051752668,0.19647479,0.7944304,0.00086191867,0.0041843574,0.00005656114],"about_ca_topic_score_codex":0.0017761422,"about_ca_topic_score_gemma":0.0031556468,"teacher_disagreement_score":0.0017761422,"about_ca_system_score_codex":0.0009617176,"about_ca_system_score_gemma":0.0006171721,"threshold_uncertainty_score":0.0069777966},"labels":[],"label_agreement":null},{"id":"W4318200611","doi":"10.3390/s23031375","title":"Cushioned Footwear Effect on Pain and Gait Characteristics of Individuals with Knee Osteoarthritis: A Double-Blinded 3 Month Intervention Study","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Osteoarthritis; Gait; Physical therapy; Medicine; Physical medicine and rehabilitation; Cadence; WOMAC; Barefoot; Knee pain; Knee Joint; Gait analysis; Surgery","score_opus":0.01693422914900018,"score_gpt":0.272188785107034,"score_spread":0.2552545559580338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318200611","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969162,0.00046396526,0.00037161922,0.000050672268,0.00009217134,0.0017923238,0.000115908384,0.0000242764,0.00017289646],"genre_scores_gemma":[0.98642,0.00075339334,0.0026944275,0.00031140374,0.00031690937,0.006955809,0.0002939389,0.000011227893,0.0022430276],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99876297,0.0005472771,0.00016608155,0.00021072627,0.00013627131,0.00017660702],"domain_scores_gemma":[0.99831945,0.00031988893,0.000425091,0.00021534576,0.00029846234,0.00042174637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017497379,0.0015419965,0.0031738442,0.0005031044,0.0008772677,0.0005044614,0.0006525089,0.0016634617,0.0026125642],"category_scores_gemma":[0.0015457026,0.00056917773,0.0010051635,0.00039053982,0.0008998206,0.00045757776,0.0004961563,0.0010767931,0.00034704327],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.7766013,0.16464132,0.0018254952,0.0010373639,0.0006254104,0.00008023107,0.00024338611,0.00020657526,0.031755257,0.000034192242,0.00030181083,0.022647798],"study_design_scores_gemma":[0.2583181,0.7176996,0.01846655,0.00003889918,0.0005301311,0.000035860492,0.00008877107,0.00037106534,0.0038009428,0.000056993045,0.0005524077,0.00004067093],"about_ca_topic_score_codex":0.0008685576,"about_ca_topic_score_gemma":0.0015811996,"teacher_disagreement_score":0.0031738442,"about_ca_system_score_codex":0.0003463457,"about_ca_system_score_gemma":0.0008815797,"threshold_uncertainty_score":0.009253621},"labels":[],"label_agreement":null},{"id":"W4318464330","doi":"10.3390/s23031503","title":"Design and Fabrication of Embroidered Textile Strain Sensors: An Alternative to Stitch-Based Strain Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología; Canada Research Chairs","keywords":"Textile; Strain (injury); Fabrication; Engineering; Mechanical engineering; Materials science; Composite material","score_opus":0.03313464184737593,"score_gpt":0.266084221494643,"score_spread":0.23294957964726706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5387883,0.0033863415,0.45179686,0.00033440758,0.0002424806,0.00045647414,0.00025084516,0.0007669608,0.0039773267],"genre_scores_gemma":[0.46980342,0.0015276875,0.5244008,0.00013507217,0.00003544674,0.00024989963,0.00016256046,0.00004200001,0.003643186],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938285,0.000059069444,0.00004771861,0.00016213137,0.00030751317,0.000040696723],"domain_scores_gemma":[0.99965537,0.00005626864,0.000108673616,0.000052311763,0.00008972173,0.000037655762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005101281,0.00054870406,0.0004727465,0.00042206873,0.00017168642,0.0004930099,0.0008648632,0.0008713378,0.00051885785],"category_scores_gemma":[0.0005714077,0.00043128274,0.0003808329,0.00035625178,0.00036866855,0.0007994868,0.0003734651,0.0005851131,0.00029134256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026544441,0.000016316339,0.00008948734,0.00007040929,0.0000044284257,0.00003743827,0.000026146396,0.00050473126,0.9932562,0.00019621209,0.000030348085,0.0057418295],"study_design_scores_gemma":[0.000014090032,0.0006255587,0.0012272622,0.000009237913,0.00001246484,0.000265429,0.000019592113,0.007842198,0.98643166,0.000117989366,0.0034071633,0.000027310229],"about_ca_topic_score_codex":0.00019697474,"about_ca_topic_score_gemma":0.00061071746,"teacher_disagreement_score":0.0008713378,"about_ca_system_score_codex":0.00026843447,"about_ca_system_score_gemma":0.00033480485,"threshold_uncertainty_score":0.0026978254},"labels":[],"label_agreement":null},{"id":"W4318464426","doi":"10.3390/s23031504","title":"Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"RGB color model; Computer science; Artificial intelligence; Embedding; Identification (biology); Computer vision; Deep learning","score_opus":0.20060736219307732,"score_gpt":0.38162880661906956,"score_spread":0.18102144442599225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464426","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061064303,0.16114289,0.71881706,0.0015463679,0.0024484377,0.00084722054,0.007546123,0.012535649,0.034051996],"genre_scores_gemma":[0.2866813,0.13225195,0.5091566,0.0021888767,0.0015603533,0.00062776066,0.03777321,0.001065357,0.028694654],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977488,0.00039139227,0.00017193887,0.0007692741,0.00076366606,0.00015483216],"domain_scores_gemma":[0.99827707,0.00037429764,0.0001413163,0.00058146985,0.0005677694,0.000058170805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013987209,0.0024552718,0.0022735232,0.0036027934,0.00057851034,0.0014940582,0.0020410623,0.0014238125,0.003141438],"category_scores_gemma":[0.003723192,0.0006036205,0.0015500903,0.0035995631,0.00058246596,0.0027464386,0.0017271339,0.0011871703,0.0065916106],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023062238,0.00016081685,0.0042493585,0.0019394624,0.000200817,0.00014387304,0.00012290914,0.0052213916,0.005555795,0.0015171927,0.027086603,0.95357114],"study_design_scores_gemma":[0.00007443547,0.00081249897,0.06496122,0.003325998,0.0008117171,0.009363997,0.0024463895,0.38729218,0.11538596,0.019231997,0.3955943,0.00069934194],"about_ca_topic_score_codex":0.005301963,"about_ca_topic_score_gemma":0.005818285,"teacher_disagreement_score":0.005301963,"about_ca_system_score_codex":0.00050650723,"about_ca_system_score_gemma":0.0007289637,"threshold_uncertainty_score":0.010542154},"labels":[],"label_agreement":null},{"id":"W4318464455","doi":"10.3390/s23031474","title":"Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Prognostics; Lubrication; Lubricant; Bearing (navigation); Particle filter; Fault (geology); Reaction wheel; Control theory (sociology); Torque; Filter (signal processing); Satellite; Fault detection and isolation; Engineering; Computer science; Mechanical engineering; Reliability engineering; Aerospace engineering; Physics; Artificial intelligence; Control (management)","score_opus":0.025304151327785674,"score_gpt":0.2794840068950694,"score_spread":0.25417985556728373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12541282,0.00043121315,0.87248755,0.00012495265,0.000076338554,0.000030972933,0.000052916257,0.0005972676,0.00078591215],"genre_scores_gemma":[0.97539574,0.00017383017,0.02367168,0.000022879776,0.000020615722,0.000025121304,0.00006761434,0.000011584518,0.00061108905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998012,0.000023396406,0.00001833292,0.000064477004,0.0000665354,0.000026042124],"domain_scores_gemma":[0.9996008,0.00015007758,0.00007964747,0.000029490793,0.00012039595,0.000019702122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041802367,0.00057147775,0.0007003333,0.00041082915,0.0002276948,0.0005381025,0.0006085589,0.00049962936,0.0003803745],"category_scores_gemma":[0.0013962766,0.00027239264,0.00053335266,0.0002848363,0.00025255416,0.00051617064,0.00037506514,0.00059557764,0.00013167811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038763095,0.00011061049,0.011451561,0.00015181296,0.0000934261,0.00024884279,0.00014731556,0.8239717,0.02450665,0.0013948468,0.00076488726,0.13677074],"study_design_scores_gemma":[0.0000037245811,0.000015296127,0.0007137297,0.000001789451,0.0000054418188,0.000009077326,0.0000029291334,0.9980306,0.001040543,0.00010457134,0.000069003174,0.0000032636108],"about_ca_topic_score_codex":0.010325487,"about_ca_topic_score_gemma":0.0043655625,"teacher_disagreement_score":0.010325487,"about_ca_system_score_codex":0.0003921982,"about_ca_system_score_gemma":0.00052829855,"threshold_uncertainty_score":0.02053076},"labels":[],"label_agreement":null},{"id":"W4318464697","doi":"10.3390/s23031424","title":"Electrochemical Sensing of Lead in Drinking Water Using Copper Foil Bonded with Polymer","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Tap water; Materials science; Copper; FOIL method; Detection limit; Electrochemistry; Electrochemical gas sensor; Fabrication; Electrode; Polymer; Polyester; Cadmium; Chemical engineering; Analytical Chemistry (journal); Composite material; Metallurgy; Chemistry; Chromatography","score_opus":0.01276636743896281,"score_gpt":0.24137925840945235,"score_spread":0.22861289097048954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93529433,0.004438588,0.05508689,0.00022536602,0.00016056049,0.000105632156,0.00014368782,0.00055346504,0.0039915037],"genre_scores_gemma":[0.926917,0.0033435137,0.062196672,0.0001551009,0.00004353056,0.00009398719,0.0001756287,0.000058820508,0.007015785],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995357,0.0000854661,0.000026995785,0.00013554923,0.0001679473,0.00004835609],"domain_scores_gemma":[0.9998369,0.000043458418,0.00004091739,0.000015983647,0.000049222766,0.00001350279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003033555,0.00083587726,0.00030152051,0.0004276781,0.0002361115,0.00043197974,0.0010435614,0.00060563686,0.0005574846],"category_scores_gemma":[0.0006035506,0.00044417454,0.00019917647,0.0004683017,0.00028141655,0.0004947119,0.0004867999,0.0003670676,0.0005970414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026775733,0.000017321296,0.00015272679,0.00008416087,0.000007921922,0.00006948092,0.000021468504,0.00015993776,0.9936115,0.000062237574,0.000043136046,0.0057432754],"study_design_scores_gemma":[0.0000037842715,0.000103217244,0.0002388229,0.0000032796772,0.00000905915,0.00008570601,0.000014828815,0.0012685894,0.99719465,0.000016484077,0.0010560922,0.000005406624],"about_ca_topic_score_codex":0.00077785767,"about_ca_topic_score_gemma":0.0010346209,"teacher_disagreement_score":0.0010435614,"about_ca_system_score_codex":0.00038102863,"about_ca_system_score_gemma":0.00025066404,"threshold_uncertainty_score":0.002764523},"labels":[],"label_agreement":null},{"id":"W4318464874","doi":"10.3390/s23031412","title":"Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Gyroscope; Wearable computer; Dynamic time warping; Physical medicine and rehabilitation; Gait training; Rehabilitation; Gait analysis; Inertial measurement unit; Artificial intelligence; Computer science; Physical therapy; Engineering; Medicine","score_opus":0.04164637373845552,"score_gpt":0.3830581686748421,"score_spread":0.34141179493638657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8319936,0.0006521526,0.16520546,0.00012398782,0.00007727995,0.00009516635,0.00027368614,0.00052464486,0.001054003],"genre_scores_gemma":[0.9715759,0.00026716612,0.027201444,0.00004721617,0.000022748614,0.000051999345,0.00023147401,0.000012642615,0.00058946165],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995963,0.0000825358,0.00004593822,0.00010949933,0.00012728022,0.00003839695],"domain_scores_gemma":[0.9994795,0.00017370275,0.00012985898,0.000050602815,0.00013373682,0.000032699656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004962447,0.0005671162,0.0005039856,0.0013980403,0.00012735584,0.0005246819,0.00026119058,0.00046761322,0.00042005096],"category_scores_gemma":[0.0019868228,0.00015646739,0.0003751064,0.0007762186,0.00022262406,0.00044760483,0.0003257686,0.00023081989,0.00023479709],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010499099,0.0007492498,0.106083825,0.00035660644,0.0003802027,0.0005615538,0.00039349607,0.0812287,0.09383192,0.00043737362,0.0013563134,0.7135709],"study_design_scores_gemma":[0.000036393216,0.0010692348,0.21423548,0.00009477802,0.00009151269,0.00068319944,0.00038650443,0.7617409,0.019516982,0.00093422376,0.0011372652,0.00007349661],"about_ca_topic_score_codex":0.0018215941,"about_ca_topic_score_gemma":0.002779089,"teacher_disagreement_score":0.0018215941,"about_ca_system_score_codex":0.00021145387,"about_ca_system_score_gemma":0.00021040726,"threshold_uncertainty_score":0.0036219358},"labels":[],"label_agreement":null},{"id":"W4318464914","doi":"10.3390/s23031418","title":"A Low-Loss, 77 GHz, 8 × 8 Microstrip Butler Matrix on a High-Purity Fused-Silica (HPFS) Glass Substrate","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor; CMC Microsystems","keywords":"Microstrip; Materials science; Footprint; Insertion loss; Substrate (aquarium); Return loss; Finite element method; Matrix (chemical analysis); Optoelectronics; Electronic engineering; Electrical engineering; Engineering; Composite material; Antenna (radio); Structural engineering","score_opus":0.008261538867598542,"score_gpt":0.21468501272037688,"score_spread":0.20642347385277834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464914","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.921369,0.0005523112,0.06630284,0.00011719729,0.0000735541,0.00011582208,0.00025116728,0.0005842867,0.010633774],"genre_scores_gemma":[0.92221117,0.0003800866,0.07157053,0.00003375726,0.000018522713,0.00006743013,0.00017689716,0.000059402213,0.0054822634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987733,0.00001161274,0.000004141274,0.000030610612,0.00006033433,0.000015974387],"domain_scores_gemma":[0.9999058,0.000015769949,0.00003187474,0.0000146478515,0.00001908885,0.000012839574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011579528,0.0003688603,0.00019212031,0.00020264852,0.00012456485,0.00024744368,0.0003660097,0.00025223242,0.00067065435],"category_scores_gemma":[0.0001373883,0.00021915392,0.00022206461,0.00019264015,0.00017854331,0.0002648755,0.00018756531,0.00023143803,0.00062763115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002970002,0.000013916642,0.0002764865,0.00004861593,0.000010733313,0.00017306951,0.00005166386,0.001991714,0.9927891,0.0009007511,0.00017595782,0.0035383622],"study_design_scores_gemma":[0.000022920753,0.0003664241,0.0015083696,0.0000085634965,0.00002263847,0.00047754028,0.000039526763,0.010861152,0.97758263,0.000098184486,0.0089971125,0.000015027698],"about_ca_topic_score_codex":0.00060357706,"about_ca_topic_score_gemma":0.0014750978,"teacher_disagreement_score":0.00067065435,"about_ca_system_score_codex":0.00034641242,"about_ca_system_score_gemma":0.00022788657,"threshold_uncertainty_score":0.002513349},"labels":[],"label_agreement":null},{"id":"W4318478874","doi":"10.3390/s23031527","title":"An Adaptive Kernels Layer for Deep Neural Networks Based on Spectral Analysis for Image Applications","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Pixel; Convolution (computer science); Convolutional neural network; Pattern recognition (psychology); Computer vision; Image (mathematics); Image resolution; Invariant (physics); Artificial neural network; Mathematics","score_opus":0.027882640690896257,"score_gpt":0.2752975462856091,"score_spread":0.24741490559471285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318478874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0635781,0.0010074019,0.92435014,0.00027819062,0.00014345834,0.000064997635,0.00032797988,0.0061612814,0.0040885718],"genre_scores_gemma":[0.71928716,0.00059446937,0.2689043,0.00031679764,0.000035602094,0.00013688229,0.00084731594,0.0002643969,0.009613245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987614,0.000012105533,0.0000069436855,0.000032077343,0.000046225483,0.000026494834],"domain_scores_gemma":[0.9998374,0.00003536274,0.000018100733,0.00002969736,0.00006767159,0.000011668448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027801294,0.0006635794,0.00031336478,0.00033422155,0.000193297,0.00045116094,0.0011356636,0.0005459493,0.0022076028],"category_scores_gemma":[0.00070285634,0.000258933,0.0004445897,0.00031706292,0.0002417345,0.0008025106,0.00059228163,0.00087634777,0.0008434714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035972,0.0002488096,0.0020692642,0.00022999103,0.00017258535,0.00016472983,0.00008455598,0.33812502,0.086563066,0.009313167,0.010628433,0.5520407],"study_design_scores_gemma":[0.000006314547,0.000043369317,0.00030635964,0.000009903453,0.000019401821,0.000033298547,0.0000062473405,0.9821988,0.014336649,0.0011722242,0.0018583105,0.000009173919],"about_ca_topic_score_codex":0.0068606096,"about_ca_topic_score_gemma":0.010955871,"teacher_disagreement_score":0.0068606096,"about_ca_system_score_codex":0.00075874914,"about_ca_system_score_gemma":0.00070747075,"threshold_uncertainty_score":0.013641357},"labels":[],"label_agreement":null},{"id":"W4318817889","doi":"10.3390/s23031547","title":"Validation of Inertial Sensors to Evaluate Gait Stability","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Gait; Motion capture; Units of measurement; Stability (learning theory); Intraclass correlation; Gait analysis; Inertial frame of reference; Computer science; Artificial intelligence; Simulation; Motion (physics); Physical medicine and rehabilitation; Mathematics; Physics; Reproducibility; Medicine; Statistics; Machine learning","score_opus":0.06714325239039828,"score_gpt":0.3972995575569151,"score_spread":0.33015630516651684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318817889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84340566,0.0014776093,0.14811087,0.00018104064,0.00031234216,0.0007259829,0.001157989,0.0003583653,0.0042701336],"genre_scores_gemma":[0.9471286,0.0004799555,0.049640205,0.00018704204,0.00008114115,0.00071553164,0.0007701965,0.00004253283,0.00095469115],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9968257,0.0012176611,0.0003670594,0.0003717953,0.0011105307,0.00010737687],"domain_scores_gemma":[0.9961163,0.0010343533,0.00036747236,0.0003611888,0.0020259186,0.00009470007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039482056,0.00091145624,0.000387942,0.0010367695,0.00034835178,0.00055700634,0.00070376083,0.0007588602,0.00096792565],"category_scores_gemma":[0.009643229,0.00026070964,0.00041901012,0.0006259555,0.0004150534,0.0004692352,0.00067544717,0.00025459513,0.00047011985],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028981564,0.0010633492,0.388559,0.00075701013,0.0004593616,0.00019110549,0.000862536,0.006972421,0.39009044,0.0010219194,0.0015469664,0.20557766],"study_design_scores_gemma":[0.0003501162,0.012969685,0.6270101,0.00044275765,0.0005387669,0.0012567353,0.0009428323,0.09429956,0.25036216,0.0010681786,0.0106246695,0.0001344416],"about_ca_topic_score_codex":0.0011946469,"about_ca_topic_score_gemma":0.0015631298,"teacher_disagreement_score":0.0039482056,"about_ca_system_score_codex":0.0001924795,"about_ca_system_score_gemma":0.00035141013,"threshold_uncertainty_score":0.020880401},"labels":[],"label_agreement":null},{"id":"W4318832773","doi":"10.3390/s23031555","title":"PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Annotation; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Pose; Encoder; Encoding (memory); Partial least squares regression; RGB color model; Automatic image annotation; Embedding; Deep learning; Set (abstract data type); Image (mathematics); Machine learning; Image retrieval; Computer vision","score_opus":0.016991161608986456,"score_gpt":0.2804752527647815,"score_spread":0.2634840911557951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318832773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01867723,0.00059270614,0.9689709,0.00008621262,0.00009226692,0.000097820164,0.00049073255,0.009531653,0.0014605001],"genre_scores_gemma":[0.39240775,0.00087448646,0.5873599,0.0005033148,0.00021975992,0.00035891283,0.005654203,0.00087013404,0.011751556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992687,0.000086755375,0.000029958965,0.00035626264,0.00017050213,0.00008786596],"domain_scores_gemma":[0.99909735,0.00031356138,0.00009239521,0.00026699592,0.00017286508,0.0000569346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007742757,0.0024128857,0.0014987863,0.0007329897,0.00029237935,0.0009713475,0.0019499627,0.00139416,0.004468363],"category_scores_gemma":[0.0029841934,0.001115911,0.0011063785,0.0007203577,0.0006640868,0.0020190408,0.001828647,0.0017961236,0.0030307425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039881037,0.0001709463,0.0026041016,0.000202736,0.00018349534,0.00016358982,0.00009241759,0.13323756,0.028359996,0.0016049886,0.008985076,0.8239963],"study_design_scores_gemma":[0.00001701449,0.00012806084,0.001153463,0.000026497866,0.000026211696,0.00022533089,0.00002273089,0.9850338,0.008625296,0.002069774,0.0026488826,0.000022951464],"about_ca_topic_score_codex":0.004978442,"about_ca_topic_score_gemma":0.008522727,"teacher_disagreement_score":0.004978442,"about_ca_system_score_codex":0.00037898295,"about_ca_system_score_gemma":0.00090872304,"threshold_uncertainty_score":0.01494813},"labels":[],"label_agreement":null},{"id":"W4319027861","doi":"10.3390/s23031631","title":"A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Pseudorange; Computer science; Dilution of precision; Android (operating system); Real-time computing; Multipath propagation; Range (aeronautics); Remote sensing; Global Positioning System; Engineering; Telecommunications; Geography","score_opus":0.020540628097561735,"score_gpt":0.23818518050102455,"score_spread":0.2176445524034628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319027861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9401883,0.00048609675,0.0550565,0.00008250231,0.000027462305,0.000031542793,0.00069449365,0.000390405,0.003042708],"genre_scores_gemma":[0.9938233,0.00018234196,0.0052084457,0.000009219766,0.000005548944,0.000009214105,0.0004212197,0.000019339368,0.0003213201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99957997,0.00006134223,0.00002393557,0.00007297086,0.0002084758,0.000053222728],"domain_scores_gemma":[0.99916565,0.00031232394,0.00012584489,0.00013584645,0.00023368304,0.00002655551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034623014,0.000489616,0.00039349473,0.0005613878,0.00017742273,0.00033796253,0.00029804595,0.00046231635,0.0004558933],"category_scores_gemma":[0.0021894053,0.0001502963,0.0002985001,0.00073298835,0.0002897023,0.00049558154,0.00035709044,0.00024251083,0.00015637746],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015167758,0.00006253837,0.02489883,0.00025008954,0.00007279874,0.0005950305,0.00012601823,0.91399485,0.024181547,0.0014237142,0.00060684915,0.033636056],"study_design_scores_gemma":[0.000014860383,0.0003906088,0.07899489,0.00003652394,0.00004476398,0.00067131215,0.00022421216,0.89907575,0.018057942,0.0007462806,0.0016846908,0.000058118476],"about_ca_topic_score_codex":0.0062122894,"about_ca_topic_score_gemma":0.005269256,"teacher_disagreement_score":0.0062122894,"about_ca_system_score_codex":0.00024075141,"about_ca_system_score_gemma":0.00027109103,"threshold_uncertainty_score":0.012352288},"labels":[],"label_agreement":null},{"id":"W4319083538","doi":"10.3390/s23031699","title":"Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Crowdsensing; Crowdsourcing; Emergency management; Computer science; Disaster recovery; Preparedness; Mobile device; Data science; Computer security; Risk analysis (engineering); Process management; Knowledge management; Engineering; Business; World Wide Web","score_opus":0.14891388266612096,"score_gpt":0.36030635322130933,"score_spread":0.21139247055518837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319083538","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012137708,0.9990313,0.00011886166,0.00029234512,0.00008380871,0.000029562778,0.00005568235,0.0000042411734,0.00026290282],"genre_scores_gemma":[0.001070315,0.9983197,0.00024143659,0.0001877359,0.000045199886,0.000040520506,0.000038867613,0.0000019152903,0.000054250417],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99769944,0.00070351554,0.00065779424,0.0002495541,0.0005939123,0.00009565106],"domain_scores_gemma":[0.982653,0.013949827,0.0013286008,0.00024134199,0.0016347789,0.00019238987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004493695,0.0010854955,0.0033346892,0.008732104,0.0006609018,0.0023037428,0.001734056,0.0018805525,0.0040478855],"category_scores_gemma":[0.015941646,0.0006823546,0.0032473817,0.009546365,0.00092119654,0.002464922,0.001446717,0.0013197277,0.0006932156],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008773981,0.00005249056,0.0005033669,0.5857908,0.0009551569,0.00013720948,0.0003860924,0.00027499258,0.00025502022,0.002136075,0.008584173,0.40083697],"study_design_scores_gemma":[0.00005129054,0.00022531301,0.002966485,0.6200974,0.006506195,0.00076699216,0.0007950643,0.00021476114,0.0003385153,0.0026241327,0.36533862,0.00007526132],"about_ca_topic_score_codex":0.005731301,"about_ca_topic_score_gemma":0.0143283885,"teacher_disagreement_score":0.008732104,"about_ca_system_score_codex":0.0016718634,"about_ca_system_score_gemma":0.010265918,"threshold_uncertainty_score":0.023765206},"labels":[],"label_agreement":null},{"id":"W4319158218","doi":"10.3390/s23031686","title":"A New Approach to Quantifying Muscular Fatigue Using Wearable EMG Sensors during Surgery: An Ergonomic Case Study","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Medicine; Wearable computer; Electromyography; Rotator cuff; Muscular fatigue; Shoulder girdle; Physical medicine and rehabilitation; Lumbar; Physical therapy; Surgery; Computer science","score_opus":0.1145430826352553,"score_gpt":0.3573545552864036,"score_spread":0.24281147265114827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319158218","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88112354,0.0034927044,0.10830237,0.0017415979,0.00018793103,0.0003543593,0.00023005357,0.00018521833,0.0043822806],"genre_scores_gemma":[0.9278485,0.0024286248,0.066587016,0.00044022134,0.00021569067,0.00013121185,0.00008455861,0.000030564177,0.0022335602],"study_design_codex":"case_report","study_design_gemma":"observational","domain_scores_codex":[0.9992086,0.00021823344,0.000095938914,0.00018221763,0.00021542494,0.00007960673],"domain_scores_gemma":[0.99947494,0.0001624819,0.000120498975,0.00006831952,0.00008698357,0.00008681732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007437659,0.0009421612,0.00039362698,0.0024839018,0.00051310577,0.0009119461,0.0006359481,0.002510292,0.0011085412],"category_scores_gemma":[0.0014080395,0.000328599,0.0005626418,0.00066358,0.0011946436,0.0008883516,0.00094264385,0.0006796301,0.00048152506],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010676373,0.0013978848,0.271172,0.0012735926,0.0002057602,0.29722825,0.0075736614,0.0030707924,0.16780628,0.0025850774,0.0032647883,0.24335428],"study_design_scores_gemma":[0.000086308486,0.0041182702,0.14261296,0.00037145132,0.00029138647,0.7564225,0.006966633,0.020702368,0.049748123,0.0026465463,0.015766906,0.00026653227],"about_ca_topic_score_codex":0.0006143853,"about_ca_topic_score_gemma":0.00146391,"teacher_disagreement_score":0.002510292,"about_ca_system_score_codex":0.00034404342,"about_ca_system_score_gemma":0.0002960522,"threshold_uncertainty_score":0.0039334297},"labels":[],"label_agreement":null},{"id":"W4319159876","doi":"10.3390/s23031670","title":"Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health","keywords":"Mean squared error; Wearable computer; Mean absolute percentage error; Physical medicine and rehabilitation; Gastrocnemius muscle; Statistics; Computer science; Artificial intelligence; Medicine; Simulation; Mathematics; Physical therapy; Anatomy; Skeletal muscle","score_opus":0.014483733787942132,"score_gpt":0.2471883918977682,"score_spread":0.23270465810982607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319159876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9069184,0.003639774,0.08094467,0.0002830404,0.00045880064,0.00006667162,0.0016231525,0.0011734144,0.004892058],"genre_scores_gemma":[0.9898997,0.00038033715,0.007160643,0.00006839024,0.000055182652,0.000024763282,0.0010362781,0.00012920823,0.0012455668],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990109,0.0002292641,0.00008116301,0.00040228892,0.00019980605,0.00007639546],"domain_scores_gemma":[0.9953181,0.001702759,0.000539889,0.0005503521,0.0016963535,0.00019253348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026949279,0.0008111866,0.00045981802,0.00073405716,0.00022557288,0.0005668552,0.0003802492,0.0008596277,0.0014016432],"category_scores_gemma":[0.012761891,0.00022422559,0.00034067562,0.00026682083,0.00024523056,0.0006638947,0.0006913916,0.00041127237,0.00127184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004570199,0.00025452653,0.39906743,0.0007438547,0.0008580114,0.0008737113,0.0014379454,0.036005907,0.10222409,0.00067438214,0.008030751,0.44525918],"study_design_scores_gemma":[0.00004671985,0.0011000893,0.75694656,0.00028899586,0.0003852727,0.0023903116,0.0005705798,0.19439638,0.035011705,0.0014162601,0.0073170885,0.00013013516],"about_ca_topic_score_codex":0.002679683,"about_ca_topic_score_gemma":0.003655924,"teacher_disagreement_score":0.0026949279,"about_ca_system_score_codex":0.0001668789,"about_ca_system_score_gemma":0.00020989649,"threshold_uncertainty_score":0.014252365},"labels":[],"label_agreement":null},{"id":"W4319160273","doi":"10.3390/s23031674","title":"Low-to-Mid-Frequency Monopole Source Levels of Underwater Noise from Small Recreational Vessels in the St. Lawrence Estuary Beluga Critical Habitat","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Ministère des Forêts, de la Faune et des Parcs","keywords":"Hydrophone; Noise (video); Environmental science; Beluga Whale; Estuary; Ambient noise level; Population; Acoustics; Soundscape; Oceanography; Shore; Geology; Sound (geography); Arctic; Physics; Computer science","score_opus":0.0394971739383995,"score_gpt":0.2677589491157295,"score_spread":0.22826177517733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319160273","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99918646,0.00003293427,0.00013702082,0.000014371119,0.0000011709924,0.000002309942,0.000074620795,0.0000046556497,0.0005463168],"genre_scores_gemma":[0.9987895,0.00005726251,0.0002247531,0.000014558342,0.0000019584925,0.000005619789,0.00013242742,0.0000026290534,0.00077136967],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983275,0.0000198687,0.000007917043,0.000035669374,0.00006846257,0.000035430225],"domain_scores_gemma":[0.99965286,0.00003953482,0.00007511985,0.000009761749,0.00016304142,0.000059693466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017645332,0.00015177707,0.00015188566,0.00064749917,0.00057181576,0.0003827032,0.00019067658,0.0001716226,0.00066965865],"category_scores_gemma":[0.00036214807,0.00015064495,0.00009949065,0.000390265,0.00038042778,0.00017522185,0.00033188652,0.00016745063,0.00016525238],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001857313,0.00007013441,0.93133914,0.000047920927,0.000037045113,0.0004172742,0.004620855,0.00022352125,0.047222886,0.000037611484,0.00032359108,0.015474229],"study_design_scores_gemma":[5.6676083e-7,0.00006715461,0.9980478,0.000004559755,0.0000063628295,0.0000727034,0.0010124651,0.00009156486,0.000513422,0.000005213008,0.00017406225,0.0000040207397],"about_ca_topic_score_codex":0.17570555,"about_ca_topic_score_gemma":0.5137158,"teacher_disagreement_score":0.82429445,"about_ca_system_score_codex":0.0008918738,"about_ca_system_score_gemma":0.00049794436,"threshold_uncertainty_score":0.34936565},"labels":[],"label_agreement":null},{"id":"W4319160422","doi":"10.3390/s23031650","title":"Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Lidar; Point cloud; Estimator; Computer science; Benchmark (surveying); Computer vision; Artificial intelligence; Depth map; Stereopsis; Point (geometry); Encoder; Depth perception; Measured depth; Remote sensing; Image (mathematics); Perception; Geology; Mathematics; Geodesy","score_opus":0.03604234561636074,"score_gpt":0.3019441228024621,"score_spread":0.26590177718610136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319160422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038762014,0.00022273687,0.94884986,0.00011244038,0.00010782927,0.00012578898,0.00049934915,0.00872447,0.0025954186],"genre_scores_gemma":[0.4209136,0.00017773287,0.5709531,0.00017151759,0.000095288546,0.0001779157,0.0023931689,0.0005417645,0.0045759627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966824,0.000028988668,0.000013946517,0.000101032194,0.00012590556,0.000061828454],"domain_scores_gemma":[0.99955744,0.000058413796,0.000052493437,0.00009275186,0.00020998076,0.000028848644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027872235,0.0012132735,0.0007234759,0.0012913825,0.00041268597,0.0005283532,0.0015906563,0.0006079872,0.0028787258],"category_scores_gemma":[0.0009426972,0.00049134425,0.00055416476,0.0008435031,0.0002634236,0.0012768424,0.00092517637,0.0008461322,0.0017185541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020396862,0.00030328205,0.001891303,0.000107100306,0.00006340493,0.00012301146,0.00010223609,0.0902845,0.039876416,0.0029288325,0.0088856155,0.85523033],"study_design_scores_gemma":[0.00001015344,0.00004351855,0.0006618283,0.000006323357,0.0000081039025,0.000040704763,0.00003139867,0.9857364,0.010530802,0.0015778639,0.001342786,0.00001012644],"about_ca_topic_score_codex":0.009156942,"about_ca_topic_score_gemma":0.019642213,"teacher_disagreement_score":0.009156942,"about_ca_system_score_codex":0.0005150182,"about_ca_system_score_gemma":0.0010035273,"threshold_uncertainty_score":0.018207312},"labels":[],"label_agreement":null},{"id":"W4319317249","doi":"10.3390/s23041807","title":"Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Institute for Aging; University Health Network; University of Toronto; University of Waterloo","funders":"Mitacs; Université de Technologie de Compiègne","keywords":"Generalizability theory; Random forest; Logistic regression; Computer science; Machine learning; Generalization; Artificial intelligence; Stressor; Stress (linguistics); Cross-validation; Identification (biology); Predictive modelling; Data mining; Statistics; Mathematics; Psychology","score_opus":0.05485507594291609,"score_gpt":0.3370627939686501,"score_spread":0.28220771802573397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319317249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4686963,0.005811518,0.5007648,0.0012872997,0.0014144196,0.0029460231,0.009563681,0.003794126,0.0057218447],"genre_scores_gemma":[0.92498434,0.0004218454,0.056910764,0.0005173401,0.00020024275,0.0020925165,0.013243268,0.00038611607,0.001243635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97578317,0.012955998,0.0023097377,0.0056135166,0.0027452526,0.0005923892],"domain_scores_gemma":[0.89862657,0.07182113,0.004166108,0.01771209,0.0071938117,0.00048032825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07729213,0.0026018547,0.0017819757,0.002538374,0.001323393,0.0025571235,0.0018578281,0.0025239103,0.003838804],"category_scores_gemma":[0.11505749,0.00045369507,0.004147248,0.001361778,0.001777475,0.0023000685,0.002640363,0.003566816,0.001115096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006797796,0.002473284,0.27783275,0.0026347411,0.013756273,0.0021305268,0.0021971753,0.36173153,0.0154798515,0.012450291,0.029665021,0.27285075],"study_design_scores_gemma":[0.00038665984,0.0041497047,0.11732424,0.0005029403,0.0019612196,0.0009639302,0.0011702057,0.8339816,0.011290679,0.013207767,0.014794995,0.00026608675],"about_ca_topic_score_codex":0.0041055228,"about_ca_topic_score_gemma":0.0026076797,"teacher_disagreement_score":0.07729213,"about_ca_system_score_codex":0.0007789507,"about_ca_system_score_gemma":0.0011497511,"threshold_uncertainty_score":0.40876472},"labels":[],"label_agreement":null},{"id":"W4319598167","doi":"10.3390/s23041903","title":"Spatiotemporal Winter Wheat Water Status Assessment Improvement Using a Water Deficit Index Derived from an Unmanned Aerial System in the North China Plain","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Genetics; Institute of Genetics and Developmental Biology, Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"China; Index (typography); Environmental science; Winter wheat; Hydrology (agriculture); Water resource management; Geography; Geology; Agronomy; Geotechnical engineering; Computer science; Biology","score_opus":0.01072012481911453,"score_gpt":0.2339018961172455,"score_spread":0.22318177129813097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319598167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977865,0.000035558984,0.0017778298,0.000009267256,0.0000028699012,0.0000062032204,0.00008399063,0.000029561445,0.0002681435],"genre_scores_gemma":[0.99820864,0.00002546008,0.0015045104,0.0000042285774,0.0000014815191,0.000005388637,0.00011864243,0.0000015446037,0.00013021576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999126,0.000011287669,0.0000051457505,0.000030498746,0.000025746549,0.000014757009],"domain_scores_gemma":[0.99991703,0.000009439863,0.000023983908,0.000008955553,0.000029219982,0.000011438149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020393638,0.00023156629,0.00013592147,0.00034004918,0.00011495902,0.00022113706,0.00012301945,0.00010481679,0.00019102197],"category_scores_gemma":[0.00020799869,0.00008307622,0.00013383629,0.0003018962,0.00010293522,0.00031365443,0.00015800053,0.00009313385,0.00003753691],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004509692,0.00026977144,0.5709747,0.00015405467,0.00014474444,0.00041238297,0.0005060294,0.05533023,0.17342247,0.0002960981,0.0008192455,0.19721933],"study_design_scores_gemma":[0.000015563817,0.00022235206,0.83593035,0.0000075007265,0.00005009106,0.000066497414,0.00028881276,0.15145297,0.011344011,0.00008647682,0.00051468617,0.000020611375],"about_ca_topic_score_codex":0.011355807,"about_ca_topic_score_gemma":0.021957489,"teacher_disagreement_score":0.011355807,"about_ca_system_score_codex":0.00022287024,"about_ca_system_score_gemma":0.0001711929,"threshold_uncertainty_score":0.022579432},"labels":[],"label_agreement":null},{"id":"W4319765238","doi":"10.3390/s23041914","title":"A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; H. Lundbeck A/S; Natural Science Foundation of Beijing Municipality; Eisai; National Natural Science Foundation of China; BioClinica; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health; U.S. Department of Defense","keywords":"Convolutional neural network; Computer science; Graph; Population; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Machine learning; Theoretical computer science; Medicine","score_opus":0.0478953458124065,"score_gpt":0.33701995794186274,"score_spread":0.2891246121294562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319765238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010675645,0.0011930863,0.98234355,0.0003463994,0.00007552416,0.000107677566,0.00088260655,0.0018358567,0.0025395686],"genre_scores_gemma":[0.33057958,0.0020186002,0.6516328,0.00038690097,0.00015324625,0.00042292132,0.0039485693,0.00022321954,0.010634142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997068,0.000058055237,0.000013786144,0.000094352734,0.00008150206,0.00004543772],"domain_scores_gemma":[0.9998216,0.00004222116,0.000023213168,0.000027992097,0.000065304914,0.000019645246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052821526,0.0010441148,0.00058538397,0.0015258742,0.0003630243,0.00062507857,0.0012678299,0.00079325575,0.0017199151],"category_scores_gemma":[0.0009141659,0.0003578557,0.0010321455,0.0014986015,0.00050993945,0.00088083197,0.0006888834,0.0012922296,0.000627688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016157395,0.00016059495,0.002220019,0.00019760845,0.00022257818,0.00022992035,0.00006960698,0.58346814,0.014568233,0.05610926,0.012487276,0.33010522],"study_design_scores_gemma":[0.0000053519047,0.000028589027,0.00058811053,0.000010136835,0.000016773669,0.000043908494,0.0000051119546,0.9816096,0.001468405,0.01291998,0.003292517,0.000011524097],"about_ca_topic_score_codex":0.036710385,"about_ca_topic_score_gemma":0.04292178,"teacher_disagreement_score":0.036710385,"about_ca_system_score_codex":0.0015678438,"about_ca_system_score_gemma":0.0014134774,"threshold_uncertainty_score":0.0729934},"labels":[],"label_agreement":null},{"id":"W4319983895","doi":"10.3390/s23041945","title":"Ambient Dose and Dose Rate Measurement in SNOLAB Underground Laboratory at Sudbury, Ontario, Canada","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Snolab; Canadian Nuclear Laboratories","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ministry of Advanced Education, Government of Alberta","keywords":"Dosimeter; Environmental science; Equivalent dose; Electromagnetic shielding; Detector; Dose rate; Thermoluminescence; Water equivalent; Radiation monitoring; Thermoluminescent dosimeter; Sensitivity (control systems); Remote sensing; Radiation; Physics; Engineering; Geology; Optics; Medical physics; Nuclear physics; Electrical engineering; Meteorology","score_opus":0.014226088376630273,"score_gpt":0.20597514215007157,"score_spread":0.19174905377344129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319983895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8824617,0.00087145186,0.031527862,0.00052587205,0.00009023518,0.0005332181,0.01048979,0.0027279684,0.07077189],"genre_scores_gemma":[0.9224729,0.0006769224,0.030122813,0.00020015876,0.000017258522,0.00017662063,0.00506217,0.00030979235,0.040961396],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988249,0.000036842957,0.00001924058,0.00021733833,0.0007871146,0.000114491566],"domain_scores_gemma":[0.99915004,0.000032058422,0.000047877184,0.00003842823,0.0006647086,0.000066791974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003847215,0.00033014643,0.00029426336,0.0011919541,0.002160477,0.0008201677,0.0009912906,0.0003201029,0.0047852583],"category_scores_gemma":[0.00054031354,0.00024704367,0.00021629254,0.0015749714,0.0005290151,0.000292663,0.00049530173,0.0005018562,0.0010377049],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011559122,0.00024074751,0.110373594,0.000340357,0.00008213582,0.00081389217,0.00318108,0.0042244974,0.7944943,0.002268855,0.013749135,0.06907552],"study_design_scores_gemma":[0.00014482153,0.0006930067,0.41987252,0.00010191032,0.00015302213,0.0005569647,0.0024487851,0.010589966,0.46196026,0.0003235809,0.10299157,0.00016350194],"about_ca_topic_score_codex":0.71526563,"about_ca_topic_score_gemma":0.8628939,"teacher_disagreement_score":0.28473437,"about_ca_system_score_codex":0.00990951,"about_ca_system_score_gemma":0.009667979,"threshold_uncertainty_score":0.57282245},"labels":[],"label_agreement":null},{"id":"W4320732085","doi":"10.3390/s23042124","title":"Data Dissemination in VANETs Using Particle Swarm Optimization","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Computer network; Particle swarm optimization; Ant colony optimization algorithms; Optimization problem; Network packet; Vehicular ad hoc network; Heuristic; Mobile ad hoc network; Overhead (engineering); Node (physics); Wireless ad hoc network; Distributed computing; Wireless; Engineering; Algorithm","score_opus":0.036585179909886065,"score_gpt":0.28559387942199443,"score_spread":0.24900869951210836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320732085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033369876,0.00072666525,0.9605719,0.00024322371,0.00010209039,0.00009863669,0.000051524028,0.00053934834,0.004296832],"genre_scores_gemma":[0.7604565,0.0009860619,0.23362204,0.000089617155,0.000060698985,0.00022896986,0.000155254,0.00007080517,0.004329949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976414,0.00008063202,0.0000144625365,0.000037663845,0.00007768235,0.000025441237],"domain_scores_gemma":[0.9997254,0.00014002391,0.000038069324,0.000018440802,0.000062923275,0.000015231425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051929225,0.00061969185,0.00076941686,0.00056647847,0.0005103316,0.00093466643,0.0004901771,0.0006840253,0.0006333149],"category_scores_gemma":[0.00089674536,0.00031418385,0.00054628245,0.0007798753,0.0003685327,0.00068337144,0.0006141988,0.00051422307,0.00011778536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024759684,0.000020412746,0.0002296511,0.00003336339,0.000023948342,0.00002994799,0.00003149397,0.9789715,0.0016365433,0.0021585135,0.00031080624,0.016529068],"study_design_scores_gemma":[0.000005583697,0.000018332525,0.000045998386,0.0000020955686,0.0000037017767,0.000005667773,0.00000937667,0.99862385,0.000326881,0.0005116984,0.00044411724,0.000002684845],"about_ca_topic_score_codex":0.006554533,"about_ca_topic_score_gemma":0.0033291786,"teacher_disagreement_score":0.006554533,"about_ca_system_score_codex":0.00050069776,"about_ca_system_score_gemma":0.0005671616,"threshold_uncertainty_score":0.013032794},"labels":[],"label_agreement":null},{"id":"W4320732177","doi":"10.3390/s23042112","title":"Reviewing Federated Machine Learning and Its Use in Diseases Prediction","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Chicoutimi; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Computer science; Confidentiality; Health care; Automation; Variety (cybernetics); Artificial intelligence; Machine learning; Investment (military); Information privacy; Data science; Knowledge management; Computer security; Engineering; Economics","score_opus":0.1214832096083933,"score_gpt":0.34033548435516103,"score_spread":0.21885227474676772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320732177","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019020289,0.90209407,0.040023055,0.024915298,0.014135226,0.00006197606,0.0002400583,0.00028325518,0.016345102],"genre_scores_gemma":[0.032226346,0.89408076,0.019456988,0.013262905,0.028933175,0.00010597511,0.00061668793,0.00010977888,0.01120743],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983777,0.00053487916,0.00017747222,0.00029735183,0.0005559217,0.00005666146],"domain_scores_gemma":[0.9945312,0.003304134,0.00028753455,0.0001976643,0.0015571874,0.00012238797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022010943,0.0005504093,0.0005983368,0.0025041276,0.00052171736,0.0019593588,0.0010065755,0.0016790793,0.0021272942],"category_scores_gemma":[0.0096825445,0.0003114871,0.0007238397,0.004038463,0.0008795542,0.002587797,0.0005058479,0.0020234855,0.001807899],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058882237,0.000060745042,0.0023062695,0.0038327666,0.00011281604,0.00035776212,0.00021157385,0.008247746,0.0006289757,0.051899727,0.21146125,0.72082144],"study_design_scores_gemma":[0.0000057305633,0.00011158398,0.0030468295,0.004478105,0.00007416681,0.0011116815,0.00013853253,0.009798638,0.0008229578,0.03101958,0.9493359,0.000056292],"about_ca_topic_score_codex":0.00318286,"about_ca_topic_score_gemma":0.0028308318,"teacher_disagreement_score":0.00318286,"about_ca_system_score_codex":0.0011671293,"about_ca_system_score_gemma":0.0014396359,"threshold_uncertainty_score":0.011640668},"labels":[],"label_agreement":null},{"id":"W4320917547","doi":"10.3390/s23042194","title":"Distributed Impact Wave Detection in Steel I-Beam with a Weak Fiber Bragg Gratings Array","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Transport Canada","keywords":"Optics; Fiber Bragg grating; Distributed acoustic sensing; Materials science; Structural health monitoring; Acoustics; Beam (structure); Rayleigh scattering; Harmonic; Cantilever; Vibration; Optical fiber; Rayleigh length; Fiber optic sensor; Physics; Laser; Laser beams","score_opus":0.009270796979144924,"score_gpt":0.22132790768098967,"score_spread":0.21205711070184474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320917547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87246466,0.0006894615,0.123682946,0.00012927952,0.00006925749,0.000044560016,0.00022287066,0.0004283986,0.0022685637],"genre_scores_gemma":[0.9229506,0.00033514595,0.07479217,0.000072485294,0.000027141188,0.00002711268,0.000118722186,0.00002383603,0.0016526284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997129,0.000024963849,0.0000083495615,0.000083779625,0.000147006,0.000022916922],"domain_scores_gemma":[0.9998202,0.00002901916,0.00006116867,0.000019340554,0.00005494363,0.00001535792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020199109,0.00051262445,0.00032805844,0.0004984007,0.00013080022,0.00023235385,0.00047183776,0.00040211878,0.00058615976],"category_scores_gemma":[0.00027776413,0.00022200787,0.00014727103,0.00045243747,0.0002794437,0.00045748838,0.00032155422,0.00023637166,0.00019739909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009634835,0.000012458673,0.0010409467,0.00003866364,0.000006612588,0.000030082798,0.000029938123,0.0004308597,0.9852734,0.00006840022,0.00006846843,0.012903942],"study_design_scores_gemma":[0.000026921132,0.00033573795,0.011770415,0.000009068555,0.000033473996,0.00026080455,0.000084914805,0.028352853,0.9579608,0.00012870059,0.0010000473,0.000036272737],"about_ca_topic_score_codex":0.0010859743,"about_ca_topic_score_gemma":0.0026553783,"teacher_disagreement_score":0.0010859743,"about_ca_system_score_codex":0.00027744894,"about_ca_system_score_gemma":0.00017771717,"threshold_uncertainty_score":0.002159357},"labels":[],"label_agreement":null},{"id":"W4321100083","doi":"10.3390/s23042209","title":"Comparison of Three Motion Capture-Based Algorithms for Spatiotemporal Gait Characteristics: How Do Algorithms Affect Accuracy and Precision of Clinical Outcomes?","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Canada Research Chairs","keywords":"Algorithm; Gait; Computer science; Motion capture; Motion (physics); Gait analysis; Affect (linguistics); Artificial intelligence; Physical medicine and rehabilitation; Psychology; Medicine","score_opus":0.1412188462014146,"score_gpt":0.4700816097360691,"score_spread":0.3288627635346545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321100083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7803461,0.026086561,0.18262158,0.0015610193,0.0007126769,0.0014443514,0.002041065,0.0013385689,0.0038481231],"genre_scores_gemma":[0.9191368,0.0030570775,0.07460393,0.0005094356,0.00017436511,0.0006730004,0.0012799604,0.00021423785,0.00035116696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9840958,0.006260121,0.0029467933,0.0023906536,0.0039899093,0.00031664516],"domain_scores_gemma":[0.90974563,0.065463394,0.007949045,0.0054816953,0.010768168,0.0005920885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025538167,0.0011349623,0.0019624233,0.003497416,0.0004692457,0.0031780575,0.0011074381,0.0023007144,0.00070018775],"category_scores_gemma":[0.10628648,0.00047558406,0.0014155101,0.0023399042,0.00087541057,0.0017015615,0.00096674374,0.00086043397,0.0005463931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013974755,0.00052863656,0.39421296,0.0018946014,0.0065122982,0.00011615982,0.0006869315,0.019087596,0.008682295,0.0010181324,0.0018833153,0.55140233],"study_design_scores_gemma":[0.0017311771,0.010572299,0.74450797,0.0012391985,0.0050240527,0.0018636372,0.0012619877,0.20125912,0.020968484,0.0054100677,0.0055983094,0.0005636976],"about_ca_topic_score_codex":0.0014558225,"about_ca_topic_score_gemma":0.0013828573,"teacher_disagreement_score":0.025538167,"about_ca_system_score_codex":0.0006364981,"about_ca_system_score_gemma":0.00069636205,"threshold_uncertainty_score":0.13506031},"labels":[],"label_agreement":null},{"id":"W4321239580","doi":"10.3390/s23042241","title":"Use of Multi-Date and Multi-Spectral UAS Imagery to Classify Dominant Tree Species in the Wet Miombo Woodlands of Zambia","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Rocky Mountain Research Station; U.S. Forest Service; National Research Foundation; National Science and Technology Council; International Development Research Centre; United States Agency for International Development; U.S. Department of Agriculture","keywords":"Ecoregion; Multispectral image; Woodland; Phenology; Remote sensing; Kappa; Random forest; Canopy; Tree (set theory); Forestry; Geography; Environmental science; Computer science; Ecology; Artificial intelligence; Mathematics; Biology","score_opus":0.05318082706501204,"score_gpt":0.2745373082481699,"score_spread":0.22135648118315787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321239580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978078,0.00026782643,0.0012526459,0.000030271576,0.0000035665485,0.000020694788,0.00011668801,0.000023552515,0.00047702514],"genre_scores_gemma":[0.99248284,0.00019485291,0.0068943216,0.000013021346,0.0000023301588,0.000012821705,0.00018221083,0.0000031792351,0.00021442288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989176,0.0000221187,0.000008555867,0.00002578137,0.00002164753,0.000030058229],"domain_scores_gemma":[0.99989843,0.000023423978,0.00002945333,0.000009820946,0.000028584305,0.000010211954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030423395,0.0003076131,0.00017920064,0.001216841,0.0002891366,0.0005318234,0.00015650991,0.00020578889,0.00023159852],"category_scores_gemma":[0.00041678143,0.00018038075,0.00020659831,0.00057994464,0.00015596526,0.00035851166,0.00022248423,0.00017981167,0.000055783545],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004099448,0.0001584948,0.643762,0.0003903703,0.0001455675,0.0007511571,0.0030220863,0.0068973387,0.12584476,0.0003207441,0.0005534672,0.21774405],"study_design_scores_gemma":[0.0000069165653,0.00007584399,0.9796942,0.000054447584,0.00008186945,0.00014857111,0.001295838,0.013962783,0.0037774642,0.000036947513,0.000852395,0.00001263629],"about_ca_topic_score_codex":0.042988755,"about_ca_topic_score_gemma":0.15087458,"teacher_disagreement_score":0.042988755,"about_ca_system_score_codex":0.00038865808,"about_ca_system_score_gemma":0.0002761579,"threshold_uncertainty_score":0.085477054},"labels":[],"label_agreement":null},{"id":"W4321372012","doi":"10.3390/s23042346","title":"Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Block (permutation group theory); Pattern recognition (psychology); Process (computing); Bottleneck; Scale (ratio); Encoding (memory); Market segmentation; Filter (signal processing); Identification (biology); Computer vision; Embedded system; Biology","score_opus":0.04942361832508516,"score_gpt":0.298392424337896,"score_spread":0.24896880601281082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321372012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10276066,0.0013133966,0.8899206,0.00039550057,0.00008863732,0.00006725022,0.00012919772,0.002372303,0.0029524702],"genre_scores_gemma":[0.9134434,0.00036678702,0.08119846,0.00025017402,0.00006272599,0.00007341024,0.00034101369,0.00010038594,0.0041636606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980885,0.00003160318,0.000008034588,0.00006688366,0.000040254894,0.00004438317],"domain_scores_gemma":[0.9998043,0.00007544024,0.000028111084,0.000021674843,0.000051059447,0.000019440566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036259025,0.00072823395,0.0005191183,0.0003606361,0.00021662645,0.00036110243,0.00098965,0.00076209055,0.0012556238],"category_scores_gemma":[0.0008549967,0.0003359045,0.0005966025,0.00030159525,0.00040761215,0.00064673,0.0006974423,0.00074497185,0.00021355214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030514254,0.0001597092,0.0019127948,0.00012310926,0.000115402116,0.00027185323,0.00016268318,0.6306977,0.041935224,0.0037031586,0.0037053593,0.31690776],"study_design_scores_gemma":[0.0000041187436,0.000023118639,0.00026264307,0.0000025647844,0.000010049143,0.000017845307,0.0000045191136,0.99630105,0.0021814748,0.00096806046,0.00022096501,0.0000035132082],"about_ca_topic_score_codex":0.0075073796,"about_ca_topic_score_gemma":0.009242037,"teacher_disagreement_score":0.0075073796,"about_ca_system_score_codex":0.00074693386,"about_ca_system_score_gemma":0.00069561054,"threshold_uncertainty_score":0.014927387},"labels":[],"label_agreement":null},{"id":"W4321375532","doi":"10.3390/s23042317","title":"A Hybrid Stainless-Steel SPME Microneedle Electrode Sensor for Dual Electrochemical and GC-MS Analysis","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solid-phase microextraction; Polyaniline; Materials science; Detection limit; Coating; Electrochemistry; Electrode; Chromatography; Chemistry; Gas chromatography–mass spectrometry; Nanotechnology; Composite material; Mass spectrometry; Polymer","score_opus":0.006456451977676425,"score_gpt":0.21074242905491194,"score_spread":0.20428597707723553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321375532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3649439,0.0075344364,0.61015505,0.0008267864,0.0005021145,0.0010714488,0.002860429,0.0076515833,0.004454276],"genre_scores_gemma":[0.4370815,0.0017887639,0.54876155,0.0006964434,0.0001060444,0.0007399827,0.0013464198,0.00012695689,0.009352377],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906534,0.000060881703,0.00005926631,0.00030365755,0.0004586614,0.00005219092],"domain_scores_gemma":[0.99970466,0.000059254482,0.00004579919,0.000028339764,0.00012703703,0.000034922723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041816177,0.0009450403,0.00078777707,0.0005209998,0.00028212092,0.00029316315,0.0012344504,0.0015199519,0.0010498276],"category_scores_gemma":[0.0004076343,0.0005237178,0.0004707566,0.00036145927,0.0002402465,0.0006768136,0.00052469864,0.00063252077,0.0010214876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010432639,0.000005485548,0.00003455183,0.00003969892,0.000004303961,0.000023455896,0.0000035289984,0.000036046138,0.9971301,0.000030176043,0.000059535552,0.00262268],"study_design_scores_gemma":[0.0000074670797,0.00013913838,0.00093903986,0.0000043547784,0.000015514912,0.0004008645,0.000007377919,0.0035933675,0.99177855,0.00004266513,0.0030484723,0.000023222015],"about_ca_topic_score_codex":0.0008616409,"about_ca_topic_score_gemma":0.0033171219,"teacher_disagreement_score":0.0015199519,"about_ca_system_score_codex":0.00063697965,"about_ca_system_score_gemma":0.0004768099,"threshold_uncertainty_score":0.004621625},"labels":[],"label_agreement":null},{"id":"W4321377095","doi":"10.3390/s23042310","title":"On the Feasibility of Monitoring Power Transformer’s Winding Vibration and Temperature along with Moisture in Oil Using Optical Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"QPS Photronics (Canada); Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Moisture; Transformer; Vibration; Electrical engineering; Acoustics; Transformer oil; Electromagnetic coil; Environmental science; Temperature measurement; Engineering; Materials science; Voltage; Composite material; Physics","score_opus":0.021174974212558633,"score_gpt":0.2475759340470859,"score_spread":0.22640095983452727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321377095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9096923,0.0011976,0.08643958,0.00017651886,0.00006234033,0.000035397054,0.000053688982,0.0001567762,0.002185911],"genre_scores_gemma":[0.9726238,0.00040785642,0.026330022,0.000025693527,0.000016266515,0.000009163188,0.000021573815,0.00000863138,0.00055698317],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971133,0.00005166474,0.0000077101495,0.000066605404,0.00014108409,0.00002156356],"domain_scores_gemma":[0.9997024,0.00013462987,0.00004279763,0.000034189485,0.00007408306,0.0000118288735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036897656,0.00021927913,0.0001298804,0.00015921313,0.00009942882,0.00022782857,0.00035133224,0.0002884906,0.00053459045],"category_scores_gemma":[0.00062815804,0.00012382645,0.00013201051,0.000116091906,0.00035391503,0.00048658365,0.00014636441,0.0002395564,0.0001221542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013128726,0.000021936978,0.001086985,0.000043593576,0.000006544492,0.00004147579,0.000028031745,0.0006021108,0.9859407,0.0004273913,0.000052881252,0.011616991],"study_design_scores_gemma":[0.000006067244,0.00030352155,0.0017949893,0.0000055527903,0.000016749034,0.00015175491,0.000043614185,0.01248253,0.9841282,0.00009301059,0.0009659726,0.000007986415],"about_ca_topic_score_codex":0.0005092856,"about_ca_topic_score_gemma":0.0007594169,"teacher_disagreement_score":0.00053459045,"about_ca_system_score_codex":0.00016104842,"about_ca_system_score_gemma":0.00014425463,"threshold_uncertainty_score":0.0019513965},"labels":[],"label_agreement":null},{"id":"W4321458516","doi":"10.3390/s23042352","title":"Characterization of the Kinetyx SI Wireless Pressure-Measuring Insole during Benchtop Testing and Running Gait","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Victoria","funders":"Mitacs; University of Victoria","keywords":"Mean squared error; Correlation coefficient; Reproducibility; Intraclass correlation; Pearson product-moment correlation coefficient; Linearity; Treadmill; Biomedical engineering; Mathematics; Pressure sensor; Pressure measurement; Materials science; Statistics; Medicine; Physics; Physical therapy; Electrical engineering; Engineering","score_opus":0.01978052094841771,"score_gpt":0.18887563700171997,"score_spread":0.16909511605330227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321458516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97333956,0.00018247886,0.02540404,0.000038663635,0.00002404203,0.00006401823,0.00025083308,0.00011870299,0.0005777598],"genre_scores_gemma":[0.98749393,0.000113943956,0.01138036,0.00004024481,0.000012744572,0.000050635044,0.0002976756,0.000026520256,0.00058401516],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924314,0.00010907632,0.00006745141,0.00015974497,0.00037673078,0.000043869622],"domain_scores_gemma":[0.9984384,0.00041779515,0.0002996522,0.00014347676,0.00064946787,0.0000510436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000748663,0.0003010305,0.0003190314,0.00036055828,0.00009883405,0.00041217115,0.0005380025,0.00028221696,0.00062882],"category_scores_gemma":[0.0032838036,0.00017752037,0.00013205041,0.00041623868,0.00023259522,0.00030594514,0.00025286377,0.00017594795,0.00018984155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008173844,0.00018973996,0.09066053,0.0005663795,0.00011152343,0.00032164835,0.0004718204,0.002699036,0.79727614,0.00014861193,0.00058609736,0.106151186],"study_design_scores_gemma":[0.00006167711,0.004977705,0.77095443,0.00006942252,0.00026310794,0.0022858256,0.0005530406,0.029739304,0.18785134,0.000121826146,0.0030649868,0.000057255646],"about_ca_topic_score_codex":0.0012700417,"about_ca_topic_score_gemma":0.0026515094,"teacher_disagreement_score":0.0012700417,"about_ca_system_score_codex":0.0001623816,"about_ca_system_score_gemma":0.00022067894,"threshold_uncertainty_score":0.0039593577},"labels":[],"label_agreement":null},{"id":"W4321599425","doi":"10.3390/s23052432","title":"Towards a Smart Environment: Optimization of WLAN Technologies to Enable Concurrent Smart Services","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Zarqa University; Al-Ahliyya Amman University; Trent University; Nottingham Trent University","keywords":"Computer science; Computer network; Voice over IP; Quality of service; Service (business); Context (archaeology); The Internet; Application layer; Protocol (science); Session Initiation Protocol; Server; World Wide Web; Software engineering","score_opus":0.016193542699623444,"score_gpt":0.2432407806698237,"score_spread":0.22704723797020024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321599425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12731476,0.0004469844,0.86575735,0.00017076562,0.000024961048,0.00008699882,0.000030882242,0.00038613152,0.0057811267],"genre_scores_gemma":[0.83193797,0.00029652045,0.16596943,0.000084939544,0.00001963346,0.000079597,0.000044433236,0.00007431089,0.0014931891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918276,0.00026871494,0.000023944138,0.000121514655,0.00028226047,0.00012073426],"domain_scores_gemma":[0.9993793,0.00027998383,0.00013337252,0.00005852142,0.000102651975,0.00004605659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011448567,0.00084300817,0.0004837153,0.0007573427,0.00029539867,0.0011630844,0.000668862,0.00061002286,0.0009493084],"category_scores_gemma":[0.002011214,0.0003554071,0.00032919162,0.0006610653,0.00051242515,0.0021551298,0.0010698497,0.0005321365,0.00029716385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002133143,0.00027989622,0.004646004,0.00012928867,0.000062677165,0.00016074622,0.00014493986,0.76858157,0.063375965,0.022837942,0.00056326826,0.13900444],"study_design_scores_gemma":[0.00003078699,0.0003945902,0.0016240424,0.00001562703,0.000032052598,0.000103380255,0.0001599692,0.9701607,0.016002452,0.008243785,0.003203454,0.00002911478],"about_ca_topic_score_codex":0.000843838,"about_ca_topic_score_gemma":0.0013662805,"teacher_disagreement_score":0.0011630844,"about_ca_system_score_codex":0.00060622214,"about_ca_system_score_gemma":0.0005795985,"threshold_uncertainty_score":0.00605464},"labels":[],"label_agreement":null},{"id":"W4321599449","doi":"10.3390/s23052446","title":"Characterizing Ambient Seismic Noise in an Urban Park Environment","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Seismometer; Passive seismic; Ambient noise level; Seismic noise; Seismology; Noise (video); Geology; Remote sensing; Computer science","score_opus":0.015915414560065524,"score_gpt":0.20641340812941458,"score_spread":0.19049799356934904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321599449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7926207,0.00014924585,0.20416689,0.000027784223,0.00001994803,0.00005871921,0.0003747152,0.0005189616,0.0020630714],"genre_scores_gemma":[0.9257221,0.00021157939,0.07202591,0.000016763353,0.000025962205,0.00006148387,0.0006075779,0.000075185904,0.0012533823],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997023,0.000046223264,0.000011665379,0.00008035544,0.00012304288,0.00003643535],"domain_scores_gemma":[0.9997571,0.00006135579,0.000041513274,0.000026469253,0.00008848751,0.00002511437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018964724,0.00041761645,0.00029665825,0.00091346755,0.00016133922,0.0005354875,0.0003268297,0.00027411836,0.0007178597],"category_scores_gemma":[0.00052452786,0.000120455144,0.00014539073,0.00066212006,0.00019021027,0.0003478402,0.0004803432,0.0001456984,0.00037109718],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010713763,0.00033303324,0.13578683,0.00060241675,0.00020293577,0.0021393222,0.0011845389,0.08228747,0.36710045,0.0011362577,0.0012148547,0.40694058],"study_design_scores_gemma":[0.00008261202,0.0013107322,0.42603582,0.00010080289,0.00033065426,0.002782575,0.0021286304,0.3887996,0.16341127,0.0019754777,0.012916452,0.000125335],"about_ca_topic_score_codex":0.0015398088,"about_ca_topic_score_gemma":0.0041889558,"teacher_disagreement_score":0.0015398088,"about_ca_system_score_codex":0.0001100181,"about_ca_system_score_gemma":0.00020228098,"threshold_uncertainty_score":0.0030617118},"labels":[],"label_agreement":null},{"id":"W4321599502","doi":"10.3390/s23052430","title":"In-Bed Posture Classification Using Deep Neural Network","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Mitacs","keywords":"Artificial neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Engineering","score_opus":0.06269272518478532,"score_gpt":0.3314686701493391,"score_spread":0.2687759449645538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321599502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6965566,0.004453797,0.28243798,0.00065335067,0.00076725014,0.00021310053,0.0036653355,0.0055312174,0.0057213004],"genre_scores_gemma":[0.9634084,0.0006724413,0.028865116,0.00027246174,0.00008884419,0.00009411806,0.003049824,0.00004085268,0.003507931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997527,0.000033749737,0.000015469199,0.00007150662,0.00004657046,0.0000799892],"domain_scores_gemma":[0.99982387,0.000046226418,0.000026156187,0.000018060742,0.000061065446,0.00002474553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028792294,0.0013104455,0.00073168706,0.0007345498,0.0002099049,0.00048633627,0.0007501353,0.0006533954,0.0011913673],"category_scores_gemma":[0.00062534463,0.0003647759,0.0006947105,0.00051047804,0.00015523765,0.00034973622,0.0005132562,0.0006935066,0.0005479559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011629319,0.0011452833,0.034595765,0.00021716877,0.00037309196,0.0006058035,0.00012140764,0.20894945,0.02453913,0.00042286896,0.0116254985,0.7162417],"study_design_scores_gemma":[0.000016275362,0.00015599803,0.008041372,0.000027142281,0.00003625664,0.000073358635,0.000038820097,0.9873095,0.003350041,0.00033450002,0.00060085824,0.000015835561],"about_ca_topic_score_codex":0.011082577,"about_ca_topic_score_gemma":0.017071294,"teacher_disagreement_score":0.011082577,"about_ca_system_score_codex":0.00062297937,"about_ca_system_score_gemma":0.00048268732,"threshold_uncertainty_score":0.022036135},"labels":[],"label_agreement":null},{"id":"W4321788223","doi":"10.3390/s23052495","title":"Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Recommender system; Matrix decomposition; Computer science; Collaborative filtering; Bayesian network; Social network (sociolinguistics); Domain (mathematical analysis); Information retrieval; Cold start (automotive); Machine learning; Artificial intelligence; Social media; World Wide Web","score_opus":0.02581018177965112,"score_gpt":0.2532821163960565,"score_spread":0.2274719346164054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321788223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011566166,0.0009823865,0.98302066,0.0003860361,0.000077380406,0.0000733343,0.0005086881,0.00040999244,0.0029752513],"genre_scores_gemma":[0.6508614,0.003155998,0.3222014,0.00042584236,0.0004283326,0.0006783686,0.0020900792,0.00014337101,0.020015148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987092,0.000423944,0.00006348853,0.00040377342,0.0002777914,0.00012191244],"domain_scores_gemma":[0.9982431,0.0010308999,0.00017667662,0.000119100216,0.00037561756,0.00005459361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017650913,0.0013258625,0.0017703486,0.0013834238,0.0005121822,0.0012405596,0.0023689778,0.001839953,0.0034709922],"category_scores_gemma":[0.0043567503,0.0006989697,0.0014447826,0.0021651397,0.0005784395,0.00230085,0.0007239046,0.0014879367,0.0019046266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009897826,0.00007788744,0.0016191214,0.00013403666,0.00012461426,0.00012230601,0.000107890744,0.9060549,0.0011233267,0.023984743,0.0029535154,0.06359865],"study_design_scores_gemma":[0.000004588143,0.0000127137055,0.00010975762,0.000004556382,0.000011579802,0.000015849842,0.000003644393,0.9971213,0.00006277475,0.0021445889,0.0005032748,0.0000054793304],"about_ca_topic_score_codex":0.03317109,"about_ca_topic_score_gemma":0.028886668,"teacher_disagreement_score":0.03317109,"about_ca_system_score_codex":0.0011263367,"about_ca_system_score_gemma":0.0010999622,"threshold_uncertainty_score":0.065956},"labels":[],"label_agreement":null},{"id":"W4321789369","doi":"10.3390/s23052506","title":"Acceptability, Feasibility, and Effectiveness of Immersive Virtual Technologies to Promote Exercise in Older Adults: A Systematic Review and Meta-Analysis","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université Laval","funders":"Mitacs","keywords":"CINAHL; Scopus; Meta-analysis; Virtual reality; Context (archaeology); Psychological intervention; Medicine; MEDLINE; Physical therapy; Population; Psychology; Physical medicine and rehabilitation; Computer science; Nursing; Artificial intelligence","score_opus":0.0732957093281152,"score_gpt":0.36598784852579197,"score_spread":0.2926921391976768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321789369","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029091593,0.9955082,0.00037679626,0.0001447108,0.000104799816,0.0005331849,0.00021294878,0.000010893523,0.00019934887],"genre_scores_gemma":[0.079059154,0.9143621,0.00261156,0.00075496826,0.00022680128,0.002440449,0.000374661,0.00001639123,0.00015394502],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.97900635,0.009024297,0.0075350185,0.0013164856,0.0027708712,0.0003470006],"domain_scores_gemma":[0.9356273,0.05138906,0.008320529,0.0011047531,0.0031727608,0.00038555387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02924246,0.002531847,0.017629096,0.009202177,0.00072594016,0.004384474,0.0022995938,0.002640368,0.002810681],"category_scores_gemma":[0.0782879,0.001572227,0.027648669,0.007980128,0.0011209933,0.0025373455,0.0018230816,0.0017072286,0.00021735783],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009027827,0.000047184498,0.0017289446,0.817714,0.15882394,0.00007378276,0.00015274166,0.00016501143,0.00018191479,0.00011923654,0.00027310103,0.019817416],"study_design_scores_gemma":[0.0007151847,0.0006208127,0.004961275,0.24288803,0.7467351,0.0001776938,0.00017011784,0.0001519029,0.00028847408,0.00022559486,0.0030218605,0.000043979533],"about_ca_topic_score_codex":0.004106876,"about_ca_topic_score_gemma":0.00915269,"teacher_disagreement_score":0.02924246,"about_ca_system_score_codex":0.0032604018,"about_ca_system_score_gemma":0.006026957,"threshold_uncertainty_score":0.15465075},"labels":[],"label_agreement":null},{"id":"W4321789582","doi":"10.3390/s23052516","title":"Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Shenzhen Science and Technology Innovation Program; National Natural Science Foundation of China","keywords":"Tone mapping; Hue; High dynamic range; Computer science; Computer vision; Gamma correction; Artificial intelligence; Operator (biology); Tone (literature); Compensation (psychology); Dynamic range; High-dynamic-range imaging; Reduction (mathematics); Range (aeronautics); Image (mathematics); Scale (ratio); Mathematics; Engineering; Geography","score_opus":0.01933495047820745,"score_gpt":0.2868652313833108,"score_spread":0.2675302809051034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321789582","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0716332,0.00011401008,0.92621523,0.000059894544,0.00005695502,0.00006095598,0.00001645726,0.00039580872,0.0014474913],"genre_scores_gemma":[0.43341184,0.00020769422,0.56351745,0.000100450925,0.00004914715,0.00006573261,0.00007642982,0.00006784584,0.002503416],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997811,0.0000354409,0.00001294192,0.000043376698,0.00010850417,0.000018694074],"domain_scores_gemma":[0.99966776,0.000094956464,0.000040418738,0.000063571344,0.00010527947,0.000028074242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003726054,0.00032053379,0.00023183622,0.00030306526,0.0001398806,0.00042324376,0.00045786728,0.00032830873,0.0013386747],"category_scores_gemma":[0.0009440212,0.000095192176,0.0003263191,0.00025738333,0.0002631254,0.0006567705,0.00041880624,0.00049156207,0.00024998837],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049350323,0.00012280824,0.0020817663,0.00021503534,0.000040610787,0.0002371883,0.00022222052,0.020629007,0.50319916,0.009411505,0.0014491505,0.461898],"study_design_scores_gemma":[0.000055430068,0.00077996054,0.005019442,0.000022191078,0.00005953134,0.0014284627,0.00012968906,0.76396185,0.21440226,0.003101654,0.010979495,0.00006007445],"about_ca_topic_score_codex":0.00033184444,"about_ca_topic_score_gemma":0.00039572624,"teacher_disagreement_score":0.0013386747,"about_ca_system_score_codex":0.00012652947,"about_ca_system_score_gemma":0.00018176352,"threshold_uncertainty_score":0.004478276},"labels":[],"label_agreement":null},{"id":"W4322502968","doi":"10.3390/s23052590","title":"Generation of Mixed-OAM-Carrying Waves Using Huygens’ Metasurface for Mm-Wave Applications","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Orbital Angular Momentum in Optics","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Physics; Optics; Angular momentum; Antenna (radio); Position (finance); Phase (matter); Aperture (computer memory); Computer science; Topology (electrical circuits); Acoustics; Telecommunications; Engineering; Electrical engineering","score_opus":0.11005156514291975,"score_gpt":0.30873037255727553,"score_spread":0.19867880741435578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322502968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83493924,0.00068484165,0.1559711,0.0002911901,0.00014167889,0.000038891423,0.0001153037,0.00044648827,0.0073712994],"genre_scores_gemma":[0.929992,0.00027826775,0.067899324,0.000044483026,0.0000149670395,0.000025173496,0.000060869956,0.00003372572,0.001651101],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999083,0.0000097236725,0.000004349077,0.000010140705,0.00005011501,0.000017474207],"domain_scores_gemma":[0.9998486,0.00003305928,0.000052693333,0.000021300246,0.000031187446,0.0000131251045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014328283,0.00027911857,0.00019012684,0.00018491164,0.0000845782,0.00035102456,0.0002685156,0.00037688392,0.0006162572],"category_scores_gemma":[0.00022721771,0.00014466526,0.00021775257,0.0002143751,0.00026931992,0.00040856935,0.0003689915,0.00030226656,0.0002123056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003168621,0.000016977785,0.00029928586,0.00006647591,0.00000817495,0.00008069302,0.000074246076,0.0025935492,0.98085624,0.0036196727,0.00028003342,0.012072891],"study_design_scores_gemma":[0.000020268772,0.00017311017,0.00065366115,0.0000095435225,0.000009616667,0.00017261921,0.00007631517,0.05819093,0.93361104,0.00093477446,0.006131125,0.00001704856],"about_ca_topic_score_codex":0.00009650796,"about_ca_topic_score_gemma":0.00019746121,"teacher_disagreement_score":0.0006162572,"about_ca_system_score_codex":0.0002586813,"about_ca_system_score_gemma":0.00012912576,"threshold_uncertainty_score":0.0020615458},"labels":[],"label_agreement":null},{"id":"W4322619969","doi":"10.3390/s23052621","title":"COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada; University of Waterloo","funders":"National Research Council Canada","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Pandemic; Artificial intelligence; Deep learning; Health care; Telemedicine; Point of care; Artificial neural network; Medical physics; Medicine; Disease; Pathology; Infectious disease (medical specialty)","score_opus":0.09457917473892492,"score_gpt":0.3867877371406505,"score_spread":0.2922085624017256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322619969","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50735885,0.0034310073,0.46177477,0.0040372303,0.0006674905,0.00038146393,0.006389672,0.006884527,0.009075032],"genre_scores_gemma":[0.92511696,0.00045818597,0.058936786,0.001220018,0.000108069675,0.00022803723,0.007929665,0.00012283245,0.005879349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970526,0.00006913899,0.000013911751,0.000104431616,0.00004845127,0.000058731304],"domain_scores_gemma":[0.99945146,0.000277192,0.00006121012,0.000049628117,0.00011017695,0.000050236926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063606934,0.0012647616,0.00061910774,0.00062940246,0.00038227096,0.00054413103,0.0014372249,0.001638892,0.001573405],"category_scores_gemma":[0.002374591,0.00039233433,0.0006723878,0.00030544997,0.00048200315,0.00082195626,0.000911036,0.0011848012,0.00034226882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008412748,0.00043992716,0.014041816,0.00030527462,0.00031458185,0.00078132196,0.00013912815,0.7844163,0.009922169,0.0033551895,0.01920594,0.16623704],"study_design_scores_gemma":[0.000017796308,0.0000900768,0.0008416568,0.000012858763,0.000016643346,0.000077951634,0.000013745915,0.9947402,0.0014718602,0.0019918485,0.00071355054,0.000011970891],"about_ca_topic_score_codex":0.010326301,"about_ca_topic_score_gemma":0.015029699,"teacher_disagreement_score":0.010326301,"about_ca_system_score_codex":0.0011247692,"about_ca_system_score_gemma":0.00080684706,"threshold_uncertainty_score":0.02053237},"labels":[],"label_agreement":null},{"id":"W4322743804","doi":"10.3390/s23052673","title":"A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Assisted living; Scalability; Proof of concept; Computer science; Assisted Living Facility; Interface (matter); Independent living; Augmented reality; Human–computer interaction; Mode (computer interface); Embedded system; Operating system; Gerontology; Medicine","score_opus":0.027266247957225234,"score_gpt":0.2766716667810606,"score_spread":0.24940541882383535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322743804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27751356,0.0014762164,0.6962671,0.0005048905,0.00042992807,0.00068338134,0.0008626468,0.008169377,0.014092945],"genre_scores_gemma":[0.87733704,0.00038164583,0.11596453,0.0003041345,0.000046073423,0.00033227087,0.000399173,0.00003120375,0.005203922],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998548,0.000024216706,0.000016326974,0.000043449912,0.000046278,0.000014924872],"domain_scores_gemma":[0.99989843,0.000019467463,0.00001083728,0.000014013037,0.00003966812,0.000017565411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019440899,0.00036644004,0.0003746786,0.00024761315,0.00024598578,0.0003544252,0.0006613075,0.00043260818,0.0019275546],"category_scores_gemma":[0.00033125558,0.000119503384,0.00023976272,0.00013980754,0.00010316467,0.000477031,0.00052237744,0.00026479244,0.00051593874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001458753,0.0011693637,0.019855177,0.0014627372,0.00030460197,0.0030681086,0.0014053873,0.019673936,0.29595485,0.009305681,0.020926934,0.6254145],"study_design_scores_gemma":[0.00037921083,0.003500144,0.03726884,0.00030554924,0.0006191631,0.004661292,0.00075540756,0.7252388,0.13609514,0.0059109563,0.085001305,0.00026422713],"about_ca_topic_score_codex":0.00065357325,"about_ca_topic_score_gemma":0.0010157702,"teacher_disagreement_score":0.0019275546,"about_ca_system_score_codex":0.00012898547,"about_ca_system_score_gemma":0.00026931992,"threshold_uncertainty_score":0.0064483285},"labels":[],"label_agreement":null},{"id":"W4322743857","doi":"10.3390/s23052677","title":"UWB Frequency-Selective Surface Absorber Based on Graphene Featuring Wide-Angle Stability","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Antenna and Metasurface Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Broadband; Materials science; Resonator; Tunable metamaterials; Wideband; Graphene; Bandwidth (computing); Polarization (electrochemistry); Impedance matching; Electrical impedance; Optics; Metamaterial absorber; Oblique case; Absorption (acoustics); Acoustics; Optoelectronics; Computer science; Engineering; Physics; Telecommunications; Electrical engineering; Metamaterial; Composite material; Nanotechnology","score_opus":0.01851607171730132,"score_gpt":0.2283488522428735,"score_spread":0.20983278052557217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322743857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90618443,0.0023051198,0.08395652,0.0003339947,0.00019231309,0.000032750097,0.000097027085,0.00079671585,0.0061012064],"genre_scores_gemma":[0.97118485,0.0005386861,0.025002746,0.00008565159,0.000032519198,0.000017947848,0.000051604715,0.00004056291,0.0030453943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998826,0.000011831129,0.0000041084404,0.000028220735,0.00004986391,0.000023438623],"domain_scores_gemma":[0.9998704,0.000029919907,0.000047136302,0.000017681625,0.000024423296,0.000010449455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011167641,0.0003582117,0.00026419957,0.00036712186,0.00017569598,0.00029298794,0.0003918923,0.0005997393,0.00057667657],"category_scores_gemma":[0.00020407254,0.00014497002,0.0003186587,0.00026058988,0.00025942177,0.00046129263,0.00028751613,0.0003355775,0.00032588973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029582652,0.0000059516738,0.00007033754,0.00003698156,0.000005126247,0.00006936139,0.00001844278,0.00018892613,0.99616736,0.00035718118,0.00007442903,0.0029763593],"study_design_scores_gemma":[0.000008972059,0.00016543514,0.0006185416,0.0000064714095,0.000030506817,0.00036312328,0.000023938293,0.008574772,0.9860692,0.00019746683,0.003920498,0.000020987234],"about_ca_topic_score_codex":0.00015716463,"about_ca_topic_score_gemma":0.0003388586,"teacher_disagreement_score":0.0005997393,"about_ca_system_score_codex":0.00016607267,"about_ca_system_score_gemma":0.00007908078,"threshold_uncertainty_score":0.0019291639},"labels":[],"label_agreement":null},{"id":"W4322772100","doi":"10.3390/s23052697","title":"Evaluation of GAN-Based Model for Adversarial Training","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Training (meteorology); Computer science; Training set; Artificial intelligence; Engineering; Physics","score_opus":0.1395842735723996,"score_gpt":0.3546751449782767,"score_spread":0.21509087140587707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322772100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23815908,0.0032371883,0.7328173,0.0013975395,0.0005234529,0.0003601757,0.00045911016,0.0026525266,0.020393632],"genre_scores_gemma":[0.9506383,0.0005812429,0.04580206,0.0002167224,0.000031859523,0.00012290046,0.0003800897,0.000118834636,0.0021079516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998623,0.0005356197,0.000053776697,0.00017592355,0.00048133894,0.00013033394],"domain_scores_gemma":[0.99724627,0.0016785221,0.00017065126,0.00032084287,0.00046837368,0.00011536175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025709257,0.001223175,0.0007570985,0.000550032,0.00028849888,0.00078359403,0.0011016857,0.0008867338,0.0017653416],"category_scores_gemma":[0.0059704203,0.00022933783,0.00047642709,0.00031160115,0.0008241817,0.0011176888,0.0011098266,0.0015220093,0.00036307066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027181703,0.000080012054,0.0012161264,0.00013652108,0.000060538816,0.00007747538,0.000029851992,0.9548668,0.0044302745,0.0059787575,0.0017589604,0.03109282],"study_design_scores_gemma":[0.000004789216,0.00006999489,0.00014920454,0.000009090553,0.000005743048,0.000024648669,0.0000053144813,0.995915,0.0027720646,0.000742034,0.00029677735,0.0000053780086],"about_ca_topic_score_codex":0.0028789095,"about_ca_topic_score_gemma":0.0021377727,"teacher_disagreement_score":0.0028789095,"about_ca_system_score_codex":0.0011605778,"about_ca_system_score_gemma":0.00068523694,"threshold_uncertainty_score":0.013596535},"labels":[],"label_agreement":null},{"id":"W4323044453","doi":"10.3390/s23052753","title":"Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation","keywords":"MNIST database; Qubit; Computer science; Artificial neural network; Quantum; Quantum computer; Quantum gate; Benchmark (surveying); Quantum entanglement; Algorithm; Quantum circuit; Contextual image classification; Artificial intelligence; Computer engineering; Image (mathematics); Pattern recognition (psychology); Quantum error correction; Physics; Quantum mechanics","score_opus":0.03867953353123705,"score_gpt":0.266905229382246,"score_spread":0.22822569585100894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323044453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12861821,0.0013442768,0.8609558,0.0016212492,0.00016199486,0.0000954906,0.0003156124,0.000919826,0.0059675314],"genre_scores_gemma":[0.9316681,0.0002998823,0.060714502,0.00028909673,0.000097655466,0.00014105799,0.00032171048,0.000049547565,0.006418579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971706,0.00005824383,0.000012985771,0.0000963681,0.00006737035,0.00004801335],"domain_scores_gemma":[0.99950254,0.0001859098,0.00006799491,0.00007162365,0.000141228,0.000030726398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075379806,0.00061920687,0.0009196229,0.0006122438,0.00041636688,0.00080186385,0.002178269,0.0014334079,0.0019484691],"category_scores_gemma":[0.0015556706,0.00034883764,0.000726555,0.000543737,0.0006436779,0.0016060163,0.00085501996,0.0017073886,0.00030874842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001173621,0.00007413698,0.0013494525,0.00006502339,0.00007070948,0.00011921267,0.000052594107,0.9089893,0.0033645306,0.019190254,0.0022026068,0.06440474],"study_design_scores_gemma":[0.0000013920898,0.0000030475096,0.000043762255,9.396973e-7,0.0000024002193,0.00000416986,7.5303655e-7,0.9986663,0.00013184806,0.001092058,0.000051831812,0.0000014032677],"about_ca_topic_score_codex":0.0076198583,"about_ca_topic_score_gemma":0.005855113,"teacher_disagreement_score":0.0076198583,"about_ca_system_score_codex":0.0015252496,"about_ca_system_score_gemma":0.00088020146,"threshold_uncertainty_score":0.015151024},"labels":[],"label_agreement":null},{"id":"W4323044481","doi":"10.3390/s23052736","title":"Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Calgary; University of Victoria","funders":"","keywords":"Ground reaction force; Algorithm; Treadmill; Gait; Foot (prosody); Kinematics; Force platform; Foot pressure; Mathematics; Simulation; Contact area; Gait analysis; Biomechanics; Pressure measurement; Contact force; Plantar pressure; Engineering; Pressure sensor; Materials science; Physics; Physical medicine and rehabilitation; Physical therapy; Medicine; Mechanical engineering","score_opus":0.03491107904915748,"score_gpt":0.2811048686028307,"score_spread":0.24619378955367321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323044481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97221655,0.00020057682,0.026250688,0.000027677528,0.000043399876,0.0001606216,0.00020375119,0.0003739133,0.0005227872],"genre_scores_gemma":[0.95253825,0.00015840013,0.045569494,0.000032177864,0.000020186733,0.00019440171,0.0006640272,0.00009458232,0.000728553],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986846,0.00033563757,0.00019585091,0.0003457838,0.00029504744,0.00014310842],"domain_scores_gemma":[0.9940102,0.0031749343,0.0003731021,0.0002973739,0.001953551,0.00019082561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026616568,0.0010352781,0.0006647301,0.0011647808,0.00025423832,0.00074307167,0.00052838546,0.000845859,0.0006322318],"category_scores_gemma":[0.009130192,0.00024993977,0.00040936857,0.0005454809,0.00025080677,0.0005868918,0.00048042386,0.00034280418,0.0002816635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020673431,0.0036019662,0.1649707,0.0012746869,0.0013798908,0.00029300858,0.00087654416,0.09305607,0.16496803,0.0004977741,0.0014270161,0.5469809],"study_design_scores_gemma":[0.0009516445,0.018459233,0.4063809,0.00011326916,0.0010945279,0.00052309484,0.00053885055,0.4510954,0.11876828,0.0004095346,0.0014665243,0.00019870211],"about_ca_topic_score_codex":0.0021288828,"about_ca_topic_score_gemma":0.0020606047,"teacher_disagreement_score":0.0026616568,"about_ca_system_score_codex":0.0002232871,"about_ca_system_score_gemma":0.0003746747,"threshold_uncertainty_score":0.014076352},"labels":[],"label_agreement":null},{"id":"W4323314808","doi":"10.3390/s23052846","title":"An Adaptive Pedaling Assistive Device for Asymmetric Torque Assistant in Cycling","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cycling; Torque; Assistive device; Computer science; Simulation; Physical medicine and rehabilitation; Engineering; Physics; Medicine","score_opus":0.04654834596131373,"score_gpt":0.32063487005205515,"score_spread":0.2740865240907414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323314808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85806215,0.0014244837,0.13514972,0.00018023673,0.00023679766,0.00038364567,0.0002656739,0.00072149304,0.0035757702],"genre_scores_gemma":[0.9708712,0.00030013904,0.02589482,0.000108676824,0.000026800715,0.00011585105,0.00010327439,0.0000138210835,0.002565293],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982136,0.000027191889,0.000019674397,0.000039695187,0.00007579728,0.000016282547],"domain_scores_gemma":[0.9998721,0.00003596934,0.000029123361,0.000014802445,0.000036164398,0.000011874695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019306455,0.00033009044,0.00023640883,0.00026354997,0.00011553388,0.00021630613,0.00065437844,0.00038778965,0.0012580551],"category_scores_gemma":[0.00039721368,0.00013966803,0.00013986714,0.00012303154,0.00015070358,0.00025349646,0.00031272284,0.00014195405,0.00031123718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006152777,0.00033902423,0.002838262,0.0005436846,0.000023115772,0.0002895978,0.000102422004,0.0006810493,0.8805617,0.00019315653,0.00059945625,0.11321327],"study_design_scores_gemma":[0.00042755026,0.007652818,0.12538736,0.00017817796,0.0002568621,0.0055988445,0.0001916799,0.07887144,0.7582342,0.0003860549,0.022675758,0.00013929683],"about_ca_topic_score_codex":0.00028046916,"about_ca_topic_score_gemma":0.00048994186,"teacher_disagreement_score":0.0012580551,"about_ca_system_score_codex":0.000079336845,"about_ca_system_score_gemma":0.000118107346,"threshold_uncertainty_score":0.0042085648},"labels":[],"label_agreement":null},{"id":"W4323314998","doi":"10.3390/s23052832","title":"Characterizing Cold Days and Spells and Their Relationship with Cold-Related Mortality in Bangladesh","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cold war; Common cold; Biology; Political science; Immunology","score_opus":0.06111746342486484,"score_gpt":0.28439841616606304,"score_spread":0.2232809527411982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323314998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964251,0.0003096367,0.00019499658,0.00003998154,0.000006050932,0.000009687822,0.0017347384,0.000006381916,0.0012734045],"genre_scores_gemma":[0.9984459,0.00017112984,0.00013156026,0.000008160535,0.0000042014726,0.000013027199,0.00091800425,0.0000015573734,0.00030643685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997116,0.00005387754,0.00006449035,0.000058153593,0.000059021626,0.000052878397],"domain_scores_gemma":[0.9992593,0.000095346506,0.00034654068,0.000038700593,0.00015216436,0.00010800529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040506085,0.00022681811,0.00017371947,0.00081346347,0.0002548912,0.00043998237,0.00019838139,0.00016633082,0.0010927686],"category_scores_gemma":[0.0009832296,0.00010839652,0.00022530332,0.0011882966,0.00017215461,0.00019521907,0.00032778393,0.00021742105,0.00024813804],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056085733,0.00000989518,0.9940758,0.000029202169,0.000041450538,0.0000675505,0.00025533588,0.00014625139,0.00076080544,0.000029816436,0.000201608,0.0043261894],"study_design_scores_gemma":[9.365462e-7,0.000035947312,0.998869,0.000006566087,0.0000073148494,0.000077327684,0.00043034888,0.00012886751,0.00009609298,0.000019924746,0.00032354062,0.000004221971],"about_ca_topic_score_codex":0.0206746,"about_ca_topic_score_gemma":0.03595156,"teacher_disagreement_score":0.0206746,"about_ca_system_score_codex":0.0005733575,"about_ca_system_score_gemma":0.00025410866,"threshold_uncertainty_score":0.04110849},"labels":[],"label_agreement":null},{"id":"W4323317762","doi":"10.3390/s23052819","title":"Sharding-Based Proof-of-Stake Blockchain Protocols: Key Components &amp; Probabilistic Security Analysis","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université de Montréal","funders":"","keywords":"Blockchain; Computer science; Scalability; Proof-of-work system; Cryptocurrency; Byzantine fault tolerance; Probabilistic logic; Latency (audio); Fault tolerance; Distributed computing; Key (lock); Context (archaeology); Proof of concept; Computer security; Computer network; Artificial intelligence","score_opus":0.03706903574644865,"score_gpt":0.29005525426692375,"score_spread":0.2529862185204751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323317762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04323218,0.00040077107,0.9513259,0.00047877512,0.000017869528,0.00009946218,0.00008868142,0.00023712403,0.0041192505],"genre_scores_gemma":[0.9386628,0.00064524997,0.057611898,0.00008705053,0.00004084874,0.00021255371,0.00013697192,0.00007368764,0.0025289725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974656,0.0007933841,0.000095304524,0.0002643556,0.0011057903,0.00027545288],"domain_scores_gemma":[0.988337,0.007527914,0.0015497047,0.0011331841,0.0011708545,0.00028131582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039584967,0.0007014354,0.0010762726,0.0013163271,0.00089590624,0.0018249169,0.0016139154,0.0012106692,0.0028033599],"category_scores_gemma":[0.014446766,0.0007147303,0.0012060306,0.0012999715,0.0029629415,0.0037035772,0.0022237867,0.0020573158,0.00033309072],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102667815,0.000046371435,0.0013116187,0.00011139969,0.000058059173,0.0001332901,0.00017483813,0.6168497,0.0034940995,0.36317682,0.00080033194,0.013740752],"study_design_scores_gemma":[0.000007550914,0.00001617015,0.00012242027,0.000011031112,0.0000071392365,0.000031497206,0.000013261115,0.9439738,0.00067736,0.054794498,0.00033582363,0.000009402498],"about_ca_topic_score_codex":0.0019779736,"about_ca_topic_score_gemma":0.0011104251,"teacher_disagreement_score":0.0039584967,"about_ca_system_score_codex":0.0023717317,"about_ca_system_score_gemma":0.0023218421,"threshold_uncertainty_score":0.02093476},"labels":[],"label_agreement":null},{"id":"W4323318961","doi":"10.3390/s23052859","title":"Natural Intelligence as the Brain of Intelligent Systems","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cognitive radio; Radar; Focus (optics); Cognition; Communications system; Real-time computing; Artificial intelligence; Electronic engineering; Telecommunications; Engineering; Wireless","score_opus":0.05901027608945674,"score_gpt":0.33070379135621425,"score_spread":0.2716935152667575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323318961","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047981236,0.9712343,0.002885717,0.0032994007,0.00053463393,0.000010782879,0.000015232655,0.000035823196,0.02150424],"genre_scores_gemma":[0.015393737,0.9761973,0.0015494212,0.0018089573,0.0011142271,0.00003572639,0.000026392925,0.000009902408,0.0038642443],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996575,0.00011364683,0.000021737173,0.00005442465,0.00012472217,0.00002799017],"domain_scores_gemma":[0.99959165,0.00026887647,0.00002629962,0.000022946115,0.00006580868,0.000024469424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048027962,0.0007018807,0.00057584886,0.0015887341,0.0004997765,0.0019896708,0.000668522,0.0021503845,0.0026707062],"category_scores_gemma":[0.0007385492,0.00020808734,0.0003263882,0.0013548415,0.0027664548,0.0029220309,0.0009336278,0.0022202928,0.0011265508],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002530154,0.000057226705,0.00024199199,0.0063101836,0.000065974215,0.00025082254,0.0005473452,0.0014706359,0.0010317964,0.42183676,0.031676114,0.5364859],"study_design_scores_gemma":[0.0000063187413,0.00003662974,0.00053200335,0.0018809013,0.00001938982,0.00049453636,0.00014255979,0.00039775806,0.0002736384,0.104393244,0.89180267,0.00002037891],"about_ca_topic_score_codex":0.001299326,"about_ca_topic_score_gemma":0.0015812749,"teacher_disagreement_score":0.0026707062,"about_ca_system_score_codex":0.001264946,"about_ca_system_score_gemma":0.0014104727,"threshold_uncertainty_score":0.009177864},"labels":[],"label_agreement":null},{"id":"W4323566421","doi":"10.3390/s23062900","title":"Measurement Uncertainty in Clinical Validation Studies of Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"Measurement uncertainty; Computer science; Process (computing); Clinical decision making; Observational error; Data mining; Uncertainty analysis; Reliability engineering; Simulation; Statistics; Engineering; Mathematics; Medicine","score_opus":0.17021409453308786,"score_gpt":0.42288789342662264,"score_spread":0.2526737988935348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323566421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038796753,0.02091553,0.92313236,0.0077866185,0.0007379492,0.00094301684,0.00035182477,0.00014568339,0.007190273],"genre_scores_gemma":[0.7476705,0.005540261,0.23972811,0.0032930404,0.0006266546,0.0019421576,0.00027258563,0.00011748782,0.0008092262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.62168604,0.3021309,0.017428057,0.011427932,0.045440346,0.0018867744],"domain_scores_gemma":[0.26926118,0.6550415,0.027068678,0.024736958,0.02286121,0.001030485],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3006375,0.0015440572,0.0028781358,0.0027966113,0.0015530842,0.0063612675,0.0038976115,0.005050947,0.0010247822],"category_scores_gemma":[0.5900207,0.0012700303,0.002528795,0.0024756864,0.006929907,0.005937477,0.0054917117,0.0038461448,0.00023961946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026151075,0.0006022329,0.086312376,0.0052027316,0.0041090967,0.0009874966,0.0037660007,0.3373255,0.004373417,0.28315634,0.005727657,0.26582205],"study_design_scores_gemma":[0.00042416964,0.0031351342,0.025227416,0.006466763,0.0018440137,0.001826717,0.001757706,0.36719814,0.01771307,0.54291445,0.030817121,0.00067523855],"about_ca_topic_score_codex":0.004155693,"about_ca_topic_score_gemma":0.0023789892,"teacher_disagreement_score":0.6993625,"about_ca_system_score_codex":0.0049190973,"about_ca_system_score_gemma":0.0066991244,"threshold_uncertainty_score":0.86243844},"labels":[],"label_agreement":null},{"id":"W4323569095","doi":"10.3390/s23062927","title":"Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":150,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"SIGNAL (programming language); Computer science; Noise (video); Noise reduction; Focus (optics); Interference (communication); Electromyography; Subtraction; Signal processing; Artificial intelligence; Channel (broadcasting); Pattern recognition (psychology); Speech recognition; Digital signal processing; Medicine; Telecommunications; Physical medicine and rehabilitation; Computer hardware; Mathematics","score_opus":0.0872818236594036,"score_gpt":0.2990916953125012,"score_spread":0.2118098716530976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323569095","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023764171,0.9980348,0.0006258506,0.00014215686,0.00012797382,0.000010047362,0.000029203138,0.0000134314,0.00077896204],"genre_scores_gemma":[0.0010888965,0.9973907,0.0008564972,0.00008507806,0.00009594912,0.000011941864,0.000043893906,0.000004414909,0.00042273413],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99949396,0.00007648723,0.00011519329,0.000093532326,0.00019321828,0.000027480417],"domain_scores_gemma":[0.99866223,0.0008253739,0.00015654892,0.000032477386,0.00028943742,0.00003392744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011224123,0.001185744,0.0016033204,0.0045467787,0.00028738292,0.0011085491,0.0009921266,0.0012331611,0.0038150612],"category_scores_gemma":[0.0018757918,0.00045038512,0.00092495116,0.0035167246,0.0005062398,0.0015978378,0.00060543255,0.00090638007,0.0021570842],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058901263,0.000086286134,0.00025452152,0.046219263,0.00013447898,0.00015454835,0.00008701881,0.00047942827,0.0032217556,0.0020666276,0.0076377336,0.9395994],"study_design_scores_gemma":[0.000015157552,0.00030345624,0.002919049,0.017117234,0.00061333756,0.002457084,0.00019618055,0.00043985917,0.0040921555,0.0026497764,0.9691196,0.00007704184],"about_ca_topic_score_codex":0.0011871578,"about_ca_topic_score_gemma":0.0016586478,"teacher_disagreement_score":0.0045467787,"about_ca_system_score_codex":0.0003685243,"about_ca_system_score_gemma":0.0011559933,"threshold_uncertainty_score":0.012762606},"labels":[],"label_agreement":null},{"id":"W4324092079","doi":"10.3390/s23063048","title":"A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Whale; Balaenoptera; Endangered species; Minke whale; Beaked whale; Fishery; Computer science; Habitat; Artificial intelligence; Ecology; Biology","score_opus":0.08430286048338473,"score_gpt":0.3532156125027004,"score_spread":0.26891275201931564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324092079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4876644,0.0009862066,0.5038266,0.00065157015,0.00026983084,0.00011973555,0.00023796277,0.0010267113,0.005216965],"genre_scores_gemma":[0.8541235,0.0002960004,0.13837089,0.00016304252,0.00009797233,0.00005944187,0.00036339433,0.00006333036,0.006462448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970204,0.00006260928,0.0000133813355,0.0000823753,0.000097792676,0.000041744974],"domain_scores_gemma":[0.9996463,0.00012489464,0.000026048046,0.00004610287,0.0001276184,0.000028993394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000483701,0.0004655258,0.00032859773,0.0005926255,0.00027653598,0.00043716352,0.00054258306,0.0007161658,0.0005444742],"category_scores_gemma":[0.0008836174,0.00009581966,0.00030096047,0.00026886375,0.00026082052,0.0004035654,0.00044499498,0.00037063882,0.00036597482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003001191,0.00027407715,0.04496666,0.0003519781,0.00022943153,0.0017791511,0.00050770905,0.071171306,0.13541427,0.0023114746,0.0047139893,0.7379798],"study_design_scores_gemma":[0.000018268713,0.00024238226,0.03194094,0.000023201786,0.00008815484,0.0012150661,0.0005464758,0.9223954,0.034779552,0.0015139042,0.007188676,0.000047922378],"about_ca_topic_score_codex":0.0029609292,"about_ca_topic_score_gemma":0.005997444,"teacher_disagreement_score":0.0029609292,"about_ca_system_score_codex":0.0001391443,"about_ca_system_score_gemma":0.00022870916,"threshold_uncertainty_score":0.005887389},"labels":[],"label_agreement":null},{"id":"W4324143150","doi":"10.3390/s23063103","title":"Laser Sintering of CNT/PZT Composite Film","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Selective laser sintering; Sintering; Piezoelectricity; Composite material; Lead zirconate titanate; Ceramic; Laser; Carbon nanotube; Dielectric; Laser power scaling; Composite number; Ferroelectricity; Optoelectronics; Optics","score_opus":0.0144314580923959,"score_gpt":0.22539693272394282,"score_spread":0.2109654746315469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324143150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98666734,0.0015140905,0.0068200706,0.00008528686,0.00016439255,0.000056484023,0.00052053167,0.00027073885,0.003901014],"genre_scores_gemma":[0.98428637,0.0012558454,0.011113782,0.000042463736,0.00002504374,0.00007921507,0.00031862632,0.00007999077,0.0027987459],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991,0.0000025445447,0.0000051084494,0.000032076226,0.000037705784,0.000012635097],"domain_scores_gemma":[0.99991596,0.000026930855,0.000016640328,0.000009170987,0.000023836852,0.000007371345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010018134,0.00027578272,0.0002277302,0.00024030195,0.00017155879,0.00013205444,0.00017713335,0.0002837129,0.0021143549],"category_scores_gemma":[0.00018458805,0.00019499395,0.0001798619,0.00030895483,0.00011375189,0.00022524812,0.00009866577,0.00039164658,0.0003175436],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024495708,0.0000068355857,0.00006650906,0.000076482436,0.0000027551944,0.00008865207,0.00001885581,0.0003723484,0.9976914,0.00008911447,0.0000838221,0.0014786645],"study_design_scores_gemma":[0.0000063326816,0.000051888306,0.0006468162,0.000005215982,0.0000038855824,0.00003951141,0.000011960127,0.0033100161,0.9950294,0.000025411226,0.0008662414,0.0000033513768],"about_ca_topic_score_codex":0.0007066907,"about_ca_topic_score_gemma":0.0020419338,"teacher_disagreement_score":0.0021143549,"about_ca_system_score_codex":0.00017773581,"about_ca_system_score_gemma":0.0001541501,"threshold_uncertainty_score":0.0070732236},"labels":[],"label_agreement":null},{"id":"W4324148957","doi":"10.3390/s23063081","title":"Domain Adaptation Methods for Lab-to-Field Human Context Recognition","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Defense Advanced Research Projects Agency; Worcester Polytechnic Institute","keywords":"Discriminative model; Computer science; Artificial intelligence; Machine learning; Context (archaeology); Domain adaptation; Field (mathematics); Fidelity; Feature learning; Pattern recognition (psychology); Support vector machine; Deep learning; Classifier (UML)","score_opus":0.11872115586070085,"score_gpt":0.38401585735488863,"score_spread":0.26529470149418777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324148957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04804971,0.0016169011,0.9431218,0.00023099661,0.00022884495,0.00014512514,0.0004481,0.003795908,0.0023626096],"genre_scores_gemma":[0.6706228,0.0008636391,0.31945068,0.0005785525,0.00018346566,0.0004011728,0.002405537,0.0002825288,0.005211565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990201,0.0002604401,0.000039057388,0.0004131655,0.00018658701,0.000080583224],"domain_scores_gemma":[0.9990809,0.00024011356,0.00009823071,0.00026590572,0.0002464217,0.00006834666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012865444,0.001244,0.00086191884,0.0007085648,0.00031434835,0.00052154285,0.0015152863,0.00078680774,0.0018442842],"category_scores_gemma":[0.003261161,0.00031864413,0.00084889575,0.0007338183,0.00054101984,0.0013834495,0.0016986026,0.0016911535,0.0014497505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034765288,0.00041953268,0.0035163688,0.00015274635,0.00011831769,0.00012889007,0.00014345265,0.15396559,0.02611444,0.0028496417,0.0081459405,0.8040975],"study_design_scores_gemma":[0.000015966058,0.00015763211,0.0019569255,0.000019741488,0.000017923205,0.0001561763,0.000080580496,0.97852683,0.010179212,0.004933119,0.0039284686,0.000027556269],"about_ca_topic_score_codex":0.0025618856,"about_ca_topic_score_gemma":0.0032110354,"teacher_disagreement_score":0.0025618856,"about_ca_system_score_codex":0.0005788576,"about_ca_system_score_gemma":0.0006267731,"threshold_uncertainty_score":0.0068039894},"labels":[],"label_agreement":null},{"id":"W4324382080","doi":"10.3390/s23063114","title":"Feature-Based Occupancy Map-Merging for Collaborative SLAM","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Occupancy grid mapping; Computer science; Simultaneous localization and mapping; Probabilistic logic; Artificial intelligence; Feature (linguistics); Grid; Occupancy; Merge (version control); Grid reference; Data mining; Inference; Bayesian probability; Transformation (genetics); Robot; Mobile robot; Geography; Engineering","score_opus":0.014019058191370606,"score_gpt":0.23706317261689155,"score_spread":0.22304411442552094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324382080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007912302,0.000042124215,0.9910061,0.0000147702885,0.000012579319,0.000018562736,0.000024163477,0.00053262163,0.00043673866],"genre_scores_gemma":[0.5712978,0.00006924078,0.427268,0.0000328232,0.000027406875,0.00009550279,0.00021117376,0.00013366838,0.000864397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986644,0.00026657572,0.000060049904,0.00025890756,0.0005803883,0.00016980528],"domain_scores_gemma":[0.99910754,0.00028007675,0.000117550626,0.00029250325,0.00016188635,0.00004045283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093376206,0.00064505235,0.0009976836,0.0008897772,0.0006308345,0.0006526841,0.0015574049,0.00068266096,0.0016509002],"category_scores_gemma":[0.0030212726,0.0004287421,0.0008575999,0.0013893811,0.00064776675,0.0018595983,0.0023011796,0.00073851965,0.00046502863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034593578,0.00013399821,0.0017257568,0.00015624664,0.00013640022,0.00019045523,0.00058361475,0.49314103,0.045669768,0.019050373,0.0021276532,0.43673873],"study_design_scores_gemma":[0.000013611036,0.000052896346,0.0006444494,0.0000038622497,0.000015325806,0.00007193361,0.000040928102,0.97789705,0.012766471,0.0066872067,0.0017824237,0.000023836705],"about_ca_topic_score_codex":0.0038236482,"about_ca_topic_score_gemma":0.0034945172,"teacher_disagreement_score":0.0038236482,"about_ca_system_score_codex":0.0006120122,"about_ca_system_score_gemma":0.00074889284,"threshold_uncertainty_score":0.0076027513},"labels":[],"label_agreement":null},{"id":"W4327568718","doi":"10.3390/s23063146","title":"Motion Smoothness-Based Assessment of Surgical Expertise: The Importance of Selecting Proper Metrics","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Jerk; Motion (physics); Smoothness; Logarithm; Computer science; Physical medicine and rehabilitation; Artificial intelligence; Mathematics; Medicine; Physics; Mathematical analysis; Acceleration","score_opus":0.06164462015763054,"score_gpt":0.3704221383450329,"score_spread":0.30877751818740234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327568718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84993935,0.001780462,0.14295565,0.0002722904,0.00007679828,0.00033196207,0.0005427459,0.00023779349,0.003863019],"genre_scores_gemma":[0.96292436,0.00040878268,0.03593817,0.00004433595,0.000032538028,0.00012513588,0.00025490593,0.00002284442,0.0002490115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99862516,0.00049436203,0.00021782871,0.00017661146,0.00040320304,0.000082772975],"domain_scores_gemma":[0.99453133,0.0028669059,0.0011126059,0.00025218344,0.0009059529,0.00033110374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028790792,0.00067282264,0.0005209367,0.0023173431,0.00019217307,0.000736743,0.00034342785,0.00059747545,0.00071811565],"category_scores_gemma":[0.014030825,0.00018150565,0.00025066955,0.00096388796,0.0003883916,0.0009877383,0.0008191855,0.00036107094,0.00023547884],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013449898,0.00039717613,0.5039801,0.0015107596,0.00031223267,0.00025859635,0.0016231117,0.013096667,0.067587085,0.0011403583,0.0013920956,0.40735683],"study_design_scores_gemma":[0.000045401488,0.0016837318,0.9276137,0.00026642944,0.000113232505,0.0008046118,0.001472343,0.052175146,0.0112120835,0.002410433,0.002058614,0.00014431479],"about_ca_topic_score_codex":0.001042888,"about_ca_topic_score_gemma":0.0020808498,"teacher_disagreement_score":0.0028790792,"about_ca_system_score_codex":0.00016147911,"about_ca_system_score_gemma":0.0003827375,"threshold_uncertainty_score":0.015226245},"labels":[],"label_agreement":null},{"id":"W4327700031","doi":"10.3390/s23063205","title":"EDPNet: An Encoding–Decoding Network with Pyramidal Representation for Semantic Image Segmentation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Decoding methods; Encoding (memory); Representation (politics); Computer science; Segmentation; Artificial intelligence; Image (mathematics); Computer vision; Pattern recognition (psychology); Natural language processing; Algorithm","score_opus":0.034309431348626206,"score_gpt":0.3186547016821164,"score_spread":0.2843452703334902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327700031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022965433,0.0007212608,0.9644403,0.00022854534,0.0001427483,0.0001242647,0.00097310107,0.006241787,0.0041625802],"genre_scores_gemma":[0.37228426,0.0009725598,0.60440195,0.0006676533,0.00010615547,0.00034404313,0.006411994,0.00068185304,0.01412952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997367,0.000027775635,0.00001618324,0.000102953745,0.000068102934,0.000048290924],"domain_scores_gemma":[0.99981517,0.000043245833,0.000020657628,0.000046387115,0.000055052868,0.000019531706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034711126,0.0013836714,0.0009073621,0.0009295186,0.00034836473,0.0009469219,0.002230687,0.0012475924,0.0035372288],"category_scores_gemma":[0.0011726929,0.0005508352,0.0007486138,0.0012326004,0.0006255434,0.0023712262,0.0014794742,0.0013724288,0.0012781243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004999347,0.00020708649,0.0011119243,0.00024296468,0.00014945412,0.0002811224,0.000114396425,0.23361321,0.035164505,0.013018819,0.017056292,0.6985403],"study_design_scores_gemma":[0.000024745119,0.000091104084,0.000269732,0.000021819234,0.000034079585,0.00011840652,0.000023441818,0.97327256,0.013446859,0.007936462,0.0047407844,0.000019907493],"about_ca_topic_score_codex":0.007044313,"about_ca_topic_score_gemma":0.009006037,"teacher_disagreement_score":0.007044313,"about_ca_system_score_codex":0.0009540892,"about_ca_system_score_gemma":0.0013480714,"threshold_uncertainty_score":0.014006615},"labels":[],"label_agreement":null},{"id":"W4327954402","doi":"10.3390/s23063271","title":"Disposable Sensor Chips with Molecularly Imprinted Carbon Paste Electrodes for Monitoring Anti-Epileptic Drugs","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Eisai Canada; Eisai","keywords":"Molecularly imprinted polymer; Differential pulse voltammetry; Methacrylic acid; Materials science; Ethylene glycol dimethacrylate; Ethylene glycol; Levetiracetam; Cyclic voltammetry; Nuclear chemistry; Chemical engineering; Chromatography; Monomer; Electrode; Chemistry; Polymer; Electrochemistry; Organic chemistry; Medicine; Epilepsy; Selectivity; Composite material","score_opus":0.005605259316355236,"score_gpt":0.19960267326522108,"score_spread":0.19399741394886583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327954402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47640207,0.021230973,0.4825133,0.0011792012,0.0014657864,0.00094346347,0.0025252043,0.0055635027,0.008176389],"genre_scores_gemma":[0.48788077,0.0057568736,0.4942099,0.0009442217,0.00017988634,0.00078876765,0.0014408828,0.00015290754,0.008645821],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992163,0.00007787449,0.000054943863,0.00021784128,0.00038970736,0.00004324838],"domain_scores_gemma":[0.99948883,0.00019917738,0.000099051205,0.000051223273,0.00013282089,0.000028851726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004051346,0.0007968848,0.00041112927,0.0005647328,0.0001225364,0.0003282167,0.001229561,0.0011620157,0.0010241191],"category_scores_gemma":[0.00085898733,0.0004258235,0.00043232518,0.00045425576,0.00023103632,0.00063132105,0.00024421906,0.0006659758,0.00086347165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034769,0.000034457655,0.00018878508,0.00012779637,0.00001963995,0.00010235944,0.000011220932,0.00017595966,0.9879099,0.00011948009,0.00023675319,0.011038822],"study_design_scores_gemma":[0.0000074020054,0.00014909623,0.0013108655,0.000006358676,0.000026678228,0.0002713027,0.0000070744154,0.0025634826,0.9926905,0.000042401218,0.0029111558,0.00001367754],"about_ca_topic_score_codex":0.00030307568,"about_ca_topic_score_gemma":0.0005945213,"teacher_disagreement_score":0.001229561,"about_ca_system_score_codex":0.0003162849,"about_ca_system_score_gemma":0.00019835019,"threshold_uncertainty_score":0.003426075},"labels":[],"label_agreement":null},{"id":"W4328118448","doi":"10.3390/s23063291","title":"Measures of Maximal Tactile Pressures during a Sustained Grasp Task Using a TactArray Device Have Satisfactory Reliability and Concurrent Validity in People with Stroke","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University Health Network","funders":"University of Newcastle Australia","keywords":"Intraclass correlation; Concurrent validity; Reliability (semiconductor); GRASP; Physical medicine and rehabilitation; Standard error; Standard deviation; Stroke (engine); Physical therapy; Task (project management); Psychology; Statistics; Mathematics; Computer science; Medicine; Reproducibility; Psychometrics; Internal consistency; Engineering","score_opus":0.025799164452085625,"score_gpt":0.23792803840420393,"score_spread":0.2121288739521183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328118448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99668866,0.00031064745,0.0019774588,0.000023608382,0.00001866738,0.000055276607,0.00008014492,0.000024832214,0.00082075055],"genre_scores_gemma":[0.9980426,0.00010310316,0.0015015741,0.00001547349,0.000012974793,0.000047640624,0.000104260114,0.000007163161,0.00016529899],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99474585,0.0012145799,0.0009960856,0.00087158184,0.001954622,0.00021731034],"domain_scores_gemma":[0.9806737,0.008538172,0.004038456,0.0013500719,0.0051025283,0.00029704883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051859403,0.0005182358,0.0008454738,0.0011089151,0.0005356822,0.0009567564,0.0005911869,0.0006776823,0.0006623994],"category_scores_gemma":[0.026380155,0.00049715873,0.000917036,0.0007163201,0.0007748049,0.0007896176,0.0009963124,0.0005132677,0.00028223312],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006592898,0.00022080676,0.95144415,0.00027064956,0.0005856622,0.00015979218,0.0032354493,0.00040296663,0.0056057386,0.00007900794,0.00019946232,0.037136972],"study_design_scores_gemma":[0.000016671898,0.000725824,0.9954094,0.000034546065,0.00009866819,0.00034749773,0.0006641662,0.0010181875,0.0013503678,0.00010004685,0.0002138018,0.00002076031],"about_ca_topic_score_codex":0.0020820918,"about_ca_topic_score_gemma":0.0034776935,"teacher_disagreement_score":0.0051859403,"about_ca_system_score_codex":0.00022980757,"about_ca_system_score_gemma":0.00039568514,"threshold_uncertainty_score":0.027426243},"labels":[],"label_agreement":null},{"id":"W4353070794","doi":"10.3390/s23063346","title":"Binding of SARS-CoV-2 Structural Proteins to Hemoglobin and Myoglobin Studied by SPR and DR LPG","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Hemoglobin structure and function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Bulgarian National Science Fund","keywords":"Myoglobin; Surface plasmon resonance; Chemistry; Biophysics; Binding site; Plasma protein binding; Protein–protein interaction; Hemoglobin; Biochemistry; Biology; Nanotechnology; Materials science; Nanoparticle","score_opus":0.016537959582539157,"score_gpt":0.2807763686605004,"score_spread":0.2642384090779612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353070794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946802,0.00056908216,0.0038005952,0.00006668585,0.000009178005,0.00001199737,0.000061006685,0.000046685025,0.00075447484],"genre_scores_gemma":[0.9910943,0.0004677217,0.0068056877,0.00013449165,0.0000110311385,0.000031779844,0.00014858389,0.000012640619,0.0012937828],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959177,0.00013356534,0.000014215998,0.00007189592,0.000106358384,0.00008217446],"domain_scores_gemma":[0.9998031,0.000091880334,0.000036321413,0.000013130579,0.000032430165,0.000023114348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004054116,0.0004395845,0.0002905786,0.0002830612,0.00017878249,0.00014845282,0.00024624576,0.00047373484,0.00044498313],"category_scores_gemma":[0.00034992665,0.00019008244,0.00027952576,0.00021715234,0.000271264,0.00019089105,0.00022292037,0.00045622152,0.00022539936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007539553,0.000023737568,0.00032837616,0.000024548533,0.0000048809943,0.00003567916,0.000045470522,0.00018256226,0.9984498,0.00005378286,0.00003101317,0.00074487703],"study_design_scores_gemma":[0.00001479068,0.0005868639,0.009132166,0.0000075557673,0.000016025178,0.00022588644,0.00013164246,0.014050832,0.97491986,0.00010622785,0.0007932446,0.000014893527],"about_ca_topic_score_codex":0.0009613155,"about_ca_topic_score_gemma":0.0005875998,"teacher_disagreement_score":0.0009613155,"about_ca_system_score_codex":0.0002000373,"about_ca_system_score_gemma":0.000108279055,"threshold_uncertainty_score":0.0021440983},"labels":[],"label_agreement":null},{"id":"W4353072703","doi":"10.3390/s23063322","title":"Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fibers Embedded in Stretchable Polydimethylsiloxane","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hôpital du Sacré-Cœur de Montréal; École de Technologie Supérieure","funders":"","keywords":"Polydimethylsiloxane; Materials science; Pressure sensor; Optical fiber; Photodiode; Bending; Optoelectronics; Repeatability; Fiber optic sensor; Sensitivity (control systems); Light intensity; Optics; Composite material; Fiber; Electronic engineering; Mechanical engineering","score_opus":0.010990592112262698,"score_gpt":0.22634858983507153,"score_spread":0.21535799772280884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353072703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93102336,0.0008126591,0.06581109,0.00011020024,0.00007487033,0.00006346072,0.00027775823,0.0002770159,0.001549465],"genre_scores_gemma":[0.934989,0.00060078496,0.062127545,0.000050234743,0.000017913528,0.000041844167,0.00012256906,0.000016297174,0.0020336742],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998053,0.000017990047,0.0000098078835,0.000046417765,0.00010186643,0.000018634133],"domain_scores_gemma":[0.9997577,0.000060773247,0.0000942764,0.000028441775,0.000044365323,0.000014462443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018223729,0.00033249077,0.00015256759,0.00022915773,0.00012484012,0.00024730738,0.00044002075,0.0002226387,0.00050134794],"category_scores_gemma":[0.00027964968,0.000182356,0.0001269205,0.00019487296,0.0002478049,0.00038764477,0.00022573335,0.00019289233,0.0001493332],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041179854,0.000019830288,0.0005500933,0.000061882274,0.0000059052336,0.0000646758,0.000017944685,0.0004972049,0.99189293,0.00010768475,0.000039785416,0.006701029],"study_design_scores_gemma":[0.0000053853187,0.00020582044,0.0022039267,0.0000041229323,0.00000941909,0.00012511709,0.000012526483,0.0038190016,0.9927603,0.00003133932,0.0008162146,0.000006739787],"about_ca_topic_score_codex":0.0002541392,"about_ca_topic_score_gemma":0.00041779864,"teacher_disagreement_score":0.00050134794,"about_ca_system_score_codex":0.00016588118,"about_ca_system_score_gemma":0.00013064653,"threshold_uncertainty_score":0.0016771555},"labels":[],"label_agreement":null},{"id":"W4360615356","doi":"10.3390/s23073376","title":"Towards a Multi-Pixel Photon-to-Digital Converter for Time-Bin Quantum Key Distribution","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; CMC Microsystems; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canada First Research Excellence Fund","keywords":"Quantum key distribution; Time-to-digital converter; Bin; Photon; Computer science; Electronic engineering; Chip; Avalanche photodiode; Physics; Detector; Optics; Computer hardware; Engineering; Clock signal; Telecommunications; Jitter","score_opus":0.018246204223303596,"score_gpt":0.2729910662012249,"score_spread":0.2547448619779213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360615356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24458314,0.0016654952,0.73524535,0.0005804109,0.0004333822,0.0005970659,0.00041737026,0.0029723314,0.013505473],"genre_scores_gemma":[0.4301501,0.00029530487,0.56457895,0.00026980182,0.000059404425,0.00014669976,0.00013530838,0.00008463297,0.0042797932],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945253,0.000047630278,0.000021656826,0.00013493399,0.00029269583,0.00005043384],"domain_scores_gemma":[0.9996234,0.00008944334,0.000047989168,0.000069191316,0.00012323586,0.000046723027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005968562,0.00035654803,0.00044388924,0.00041979997,0.0003262295,0.0012530772,0.0016329398,0.00071976066,0.0029422278],"category_scores_gemma":[0.0007392432,0.00036312037,0.00020366005,0.0004443393,0.0004378859,0.0009866557,0.0005596789,0.0009254016,0.00090713514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024840233,0.00024407354,0.00071420736,0.00017146993,0.000027792625,0.00011893131,0.00007547256,0.0029522355,0.9268231,0.012861485,0.0010079923,0.054754846],"study_design_scores_gemma":[0.000041579173,0.00044894312,0.00062900595,0.000016645577,0.00002367565,0.0004218287,0.000017364147,0.05972474,0.92261,0.001062623,0.014969796,0.000033848533],"about_ca_topic_score_codex":0.0003743901,"about_ca_topic_score_gemma":0.00062105176,"teacher_disagreement_score":0.0029422278,"about_ca_system_score_codex":0.00091159344,"about_ca_system_score_gemma":0.0007751882,"threshold_uncertainty_score":0.009842753},"labels":[],"label_agreement":null},{"id":"W4360619057","doi":"10.3390/s23073365","title":"Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"University of Illinois at Urbana-Champaign; Mitacs; Harbin Institute of Technology","keywords":"Interpretability; Random forest; Decision tree; Computer science; Anomaly detection; Artificial intelligence; Structural health monitoring; Machine learning; Data mining; Decision tree learning; Engineering","score_opus":0.025915178649662887,"score_gpt":0.3046708510509915,"score_spread":0.27875567240132865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360619057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033460256,0.00022735645,0.96443486,0.000088932065,0.00003974372,0.000023885686,0.000055726883,0.0012198687,0.00044933325],"genre_scores_gemma":[0.667301,0.00018124419,0.3308616,0.00007613841,0.00006114463,0.00006933116,0.00025819737,0.000057432782,0.0011338566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994998,0.00011110948,0.000031998246,0.00011169422,0.00018635449,0.000058954225],"domain_scores_gemma":[0.9992803,0.00030158588,0.00008918092,0.000067413785,0.00024091826,0.000020638268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000826612,0.00046114848,0.00059778785,0.00088821916,0.00022648381,0.00035533306,0.0008185199,0.00057627604,0.0006919493],"category_scores_gemma":[0.002151773,0.00017016551,0.00046257005,0.0008292624,0.00024887468,0.0006792542,0.00039846997,0.00075973285,0.00033783412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015510258,0.00025954167,0.0037731233,0.00007759416,0.00006340169,0.0001077914,0.000109683155,0.28979355,0.042470418,0.003186779,0.0021364714,0.6578666],"study_design_scores_gemma":[0.0000019004494,0.000025896135,0.0005129761,0.0000020906898,0.000003966857,0.000019014149,0.0000036342194,0.9956454,0.0029052016,0.0006137348,0.00026249196,0.0000037211903],"about_ca_topic_score_codex":0.0023410064,"about_ca_topic_score_gemma":0.002834072,"teacher_disagreement_score":0.0023410064,"about_ca_system_score_codex":0.00035262294,"about_ca_system_score_gemma":0.00044568337,"threshold_uncertainty_score":0.0046548247},"labels":[],"label_agreement":null},{"id":"W4360862185","doi":"10.3390/s23073412","title":"Modeling for Single-Photon Avalanche Diodes: State-of-the-Art and Research Challenges","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Jitter; Photonics; Computer science; Photon; Single-photon avalanche diode; Diode; Popularity; Avalanche photodiode; Electronic engineering; Detector; Physics; Engineering; Electrical engineering; Telecommunications; Optoelectronics; Optics","score_opus":0.24604594521040096,"score_gpt":0.4055719363122256,"score_spread":0.15952599110182464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360862185","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012502009,0.94042563,0.05087811,0.0012273587,0.00044844483,0.000037477494,0.0001653545,0.00012830234,0.005439112],"genre_scores_gemma":[0.009590314,0.9705413,0.016647533,0.00031322014,0.00036747812,0.000076172255,0.00023251848,0.00003618097,0.0021952929],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996184,0.000065059925,0.00003273247,0.000066949586,0.00019312868,0.000023636107],"domain_scores_gemma":[0.999343,0.00031474052,0.00006375392,0.000044954817,0.00021543007,0.000018228966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011621852,0.0013031103,0.0017467602,0.001717925,0.0002941677,0.0017445767,0.0023379615,0.0016462408,0.0020117413],"category_scores_gemma":[0.0011677314,0.0006850199,0.0012436623,0.002001901,0.00063120044,0.0027491145,0.0006430378,0.002088315,0.0018240068],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005054099,0.00015273342,0.000904733,0.019993775,0.00017376077,0.00037959358,0.00023132656,0.06083163,0.010314744,0.15416034,0.023287455,0.7295193],"study_design_scores_gemma":[0.000013686477,0.00013574176,0.0008191313,0.0055091167,0.00017822423,0.0011298466,0.00019486414,0.0978047,0.006510471,0.08117982,0.8063916,0.00013274673],"about_ca_topic_score_codex":0.0021210012,"about_ca_topic_score_gemma":0.001767279,"teacher_disagreement_score":0.0023379615,"about_ca_system_score_codex":0.0009497576,"about_ca_system_score_gemma":0.0014475266,"threshold_uncertainty_score":0.006891012},"labels":[],"label_agreement":null},{"id":"W4361007733","doi":"10.3390/s23073491","title":"Real-Time Safe Landing Zone Identification Based on Airborne LiDAR","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Point cloud; Computer science; Identification (biology); Real-time computing; Software; Landing gear; Data processing; Simulation; Remote sensing; Aerospace engineering; Engineering; Artificial intelligence; Database","score_opus":0.0113588760248503,"score_gpt":0.2409291015905327,"score_spread":0.2295702255656824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361007733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22389068,0.00026697744,0.7699257,0.00012600693,0.00007911185,0.00011887348,0.00022148377,0.0024849463,0.0028862574],"genre_scores_gemma":[0.7573444,0.000110358254,0.24112314,0.000042167994,0.000015933694,0.00006714192,0.00028921186,0.000049289414,0.0009584791],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978906,0.000025175746,0.000009608032,0.00003985903,0.000094293384,0.000041946634],"domain_scores_gemma":[0.999795,0.000041453368,0.000026547212,0.0000191542,0.00009945492,0.000018377357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000224642,0.0005316733,0.0004198526,0.001100408,0.00030883672,0.00059114664,0.00055891287,0.00041181096,0.0010039877],"category_scores_gemma":[0.00052540656,0.0002690553,0.00031975124,0.000437778,0.00014350883,0.0006164153,0.00058570824,0.0002782113,0.0005553984],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050374784,0.00023406152,0.0157609,0.00025506128,0.00007171739,0.00078475673,0.00053168135,0.20936938,0.19050735,0.0019312913,0.0039208606,0.5761292],"study_design_scores_gemma":[0.000019385188,0.0000722124,0.0026612754,0.000015130236,0.000012713603,0.0001261704,0.0001797882,0.97945124,0.015659811,0.00066870893,0.0011132514,0.000020312713],"about_ca_topic_score_codex":0.0032187444,"about_ca_topic_score_gemma":0.0046120645,"teacher_disagreement_score":0.0032187444,"about_ca_system_score_codex":0.00021285264,"about_ca_system_score_gemma":0.000576201,"threshold_uncertainty_score":0.006399989},"labels":[],"label_agreement":null},{"id":"W4361009571","doi":"10.3390/s23073477","title":"Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Robustness (evolution); Control theory (sociology); Computer science; Artificial neural network; Robust control; Multi-agent system; Protocol (science); Controller (irrigation); Consensus; Sliding mode control; Upper and lower bounds; Control system; Control (management); Engineering; Artificial intelligence; Nonlinear system; Mathematics","score_opus":0.06299225844506787,"score_gpt":0.2922016420874506,"score_spread":0.22920938364238272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361009571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019721929,0.0002847274,0.9763632,0.00008500759,0.000060022583,0.00005195998,0.000019860436,0.00015574985,0.0032575321],"genre_scores_gemma":[0.9681674,0.00024050192,0.029275768,0.00006113431,0.000032972257,0.00015189342,0.000050534203,0.000015596499,0.002004218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996393,0.000065202774,0.000026419682,0.00011084408,0.00010984249,0.00004829957],"domain_scores_gemma":[0.9996792,0.00009510925,0.00007307728,0.000020819576,0.00010926107,0.00002249371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069158635,0.0008030579,0.00060310063,0.00026616405,0.0004120629,0.0009016426,0.0013538162,0.0008792124,0.0011303966],"category_scores_gemma":[0.0009946125,0.00028449355,0.00036608684,0.00027749073,0.0005072358,0.00074493565,0.0008134716,0.00067853864,0.0001232551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081479455,0.000037115726,0.000261601,0.0000809114,0.000034444423,0.0001488285,0.000058675923,0.96098983,0.0060691256,0.007423839,0.00037296757,0.024441117],"study_design_scores_gemma":[0.000005215284,0.00002531531,0.000043869648,0.0000018382773,0.0000032657583,0.0000065222766,0.0000038984826,0.99869114,0.0003731768,0.0007139229,0.00013002363,0.0000019116083],"about_ca_topic_score_codex":0.0030558591,"about_ca_topic_score_gemma":0.002090384,"teacher_disagreement_score":0.0030558591,"about_ca_system_score_codex":0.0005840373,"about_ca_system_score_gemma":0.000671553,"threshold_uncertainty_score":0.006076157},"labels":[],"label_agreement":null},{"id":"W4361018933","doi":"10.3390/s23073478","title":"Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung","keywords":"Pseudorange; GNSS applications; Global Positioning System; GLONASS; Precise Point Positioning; Computer science; Real-time computing; Remote sensing; Telecommunications; Geography","score_opus":0.04011106232073771,"score_gpt":0.25156731789783165,"score_spread":0.21145625557709394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361018933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22331838,0.00047557525,0.7720074,0.00033063276,0.000073524236,0.00007990043,0.0007479483,0.00076787145,0.002198788],"genre_scores_gemma":[0.9558699,0.00030335816,0.04046072,0.000049130154,0.000028935281,0.000095856994,0.0010119623,0.000059050155,0.0021209838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973303,0.000077772,0.000017019795,0.00006216446,0.000077509954,0.00003248085],"domain_scores_gemma":[0.99931407,0.00040088018,0.00008158978,0.00003895697,0.00014374702,0.000020721169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006807772,0.0005730185,0.0004427478,0.00041768514,0.00022514183,0.00053504563,0.00061348535,0.00066162995,0.00067167176],"category_scores_gemma":[0.0021594616,0.0003042447,0.0007609929,0.00048902293,0.00032639937,0.0003681368,0.0004811752,0.00069713395,0.0001680995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018478182,0.000008727253,0.0017239477,0.000024100254,0.000014101313,0.00005236711,0.000022665217,0.99141616,0.0009073877,0.0019541362,0.0002183114,0.0036397188],"study_design_scores_gemma":[0.0000012239175,0.0000036235303,0.000356973,0.0000013075547,0.0000013068803,0.0000053834965,0.000002816228,0.9992156,0.00013261575,0.00018388148,0.000091823546,0.0000034419663],"about_ca_topic_score_codex":0.034105178,"about_ca_topic_score_gemma":0.016236678,"teacher_disagreement_score":0.034105178,"about_ca_system_score_codex":0.0005081623,"about_ca_system_score_gemma":0.00055952504,"threshold_uncertainty_score":0.06781334},"labels":[],"label_agreement":null},{"id":"W4361019075","doi":"10.3390/s23073488","title":"Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Cloud computing; Computer science; Virtual machine; Energy consumption; Distributed computing; Smart grid; Load balancing (electrical power); Response time; Grid; Demand response; Efficient energy use; Real-time computing; Operating system; Engineering","score_opus":0.02725249160427222,"score_gpt":0.27510905805299307,"score_spread":0.24785656644872084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361019075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05055415,0.00043400496,0.93430966,0.0003212409,0.00011113054,0.00015299699,0.00012433786,0.0029295406,0.011062958],"genre_scores_gemma":[0.88655114,0.00024709763,0.10854887,0.00009178345,0.000043452164,0.00010558851,0.00016835594,0.000115924166,0.0041278885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989545,0.000016881328,0.000005958238,0.000021886684,0.000031984244,0.000027857646],"domain_scores_gemma":[0.9999405,0.000011936708,0.000009159893,0.0000068222757,0.000021332067,0.000010255082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002078397,0.000401027,0.0005041564,0.00027833332,0.0006223729,0.0008251781,0.0008046646,0.00035619337,0.0017730362],"category_scores_gemma":[0.00021219792,0.00015641427,0.0004164802,0.00033796413,0.00023244048,0.0007515334,0.0004415763,0.00037471598,0.00026317633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002824888,0.00017830929,0.0020804135,0.00012859215,0.000075175005,0.00028414454,0.00016301447,0.81273496,0.014429926,0.027280888,0.009932143,0.13242996],"study_design_scores_gemma":[0.000011128096,0.00001778614,0.00020461272,0.000003407889,0.0000092908695,0.000020112979,0.000017578708,0.9945873,0.0011458921,0.0021539794,0.0018232906,0.0000055833407],"about_ca_topic_score_codex":0.008742884,"about_ca_topic_score_gemma":0.010244772,"teacher_disagreement_score":0.008742884,"about_ca_system_score_codex":0.00065679336,"about_ca_system_score_gemma":0.00094222446,"threshold_uncertainty_score":0.017383933},"labels":[],"label_agreement":null},{"id":"W4361214465","doi":"10.3390/s23073523","title":"Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Kernel (algebra); Mycotoxin; Fusarium; Segmentation; Mathematics; Computer science; Horticulture; Biology; Botany","score_opus":0.01713925369930059,"score_gpt":0.28215655054399585,"score_spread":0.26501729684469527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361214465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9203082,0.00018219856,0.077314906,0.00009919123,0.000023871507,0.000024000801,0.00030243726,0.0005105803,0.0012345809],"genre_scores_gemma":[0.97506475,0.00010799302,0.023735497,0.00002811616,0.0000086585,0.000015703206,0.00040700677,0.000016077438,0.0006162345],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999021,0.000014376188,0.000005378099,0.00002903372,0.000029450826,0.000019617619],"domain_scores_gemma":[0.9998436,0.00004420589,0.000034362078,0.0000174151,0.00005116484,0.000009262021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032335264,0.00034002372,0.00022975454,0.0007078292,0.00011550061,0.0002889831,0.00019861836,0.00027383704,0.00042791016],"category_scores_gemma":[0.0005104605,0.00008499361,0.00036649837,0.00042579893,0.00015844981,0.00028287992,0.00016827768,0.00021933368,0.00015571354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006880184,0.00041060857,0.030698936,0.00019604914,0.000193129,0.00036327704,0.00021441974,0.19457446,0.40386453,0.0009862902,0.0020108032,0.36579946],"study_design_scores_gemma":[0.0000038850435,0.00005658485,0.03921107,0.0000047196486,0.00001978605,0.00008546412,0.000045355904,0.9249023,0.03500778,0.00032656683,0.0003247079,0.000011777912],"about_ca_topic_score_codex":0.002118403,"about_ca_topic_score_gemma":0.0020193453,"teacher_disagreement_score":0.002118403,"about_ca_system_score_codex":0.0002822062,"about_ca_system_score_gemma":0.00014329894,"threshold_uncertainty_score":0.004212141},"labels":[],"label_agreement":null},{"id":"W4361223569","doi":"10.3390/s23073520","title":"Ultrasonic Transducers for In-Service Inspection and Continuous Monitoring in High-Temperature Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; École de technologie supérieure","keywords":"Ultrasonic sensor; Transducer; Casing; Acoustics; Piezoelectricity; Ultrasonic testing; Materials science; Electromagnetic acoustic transducer; Petrochemical; Nondestructive testing; Mechanical engineering; Engineering; Composite material","score_opus":0.007484669433526371,"score_gpt":0.20128246243011588,"score_spread":0.1937977929965895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361223569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12616529,0.032552134,0.8181038,0.0011746,0.0010472285,0.00041015964,0.0006086385,0.0030839583,0.016854206],"genre_scores_gemma":[0.5447232,0.0114416815,0.4228043,0.00044743027,0.0002762881,0.00035312586,0.00053150946,0.00032747813,0.019095058],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985373,0.000224685,0.00004744935,0.00015055377,0.0009685111,0.00007148613],"domain_scores_gemma":[0.998965,0.0003385441,0.00019930681,0.000108515014,0.00033091914,0.000057627934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008270442,0.0006502464,0.000590689,0.000579804,0.00025785321,0.0007329083,0.00077980437,0.0011429236,0.003562052],"category_scores_gemma":[0.0013103555,0.000397948,0.0002687274,0.00080635096,0.0005503223,0.00087182457,0.00049649004,0.0009736472,0.0018049653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118309144,0.00006012048,0.0008872915,0.00047563092,0.00001918349,0.00012529563,0.00014371956,0.0013253806,0.91095,0.0023238582,0.002620611,0.080950595],"study_design_scores_gemma":[0.000048569273,0.0010900851,0.0073193167,0.00014390338,0.00010022841,0.0021032311,0.00023520872,0.022060826,0.8661648,0.0019050626,0.09872311,0.00010558804],"about_ca_topic_score_codex":0.00039220552,"about_ca_topic_score_gemma":0.0008239921,"teacher_disagreement_score":0.003562052,"about_ca_system_score_codex":0.00046713342,"about_ca_system_score_gemma":0.00045861292,"threshold_uncertainty_score":0.01191628},"labels":[],"label_agreement":null},{"id":"W4362577874","doi":"10.3390/s23073660","title":"Joint Method of Moments (JMoM) and Successive Moment Cancellation (SMC) Multiuser Time Synchronization for ZP-OFDM-Based Waveforms Applicable to Joint Communication and Sensing","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Estimator; Telecommunications link; Orthogonal frequency-division multiplexing; Computer science; MIMO-OFDM; Moment (physics); MIMO; Synchronization (alternating current); Multiplexing; Algorithm; Real-time computing; Control theory (sociology); Electronic engineering; Telecommunications; Mathematics; Engineering; Statistics; Channel (broadcasting)","score_opus":0.018489125678360918,"score_gpt":0.2652579182351451,"score_spread":0.2467687925567842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362577874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047119986,0.00038231577,0.9943119,0.000041105308,0.000028271208,0.000011936071,0.00001075752,0.0000968681,0.00040476827],"genre_scores_gemma":[0.4232376,0.0010592784,0.57320255,0.0001458408,0.00018991988,0.00013540794,0.00010704451,0.000059861108,0.0018625477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915063,0.0002833326,0.000045460274,0.000090002344,0.0003763264,0.000054165812],"domain_scores_gemma":[0.9981122,0.0010902828,0.00028505514,0.00022701103,0.00024428562,0.000041144332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011649,0.0005024835,0.0005791318,0.00055435573,0.0002682063,0.0005387361,0.0006784746,0.000739856,0.00074283767],"category_scores_gemma":[0.0044450867,0.0002677678,0.0005078439,0.00063922006,0.00051329605,0.0007547421,0.000805347,0.0007710463,0.00029153706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064858695,0.00008346399,0.0026283085,0.0005301772,0.00018435362,0.0003247972,0.00029137632,0.23024325,0.115303524,0.11234292,0.002353739,0.5350655],"study_design_scores_gemma":[0.00001679563,0.000105748884,0.0005144516,0.000018518922,0.000020720992,0.00023182575,0.000013667373,0.9716224,0.01864913,0.005617015,0.0031588632,0.000030788422],"about_ca_topic_score_codex":0.0004211933,"about_ca_topic_score_gemma":0.0005520531,"teacher_disagreement_score":0.0011649,"about_ca_system_score_codex":0.000237321,"about_ca_system_score_gemma":0.0006285653,"threshold_uncertainty_score":0.006160617},"labels":[],"label_agreement":null},{"id":"W4362583342","doi":"10.3390/s23073683","title":"A New Recursive Trigonometric Technique for FPGA-Design Implementation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CORDIC; Field-programmable gate array; Lookup table; Trigonometry; Computer science; Trigonometric functions; Algorithm; Waveform; Process (computing); Logic block; Transformation (genetics); Computer hardware; Mathematics","score_opus":0.062452244769170794,"score_gpt":0.3659777004172674,"score_spread":0.30352545564809663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362583342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043731625,0.00046356852,0.98511755,0.00009286723,0.0001420681,0.00006585531,0.00005017634,0.0016542426,0.008040555],"genre_scores_gemma":[0.12730467,0.00068946794,0.86293596,0.00016212669,0.00008764397,0.00011794618,0.00019099038,0.00023773286,0.008273462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966574,0.000055900753,0.00002870231,0.000046756566,0.0001757125,0.000027251619],"domain_scores_gemma":[0.9997545,0.000048348786,0.000028085942,0.000062074556,0.00009825861,0.000008778231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021331504,0.00065077463,0.00030012324,0.0007573171,0.00032355214,0.0005688729,0.00062171713,0.00035472942,0.006260833],"category_scores_gemma":[0.00077246566,0.00020834606,0.0003911973,0.00071687734,0.00024986587,0.00056683813,0.0002941806,0.0005248914,0.0030143876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014250839,0.00004585009,0.00051840756,0.00046257823,0.000046345802,0.00046928064,0.00017918914,0.03206566,0.13943519,0.041498046,0.009548464,0.77558845],"study_design_scores_gemma":[0.00013144354,0.0007169255,0.0014339015,0.00020077104,0.00010788118,0.0034940273,0.00012915529,0.53581274,0.2120023,0.0141607495,0.23168579,0.00012431206],"about_ca_topic_score_codex":0.0010351618,"about_ca_topic_score_gemma":0.0015925226,"teacher_disagreement_score":0.006260833,"about_ca_system_score_codex":0.0003819322,"about_ca_system_score_gemma":0.00048772368,"threshold_uncertainty_score":0.020944595},"labels":[],"label_agreement":null},{"id":"W4362671724","doi":"10.3390/s23073764","title":"Sensing and Processing for Infrared Vision: Methods and Applications","year":2023,"lang":"en","type":"editorial","venue":"Sensors","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Infrared; Computer science; Data science; Optics; Physics","score_opus":0.025691991717303155,"score_gpt":0.3625002463153392,"score_spread":0.3368082545980361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362671724","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024312641,0.042369705,0.00078224484,0.02159803,0.9330021,0.00001755076,0.000035892575,0.00007304175,0.0020971624],"genre_scores_gemma":[0.00039190092,0.042772748,0.0006567573,0.010933811,0.9301756,0.00002399995,0.000044653036,0.00007838392,0.014922218],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99442625,0.0008142361,0.0005574196,0.0005635309,0.00345181,0.00018678895],"domain_scores_gemma":[0.9761716,0.009291015,0.0009603932,0.00065223017,0.011110188,0.001814576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068726963,0.0033246535,0.0028524133,0.0039867666,0.0017455843,0.0067539695,0.0034369621,0.010203713,0.010326043],"category_scores_gemma":[0.015046063,0.0011300095,0.0021331515,0.0020192687,0.0037479268,0.004417441,0.0013112132,0.018282237,0.016685564],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034045206,0.0000087795615,0.00001834602,0.00030946068,0.000018342398,0.000045260444,0.00000820635,0.000030501747,0.00014789437,0.00074007513,0.97890306,0.01973609],"study_design_scores_gemma":[0.000011772067,0.000022584343,0.00009431922,0.00018456261,0.000017381686,0.00016628901,0.000015535083,0.00011505527,0.00014479454,0.001418242,0.9977957,0.0000137304805],"about_ca_topic_score_codex":0.0013700717,"about_ca_topic_score_gemma":0.0048427614,"teacher_disagreement_score":0.010326043,"about_ca_system_score_codex":0.0018841273,"about_ca_system_score_gemma":0.0022306778,"threshold_uncertainty_score":0.036346734},"labels":[],"label_agreement":null},{"id":"W4363674192","doi":"10.3390/s23083859","title":"Review of Zinc Oxide Piezoelectric Nanogenerators: Piezoelectric Properties, Composite Structures and Power Output","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Piezoelectricity; Materials science; Nanogenerator; Energy harvesting; Piezoelectric coefficient; Composite number; Nanorod; Power density; Power (physics); Optoelectronics; Composite material; Nanotechnology; Physics","score_opus":0.03510541272750347,"score_gpt":0.26252654357137517,"score_spread":0.2274211308438717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4363674192","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048521484,0.99745435,0.00025602686,0.00011759171,0.00016445364,0.000007098657,0.00004844343,0.000011792239,0.0014549562],"genre_scores_gemma":[0.0014584259,0.9970145,0.00035973027,0.000100073135,0.00008074122,0.0000106914595,0.00005207445,0.0000026534535,0.0009210667],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998493,0.00001594916,0.000023007167,0.00003332997,0.000064080785,0.000014303156],"domain_scores_gemma":[0.9998312,0.000076346994,0.000031176703,0.000005295515,0.000043173175,0.000012762588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030056966,0.0008917808,0.0010071784,0.0023159755,0.00023832892,0.0005893821,0.0005440846,0.0006416732,0.0028855063],"category_scores_gemma":[0.00045277615,0.00041603658,0.0005398755,0.0025568584,0.00017878489,0.0008896756,0.00041922551,0.0007199977,0.0013369266],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068453024,0.00010312989,0.00023595928,0.06903506,0.00015395,0.00030108084,0.0000979576,0.0007861849,0.013783759,0.0042872634,0.029442402,0.88170475],"study_design_scores_gemma":[0.000009879005,0.00016572936,0.0011063782,0.0044147805,0.00019160313,0.00094985176,0.00005204949,0.00016987258,0.00341072,0.0010718583,0.9884327,0.000024472165],"about_ca_topic_score_codex":0.00080280117,"about_ca_topic_score_gemma":0.0014708493,"teacher_disagreement_score":0.0028855063,"about_ca_system_score_codex":0.00029563505,"about_ca_system_score_gemma":0.0006804149,"threshold_uncertainty_score":0.009653032},"labels":[],"label_agreement":null},{"id":"W4363676412","doi":"10.3390/s23083842","title":"Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Complex wavelet transform; Artificial intelligence; Pattern recognition (psychology); Invariant (physics); Fourier transform; Wavelet transform; Scaling; Computer science; Wavelet; Harmonic wavelet transform; Mathematics; Computer vision; Discrete wavelet transform; Geometry; Mathematical analysis","score_opus":0.020574052228309433,"score_gpt":0.22342758705446947,"score_spread":0.20285353482616003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4363676412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064606005,0.00029918776,0.99135613,0.000059498856,0.00006235528,0.000039533574,0.000119181415,0.0008388008,0.0007647375],"genre_scores_gemma":[0.1247219,0.00088283926,0.87014294,0.0001828001,0.00013941729,0.00015608755,0.0011987804,0.0002521489,0.0023230521],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920875,0.000067487694,0.000048746973,0.00019346144,0.00041397483,0.00006774699],"domain_scores_gemma":[0.9994543,0.00012523391,0.00007449303,0.00013051435,0.00018507209,0.000030484563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003924744,0.0007745277,0.00089252845,0.0026184032,0.00025461818,0.0012216764,0.00082667347,0.00065530493,0.0019675836],"category_scores_gemma":[0.001601925,0.00036429893,0.0009255607,0.0024577745,0.0004708264,0.0019970834,0.00086683995,0.000881452,0.001897562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012002304,0.00008026597,0.00095308066,0.00015488442,0.00004445857,0.00016049526,0.00005059565,0.011926564,0.057184853,0.003412117,0.0034917824,0.9224208],"study_design_scores_gemma":[0.000040864157,0.00018536359,0.006386472,0.000041763742,0.000073740564,0.0014961843,0.00010273954,0.881987,0.081417866,0.010068202,0.018104518,0.00009536896],"about_ca_topic_score_codex":0.0010248779,"about_ca_topic_score_gemma":0.0010075724,"teacher_disagreement_score":0.0026184032,"about_ca_system_score_codex":0.00023892,"about_ca_system_score_gemma":0.00040712245,"threshold_uncertainty_score":0.0065822005},"labels":[],"label_agreement":null},{"id":"W4365147995","doi":"10.3390/s23083907","title":"Auto Sizing of CANDU Nuclear Reactor Fuel Channel Flaws from UT Scans","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Power Generation; Dalhousie University","funders":"Ontario Power Generation","keywords":"Sizing; Nuclear power plant; Process (computing); Channel (broadcasting); Nuclear fuel; Nuclear engineering; Spent nuclear fuel; Computer science; Uranium; Engineering; Materials science; Electrical engineering; Chemistry; Nuclear physics","score_opus":0.020227486337291843,"score_gpt":0.22698247450270292,"score_spread":0.20675498816541107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365147995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13452576,0.00018539479,0.8600776,0.000068561414,0.000029367387,0.00008854298,0.00011368516,0.0032018365,0.0017092315],"genre_scores_gemma":[0.36149293,0.00014336302,0.6356506,0.00004393995,0.000013946765,0.00006379357,0.00033138,0.00036332733,0.0018967716],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994517,0.000048120688,0.00002965198,0.00010890657,0.00031027995,0.00005139076],"domain_scores_gemma":[0.9980122,0.00055649405,0.0003920235,0.00022650152,0.00076916744,0.000043644926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006387572,0.00067687675,0.00039870007,0.0013478827,0.00020195646,0.0007116266,0.00080300396,0.00060357933,0.0013678123],"category_scores_gemma":[0.003025501,0.0003508466,0.00041068273,0.00055345846,0.00030276782,0.0006424895,0.00047488417,0.00043556435,0.00066747254],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003120969,0.00009138405,0.01193754,0.00026619816,0.000032688808,0.00017416866,0.00035347007,0.017360078,0.46329436,0.00150276,0.0013397542,0.5033355],"study_design_scores_gemma":[0.000019184587,0.0002778705,0.01995519,0.000027992766,0.00004818701,0.00077671977,0.00020929695,0.41595292,0.5564379,0.0008789661,0.005356132,0.00005970241],"about_ca_topic_score_codex":0.0018068503,"about_ca_topic_score_gemma":0.004938304,"teacher_disagreement_score":0.0018068503,"about_ca_system_score_codex":0.0003898043,"about_ca_system_score_gemma":0.0007559392,"threshold_uncertainty_score":0.004575789},"labels":[],"label_agreement":null},{"id":"W4365149431","doi":"10.3390/s23083894","title":"A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"","keywords":"Autoencoder; Machining; Process (computing); Computer science; Wavelet; Artificial intelligence; Representation (politics); Artificial neural network; Machine learning; Pattern recognition (psychology); Engineering; Mechanical engineering","score_opus":0.008271034634781797,"score_gpt":0.2269999828167959,"score_spread":0.2187289481820141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365149431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06484562,0.0004096396,0.932649,0.00014103933,0.000049062568,0.000038832713,0.00006319115,0.00087457435,0.000929081],"genre_scores_gemma":[0.88142,0.00025514752,0.115697876,0.00009757759,0.000034832134,0.0000997329,0.000171134,0.00003126614,0.0021924046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981517,0.00002386329,0.0000132191535,0.000054517655,0.000057805835,0.000035441903],"domain_scores_gemma":[0.9997578,0.00009043797,0.000036430287,0.000020111043,0.00008040582,0.000014682339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004687859,0.00067459734,0.00042522902,0.00037394746,0.00019283034,0.00034061942,0.00074940856,0.0006702972,0.00075409986],"category_scores_gemma":[0.0008622084,0.0003067986,0.00039809028,0.00028756878,0.0002457994,0.00048699384,0.00046631438,0.00094906514,0.00019229607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015907655,0.00018345131,0.0015638783,0.00008570666,0.000052435218,0.00011354937,0.000053971475,0.6591381,0.02571351,0.0015661187,0.0009977256,0.31037247],"study_design_scores_gemma":[0.0000013194627,0.000021023321,0.0001712773,0.0000019976314,0.0000027728074,0.0000053202193,0.0000018136523,0.9981481,0.0013567362,0.00019264295,0.000094958246,0.000002160202],"about_ca_topic_score_codex":0.0038904075,"about_ca_topic_score_gemma":0.004668569,"teacher_disagreement_score":0.0038904075,"about_ca_system_score_codex":0.00042808233,"about_ca_system_score_gemma":0.00068096304,"threshold_uncertainty_score":0.007735491},"labels":[],"label_agreement":null},{"id":"W4365448299","doi":"10.3390/s23083945","title":"Enhancing Evanescent Wave Coupling of Near-Surface Waveguides with Plasmonic Nanoparticles","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ciena (Canada); Université Laval","funders":"Canada First Research Excellence Fund","keywords":"Materials science; Waveguide; Optics; Femtosecond; Excitation; Surface plasmon; Refractive index; Perpendicular; Nanoparticle; Optoelectronics; Surface plasmon resonance; Surface plasmon polariton; Near-field scanning optical microscope; Plasmon; Laser; Nanotechnology; Optical microscope; Physics; Scanning electron microscope","score_opus":0.01328041662744152,"score_gpt":0.21438079472760319,"score_spread":0.20110037810016165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365448299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98240983,0.00036084335,0.016096411,0.00004208826,0.000016444053,0.000020542642,0.000027182181,0.00006568093,0.0009610259],"genre_scores_gemma":[0.9750503,0.0005668224,0.022136247,0.00003853914,0.000010575164,0.000032618376,0.00006828012,0.000035554713,0.002061082],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998124,0.000026006503,0.0000117561385,0.000049000962,0.00006617248,0.000034593006],"domain_scores_gemma":[0.99975556,0.00009753804,0.00008092684,0.000020412042,0.000031598844,0.000014011409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020530364,0.0004784455,0.00019024164,0.0001587547,0.00012165297,0.0002785996,0.00021954231,0.00031303338,0.00039775512],"category_scores_gemma":[0.00030879639,0.00024191018,0.00022593749,0.00013536503,0.00035471137,0.0003323792,0.0002864111,0.00041113593,0.00018729224],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005723281,0.000005274435,0.00004049902,0.000014258641,0.0000013392065,0.000009731907,0.000010344505,0.00009759553,0.99935216,0.00004774455,0.000005052835,0.00041034704],"study_design_scores_gemma":[9.679021e-7,0.000025663567,0.00017711711,0.0000010895479,0.0000013908154,0.000011140693,0.000006026718,0.00063060195,0.9989899,0.000010585788,0.0001441396,0.0000014478821],"about_ca_topic_score_codex":0.00043961828,"about_ca_topic_score_gemma":0.0009007002,"teacher_disagreement_score":0.0004784455,"about_ca_system_score_codex":0.0002741934,"about_ca_system_score_gemma":0.00017414337,"threshold_uncertainty_score":0.0019894242},"labels":[],"label_agreement":null},{"id":"W4366211339","doi":"10.3390/s23084012","title":"Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Convolutional neural network; Computer science; Algorithm; Seismic migration; Computation; Residual; Artificial neural network; Aperture (computer memory); Artificial intelligence; Geology; Acoustics","score_opus":0.01992785176511779,"score_gpt":0.228249813578634,"score_spread":0.2083219618135162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366211339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048742454,0.0003985044,0.94491667,0.00022300816,0.0001245875,0.000043603723,0.00024134351,0.003164315,0.0021454813],"genre_scores_gemma":[0.55055714,0.00047564995,0.4391707,0.00023340539,0.000061694926,0.00011589321,0.0012453838,0.00029604218,0.007844166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998282,0.000016371163,0.000009977624,0.00005588537,0.00005567445,0.000033847704],"domain_scores_gemma":[0.9997478,0.00006297937,0.00004204599,0.000042273197,0.00008702325,0.000017816572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035445628,0.0010904534,0.00047499256,0.00053636555,0.00026009962,0.0005411431,0.0013740557,0.0007452768,0.0014288682],"category_scores_gemma":[0.000993149,0.0005830575,0.000813343,0.0005873882,0.00038156682,0.00083165814,0.00065980764,0.0010296283,0.0005357747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014967669,0.00008733396,0.0024905514,0.000116283634,0.00016193758,0.00017268375,0.000072093295,0.64895403,0.03416645,0.0036461854,0.003629024,0.30635384],"study_design_scores_gemma":[0.0000026177945,0.000009253174,0.00017841939,0.0000031619243,0.0000069206108,0.000017032049,0.000002727377,0.9947266,0.0042527593,0.00036745382,0.0004280085,0.0000051675484],"about_ca_topic_score_codex":0.01699913,"about_ca_topic_score_gemma":0.022576502,"teacher_disagreement_score":0.01699913,"about_ca_system_score_codex":0.00089477893,"about_ca_system_score_gemma":0.0011422902,"threshold_uncertainty_score":0.033800364},"labels":[],"label_agreement":null},{"id":"W4366425237","doi":"10.3390/s23084080","title":"Regularization for Unsupervised Learning of Optical Flow","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regularization (linguistics); Computer science; Inference; Artificial intelligence; Convolutional neural network; Computation; Deep learning; Optical flow; Artificial neural network; Generalization; Machine learning; Deep neural networks; Unsupervised learning; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.01974733393849838,"score_gpt":0.28027516795456553,"score_spread":0.26052783401606716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366425237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004190636,0.00013711289,0.9928767,0.00011600432,0.000031038344,0.00003548522,0.000087184766,0.001732083,0.0007937329],"genre_scores_gemma":[0.28127733,0.00038873954,0.7075985,0.0004372578,0.00020166485,0.00040937483,0.0014182503,0.00093985215,0.007329125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993956,0.00014524376,0.00002585054,0.00021049146,0.00016204163,0.00006081396],"domain_scores_gemma":[0.99883157,0.00042885868,0.00014126541,0.00029280392,0.00025787103,0.000047705846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001291238,0.0013909724,0.00087906787,0.00094414625,0.0005599305,0.0006670236,0.002144902,0.0013705642,0.0022297068],"category_scores_gemma":[0.0045329803,0.0007314835,0.0010253238,0.000890917,0.0011856937,0.0016004264,0.0013529037,0.002642995,0.00097070733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012213703,0.00012031425,0.0011290933,0.00016659372,0.00014796155,0.000065119064,0.00008697049,0.5759594,0.020137943,0.027402971,0.012728668,0.36193275],"study_design_scores_gemma":[0.0000052732275,0.000010740735,0.00012659354,0.000005266759,0.000004045897,0.000012720209,0.000002677058,0.9903418,0.0027211343,0.0056987912,0.0010659758,0.0000050035114],"about_ca_topic_score_codex":0.006701358,"about_ca_topic_score_gemma":0.010666788,"teacher_disagreement_score":0.006701358,"about_ca_system_score_codex":0.0012744707,"about_ca_system_score_gemma":0.0018924457,"threshold_uncertainty_score":0.013324678},"labels":[],"label_agreement":null},{"id":"W4366590374","doi":"10.3390/s23084101","title":"Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University","funders":"National Natural Science Foundation of China","keywords":"Geospatial analysis; Environmental resource management; Resilience (materials science); Analytic hierarchy process; Ecosystem; Vulnerability (computing); Process (computing); Quality (philosophy); Computer science; Ecosystem health; Ecosystem services; Environmental science; Geography; Ecology; Remote sensing; Engineering; Operations research","score_opus":0.11573767193355847,"score_gpt":0.37712207362260974,"score_spread":0.2613844016890513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366590374","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010695096,0.9985655,0.0002638107,0.00019483673,0.00009769604,0.000010238188,0.00006340461,0.000008095613,0.00068950636],"genre_scores_gemma":[0.0007291002,0.9985784,0.00038674468,0.00006981511,0.000053636264,0.000008324497,0.000059620943,0.0000021904004,0.000112174486],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958354,0.00007835396,0.000088803776,0.00007426396,0.00015239588,0.000022674383],"domain_scores_gemma":[0.9980196,0.0012304693,0.00022703932,0.00004875206,0.00041355155,0.0000606004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014746373,0.0012190024,0.001801209,0.006546459,0.00032026987,0.0015224607,0.0013634787,0.000956468,0.003471677],"category_scores_gemma":[0.0029734573,0.00044375766,0.0012026763,0.007881235,0.0006026111,0.0022769112,0.00080883934,0.0010055456,0.001294574],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029206738,0.000042520685,0.00047726187,0.046568733,0.00026586762,0.00009521153,0.000086496526,0.00069305825,0.00046421363,0.004128473,0.01553827,0.93161064],"study_design_scores_gemma":[0.000014594833,0.00008058715,0.0029986752,0.03208713,0.0008451895,0.00073709874,0.00018653525,0.00030525116,0.0004997489,0.0039801113,0.9582054,0.000059689086],"about_ca_topic_score_codex":0.0057774796,"about_ca_topic_score_gemma":0.007862381,"teacher_disagreement_score":0.006546459,"about_ca_system_score_codex":0.0008023339,"about_ca_system_score_gemma":0.0026172665,"threshold_uncertainty_score":0.011613905},"labels":[],"label_agreement":null},{"id":"W4366772416","doi":"10.3390/s23084171","title":"Improving Pedestrian Safety Using Ultra-Wideband Sensors: A Study of Time-to-Collision Estimation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic and Road Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Transport Canada","keywords":"Collision; Beacon; Pedestrian; Computer science; Phone; Real-time computing; Wireless; Robustness (evolution); Wideband; Collision avoidance; Simulation; Engineering; Computer security; Transport engineering; Telecommunications","score_opus":0.012613875479421818,"score_gpt":0.23479314499490597,"score_spread":0.22217926951548417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366772416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7989531,0.00251495,0.19499893,0.00014831421,0.00008610209,0.000087619876,0.00010772805,0.00014959053,0.0029536705],"genre_scores_gemma":[0.9784718,0.0010270296,0.019643249,0.000025728219,0.00002763045,0.000020897553,0.000088624445,0.000009747958,0.0006852328],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989343,0.00034441752,0.000052920295,0.00020172655,0.00038734917,0.000079248144],"domain_scores_gemma":[0.9971348,0.0014088288,0.00051202567,0.00017678771,0.0007049948,0.00006246029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010661874,0.0006230907,0.00040952297,0.00096465513,0.00022281031,0.00054190523,0.00056399213,0.0005442545,0.00031936492],"category_scores_gemma":[0.0052316375,0.00022776234,0.00039468828,0.00088406575,0.00023924051,0.00089477334,0.00041262637,0.0003049364,0.00013065127],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015004317,0.0010563281,0.21612002,0.0009883017,0.0006495023,0.0004860847,0.00093374733,0.32450438,0.040114786,0.004005387,0.001211402,0.4084297],"study_design_scores_gemma":[0.000019274805,0.002266044,0.08395871,0.00012803101,0.00027783195,0.0008397491,0.00076035154,0.8777945,0.030594679,0.0012735047,0.0020062076,0.00008109186],"about_ca_topic_score_codex":0.004014497,"about_ca_topic_score_gemma":0.002735395,"teacher_disagreement_score":0.004014497,"about_ca_system_score_codex":0.00041046258,"about_ca_system_score_gemma":0.00034019072,"threshold_uncertainty_score":0.007982254},"labels":[],"label_agreement":null},{"id":"W4366773682","doi":"10.3390/s23084137","title":"Silicon Fresnel Zone Plate Metalens with Subwavelength Gratings","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zone plate; Optics; Fresnel zone; Materials science; Grating; Holography; Wavelength; Amorphous silicon; Planar; Fabrication; Optoelectronics; Silicon; Physics; Crystalline silicon; Diffraction; Computer science","score_opus":0.022644041203723164,"score_gpt":0.2479972187737775,"score_spread":0.22535317757005435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366773682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9549592,0.00093073363,0.032036398,0.00012236288,0.00010073919,0.000055582655,0.00021043236,0.0006399951,0.010944567],"genre_scores_gemma":[0.9377284,0.00029850207,0.05793038,0.00007429588,0.000021570442,0.000033785043,0.00017329652,0.000041453717,0.0036982722],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998266,0.000011159503,0.000010358106,0.00004313409,0.00008617423,0.000022552795],"domain_scores_gemma":[0.9998566,0.000016512813,0.00006019928,0.000025335428,0.00002380125,0.000017497248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008635484,0.00037429054,0.00017641514,0.000306815,0.00014005652,0.00033733805,0.00057906006,0.00042539585,0.0005520046],"category_scores_gemma":[0.00015508644,0.00028241347,0.00016303321,0.00022372503,0.00026400216,0.0003289074,0.00028292605,0.00022399481,0.00040073102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007715387,0.00003047012,0.00029153298,0.00007974136,0.0000072353005,0.00006253244,0.000029109557,0.00069872435,0.98945755,0.0013120262,0.00021067094,0.00774337],"study_design_scores_gemma":[0.000017384491,0.00016904117,0.0013249285,0.000004801063,0.000008222118,0.00021974139,0.000014878822,0.009799723,0.9836128,0.00012236797,0.004694814,0.000011358518],"about_ca_topic_score_codex":0.00047218692,"about_ca_topic_score_gemma":0.001012587,"teacher_disagreement_score":0.00057906006,"about_ca_system_score_codex":0.00045735802,"about_ca_system_score_gemma":0.0001652812,"threshold_uncertainty_score":0.003318429},"labels":[],"label_agreement":null},{"id":"W4366773847","doi":"10.3390/s23084145","title":"A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Photoplethysmogram; Sphygmomanometer; Blood pressure; Calibration; Cuff; Medicine; Biomedical engineering; Pulse pressure; Cardiology; Internal medicine; Mathematics; Computer science; Surgery; Statistics; Computer vision","score_opus":0.01666280354831112,"score_gpt":0.24675160042812913,"score_spread":0.230088796879818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366773847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07640781,0.00089881686,0.91912633,0.00017666661,0.00021257329,0.00015100882,0.0002417516,0.0018106333,0.00097444025],"genre_scores_gemma":[0.51574224,0.00089090626,0.4804014,0.0003577745,0.00021564415,0.00030705443,0.00040911965,0.00020325856,0.001472693],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861014,0.0003937563,0.00010058549,0.00033212383,0.0005314207,0.00003200772],"domain_scores_gemma":[0.9987816,0.0004648186,0.00014488318,0.00022402633,0.00034397954,0.00004067608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091726886,0.0007430522,0.0008066572,0.0007797397,0.00019014807,0.0006232906,0.0005931598,0.0008541348,0.00083066535],"category_scores_gemma":[0.00362894,0.00036790085,0.00046012097,0.0007133336,0.00027933554,0.00083018147,0.0006550661,0.00062668585,0.0007367486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068879157,0.00019807395,0.020653773,0.0007044664,0.00027189145,0.00027857776,0.00019575063,0.0029817582,0.35036415,0.0006270203,0.0017291672,0.62130666],"study_design_scores_gemma":[0.0005140441,0.0033889837,0.22191109,0.0001992258,0.0011212294,0.014532423,0.00016247918,0.28975162,0.45121905,0.002299239,0.0144590875,0.00044148444],"about_ca_topic_score_codex":0.00026405702,"about_ca_topic_score_gemma":0.0004289144,"teacher_disagreement_score":0.00091726886,"about_ca_system_score_codex":0.00011538986,"about_ca_system_score_gemma":0.00030470538,"threshold_uncertainty_score":0.004851043},"labels":[],"label_agreement":null},{"id":"W4366775507","doi":"10.3390/s23084152","title":"Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute on Aging; National Institutes of Health","keywords":"Covariate; Demography; Cognitive decline; Demographics; Gerontology; Variance (accounting); Explained variation; Metric (unit); Multivariate statistics; Statistics; Medicine; Mathematics; Operations management","score_opus":0.14326909000501956,"score_gpt":0.3199054811918311,"score_spread":0.17663639118681154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366775507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99870443,0.00013510302,0.00030444545,0.000018539084,0.000002890494,0.0000057216885,0.00064936467,0.0000077740715,0.00017170813],"genre_scores_gemma":[0.99891603,0.00005180948,0.00020017366,0.000006257566,0.000004014585,0.000009088707,0.00070835254,0.000002567654,0.00010155274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99942064,0.0001724408,0.00008322803,0.00016091333,0.00010854608,0.000054205517],"domain_scores_gemma":[0.99785525,0.00042907184,0.0009368435,0.00026149052,0.00039792276,0.00011936339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011270554,0.00018928251,0.0003432563,0.0006014953,0.00017043036,0.00037791976,0.00029172556,0.00025580465,0.00048265944],"category_scores_gemma":[0.0058646128,0.000116913994,0.0002941834,0.0006998629,0.00013843125,0.0003230913,0.00042535106,0.00031225005,0.00015956693],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006227762,0.000017961922,0.9958423,0.000007799852,0.00006411956,0.000014455346,0.00008833853,0.00011209224,0.00012279922,0.000009562733,0.000085171574,0.0035731944],"study_design_scores_gemma":[7.475287e-7,0.000027079275,0.99955565,0.0000016474304,0.00000850054,0.000038622904,0.000044952336,0.00020061419,0.000037495483,0.0000121449975,0.00007087504,0.0000016507503],"about_ca_topic_score_codex":0.005108705,"about_ca_topic_score_gemma":0.008218803,"teacher_disagreement_score":0.005108705,"about_ca_system_score_codex":0.00014249109,"about_ca_system_score_gemma":0.00012820131,"threshold_uncertainty_score":0.010157943},"labels":[],"label_agreement":null},{"id":"W4366814603","doi":"10.3390/s23094228","title":"Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Università Campus Bio-Medico di Roma","keywords":"Medicine; Internal medicine; Primary care; Medical record; Heart failure; Body mass index; Glycated hemoglobin; Population; Cardiology; Diabetes mellitus; Type 2 diabetes; Endocrinology","score_opus":0.010967737578293772,"score_gpt":0.2703340954801473,"score_spread":0.25936635790185353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366814603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98654073,0.00050848344,0.009033954,0.00018975016,0.000033463715,0.00008121781,0.0026052892,0.000116466916,0.000890596],"genre_scores_gemma":[0.9891762,0.00013187146,0.005460468,0.00005774337,0.000043003405,0.000037688882,0.0049666823,0.0000049060664,0.00012148846],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987494,0.00049173686,0.00016808347,0.00021487135,0.00022560368,0.000150233],"domain_scores_gemma":[0.9949071,0.0026853099,0.0010193302,0.00042887768,0.0006364335,0.00032291535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028735825,0.00051062356,0.00056734594,0.001652605,0.00021782961,0.0012403782,0.00031962077,0.0005493187,0.0005610885],"category_scores_gemma":[0.0106801465,0.00012530692,0.0004940331,0.0008535026,0.00018035152,0.00046767874,0.0006414237,0.00043182535,0.00035097805],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063940755,0.00014713359,0.95399153,0.000057114907,0.00015220899,0.00013996937,0.00007745842,0.007784499,0.0015075979,0.00013273094,0.00077808945,0.03459231],"study_design_scores_gemma":[0.00004619242,0.000560134,0.8117848,0.000077096076,0.00013449923,0.00054714974,0.000267732,0.18188183,0.0023670692,0.0009055002,0.0013926487,0.000035413414],"about_ca_topic_score_codex":0.0019669405,"about_ca_topic_score_gemma":0.0022652638,"teacher_disagreement_score":0.0028735825,"about_ca_system_score_codex":0.00027117995,"about_ca_system_score_gemma":0.00052563444,"threshold_uncertainty_score":0.015197158},"labels":[],"label_agreement":null},{"id":"W4366990643","doi":"10.3390/s23094253","title":"Development of a Quick-Install Rapid Phenotyping System","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Precision agriculture; Multispectral image; Throughput; Agricultural engineering; Field (mathematics); Computer science; Scale (ratio); Canopy; Row; Vegetation (pathology); Environmental science; Biochemical engineering; Agriculture; Engineering; Artificial intelligence; Database; Mathematics; Geography; Cartography; Biology; Telecommunications; Ecology","score_opus":0.024395984664099618,"score_gpt":0.2041549842991213,"score_spread":0.17975899963502168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366990643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03465127,0.00023120591,0.88323545,0.00023910594,0.0003201085,0.0013081679,0.00521164,0.07049813,0.0043048733],"genre_scores_gemma":[0.09134745,0.0002917932,0.8866137,0.00036099183,0.00010192332,0.0021177582,0.007536937,0.0022027427,0.009426691],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988927,0.00008572546,0.00006850919,0.00039121517,0.00046337757,0.00009856426],"domain_scores_gemma":[0.9990821,0.0001743136,0.000070755035,0.000186906,0.0003554431,0.00013049386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014232458,0.000850894,0.0008601221,0.0010921842,0.00042732438,0.0007474426,0.0015819655,0.00070110254,0.010828752],"category_scores_gemma":[0.0012350607,0.0007191673,0.00052521273,0.00046150974,0.00019211957,0.00085269747,0.0011433418,0.0009881636,0.007925762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007121418,0.00021889024,0.0049322858,0.00045266526,0.00009667681,0.00035079464,0.00017997317,0.005171856,0.7442894,0.0014078614,0.021584984,0.22060248],"study_design_scores_gemma":[0.0002501581,0.0014612048,0.028688852,0.00013074468,0.00018979821,0.0017073078,0.00013808798,0.12447498,0.6017013,0.0018201411,0.23903914,0.00039824692],"about_ca_topic_score_codex":0.0011988311,"about_ca_topic_score_gemma":0.0011514089,"teacher_disagreement_score":0.010828752,"about_ca_system_score_codex":0.00032327525,"about_ca_system_score_gemma":0.00071553345,"threshold_uncertainty_score":0.036225796},"labels":[],"label_agreement":null},{"id":"W4367181929","doi":"10.3390/s23094324","title":"Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Artificial intelligence; Nondestructive testing; Deep learning; Image quality; Digital radiography; Computer science; Artificial neural network; Noise (video); Radiography; Pattern recognition (psychology); Machine learning; Image (mathematics); Medicine","score_opus":0.01746647236080209,"score_gpt":0.2959973123200526,"score_spread":0.27853083995925054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367181929","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98105717,0.0006136678,0.015716093,0.00015960829,0.000054931425,0.000043806594,0.00021214448,0.00039006726,0.0017524142],"genre_scores_gemma":[0.9864893,0.0002456176,0.011516087,0.00005959628,0.0000083756695,0.000035480127,0.00044561186,0.00003527789,0.0011646679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996288,0.000064343534,0.000034273995,0.00008272856,0.00011119719,0.00007866727],"domain_scores_gemma":[0.9986727,0.00058028824,0.00013744023,0.000108951695,0.00042653753,0.0000741057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012574375,0.0008912312,0.0004878278,0.00060298305,0.0002291064,0.00056836486,0.00063505775,0.0009735841,0.0010513559],"category_scores_gemma":[0.0038051629,0.00024219544,0.00036920403,0.000445769,0.00054029014,0.00064356107,0.0005910621,0.00064433133,0.00026316557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034905325,0.0017052868,0.015237956,0.0008737952,0.00028816605,0.00045224294,0.00030250187,0.5887787,0.11916866,0.001255732,0.002437814,0.26600868],"study_design_scores_gemma":[0.00003146458,0.0007956215,0.006925128,0.000045076442,0.000049184662,0.00007970336,0.00007211017,0.94489384,0.046355963,0.00031519204,0.000413766,0.000022909644],"about_ca_topic_score_codex":0.0069821435,"about_ca_topic_score_gemma":0.004713432,"teacher_disagreement_score":0.0069821435,"about_ca_system_score_codex":0.0006878587,"about_ca_system_score_gemma":0.00044566402,"threshold_uncertainty_score":0.013882995},"labels":[],"label_agreement":null},{"id":"W4367298443","doi":"10.3390/s23094364","title":"DOPESLAM: High-Precision ROS-Based Semantic 3D SLAM in a Dynamic Environment","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Mitacs","keywords":"Computer science; Artificial intelligence; Pipeline (software); Object (grammar); Key (lock); Computer vision; Speedup; Inference; Ground truth; Robot; Pose; Point cloud; Filter (signal processing); Simultaneous localization and mapping; Deep learning; Mobile robot","score_opus":0.006265961455402776,"score_gpt":0.2005321580297693,"score_spread":0.1942661965743665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367298443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022598512,0.00027090157,0.9451955,0.00017554035,0.00014648354,0.00009302317,0.0007467301,0.027255286,0.0035181504],"genre_scores_gemma":[0.4178564,0.00022861111,0.57298285,0.00023729868,0.00006691159,0.00016814035,0.0030902084,0.0010807206,0.0042888797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994892,0.00005577326,0.00001459339,0.00015130382,0.0002240273,0.00006513892],"domain_scores_gemma":[0.9996841,0.00004927122,0.000029249168,0.00012496274,0.00007839533,0.000034114666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000581232,0.0012325142,0.0008540168,0.000734232,0.00053672306,0.0009773127,0.0017905436,0.0008882745,0.0033753067],"category_scores_gemma":[0.001251368,0.000692416,0.0005524273,0.00072050496,0.0005672654,0.0016591516,0.002719957,0.0015604285,0.0018848708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052425795,0.00027641977,0.002484486,0.00030662105,0.00031390763,0.00031782026,0.00041148302,0.23868021,0.055170953,0.0073652174,0.027753757,0.66639477],"study_design_scores_gemma":[0.00006530687,0.000098339035,0.0012018895,0.000021702805,0.000017846927,0.000105755935,0.00008828894,0.9669883,0.013831118,0.0054624565,0.012082846,0.000036219473],"about_ca_topic_score_codex":0.0056806444,"about_ca_topic_score_gemma":0.009447278,"teacher_disagreement_score":0.0056806444,"about_ca_system_score_codex":0.00048583798,"about_ca_system_score_gemma":0.0012188149,"threshold_uncertainty_score":0.011295199},"labels":[],"label_agreement":null},{"id":"W4367598841","doi":"10.3390/s23094433","title":"Slip Detection Strategies for Automatic Grasping in Prosthetic Hands","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Leverhulme Trust","keywords":"GRASP; Slipping; Slip (aerodynamics); Computer science; Vibration; Acoustics; Prosthetic hand; Computer vision; Artificial intelligence; Engineering; Simulation; Mechanical engineering; Physics; Aerospace engineering","score_opus":0.014185371564713627,"score_gpt":0.23382376785571796,"score_spread":0.21963839629100432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367598841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087657385,0.0018620364,0.9014261,0.00014341144,0.0001337744,0.00021368772,0.000046711204,0.0011145842,0.0074023195],"genre_scores_gemma":[0.76495177,0.0006920559,0.22754323,0.00011928275,0.000055143235,0.00017803477,0.0000615213,0.00011199872,0.0062868623],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966383,0.000056538178,0.000032883392,0.00006684914,0.00013333635,0.000046519854],"domain_scores_gemma":[0.999655,0.00016204688,0.000047899186,0.000028277344,0.00008542098,0.000021277141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054438005,0.0005357412,0.00053539843,0.00060809305,0.0003514175,0.0007112158,0.0006382932,0.0005747807,0.0044201156],"category_scores_gemma":[0.0014649524,0.00035348174,0.00021313391,0.00029557644,0.00047100216,0.00056142337,0.0006640529,0.00033813927,0.0010593004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049818744,0.000092443246,0.00068798405,0.00040381824,0.000033256914,0.00025029876,0.0003095736,0.016416153,0.3269768,0.0047048233,0.0012143917,0.64841235],"study_design_scores_gemma":[0.00018720332,0.0022507503,0.01584145,0.00038157948,0.000118346776,0.0020829728,0.00066528143,0.6965249,0.2376785,0.022851171,0.021259042,0.0001587594],"about_ca_topic_score_codex":0.00063590234,"about_ca_topic_score_gemma":0.0009964354,"teacher_disagreement_score":0.0044201156,"about_ca_system_score_codex":0.00031669918,"about_ca_system_score_gemma":0.00034615592,"threshold_uncertainty_score":0.01478672},"labels":[],"label_agreement":null},{"id":"W4367624326","doi":"10.3390/s23094444","title":"Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Deep learning; Segmentation; Computer science; Thermography; Identification (biology); Artificial neural network; Pattern recognition (psychology); Image segmentation; Algorithm; Field (mathematics); Machine learning; Infrared; Mathematics; Optics","score_opus":0.009987959816891797,"score_gpt":0.2198290319901243,"score_spread":0.2098410721732325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367624326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1726445,0.00059316785,0.821457,0.00016860153,0.000060698916,0.00010210607,0.00042189474,0.002872677,0.0016793837],"genre_scores_gemma":[0.6949225,0.00051433773,0.30006158,0.0001412582,0.000027238813,0.00016351364,0.0011909668,0.0001327736,0.0028458207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997118,0.00003107589,0.00001993768,0.00009480165,0.000092568494,0.000049809867],"domain_scores_gemma":[0.99944526,0.00015392764,0.00010914387,0.000067183835,0.00020103174,0.000023442139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063500996,0.0009749129,0.0005234066,0.0011958098,0.00017746065,0.0006284203,0.00094276824,0.0007582303,0.0010355524],"category_scores_gemma":[0.0014844877,0.00033805126,0.0006037528,0.0006647637,0.00038768025,0.0009708854,0.00063888443,0.0008597309,0.0005762346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039836197,0.00035555352,0.0067846132,0.0003086448,0.0000952343,0.00025205838,0.00012967475,0.19320257,0.14234477,0.0018429752,0.0029881278,0.65129745],"study_design_scores_gemma":[0.000005045201,0.00006909114,0.0017259738,0.000012833278,0.000014462471,0.000037726586,0.000022168333,0.9690317,0.02778449,0.00074667874,0.0005402294,0.000009706709],"about_ca_topic_score_codex":0.0022830311,"about_ca_topic_score_gemma":0.003170004,"teacher_disagreement_score":0.0022830311,"about_ca_system_score_codex":0.00053951924,"about_ca_system_score_gemma":0.00050918566,"threshold_uncertainty_score":0.0045394897},"labels":[],"label_agreement":null},{"id":"W4367626873","doi":"10.3390/s23094389","title":"Gain Enhancement and Cross-Polarization Suppression of Cavity-Backed Antennas Using a Flared Ground Cavity and Iris","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Optics; Polarization (electrochemistry); Antenna gain; Physics; Dual-polarization interferometry; Materials science; Antenna aperture; Antenna (radio); Dipole antenna; Engineering; Electrical engineering","score_opus":0.020625991713240057,"score_gpt":0.26470913927038164,"score_spread":0.24408314755714158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367626873","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9291907,0.00043885908,0.0660092,0.00010842596,0.00004607729,0.000020585767,0.000032613607,0.00023026657,0.0039232858],"genre_scores_gemma":[0.9599337,0.00017265427,0.038575638,0.000044070548,0.000013047234,0.000015591715,0.00002545342,0.000025530899,0.0011943022],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997408,0.000030403951,0.0000131179195,0.00006272048,0.00009717321,0.000055892207],"domain_scores_gemma":[0.9995952,0.00007144996,0.00016217603,0.00006591614,0.00007374046,0.000031515447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022763424,0.00039286728,0.0002425305,0.00016945729,0.00009832858,0.00036889073,0.000434411,0.0004215773,0.0003570445],"category_scores_gemma":[0.00034912842,0.00019457273,0.00029328643,0.00018101861,0.00043647457,0.00030691066,0.0004875403,0.00046919027,0.00021410342],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006061344,0.0000151954155,0.00040156164,0.000042999283,0.0000075867993,0.00011573878,0.00005304085,0.00097349525,0.9918887,0.0006694993,0.000053946766,0.0057176272],"study_design_scores_gemma":[0.00001450921,0.00021321292,0.0015981754,0.000008672822,0.000014518719,0.00029740448,0.000038821367,0.013343987,0.9818647,0.00016711987,0.0024198769,0.000019036563],"about_ca_topic_score_codex":0.00018611376,"about_ca_topic_score_gemma":0.00034732596,"teacher_disagreement_score":0.000434411,"about_ca_system_score_codex":0.00026645933,"about_ca_system_score_gemma":0.00015026965,"threshold_uncertainty_score":0.0019333363},"labels":[],"label_agreement":null},{"id":"W4375850331","doi":"10.3390/s23094535","title":"Exploring Tactile Temporal Features for Object Pose Estimation during Robotic Manipulation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Memorial University of Newfoundland","funders":"Memorial University of Newfoundland; Compute Canada; University of Ottawa","keywords":"Pose; Artificial intelligence; Robustness (evolution); Computer vision; Tactile sensor; Computer science; Orientation (vector space); Window (computing); Mean squared error; Pattern recognition (psychology); Robot; Mathematics; Statistics","score_opus":0.1629679316187359,"score_gpt":0.3246106524490795,"score_spread":0.1616427208303436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375850331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54571366,0.0013493344,0.4487349,0.00017347604,0.0001485742,0.000055513523,0.00037452925,0.00069191644,0.0027581064],"genre_scores_gemma":[0.9547754,0.0003430721,0.04379524,0.000046286696,0.000031860774,0.000026706166,0.0001498058,0.00003970572,0.000791923],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998528,0.00002293806,0.000008703325,0.000040788884,0.0000535159,0.000021225145],"domain_scores_gemma":[0.99962664,0.00018195325,0.00006894821,0.000031357722,0.00007126194,0.000019735013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026440417,0.00049576483,0.00026332116,0.00042532003,0.0001119627,0.00043546845,0.000227682,0.00033982555,0.0012994213],"category_scores_gemma":[0.0018940062,0.00017068845,0.00023200562,0.00042427587,0.00018280656,0.0007368845,0.00046894187,0.0002648414,0.00020967526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085687713,0.00015339829,0.008685953,0.00044998914,0.00007552073,0.00033254866,0.00025325522,0.0455346,0.50478125,0.0007302251,0.0009100391,0.43723637],"study_design_scores_gemma":[0.000028774997,0.0008081964,0.07163849,0.00009837675,0.00011000886,0.00090720714,0.0003457287,0.7523513,0.16716293,0.0033673723,0.0031051908,0.00007646072],"about_ca_topic_score_codex":0.0006775613,"about_ca_topic_score_gemma":0.0015742802,"teacher_disagreement_score":0.0012994213,"about_ca_system_score_codex":0.00011585591,"about_ca_system_score_gemma":0.00023165107,"threshold_uncertainty_score":0.0043469667},"labels":[],"label_agreement":null},{"id":"W4376630408","doi":"10.3390/s23094457","title":"Multi-Transduction-Mechanism Technology, an Emerging Approach to Enhance Sensor Performance","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Windsor Cancer Centre Foundation; University of Windsor; CMC Microsystems","keywords":"Miniaturization; Transduction (biophysics); Capacitive sensing; Mechanism (biology); Piezoresistive effect; Robustness (evolution); Electronic engineering; Computer science; Engineering; Electrical engineering; Physics","score_opus":0.049961296585584854,"score_gpt":0.3179440232876079,"score_spread":0.26798272670202306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376630408","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047891573,0.95850646,0.022618782,0.0006287869,0.00056852825,0.00007128029,0.000054696393,0.0001428083,0.012619422],"genre_scores_gemma":[0.041699417,0.93090546,0.019442594,0.00074162876,0.00044481878,0.00015334281,0.00011126198,0.000032610413,0.0064687766],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995516,0.000053763906,0.000025998526,0.00010862131,0.00021419412,0.000045772227],"domain_scores_gemma":[0.999728,0.00012028581,0.00004556444,0.00001644458,0.00007386731,0.000015798527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072507624,0.0010752585,0.0009147196,0.0020610746,0.00029379802,0.0009710089,0.0012145923,0.0013958692,0.001900256],"category_scores_gemma":[0.00049674825,0.00054771546,0.00055784016,0.0016038716,0.00071596046,0.0021066172,0.0007947395,0.001592631,0.0011686689],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007297434,0.00023054652,0.0005353851,0.021952147,0.00013360925,0.00049054576,0.00019045384,0.0018234262,0.22884074,0.055205394,0.010247329,0.6802775],"study_design_scores_gemma":[0.000018072515,0.0005643532,0.0009218214,0.0010215141,0.0001350649,0.0023077347,0.000113123126,0.003024107,0.14419165,0.0077027264,0.83991617,0.00008363798],"about_ca_topic_score_codex":0.00032985103,"about_ca_topic_score_gemma":0.0005318786,"teacher_disagreement_score":0.0020610746,"about_ca_system_score_codex":0.00063399103,"about_ca_system_score_gemma":0.0006022172,"threshold_uncertainty_score":0.006357014},"labels":[],"label_agreement":null},{"id":"W4376630911","doi":"10.3390/s23094484","title":"Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"KU Leuven; Fonds Wetenschappelijk Onderzoek","keywords":"Osteoarthritis; Motion capture; Inertial measurement unit; Ground reaction force; Context (archaeology); Knee Joint; Contact force; Physical medicine and rehabilitation; Gait; Computer science; Accelerometer; Workflow; Physical therapy; Medicine; Motion (physics); Artificial intelligence; Kinematics; Physics; Surgery","score_opus":0.04424570483269032,"score_gpt":0.2169220907591309,"score_spread":0.17267638592644058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376630911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9929363,0.0004976968,0.005648628,0.000038292343,0.000010070285,0.000018279365,0.00030454455,0.00004670738,0.0004995321],"genre_scores_gemma":[0.99866354,0.000108636006,0.00085317827,0.00001155501,0.000005936698,0.000008326349,0.00019772553,0.0000032492098,0.00014787253],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99968207,0.00009236046,0.000038795435,0.0000816178,0.00007100186,0.000034101617],"domain_scores_gemma":[0.9994405,0.00020317269,0.00017050137,0.000058390924,0.00007605995,0.0000514585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005174073,0.00043415246,0.00037906007,0.000665072,0.00015421964,0.0005047529,0.00019703504,0.00037084677,0.0010204876],"category_scores_gemma":[0.0030862498,0.00017565761,0.00021509353,0.00045382787,0.00016008096,0.00025515695,0.00039959405,0.00015858191,0.0003556296],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017025768,0.00028293443,0.8166944,0.0003751881,0.00025422522,0.0005563794,0.0007215406,0.0048838947,0.030315975,0.00012236903,0.00063794013,0.14345257],"study_design_scores_gemma":[0.000031527212,0.0006550141,0.9800812,0.000037159258,0.00010815296,0.0010153275,0.00039685084,0.013872173,0.00320779,0.00014878184,0.00042366784,0.00002235128],"about_ca_topic_score_codex":0.0019169068,"about_ca_topic_score_gemma":0.0036384542,"teacher_disagreement_score":0.0019169068,"about_ca_system_score_codex":0.00009798478,"about_ca_system_score_gemma":0.00015581408,"threshold_uncertainty_score":0.0038114786},"labels":[],"label_agreement":null},{"id":"W4376890740","doi":"10.3390/s23104759","title":"Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpolation (computer graphics); Channel (broadcasting); Electromyography; Computer science; Spline interpolation; Noise (video); Artificial intelligence; Pattern recognition (psychology); Mathematics; Computer vision; Bilinear interpolation; Medicine; Telecommunications","score_opus":0.032267292702831435,"score_gpt":0.24031344970586616,"score_spread":0.20804615700303472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376890740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2764371,0.0011627333,0.71918035,0.00014101398,0.000107362735,0.00011908885,0.00022912068,0.001397761,0.0012255007],"genre_scores_gemma":[0.7034985,0.0005572961,0.29395038,0.00018979536,0.000064838176,0.00009547323,0.0004070059,0.00016282525,0.0010739951],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981791,0.00034723635,0.00016784816,0.000383393,0.0007886269,0.00013374929],"domain_scores_gemma":[0.9956175,0.001818856,0.00072755496,0.0004664278,0.0012809968,0.000088715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024298043,0.00075415836,0.0007503151,0.0013758569,0.00031082824,0.0007302469,0.00065363426,0.00089612947,0.0005474336],"category_scores_gemma":[0.0084536085,0.00026198465,0.00048270385,0.0007835706,0.00050744764,0.0008246676,0.00066623325,0.0005933136,0.00047551602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001225246,0.0002116485,0.076785386,0.0010844585,0.0002513556,0.000825321,0.00066455436,0.020888645,0.4158035,0.0011079598,0.0020905319,0.47906137],"study_design_scores_gemma":[0.00005409511,0.00057237566,0.20700145,0.0001663679,0.00030487453,0.0032059387,0.00041522877,0.36044946,0.41841152,0.002270545,0.0069698114,0.00017828665],"about_ca_topic_score_codex":0.0010146473,"about_ca_topic_score_gemma":0.0021706035,"teacher_disagreement_score":0.0024298043,"about_ca_system_score_codex":0.00024559675,"about_ca_system_score_gemma":0.00033544816,"threshold_uncertainty_score":0.012850165},"labels":[],"label_agreement":null},{"id":"W4377041912","doi":"10.3390/s23104802","title":"Temporal, Kinematic and Kinetic Variables Derived from a Wearable 3D Inertial Sensor to Estimate Muscle Power during the 5 Sit to Stand Test in Older Individuals: A Validation Study","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fondazione Cassa Di Risparmio Di Trento E Rovereto","keywords":"Inertial measurement unit; Limits of agreement; Bland–Altman plot; Pearson product-moment correlation coefficient; Mathematics; Correlation coefficient; Statistics; Medicine; Nuclear medicine; Computer science; Artificial intelligence","score_opus":0.023068946840786943,"score_gpt":0.3444016804746101,"score_spread":0.3213327336338232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377041912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975352,0.000077514116,0.001986017,0.0000059682866,0.000008704576,0.00006433197,0.00015163464,0.000008077095,0.00016255537],"genre_scores_gemma":[0.9966313,0.000071236136,0.0021979304,0.00003491998,0.000017417937,0.00016287804,0.0006166344,0.000008388454,0.00025924412],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993907,0.00018658453,0.00006312088,0.000120928096,0.00018432284,0.00005427403],"domain_scores_gemma":[0.99847156,0.00035818713,0.0002056933,0.00017541708,0.00066273607,0.00012645572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023178007,0.00067322294,0.00044095836,0.00047601786,0.0002814225,0.00042800847,0.00037581316,0.00069117756,0.0004657493],"category_scores_gemma":[0.0035901603,0.00023751432,0.0005397265,0.0002835331,0.0003695301,0.00028219007,0.00039546774,0.00037244646,0.0003617595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028653068,0.002002911,0.92072654,0.00016601944,0.00059924094,0.0003578905,0.0019234685,0.001854206,0.03682428,0.00007240965,0.0003705866,0.032237146],"study_design_scores_gemma":[0.00007446373,0.0034022527,0.9895901,0.000019749788,0.00013881203,0.00037874546,0.000282016,0.0040496234,0.0016279668,0.000025886662,0.0003938613,0.000016411575],"about_ca_topic_score_codex":0.002687809,"about_ca_topic_score_gemma":0.0032584574,"teacher_disagreement_score":0.002687809,"about_ca_system_score_codex":0.00016346143,"about_ca_system_score_gemma":0.00027298776,"threshold_uncertainty_score":0.012257874},"labels":[],"label_agreement":null},{"id":"W4377042701","doi":"10.3390/s23104793","title":"Image Generation and Recognition for Railway Surface Defect Detection","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Transport Canada","keywords":"Obstacle; Artificial intelligence; Nondestructive testing; Artificial neural network; Segmentation; Computer science; Pattern recognition (psychology); Identification (biology); Track (disk drive); Pixel; Computer vision; Image segmentation; Sampling (signal processing); Filter (signal processing)","score_opus":0.016226932949881045,"score_gpt":0.2228881358327438,"score_spread":0.20666120288286277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377042701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0921568,0.00056006166,0.89637136,0.00021424428,0.0000974363,0.0001490398,0.00035111106,0.0047796573,0.00532025],"genre_scores_gemma":[0.5495417,0.00054142566,0.44148907,0.0001557053,0.000040717445,0.00010725182,0.0012413773,0.00023528821,0.00664746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998411,0.000015295902,0.0000062782274,0.000049631086,0.00006631184,0.000021335238],"domain_scores_gemma":[0.99984515,0.00003011197,0.000020409725,0.000031424814,0.00006570338,0.000007245215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025440272,0.0004136734,0.00029743311,0.00077213085,0.00012570772,0.00030016043,0.0005485367,0.0004969235,0.002167433],"category_scores_gemma":[0.000485979,0.00018689728,0.00046652107,0.0004826077,0.00019677276,0.00039941983,0.00030581566,0.0003545746,0.0010015024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002161778,0.00010763296,0.0028000644,0.00017550711,0.000038086066,0.00022413947,0.00006551508,0.06455591,0.17325595,0.002572892,0.0056600305,0.75032806],"study_design_scores_gemma":[0.000013016281,0.00013118914,0.006357953,0.000016584658,0.000029298646,0.00033159417,0.000034275447,0.89068145,0.09404658,0.0013147565,0.0070229294,0.000020387884],"about_ca_topic_score_codex":0.0024996009,"about_ca_topic_score_gemma":0.002695148,"teacher_disagreement_score":0.0024996009,"about_ca_system_score_codex":0.0003755397,"about_ca_system_score_gemma":0.0003348859,"threshold_uncertainty_score":0.007250786},"labels":[],"label_agreement":null},{"id":"W4377043137","doi":"10.3390/s23104799","title":"A Comparison of Multiple Odor Source Localization Algorithms","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Insect Pheromone Research and Control","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Algorithm; Computer science; Odor; Grid; Data mining; Artificial intelligence; Mathematics","score_opus":0.04269622856563747,"score_gpt":0.2928087743813433,"score_spread":0.2501125458157058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377043137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1812515,0.0030572792,0.8005785,0.00070379005,0.0002864864,0.000250262,0.00034314056,0.0042482605,0.009280772],"genre_scores_gemma":[0.5863553,0.001119276,0.40827712,0.00019527809,0.00008721508,0.00016405391,0.00081704586,0.0002904182,0.0026942915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979837,0.00045483743,0.00017874292,0.0003705445,0.0008461222,0.00016612839],"domain_scores_gemma":[0.9923822,0.0045155664,0.0004015121,0.0006220948,0.0018716858,0.00020694382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040494218,0.0013386371,0.0014016972,0.00311562,0.00066195446,0.0015040203,0.0021923375,0.0014110807,0.0024938958],"category_scores_gemma":[0.011453254,0.00040729812,0.0010454943,0.0018983969,0.00058988424,0.0029885082,0.0015346323,0.00073681003,0.00071248674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010561988,0.00029537824,0.00842894,0.0003198563,0.00026529247,0.0000814209,0.00010846355,0.3571795,0.004057831,0.005759085,0.0024364966,0.62001157],"study_design_scores_gemma":[0.000106868756,0.00022218542,0.0023975922,0.000030704443,0.00005107916,0.0001462606,0.00008763597,0.98857456,0.0044494527,0.0025714682,0.0013303458,0.000031707365],"about_ca_topic_score_codex":0.005731445,"about_ca_topic_score_gemma":0.0041325195,"teacher_disagreement_score":0.005731445,"about_ca_system_score_codex":0.0013667953,"about_ca_system_score_gemma":0.001958257,"threshold_uncertainty_score":0.02141565},"labels":[],"label_agreement":null},{"id":"W4377043242","doi":"10.3390/s23104810","title":"A Direct Immunoassay Based on Surface-Enhanced Spectroscopy Using AuNP/PS-b-P2VP Nanocomposites","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; University of Victoria","keywords":"Analyte; Surface modification; Biomolecule; Detection limit; Immunoassay; Biosensor; Colloidal gold; Chemistry; Covalent bond; Nanocomposite; Polystyrene; Substrate (aquarium); Nanoparticle; Aptamer; Chromatography; Materials science; Nanotechnology; Polymer; Organic chemistry; Antibody; Molecular biology","score_opus":0.011408052765231703,"score_gpt":0.28655502006287237,"score_spread":0.27514696729764065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377043242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61085314,0.0059118564,0.3764351,0.00031094375,0.00023654765,0.00033509528,0.00036697154,0.0022496602,0.0033007131],"genre_scores_gemma":[0.74320203,0.0023116188,0.24782613,0.0001408422,0.000053699863,0.0002680287,0.00035538204,0.00006570509,0.005776573],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994356,0.000090481444,0.000026634065,0.00017765384,0.00023314194,0.000036384838],"domain_scores_gemma":[0.99986494,0.000047576643,0.000026905887,0.000010199855,0.00003533553,0.000015089683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030349175,0.0009388711,0.00040533236,0.00038506862,0.00013005278,0.00051182375,0.0006806033,0.00087811565,0.00045691154],"category_scores_gemma":[0.0004100419,0.0004265839,0.00023982242,0.0002969537,0.00033014832,0.00036177383,0.00040985824,0.000651403,0.0004513838],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009740265,0.000009597463,0.000044012922,0.00003593491,0.0000037719362,0.000024168829,0.0000053647464,0.00006686032,0.99760026,0.000044556753,0.0000206433,0.0021350419],"study_design_scores_gemma":[0.0000041106555,0.00007623866,0.00041559708,0.0000029727114,0.000006903658,0.00016191571,0.0000052777436,0.0030301823,0.99556005,0.000044139,0.00068751466,0.000005111965],"about_ca_topic_score_codex":0.00033339637,"about_ca_topic_score_gemma":0.00043528498,"teacher_disagreement_score":0.0009388711,"about_ca_system_score_codex":0.00033370958,"about_ca_system_score_gemma":0.00021377583,"threshold_uncertainty_score":0.0024212599},"labels":[],"label_agreement":null},{"id":"W4377092359","doi":"10.3390/s23104909","title":"Design and Psychophysical Evaluation of a Novel Wearable Upper-Arm Tactile Display Device","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wearable computer; Actuator; Tactile display; Tactile perception; Computer science; Vibration; Stimulus (psychology); Wearable technology; Perception; Just-noticeable difference; Acoustics; Simulation; Computer vision; Artificial intelligence; Physics; Psychology","score_opus":0.11955463960726682,"score_gpt":0.3622760622475376,"score_spread":0.2427214226402708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377092359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82959217,0.0011400447,0.1642903,0.00027367388,0.00019080205,0.0007343402,0.00030708069,0.0005591216,0.0029125821],"genre_scores_gemma":[0.8739231,0.0007517908,0.12107633,0.0002940985,0.000055634162,0.00049192144,0.00017728671,0.000051595838,0.0031782456],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995814,0.00008891104,0.000044494856,0.00009577729,0.0001480234,0.000041498613],"domain_scores_gemma":[0.99931455,0.00024211734,0.0000790574,0.00008220151,0.00018145342,0.00010062563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054749806,0.0005750473,0.00060987374,0.00028179752,0.00017443868,0.00059320335,0.0007372768,0.0008084136,0.002791755],"category_scores_gemma":[0.0012149098,0.0002575984,0.000422645,0.0001948241,0.000247165,0.00060825417,0.00052571413,0.00028135514,0.0004437158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005014093,0.0003114454,0.000629634,0.00042948918,0.0000314714,0.0002799409,0.00010718502,0.00039730442,0.97987956,0.00012028195,0.00017670407,0.017135657],"study_design_scores_gemma":[0.00077856577,0.02811952,0.07309737,0.0002533611,0.00058512815,0.007694783,0.00041698554,0.04226831,0.83391947,0.0005480217,0.012070382,0.00024803123],"about_ca_topic_score_codex":0.00016736062,"about_ca_topic_score_gemma":0.00018343984,"teacher_disagreement_score":0.002791755,"about_ca_system_score_codex":0.00017958584,"about_ca_system_score_gemma":0.00021341821,"threshold_uncertainty_score":0.009339392},"labels":[],"label_agreement":null},{"id":"W4377108362","doi":"10.3390/s23104839","title":"Automated Implementation of the Edinburgh Visual Gait Score (EVGS) Using OpenPose and Handheld Smartphone Video","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Gait; Computer science; Artificial intelligence; Accelerometer; Computer vision; Gait analysis; Ground truth; Mobile device; Physical medicine and rehabilitation; Medicine","score_opus":0.030426483005578764,"score_gpt":0.3541102345911024,"score_spread":0.32368375158552365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377108362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26617995,0.00016700971,0.7202687,0.0001009609,0.000118444914,0.0007493798,0.002251634,0.0071131974,0.003050705],"genre_scores_gemma":[0.6247425,0.00020439904,0.36967635,0.00009371681,0.00005616312,0.00058972166,0.0021180706,0.00023666503,0.0022824586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995858,0.00006759408,0.000031421278,0.00010386436,0.00018229717,0.000028953704],"domain_scores_gemma":[0.9992816,0.00016803709,0.00007443336,0.00006499547,0.00036472382,0.000046140143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047889992,0.00065844704,0.00044613032,0.0013256838,0.000112902184,0.000544441,0.0006171736,0.00033289645,0.0032481952],"category_scores_gemma":[0.002455769,0.00020143701,0.00033594083,0.0004003917,0.00014002727,0.00035655792,0.0006033588,0.00022883569,0.0011025796],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011224564,0.00024856196,0.028303795,0.0004613538,0.000116888485,0.00045769676,0.00032048696,0.009728713,0.15543142,0.001161948,0.005886742,0.79676],"study_design_scores_gemma":[0.00025872485,0.0014456044,0.18764701,0.00018000815,0.00014046751,0.001912707,0.00069301313,0.62199676,0.17239061,0.0027186503,0.010386963,0.00022954661],"about_ca_topic_score_codex":0.002502925,"about_ca_topic_score_gemma":0.004917433,"teacher_disagreement_score":0.0032481952,"about_ca_system_score_codex":0.00019651926,"about_ca_system_score_gemma":0.00029856784,"threshold_uncertainty_score":0.010866284},"labels":[],"label_agreement":null},{"id":"W4377293169","doi":"10.3390/s23104924","title":"Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transmission (telecommunications); Computer science; Tree (set theory); Data transmission; Wearable computer; Real-time computing; Process (computing); Interval (graph theory); Sequence (biology); Data aggregator; Wireless sensor network; Data mining; Scheme (mathematics); Missing data; Artificial intelligence; Machine learning; Computer network; Telecommunications; Embedded system; Mathematics","score_opus":0.3780418867892188,"score_gpt":0.3937205384757478,"score_spread":0.015678651686528988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377293169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061279844,0.0006929841,0.93381286,0.00023349671,0.00013020934,0.00015420213,0.0007602102,0.0015747137,0.0013614893],"genre_scores_gemma":[0.70327544,0.0007172327,0.2887893,0.00017626413,0.00011739484,0.00039820693,0.0030139615,0.00009686317,0.0034153096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909806,0.00013127859,0.00009720747,0.0002692568,0.0002706537,0.00013367341],"domain_scores_gemma":[0.99863845,0.00049834914,0.00019708143,0.0001563423,0.00044236213,0.00006740711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001170578,0.0009415461,0.00109521,0.0021554253,0.00056518085,0.0007567151,0.0012905126,0.00087616785,0.0018319982],"category_scores_gemma":[0.00374111,0.00026357008,0.001108587,0.0019986967,0.00025162785,0.0012918125,0.0006765195,0.0010941926,0.0010181245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041384788,0.0005730211,0.018414782,0.00026233745,0.00014008352,0.0003380651,0.00022311092,0.11809949,0.010584598,0.0040035537,0.006890223,0.84005696],"study_design_scores_gemma":[0.0000086543305,0.00013062335,0.0021045152,0.000021429541,0.000028242626,0.00011505591,0.00005525067,0.9906578,0.0029609413,0.0026879616,0.0012159143,0.000013695691],"about_ca_topic_score_codex":0.0044332813,"about_ca_topic_score_gemma":0.004487279,"teacher_disagreement_score":0.0044332813,"about_ca_system_score_codex":0.0005280982,"about_ca_system_score_gemma":0.0010381696,"threshold_uncertainty_score":0.008814931},"labels":[],"label_agreement":null},{"id":"W4377294066","doi":"10.3390/s23104929","title":"Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Channel state information; Overhead (engineering); Internet of Things; Scheme (mathematics); Base station; Coding (social sciences); Channel (broadcasting); Real-time computing; Scheduling (production processes); Computer network; Computer engineering; Wireless; Embedded system; Engineering; Telecommunications; Operating system","score_opus":0.018786459761761357,"score_gpt":0.23877510471464397,"score_spread":0.21998864495288262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377294066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023646489,0.0007197388,0.97132236,0.00042360317,0.000253624,0.00007580379,0.00010757433,0.0010122404,0.0024385736],"genre_scores_gemma":[0.94944286,0.00032011818,0.04766919,0.0003450264,0.00011233825,0.00012709067,0.000098357465,0.000036608395,0.0018483932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935216,0.00012709411,0.00004989838,0.00016582492,0.00019669803,0.00010836747],"domain_scores_gemma":[0.9992231,0.00030467735,0.00009920995,0.00007633041,0.00025258228,0.000044099997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089606753,0.0008904142,0.0009106897,0.0003046011,0.0007875211,0.0006914924,0.0016045567,0.0008547572,0.002305382],"category_scores_gemma":[0.0023695359,0.0002972018,0.0004722821,0.00048678849,0.00072468177,0.0016190021,0.0011645273,0.0013317417,0.000323967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055503537,0.0002177685,0.0012665103,0.00031791185,0.00008963345,0.00032016856,0.00036340312,0.7560935,0.034749046,0.01839623,0.0067835297,0.1808473],"study_design_scores_gemma":[0.000015120068,0.000056932302,0.00008141328,0.000007319387,0.000010776311,0.0000313969,0.00001043721,0.9955503,0.0016696871,0.0020951715,0.00045957026,0.000011994347],"about_ca_topic_score_codex":0.0077238497,"about_ca_topic_score_gemma":0.009014072,"teacher_disagreement_score":0.0077238497,"about_ca_system_score_codex":0.00093631534,"about_ca_system_score_gemma":0.0018220622,"threshold_uncertainty_score":0.015357733},"labels":[],"label_agreement":null},{"id":"W4378082694","doi":"10.3390/s23114999","title":"A Dynamic Procedure to Detect Maximum Voluntary Contractions in Low Back","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Amplitude; Wilcoxon signed-rank test; Electromyography; SIGNAL (programming language); Computer science; Biomedical engineering; Mathematics; Physical medicine and rehabilitation; Statistics; Engineering; Medicine; Physics; Mann–Whitney U test","score_opus":0.007016499156123454,"score_gpt":0.21999470825424727,"score_spread":0.21297820909812382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378082694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35192505,0.0027592634,0.6356211,0.00024321173,0.00042320555,0.0015099496,0.001300412,0.0013546078,0.0048632724],"genre_scores_gemma":[0.59017354,0.0015425399,0.40028793,0.00052319013,0.00021917233,0.0026967851,0.0010633846,0.00027035343,0.0032231628],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989385,0.00015338023,0.00007784519,0.00030531298,0.00045978033,0.000065175205],"domain_scores_gemma":[0.9988857,0.00041569158,0.00013551858,0.000114259215,0.000395806,0.000053085176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008565056,0.00082354585,0.0006435669,0.0013267613,0.00038310752,0.00037651474,0.00062261877,0.0008017489,0.002162043],"category_scores_gemma":[0.0028297673,0.00028489306,0.00029509212,0.0009040375,0.00044142522,0.00045208435,0.0005757995,0.00049412943,0.0006052269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039078295,0.00018851947,0.004886739,0.00059523643,0.000050777373,0.0001493552,0.00023110447,0.0007674964,0.7917142,0.00049557735,0.0010137128,0.19951645],"study_design_scores_gemma":[0.0001682354,0.0069380803,0.30142823,0.00027183598,0.00035163094,0.0048458176,0.0005372957,0.04930049,0.60884607,0.001464499,0.025512634,0.00033515508],"about_ca_topic_score_codex":0.00077489426,"about_ca_topic_score_gemma":0.0018652413,"teacher_disagreement_score":0.002162043,"about_ca_system_score_codex":0.00018384733,"about_ca_system_score_gemma":0.0003589357,"threshold_uncertainty_score":0.0072327256},"labels":[],"label_agreement":null},{"id":"W4378228260","doi":"10.3390/s23115054","title":"Will Your Next Therapist Be a Robot?—A Review of the Advancements in Robotic Upper Extremity Rehabilitation","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College Ahuntsic","funders":"","keywords":"Rehabilitation; Robot; Robotics; Field (mathematics); Physical medicine and rehabilitation; Computer science; Medicine; Artificial intelligence; Physical therapy","score_opus":0.07873158598585339,"score_gpt":0.3805139396219617,"score_spread":0.30178235363610834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378228260","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000791857,0.9991567,0.000102629216,0.00016928106,0.00008060977,0.000005973355,0.0000147225,0.00000351136,0.00038735318],"genre_scores_gemma":[0.00057387206,0.9989328,0.00017558182,0.00012012413,0.000071469105,0.000007035782,0.000014832748,0.0000012862965,0.00010297166],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992747,0.0001686923,0.00019562233,0.00011123382,0.00021716616,0.000032607604],"domain_scores_gemma":[0.998108,0.0014333202,0.00018994047,0.000025260679,0.00020463306,0.000038823586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013742737,0.0008740486,0.0016219235,0.004178667,0.00036393633,0.0012572076,0.0009766364,0.0013080431,0.004101635],"category_scores_gemma":[0.0029558614,0.0004409708,0.0013531768,0.0032650586,0.0004772882,0.0016686566,0.0006751274,0.0011682787,0.0012188415],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082369086,0.000076972465,0.00023077111,0.1131321,0.00022155231,0.00013942088,0.00013525343,0.00032373937,0.0004864777,0.0019664872,0.012298987,0.8709059],"study_design_scores_gemma":[0.0000382963,0.00029711507,0.0027901358,0.11850204,0.0011408511,0.002983229,0.0003423234,0.00029151732,0.000540005,0.0025376424,0.8704757,0.000061123996],"about_ca_topic_score_codex":0.0016573825,"about_ca_topic_score_gemma":0.0028921745,"teacher_disagreement_score":0.004178667,"about_ca_system_score_codex":0.00071710494,"about_ca_system_score_gemma":0.0017491224,"threshold_uncertainty_score":0.013721347},"labels":[],"label_agreement":null},{"id":"W4378228333","doi":"10.3390/s23115055","title":"An Intelligent Healthcare System Using IoT in Wireless Sensor Network","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"King Saud University","keywords":"Wireless sensor network; Computer science; Encryption; Authentication (law); Energy consumption; Computer network; Data transmission; Wireless; Internet of Things; Computer security; Embedded system; Engineering; Telecommunications","score_opus":0.030297066691706124,"score_gpt":0.2736608013008524,"score_spread":0.2433637346091463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378228333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09916634,0.0020016525,0.86353225,0.0012996555,0.0007344868,0.000520093,0.00030231514,0.004910788,0.027532369],"genre_scores_gemma":[0.80106956,0.0012998009,0.17819682,0.0007895782,0.00014061056,0.00031690978,0.00039826342,0.000056399047,0.017732082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997843,0.000035687317,0.00002063724,0.00005521321,0.00008039423,0.000023709661],"domain_scores_gemma":[0.99992085,0.000013937542,0.000012413792,0.000011634573,0.00003146828,0.0000096599815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017982145,0.00028330882,0.00037609827,0.00026187953,0.00042088848,0.00050271535,0.00042974844,0.0005481905,0.0016870984],"category_scores_gemma":[0.00019868198,0.00013188204,0.00024574032,0.0002543845,0.00018130397,0.0006752376,0.0004325884,0.00026898246,0.000627962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010211909,0.00055084756,0.008416526,0.0010946312,0.0002142873,0.0021116869,0.0004693602,0.042101596,0.36045533,0.026019756,0.02315164,0.5343932],"study_design_scores_gemma":[0.00020094696,0.001680842,0.010095396,0.00016323026,0.0003412933,0.0045918874,0.0002468253,0.6578291,0.21751006,0.010297008,0.09689376,0.00014961249],"about_ca_topic_score_codex":0.0004196563,"about_ca_topic_score_gemma":0.00052805565,"teacher_disagreement_score":0.0016870984,"about_ca_system_score_codex":0.00021055575,"about_ca_system_score_gemma":0.0003174398,"threshold_uncertainty_score":0.005643964},"labels":[],"label_agreement":null},{"id":"W4378228784","doi":"10.3390/s23115018","title":"Solid-Phase Optical Sensing Techniques for Sensitive Virus Detection","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Boston University","keywords":"Computer science; Interferometry; Surface plasmon resonance; Nanotechnology; Materials science; Optics; Nanoparticle; Physics","score_opus":0.04746489592137581,"score_gpt":0.4423218296467711,"score_spread":0.39485693372539526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378228784","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037555285,0.41647056,0.509344,0.0025444133,0.0030919444,0.00048785363,0.0007344848,0.002168649,0.027602926],"genre_scores_gemma":[0.24941094,0.3640329,0.3587546,0.0019459535,0.0010890117,0.0007478874,0.00087894214,0.00027742967,0.022862317],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.998613,0.00021161308,0.000059461687,0.00028133087,0.0007678015,0.0000667436],"domain_scores_gemma":[0.9995734,0.00017937031,0.000104016115,0.000022557964,0.000103046936,0.000017583816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008435276,0.0017067223,0.0010073824,0.0012562159,0.00035924604,0.0010212731,0.0011087175,0.0017439655,0.0023757298],"category_scores_gemma":[0.00079128414,0.0008363525,0.00068525656,0.0014414116,0.0007768958,0.001596749,0.0006945272,0.0018211016,0.00271855],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006970686,0.00011225988,0.00019779033,0.004998415,0.000061021776,0.00027923356,0.00011669428,0.0012840407,0.8352088,0.00911639,0.006075614,0.14248015],"study_design_scores_gemma":[0.000021829916,0.00030793497,0.0003383072,0.00018102721,0.00005983094,0.0009447092,0.000075401855,0.010316941,0.79565775,0.0028024008,0.18921931,0.00007450101],"about_ca_topic_score_codex":0.0004179147,"about_ca_topic_score_gemma":0.0005242272,"teacher_disagreement_score":0.0023757298,"about_ca_system_score_codex":0.00055595534,"about_ca_system_score_gemma":0.0005800486,"threshold_uncertainty_score":0.007947564},"labels":[],"label_agreement":null},{"id":"W4378228953","doi":"10.3390/s23115022","title":"Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; American College of Sports Medicine","keywords":"Accelerometer; Gyroscope; Gait; Wearable computer; Event (particle physics); Identification (biology); Computer science; Foot (prosody); Gait analysis; Artificial intelligence; Simulation; Physical medicine and rehabilitation; Engineering; Medicine; Physics; Embedded system; Aerospace engineering","score_opus":0.06607929543947197,"score_gpt":0.3514649728174078,"score_spread":0.28538567737793585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378228953","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7169871,0.001350773,0.27604058,0.00010552431,0.00014870928,0.00025252096,0.0007663076,0.0024720049,0.0018765345],"genre_scores_gemma":[0.85564923,0.00050601247,0.13954304,0.000085682324,0.000095813346,0.00017857339,0.0019965111,0.00026722602,0.0016779475],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99892837,0.00023690842,0.000106645195,0.00038842807,0.00025292014,0.00008659624],"domain_scores_gemma":[0.997546,0.0012366147,0.00028370536,0.00025495002,0.0005790183,0.00009962158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014647271,0.0008585672,0.00094113173,0.0015144431,0.00024981372,0.0006013681,0.0006695371,0.00051437406,0.0006072295],"category_scores_gemma":[0.0039567337,0.00018650317,0.0006097197,0.0007166185,0.0002281912,0.00061628013,0.0005614105,0.00034772864,0.000508392],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00171898,0.0006115226,0.08558627,0.00050905574,0.0010232807,0.0001557891,0.0002794667,0.04291872,0.03180286,0.00030033058,0.0023455026,0.8327482],"study_design_scores_gemma":[0.00018692669,0.0011449255,0.19484949,0.000100257515,0.00042819176,0.0006307615,0.00039485245,0.779393,0.01964125,0.00092444004,0.0022010282,0.00010484917],"about_ca_topic_score_codex":0.0030151515,"about_ca_topic_score_gemma":0.0048297425,"teacher_disagreement_score":0.0030151515,"about_ca_system_score_codex":0.00016724512,"about_ca_system_score_gemma":0.00040981098,"threshold_uncertainty_score":0.0077462792},"labels":[],"label_agreement":null},{"id":"W4378575555","doi":"10.3390/s23115123","title":"Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"University of Manitoba; Natural Sciences and Engineering Research Council of Canada; CancerCare Manitoba Foundation","keywords":"Image quality; Computer science; Modality (human–computer interaction); Metric (unit); Artificial intelligence; Quality (philosophy); Image (mathematics); Pattern recognition (psychology); Data mining; Medical physics; Computer vision; Medicine; Engineering","score_opus":0.030118000865568425,"score_gpt":0.32105437077264565,"score_spread":0.2909363699070772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378575555","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027130908,0.9983333,0.00048163524,0.00011580524,0.00009975536,0.000014939684,0.00003858207,0.000008568722,0.0006361576],"genre_scores_gemma":[0.0012673076,0.99768543,0.0005196949,0.00013101361,0.0000755887,0.000018040084,0.000047363315,0.0000035957123,0.00025186024],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993352,0.00014552267,0.00014701116,0.00009696978,0.0002470278,0.00002820846],"domain_scores_gemma":[0.9977775,0.0014707184,0.0002715905,0.00005720182,0.00038710836,0.000035894886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014723137,0.0009881939,0.0019275004,0.004302545,0.00024265285,0.0010032625,0.0009507952,0.0011263813,0.0026896668],"category_scores_gemma":[0.002754236,0.00054514257,0.0012423185,0.0035992207,0.0005716804,0.0013429117,0.0005206361,0.00084548467,0.00096489047],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012981717,0.0000843714,0.0003778142,0.1699709,0.0005674779,0.00018985449,0.0001309969,0.0007114259,0.0064181117,0.0027554203,0.013214506,0.80544937],"study_design_scores_gemma":[0.000029221523,0.0004357324,0.0041630785,0.038706914,0.0021236,0.0023826761,0.0001749954,0.00050286396,0.009226277,0.0023883784,0.9397574,0.00010886829],"about_ca_topic_score_codex":0.0015291483,"about_ca_topic_score_gemma":0.0019445561,"teacher_disagreement_score":0.004302545,"about_ca_system_score_codex":0.00063851767,"about_ca_system_score_gemma":0.0015686383,"threshold_uncertainty_score":0.008997798},"labels":[],"label_agreement":null},{"id":"W4378575687","doi":"10.3390/s23115119","title":"Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"GNSS applications; Inertial navigation system; Air navigation; Computer science; Radar; Sensor fusion; Real-time computing; Multipath propagation; GNSS augmentation; Inertial measurement unit; Kalman filter; Dead reckoning; Remote sensing; Global Positioning System; Computer vision; Geography; Artificial intelligence; Telecommunications; Inertial frame of reference","score_opus":0.009461321748732541,"score_gpt":0.21557416805627216,"score_spread":0.20611284630753962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378575687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06995052,0.00018090106,0.92574024,0.000047650607,0.00004778927,0.00005411584,0.00005524189,0.0019189592,0.0020045561],"genre_scores_gemma":[0.46381083,0.00014130452,0.5331596,0.00004741042,0.000025968364,0.000052540046,0.00025835208,0.00010167287,0.0024022618],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996717,0.0000487524,0.000013790974,0.00007716859,0.0001507054,0.000037843653],"domain_scores_gemma":[0.9998037,0.00002754701,0.000030728537,0.00003137572,0.0000972816,0.000009321793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038188632,0.0005826128,0.0004205754,0.0009972241,0.00032009772,0.00042568386,0.00058769085,0.0003539707,0.0011570002],"category_scores_gemma":[0.00069878425,0.00028813467,0.00030150154,0.0011681152,0.00022046527,0.0006861109,0.0006416081,0.00040422997,0.00073403365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001768429,0.00014262772,0.0051489286,0.00008378903,0.00007921699,0.00016869722,0.00016184324,0.085842244,0.062079307,0.0021453,0.0012298954,0.8427413],"study_design_scores_gemma":[0.00003794595,0.00013618157,0.009960232,0.000015126884,0.000062322666,0.00027378858,0.00012324574,0.91403764,0.06684847,0.0015289455,0.0069432035,0.000032899417],"about_ca_topic_score_codex":0.0039896322,"about_ca_topic_score_gemma":0.0054495465,"teacher_disagreement_score":0.0039896322,"about_ca_system_score_codex":0.0003078901,"about_ca_system_score_gemma":0.0006563525,"threshold_uncertainty_score":0.007932842},"labels":[],"label_agreement":null},{"id":"W4378619394","doi":"10.3390/s23115154","title":"A Mobile Sensing Framework for Bridge Modal Identification through an Inverse Problem Solution Procedure and Moving-Window Time Series Models","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modal; Series (stratigraphy); Window (computing); Bridge (graph theory); Identification (biology); Computer science; Inverse problem; Inverse; Algorithm; Mathematics; Geology; Mathematical analysis; Materials science; Geometry","score_opus":0.03138794591326007,"score_gpt":0.2969495217224935,"score_spread":0.26556157580923345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378619394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014811319,0.00008303631,0.9979291,0.000022973802,0.000014288203,0.000008247491,0.0000116773845,0.000045964716,0.0004035388],"genre_scores_gemma":[0.42565006,0.0010799513,0.56405056,0.00009421973,0.00015813002,0.00034090492,0.00030908536,0.00012515343,0.008191951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997838,0.00005382483,0.000011528874,0.000065608445,0.00006676752,0.000018423458],"domain_scores_gemma":[0.9997906,0.00009416036,0.0000364169,0.000017286702,0.000049909562,0.000011570346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006162552,0.00092050125,0.0004870619,0.0004972054,0.0002786429,0.0006014661,0.0010261935,0.00089365547,0.0021866104],"category_scores_gemma":[0.0010704492,0.00035903495,0.0009832161,0.0004391663,0.00046258463,0.0008091959,0.00075023784,0.0010711786,0.00052765495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044267043,0.000049259612,0.0005238408,0.0001415017,0.000055851837,0.00016857931,0.0001449459,0.81859076,0.009614777,0.0907915,0.0009937661,0.07888089],"study_design_scores_gemma":[0.0000010437076,0.00001363977,0.000053502128,0.0000029380465,0.0000031762042,0.000009928245,0.0000049714454,0.996811,0.00025963972,0.0023044867,0.000531833,0.0000038719018],"about_ca_topic_score_codex":0.0047199284,"about_ca_topic_score_gemma":0.0036404426,"teacher_disagreement_score":0.0047199284,"about_ca_system_score_codex":0.00034138194,"about_ca_system_score_gemma":0.00058441644,"threshold_uncertainty_score":0.00938493},"labels":[],"label_agreement":null},{"id":"W4378625577","doi":"10.3390/s23104957","title":"Co-Designing Digital Technologies for Improving Clinical Care in People with Parkinson’s Disease: What Did We Learn?","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Instituto de Salud Carlos III; Agence Nationale de la Recherche; Canadian Institutes of Health Research; EU Joint Programme – Neurodegenerative Disease Research","keywords":"Parkinson's disease; Disease; Medicine; Physical medicine and rehabilitation; Human–computer interaction; Psychology; Computer science; Gerontology","score_opus":0.04096063148909861,"score_gpt":0.33776594806692717,"score_spread":0.29680531657782855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378625577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47547492,0.036669016,0.17614141,0.26274922,0.0030521473,0.0031084465,0.00029259454,0.0011885001,0.041323744],"genre_scores_gemma":[0.7142701,0.024530984,0.23892727,0.01445496,0.00052883057,0.0014178056,0.00026393196,0.00014800427,0.005458108],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9721923,0.020210061,0.0011270824,0.0013995081,0.0034937954,0.0015772885],"domain_scores_gemma":[0.92664117,0.05167208,0.0033051623,0.003645657,0.009974108,0.00476174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03210988,0.0010531247,0.0006921491,0.0018619159,0.0021519104,0.008843956,0.0023393566,0.0044611,0.0031004085],"category_scores_gemma":[0.070598446,0.00049304613,0.0010517861,0.0012399364,0.0044078026,0.014413652,0.005226962,0.00485579,0.0010369716],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024826807,0.0017500783,0.02991146,0.0047336845,0.0001536282,0.0005271211,0.061437618,0.00097153947,0.0025143058,0.0110518355,0.012534334,0.8741661],"study_design_scores_gemma":[0.0006861277,0.0072904364,0.042400368,0.023392182,0.0008453856,0.005162833,0.46411383,0.015310312,0.021904573,0.082551755,0.33559835,0.00074379187],"about_ca_topic_score_codex":0.0026618834,"about_ca_topic_score_gemma":0.0045689037,"teacher_disagreement_score":0.03210988,"about_ca_system_score_codex":0.0025345231,"about_ca_system_score_gemma":0.010048512,"threshold_uncertainty_score":0.16981524},"labels":[],"label_agreement":null},{"id":"W4378882813","doi":"10.3390/s23115200","title":"Ratiometric Sensing of Glyphosate in Water Using Dual Fluorescent Carbon Dots","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Nanosensor; Glyphosate; Fluorescence; Quenching (fluorescence); Pesticide; Environmental chemistry; Chemistry; Nanotechnology; Materials science; Biotechnology; Biology; Ecology","score_opus":0.033707834211498054,"score_gpt":0.28097119203936505,"score_spread":0.247263357827867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378882813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9421288,0.0015612679,0.051992506,0.000326754,0.00007549258,0.00009161281,0.00029332683,0.00026479282,0.0032655604],"genre_scores_gemma":[0.9405116,0.00094701553,0.055157863,0.00021433894,0.000015117641,0.00009467342,0.00020606467,0.000027355098,0.002825952],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955505,0.00007334299,0.000023463614,0.0001754869,0.00013850526,0.000034181772],"domain_scores_gemma":[0.9998596,0.000042420488,0.000034018893,0.000009062361,0.00004155274,0.000013346379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024539966,0.00052782544,0.00025558867,0.0002859568,0.0001959895,0.0003512008,0.00040404926,0.0006705243,0.0007641252],"category_scores_gemma":[0.00038526248,0.00024141064,0.00017425482,0.00024372013,0.00041675183,0.0004963642,0.00037704312,0.00047284857,0.00019205766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017739661,0.000008416657,0.000082315586,0.000034012577,0.0000026043417,0.000028861945,0.000012105904,0.000090058,0.9983469,0.000089430134,0.000039882543,0.0012476889],"study_design_scores_gemma":[0.0000041331728,0.000044886332,0.00021692191,0.0000025360932,0.0000031719887,0.000050853803,0.000012880524,0.0017958094,0.99728775,0.000029740599,0.00054588384,0.000005375801],"about_ca_topic_score_codex":0.000601528,"about_ca_topic_score_gemma":0.0014451618,"teacher_disagreement_score":0.0007641252,"about_ca_system_score_codex":0.00045674565,"about_ca_system_score_gemma":0.00015887707,"threshold_uncertainty_score":0.0033139586},"labels":[],"label_agreement":null},{"id":"W4379054030","doi":"10.3390/s23115248","title":"Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Université Polytechnique Hauts-de-France; Centre National de la Recherche Scientifique; Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi; Polytechnique Montréal","keywords":"Occupancy; Cluster analysis; Transferability; Software deployment; Computer science; Process (computing); Parking lot; Data mining; Dimension (graph theory); Machine learning; Engineering","score_opus":0.2351732547900467,"score_gpt":0.3151517713477502,"score_spread":0.0799785165577035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379054030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2864432,0.00022094551,0.7080335,0.0002661336,0.000058515037,0.0000737257,0.000932695,0.0014111716,0.0025602316],"genre_scores_gemma":[0.9511766,0.00008827576,0.046994157,0.000027989987,0.000015518912,0.000048779013,0.0009382497,0.000051096573,0.00065932644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997944,0.000043526252,0.00001378667,0.00007213826,0.0000376767,0.00003843439],"domain_scores_gemma":[0.99961424,0.00009755374,0.000056891957,0.00009549233,0.000101925965,0.000034016935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042150277,0.0005156654,0.0006064019,0.0007481096,0.00031604586,0.00062641746,0.0013178498,0.0004761933,0.000948338],"category_scores_gemma":[0.0014661227,0.0004002021,0.00078188896,0.0010025701,0.0002457764,0.0012286848,0.0008122493,0.0005787434,0.0003677103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045940087,0.00003578531,0.0036879592,0.000020877525,0.000037608228,0.000037845002,0.000046866237,0.97325605,0.0013345525,0.0018632718,0.0007455006,0.018887749],"study_design_scores_gemma":[8.7972006e-7,0.000003831602,0.0003800971,0.0000012015109,0.0000028683203,0.000005820862,0.000011627149,0.9985007,0.0002612921,0.00066720915,0.0001616802,0.000002847291],"about_ca_topic_score_codex":0.020890307,"about_ca_topic_score_gemma":0.022149825,"teacher_disagreement_score":0.020890307,"about_ca_system_score_codex":0.0007100922,"about_ca_system_score_gemma":0.0008624364,"threshold_uncertainty_score":0.041537404},"labels":[],"label_agreement":null},{"id":"W4379185278","doi":"10.3390/s23115300","title":"Recent Developments in Inertial and Centrifugal Microfluidic Systems along with the Involved Forces for Cancer Cell Separation: A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Circulating tumor cell; Context (archaeology); Microfluidics; Metastasis; Cancer; Computer science; Cancer cell; Process (computing); Blood circulation; Nanotechnology; Biology; Medicine; Materials science; Internal medicine","score_opus":0.04875687820094877,"score_gpt":0.2995689854918421,"score_spread":0.2508121072908933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379185278","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026343574,0.9968836,0.00064516277,0.00021599667,0.000269329,0.000009812873,0.000031942778,0.000019239991,0.0016614819],"genre_scores_gemma":[0.00094782416,0.9973042,0.0006125887,0.00013863081,0.00015395191,0.000011591717,0.000041145737,0.0000032037715,0.00078679435],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99975425,0.0000299343,0.000034921126,0.000059156577,0.00009773476,0.000023975614],"domain_scores_gemma":[0.99951684,0.00026661938,0.000062924184,0.000013765647,0.00011327617,0.00002660749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005597838,0.0011960411,0.0012072743,0.0030795038,0.00028802946,0.0010597015,0.00080967793,0.0009738977,0.0043434543],"category_scores_gemma":[0.00077516935,0.0004713666,0.0007830081,0.0030087123,0.00037550053,0.001740397,0.0005994864,0.0013709487,0.0023439005],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056710956,0.00009318117,0.00020735027,0.028756168,0.000082621365,0.00016313055,0.00007469487,0.0007599745,0.006238283,0.004732207,0.01881041,0.9400252],"study_design_scores_gemma":[0.000009684144,0.00016636871,0.00062905275,0.0025043702,0.00014898625,0.00085481594,0.00006277017,0.00030111725,0.0025512828,0.0015796046,0.99115163,0.00004028631],"about_ca_topic_score_codex":0.00094350154,"about_ca_topic_score_gemma":0.0012362741,"teacher_disagreement_score":0.0043434543,"about_ca_system_score_codex":0.00045826484,"about_ca_system_score_gemma":0.00092287234,"threshold_uncertainty_score":0.014530301},"labels":[],"label_agreement":null},{"id":"W4379230580","doi":"10.3390/s23115292","title":"An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Ambiguity resolution; Ambiguity; Residual; Kinematics; GNSS applications; Computer science; Algorithm; Float (project management); Real Time Kinematic; Artificial intelligence; Engineering; Global Positioning System; Telecommunications","score_opus":0.010474768507009487,"score_gpt":0.24001367089503844,"score_spread":0.22953890238802896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379230580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025321404,0.0003642629,0.97128403,0.00007562159,0.00010978255,0.000031294203,0.00008214503,0.00094939995,0.0017821703],"genre_scores_gemma":[0.25760046,0.0002885953,0.7381917,0.00007729065,0.00009933244,0.00006429914,0.00038072446,0.00013834679,0.0031592606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994555,0.00006454815,0.00003106591,0.00010584901,0.00030022959,0.000042714368],"domain_scores_gemma":[0.99954563,0.00007785975,0.00006002179,0.000094078765,0.00020484554,0.00001750429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036222851,0.00071785564,0.0006073105,0.0009494496,0.00033278283,0.00055308535,0.000597535,0.0005359833,0.0019278794],"category_scores_gemma":[0.002013656,0.00030075942,0.00054639287,0.0008505969,0.0003267509,0.0010418672,0.0008800535,0.0007780871,0.0013884889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026680663,0.00004511758,0.001697957,0.00016502255,0.000059754777,0.00016295385,0.00018934422,0.048262615,0.0982009,0.006747196,0.0022749256,0.8419274],"study_design_scores_gemma":[0.00008822724,0.0002827783,0.005406332,0.00003516714,0.00006833256,0.0009651534,0.00013179689,0.8942967,0.078032814,0.0039940355,0.016564747,0.00013395361],"about_ca_topic_score_codex":0.0012469284,"about_ca_topic_score_gemma":0.0019614394,"teacher_disagreement_score":0.0019278794,"about_ca_system_score_codex":0.00019807728,"about_ca_system_score_gemma":0.0005640983,"threshold_uncertainty_score":0.0064494014},"labels":[],"label_agreement":null},{"id":"W4379232043","doi":"10.3390/s23115298","title":"XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Computer science; Malware; Internet of Things; Resource consumption; Computer security; Popularity; Resource (disambiguation); Real-time computing; Embedded system; Artificial intelligence; Computer network","score_opus":0.03455662257225686,"score_gpt":0.25679174302202507,"score_spread":0.2222351204497682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379232043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12543237,0.0005272001,0.83033067,0.0003757501,0.00017211394,0.0003207867,0.0010029376,0.037689243,0.0041490323],"genre_scores_gemma":[0.69857246,0.0003110926,0.29043582,0.0003966198,0.000080719416,0.00024289376,0.0025235587,0.0003388689,0.0070980336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958307,0.00005012934,0.000024501953,0.00013494195,0.00015968582,0.000047605074],"domain_scores_gemma":[0.9995184,0.00012923394,0.000065133994,0.000099406454,0.00015837986,0.000029448594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006230759,0.0007566653,0.00084012776,0.00085659744,0.00031590852,0.00051290455,0.0010017233,0.00067762,0.0017785439],"category_scores_gemma":[0.0012672528,0.00026661306,0.0005369037,0.00041547755,0.00014673403,0.0010245264,0.00089400465,0.0008832407,0.00074923545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068139494,0.0008534325,0.026564393,0.00016535008,0.00052618753,0.0007134229,0.00017314746,0.089014545,0.04582999,0.0022338426,0.016935252,0.81630903],"study_design_scores_gemma":[0.000026442869,0.00016144152,0.0044936757,0.000009565088,0.000056459998,0.00018665475,0.000017827215,0.9796128,0.011312097,0.00076043955,0.0033314978,0.000031008032],"about_ca_topic_score_codex":0.003057699,"about_ca_topic_score_gemma":0.004808009,"teacher_disagreement_score":0.003057699,"about_ca_system_score_codex":0.0003566764,"about_ca_system_score_gemma":0.0003925996,"threshold_uncertainty_score":0.006079793},"labels":[],"label_agreement":null},{"id":"W4379232896","doi":"10.3390/s23115293","title":"Multimodality Video Acquisition System for the Assessment of Vital Distress in Children","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Centre hospitalier universitaire Sainte-Justine","keywords":"Vital signs; Computer science; Interface (matter); Fidelity; Distress; Medicine; Database; Medical emergency; Operating system","score_opus":0.027205101897556073,"score_gpt":0.3569874849935774,"score_spread":0.3297823830960213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379232896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1968506,0.0020130915,0.76082087,0.00042673395,0.00019929996,0.0017876647,0.01583442,0.011306256,0.010761069],"genre_scores_gemma":[0.52044094,0.001519058,0.4614249,0.00040880247,0.00016553399,0.0021466096,0.007742154,0.0005006429,0.0056513017],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995479,0.00012254892,0.000034908295,0.00012451512,0.00014207792,0.00002800889],"domain_scores_gemma":[0.9993574,0.0001740986,0.000065456865,0.00005971066,0.0002812588,0.00006216664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008651336,0.0004623673,0.00040779737,0.0012768115,0.00020311435,0.000521831,0.00062950235,0.0004443268,0.0061943964],"category_scores_gemma":[0.0019847949,0.00018364024,0.0002568215,0.0006925262,0.00009763939,0.00044890403,0.00064607087,0.00034913156,0.0013795584],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017091791,0.00034292592,0.053101983,0.00081601564,0.00018056206,0.00067776575,0.00056979025,0.0071415007,0.17680061,0.0021598672,0.02207648,0.7344233],"study_design_scores_gemma":[0.0003639227,0.0022961895,0.34014457,0.0005979473,0.00051068625,0.0055003464,0.0007692218,0.38793892,0.18630959,0.003235936,0.07200907,0.0003236589],"about_ca_topic_score_codex":0.0031404928,"about_ca_topic_score_gemma":0.0048385146,"teacher_disagreement_score":0.0061943964,"about_ca_system_score_codex":0.00037791577,"about_ca_system_score_gemma":0.0005492323,"threshold_uncertainty_score":0.02072227},"labels":[],"label_agreement":null},{"id":"W4379472855","doi":"10.3390/s23115306","title":"Design of a Planar Sensor Based on Split-Ring Resonators for Non-Invasive Permittivity Measurement","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Universidad Carlos III de Madrid; King Saud University; European Commission","keywords":"Permittivity; Resonator; Metamaterial; Planar; Electric field; Materials science; Split-ring resonator; Microstrip; Optoelectronics; Microwave; Relative permittivity; Acoustics; Electronic engineering; Optics; Dielectric; Computer science; Physics; Engineering; Telecommunications","score_opus":0.05573107843480699,"score_gpt":0.24245276735520216,"score_spread":0.18672168892039517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379472855","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25248405,0.0015103389,0.7394567,0.00041382574,0.00030058355,0.00040035532,0.00030179336,0.0019077285,0.003224546],"genre_scores_gemma":[0.4258276,0.00055193773,0.5713445,0.00016101597,0.0000528635,0.00017841585,0.00014415497,0.000045824825,0.0016936747],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955136,0.000059633014,0.000022045728,0.00014228234,0.00019078869,0.000033856053],"domain_scores_gemma":[0.99968135,0.00005479985,0.00009384313,0.00004231072,0.00009568726,0.000032100343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003481886,0.00052012794,0.00074180134,0.00035078154,0.00014551445,0.00045259017,0.0013856435,0.00078671623,0.00060567784],"category_scores_gemma":[0.000332905,0.00037727953,0.00041098605,0.00025415907,0.00026545697,0.00068095885,0.0003254169,0.00040293942,0.000657596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063739724,0.00003057402,0.00024411923,0.000087757544,0.000014540113,0.00007803233,0.000018551298,0.0006652037,0.9892608,0.00055360585,0.00015092912,0.008832244],"study_design_scores_gemma":[0.000025052204,0.00068672776,0.001246292,0.000008218991,0.00004234494,0.00077477033,0.00002594339,0.041362867,0.9507554,0.00014653997,0.004884561,0.00004127276],"about_ca_topic_score_codex":0.00016502074,"about_ca_topic_score_gemma":0.0002771684,"teacher_disagreement_score":0.0013856435,"about_ca_system_score_codex":0.00031393443,"about_ca_system_score_gemma":0.00036247325,"threshold_uncertainty_score":0.0022777915},"labels":[],"label_agreement":null},{"id":"W4380085139","doi":"10.3390/s23125440","title":"Hybrid FSK–FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"King Saud University","keywords":"Frequency-shift keying; Electronic engineering; Computer science; Modulation (music); Bit error rate; Radar; Keying; Transmission (telecommunications); Quadrature amplitude modulation; Side lobe; Pulse-amplitude modulation; Telecommunications; Channel (broadcasting); Engineering; Demodulation; Acoustics; Physics","score_opus":0.03526119362900236,"score_gpt":0.25946722237345954,"score_spread":0.22420602874445716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380085139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10540464,0.0041714488,0.88176495,0.0004565675,0.00023092468,0.00008615786,0.00006986525,0.00045029286,0.007365148],"genre_scores_gemma":[0.6533034,0.0015480582,0.3402999,0.00025631333,0.00018804916,0.00008626721,0.00007262252,0.000035228048,0.0042100814],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995957,0.000078352525,0.000028353543,0.00008523697,0.00016902741,0.00004340958],"domain_scores_gemma":[0.9994784,0.00016386026,0.00011535178,0.00009408186,0.00012922398,0.000019066027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004785036,0.00048229014,0.00035149406,0.0006695422,0.00034268035,0.00044080516,0.00066816533,0.00063866714,0.001168615],"category_scores_gemma":[0.00078631006,0.00018937059,0.0002463646,0.00052051555,0.00040567332,0.0010473961,0.00041997386,0.0005545219,0.00052685686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048317944,0.0001222908,0.00090798637,0.00052690826,0.0000518887,0.0003006287,0.00035502363,0.013814072,0.6648146,0.029160133,0.0018109839,0.28765228],"study_design_scores_gemma":[0.00010311994,0.0009207781,0.0014098678,0.000102075945,0.00008901881,0.0026312766,0.00009845208,0.42978993,0.51533115,0.006509309,0.042890973,0.00012398088],"about_ca_topic_score_codex":0.00021208505,"about_ca_topic_score_gemma":0.00031912632,"teacher_disagreement_score":0.001168615,"about_ca_system_score_codex":0.00041599513,"about_ca_system_score_gemma":0.00020857164,"threshold_uncertainty_score":0.003909409},"labels":[],"label_agreement":null},{"id":"W4380225032","doi":"10.3390/s23115186","title":"MIMO 5G Smartphone Antenna with Tri-Band and Decoupled Elements","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"MIMO; Antenna (radio); Computer science; 3G MIMO; Electronic engineering; Telecommunications; Embedded system; Engineering; Beamforming","score_opus":0.009522933396235679,"score_gpt":0.20208035337173932,"score_spread":0.19255741997550366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380225032","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30378994,0.0011143732,0.6585418,0.0006882899,0.00053636235,0.000089982794,0.00024259769,0.002026101,0.032970566],"genre_scores_gemma":[0.86995876,0.0004445236,0.11900268,0.00046795752,0.00012716286,0.00010370508,0.00026073222,0.00003740411,0.00959704],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965143,0.00006750516,0.000017716453,0.00008931058,0.00010956908,0.00006444516],"domain_scores_gemma":[0.99979395,0.000027055088,0.000048355443,0.000041345105,0.000068382586,0.000020926798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013842834,0.0007222721,0.0004455375,0.00037897177,0.0001262016,0.0004849475,0.00060680683,0.0009561295,0.0019285879],"category_scores_gemma":[0.00022664951,0.00033773607,0.0006997378,0.00044265753,0.00019528161,0.00044317835,0.00045186596,0.00037643308,0.002040071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000633377,0.0001349328,0.0046802545,0.0004133817,0.00022537546,0.0013467818,0.00022038046,0.033091165,0.7950817,0.0072001745,0.006531726,0.15044065],"study_design_scores_gemma":[0.00021864225,0.0029835876,0.013207948,0.00010919115,0.00032390302,0.006623953,0.0003574171,0.42684966,0.48365432,0.0025787894,0.06283517,0.00025740595],"about_ca_topic_score_codex":0.00032803338,"about_ca_topic_score_gemma":0.00053329166,"teacher_disagreement_score":0.0019285879,"about_ca_system_score_codex":0.00039083837,"about_ca_system_score_gemma":0.00017164157,"threshold_uncertainty_score":0.0064517856},"labels":[],"label_agreement":null},{"id":"W4380358040","doi":"10.3390/s23125488","title":"DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Shanghai Municipal Education Commission; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"Convolution (computer science); Computer science; Feature (linguistics); Artificial intelligence; Redundancy (engineering); Channel (broadcasting); Pattern recognition (psychology); Computer vision; Artificial neural network","score_opus":0.027130865474849646,"score_gpt":0.24987660969703468,"score_spread":0.22274574422218504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380358040","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062656105,0.0005820177,0.92534053,0.00027350936,0.00014063569,0.000086604145,0.0006608381,0.007309417,0.0029503086],"genre_scores_gemma":[0.75241554,0.00045821146,0.23262331,0.00033446966,0.00006474978,0.0002063537,0.0020851383,0.0003059431,0.011506243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998425,0.000013714655,0.0000076164265,0.00006207339,0.00004927687,0.000024800473],"domain_scores_gemma":[0.99983275,0.000041374322,0.00001824188,0.000023240895,0.00006754156,0.000016843172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003099535,0.000845388,0.0006086436,0.00052502577,0.0002092802,0.0005265679,0.0016784118,0.0006387517,0.0021161765],"category_scores_gemma":[0.00065253134,0.000378326,0.00089787436,0.00031740166,0.00026924905,0.00086581433,0.0007383241,0.00080093835,0.0007717593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004708504,0.00018447783,0.0051196143,0.000119830285,0.00017190713,0.00021163782,0.00007166422,0.66658604,0.023342026,0.0026811701,0.007102832,0.29393795],"study_design_scores_gemma":[0.000005314628,0.000023369963,0.00030263394,0.0000018056747,0.000010582372,0.000022003664,0.0000030376652,0.9964309,0.0021891547,0.0003597382,0.00064742775,0.0000041121802],"about_ca_topic_score_codex":0.011720141,"about_ca_topic_score_gemma":0.013341885,"teacher_disagreement_score":0.011720141,"about_ca_system_score_codex":0.0008332533,"about_ca_system_score_gemma":0.0007525256,"threshold_uncertainty_score":0.023303866},"labels":[],"label_agreement":null},{"id":"W4380536579","doi":"10.3390/s23125558","title":"Enabling the ActiGraph GT9X Link’s Idle Sleep Mode and Inertial Measurement Unit Settings Directly Impacts Data Acquisition","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Sleep and related disorders","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Accelerometer; Hexapod; Data acquisition; Range (aeronautics); Units of measurement; Simulation; Computer science; Idle; Mode (computer interface); Engineering; Artificial intelligence; Robot; Human–computer interaction; Physics","score_opus":0.0544842009834312,"score_gpt":0.318656187808464,"score_spread":0.2641719868250328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380536579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9246437,0.0010591737,0.058481265,0.0009506712,0.0006166874,0.0008773559,0.0021292076,0.0018136102,0.0094283195],"genre_scores_gemma":[0.9496302,0.00055870396,0.041840147,0.00075903954,0.00012407855,0.0009637698,0.00090873,0.00026695538,0.004948363],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99819297,0.0006502724,0.00018022678,0.00028765298,0.0005444714,0.00014430095],"domain_scores_gemma":[0.99663,0.0015503734,0.00044899396,0.0004237905,0.0007581335,0.00018868627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019192657,0.0009828031,0.00059731345,0.00054509373,0.00031527216,0.0008794223,0.0008925123,0.00082540774,0.0075597735],"category_scores_gemma":[0.009107727,0.00036308885,0.00036423153,0.00047777596,0.00069361506,0.0007847422,0.0010428936,0.00051319133,0.0020195318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01446103,0.0012074494,0.11204564,0.003349933,0.0003681631,0.0010004466,0.0041687246,0.003023971,0.42795864,0.0016295522,0.013299752,0.41748667],"study_design_scores_gemma":[0.0010876502,0.028476428,0.66068274,0.0008650804,0.0010487288,0.0053883945,0.00351286,0.015922239,0.22389853,0.003047131,0.05569685,0.0003734354],"about_ca_topic_score_codex":0.00078720524,"about_ca_topic_score_gemma":0.001429275,"teacher_disagreement_score":0.0075597735,"about_ca_system_score_codex":0.0001805945,"about_ca_system_score_gemma":0.0004858067,"threshold_uncertainty_score":0.025289953},"labels":[],"label_agreement":null},{"id":"W4380626921","doi":"10.3390/s23125524","title":"Cloud and Precipitation Profiling Radars: The First Combined W- and K-Band Radar Profiler Measurements in Italy","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Università degli Studi dell'Aquila; Ministero dell’Istruzione, dell’Università e della Ricerca; Ministero della transizione ecologica","keywords":"Radar; Global Precipitation Measurement; Remote sensing; Environmental science; Satellite; Meteorology; Precipitation; Cloud computing; Observatory; Cloud cover; Computer science; Geology; Geography; Aerospace engineering; Engineering; Telecommunications; Physics","score_opus":0.09702609303676851,"score_gpt":0.28812332940607444,"score_spread":0.19109723636930592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380626921","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8956575,0.023804266,0.030334491,0.002421336,0.0004607684,0.00036466576,0.0031154652,0.0010586086,0.0427829],"genre_scores_gemma":[0.948945,0.0051630395,0.034103733,0.000455421,0.00056518713,0.000082833896,0.004270789,0.00012298858,0.0062911054],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996511,0.00006015271,0.000011279465,0.00011827404,0.000081535145,0.0000776361],"domain_scores_gemma":[0.99977,0.000031882602,0.000041283474,0.00004364248,0.00006302745,0.00005025356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072664156,0.00048557334,0.00032175265,0.0008011772,0.00023334,0.0006143677,0.00043765057,0.0005598753,0.00056143536],"category_scores_gemma":[0.00048108076,0.0002702995,0.00033272067,0.0009959086,0.0003155807,0.00047098487,0.0005101862,0.0005039765,0.0004872834],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013045525,0.00060699706,0.12507695,0.0009578865,0.00019054709,0.0028314062,0.0011061019,0.00888757,0.20609196,0.001987107,0.016909743,0.63404924],"study_design_scores_gemma":[0.00014193021,0.0014036065,0.87305117,0.0002881361,0.0002605194,0.0018699919,0.0002447584,0.012023547,0.026729943,0.00060281926,0.08328684,0.000096784395],"about_ca_topic_score_codex":0.006523534,"about_ca_topic_score_gemma":0.005600644,"teacher_disagreement_score":0.006523534,"about_ca_system_score_codex":0.00043179386,"about_ca_system_score_gemma":0.00052603614,"threshold_uncertainty_score":0.012971103},"labels":[],"label_agreement":null},{"id":"W4380632956","doi":"10.3390/s23125513","title":"Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Computer science; Wearable computer; Artificial intelligence; Machine learning; Stroke (engine); Rehabilitation; Trunk; Data mining; Medicine; Engineering; Physical therapy","score_opus":0.033419605845001046,"score_gpt":0.32726834242706904,"score_spread":0.293848736582068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380632956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13887857,0.00031573814,0.85749763,0.0001563448,0.00007133379,0.00012579886,0.00028709217,0.0015047253,0.0011626973],"genre_scores_gemma":[0.8555817,0.00013560169,0.14119938,0.00009776156,0.000056200486,0.00014738948,0.0012368778,0.000084625106,0.0014604131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991528,0.00016628888,0.000062740764,0.00032519616,0.0001891251,0.00010384372],"domain_scores_gemma":[0.99844164,0.0005052654,0.00017917815,0.00016233126,0.00062657584,0.00008506617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013459405,0.00087712094,0.000919803,0.0013133793,0.00041731817,0.0006197323,0.001068487,0.00082438946,0.00058281474],"category_scores_gemma":[0.0039331536,0.000252783,0.0007933805,0.00079738995,0.00031611454,0.00087607186,0.0007758163,0.00087065983,0.00034508275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053318014,0.00044510327,0.013980288,0.00017161314,0.0002857704,0.00025676485,0.00024968447,0.39693925,0.019830812,0.0013276656,0.0044385954,0.5615414],"study_design_scores_gemma":[0.000008799437,0.00004777257,0.002573106,0.0000060319417,0.000013068605,0.000031110434,0.000040028353,0.993046,0.0029841848,0.00097017054,0.00026766196,0.00001213203],"about_ca_topic_score_codex":0.0060135005,"about_ca_topic_score_gemma":0.00749792,"teacher_disagreement_score":0.0060135005,"about_ca_system_score_codex":0.0005730453,"about_ca_system_score_gemma":0.0008745898,"threshold_uncertainty_score":0.011957049},"labels":[],"label_agreement":null},{"id":"W4380985700","doi":"10.3390/s23125621","title":"A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Högskolan i Halmstad","keywords":"Snapshot (computer storage); Computer science; Heuristic; Machine learning; Ensemble learning; Warranty; Artificial neural network; Artificial intelligence; Curse of dimensionality; Dimensionality reduction; Data mining; Task (project management); Engineering; Database","score_opus":0.00903436928456349,"score_gpt":0.2030719345749339,"score_spread":0.1940375652903704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380985700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09730826,0.0014219389,0.8977757,0.0004419602,0.00011252556,0.000034005658,0.00042249006,0.0009322155,0.0015508037],"genre_scores_gemma":[0.9129915,0.0006355719,0.08246902,0.00018301992,0.00012039458,0.000082650244,0.0011136649,0.000053901484,0.002350327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970883,0.000063749896,0.000019158286,0.00008736378,0.000059802394,0.00006105189],"domain_scores_gemma":[0.9995061,0.00018828695,0.00006102264,0.000044525037,0.00016653372,0.000033486664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006775025,0.0010318181,0.0010063512,0.000596569,0.00030502336,0.0006020901,0.001142196,0.00079996843,0.0008340517],"category_scores_gemma":[0.0015358083,0.00050870486,0.00077175786,0.0007201933,0.00027269227,0.0010066504,0.00071465643,0.001348779,0.00020406552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070919436,0.00006181947,0.0029787365,0.000029335679,0.00009712877,0.00006774954,0.000034541954,0.9277026,0.0012627702,0.0010986653,0.0012225742,0.065373175],"study_design_scores_gemma":[6.831808e-7,0.000006171939,0.00018631184,0.0000013634647,0.0000043806813,0.0000026582495,0.0000026094135,0.99927765,0.000107434855,0.00035733957,0.000051496314,0.0000018121947],"about_ca_topic_score_codex":0.014040717,"about_ca_topic_score_gemma":0.01254691,"teacher_disagreement_score":0.014040717,"about_ca_system_score_codex":0.00058413466,"about_ca_system_score_gemma":0.0007676888,"threshold_uncertainty_score":0.027917981},"labels":[],"label_agreement":null},{"id":"W4380987936","doi":"10.3390/s23125648","title":"An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; King Abdulaziz University; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Entorhinal cortex; Deep learning; Hippocampus; Magnetic resonance imaging; Atrophy; Cognition; Artificial neural network; Artificial intelligence; Recall; Dementia; Psychology; Neuroscience; Pattern recognition (psychology); Computer science; Medicine; Disease; Pathology; Cognitive psychology; Radiology","score_opus":0.04244284395508885,"score_gpt":0.3675219632670021,"score_spread":0.32507911931191324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380987936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4118442,0.0020918474,0.57532126,0.00092981016,0.00018983452,0.00019017464,0.0014539559,0.0030679111,0.004910999],"genre_scores_gemma":[0.92900985,0.00039874803,0.06477811,0.0002031723,0.00003401994,0.00018718358,0.0014840988,0.000051872732,0.0038529024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998591,0.000026106418,0.000010037775,0.00004199416,0.00002786176,0.00003489343],"domain_scores_gemma":[0.9998233,0.00006557206,0.000017523182,0.000011360786,0.00007155206,0.0000107332735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053087756,0.0010348901,0.00052268046,0.000504372,0.00022048314,0.00049756054,0.00076306507,0.0008348365,0.0009904512],"category_scores_gemma":[0.00097452564,0.00029007345,0.0006692184,0.0003701905,0.00020950862,0.0003879544,0.00043862595,0.0007596351,0.0003030231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000165555,0.00011640675,0.0023130982,0.000037730304,0.000050620783,0.00009260015,0.000020866919,0.91176707,0.0032506639,0.0005872637,0.0017370785,0.07986114],"study_design_scores_gemma":[0.000004644973,0.000017104556,0.00024947664,0.0000033069891,0.0000062708546,0.0000075785065,0.0000020340765,0.9988279,0.0005529289,0.00023592483,0.00009032412,0.0000025380123],"about_ca_topic_score_codex":0.020856982,"about_ca_topic_score_gemma":0.016073912,"teacher_disagreement_score":0.020856982,"about_ca_system_score_codex":0.0008197383,"about_ca_system_score_gemma":0.0014382661,"threshold_uncertainty_score":0.041471124},"labels":[],"label_agreement":null},{"id":"W4380988651","doi":"10.3390/s23125613","title":"Prediction of Continuous Emotional Measures through Physiological and Visual Data","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre; University of Ottawa; Université du Québec en Outaouais","funders":"Canada Research Chairs","keywords":"Valence (chemistry); Arousal; Computer science; Machine learning; Artificial intelligence; Data pre-processing; Concordance correlation coefficient; Personalization; Feature selection; Preprocessor; Data mining; Psychology; Statistics","score_opus":0.1940617333012068,"score_gpt":0.37180018907115675,"score_spread":0.17773845576994995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380988651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50039375,0.0030485592,0.46758667,0.00070414686,0.0007562679,0.0003719697,0.012772304,0.0048079593,0.009558441],"genre_scores_gemma":[0.9174788,0.0008455809,0.071923986,0.00016937182,0.00020009345,0.00023382879,0.0074042245,0.00010630522,0.0016378387],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994332,0.0001254584,0.00003915027,0.00020776584,0.00013845667,0.000055840334],"domain_scores_gemma":[0.99853957,0.00060285855,0.00020264214,0.00019989813,0.00039768557,0.00005738626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068584946,0.0011370541,0.0005377777,0.001675913,0.00013021658,0.0009972194,0.00043951935,0.00066426274,0.0016033197],"category_scores_gemma":[0.004244603,0.00017333875,0.00053835625,0.00092977105,0.00020508295,0.00083980116,0.00055394927,0.0007415031,0.001344699],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013528172,0.0010888088,0.10784491,0.0010219131,0.0004025584,0.00038329634,0.00029686192,0.034967862,0.09044755,0.0014387417,0.0133426115,0.7474121],"study_design_scores_gemma":[0.000058280602,0.0007090213,0.3078276,0.00020469297,0.00018011332,0.00070741144,0.00051059935,0.63471925,0.041445706,0.005785964,0.0077229184,0.00012842777],"about_ca_topic_score_codex":0.0010082931,"about_ca_topic_score_gemma":0.0016776857,"teacher_disagreement_score":0.001675913,"about_ca_system_score_codex":0.00019188761,"about_ca_system_score_gemma":0.00014389992,"threshold_uncertainty_score":0.0053635836},"labels":[],"label_agreement":null},{"id":"W4380990075","doi":"10.3390/s23125638","title":"Backscattering Echo Intensity Characteristics of Laser in Soil Explosion Dust","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council","keywords":"Laser; Explosive material; Environmental science; Impact crater; Water content; Intensity (physics); Soil water; Echo (communications protocol); Remote sensing; Optics; Materials science; Soil science; Geology; Chemistry; Physics; Geotechnical engineering; Astrobiology","score_opus":0.01864301363261858,"score_gpt":0.25082433474070703,"score_spread":0.23218132110808845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380990075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958668,0.00008285776,0.0036718678,0.000008399897,0.0000022974552,0.0000026292716,0.00003475931,0.000026609157,0.00030386078],"genre_scores_gemma":[0.99824035,0.00004839467,0.0013786281,0.000011311861,0.0000018131835,0.00000406251,0.00003788997,0.0000057957664,0.00027169107],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998925,0.000009224286,0.000005035939,0.000027829894,0.00004600678,0.000019499415],"domain_scores_gemma":[0.999678,0.0001321726,0.000056433448,0.0000106715515,0.00009864682,0.000023989978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014207633,0.0001219895,0.00010360308,0.000508203,0.00009677899,0.0001639028,0.00013208517,0.0002398693,0.00073359837],"category_scores_gemma":[0.00045208287,0.00010285858,0.000111891306,0.00024070941,0.00016902562,0.00023165748,0.00015012843,0.00018562283,0.0001285817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018311573,0.00003862082,0.027512535,0.000043307133,0.000013364933,0.000127739,0.00023678935,0.0012685878,0.95709705,0.00008304932,0.00008089765,0.013314944],"study_design_scores_gemma":[0.0000162244,0.0005021993,0.2817253,0.000017239812,0.000036198682,0.0007884513,0.00074500503,0.02744265,0.6880135,0.00016377236,0.0005138454,0.00003571525],"about_ca_topic_score_codex":0.0007114839,"about_ca_topic_score_gemma":0.00090535166,"teacher_disagreement_score":0.00073359837,"about_ca_system_score_codex":0.0001369278,"about_ca_system_score_gemma":0.00007510734,"threshold_uncertainty_score":0.002454102},"labels":[],"label_agreement":null},{"id":"W4380990964","doi":"10.3390/s23125606","title":"Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Mitacs","keywords":"Photoplethysmogram; Metric (unit); Benchmark (surveying); Computer science; Modulation (music); SIGNAL (programming language); Artificial intelligence; Spectrogram; Pattern recognition (psychology); Frequency modulation; Radio frequency; Speech recognition; Computer vision; Acoustics; Telecommunications; Engineering; Physics","score_opus":0.05137319225405836,"score_gpt":0.32810638374133716,"score_spread":0.2767331914872788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380990964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6453649,0.004894945,0.3413909,0.00029415512,0.00024782808,0.00022360565,0.0019305189,0.0022406192,0.003412591],"genre_scores_gemma":[0.93010986,0.0014274288,0.06447586,0.000117985954,0.00017103288,0.00009274126,0.0021108135,0.00009582444,0.0013984088],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993913,0.00010569968,0.00005166333,0.0001459084,0.00026253672,0.000042763873],"domain_scores_gemma":[0.99891615,0.0003130498,0.00021941337,0.00012947214,0.0003653022,0.000056564357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007232447,0.0006022637,0.00059625006,0.001403465,0.00012462448,0.0006646259,0.00035194488,0.00061539124,0.00060516945],"category_scores_gemma":[0.0025158394,0.00011482887,0.00042450364,0.00078738073,0.00017220386,0.0004923696,0.0006469538,0.00037351475,0.00046071527],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015327364,0.00027449278,0.04041162,0.00066706014,0.00035332603,0.0004947384,0.00014037086,0.011842763,0.22715384,0.0005466409,0.004513615,0.71206886],"study_design_scores_gemma":[0.00015435301,0.0010235119,0.3023526,0.00011985013,0.00039696574,0.0038060136,0.00021781634,0.5469747,0.13610797,0.0021676777,0.0065609044,0.000117536394],"about_ca_topic_score_codex":0.0008103835,"about_ca_topic_score_gemma":0.0013680618,"teacher_disagreement_score":0.001403465,"about_ca_system_score_codex":0.00015571219,"about_ca_system_score_gemma":0.00017577944,"threshold_uncertainty_score":0.0038248897},"labels":[],"label_agreement":null},{"id":"W4380996200","doi":"10.3390/s23125592","title":"Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institutes of Health; Michael Smith Health Research BC","keywords":"Convolutional neural network; Artificial intelligence; Deep learning; Computer science; Wearable computer; Pattern recognition (psychology); Breathing; Residual; Machine learning; Medicine; Embedded system","score_opus":0.020635318429285416,"score_gpt":0.24900503289930387,"score_spread":0.22836971447001844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380996200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26825702,0.0006345894,0.7265097,0.0006834588,0.00014846299,0.00012666697,0.00025545055,0.0016522661,0.0017323355],"genre_scores_gemma":[0.87354165,0.00030825203,0.122617565,0.00036003478,0.000046100144,0.00013467035,0.00040609256,0.00006303054,0.0025226395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997577,0.00004833165,0.000017672433,0.0000831669,0.000059613703,0.00003360307],"domain_scores_gemma":[0.9996648,0.00011618097,0.000040991854,0.00004560007,0.000109280445,0.000023220064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000607792,0.0006756609,0.00038082385,0.00026504474,0.000105061954,0.0003769238,0.00069714064,0.00057604373,0.0006488105],"category_scores_gemma":[0.0014550532,0.00024356236,0.00036193785,0.00016745063,0.0001959486,0.00060511206,0.00035298572,0.0007279353,0.00026614883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006178391,0.0006055139,0.026813898,0.00014081836,0.00018748002,0.00022797514,0.000119552584,0.30309123,0.10019137,0.0011599271,0.002943464,0.563901],"study_design_scores_gemma":[0.000008025497,0.00010977659,0.0030398467,0.000007183369,0.000015117252,0.000042118307,0.000008514775,0.9887158,0.0073448764,0.00027221246,0.00042676175,0.00000981208],"about_ca_topic_score_codex":0.0053163012,"about_ca_topic_score_gemma":0.0063685654,"teacher_disagreement_score":0.0053163012,"about_ca_system_score_codex":0.00038148224,"about_ca_system_score_gemma":0.00061072473,"threshold_uncertainty_score":0.010570705},"labels":[],"label_agreement":null},{"id":"W4380996230","doi":"10.3390/s23125581","title":"Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satellite; Computer science; Scheduling (production processes); Constellation; Low earth orbit; Real-time computing; Satellite constellation; Energy consumption; Communications satellite; Wireless; Wireless sensor network; Remote sensing; Computer network; Telecommunications; Engineering; Electrical engineering; Aerospace engineering; Geography","score_opus":0.08195112128001246,"score_gpt":0.2954840789622386,"score_spread":0.21353295768222613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380996230","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34594837,0.0002486305,0.64954823,0.00030168056,0.00005242432,0.00007019486,0.000060447153,0.00035659113,0.00341341],"genre_scores_gemma":[0.98516023,0.000041189167,0.014277397,0.00003516269,0.000011684987,0.000018595874,0.000019247493,0.000011503796,0.00042508796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977976,0.00007538424,0.000008824357,0.000045590616,0.00003930423,0.00005111305],"domain_scores_gemma":[0.9989937,0.00062844844,0.0001701252,0.00006299743,0.00007246481,0.000072270195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004593519,0.0003070321,0.00037567504,0.00015570417,0.00036153937,0.0002906354,0.0005452784,0.00032139948,0.00074506307],"category_scores_gemma":[0.0020883458,0.00016666057,0.00013254986,0.00019653764,0.00040190914,0.0005684315,0.00046143122,0.0004610786,0.00011548024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025177706,0.000135562,0.0021825624,0.000034685323,0.000021869271,0.000064887725,0.00008343254,0.95305425,0.005851132,0.002068841,0.0004576265,0.035793383],"study_design_scores_gemma":[0.000007300086,0.000051894935,0.00035459848,0.0000015217826,0.0000033778035,0.000010648287,0.00001559181,0.99755293,0.0011291206,0.0007067191,0.00016321271,0.0000031001491],"about_ca_topic_score_codex":0.003975386,"about_ca_topic_score_gemma":0.0065445206,"teacher_disagreement_score":0.003975386,"about_ca_system_score_codex":0.00042573136,"about_ca_system_score_gemma":0.0005506901,"threshold_uncertainty_score":0.00790453},"labels":[],"label_agreement":null},{"id":"W4381149282","doi":"10.3390/s23125681","title":"Modelling Polarization Effects in a CdZnTe Sensor at Low Bias","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Redlen Technologies (Canada)","funders":"Grantová Agentura České Republiky","keywords":"Pockels effect; Detector; Electric field; Polarization (electrochemistry); Optics; Optoelectronics; Materials science; Photon; Photon counting; Physics; Laser; Chemistry","score_opus":0.02077386653184882,"score_gpt":0.2243828519899016,"score_spread":0.20360898545805278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381149282","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846064,0.00011294994,0.013513821,0.00008828445,0.000009017078,0.000014261996,0.000072197436,0.00006960841,0.0015134271],"genre_scores_gemma":[0.99734527,0.00007999461,0.0021359012,0.000010665174,0.000001271955,0.000007625263,0.000015841553,0.000007631737,0.00039584326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989927,0.000018319022,0.0000042357997,0.000019810135,0.000036007525,0.00002226876],"domain_scores_gemma":[0.99974173,0.00015790258,0.000036345933,0.000015430369,0.000039555947,0.000008983663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002220735,0.00024285559,0.00023133832,0.00020319241,0.00017433875,0.00044979542,0.00040020264,0.0007308752,0.0005150869],"category_scores_gemma":[0.0007230286,0.00019132528,0.00023515106,0.00026636638,0.00043837057,0.00041425767,0.00018478684,0.00025021107,0.00007512604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020601597,0.00007559552,0.00465263,0.00012770868,0.000027859727,0.00049733376,0.00022497735,0.7842705,0.20252635,0.003113884,0.0001379348,0.0041391947],"study_design_scores_gemma":[0.000010337294,0.000055298286,0.00073902926,0.0000055606833,0.000007700098,0.000037081812,0.000029552699,0.9687575,0.03002657,0.00020117924,0.00012317901,0.0000070254623],"about_ca_topic_score_codex":0.00324283,"about_ca_topic_score_gemma":0.0016491038,"teacher_disagreement_score":0.00324283,"about_ca_system_score_codex":0.00061375496,"about_ca_system_score_gemma":0.0003960489,"threshold_uncertainty_score":0.0064479113},"labels":[],"label_agreement":null},{"id":"W4381166022","doi":"10.3390/s23125665","title":"All-Optical, Air-Coupled Ultrasonic Detection of Low-Pressure Gas Leaks and Observation of Jet Tones in the MHz Range","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Government of Alberta","keywords":"Ultrasonic sensor; Jet (fluid); Acoustics; Range (aeronautics); Materials science; Optics; Physics; Engineering; Aerospace engineering; Composite material","score_opus":0.02219520733051531,"score_gpt":0.22002772021499997,"score_spread":0.19783251288448467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381166022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98094785,0.0006882879,0.017185953,0.0000578561,0.000015666405,0.000018362054,0.000058312875,0.00008708519,0.0009406322],"genre_scores_gemma":[0.98732543,0.00026351507,0.011519573,0.00005839174,0.000012106234,0.000021507112,0.000028544815,0.000009146234,0.0007618134],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997744,0.000038546932,0.000009280388,0.000047877824,0.00009982126,0.000029992796],"domain_scores_gemma":[0.9996538,0.00016180548,0.00009005583,0.000019677535,0.00004793491,0.000026733434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023807479,0.0002248803,0.00015116973,0.00028816157,0.00012516827,0.00018556038,0.00029942047,0.0002874413,0.0007269082],"category_scores_gemma":[0.00060463476,0.00013985221,0.00006813326,0.00017394406,0.00042915222,0.0003648684,0.00033110465,0.00024596465,0.000096663054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042462143,0.0000071374893,0.0005997943,0.000035849615,0.0000016780413,0.000026089432,0.000041482977,0.00006976184,0.99708956,0.000056967714,0.000016730231,0.0020124891],"study_design_scores_gemma":[0.000008553883,0.000320752,0.010057944,0.000008538398,0.000012529143,0.00026529827,0.000091830756,0.003363025,0.985114,0.00010878663,0.000634624,0.00001411947],"about_ca_topic_score_codex":0.00043394495,"about_ca_topic_score_gemma":0.0007142645,"teacher_disagreement_score":0.0007269082,"about_ca_system_score_codex":0.00016774533,"about_ca_system_score_gemma":0.00009582405,"threshold_uncertainty_score":0.0024317503},"labels":[],"label_agreement":null},{"id":"W4381465825","doi":"10.3390/s23104609","title":"Evaluation of a Restoration Algorithm Applied to Clipped Tibial Acceleration Signals","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Accelerometer; Acceleration; Range (aeronautics); Acoustics; Algorithm; Computer science; Mathematics; Materials science; Physics","score_opus":0.06573948425130866,"score_gpt":0.2926712031347807,"score_spread":0.22693171888347202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381465825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074269935,0.00033182924,0.92177755,0.00009496238,0.00016013959,0.00017376372,0.000056123266,0.0022340268,0.00090163044],"genre_scores_gemma":[0.21240653,0.00020495517,0.7854336,0.00006205242,0.000037266393,0.00012496847,0.00022219539,0.00021963373,0.0012888154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978072,0.00043324288,0.00017863687,0.00047641085,0.0009622727,0.00014222627],"domain_scores_gemma":[0.9945686,0.0019599383,0.00042187344,0.00046361046,0.0024344686,0.00015156854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004261619,0.0009571521,0.000769631,0.0013819949,0.0005242609,0.001239685,0.0009451795,0.0011817237,0.001659808],"category_scores_gemma":[0.013146723,0.0003574702,0.00079742,0.0010007251,0.0005393377,0.00079132523,0.00078229327,0.0010313438,0.0008910147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016379944,0.00033337608,0.008206744,0.00037120646,0.00019451878,0.00018228339,0.00043102365,0.05493181,0.06518845,0.0018633181,0.0015752378,0.86508393],"study_design_scores_gemma":[0.000076725395,0.0007478313,0.012056344,0.00004692496,0.00009751577,0.0005204649,0.00015952591,0.9325196,0.049186084,0.0007456019,0.0037769598,0.00006646935],"about_ca_topic_score_codex":0.0046229246,"about_ca_topic_score_gemma":0.003521952,"teacher_disagreement_score":0.0046229246,"about_ca_system_score_codex":0.00046825196,"about_ca_system_score_gemma":0.0014536662,"threshold_uncertainty_score":0.022537827},"labels":[],"label_agreement":null},{"id":"W4381512110","doi":"10.3390/s23135782","title":"Retinal Prostheses: Engineering and Clinical Perspectives for Vision Restoration","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal; University of Calgary; Université de Sherbrooke","funders":"","keywords":"Retinal implant; Retinitis pigmentosa; Retinal; Retinal Prosthesis; Visual prosthesis; Macular degeneration; Retina; Retinal degeneration; Neuroscience; Medicine; Ophthalmology; Psychology","score_opus":0.16470300231045784,"score_gpt":0.4291236936477535,"score_spread":0.2644206913372956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381512110","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005902308,0.99175984,0.0010297726,0.0024112992,0.0005602723,0.0000056006447,0.000013371082,0.000019407184,0.003610251],"genre_scores_gemma":[0.0068700984,0.986024,0.0021385704,0.0015335331,0.0013775912,0.000015837357,0.000027397558,0.00000916269,0.0020039026],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954706,0.000097546166,0.000060639806,0.00006369012,0.0001884941,0.000042538024],"domain_scores_gemma":[0.9994174,0.00031353923,0.00008123352,0.000022531483,0.00012559792,0.000039673396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009589965,0.0006489686,0.0006588478,0.0018078533,0.00035672824,0.0017974578,0.000589495,0.0018387536,0.0051452536],"category_scores_gemma":[0.0011798245,0.00020159034,0.0008080855,0.00084819074,0.0010697106,0.0023527348,0.0008378297,0.0022825024,0.0014544458],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013609194,0.00013321501,0.0006524823,0.016702548,0.00008122012,0.0012563225,0.00033525948,0.0007149229,0.010919694,0.04146186,0.028223846,0.8993826],"study_design_scores_gemma":[0.000018882107,0.00041842955,0.001668315,0.00661655,0.00009422633,0.007801821,0.0003483189,0.0004336474,0.0027729848,0.018138513,0.9616326,0.000055791268],"about_ca_topic_score_codex":0.00057974405,"about_ca_topic_score_gemma":0.0012616947,"teacher_disagreement_score":0.0051452536,"about_ca_system_score_codex":0.0006542818,"about_ca_system_score_gemma":0.0009999893,"threshold_uncertainty_score":0.01721257},"labels":[],"label_agreement":null},{"id":"W4381662144","doi":"10.3390/s23125761","title":"An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"BC Cancer Agency; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Michael Smith Health Research BC","keywords":"Breast cancer; Medicine; Cancer; Residual; Oncology; Chemotherapy; Internal medicine; Optical imaging; Radiology; Computer science; Algorithm","score_opus":0.020831589575283277,"score_gpt":0.3715922216211076,"score_spread":0.3507606320458243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381662144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86004335,0.001340264,0.13437618,0.00035851402,0.00011609338,0.00010599131,0.00054431695,0.001027595,0.0020877118],"genre_scores_gemma":[0.9775589,0.0002859804,0.021242773,0.00014425971,0.000019776166,0.000058760263,0.00012715792,0.000017352348,0.00054497493],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997472,0.000069665046,0.000011755629,0.000055264216,0.000093968076,0.000022110526],"domain_scores_gemma":[0.999706,0.000113626986,0.00006318441,0.000022537903,0.000080121274,0.000014574646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041103564,0.0003477086,0.00029376973,0.00040501627,0.00009813917,0.0003239336,0.00030584977,0.00043046332,0.00058970513],"category_scores_gemma":[0.001094617,0.00014951969,0.00025400522,0.00030468532,0.00016428976,0.00032683063,0.00033376145,0.00030143888,0.00018324371],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001849677,0.00042559713,0.15937608,0.00051764474,0.00019022684,0.00041658356,0.00036059367,0.034978747,0.47096246,0.00050372706,0.0020156475,0.328403],"study_design_scores_gemma":[0.00005398571,0.002345737,0.154054,0.00006735683,0.0002204032,0.001472165,0.00034266923,0.65175873,0.186077,0.0007890512,0.0026779885,0.00014095035],"about_ca_topic_score_codex":0.0007686426,"about_ca_topic_score_gemma":0.0010934455,"teacher_disagreement_score":0.0007686426,"about_ca_system_score_codex":0.00026146165,"about_ca_system_score_gemma":0.00019579582,"threshold_uncertainty_score":0.0021737814},"labels":[],"label_agreement":null},{"id":"W4381730776","doi":"10.3390/s23104659","title":"Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"CMA-ES; Reinforcement learning; Computer science; Evolution strategy; Speed limit; Artificial neural network; Differential evolution; Artificial intelligence; Limit (mathematics); Controller (irrigation); Mathematical optimization; Deep learning; Evolutionary algorithm; Mathematics; Engineering","score_opus":0.011276717467887316,"score_gpt":0.19914921245127537,"score_spread":0.18787249498338807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381730776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07433952,0.0006190633,0.9177706,0.00032082773,0.00012059182,0.00004813694,0.000037793354,0.0008506307,0.0058928574],"genre_scores_gemma":[0.9395883,0.00015387355,0.05694132,0.00015399717,0.00002422927,0.00011225157,0.00006353983,0.000044210134,0.0029182476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986744,0.000020676565,0.0000063387483,0.000032929573,0.00004155992,0.000031039675],"domain_scores_gemma":[0.9998043,0.00007913553,0.000031383577,0.000011622449,0.000056097517,0.000017421724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040675333,0.0009265313,0.00071807427,0.0003097724,0.00026491162,0.00055215857,0.0009585264,0.00079501595,0.00096386595],"category_scores_gemma":[0.0007230803,0.00045619326,0.0005794745,0.00029106464,0.00045449007,0.0005058747,0.0006791801,0.0009540561,0.00014242453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026483678,0.000027853137,0.00027404833,0.00001757934,0.000024779425,0.000035418096,0.000016327765,0.97098976,0.001955493,0.0016255487,0.00039453834,0.024612118],"study_design_scores_gemma":[0.000001981611,0.000005951779,0.000016254004,8.6088045e-7,0.0000019596155,0.0000016436405,6.294493e-7,0.99959856,0.000121684585,0.00020016759,0.000049293183,8.6886865e-7],"about_ca_topic_score_codex":0.008665632,"about_ca_topic_score_gemma":0.008933073,"teacher_disagreement_score":0.008665632,"about_ca_system_score_codex":0.0007944696,"about_ca_system_score_gemma":0.000953083,"threshold_uncertainty_score":0.017230392},"labels":[],"label_agreement":null},{"id":"W4382135409","doi":"10.3390/s23135863","title":"On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Mitacs","keywords":"Wearable computer; Reliability (semiconductor); Fidelity; Wearable technology; Computer science; Reliability engineering; Human–computer interaction; Engineering; Embedded system; Telecommunications","score_opus":0.022421383768365274,"score_gpt":0.2554784326132457,"score_spread":0.23305704884488043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382135409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044671553,0.44842193,0.4510564,0.024924383,0.010224406,0.00050379394,0.0010360621,0.0021345562,0.05723133],"genre_scores_gemma":[0.039863598,0.5545449,0.26966807,0.020056719,0.029929703,0.001600654,0.0013417348,0.0026841736,0.08031035],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972843,0.0012365609,0.00017960447,0.00038193437,0.0008072676,0.000110352754],"domain_scores_gemma":[0.9881059,0.009933903,0.00034570304,0.00029806496,0.0010260695,0.0002902825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005272741,0.0025986445,0.0013217549,0.0029308384,0.00063113665,0.003104442,0.0014913013,0.0029454932,0.012134137],"category_scores_gemma":[0.014607795,0.0010988311,0.0011897846,0.002024025,0.0027630145,0.0067901127,0.0022926913,0.004795092,0.009539343],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018002298,0.00025147473,0.0016878571,0.0037442837,0.00014218218,0.00058812776,0.0021758054,0.0039048751,0.010896373,0.06226096,0.27510917,0.6390589],"study_design_scores_gemma":[0.000032421365,0.00044758405,0.0048165,0.004095436,0.00006124255,0.0023812794,0.00059741567,0.0036993541,0.002972768,0.07604417,0.90464896,0.00020287972],"about_ca_topic_score_codex":0.0014139237,"about_ca_topic_score_gemma":0.0017076804,"teacher_disagreement_score":0.012134137,"about_ca_system_score_codex":0.0010326442,"about_ca_system_score_gemma":0.0009152505,"threshold_uncertainty_score":0.04059273},"labels":[],"label_agreement":null},{"id":"W4382281941","doi":"10.3390/s23135941","title":"CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":793,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Internet of Things; Computer security; Interoperability; Spoofing attack; Denial-of-service attack; Analytics; Benchmark (surveying); Data science; World Wide Web; The Internet","score_opus":0.012001293171436332,"score_gpt":0.24592530762358875,"score_spread":0.2339240144521524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382281941","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09707134,0.002550967,0.009429261,0.002208531,0.00097500195,0.00086318515,0.85838294,0.017473096,0.011045682],"genre_scores_gemma":[0.07495771,0.0004495636,0.007698293,0.00030001646,0.00009901763,0.00039648145,0.9146707,0.00028432335,0.0011439921],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972849,0.0003389515,0.00036870287,0.0006000143,0.0010126956,0.00039484698],"domain_scores_gemma":[0.996197,0.0007649547,0.0005113563,0.0011663865,0.0009264501,0.0004338635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016475713,0.002336804,0.001208014,0.0036456997,0.0012561267,0.0017976364,0.0028944556,0.0028847463,0.002074627],"category_scores_gemma":[0.006641855,0.00038652742,0.0014421227,0.0043959706,0.0008250401,0.0023833322,0.0018314327,0.0021897617,0.0034999577],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096735026,0.00094210816,0.041856628,0.0015078654,0.0004780677,0.001103201,0.00027139156,0.03633659,0.0044860374,0.003099587,0.8639918,0.04495946],"study_design_scores_gemma":[0.0006439033,0.00084962975,0.14342989,0.00051905337,0.00031155188,0.0034580843,0.0014000882,0.28547993,0.014858802,0.007185429,0.5415345,0.00032914156],"about_ca_topic_score_codex":0.017896622,"about_ca_topic_score_gemma":0.023528885,"teacher_disagreement_score":0.017896622,"about_ca_system_score_codex":0.0017430934,"about_ca_system_score_gemma":0.001576537,"threshold_uncertainty_score":0.035584927},"labels":[],"label_agreement":null},{"id":"W4382282019","doi":"10.3390/s23135952","title":"Enhanced Performance of Artificial-Neural-Network-Based Equalization for Short-Haul Fiber-Optic Communications","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Network Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Equalization (audio); Robustness (evolution); Bit error rate; Equalizer; Electronic engineering; Compensation (psychology); Optical fiber; Optical communication; Adaptive equalizer; Estimator; Telecommunications; Engineering; Artificial intelligence; Decoding methods; Channel (broadcasting)","score_opus":0.03938559625973956,"score_gpt":0.27562573105158084,"score_spread":0.23624013479184128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382282019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37186995,0.0016676694,0.6156104,0.00025708642,0.00018827904,0.000024006422,0.000060570957,0.00090656686,0.009415483],"genre_scores_gemma":[0.9432308,0.00032861644,0.052696012,0.000051399576,0.00003301025,0.0000101517635,0.00005297982,0.000022754499,0.0035743206],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983287,0.000029966559,0.000009560777,0.000023868082,0.00008126838,0.000022466293],"domain_scores_gemma":[0.9998253,0.00007284212,0.000018762272,0.000013160633,0.00006503752,0.0000049697437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020339614,0.0002473963,0.00020009222,0.00016952629,0.00016153995,0.00022423052,0.00026192947,0.00034020923,0.0011691705],"category_scores_gemma":[0.0005048525,0.000073615556,0.00010085662,0.00017354198,0.00016619163,0.0006633957,0.00023653804,0.0002785733,0.0002959983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079975795,0.00015525661,0.0018184822,0.0002039831,0.0000699958,0.00017095859,0.000084200576,0.13748188,0.5594492,0.0054752547,0.0012357384,0.29305527],"study_design_scores_gemma":[0.000011645776,0.00012265208,0.0011240735,0.0000106403,0.000018233475,0.00010888282,0.000011458996,0.7723903,0.22354703,0.0005186821,0.0021179754,0.000018492232],"about_ca_topic_score_codex":0.00067025406,"about_ca_topic_score_gemma":0.0014420555,"teacher_disagreement_score":0.0011691705,"about_ca_system_score_codex":0.00021035892,"about_ca_system_score_gemma":0.00018951148,"threshold_uncertainty_score":0.003911257},"labels":[],"label_agreement":null},{"id":"W4382560805","doi":"10.3390/s23136015","title":"Crop Disease Identification by Fusing Multiscale Convolution and Vision Transformer","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Residual; Deep learning; Pattern recognition (psychology); Adaptability; Machine learning; Convolution (computer science); Artificial neural network; Algorithm","score_opus":0.009708834749210223,"score_gpt":0.2346685095810885,"score_spread":0.2249596748318783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382560805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1705683,0.0008884973,0.82302415,0.00018448589,0.00008925035,0.000070136964,0.0002031723,0.0017344314,0.0032375755],"genre_scores_gemma":[0.91533464,0.0004264919,0.08062253,0.00014621507,0.000037460715,0.000034584726,0.00035329728,0.000041743922,0.0030031113],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999833,0.000013196401,0.000008780854,0.000064361215,0.00004689806,0.000033751196],"domain_scores_gemma":[0.9998778,0.000028614535,0.000020501027,0.000020061294,0.00003887811,0.000014142289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033986298,0.00064778153,0.00058345695,0.0007522162,0.00012590963,0.00046127904,0.0005940883,0.00045841356,0.00072178873],"category_scores_gemma":[0.0004707414,0.00025145613,0.0008584178,0.0004084113,0.00022864791,0.00083217665,0.00052060705,0.00034605627,0.00028383872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003476559,0.00023521567,0.010343738,0.00014691892,0.00021095376,0.0003781542,0.00007595792,0.24551943,0.16144906,0.0031410519,0.0022930622,0.57585883],"study_design_scores_gemma":[0.0000043408963,0.00006702708,0.0018708714,0.000003819173,0.000031643085,0.000121186225,0.000007077056,0.9854167,0.011158217,0.0007505202,0.0005597636,0.000008868376],"about_ca_topic_score_codex":0.0045548356,"about_ca_topic_score_gemma":0.0054162657,"teacher_disagreement_score":0.0045548356,"about_ca_system_score_codex":0.00048382557,"about_ca_system_score_gemma":0.00047113688,"threshold_uncertainty_score":0.009056628},"labels":[],"label_agreement":null},{"id":"W4382599528","doi":"10.3390/s23136048","title":"The Effects of a Simple Sensor Reorientation Procedure on Peak Tibial Accelerations during Running and Correlations with Ground Reaction Forces","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Ground reaction force; Inertial measurement unit; Orientation (vector space); Accelerometer; Treadmill; Mathematics; Physics; Geodesy; Geology; Engineering; Medicine; Physical therapy; Kinematics; Geometry; Classical mechanics","score_opus":0.00902066068347926,"score_gpt":0.220283588314229,"score_spread":0.21126292763074975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382599528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99634486,0.00019211933,0.0031203888,0.000010214942,0.00002280785,0.000039628903,0.000048270267,0.000027860966,0.00019383676],"genre_scores_gemma":[0.9942675,0.00014131969,0.0050187777,0.000028375936,0.000016576212,0.00007128303,0.00016636337,0.00003512549,0.0002547641],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985929,0.00055087765,0.00017447169,0.0003128572,0.00026567598,0.00010312558],"domain_scores_gemma":[0.99294186,0.0033766069,0.001713206,0.0010169585,0.00067844504,0.00027284774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018998374,0.00070204696,0.00039420283,0.00030260478,0.00016705578,0.00036404774,0.00023953631,0.00027964197,0.00046801078],"category_scores_gemma":[0.0084115295,0.00029864555,0.00030841038,0.00028669822,0.000408054,0.0002581169,0.00038053896,0.0004516866,0.00017581698],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014526124,0.0014348743,0.28051046,0.00053655595,0.000663524,0.00030098646,0.001325371,0.0031629356,0.605746,0.00009288178,0.00023947295,0.09146075],"study_design_scores_gemma":[0.000042021868,0.0051850392,0.96906114,0.000013205768,0.00009567066,0.0001314031,0.00010857596,0.0009812075,0.024100982,0.000026657946,0.00022825497,0.000025865576],"about_ca_topic_score_codex":0.0009272321,"about_ca_topic_score_gemma":0.0020405564,"teacher_disagreement_score":0.0018998374,"about_ca_system_score_codex":0.00012063641,"about_ca_system_score_gemma":0.00021946571,"threshold_uncertainty_score":0.010047436},"labels":[],"label_agreement":null},{"id":"W4382699516","doi":"10.3390/s23136019","title":"Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Simultaneous localization and mapping; Artificial intelligence; Computer vision; GNSS applications; Inertial measurement unit; Computer science; Extended Kalman filter; Monocular; Odometry; Monocular vision; Kalman filter; Visual odometry; Remote sensing; Mean squared error; Geography; Global Positioning System; Mathematics; Robot; Mobile robot","score_opus":0.01328915399936704,"score_gpt":0.23367676804171914,"score_spread":0.2203876140423521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382699516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082467824,0.00050921453,0.90856785,0.00012727959,0.00011582983,0.000053649834,0.0002588999,0.0054997844,0.0023997044],"genre_scores_gemma":[0.694004,0.00019423527,0.300814,0.00016144676,0.000043862332,0.00007224376,0.0010572878,0.00015484603,0.0034980942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974686,0.000026216405,0.00001090004,0.0000669785,0.00008886632,0.00006014832],"domain_scores_gemma":[0.99979514,0.00003030573,0.000026513917,0.000036793943,0.00009604862,0.00001528948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029219376,0.000903727,0.0004946466,0.00061109185,0.0002927506,0.00043242922,0.00102473,0.00046316042,0.0014452072],"category_scores_gemma":[0.0007161099,0.00033180235,0.0004179687,0.0007279849,0.00020199169,0.000689981,0.0011355483,0.0006879305,0.00079472363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001495444,0.0001706252,0.0031935684,0.00010003055,0.00011786512,0.000105823834,0.00008419683,0.19440064,0.035904724,0.0010962904,0.0037836216,0.76089305],"study_design_scores_gemma":[0.0000137633715,0.000059256436,0.0016681117,0.000010137465,0.000017027356,0.000044110588,0.000031849293,0.98613226,0.009322702,0.00088886986,0.0018013251,0.000010530143],"about_ca_topic_score_codex":0.009695048,"about_ca_topic_score_gemma":0.018735565,"teacher_disagreement_score":0.009695048,"about_ca_system_score_codex":0.00036018773,"about_ca_system_score_gemma":0.00095565134,"threshold_uncertainty_score":0.019277215},"labels":[],"label_agreement":null},{"id":"W4382727729","doi":"10.3390/s23136005","title":"Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; University of Ottawa","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Electroencephalography; Brain–computer interface; Support vector machine; Computer science; Artificial intelligence; Stroop effect; Pattern recognition (psychology); Gait; Speech recognition; Physical medicine and rehabilitation; Psychology; Cognition; Neuroscience; Medicine","score_opus":0.05127959695391133,"score_gpt":0.2808437448630336,"score_spread":0.22956414790912227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382727729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6942679,0.0009834771,0.30213174,0.0002116286,0.00006467845,0.000164685,0.00022830213,0.00036180063,0.0015858277],"genre_scores_gemma":[0.9184295,0.0003700222,0.08045231,0.0000395241,0.000026264775,0.00006238024,0.000193793,0.000014472827,0.0004116303],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970216,0.00008513896,0.000046071513,0.000065280925,0.00007377775,0.000027510845],"domain_scores_gemma":[0.99927884,0.00041386078,0.00011581669,0.000038639097,0.00012644962,0.000026360234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080204697,0.0005817764,0.0004394431,0.0014528297,0.00016189754,0.0006311433,0.0002170861,0.00042946814,0.0005263614],"category_scores_gemma":[0.0033105118,0.00012173548,0.0003291229,0.0008439348,0.0002542812,0.0006878652,0.00022303729,0.0002588528,0.00016737466],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006774435,0.0005287362,0.09341499,0.00031232362,0.0002618022,0.00036330894,0.00023453074,0.05897496,0.04520063,0.001574632,0.0009408808,0.7975157],"study_design_scores_gemma":[0.000056125788,0.0008623528,0.09361977,0.000074933545,0.000094186114,0.0004821002,0.00027869773,0.88537925,0.013773388,0.0045348443,0.00080140086,0.000042939013],"about_ca_topic_score_codex":0.0010483185,"about_ca_topic_score_gemma":0.0014097213,"teacher_disagreement_score":0.0014528297,"about_ca_system_score_codex":0.00014039093,"about_ca_system_score_gemma":0.00022670288,"threshold_uncertainty_score":0.0042416453},"labels":[],"label_agreement":null},{"id":"W4382815681","doi":"10.3390/s23104727","title":"Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; University of Victoria","funders":"","keywords":"Computer science; Robustness (evolution); Machine learning; Authentication (law); Feature extraction; Benchmark (surveying); Preprocessor; Artificial intelligence; Data mining; Data pre-processing; Computer security","score_opus":0.07366105419527054,"score_gpt":0.3671068866359111,"score_spread":0.2934458324406406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382815681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.115607254,0.00082431966,0.8796397,0.0007117728,0.0002103838,0.00008445771,0.00015953692,0.00073039834,0.00203218],"genre_scores_gemma":[0.92096186,0.00043537183,0.07429098,0.00023590698,0.00008991473,0.00006376007,0.0003164615,0.00002882213,0.0035769192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995246,0.00015943317,0.00003112387,0.00013832719,0.000091363145,0.000055184923],"domain_scores_gemma":[0.9991698,0.000391237,0.00006134338,0.000072453324,0.00026270968,0.00004244738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012773777,0.0006986391,0.0006401507,0.00043525678,0.00033940095,0.00047417716,0.00057843403,0.00066376786,0.0012761264],"category_scores_gemma":[0.002218806,0.0002299244,0.0005251609,0.00041701473,0.0004037713,0.0010542093,0.00055753696,0.0009942254,0.00029591453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032054103,0.00016094116,0.004694663,0.00006021208,0.0001090558,0.00017742877,0.000094471136,0.7909811,0.007540306,0.0033661316,0.0023135545,0.19018155],"study_design_scores_gemma":[0.0000017206784,0.000026981372,0.00027281736,0.0000013320765,0.000005397355,0.000018687739,0.0000032398818,0.99840575,0.0006275616,0.0004798795,0.00015364734,0.0000029893563],"about_ca_topic_score_codex":0.0076006134,"about_ca_topic_score_gemma":0.0076443795,"teacher_disagreement_score":0.0076006134,"about_ca_system_score_codex":0.00051575364,"about_ca_system_score_gemma":0.0005859757,"threshold_uncertainty_score":0.015112758},"labels":[],"label_agreement":null},{"id":"W4382982090","doi":"10.3390/s23136097","title":"Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"GNSS applications; Extended Kalman filter; Inertial navigation system; Inertial measurement unit; Kalman filter; Navigation system; Computer science; Control theory (sociology); Air navigation; GNSS augmentation; Engineering; Global Positioning System; Real-time computing; Artificial intelligence; Inertial frame of reference; Telecommunications; Physics","score_opus":0.01631979225825427,"score_gpt":0.24195207615304135,"score_spread":0.22563228389478707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382982090","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080868974,0.00021538226,0.9134092,0.00005241761,0.00010701323,0.000060048962,0.00007340386,0.0019677798,0.0032458373],"genre_scores_gemma":[0.79351926,0.00013279606,0.2027925,0.00006611952,0.000030896084,0.00005840027,0.00031781956,0.000070934955,0.0030112357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963164,0.000043210483,0.000020350777,0.00008665456,0.00018633605,0.00003169201],"domain_scores_gemma":[0.99986243,0.000012257784,0.000018979252,0.000027512238,0.00006938417,0.000009497916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027500998,0.00056289416,0.0006063941,0.00040734684,0.00028507892,0.0004922865,0.00056455604,0.00042678212,0.00067224714],"category_scores_gemma":[0.0004160229,0.00023330463,0.00049539027,0.00040223112,0.000180113,0.0005543132,0.0006259397,0.00041157022,0.0005234592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004738857,0.00022036575,0.01060655,0.00016734951,0.00028407946,0.0004196657,0.0002882985,0.32827297,0.1643406,0.0031119809,0.0022550556,0.4895591],"study_design_scores_gemma":[0.000025771069,0.0001975941,0.0055561527,0.000007996283,0.000068856476,0.00012548197,0.000024124216,0.9741794,0.015854014,0.00039197376,0.0035283107,0.000040388364],"about_ca_topic_score_codex":0.0068713203,"about_ca_topic_score_gemma":0.0058151446,"teacher_disagreement_score":0.0068713203,"about_ca_system_score_codex":0.00028067167,"about_ca_system_score_gemma":0.00043633708,"threshold_uncertainty_score":0.013662636},"labels":[],"label_agreement":null},{"id":"W4382982296","doi":"10.3390/s23136077","title":"k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autocorrelation; Classifier (UML); Cross-validation; Brain–computer interface; Statistics; Artificial intelligence; Mathematics; Pattern recognition (psychology); Seriousness; Correlation; Computer science; Block design; Electroencephalography; Psychology","score_opus":0.1320162087649119,"score_gpt":0.40273583029156174,"score_spread":0.27071962152664986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382982296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67787373,0.005416725,0.30951765,0.0005398195,0.0004222149,0.0008739828,0.00076308765,0.0010177607,0.0035749953],"genre_scores_gemma":[0.9232627,0.00040515955,0.072413355,0.0003817094,0.00008323659,0.0006481369,0.0012612856,0.00025101306,0.0012934551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95872974,0.025207898,0.0030758195,0.0065173437,0.005681959,0.0007872824],"domain_scores_gemma":[0.6127242,0.33322018,0.013518462,0.02510024,0.014564672,0.00087221],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.068339765,0.0017462965,0.0017097847,0.00092817214,0.0013376437,0.0017550813,0.0020346397,0.0021304453,0.0011890653],"category_scores_gemma":[0.20653373,0.0005878827,0.0016818854,0.0012786065,0.0023960504,0.0026227068,0.001986526,0.002875194,0.0007576084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010666983,0.0028579005,0.26156944,0.004203891,0.0047211694,0.0007424046,0.006022715,0.23560165,0.026745629,0.011565505,0.011314482,0.42398828],"study_design_scores_gemma":[0.00038993554,0.0065246294,0.156057,0.0010108709,0.0010239785,0.0014632047,0.0011506082,0.76912516,0.033683933,0.02083798,0.008343305,0.00038941673],"about_ca_topic_score_codex":0.0029888917,"about_ca_topic_score_gemma":0.0026698515,"teacher_disagreement_score":0.93166023,"about_ca_system_score_codex":0.0012916402,"about_ca_system_score_gemma":0.001078226,"threshold_uncertainty_score":0.36141956},"labels":[],"label_agreement":null},{"id":"W4382982443","doi":"10.3390/s23136095","title":"Investigation of a HAP-UAV Collaboration Scheme for Throughput Maximization via Joint User Association and 3D UAV Placement","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Throughput; Computer science; Scheme (mathematics); Convergence (economics); Genetic algorithm; Telecommunications link; Maximization; Real-time computing; Joint (building); Wireless; Computer network; Distributed computing; Mathematical optimization; Engineering; Machine learning; Mathematics","score_opus":0.015342270670698762,"score_gpt":0.2179385115213395,"score_spread":0.20259624085064074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382982443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09127198,0.00037843606,0.9050492,0.00013914132,0.0000490527,0.00006764362,0.000018791587,0.00013287445,0.0028928816],"genre_scores_gemma":[0.9394326,0.00016170164,0.0593393,0.000029256871,0.000019463607,0.000043959906,0.00001546407,0.000009278481,0.00094888185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992086,0.00029532163,0.000024272222,0.00015311563,0.0001734253,0.00014521062],"domain_scores_gemma":[0.99903476,0.00039965857,0.00015125379,0.000118904136,0.00019107874,0.000104383675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078014814,0.0008251251,0.0007630959,0.00038725862,0.00075471663,0.00078849803,0.0011038717,0.0006828337,0.0008591855],"category_scores_gemma":[0.0017505835,0.0002773085,0.00044114608,0.0007765012,0.0005645458,0.0011518159,0.0013195373,0.00056232605,0.00014558258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002813326,0.00014889202,0.0022357726,0.00012950086,0.00010796988,0.0005238966,0.00024403079,0.889489,0.029190581,0.01591081,0.000773567,0.060964633],"study_design_scores_gemma":[0.000007911839,0.00012408616,0.00022218301,0.0000033875112,0.00001508358,0.00011979269,0.00005453024,0.9946321,0.0029650037,0.0015433035,0.00030603644,0.0000066271778],"about_ca_topic_score_codex":0.0019239782,"about_ca_topic_score_gemma":0.002132701,"teacher_disagreement_score":0.0019239782,"about_ca_system_score_codex":0.0006467017,"about_ca_system_score_gemma":0.0011192514,"threshold_uncertainty_score":0.0046921372},"labels":[],"label_agreement":null},{"id":"W4382982627","doi":"10.3390/s23136056","title":"Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Fonds de Recherche du Québec - Santé","keywords":"Computer science; Joystick; Robot; Task (project management); Human–computer interaction; Interface (matter); Leverage (statistics); Voice command device; Speech recognition; Artificial intelligence; Simulation; Engineering","score_opus":0.09773677471707368,"score_gpt":0.41046435436314965,"score_spread":0.312727579646076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382982627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095150016,0.0015137766,0.89361227,0.00036398036,0.00023752468,0.000110465866,0.0003998152,0.0047771777,0.0038349824],"genre_scores_gemma":[0.94739664,0.00033367617,0.04569748,0.00021291552,0.0000852645,0.00018000712,0.00070007326,0.00011920061,0.0052747806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971825,0.000045136,0.000018902641,0.00010874805,0.0000581795,0.000050703606],"domain_scores_gemma":[0.99966335,0.00013663436,0.000026115127,0.000028842182,0.0001291386,0.000015850792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050926814,0.0007095815,0.00068103574,0.00039853604,0.00023786761,0.0005578724,0.0010681734,0.00062936527,0.0019461486],"category_scores_gemma":[0.0011238974,0.00022305177,0.00060867256,0.00028370478,0.00027947937,0.00060080114,0.0005409151,0.0010261432,0.00093905366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037918214,0.0002902506,0.001835091,0.00015970884,0.00010666516,0.0001872049,0.00015829201,0.5523973,0.025292097,0.0027184174,0.004933942,0.41154194],"study_design_scores_gemma":[0.0000030164367,0.000027051818,0.00025965893,0.000002589057,0.000006301031,0.000010748663,0.0000061245323,0.99722594,0.0017747292,0.00040370447,0.0002766613,0.0000034358302],"about_ca_topic_score_codex":0.013255264,"about_ca_topic_score_gemma":0.009942608,"teacher_disagreement_score":0.013255264,"about_ca_system_score_codex":0.0007345946,"about_ca_system_score_gemma":0.0007441424,"threshold_uncertainty_score":0.02635622},"labels":[],"label_agreement":null},{"id":"W4382982764","doi":"10.3390/s23136062","title":"Technology Trends for Massive MIMO towards 6G","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"MIMO; Wireless; Scalability; Computer science; Cornerstone; Telecommunications; Computer architecture; Adaptability; Engineering; Channel (broadcasting)","score_opus":0.021318729666892345,"score_gpt":0.2745035885830964,"score_spread":0.25318485891620407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382982764","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043979693,0.163805,0.46511748,0.07556572,0.006331985,0.00021712213,0.0006809317,0.0015536268,0.2427485],"genre_scores_gemma":[0.50999886,0.1589576,0.24744268,0.020321602,0.0070266225,0.00032744068,0.0007627268,0.00020587868,0.05495659],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950683,0.00010482155,0.000020828915,0.000094200805,0.00019299334,0.00008027655],"domain_scores_gemma":[0.9992316,0.00024939424,0.000050121605,0.00006433656,0.0003094744,0.00009508182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011958133,0.00064166955,0.0003550732,0.00069879356,0.0004770688,0.0015831527,0.00057976256,0.0012771259,0.006629691],"category_scores_gemma":[0.0017558058,0.00022997349,0.00031920298,0.00071251765,0.0010523275,0.0027058085,0.001391578,0.002499489,0.002506343],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021298938,0.00010186891,0.0015720595,0.00072187296,0.000033726308,0.00035478076,0.0006038689,0.009598674,0.016563654,0.5939532,0.044738974,0.33154428],"study_design_scores_gemma":[0.000048175476,0.000559407,0.0017768139,0.0006231573,0.000049985803,0.0010395473,0.00051485,0.03397848,0.0062218877,0.20893176,0.7461835,0.00007238391],"about_ca_topic_score_codex":0.0010114041,"about_ca_topic_score_gemma":0.000984944,"teacher_disagreement_score":0.006629691,"about_ca_system_score_codex":0.0009156608,"about_ca_system_score_gemma":0.0010677376,"threshold_uncertainty_score":0.02217859},"labels":[],"label_agreement":null},{"id":"W4383228285","doi":"10.3390/s23136133","title":"ECKN: An Integrated Approach for Position Estimation, Packet Routing, and Sleep Scheduling in Wireless Sensor Networks","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer network; Computer science; Packet forwarding; Network packet; Routing protocol; Scheduling (production processes); Geographic routing; Node (physics); Key distribution in wireless sensor networks; Distributed computing; Wireless network; Wireless; Static routing; Engineering","score_opus":0.014699156734564364,"score_gpt":0.2466268323604519,"score_spread":0.23192767562588754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383228285","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004522972,0.000780293,0.99245536,0.00007977568,0.000099022305,0.00007788102,0.000031112875,0.0008715987,0.0010820125],"genre_scores_gemma":[0.29000252,0.0018862515,0.7017261,0.00026660526,0.00012828203,0.00024916668,0.0003154313,0.00022841238,0.0051972237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991259,0.00018052156,0.000057028774,0.00019044123,0.00037025713,0.000075847995],"domain_scores_gemma":[0.99950373,0.0001354233,0.00006257221,0.00011335346,0.00015365299,0.000031250274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011182075,0.00067996053,0.00074739207,0.00081673503,0.00056055153,0.0008270497,0.0019330224,0.00065423537,0.0007153214],"category_scores_gemma":[0.0016209933,0.00034478251,0.0005689104,0.00097652944,0.00040355464,0.0016093126,0.0014739606,0.00063631433,0.00027242771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002445086,0.00015425714,0.0012931162,0.00031602575,0.00015257215,0.00027598173,0.0002085796,0.42166898,0.0193981,0.025781011,0.005759765,0.5247471],"study_design_scores_gemma":[0.000016626227,0.00011803965,0.00039357832,0.000022583159,0.000042114632,0.00019179142,0.000047381247,0.97910607,0.005119867,0.0053622033,0.009553306,0.000026398911],"about_ca_topic_score_codex":0.004562173,"about_ca_topic_score_gemma":0.007219901,"teacher_disagreement_score":0.004562173,"about_ca_system_score_codex":0.0005702983,"about_ca_system_score_gemma":0.0012054283,"threshold_uncertainty_score":0.009071231},"labels":[],"label_agreement":null},{"id":"W4383818654","doi":"10.3390/s23136236","title":"AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Canada Research Chairs; University of Toronto","funders":"Institució Catalana de Recerca i Estudis Avançats","keywords":"Ternary operation; Selectivity; Sensitivity (control systems); Resonator; Artificial neural network; Materials science; Binary number; Microwave; Computer science; Electronic engineering; Optoelectronics; Artificial intelligence; Mathematics; Chemistry; Engineering; Telecommunications","score_opus":0.010013404962165283,"score_gpt":0.21845688594223053,"score_spread":0.20844348098006524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383818654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73373485,0.0017731363,0.25569403,0.0005496614,0.00024179684,0.00014845657,0.00030717545,0.0016409412,0.0059099183],"genre_scores_gemma":[0.81358474,0.0005665576,0.18202762,0.00034507917,0.000051257015,0.000058279114,0.00015503999,0.00005238459,0.0031590294],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951327,0.00005293193,0.000023845232,0.00011105653,0.00025214776,0.000046841138],"domain_scores_gemma":[0.9996742,0.0000965672,0.000074763804,0.000032460666,0.000102287806,0.000019768577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006453955,0.00042864113,0.00044275625,0.00026528418,0.00012224812,0.00037720936,0.00092535664,0.00057909184,0.0006567536],"category_scores_gemma":[0.00057713105,0.000261423,0.00028433654,0.0002529677,0.00039279822,0.00085818925,0.0005138431,0.00056452403,0.0005316419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025270836,0.000015200093,0.0000843432,0.00004231435,0.0000042883485,0.000021720409,0.0000124151275,0.00028717503,0.99446386,0.00018426201,0.000068321504,0.004790881],"study_design_scores_gemma":[0.000005658071,0.00006156618,0.00026205872,0.0000023062616,0.000008853629,0.00006965021,0.000008970465,0.01618127,0.9823268,0.000046544134,0.0010168739,0.000009501013],"about_ca_topic_score_codex":0.0005865054,"about_ca_topic_score_gemma":0.001753278,"teacher_disagreement_score":0.00092535664,"about_ca_system_score_codex":0.00045800433,"about_ca_system_score_gemma":0.0002327847,"threshold_uncertainty_score":0.00341326},"labels":[],"label_agreement":null},{"id":"W4383820627","doi":"10.3390/s23146251","title":"Microfabrication Process Development for a Polymer-Based Lab-on-Chip Concept Applied in Attenuated Total Reflection Fourier Transform Infrared Spectroelectrochemistry","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"Government of Saskatchewan; National Research Council Canada; Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Saskatchewan; Canadian Light Source","keywords":"Microfabrication; Materials science; Attenuated total reflection; Surface modification; Nanotechnology; Polydimethylsiloxane; Microfluidics; Fourier transform infrared spectroscopy; Optoelectronics; Fabrication; Chemical engineering","score_opus":0.014536118087848935,"score_gpt":0.25833493218283166,"score_spread":0.24379881409498272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383820627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0850595,0.0021857268,0.90284896,0.0002926681,0.00032570027,0.0007086376,0.00033307952,0.0026210481,0.0056247474],"genre_scores_gemma":[0.17191035,0.0014833472,0.8200444,0.0001988183,0.000082456754,0.00076115335,0.00045593717,0.00018710892,0.004876464],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956614,0.000031890635,0.000024579378,0.000116744355,0.00021272339,0.000047958598],"domain_scores_gemma":[0.99976665,0.00004903612,0.00005947375,0.000044555334,0.00005954242,0.000020722231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041565046,0.00081880967,0.00041328144,0.0003695715,0.00032585463,0.00046275684,0.0010704196,0.00069798686,0.0016683239],"category_scores_gemma":[0.00036996612,0.00045014734,0.00064753776,0.00025808,0.0003382149,0.0005385017,0.00042956395,0.0011624419,0.001547058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011505587,0.000029170315,0.000038259463,0.000087330656,0.0000055485575,0.000049725728,0.000019674702,0.00037035724,0.9902678,0.0010790625,0.00018208945,0.00785959],"study_design_scores_gemma":[0.0000128956235,0.00025296333,0.00055540615,0.0000075586822,0.000015573467,0.00035750723,0.000006799626,0.0047304416,0.98258173,0.0001850682,0.011274457,0.000019653786],"about_ca_topic_score_codex":0.00045420244,"about_ca_topic_score_gemma":0.0005702611,"teacher_disagreement_score":0.0016683239,"about_ca_system_score_codex":0.00054279016,"about_ca_system_score_gemma":0.00071139407,"threshold_uncertainty_score":0.0055811405},"labels":[],"label_agreement":null},{"id":"W4384202280","doi":"10.3390/s23146339","title":"Initial Testing of Robotic Exoskeleton Hand Device for Stroke Rehabilitation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Rehabilitation; Physical medicine and rehabilitation; Stroke (engine); Powered exoskeleton; Computer science; Simulation; Engineering; Medicine; Physical therapy; Mechanical engineering","score_opus":0.047533394772525495,"score_gpt":0.3366150755769989,"score_spread":0.2890816808044734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384202280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9405348,0.0008768765,0.053352073,0.00021578466,0.00018204714,0.0013934157,0.0005396266,0.0003300394,0.002575323],"genre_scores_gemma":[0.9667553,0.00039047195,0.027218878,0.00017539194,0.000048627306,0.0006282968,0.00043180745,0.000040417042,0.00431074],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99875116,0.00041601414,0.0001921511,0.00015288431,0.00035610155,0.00013162357],"domain_scores_gemma":[0.9981236,0.000896268,0.0000608416,0.00018278776,0.0006316569,0.00010482432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022248568,0.0007585555,0.0006257324,0.00044233,0.00024750197,0.00026552685,0.00072688365,0.0009537293,0.0043947203],"category_scores_gemma":[0.0039704232,0.00022809963,0.00045121557,0.00018525329,0.0003301348,0.0005728288,0.00042035637,0.00016593623,0.00066523714],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004836109,0.0024972649,0.008877204,0.002454286,0.00017612318,0.0015152681,0.0019221511,0.002743257,0.82275045,0.00044834762,0.0019903611,0.14978915],"study_design_scores_gemma":[0.0011949035,0.24651341,0.14580204,0.00055556203,0.0005476095,0.0063857497,0.0016596159,0.014702717,0.55547017,0.0006725311,0.026297908,0.00019780705],"about_ca_topic_score_codex":0.00021190962,"about_ca_topic_score_gemma":0.0004699877,"teacher_disagreement_score":0.0043947203,"about_ca_system_score_codex":0.00010540896,"about_ca_system_score_gemma":0.00026177318,"threshold_uncertainty_score":0.014701784},"labels":[],"label_agreement":null},{"id":"W4384298253","doi":"10.3390/s23146404","title":"Sensors for Biomass Monitoring in Vegetated Green Infrastructure: A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Victoria","funders":"University of Victoria","keywords":"Environmental science; Bioretention; Biomass (ecology); Bioremediation; Process (computing); Remote sensing; Calibration; Stormwater; Computer science; Contamination; Surface runoff; Ecology; Geology","score_opus":0.06269945557398653,"score_gpt":0.32469028159009594,"score_spread":0.2619908260161094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384298253","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028992057,0.9970462,0.00089409284,0.00016808513,0.00025917127,0.000014954341,0.000035543515,0.00001831203,0.0012738215],"genre_scores_gemma":[0.0014202977,0.996086,0.0012617236,0.00016488857,0.00014703121,0.000018902017,0.00006236164,0.000004697977,0.00083413575],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951637,0.00006245252,0.00006606178,0.00010888651,0.00020895596,0.000037319638],"domain_scores_gemma":[0.99909484,0.00043127706,0.00015481158,0.000027326598,0.0002528528,0.000038862196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008873567,0.001547329,0.0016809121,0.0035888352,0.0003176526,0.0012721798,0.0012188891,0.0014970339,0.0028804343],"category_scores_gemma":[0.0010887018,0.0005748248,0.0010518192,0.0037447088,0.0004593852,0.0023881316,0.0006977737,0.0014574428,0.0023605027],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054666227,0.0001268064,0.0003341512,0.037019473,0.00013250396,0.00018684022,0.00009508099,0.0011564267,0.00780943,0.0036393567,0.016090376,0.93335485],"study_design_scores_gemma":[0.000007983931,0.0002585725,0.0014939703,0.0061394274,0.00025809108,0.0010999143,0.00012744564,0.0005544106,0.0032945343,0.001715734,0.98498905,0.000060942413],"about_ca_topic_score_codex":0.0012897323,"about_ca_topic_score_gemma":0.0015165781,"teacher_disagreement_score":0.0035888352,"about_ca_system_score_codex":0.00048246188,"about_ca_system_score_gemma":0.0010701469,"threshold_uncertainty_score":0.009635985},"labels":[],"label_agreement":null},{"id":"W4384343138","doi":"10.3390/s23146393","title":"A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Inertial measurement unit; GNSS applications; Computer science; Robustness (evolution); Real-time computing; Inertial navigation system; Kalman filter; Encoder; Global Positioning System; Indoor positioning system; Navigation system; Extended Kalman filter; Artificial intelligence; Orientation (vector space); Accelerometer; Telecommunications","score_opus":0.01267645800439426,"score_gpt":0.21797814050742806,"score_spread":0.2053016825030338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384343138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13421205,0.00046023645,0.85208136,0.00028412108,0.00016081326,0.00026080076,0.00021053322,0.0055641923,0.006765866],"genre_scores_gemma":[0.9456061,0.00013911018,0.050639603,0.00006526034,0.000030755473,0.0001628728,0.00021253077,0.000051153376,0.0030924755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999213,0.00015719525,0.00006014091,0.00015027165,0.00028193998,0.00013743907],"domain_scores_gemma":[0.9990594,0.00016223014,0.00012020147,0.0002914186,0.00025895194,0.00010787755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006867013,0.00048215617,0.00070146914,0.0003836846,0.001048766,0.00070810644,0.001193497,0.0007457941,0.0027945573],"category_scores_gemma":[0.0012663829,0.00022637156,0.00020748963,0.00050242554,0.00048657673,0.001559138,0.0019240791,0.0005244701,0.0011572972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018037787,0.0003481083,0.008026292,0.00046262858,0.000117881216,0.0020106474,0.00086178497,0.34023952,0.12588732,0.026901567,0.008626809,0.48471373],"study_design_scores_gemma":[0.0001647528,0.00035316433,0.000881936,0.000029524494,0.000034700948,0.0003151031,0.00008524981,0.947847,0.03187683,0.0071663396,0.011188589,0.00005674639],"about_ca_topic_score_codex":0.0036295333,"about_ca_topic_score_gemma":0.0030588808,"teacher_disagreement_score":0.0036295333,"about_ca_system_score_codex":0.00045698296,"about_ca_system_score_gemma":0.0014324943,"threshold_uncertainty_score":0.00934875},"labels":[],"label_agreement":null},{"id":"W4384343170","doi":"10.3390/s23146392","title":"A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Bearing (navigation); Fault (geology); Vibration; SIGNAL (programming language); Fault detection and isolation; Envelope (radar); Signal processing; Envelope detector; Modal; Computer science; Train; Engineering; Modal analysis; Electronic engineering; Acoustics; Artificial intelligence; Digital signal processing; Telecommunications; Physics","score_opus":0.014263964977900043,"score_gpt":0.2826297593578692,"score_spread":0.26836579437996916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384343170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042000722,0.0021121446,0.9511752,0.00033190625,0.00028192223,0.00007858956,0.00017696046,0.0010541525,0.0027884804],"genre_scores_gemma":[0.74436307,0.0027910923,0.2460918,0.00034680043,0.00041478642,0.00014029429,0.00039826482,0.000074989344,0.005378827],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992176,0.00008668413,0.000034173816,0.00015597681,0.00046012594,0.00004541735],"domain_scores_gemma":[0.99953926,0.00008575346,0.00009476676,0.00004884352,0.00020278506,0.00002862586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045795547,0.0005447987,0.00049729215,0.0009694092,0.0003039055,0.00047019136,0.00063106447,0.0007250583,0.0012430364],"category_scores_gemma":[0.000745442,0.0002423049,0.0003211863,0.0009932498,0.00029514782,0.0017938414,0.00050148653,0.0006651179,0.0005219929],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021002152,0.000083631974,0.0056456765,0.00066148466,0.00007491162,0.00026220112,0.00020307237,0.0046950015,0.6452985,0.0035941412,0.0030816968,0.3361897],"study_design_scores_gemma":[0.0001098458,0.0017770896,0.03154386,0.00012047829,0.00040585684,0.003185405,0.00024156792,0.39415953,0.49239364,0.0031116626,0.07277968,0.00017152133],"about_ca_topic_score_codex":0.00064333447,"about_ca_topic_score_gemma":0.0008528384,"teacher_disagreement_score":0.0012430364,"about_ca_system_score_codex":0.0003948332,"about_ca_system_score_gemma":0.00038169743,"threshold_uncertainty_score":0.004158318},"labels":[],"label_agreement":null},{"id":"W4384525922","doi":"10.3390/s23146450","title":"CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Salient; Artificial neural network; Visual cortex; Deep learning; Pattern recognition (psychology)","score_opus":0.01630957203593607,"score_gpt":0.26135589431279965,"score_spread":0.24504632227686357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384525922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123929195,0.0031136295,0.85106856,0.00060733594,0.00054683257,0.00023139975,0.0016037729,0.009183876,0.009715388],"genre_scores_gemma":[0.77300274,0.0012268223,0.20416662,0.0006885473,0.000128093,0.00020772022,0.004541354,0.00038631816,0.015651833],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998266,0.000016765856,0.0000055353394,0.00006207376,0.00005529011,0.000033790722],"domain_scores_gemma":[0.999813,0.000044474153,0.000029063154,0.00003079433,0.000057955796,0.000024725234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040567605,0.0012417447,0.00058675086,0.0005480617,0.00022852042,0.0005182479,0.001679374,0.00071898807,0.002085611],"category_scores_gemma":[0.0009013849,0.00031854786,0.00048621255,0.0004895787,0.00034398944,0.0009311514,0.0008982328,0.00077462476,0.00079410133],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052067824,0.0003363775,0.004559283,0.0003926981,0.00023897683,0.00030975428,0.00008195978,0.26383224,0.0572929,0.008871606,0.0320017,0.63156176],"study_design_scores_gemma":[0.000014167263,0.00007883475,0.0009375865,0.000013491231,0.000019684663,0.00006649108,0.000009476113,0.98328215,0.0099506145,0.0024834021,0.0031331102,0.000011065498],"about_ca_topic_score_codex":0.0075430386,"about_ca_topic_score_gemma":0.014311715,"teacher_disagreement_score":0.0075430386,"about_ca_system_score_codex":0.00081406446,"about_ca_system_score_gemma":0.00097029674,"threshold_uncertainty_score":0.014998257},"labels":[],"label_agreement":null},{"id":"W4384525924","doi":"10.3390/s23146443","title":"Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Computer science; Data mining; Data quality; Quality assurance; Profiling (computer programming); Outlier; Classifier (UML); Data integrity; Metadata; Machine learning; Artificial intelligence; Database; Engineering","score_opus":0.36624541897583285,"score_gpt":0.49565355172024805,"score_spread":0.1294081327444152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384525924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048072625,0.00036940022,0.94821,0.00077768957,0.00006461433,0.00014693136,0.00026629568,0.000936846,0.0011555934],"genre_scores_gemma":[0.83851224,0.00027572102,0.15911117,0.000371672,0.00005716661,0.00012672861,0.0006385244,0.000044513847,0.0008622728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961498,0.0015599827,0.00028458805,0.00080745504,0.000925216,0.00027292507],"domain_scores_gemma":[0.9948827,0.0019068435,0.00089959684,0.0009662842,0.0010256352,0.0003189647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004711057,0.00076230627,0.0010699002,0.0017026407,0.0006853907,0.0019102314,0.001723545,0.0010983021,0.0006321236],"category_scores_gemma":[0.013847757,0.0002848211,0.00064575236,0.0018363302,0.00066676777,0.003596701,0.0033921166,0.0012681524,0.00023808295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073540705,0.0005158082,0.036846187,0.0002979565,0.0002335,0.00063330756,0.0009221841,0.34853584,0.0083317235,0.023942897,0.0048441277,0.5741611],"study_design_scores_gemma":[0.000015602518,0.00015197275,0.0023424346,0.000035206518,0.00003821664,0.00035074496,0.00020673084,0.96677196,0.004391476,0.023054283,0.0026179687,0.000023471996],"about_ca_topic_score_codex":0.0017148041,"about_ca_topic_score_gemma":0.0018705487,"teacher_disagreement_score":0.004711057,"about_ca_system_score_codex":0.0012476209,"about_ca_system_score_gemma":0.0012648219,"threshold_uncertainty_score":0.024914801},"labels":[],"label_agreement":null},{"id":"W4384557778","doi":"10.3390/s23146434","title":"Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":253,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China","keywords":"Electroencephalography; Signal processing; Computer science; Preprocessor; SIGNAL (programming language); Feature extraction; Artificial intelligence; Pattern recognition (psychology); Digital signal processing; Psychology; Neuroscience","score_opus":0.10889998751907519,"score_gpt":0.43460178133802496,"score_spread":0.32570179381894976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384557778","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030274747,0.99459594,0.002383322,0.00053139305,0.00032962614,0.000018260393,0.000061286315,0.00003945114,0.0017379283],"genre_scores_gemma":[0.0014991895,0.9957908,0.0015753787,0.00014135758,0.0004013886,0.000018416127,0.00006738678,0.00000708865,0.0004989739],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994647,0.000096956726,0.00010777506,0.0000841059,0.00021982276,0.000026554515],"domain_scores_gemma":[0.99872464,0.0007604466,0.000105857376,0.0000442595,0.00033248586,0.00003222629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010652061,0.0011484557,0.0011781147,0.0036971872,0.00028559906,0.0012121934,0.0009809943,0.00093788945,0.004097763],"category_scores_gemma":[0.0022542549,0.00036405798,0.0006803799,0.00425856,0.0005423819,0.0017496905,0.00068447395,0.0011096717,0.00237735],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041844356,0.000044026743,0.00034447323,0.021180572,0.000092183865,0.00015054166,0.00009980846,0.0005277431,0.0015158157,0.0025558851,0.019354185,0.9540928],"study_design_scores_gemma":[0.000015103061,0.00019219342,0.0031571162,0.011330905,0.00034911346,0.0023517162,0.00015832907,0.0008331414,0.0019644715,0.0063158767,0.9732574,0.00007458067],"about_ca_topic_score_codex":0.0013349417,"about_ca_topic_score_gemma":0.0013986096,"teacher_disagreement_score":0.004097763,"about_ca_system_score_codex":0.00052273157,"about_ca_system_score_gemma":0.0015655085,"threshold_uncertainty_score":0.013708353},"labels":[],"label_agreement":null},{"id":"W4384636546","doi":"10.3390/s23146474","title":"Optimal Control of Semi-Active Suspension for Agricultural Tractors Using Linear Quadratic Gaussian Control","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Ministry of Agriculture, Food and Rural Affairs","keywords":"Linear-quadratic-Gaussian control; Control theory (sociology); Tractor; Kalman filter; Active suspension; Suspension (topology); Controller (irrigation); Observer (physics); State observer; Acceleration; Optimal control; Engineering; Deflection (physics); Computer science; Mathematics; Automotive engineering; Actuator; Control (management); Mathematical optimization","score_opus":0.014766906408174796,"score_gpt":0.2408841426711804,"score_spread":0.22611723626300562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384636546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052615806,0.0001681671,0.94513214,0.00008675731,0.000035036715,0.00003301848,0.000012549027,0.00025147,0.0016650861],"genre_scores_gemma":[0.97685826,0.000094332434,0.021270193,0.00003055163,0.000014525682,0.000056544835,0.000026476137,0.000015566831,0.0016334888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956733,0.00009408745,0.000021709047,0.000094113835,0.00016004768,0.00006277003],"domain_scores_gemma":[0.99945015,0.000188093,0.0001201766,0.00002369116,0.00019561395,0.000022233384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007536415,0.00050840894,0.00044862725,0.00024077646,0.00029887457,0.0006431227,0.00051623635,0.00040214637,0.00060679903],"category_scores_gemma":[0.00090790255,0.00024994308,0.000341988,0.00023539872,0.0005860902,0.00035673165,0.0004980336,0.0004358177,0.00015573282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024909727,0.00014530616,0.0008736114,0.00021911236,0.0000460908,0.00007278814,0.00017534858,0.8777098,0.049319927,0.0042230305,0.00049028837,0.06647555],"study_design_scores_gemma":[0.000014856377,0.00014684269,0.00035914005,0.000003057588,0.0000064450714,0.000004924267,0.000011427981,0.9968316,0.0019538112,0.00032864348,0.0003339507,0.0000052238247],"about_ca_topic_score_codex":0.0064380416,"about_ca_topic_score_gemma":0.006413267,"teacher_disagreement_score":0.0064380416,"about_ca_system_score_codex":0.0004818242,"about_ca_system_score_gemma":0.0007843497,"threshold_uncertainty_score":0.01280117},"labels":[],"label_agreement":null},{"id":"W4384665904","doi":"10.3390/s23146453","title":"A Highly Sensitive 3D Resonator Sensor for Fluid Measurement","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"King Saud University","keywords":"Resonator; Sensitivity (control systems); Materials science; Planar; Split-ring resonator; Dielectric; Optoelectronics; Microfluidics; Electric field; Wavelength; Acoustics; Electronic engineering; Computer science; Engineering; Nanotechnology; Physics","score_opus":0.02590286194459885,"score_gpt":0.2372719457371363,"score_spread":0.21136908379253747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384665904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34844077,0.0045302003,0.63530946,0.0009581141,0.0011355897,0.000274882,0.00095638225,0.0035221763,0.0048724893],"genre_scores_gemma":[0.4590071,0.0009938102,0.5355244,0.00036220445,0.00008400679,0.00013306637,0.0002461761,0.00007281164,0.0035763762],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991571,0.00011388693,0.000032722608,0.00021390998,0.00043460584,0.00004773614],"domain_scores_gemma":[0.999637,0.000094073905,0.00008943237,0.00004417624,0.00010529269,0.000029990792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005215282,0.00071542297,0.00058830425,0.00043531304,0.0002613906,0.000450004,0.000888692,0.0012558779,0.0006886166],"category_scores_gemma":[0.0006854489,0.0004275862,0.00063001824,0.00028462594,0.00040455128,0.00068179006,0.00057317805,0.0004937128,0.00054093683],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020112666,0.000009298605,0.000120405726,0.000057886118,0.000006727538,0.000051905314,0.000015815951,0.00048588158,0.9931813,0.00026498825,0.00022445078,0.005561272],"study_design_scores_gemma":[0.000015369873,0.00019982335,0.000817251,0.0000069309694,0.000023685532,0.00048659684,0.000017369637,0.025008984,0.96591514,0.0001261617,0.007320365,0.000062329134],"about_ca_topic_score_codex":0.0005905908,"about_ca_topic_score_gemma":0.0010308665,"teacher_disagreement_score":0.0012558779,"about_ca_system_score_codex":0.0005183966,"about_ca_system_score_gemma":0.00048741107,"threshold_uncertainty_score":0.003761232},"labels":[],"label_agreement":null},{"id":"W4384827404","doi":"10.3390/s23146496","title":"Electromyographic Activity of the Pelvic Floor Muscles and Internal Oblique Muscles in Women during Running with Traditional and Minimalist Shoes: A Cross-Over Clinical Trial","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Oblique case; Physical medicine and rehabilitation; Electromyography; Medicine; Pelvic floor; Anatomy","score_opus":0.02866331244259598,"score_gpt":0.2669542057540302,"score_spread":0.23829089331143424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384827404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931995,0.004472011,0.00019527687,0.0000696984,0.00008007371,0.0013392824,0.00010045657,0.000005622753,0.0005379795],"genre_scores_gemma":[0.9921327,0.0028182492,0.00065390544,0.00032944477,0.00026783798,0.0025020938,0.00023551991,0.0000035591995,0.0010566259],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99938905,0.00021966257,0.00009113652,0.00011524372,0.00008973119,0.00009523312],"domain_scores_gemma":[0.9993383,0.00015717493,0.00014718303,0.00004836504,0.00011155367,0.00019748438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017899283,0.00091866875,0.0022347164,0.00031641548,0.0005072138,0.0005407695,0.00037363372,0.0014021832,0.0023369095],"category_scores_gemma":[0.00137602,0.00038288315,0.0009524372,0.00039765562,0.0005196507,0.00054678903,0.00038095092,0.00077345164,0.0003594103],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.757706,0.08540763,0.045065604,0.0040711034,0.002856871,0.00041534635,0.0005962473,0.00036639717,0.018679732,0.00010258156,0.00044304418,0.08428945],"study_design_scores_gemma":[0.09355335,0.8055865,0.09562051,0.00019914113,0.0014973527,0.00017695212,0.00032588543,0.00024969343,0.0013181217,0.00006773458,0.001368776,0.000035949386],"about_ca_topic_score_codex":0.00047349674,"about_ca_topic_score_gemma":0.0010791057,"teacher_disagreement_score":0.0023369095,"about_ca_system_score_codex":0.0002810452,"about_ca_system_score_gemma":0.0005367177,"threshold_uncertainty_score":0.009466171},"labels":[],"label_agreement":null},{"id":"W4384929625","doi":"10.3390/s23146542","title":"Approaches for Hybrid Coregistration of Marker-Based and Markerless Coordinates Describing Complex Body/Object Interactions","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Human Motion and Animation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Ball (mathematics); Motion capture; Match moving; Motion (physics); Mathematics; Geometry","score_opus":0.11614359439170628,"score_gpt":0.2626381557323247,"score_spread":0.1464945613406184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384929625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017553136,0.000054871496,0.99754435,0.000017095845,0.000007094709,0.000024582425,0.000018822044,0.0003797267,0.00019813841],"genre_scores_gemma":[0.06105464,0.00030143137,0.9364433,0.000058140027,0.000030793537,0.0001665863,0.00025441014,0.00038251886,0.0013081501],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987931,0.0003242694,0.00010017587,0.00028542813,0.0004188945,0.0000781487],"domain_scores_gemma":[0.9976478,0.0007353899,0.00042110425,0.00065643265,0.000453628,0.00008566028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001769785,0.0020009752,0.0010310443,0.0021148317,0.00038703793,0.001967683,0.0021621305,0.00112329,0.0024129224],"category_scores_gemma":[0.0059102704,0.0012769634,0.0010040102,0.0020169655,0.0011272067,0.0020590043,0.0028067252,0.0014717396,0.001169131],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024186254,0.00018633164,0.0018778697,0.00042901194,0.00024132982,0.0001525527,0.0006195614,0.25630245,0.071286164,0.025844393,0.00273875,0.6400798],"study_design_scores_gemma":[0.000024826992,0.00014024034,0.002102856,0.000054615764,0.000041423824,0.0002727475,0.000112282934,0.9375975,0.03953268,0.010353464,0.009684763,0.00008253902],"about_ca_topic_score_codex":0.004210687,"about_ca_topic_score_gemma":0.0059516924,"teacher_disagreement_score":0.004210687,"about_ca_system_score_codex":0.0006837131,"about_ca_system_score_gemma":0.0014595232,"threshold_uncertainty_score":0.009359598},"labels":[],"label_agreement":null},{"id":"W4384931048","doi":"10.3390/s23146539","title":"Classification of Low Earth Orbit (LEO) Resident Space Objects’ (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM)","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Light curve; Support vector machine; Artificial intelligence; Computer science; Geostationary orbit; Brightness; Algorithm; Machine learning; Pattern recognition (psychology); Physics; Astrophysics; Optics; Astronomy; Satellite","score_opus":0.028835201763697135,"score_gpt":0.2590654822581476,"score_spread":0.23023028049445043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384931048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8894942,0.00028953105,0.10540432,0.00016807442,0.00015606551,0.00004413602,0.0008133968,0.0013901255,0.0022401235],"genre_scores_gemma":[0.9693028,0.00015485924,0.027793745,0.000033875163,0.00003728849,0.000029119843,0.0013816968,0.000048745824,0.0012178625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999866,0.000010717101,0.0000097996335,0.000035071724,0.00004818896,0.000030239395],"domain_scores_gemma":[0.99978083,0.000048380916,0.000042769894,0.000021486436,0.000080356775,0.000026241558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019456013,0.00050819234,0.0003059638,0.0009829492,0.0001666515,0.00044085266,0.00035259422,0.0003703596,0.00067978655],"category_scores_gemma":[0.0006440311,0.000088413886,0.0003998564,0.00090193126,0.00019163969,0.0004972777,0.00024777255,0.0004461616,0.00038117563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005460144,0.00060419214,0.07578759,0.00023210127,0.00014713491,0.0004850664,0.00033706252,0.110458545,0.104836285,0.0012612409,0.0075656124,0.6977391],"study_design_scores_gemma":[0.000007860009,0.00012141779,0.04407165,0.00001252281,0.000016992748,0.00009995682,0.00012909841,0.9316683,0.021729324,0.00059318636,0.0015238303,0.000025912725],"about_ca_topic_score_codex":0.0035334527,"about_ca_topic_score_gemma":0.0038421825,"teacher_disagreement_score":0.0035334527,"about_ca_system_score_codex":0.0002659809,"about_ca_system_score_gemma":0.00021380375,"threshold_uncertainty_score":0.0070257783},"labels":[],"label_agreement":null},{"id":"W4385201682","doi":"10.3390/s23146595","title":"Stratospheric Night Sky Imaging Payload for Space Situational Awareness (SSA)","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Payload (computing); Situation awareness; Sky; Remote sensing; Space (punctuation); Computer science; Meteorology; Environmental science; Aerospace engineering; Aeronautics; Engineering; Physics; Geography; Computer security; Operating system","score_opus":0.03389338227667418,"score_gpt":0.28486621339378415,"score_spread":0.25097283111711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385201682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12583707,0.003147514,0.62466455,0.0016073859,0.0017569936,0.0022577883,0.02024826,0.031835698,0.18864465],"genre_scores_gemma":[0.45851597,0.0017023355,0.45893818,0.00071687024,0.0004080784,0.00073495175,0.043619644,0.0019520595,0.033411942],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955815,0.000047106976,0.000012040348,0.000049477647,0.00027092893,0.000062234896],"domain_scores_gemma":[0.999241,0.000058686397,0.000057823458,0.00018536356,0.00037614943,0.00008099711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008987551,0.00061968656,0.0003745697,0.0008527913,0.0005237814,0.0010820507,0.00060631003,0.0004520012,0.0071379784],"category_scores_gemma":[0.00078536686,0.00015457165,0.00030165736,0.00062506663,0.000260922,0.0008636655,0.0010404766,0.0007870549,0.003772538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041336613,0.00017629513,0.0108395815,0.00043558454,0.00010562682,0.0002710849,0.0005021637,0.00598735,0.29425597,0.016271198,0.07850687,0.59223497],"study_design_scores_gemma":[0.00010088894,0.0010437889,0.0358421,0.00018815059,0.00014147967,0.00088729407,0.0003625961,0.053003553,0.2041107,0.0042705545,0.6999433,0.0001055704],"about_ca_topic_score_codex":0.0033161938,"about_ca_topic_score_gemma":0.004305461,"teacher_disagreement_score":0.0071379784,"about_ca_system_score_codex":0.00065149326,"about_ca_system_score_gemma":0.0012743414,"threshold_uncertainty_score":0.023878932},"labels":[],"label_agreement":null},{"id":"W4385342426","doi":"10.3390/s23156752","title":"SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Government of Canada; University of Victoria","funders":"","keywords":"Leverage (statistics); Anomaly detection; Anomaly (physics); Computer science; Internet of Things; Linear correlation; Sliding window protocol; False positive rate; Artificial intelligence; Smart city; Data mining; Machine learning; Window (computing); Computer security; Mathematics; Statistics","score_opus":0.008613484670486669,"score_gpt":0.23727425268007785,"score_spread":0.22866076800959118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385342426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16624416,0.0010261884,0.82399696,0.0005993941,0.00020033619,0.00019439761,0.0012189334,0.0039483276,0.0025712785],"genre_scores_gemma":[0.89768916,0.00023417584,0.09918786,0.00010611572,0.00013682472,0.0001079129,0.0010976569,0.000082574465,0.0013576403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986327,0.0003554771,0.000096031785,0.00039214102,0.00039139992,0.00013218961],"domain_scores_gemma":[0.9970511,0.0011706693,0.00052012457,0.00047250514,0.00063107297,0.00015454767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015850359,0.00079876615,0.0010908529,0.0018396659,0.00047189984,0.0006947503,0.0014388012,0.0006233924,0.0011450883],"category_scores_gemma":[0.006959019,0.00023947716,0.0007959468,0.0013691554,0.0006064932,0.0014203314,0.0015463377,0.0010435852,0.00047513883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089486997,0.0008239927,0.1153332,0.0003632395,0.00062974694,0.00059918227,0.00057258026,0.25884917,0.011482252,0.014209989,0.013129742,0.58311194],"study_design_scores_gemma":[0.000009653723,0.0001214076,0.00706319,0.0000114797795,0.000029870938,0.00016666568,0.00005364165,0.984082,0.0023764994,0.0045591053,0.0015028433,0.000023579567],"about_ca_topic_score_codex":0.005811832,"about_ca_topic_score_gemma":0.008371518,"teacher_disagreement_score":0.005811832,"about_ca_system_score_codex":0.0006938477,"about_ca_system_score_gemma":0.000986612,"threshold_uncertainty_score":0.011556029},"labels":[],"label_agreement":null},{"id":"W4385347012","doi":"10.3390/s23156712","title":"Preventative Sensor-Based Remote Monitoring of the Diabetic Foot in Clinical Practice","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Diabetic foot; Medicine; Diabetes management; Intensive care medicine; Diabetes mellitus; Amputation; Risk analysis (engineering); Health care; Type 2 diabetes; Surgery","score_opus":0.051676252679245395,"score_gpt":0.3998690732535794,"score_spread":0.348192820574334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385347012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27577093,0.048284177,0.544909,0.02896204,0.0018463897,0.0024976789,0.0036565422,0.0060629733,0.08801025],"genre_scores_gemma":[0.76013094,0.017429257,0.21253513,0.0034060122,0.0007005949,0.0006074859,0.0010048,0.00017835932,0.0040074764],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.997477,0.0011495014,0.00025515418,0.00035325484,0.0006175665,0.00014749625],"domain_scores_gemma":[0.9960692,0.0016215718,0.0007511209,0.00033173908,0.00089430687,0.0003319353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023710737,0.00067285926,0.00065555103,0.001436709,0.00042006373,0.002250321,0.0011200048,0.0009510579,0.0039578313],"category_scores_gemma":[0.00894032,0.0003431392,0.00046789146,0.0012703055,0.0004274764,0.0014687001,0.0016240313,0.0009618551,0.0011286185],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009110584,0.0007492094,0.07091157,0.0023097584,0.00027650886,0.0015471171,0.0019722902,0.0068781194,0.016374702,0.0053339973,0.02199685,0.8707388],"study_design_scores_gemma":[0.00088899647,0.0062032877,0.24559596,0.01326618,0.0015743368,0.015946725,0.014966286,0.19143409,0.07676902,0.056422092,0.3761186,0.0008144442],"about_ca_topic_score_codex":0.0019375371,"about_ca_topic_score_gemma":0.0030878522,"teacher_disagreement_score":0.0039578313,"about_ca_system_score_codex":0.00059241825,"about_ca_system_score_gemma":0.0010799016,"threshold_uncertainty_score":0.013240278},"labels":[],"label_agreement":null},{"id":"W4385347109","doi":"10.3390/s23156716","title":"Cybersecurity Risk Analysis of Electric Vehicles Charging Stations","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Context (archaeology); Electric vehicle; Computer security; Engineering; Adaptation (eye); Risk analysis (engineering); Computer science; Business","score_opus":0.025530729951259407,"score_gpt":0.2827868527720169,"score_spread":0.2572561228207575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385347109","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00633885,0.9777575,0.0047795456,0.0007435276,0.0002741247,0.00005066408,0.0000797479,0.000024704043,0.009951287],"genre_scores_gemma":[0.061864763,0.9326974,0.0022724885,0.00019638814,0.000236566,0.000036729885,0.00013296043,0.00000538308,0.0025572826],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995369,0.00011195358,0.00004090312,0.000051313258,0.00022485886,0.000034143195],"domain_scores_gemma":[0.99900585,0.00055470923,0.00015465645,0.000025746394,0.00023814145,0.000020793354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071215443,0.00088892813,0.0007142152,0.0024525507,0.0002928469,0.0012666621,0.00051848806,0.00079237035,0.0015752591],"category_scores_gemma":[0.0016251226,0.00021904719,0.00073764083,0.0018193949,0.00027033273,0.00118094,0.00046722314,0.0005313063,0.00036431488],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006398892,0.00014260296,0.0032890602,0.018418128,0.00036621184,0.00044912327,0.00030146845,0.008249922,0.0016041795,0.026387202,0.0159308,0.9247974],"study_design_scores_gemma":[0.000021927832,0.0006982957,0.015065578,0.025789998,0.001119395,0.0035942285,0.0018032752,0.01886691,0.008686685,0.030383436,0.8937948,0.00017554691],"about_ca_topic_score_codex":0.001632391,"about_ca_topic_score_gemma":0.0018767891,"teacher_disagreement_score":0.0024525507,"about_ca_system_score_codex":0.0005079853,"about_ca_system_score_gemma":0.0009775902,"threshold_uncertainty_score":0.005269766},"labels":[],"label_agreement":null},{"id":"W4385446408","doi":"10.3390/s23156845","title":"Analysis of Hyperspectral Data to Develop an Approach for Document Images","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Hyperspectral imaging; Preprocessor; Computer science; Data pre-processing; Data science; Field (mathematics); Feature extraction; Image processing; Data mining; Artificial intelligence; Information retrieval; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.19035495158476096,"score_gpt":0.37987170346447613,"score_spread":0.18951675187971517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385446408","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077212634,0.11884141,0.8354301,0.0024101858,0.0012051153,0.00042872003,0.001204322,0.0017041388,0.03105478],"genre_scores_gemma":[0.08717895,0.18174306,0.70721275,0.0016436249,0.0010112944,0.000504244,0.0024078903,0.00043290036,0.017865334],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993311,0.00008791026,0.0000465305,0.00014313457,0.00035932384,0.000031955708],"domain_scores_gemma":[0.99938905,0.00015457712,0.00006364715,0.000055299766,0.00032224783,0.000015172307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011002693,0.001046783,0.0007927223,0.0041917353,0.00036606466,0.0020163034,0.0011251373,0.00097043,0.003159112],"category_scores_gemma":[0.0013269293,0.00032853478,0.0009275498,0.003584011,0.00086825475,0.002050886,0.00070238725,0.001725125,0.0030098467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003630296,0.00006690293,0.0013450853,0.0043735025,0.00016112332,0.00018129236,0.000220266,0.0042816326,0.048446003,0.024832625,0.015643878,0.9004115],"study_design_scores_gemma":[0.000018341902,0.00019890977,0.009518258,0.0024633382,0.0003053537,0.0025090177,0.0008809408,0.080835864,0.13109925,0.048487406,0.7234646,0.00021867787],"about_ca_topic_score_codex":0.0012521842,"about_ca_topic_score_gemma":0.0020672637,"teacher_disagreement_score":0.0041917353,"about_ca_system_score_codex":0.0006544233,"about_ca_system_score_gemma":0.0007088188,"threshold_uncertainty_score":0.010568261},"labels":[],"label_agreement":null},{"id":"W4385490911","doi":"10.3390/s23156872","title":"Optimization of Spatial and Temporal Configuration of a Pressure Sensing Array to Predict Posture and Mobility in Lying","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Receiver operating characteristic; Sampling (signal processing); Temporal resolution; Simulation; Convolutional neural network; Computer science; Acoustics; Artificial intelligence; Biomedical engineering; Environmental science; Statistics; Mathematics; Engineering; Detector; Telecommunications; Physics; Optics","score_opus":0.031058692080314816,"score_gpt":0.34864649348846005,"score_spread":0.31758780140814524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385490911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83769107,0.0006982586,0.15957014,0.00010794988,0.00006211615,0.00006326769,0.00023345409,0.000400114,0.0011735535],"genre_scores_gemma":[0.97024554,0.00017903074,0.028945714,0.000037603473,0.000011018562,0.000056933815,0.00013797768,0.000009680525,0.00037653322],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984074,0.000028426184,0.000011293274,0.000051864416,0.00004221017,0.000025406596],"domain_scores_gemma":[0.99975973,0.000088492816,0.000036477184,0.000015759213,0.00008117538,0.00001836607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003103426,0.00053567666,0.00038958993,0.0003591815,0.00011294634,0.00031840178,0.00033142584,0.00031520482,0.000514842],"category_scores_gemma":[0.0009576405,0.00024217788,0.000254838,0.00020306128,0.00013312747,0.00025853806,0.0002253607,0.00022939513,0.00019479085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022256777,0.0005664849,0.0659293,0.00040807668,0.00012829195,0.00059666624,0.00015346076,0.10827774,0.5556074,0.00023960027,0.0012169541,0.26465037],"study_design_scores_gemma":[0.00006680889,0.0019588456,0.12669462,0.00004542278,0.00019296356,0.0006482121,0.00014273143,0.75738347,0.11166013,0.00030112278,0.0008509535,0.000054806067],"about_ca_topic_score_codex":0.0019494736,"about_ca_topic_score_gemma":0.0030850645,"teacher_disagreement_score":0.0019494736,"about_ca_system_score_codex":0.00014226626,"about_ca_system_score_gemma":0.00026239638,"threshold_uncertainty_score":0.0038762689},"labels":[],"label_agreement":null},{"id":"W4385498095","doi":"10.3390/s23156871","title":"The Objective Dementia Severity Scale Based on MRI with Contrastive Learning: A Whole Brain Neuroimaging Perspective","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; National Natural Science Foundation of China; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; BioClinica; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; U.S. Department of Defense; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuroimaging; Dementia; Magnetic resonance imaging; Rating scale; Perspective (graphical); Medicine; Physical medicine and rehabilitation; Disease; Artificial intelligence; Psychology; Machine learning; Computer science; Radiology; Psychiatry; Pathology; Developmental psychology","score_opus":0.009547967689511862,"score_gpt":0.28841672227184534,"score_spread":0.27886875458233346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385498095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43074694,0.008713241,0.5480857,0.0013231747,0.00032042945,0.0003791747,0.0028401432,0.00094360695,0.006647584],"genre_scores_gemma":[0.899485,0.0017818422,0.09495687,0.00029014357,0.00021769827,0.0001496077,0.0016751729,0.000036745798,0.0014069235],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99944466,0.00014303757,0.000063694046,0.00014500314,0.00015580558,0.000047746325],"domain_scores_gemma":[0.9991379,0.00027435846,0.00020821892,0.00007757389,0.00023882184,0.00006314211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010040896,0.0010889511,0.0005909855,0.0016274049,0.0001106824,0.0008651559,0.0005619413,0.0005641892,0.00053881516],"category_scores_gemma":[0.0031459928,0.0001583891,0.00046393237,0.0008002472,0.00046258725,0.0010000846,0.0005603141,0.000684563,0.00023030155],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010737593,0.00069574686,0.123849325,0.0008519203,0.00063318,0.00066115934,0.00036938404,0.059953775,0.04661233,0.005805014,0.008255232,0.7512391],"study_design_scores_gemma":[0.00009354825,0.0014678932,0.14465342,0.00025871734,0.00038264174,0.002150587,0.0003497302,0.7702391,0.050325092,0.020003112,0.0098287035,0.00024742013],"about_ca_topic_score_codex":0.0020404605,"about_ca_topic_score_gemma":0.0042192885,"teacher_disagreement_score":0.0020404605,"about_ca_system_score_codex":0.00033936618,"about_ca_system_score_gemma":0.00035777228,"threshold_uncertainty_score":0.005310178},"labels":[],"label_agreement":null},{"id":"W4385614860","doi":"10.3390/s23156985","title":"A Novel Monopole Ultra-Wide-Band Multiple-Input Multiple-Output Antenna with Triple-Notched Characteristics for Enhanced Wireless Communication and Portable Systems","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"MIMO; Ground plane; Antenna (radio); Monopole antenna; Notching; Electronic engineering; Narrowband; Ultra-wideband; Wireless; Computer science; Electrical engineering; Acoustics; Engineering; Physics; Telecommunications; Channel (broadcasting)","score_opus":0.01729088857080488,"score_gpt":0.21673870839203532,"score_spread":0.19944781982123044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385614860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24181065,0.000864263,0.74338806,0.00031565042,0.00027653552,0.00003923144,0.00010904099,0.0012478628,0.011948756],"genre_scores_gemma":[0.75882924,0.0003911409,0.23213363,0.00028451288,0.00012635536,0.000046832123,0.00016444239,0.00007099468,0.00795281],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998017,0.00003975377,0.00000992399,0.00004350789,0.00007757347,0.00002760073],"domain_scores_gemma":[0.9997638,0.000033743167,0.00006707155,0.000038418075,0.00006928648,0.000027655884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014207524,0.00045051222,0.00035165006,0.00023283197,0.00010330613,0.00037828955,0.0007177629,0.00070803764,0.0010181915],"category_scores_gemma":[0.0001872917,0.00020430499,0.000445673,0.0002939437,0.00014282326,0.00038628155,0.00036193785,0.00035752272,0.0010735143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001247839,0.000029913566,0.0006447601,0.00011890916,0.00003880773,0.00035818297,0.000040982562,0.002497042,0.94735885,0.0020682195,0.0006937515,0.046025697],"study_design_scores_gemma":[0.000070433394,0.0017285931,0.004368253,0.000039774648,0.00013560025,0.0066550383,0.00008852282,0.13651973,0.8145911,0.0013518371,0.034341086,0.00011008294],"about_ca_topic_score_codex":0.000047134257,"about_ca_topic_score_gemma":0.00008238639,"teacher_disagreement_score":0.0010181915,"about_ca_system_score_codex":0.00017656271,"about_ca_system_score_gemma":0.00010803709,"threshold_uncertainty_score":0.0034062266},"labels":[],"label_agreement":null},{"id":"W4385635945","doi":"10.3390/s23156989","title":"A Miniaturized Tri-Band Implantable Antenna for ISM/WMTS/Lower UWB/Wi-Fi Frequencies","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Ground plane; ISM band; Antenna (radio); Electrical engineering; Monopole antenna; Bandwidth (computing); Imaging phantom; Computer science; Acoustics; Physics; Telecommunications; Engineering; Optics","score_opus":0.015212868107911077,"score_gpt":0.22191165275574481,"score_spread":0.20669878464783373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385635945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11301206,0.0019275887,0.87170786,0.0005366347,0.0005920177,0.000106535495,0.0002058981,0.0012615386,0.010649883],"genre_scores_gemma":[0.68747234,0.0014913811,0.2969415,0.0006324189,0.00021840155,0.00020736732,0.00038041043,0.00012258042,0.012533668],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998086,0.00003843867,0.000015048107,0.000046257286,0.00006401966,0.000027722746],"domain_scores_gemma":[0.9997379,0.000038985538,0.00008920781,0.00005189129,0.00005816733,0.000023776262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019062078,0.00052617554,0.00041688257,0.00033301135,0.00008668767,0.0005069671,0.0008080039,0.0008830291,0.001528352],"category_scores_gemma":[0.00034386225,0.00021331465,0.00065766525,0.0003623506,0.00019651027,0.00046759497,0.00029585752,0.0003653813,0.0013744406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031899655,0.00008931037,0.0017520197,0.0005480443,0.00010773534,0.00069835474,0.00010857048,0.013950603,0.83228177,0.0042097326,0.0045157573,0.14141905],"study_design_scores_gemma":[0.00015420833,0.0050675906,0.01221501,0.00014288705,0.0004477604,0.01720955,0.00022088311,0.14734262,0.64773214,0.0022909746,0.16695319,0.00022320695],"about_ca_topic_score_codex":0.000065035114,"about_ca_topic_score_gemma":0.00007623714,"teacher_disagreement_score":0.001528352,"about_ca_system_score_codex":0.0002470512,"about_ca_system_score_gemma":0.00016665214,"threshold_uncertainty_score":0.005112827},"labels":[],"label_agreement":null},{"id":"W4385665964","doi":"10.3390/s23167020","title":"Power Transformers OLTC Condition Monitoring Based on Feature Extraction from Vibro-Acoustic Signals: Main Peaks and Euclidean Distance","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Euclidean distance; Transformer; Acoustic emission; SIGNAL (programming language); Engineering; Fault (geology); Feature extraction; Computer science; Fault detection and isolation; Acoustics; Artificial intelligence; Voltage; Electrical engineering","score_opus":0.007940697891624672,"score_gpt":0.23335133647639034,"score_spread":0.22541063858476568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385665964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.335878,0.0011319349,0.65737593,0.00013360681,0.000098911994,0.0001534175,0.001195877,0.0015302991,0.0025019797],"genre_scores_gemma":[0.87272125,0.00054391724,0.12363732,0.000028250759,0.000044604254,0.00010432898,0.0016429203,0.00004082318,0.001236596],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948007,0.00005694772,0.0000559421,0.000147982,0.00020638331,0.0000526723],"domain_scores_gemma":[0.9994822,0.00014139127,0.00011664708,0.000050293937,0.00018266548,0.000026851993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033491498,0.00057274144,0.0005663705,0.0022868488,0.00015117522,0.0006291449,0.00049538055,0.00044221935,0.0005976577],"category_scores_gemma":[0.0012144181,0.000121510835,0.00036665858,0.0016539595,0.00020946006,0.0006030159,0.0005127938,0.00034211465,0.00035686395],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007006248,0.00032582466,0.017535802,0.0004692649,0.00016289564,0.00038431093,0.00018353228,0.05112726,0.1247567,0.0010175504,0.0025715155,0.80076474],"study_design_scores_gemma":[0.000034395773,0.0004079945,0.07818302,0.00004562544,0.000082821556,0.0009152979,0.00027869042,0.83301526,0.08163555,0.0012748413,0.004047077,0.000079412],"about_ca_topic_score_codex":0.001909865,"about_ca_topic_score_gemma":0.0018358513,"teacher_disagreement_score":0.0022868488,"about_ca_system_score_codex":0.0002184057,"about_ca_system_score_gemma":0.00025711942,"threshold_uncertainty_score":0.0037974715},"labels":[],"label_agreement":null},{"id":"W4385707598","doi":"10.3390/s23167045","title":"Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Traceability; Livestock; Productivity; Business; Supply chain; Adaptability; Animal welfare; Sustainability; Computer science; Marketing; Economics","score_opus":0.10436357125254114,"score_gpt":0.298034988899518,"score_spread":0.19367141764697687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385707598","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021190164,0.99356306,0.0004314182,0.0019052398,0.00053647405,0.000007999845,0.000011564942,0.0000068528834,0.0033253792],"genre_scores_gemma":[0.0017823561,0.9955089,0.0005759934,0.0008466881,0.0002982409,0.0000099769695,0.000016238266,0.0000030991746,0.00095846807],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945027,0.00015963492,0.000060964667,0.000061868646,0.00022175566,0.00004552967],"domain_scores_gemma":[0.99877256,0.0008315269,0.00008064874,0.000030883923,0.00024298806,0.00004139147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016857625,0.00055347534,0.0007579796,0.0022339139,0.00034143403,0.0019366662,0.0006343531,0.0015577436,0.0022038312],"category_scores_gemma":[0.0016654307,0.00025996153,0.000547184,0.0026838775,0.0010222679,0.0028428829,0.0009434389,0.0027144393,0.00086563575],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038332186,0.00006042531,0.0002321268,0.014331249,0.00006979155,0.0001710315,0.0003262256,0.00067519775,0.001134636,0.05644253,0.02477198,0.9017465],"study_design_scores_gemma":[0.000005127537,0.00007073489,0.0004857679,0.0058608907,0.00004075676,0.00030726154,0.00023631647,0.00014720667,0.00034990467,0.0075562587,0.984925,0.000014748145],"about_ca_topic_score_codex":0.0019546584,"about_ca_topic_score_gemma":0.0028502606,"teacher_disagreement_score":0.0022339139,"about_ca_system_score_codex":0.0011394318,"about_ca_system_score_gemma":0.002501943,"threshold_uncertainty_score":0.008915305},"labels":[],"label_agreement":null},{"id":"W4385737971","doi":"10.3390/s23167065","title":"Microbial Fuel Cell Biosensor with Capillary Carbon Source Delivery for Real-Time Toxicity Detection","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"","keywords":"Biosensor; Microbial fuel cell; Chemistry; Environmental chemistry; Anode; Nanotechnology; Materials science; Electrode","score_opus":0.0052003910663956585,"score_gpt":0.17459940225246798,"score_spread":0.1693990111860723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385737971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83292836,0.0038651288,0.15804517,0.00047900315,0.00020200151,0.00028158445,0.0005386738,0.0012220482,0.002438146],"genre_scores_gemma":[0.83870965,0.0021370363,0.15497658,0.00024817814,0.00005223363,0.00024352584,0.0003704788,0.000057595687,0.0032048193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994474,0.00004974306,0.000028668963,0.000113465285,0.00031290253,0.000047744365],"domain_scores_gemma":[0.99968183,0.00007844646,0.00005442291,0.000016048796,0.00014499495,0.000024237188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041703103,0.00079517503,0.0005627276,0.00047623666,0.000224337,0.0004938987,0.0007044979,0.0012699692,0.0004929082],"category_scores_gemma":[0.0006182545,0.0003114363,0.0004017697,0.00036484998,0.0002886824,0.00048796032,0.0003566939,0.00064656243,0.00032862782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000113789865,0.000008917393,0.000054864402,0.000024222629,0.0000024694125,0.000014457429,0.0000060894668,0.000063041494,0.9981357,0.000032350832,0.000022448916,0.0016239313],"study_design_scores_gemma":[0.0000019490208,0.00006195939,0.00021624468,0.0000021733692,0.000004503613,0.00005415054,0.0000042953293,0.002138382,0.9969304,0.000011087052,0.0005679395,0.0000069308057],"about_ca_topic_score_codex":0.0015223839,"about_ca_topic_score_gemma":0.0028516825,"teacher_disagreement_score":0.0015223839,"about_ca_system_score_codex":0.00068636273,"about_ca_system_score_gemma":0.0004655784,"threshold_uncertainty_score":0.0049799085},"labels":[],"label_agreement":null},{"id":"W4385740861","doi":"10.3390/s23167079","title":"Architecture for a Mobile Robotic Camera Positioning System for Photogrammetric Data Acquisition in Hydroelectric Tunnels","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; New Brunswick Innovation Foundation","keywords":"Photogrammetry; Photography; Computer vision; Artificial intelligence; Hydroelectricity; Data collection; Channel (broadcasting); Computer science; Engineering; Real-time computing","score_opus":0.03356476797157612,"score_gpt":0.2580504835727167,"score_spread":0.22448571560114058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385740861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05518111,0.00022123737,0.92117,0.00018341519,0.00013288092,0.00045444295,0.00025329864,0.011817164,0.010586383],"genre_scores_gemma":[0.598193,0.00018900746,0.38182208,0.00018240571,0.00005540241,0.0006208609,0.0006407524,0.00019482168,0.018101659],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996388,0.00002589592,0.000019629968,0.00010699711,0.0001594818,0.0000491616],"domain_scores_gemma":[0.9997969,0.000014879313,0.00002958817,0.000028034096,0.000104758015,0.000025913012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030883146,0.00052986114,0.0004151976,0.00067044,0.0003572866,0.0005572033,0.0016086164,0.00061301835,0.005421618],"category_scores_gemma":[0.00037450524,0.00032414027,0.00024650816,0.00034523496,0.00022486788,0.00056459266,0.00070630934,0.00043186898,0.0030479566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006002552,0.00029364595,0.0065792426,0.00038091515,0.000086408225,0.00093372073,0.0006843473,0.044097472,0.3784004,0.0065989266,0.013807943,0.5475367],"study_design_scores_gemma":[0.00018621479,0.0017349033,0.017940404,0.00012002721,0.0001436076,0.0018868863,0.0003031863,0.7038919,0.15651985,0.0019849057,0.11507724,0.00021087483],"about_ca_topic_score_codex":0.0038856484,"about_ca_topic_score_gemma":0.0034325207,"teacher_disagreement_score":0.005421618,"about_ca_system_score_codex":0.00045901988,"about_ca_system_score_gemma":0.00089097564,"threshold_uncertainty_score":0.018137097},"labels":[],"label_agreement":null},{"id":"W4385805592","doi":"10.3390/s23167151","title":"Graphene Inks Printed by Aerosol Jet for Sensing Applications: The Role of Dispersant on the Inks’ Formulation and Performance","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Dispersant; Aerosol; Materials science; Jet (fluid); Graphene; Nanotechnology; Jet fuel; Engineering; Chemistry; Aerospace engineering; Organic chemistry; Dispersion (optics); Physics; Optics","score_opus":0.009276185012625542,"score_gpt":0.1995423867993933,"score_spread":0.19026620178676776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385805592","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97888446,0.00423334,0.0135856625,0.00018587548,0.00008876105,0.00003496569,0.000102482736,0.00024409393,0.002640379],"genre_scores_gemma":[0.9792322,0.0018899353,0.016455622,0.00006493436,0.000018863737,0.000014896934,0.00009566229,0.00006737902,0.002160557],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981815,0.000028448918,0.000019394263,0.000042780383,0.00007077219,0.000020427691],"domain_scores_gemma":[0.9998215,0.00007143369,0.00004215887,0.000020428892,0.000032300413,0.000012219606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019310966,0.00040973164,0.00018599896,0.0002428424,0.00013525058,0.00048827304,0.00027086755,0.00039314904,0.00065576634],"category_scores_gemma":[0.00042873537,0.00011047088,0.00017834033,0.00018239443,0.00019037865,0.0005194098,0.00022053822,0.00032026376,0.00029240304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020096526,0.000009873577,0.00007980109,0.000045270084,0.0000029432124,0.00005855463,0.000011781161,0.000116552,0.9959359,0.00004802528,0.000018149449,0.0036530097],"study_design_scores_gemma":[0.0000016919615,0.000050504288,0.00023092632,0.0000022794013,0.0000034269592,0.000050532664,0.000006176821,0.0007021388,0.9984566,0.000013016806,0.00047977924,0.0000028808229],"about_ca_topic_score_codex":0.00027004498,"about_ca_topic_score_gemma":0.0005737673,"teacher_disagreement_score":0.00065576634,"about_ca_system_score_codex":0.0001775969,"about_ca_system_score_gemma":0.000093594586,"threshold_uncertainty_score":0.002193749},"labels":[],"label_agreement":null},{"id":"W4385812051","doi":"10.3390/s23167155","title":"The Validity of a Three-Dimensional Motion Capture System and the Garmin Running Dynamics Pod in Connection with an Assessment of Ground Contact Time While Running in Place","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Javna Agencija za Raziskovalno Dejavnost RS","keywords":"Ground reaction force; Motion capture; Simulation; Force platform; Accelerometer; Dynamics (music); Wearable computer; Computer science; Engineering; Motion (physics); Acoustics; Artificial intelligence; Physics","score_opus":0.02102632335130154,"score_gpt":0.27808839983624367,"score_spread":0.2570620764849421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385812051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88352686,0.001533508,0.10880709,0.0001873118,0.00041462126,0.0003759229,0.00084258476,0.00029122492,0.004020888],"genre_scores_gemma":[0.9707625,0.00033792964,0.02713687,0.00012485297,0.00006445085,0.00023686762,0.00026133258,0.000038392107,0.0010367865],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99595535,0.001252423,0.00038821646,0.0009662445,0.0012316075,0.00020623763],"domain_scores_gemma":[0.9927055,0.003368855,0.001200333,0.00077812554,0.0016424508,0.00030466748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035232212,0.00080987165,0.00054820353,0.0013267448,0.00033160852,0.001032144,0.0008983981,0.0014191517,0.00153431],"category_scores_gemma":[0.015292555,0.00040547075,0.00039788202,0.0008890027,0.0008837003,0.0008162382,0.0011357226,0.00042795853,0.00060770224],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0067946273,0.00037467817,0.46674597,0.0020082009,0.0005914068,0.0005076357,0.0022474753,0.002608097,0.27824518,0.0007692884,0.0009623849,0.23814504],"study_design_scores_gemma":[0.000096456395,0.0028750228,0.944507,0.00018100413,0.00026632415,0.0015556706,0.000937587,0.010747659,0.036078546,0.0004994825,0.0021649294,0.000090382826],"about_ca_topic_score_codex":0.0016625181,"about_ca_topic_score_gemma":0.0039193127,"teacher_disagreement_score":0.0035232212,"about_ca_system_score_codex":0.00028018747,"about_ca_system_score_gemma":0.00040473277,"threshold_uncertainty_score":0.01863277},"labels":[],"label_agreement":null},{"id":"W4385949190","doi":"10.3390/s23167218","title":"Correction: Bohlke et al. The Effect of a Verbal Cognitive Task on Postural Sway Does Not Persist When the Task Is Over. Sensors 2021, 21, 8428","year":2023,"lang":"en","type":"erratum","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"North York General Hospital; University of Toronto","funders":"National Institute on Aging","keywords":"Task (project management); Cognition; Cognitive psychology; Psychology; Computer science; Physical medicine and rehabilitation; Medicine; Engineering; Neuroscience","score_opus":0.015520026747705806,"score_gpt":0.33075042263862775,"score_spread":0.31523039589092194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385949190","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014288709,0.0006946909,0.0010648194,0.03461469,0.95565164,0.000050556875,0.0044485237,0.00072564324,0.002606531],"genre_scores_gemma":[0.025158813,0.01223665,0.015497501,0.12068535,0.38171622,0.0010024736,0.021131264,0.007803145,0.41476852],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99118805,0.0009761425,0.0017878077,0.0010810469,0.004466105,0.00050078495],"domain_scores_gemma":[0.9200773,0.012292635,0.0032050926,0.0045314203,0.05823113,0.0016623838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075406935,0.003343579,0.0023433587,0.0044972887,0.0038859788,0.0049809227,0.004074855,0.0071425894,0.08208794],"category_scores_gemma":[0.12383657,0.0013452587,0.0021308083,0.0032468105,0.002601412,0.0023381894,0.002937223,0.010985529,0.05837153],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018522653,0.0000036234392,0.00003766622,0.00010507687,0.000007699242,0.00008515432,0.000026469192,0.000018319186,0.000030007113,0.00024769662,0.9957754,0.0036444322],"study_design_scores_gemma":[0.00006171158,0.000020109546,0.00077723106,0.0006843597,0.00005971101,0.00045932175,0.00009771153,0.0002304706,0.00044542114,0.0011078109,0.9960026,0.00005347929],"about_ca_topic_score_codex":0.04361893,"about_ca_topic_score_gemma":0.035276234,"teacher_disagreement_score":0.08208794,"about_ca_system_score_codex":0.0046829223,"about_ca_system_score_gemma":0.009693392,"threshold_uncertainty_score":0.27461147},"labels":[],"label_agreement":null},{"id":"W4386028983","doi":"10.3390/s23167288","title":"Towards Feasible Solutions for Load Monitoring in Quebec Residences","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec; Université du Québec à Trois-Rivières","funders":"Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Identification (biology); Implementation; Computer science; Electricity; Aggregate (composite); Smart meter; Risk analysis (engineering); Data science; Engineering; Business; Software engineering","score_opus":0.04256402380893445,"score_gpt":0.2656905725245547,"score_spread":0.22312654871562024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386028983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32112864,0.0016460593,0.6299619,0.0063896812,0.00013788266,0.0009120926,0.0065965685,0.003578245,0.029648904],"genre_scores_gemma":[0.8117031,0.0005249149,0.17088929,0.00038841832,0.000040924082,0.00027093172,0.0038207043,0.00012838418,0.012233274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926966,0.00017645127,0.00002333469,0.00022754516,0.00012359093,0.0001793081],"domain_scores_gemma":[0.9990785,0.00026338227,0.00007789843,0.00009404646,0.00039551224,0.000090668305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009152243,0.00095383206,0.0005293445,0.0010009668,0.0013322427,0.0022315949,0.0019060115,0.0010782799,0.004337328],"category_scores_gemma":[0.0025663858,0.00036029692,0.00051723525,0.0013606688,0.000590606,0.0011233942,0.0013158715,0.0006977318,0.00053217425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003676232,0.0002782238,0.04261558,0.0003954576,0.00016497426,0.0009246348,0.0008915829,0.6128191,0.008880092,0.04009882,0.024685092,0.26787895],"study_design_scores_gemma":[0.00003457921,0.000041026102,0.012653016,0.000054011685,0.000029009188,0.00006272448,0.0010699532,0.9651481,0.0015436241,0.0076046097,0.011720773,0.000038551407],"about_ca_topic_score_codex":0.7930943,"about_ca_topic_score_gemma":0.8438471,"teacher_disagreement_score":0.20690572,"about_ca_system_score_codex":0.009581809,"about_ca_system_score_gemma":0.007554717,"threshold_uncertainty_score":0.41624844},"labels":[],"label_agreement":null},{"id":"W4386137613","doi":"10.3390/s23177375","title":"Creating an Autonomous Hovercraft for Bathymetric Surveying in Extremely Shallow Water (&lt;1 m)","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bathymetry; Waves and shallow water; Marine engineering; Engineering; Civil engineering; Geology; Environmental science; Remote sensing; Oceanography","score_opus":0.0678190749106178,"score_gpt":0.28935936089502445,"score_spread":0.22154028598440667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386137613","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7176768,0.000049560625,0.2688462,0.00016092474,0.000045800167,0.0008078091,0.00042890184,0.0031512284,0.008832741],"genre_scores_gemma":[0.6714327,0.000052663265,0.3232522,0.000050260307,0.0000062693084,0.00021383917,0.00044519483,0.00012494414,0.004421944],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998599,0.000016468332,0.0000043097752,0.0000386396,0.000047760426,0.000032814398],"domain_scores_gemma":[0.9997508,0.00003516554,0.000021806914,0.000059504637,0.000058983038,0.000073743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027363424,0.00027977157,0.00018036133,0.00021041589,0.00029326713,0.00038671796,0.00044331356,0.00035298706,0.0019793145],"category_scores_gemma":[0.00043686607,0.00020628968,0.00016680428,0.00009330667,0.00037454895,0.0004835551,0.00074414705,0.00031392757,0.00055064575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071067177,0.00070948974,0.054766513,0.00024667257,0.00006609442,0.0008407637,0.0013608512,0.0944597,0.29687056,0.002859931,0.0071424167,0.5399664],"study_design_scores_gemma":[0.0004208543,0.0032979678,0.07885592,0.00009159177,0.00008374434,0.0013383538,0.0018371979,0.7472981,0.1043872,0.002062572,0.06021354,0.00011293391],"about_ca_topic_score_codex":0.0045662136,"about_ca_topic_score_gemma":0.009866761,"teacher_disagreement_score":0.0045662136,"about_ca_system_score_codex":0.00017580742,"about_ca_system_score_gemma":0.00064860843,"threshold_uncertainty_score":0.0090792775},"labels":[],"label_agreement":null},{"id":"W4386137786","doi":"10.3390/s23177384","title":"Assessing the Global Cognition of Community-Dwelling Older Adults Using Motor and Sensory Factors: A Cross-Sectional Feasibility Study","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Jeonbuk National University; National Research Foundation","keywords":"Cognition; Gait; Psychology; Physical medicine and rehabilitation; Dementia; Sensory system; Effects of sleep deprivation on cognitive performance; Audiology; Cognitive psychology; Medicine; Neuroscience","score_opus":0.13422467325232684,"score_gpt":0.44723708933072026,"score_spread":0.3130124160783934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386137786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995521,0.000065953725,0.00013640083,0.000007743744,0.0000032148503,0.00004137742,0.000051390976,0.0000015577122,0.00014020994],"genre_scores_gemma":[0.9992175,0.000086102846,0.0003290561,0.00003888506,0.0000081517455,0.00004843941,0.0001282558,0.0000012116304,0.00014241113],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996835,0.00007536699,0.00003740228,0.000085226086,0.00007218572,0.000046325837],"domain_scores_gemma":[0.99924904,0.00010702539,0.00015962409,0.00006139753,0.0002371156,0.00018568565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012322712,0.00067077903,0.0003802206,0.0007552055,0.00052769564,0.00049099943,0.00016843564,0.00061044976,0.00064022245],"category_scores_gemma":[0.0018011369,0.0004120908,0.00045418623,0.00050039956,0.00031603864,0.00066991686,0.00045417325,0.00044673058,0.0002547294],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036441587,0.0008471945,0.99323297,0.000028488936,0.00011595755,0.0001246518,0.0005002912,0.000034605113,0.0012149217,0.000012846935,0.000059781327,0.003463896],"study_design_scores_gemma":[0.000031894822,0.0018527058,0.9969202,0.000005424801,0.000060162147,0.000229302,0.00047372197,0.00012945324,0.00012796666,0.00001760496,0.0001444731,0.0000070828087],"about_ca_topic_score_codex":0.0027215001,"about_ca_topic_score_gemma":0.0051657776,"teacher_disagreement_score":0.0027215001,"about_ca_system_score_codex":0.0001666005,"about_ca_system_score_gemma":0.0003067881,"threshold_uncertainty_score":0.0065169334},"labels":[],"label_agreement":null},{"id":"W4386137878","doi":"10.3390/s23177381","title":"Human Factors Considerations for Quantifiable Human States in Physical Human–Robot Interaction: A Literature Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Human–robot interaction; Context (archaeology); Robot; Expectancy theory; Population; Computer science; Human–computer interaction; Risk analysis (engineering); Psychology; Medicine; Artificial intelligence; Social psychology; Environmental health","score_opus":0.2992415907534972,"score_gpt":0.5318699133550261,"score_spread":0.23262832260152888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386137878","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007309117,0.99561876,0.0012356975,0.0007750074,0.00015879895,0.000046757872,0.000081635655,0.00000812138,0.0013443023],"genre_scores_gemma":[0.01173273,0.9849855,0.002063355,0.0005551489,0.00017985974,0.00012315733,0.00010280892,0.000006934385,0.00025052842],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948095,0.0018513936,0.001177637,0.0006090968,0.0013895938,0.00016267633],"domain_scores_gemma":[0.9506509,0.042058136,0.0024993303,0.00054394186,0.004026844,0.00022089951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068693906,0.0013647992,0.00204702,0.0120157935,0.0010325566,0.0046028495,0.0015329389,0.0019713775,0.0037234062],"category_scores_gemma":[0.022047902,0.0008044664,0.0027993093,0.011204452,0.0018590885,0.004305692,0.0016967673,0.0015972947,0.000669369],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009481721,0.000086279295,0.0039321915,0.19929855,0.0007796354,0.0003552209,0.003403179,0.0007926658,0.0004196854,0.016460875,0.009937958,0.764439],"study_design_scores_gemma":[0.000030072066,0.00027082226,0.02178168,0.4773066,0.0051261415,0.002644059,0.008266894,0.0010483573,0.0008563994,0.01549129,0.46694297,0.00023482036],"about_ca_topic_score_codex":0.009767102,"about_ca_topic_score_gemma":0.0143075865,"teacher_disagreement_score":0.0120157935,"about_ca_system_score_codex":0.0032651767,"about_ca_system_score_gemma":0.0083576,"threshold_uncertainty_score":0.03632927},"labels":[],"label_agreement":null},{"id":"W4386161991","doi":"10.3390/s23177423","title":"A Simple and Valid Method to Calculate Wheelchair Frame Rotation Using One Wheel-Mounted IMU","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; Wheelchair Rugby Canada; University of Victoria","funders":"","keywords":"Wheelchair; Inertial measurement unit; Kinematics; Frame (networking); Rotation (mathematics); Computer science; Accelerometer; Simulation; Computer vision; Physics","score_opus":0.027100002672514324,"score_gpt":0.31601715235516203,"score_spread":0.2889171496826477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386161991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029490435,0.0015185848,0.9583644,0.00020906024,0.00077323354,0.0004623487,0.0012477434,0.0034745852,0.004459608],"genre_scores_gemma":[0.26531458,0.0014412012,0.72432137,0.00025150846,0.00026723222,0.0009421634,0.0011319943,0.0004692185,0.00586068],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99702746,0.0005610662,0.00020233625,0.0004789461,0.001624476,0.00010577146],"domain_scores_gemma":[0.99715686,0.00041955305,0.00041648882,0.00033422885,0.0016078429,0.00006499101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014071722,0.0023720213,0.0015163551,0.003484878,0.00064976234,0.0012844182,0.0013644113,0.0012437984,0.004327467],"category_scores_gemma":[0.0055260616,0.00066886516,0.0008136049,0.0021631296,0.00050609244,0.0013710872,0.0011181574,0.0007904167,0.0036744934],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042451834,0.00019720447,0.033613164,0.0015273561,0.00054144335,0.00025061873,0.00049212214,0.007908248,0.09134753,0.002426074,0.011970887,0.8493008],"study_design_scores_gemma":[0.00039916657,0.0029398825,0.22415425,0.0015301417,0.0012911272,0.0047579226,0.0013669586,0.28496295,0.33929846,0.006823941,0.13093594,0.001539245],"about_ca_topic_score_codex":0.0038273348,"about_ca_topic_score_gemma":0.009823374,"teacher_disagreement_score":0.004327467,"about_ca_system_score_codex":0.00051359605,"about_ca_system_score_gemma":0.00084181695,"threshold_uncertainty_score":0.014476836},"labels":[],"label_agreement":null},{"id":"W4386168116","doi":"10.3390/s23177424","title":"INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"GNSS applications; Simultaneous localization and mapping; Lidar; Extended Kalman filter; Navigation system; Computer science; Kalman filter; Inertial navigation system; Artificial intelligence; Feature (linguistics); Computer vision; Satellite system; Remote sensing; Global Positioning System; Geography; Robot; Orientation (vector space); Mobile robot; Telecommunications","score_opus":0.0162266592267258,"score_gpt":0.23892585961811502,"score_spread":0.2226992003913892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386168116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04003949,0.00077645417,0.9522571,0.0000964265,0.0001561206,0.000040229756,0.00031480435,0.003587416,0.0027319796],"genre_scores_gemma":[0.6819449,0.00062072894,0.31257346,0.00012683426,0.00007937533,0.00009125351,0.001414641,0.00015807363,0.0029907336],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996165,0.00005038644,0.000016480091,0.00008004965,0.00019551761,0.000041075375],"domain_scores_gemma":[0.9997733,0.000023062179,0.00003319089,0.00005227445,0.00010651292,0.000011627543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031935214,0.000720056,0.00047963226,0.0007615783,0.0002836688,0.00039602135,0.00063363917,0.00042079517,0.0011360819],"category_scores_gemma":[0.00067424576,0.00029828702,0.00035385363,0.0009651943,0.00023643342,0.00059506577,0.00083707523,0.0004891677,0.0008843055],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024062385,0.000091614806,0.006239915,0.0004033844,0.00018415537,0.00035651104,0.0002329312,0.13759755,0.108939886,0.004081621,0.009515097,0.73211664],"study_design_scores_gemma":[0.000047369715,0.00014139408,0.008698365,0.000050725426,0.00007583691,0.0003120563,0.00015590254,0.9357968,0.03362519,0.0031215064,0.017927168,0.00004761574],"about_ca_topic_score_codex":0.009212564,"about_ca_topic_score_gemma":0.014264457,"teacher_disagreement_score":0.009212564,"about_ca_system_score_codex":0.00026172976,"about_ca_system_score_gemma":0.00081582455,"threshold_uncertainty_score":0.018317878},"labels":[],"label_agreement":null},{"id":"W4386223270","doi":"10.3390/s23177464","title":"Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"King Abdulaziz University","keywords":"Computer science; Smart grid; Intrusion detection system; Reliability (semiconductor); Grid; Anomaly detection; Denial-of-service attack; Deep learning; SCADA; Resilience (materials science); Real-time computing; Distributed computing; Artificial intelligence; Computer security; Engineering; The Internet; Operating system","score_opus":0.008659955713557698,"score_gpt":0.20898226102517836,"score_spread":0.20032230531162065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386223270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17308429,0.0010582834,0.8198447,0.0004011768,0.000119831464,0.00004369465,0.00029728998,0.0027294697,0.002421177],"genre_scores_gemma":[0.9518418,0.0002635156,0.045249972,0.00012858117,0.000034298173,0.000058925707,0.0005036058,0.00005341634,0.0018659211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997118,0.00007108433,0.00001844173,0.00007509104,0.00006835409,0.000055094602],"domain_scores_gemma":[0.9995284,0.00021684502,0.00004404373,0.000048507285,0.00014047083,0.00002166287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008281056,0.00070881366,0.0009906632,0.0005699777,0.00024505012,0.0005503042,0.0010382304,0.000581933,0.00078889035],"category_scores_gemma":[0.0015617211,0.00033560456,0.0005981295,0.00058788946,0.00023704195,0.00104457,0.00066381023,0.0010449741,0.00020740536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007730705,0.00006148491,0.0014659553,0.000017364362,0.000056799374,0.000030104244,0.000016151474,0.9357761,0.0010273382,0.0010425203,0.0008547189,0.059574034],"study_design_scores_gemma":[5.2738847e-7,0.000004188349,0.000047427977,4.6250508e-7,0.0000019029519,0.0000012913301,6.0923e-7,0.99966216,0.0001069535,0.00014794264,0.000025910507,7.416688e-7],"about_ca_topic_score_codex":0.011747113,"about_ca_topic_score_gemma":0.012362732,"teacher_disagreement_score":0.011747113,"about_ca_system_score_codex":0.0007025824,"about_ca_system_score_gemma":0.0005884551,"threshold_uncertainty_score":0.02335751},"labels":[],"label_agreement":null},{"id":"W4386223728","doi":"10.3390/s23177441","title":"On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dynamic time warping; Gesture; Usability; Classifier (UML); Computer science; Gesture recognition; Image warping; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Set (abstract data type); Human–computer interaction","score_opus":0.053124834481705685,"score_gpt":0.3065350790570123,"score_spread":0.25341024457530664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386223728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027744334,0.00029913566,0.96998334,0.00009194438,0.000036279995,0.000047092675,0.00025123003,0.000509225,0.0010374038],"genre_scores_gemma":[0.45588598,0.00088540796,0.53665984,0.0001083392,0.00017908236,0.00022032906,0.0010524918,0.00035192404,0.0046566995],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991732,0.00013977078,0.000046449248,0.0002581022,0.00032145734,0.000060929462],"domain_scores_gemma":[0.9986009,0.0007021231,0.00015711287,0.00022722682,0.0002506124,0.000061994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008993791,0.00071320584,0.00068778626,0.0023092236,0.0003491033,0.00079702836,0.0008824033,0.00075661484,0.002189448],"category_scores_gemma":[0.0043254932,0.00024564334,0.00044495898,0.0018870161,0.0006552728,0.00095072226,0.0006558329,0.0008764213,0.0013605386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042676704,0.0001511202,0.005732967,0.0002484293,0.00011515811,0.00019958486,0.00024115686,0.061837103,0.07983166,0.0069742594,0.0020514494,0.8421904],"study_design_scores_gemma":[0.000023223338,0.0001575802,0.01554083,0.000044823235,0.000029625417,0.00064336235,0.00009625567,0.94745046,0.023243044,0.008347201,0.0043603857,0.00006311211],"about_ca_topic_score_codex":0.0017015241,"about_ca_topic_score_gemma":0.0016376456,"teacher_disagreement_score":0.0023092236,"about_ca_system_score_codex":0.00036993544,"about_ca_system_score_gemma":0.00040162914,"threshold_uncertainty_score":0.007324457},"labels":[],"label_agreement":null},{"id":"W4386251049","doi":"10.3390/s23177489","title":"Wheelchair Rugby Sprint Force-Velocity Modeling Using Inertial Measurement Units and Sport Specific Parameters: A Proof of Concept","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; Wheelchair Rugby Canada; University of Victoria","funders":"","keywords":"Wheelchair; Sprint; Inertial measurement unit; Resistive touchscreen; Simulation; Units of measurement; Engineering; Computer science; Aerospace engineering; Physics; Electrical engineering","score_opus":0.2201242953876892,"score_gpt":0.35645872471469353,"score_spread":0.13633442932700432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386251049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23404445,0.0013229981,0.75367117,0.00065026636,0.00034976585,0.00065902225,0.00046839743,0.0021001075,0.006733829],"genre_scores_gemma":[0.81377685,0.0011779797,0.17878234,0.00016902755,0.00006825658,0.00058214983,0.00047384296,0.00012128613,0.0048482753],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995598,0.000047188663,0.000019368645,0.000071037735,0.00027734978,0.000025085861],"domain_scores_gemma":[0.99969816,0.00006259662,0.000044268607,0.000035348745,0.00013552343,0.00002419253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008571383,0.00066439126,0.000397736,0.00024227073,0.00018416312,0.0006858348,0.00086771185,0.0007134777,0.002015579],"category_scores_gemma":[0.0011885607,0.0003023585,0.00045829848,0.00013547299,0.00027045183,0.000701308,0.0005874428,0.0005530253,0.0007166014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005620576,0.001329008,0.011108192,0.0010866529,0.0003566079,0.00059647753,0.00035075741,0.107864074,0.5749222,0.0058041574,0.0041763894,0.2918435],"study_design_scores_gemma":[0.00016679602,0.0041875686,0.011764252,0.00024481773,0.00024037909,0.0007457705,0.0001843772,0.70444804,0.24096511,0.0012345673,0.035636112,0.00018217284],"about_ca_topic_score_codex":0.002742994,"about_ca_topic_score_gemma":0.00214224,"teacher_disagreement_score":0.002742994,"about_ca_system_score_codex":0.00025053287,"about_ca_system_score_gemma":0.00064725295,"threshold_uncertainty_score":0.006742835},"labels":[],"label_agreement":null},{"id":"W4386324878","doi":"10.3390/s23177551","title":"Ultra-Wideband-Based Time Occupancy Analysis for Safety Studies","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Transport Canada","keywords":"Occupancy; Ultra-wideband; Computer science; Real-time computing; Ground truth; Tracking (education); Wideband; Electronic engineering; Engineering; Telecommunications; Artificial intelligence","score_opus":0.023038194672063056,"score_gpt":0.2685842684076491,"score_spread":0.24554607373558604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386324878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14893377,0.0007746428,0.8445269,0.000042290918,0.000093623516,0.000068828806,0.00030462124,0.0006741343,0.0045812484],"genre_scores_gemma":[0.89188963,0.0005110782,0.10538348,0.000041159197,0.000045670877,0.00009517284,0.0005069784,0.000063937114,0.0014627948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951994,0.00012731661,0.000028150836,0.00008421119,0.00018501065,0.000055322565],"domain_scores_gemma":[0.9991391,0.00030321404,0.00018803841,0.00009801203,0.0002367183,0.000034956898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045381472,0.0005785217,0.0004025658,0.001688465,0.000283813,0.00065222103,0.00061272684,0.00039113782,0.0012917768],"category_scores_gemma":[0.0018479518,0.00017549908,0.00039785547,0.0012833397,0.00023543552,0.00081201945,0.000618599,0.0003258966,0.0005144162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008320229,0.0004328048,0.074996755,0.00083461526,0.00033737492,0.00062782463,0.0009267323,0.25176534,0.11654338,0.015403931,0.003381401,0.5339178],"study_design_scores_gemma":[0.000025452015,0.0007203761,0.04035192,0.00014281379,0.00013714713,0.0012018359,0.0011890956,0.88504285,0.048439544,0.0073865377,0.01524794,0.00011456011],"about_ca_topic_score_codex":0.0015561574,"about_ca_topic_score_gemma":0.0017253482,"teacher_disagreement_score":0.001688465,"about_ca_system_score_codex":0.0003294519,"about_ca_system_score_gemma":0.0003055487,"threshold_uncertainty_score":0.004321456},"labels":[],"label_agreement":null},{"id":"W4386525904","doi":"10.3390/s23187726","title":"Special Issue on Acoustic Sensors and Their Applications (Vol. 1)","year":2023,"lang":"en","type":"editorial","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Engineering; Acoustics; Physics","score_opus":0.009561794179939609,"score_gpt":0.2517500631375066,"score_spread":0.24218826895756698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386525904","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030457122,0.0075296247,0.00020354876,0.011100696,0.9746969,0.000025013762,0.00010108561,0.000105847736,0.006206762],"genre_scores_gemma":[0.00031250613,0.007708644,0.00017659325,0.008843155,0.9486223,0.000029219265,0.0001206984,0.00010815178,0.03407879],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959045,0.0004140474,0.00037826426,0.0005871422,0.0024518317,0.00026429776],"domain_scores_gemma":[0.9844023,0.0038708672,0.0011687384,0.0005148561,0.0076360344,0.0024072034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004279984,0.0033440813,0.0037814335,0.0046818713,0.002551922,0.008527249,0.0029335876,0.008455755,0.05042929],"category_scores_gemma":[0.012187989,0.0012472242,0.0022595043,0.0018527614,0.0018819378,0.003819259,0.0016073817,0.011486266,0.051942304],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027813037,0.000009646769,0.000016052969,0.00017796634,0.000008382857,0.000048902115,0.00000393581,0.000024676707,0.0001236836,0.0003145407,0.9908165,0.0084279515],"study_design_scores_gemma":[0.000013978604,0.000018303066,0.0001157339,0.00012976275,0.000012033773,0.00011916935,0.0000058571904,0.00005402449,0.00010280107,0.0004922898,0.99892837,0.0000076035008],"about_ca_topic_score_codex":0.0013200269,"about_ca_topic_score_gemma":0.0042822976,"teacher_disagreement_score":0.05042929,"about_ca_system_score_codex":0.002681562,"about_ca_system_score_gemma":0.003154252,"threshold_uncertainty_score":0.16870278},"labels":[],"label_agreement":null},{"id":"W4386542258","doi":"10.3390/s23187753","title":"Investigation of Camera-Free Eye-Tracking Glasses Compared to a Video-Based System","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Saccade; Computer vision; Eye tracking; Eye movement; Smooth pursuit; Fixation (population genetics); Artificial intelligence; Computer science; Gaze; Tracking system; Eye tracking on the ISS; Tracking (education); Saccadic masking; Kalman filter; Psychology; Medicine","score_opus":0.033866362257498894,"score_gpt":0.2731373666278565,"score_spread":0.2392710043703576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386542258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889577,0.0007400161,0.0071186204,0.000092174916,0.00007177459,0.00020844128,0.000360622,0.00009891188,0.0023517741],"genre_scores_gemma":[0.99002516,0.0005053123,0.0073297145,0.00013412996,0.00003959981,0.00009775077,0.00036110493,0.000024688128,0.0014824615],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9980585,0.00040850614,0.00022117312,0.0004250987,0.00072245137,0.0001642895],"domain_scores_gemma":[0.99500704,0.0020034479,0.00047220787,0.00034873822,0.0019590624,0.00020954816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018954038,0.00034709522,0.0003681591,0.00090268184,0.00051358284,0.00097561674,0.0005324556,0.000737527,0.0029465032],"category_scores_gemma":[0.009784116,0.00020608716,0.0003555847,0.00044399462,0.00038955038,0.0013539663,0.0010606399,0.00018919661,0.00047393923],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009472494,0.00082131574,0.17801346,0.0029988494,0.0006314945,0.0010192416,0.00609847,0.0025353748,0.4982599,0.0018009893,0.0025539128,0.29579446],"study_design_scores_gemma":[0.00037226622,0.018053705,0.8078912,0.00042639562,0.0015440068,0.0031326457,0.004519439,0.016453195,0.13752736,0.0006814602,0.0091747865,0.00022352477],"about_ca_topic_score_codex":0.006541527,"about_ca_topic_score_gemma":0.006517489,"teacher_disagreement_score":0.006541527,"about_ca_system_score_codex":0.00088035036,"about_ca_system_score_gemma":0.0007085432,"threshold_uncertainty_score":0.013006866},"labels":[],"label_agreement":null},{"id":"W4386600982","doi":"10.3390/s23187790","title":"Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Gesture; Histogram; Computer science; Gesture recognition; Computer vision; Histogram of oriented gradients; Pattern recognition (psychology); Noise (video); Range (aeronautics); Canny edge detector; Enhanced Data Rates for GSM Evolution; Image (mathematics); Edge detection; Image processing; Engineering","score_opus":0.01791551944211436,"score_gpt":0.23629699284759823,"score_spread":0.21838147340548386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386600982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13714437,0.0009048898,0.85036427,0.0002147765,0.00020556618,0.00026149553,0.00051295647,0.006929997,0.003461715],"genre_scores_gemma":[0.7535857,0.00036521975,0.24045432,0.00025920296,0.000090619156,0.00013076715,0.0010900391,0.00018732851,0.0038367822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976796,0.000022734715,0.000010964097,0.000065011474,0.000090543566,0.00004274651],"domain_scores_gemma":[0.9997701,0.00004614518,0.000025212174,0.00003072223,0.00009664952,0.00003113454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037029939,0.00097378215,0.0008444283,0.0010243967,0.0002236459,0.00044174705,0.00074499636,0.0004056542,0.0013301617],"category_scores_gemma":[0.00069858145,0.00031120927,0.0005207423,0.0006273023,0.00029240805,0.0008374904,0.00063955825,0.0005235104,0.00060128834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033614496,0.0003877251,0.004648235,0.00023599384,0.00017207405,0.0002017446,0.000053665608,0.06557836,0.1660276,0.0011026927,0.004934134,0.75632167],"study_design_scores_gemma":[0.00002185677,0.000211884,0.0053338828,0.000013930662,0.00003137034,0.00012056725,0.000019717569,0.9506817,0.040862568,0.0014343129,0.0012403647,0.000027723483],"about_ca_topic_score_codex":0.0077290293,"about_ca_topic_score_gemma":0.016980095,"teacher_disagreement_score":0.0077290293,"about_ca_system_score_codex":0.00050872914,"about_ca_system_score_gemma":0.00070187706,"threshold_uncertainty_score":0.015368044},"labels":[],"label_agreement":null},{"id":"W4386601277","doi":"10.3390/s23187792","title":"Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; Wireless; Software-defined radio; Cluster analysis; The Internet; Efficient energy use; Computer network; Telecommunications; Machine learning; Engineering; Electrical engineering; World Wide Web","score_opus":0.029082341266336985,"score_gpt":0.26669301313812716,"score_spread":0.23761067187179016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386601277","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045177917,0.9980134,0.0006367554,0.00029733204,0.00010345569,0.00009218659,0.00006895349,0.0000064094183,0.00032973266],"genre_scores_gemma":[0.0098372,0.98649675,0.002679997,0.0003825389,0.00010347884,0.00023297044,0.00009331308,0.0000051857187,0.00016856176],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957991,0.0016644486,0.0012052087,0.00033253306,0.0009101814,0.000088466295],"domain_scores_gemma":[0.98488927,0.011926653,0.001473727,0.00023830532,0.0013806274,0.00009144517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006059167,0.0010209754,0.0036423807,0.0070581953,0.00050539535,0.002192729,0.0010979605,0.0011932612,0.002414711],"category_scores_gemma":[0.0225837,0.00046611793,0.004079773,0.005545579,0.0006130224,0.0016764977,0.00077379256,0.00097303925,0.00028554272],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014625028,0.000047724476,0.0008639414,0.6157165,0.0060063824,0.0000888687,0.00021463026,0.00061214785,0.00049453473,0.0024392174,0.0035009068,0.36986896],"study_design_scores_gemma":[0.00024035892,0.00075963396,0.0064135697,0.7435677,0.06064094,0.0011064854,0.0006583721,0.0014331595,0.0014183497,0.007198143,0.17643161,0.00013172881],"about_ca_topic_score_codex":0.0039140168,"about_ca_topic_score_gemma":0.010875939,"teacher_disagreement_score":0.0070581953,"about_ca_system_score_codex":0.0017472884,"about_ca_system_score_gemma":0.007097675,"threshold_uncertainty_score":0.03204435},"labels":[],"label_agreement":null},{"id":"W4386690303","doi":"10.3390/s23187816","title":"An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Prince Sattam bin Abdulaziz University","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Cluster analysis; Segmentation; Image segmentation; Fuzzy logic; Feature extraction; Artificial neural network","score_opus":0.03371268904244685,"score_gpt":0.2946010254366234,"score_spread":0.2608883363941766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386690303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02981897,0.00041141346,0.96724063,0.00011315489,0.00007509522,0.00009890698,0.000078374724,0.0009643299,0.0011990028],"genre_scores_gemma":[0.2763203,0.0003935056,0.71998376,0.00009287932,0.000043457167,0.00018586327,0.00034158997,0.00012179044,0.0025168632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948645,0.000045951096,0.000030960808,0.0001359585,0.00025164225,0.000048964717],"domain_scores_gemma":[0.99965155,0.00006131633,0.000034818844,0.00003179936,0.00020582486,0.000014742143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005877774,0.0005751873,0.0006907485,0.001587726,0.0007448111,0.00063764263,0.000979529,0.0008718366,0.00071754755],"category_scores_gemma":[0.001090218,0.00030962774,0.0010055308,0.0011297405,0.000358452,0.0008979389,0.0005198955,0.0005318203,0.00038055965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023411954,0.00014950878,0.0026838293,0.00022975577,0.0001565133,0.00014332052,0.00026737494,0.16085815,0.05177515,0.0042482023,0.003859607,0.77539444],"study_design_scores_gemma":[0.000012897848,0.000075803844,0.0023946255,0.000015779582,0.000034551013,0.00018077245,0.00007697893,0.9709091,0.021384165,0.0019975854,0.0028711334,0.000046643443],"about_ca_topic_score_codex":0.009350318,"about_ca_topic_score_gemma":0.010833569,"teacher_disagreement_score":0.009350318,"about_ca_system_score_codex":0.0007401649,"about_ca_system_score_gemma":0.0012049444,"threshold_uncertainty_score":0.018591821},"labels":[],"label_agreement":null},{"id":"W4386746994","doi":"10.3390/s23187873","title":"Development of a Low-Cost Portable EMG for Measuring the Muscular Activity of Workers in the Field","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Electromyography; Isometric exercise; Interfacing; Dynamometer; Isotonic; Muscle fatigue; Computer science; Physical medicine and rehabilitation; Biomedical engineering; Biceps; Simulation; Medicine; Physical therapy; Computer hardware; Engineering; Automotive engineering","score_opus":0.024225194628998758,"score_gpt":0.24325245169006046,"score_spread":0.21902725706106171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386746994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23046483,0.002343358,0.75896794,0.00043851833,0.00039585616,0.001927522,0.000588244,0.0013517018,0.0035219612],"genre_scores_gemma":[0.4256475,0.0010960049,0.5656461,0.00040050162,0.00012603833,0.0013265732,0.00060374424,0.00008481583,0.005068709],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988167,0.00023525358,0.00007897597,0.00025305618,0.0005752168,0.000040838935],"domain_scores_gemma":[0.99893004,0.00032922882,0.00009659309,0.00011357658,0.00046757323,0.0000630498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013630925,0.0007609813,0.0005696337,0.000911497,0.00018729064,0.0004845312,0.0013634143,0.00089565315,0.0018612541],"category_scores_gemma":[0.002312753,0.00027489383,0.00034760602,0.000416298,0.0003150471,0.0005411325,0.0005444133,0.000350825,0.0008475092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046933265,0.00043899953,0.015449719,0.0010056781,0.00007481968,0.00038226787,0.00018317586,0.0013441194,0.6755285,0.00046000676,0.0011233843,0.30354],"study_design_scores_gemma":[0.00065163575,0.021738114,0.21540508,0.00058659865,0.00076029706,0.00970598,0.00080768,0.060199644,0.64640546,0.0011526146,0.042328965,0.00025800805],"about_ca_topic_score_codex":0.0004657109,"about_ca_topic_score_gemma":0.000868462,"teacher_disagreement_score":0.0018612541,"about_ca_system_score_codex":0.00017341015,"about_ca_system_score_gemma":0.000439445,"threshold_uncertainty_score":0.007208824},"labels":[],"label_agreement":null},{"id":"W4386747333","doi":"10.3390/s23187856","title":"HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Taif University","keywords":"Computer science; Artificial intelligence; Transfer of learning; Intrusion detection system; Process (computing); Industrial Internet; Deep learning; Machine learning; Cloud computing; Internet of Things; Automation; Measure (data warehouse); Quality (philosophy); Transfer (computing); Data mining; Computer security; Engineering","score_opus":0.021101921733171027,"score_gpt":0.22897919476894282,"score_spread":0.2078772730357718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386747333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19208628,0.0019803112,0.7852054,0.0011652507,0.0004026607,0.00022305576,0.00071408344,0.010317019,0.007905853],"genre_scores_gemma":[0.9231491,0.00043995067,0.068405144,0.0003486633,0.00005658012,0.00023379904,0.0006518263,0.000089007845,0.0066259834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982697,0.000022444661,0.000012102618,0.000068792426,0.00003564285,0.000033987366],"domain_scores_gemma":[0.99984586,0.000047430225,0.000023751541,0.000013764782,0.000058462425,0.000010843843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000496611,0.0010840688,0.00052059616,0.00054824405,0.00027133513,0.00055808405,0.0014454825,0.0008693583,0.0015898114],"category_scores_gemma":[0.00076599926,0.00033007908,0.0007441307,0.00039147784,0.00029830806,0.0011248812,0.00068228826,0.0011488884,0.00040736026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026786004,0.0002081577,0.004418979,0.000117629854,0.00019104594,0.00019771303,0.00009265465,0.73827785,0.008563245,0.0028780634,0.0049120486,0.23987481],"study_design_scores_gemma":[0.000003872875,0.000029625444,0.00019563817,0.0000033901065,0.000010014912,0.000016854365,0.0000035700377,0.9976987,0.0012225668,0.00050802645,0.00030331305,0.000004437441],"about_ca_topic_score_codex":0.0102608465,"about_ca_topic_score_gemma":0.009584529,"teacher_disagreement_score":0.0102608465,"about_ca_system_score_codex":0.0011906396,"about_ca_system_score_gemma":0.00083733845,"threshold_uncertainty_score":0.020402253},"labels":[],"label_agreement":null},{"id":"W4386781791","doi":"10.3390/s23187878","title":"Synthesis of Valid Camera Poses for the Inspection of Triangular Facets in a 3D Mesh","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Factory (object-oriented programming); Software deployment; Computer science; Automation; Visual inspection; Set (abstract data type); Computer vision; Artificial intelligence; Operator (biology); Machine vision; Quality (philosophy); Field (mathematics); Real-time computing; Engineering; Software engineering; Mathematics","score_opus":0.03755994858990269,"score_gpt":0.25450817001815024,"score_spread":0.21694822142824755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386781791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014639831,0.000039217954,0.9833815,0.000026848074,0.000010938156,0.000050043367,0.00004079072,0.00020483213,0.0016059928],"genre_scores_gemma":[0.30884483,0.0000891144,0.68990463,0.000022753693,0.000008315541,0.0000901516,0.00017549297,0.0001466279,0.000718148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994319,0.00009851313,0.000035557143,0.00013358858,0.00024573557,0.000054731343],"domain_scores_gemma":[0.99908066,0.0004149177,0.0001233502,0.00015596095,0.00017122147,0.000053771386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006506636,0.0008509033,0.0005516307,0.0005888607,0.00026103572,0.00072741276,0.00073250826,0.0007138852,0.002392623],"category_scores_gemma":[0.0029529233,0.0005252065,0.00065395044,0.00036136797,0.0006712356,0.0005331834,0.0009204083,0.0007082364,0.0004313874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015422548,0.00009243222,0.0014280688,0.0002663121,0.000028773997,0.0002274291,0.00047016362,0.7354499,0.071932204,0.01398168,0.0013477757,0.17462097],"study_design_scores_gemma":[0.0000146313,0.00011065343,0.0003460414,0.00002561753,0.000006301636,0.00008451702,0.00008917502,0.97900957,0.015171309,0.003561506,0.0015663089,0.000014400026],"about_ca_topic_score_codex":0.0015973443,"about_ca_topic_score_gemma":0.0028210015,"teacher_disagreement_score":0.002392623,"about_ca_system_score_codex":0.0004797907,"about_ca_system_score_gemma":0.000759828,"threshold_uncertainty_score":0.008004129},"labels":[],"label_agreement":null},{"id":"W4386838008","doi":"10.3390/s23187941","title":"Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Ajman University; Université Abdelmalek Essaadi","keywords":"Computer science; False positive rate; Intrusion detection system; Robustness (evolution); Anomaly detection; Abnormality; Computer security; Data mining; Artificial intelligence; Real-time computing","score_opus":0.051726099708203806,"score_gpt":0.33410757557144444,"score_spread":0.28238147586324064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386838008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11043518,0.00035640973,0.88620675,0.0002393536,0.000031503045,0.00011520631,0.00012487413,0.0013803976,0.0011103575],"genre_scores_gemma":[0.8589419,0.0001231691,0.13906416,0.00007549653,0.00003670971,0.000057955516,0.00031028898,0.000022524757,0.0013677895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992417,0.00015464456,0.000047268124,0.00019607943,0.00022883338,0.00013147715],"domain_scores_gemma":[0.9993513,0.00016373929,0.00012083054,0.00008254123,0.00022279737,0.000058777907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009295138,0.0007832312,0.00082658365,0.0014512865,0.0003788921,0.0009191849,0.0010163912,0.00074434915,0.0005110218],"category_scores_gemma":[0.0013303028,0.00031870278,0.0010270053,0.00046584147,0.00042118187,0.00090107194,0.0010004,0.0007306168,0.0002323935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031685666,0.00049069937,0.036984622,0.00009726676,0.00029317266,0.00034963913,0.00023973019,0.5900761,0.032125898,0.006752434,0.0017515541,0.33052203],"study_design_scores_gemma":[0.0000025769161,0.00008084335,0.001998701,0.0000030195285,0.000020944655,0.000051438743,0.0000158126,0.99309397,0.0029903117,0.0013793377,0.00035503937,0.000008036702],"about_ca_topic_score_codex":0.007356937,"about_ca_topic_score_gemma":0.008451691,"teacher_disagreement_score":0.007356937,"about_ca_system_score_codex":0.0006668497,"about_ca_system_score_gemma":0.001096234,"threshold_uncertainty_score":0.0146282315},"labels":[],"label_agreement":null},{"id":"W4386850196","doi":"10.3390/s23187958","title":"Shaped-Based Tightly Coupled IMU/Camera Object-Level SLAM","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canada Research Chairs","keywords":"Inertial measurement unit; Computer vision; Artificial intelligence; Object (grammar); Simultaneous localization and mapping; Computer science; Robot; Mobile robot","score_opus":0.023370295045589226,"score_gpt":0.2272656751844219,"score_spread":0.20389538013883268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386850196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008000353,0.000054564796,0.9904146,0.00001756742,0.000031489937,0.000019586383,0.000018124432,0.0005917008,0.0008519942],"genre_scores_gemma":[0.53413796,0.00013136801,0.46167246,0.00011584544,0.0000445345,0.00007972016,0.00018387933,0.00017774515,0.0034564768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908316,0.00012642315,0.00003902921,0.00023505256,0.00039107061,0.00012513729],"domain_scores_gemma":[0.9996555,0.00004025022,0.000056088524,0.000106449595,0.00011235697,0.000029275649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004965975,0.0011618746,0.0011145655,0.0006509046,0.00038507188,0.001007833,0.0015687817,0.0011218736,0.00132881],"category_scores_gemma":[0.0011915892,0.0007216419,0.0009304964,0.0010369963,0.00052299694,0.0009917789,0.0022441072,0.0007997417,0.0009249062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022144671,0.00012367778,0.0014752749,0.00014632031,0.00022106004,0.00016118336,0.00024870608,0.6459223,0.04887174,0.0058234236,0.0022465477,0.29453832],"study_design_scores_gemma":[0.000011902852,0.00007332446,0.00056128047,0.0000064287597,0.000020307614,0.000054041117,0.000021124593,0.98909956,0.007043312,0.0017101966,0.0013802508,0.00001834318],"about_ca_topic_score_codex":0.004561528,"about_ca_topic_score_gemma":0.004827577,"teacher_disagreement_score":0.004561528,"about_ca_system_score_codex":0.0005122767,"about_ca_system_score_gemma":0.00083098124,"threshold_uncertainty_score":0.00906992},"labels":[],"label_agreement":null},{"id":"W4386996682","doi":"10.3390/s23198034","title":"Modelling Weather Precipitation Intensity on Surfaces in Motion with Application to Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Precipitation; Snow; Visibility; Environmental science; Range (aeronautics); Wind speed; Intensity (physics); Meteorology; Computer science; Quantitative precipitation forecast; Simulation; Aerospace engineering; Engineering","score_opus":0.014876718675895401,"score_gpt":0.2155661072936167,"score_spread":0.2006893886177213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386996682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7091716,0.00022483751,0.28581554,0.00010098275,0.000039937753,0.00007065569,0.00026764796,0.00026768012,0.0040412],"genre_scores_gemma":[0.99285346,0.00009103473,0.006304568,0.0000057276875,0.000007282441,0.000026149082,0.00007334028,0.00001152804,0.000626772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999356,0.000012418972,0.0000032121332,0.00001907795,0.000015598525,0.000014010992],"domain_scores_gemma":[0.9998858,0.00005371955,0.000019728686,0.00000920182,0.00002445103,0.0000071018862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009467481,0.00043229634,0.00024939465,0.0002800721,0.00016254907,0.000446918,0.00033986673,0.00051494973,0.0005092599],"category_scores_gemma":[0.00041769588,0.00016208984,0.00043942448,0.0003269556,0.0002372495,0.00030729218,0.00023533657,0.00025159185,0.00008284466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012114146,0.000013968131,0.0016814375,0.000013610457,0.0000057590687,0.000030167883,0.000019153009,0.9924051,0.0022705959,0.0003782832,0.000048299393,0.0031214396],"study_design_scores_gemma":[0.0000014132331,0.000007637601,0.00060414907,7.323797e-7,0.0000014269533,0.0000045009147,0.000009322785,0.99885416,0.00031708324,0.00012032713,0.00007735627,0.0000019145732],"about_ca_topic_score_codex":0.010620677,"about_ca_topic_score_gemma":0.0049803173,"teacher_disagreement_score":0.010620677,"about_ca_system_score_codex":0.00035095512,"about_ca_system_score_gemma":0.00031691845,"threshold_uncertainty_score":0.021117747},"labels":[],"label_agreement":null},{"id":"W4387140183","doi":"10.3390/s23198127","title":"Real-Time Sensor-Embedded Neural Network for Human Activity Recognition","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Microcontroller; Activity recognition; Inertial measurement unit; Computer science; Convolutional neural network; Artificial intelligence; Artificial neural network; Inference; Real-time computing; Embedded system","score_opus":0.05936429814553438,"score_gpt":0.2999093647586779,"score_spread":0.24054506661314354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387140183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041278627,0.0022460537,0.9516502,0.00017954152,0.00020865053,0.00005059914,0.00019034497,0.0014284768,0.0027675005],"genre_scores_gemma":[0.79014915,0.0013447742,0.20202969,0.00019468395,0.00011823763,0.000108409506,0.00036366304,0.000049683502,0.0056417515],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998584,0.000025147976,0.000009264826,0.000050511033,0.00004083718,0.000015753958],"domain_scores_gemma":[0.9998791,0.000043324682,0.000021990663,0.00001489411,0.00003408649,0.0000066305915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002160728,0.00042610324,0.00028897097,0.00025543006,0.000090413494,0.0002452972,0.00043973647,0.00039331772,0.0009341438],"category_scores_gemma":[0.0006180306,0.00012032484,0.00019709254,0.00037105984,0.00013549959,0.00043204025,0.00023387835,0.00043794265,0.00030967378],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027159706,0.00027774047,0.0030791909,0.0002515134,0.00012177668,0.00019426743,0.000075975724,0.18336551,0.0744704,0.005082002,0.003359498,0.7294505],"study_design_scores_gemma":[0.0000051384745,0.00006893322,0.0018300378,0.0000108525965,0.000018033037,0.000066900495,0.000009814533,0.9835154,0.011084545,0.0016275955,0.0017532093,0.000009555123],"about_ca_topic_score_codex":0.0022150646,"about_ca_topic_score_gemma":0.00387971,"teacher_disagreement_score":0.0022150646,"about_ca_system_score_codex":0.00030133632,"about_ca_system_score_gemma":0.00024823507,"threshold_uncertainty_score":0.004404366},"labels":[],"label_agreement":null},{"id":"W4387140562","doi":"10.3390/s23198122","title":"Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada; University of Waterloo","funders":"National Research Council Canada; University of Waterloo","keywords":"Deep learning; Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Visualization; Trustworthiness; Data science; Computer security","score_opus":0.023002233752204566,"score_gpt":0.3671497307515774,"score_spread":0.34414749699937286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387140562","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042136675,0.00026478362,0.9940303,0.0005567018,0.000019930148,0.000036165045,0.00004821415,0.00051612774,0.00031405862],"genre_scores_gemma":[0.31991112,0.0007603954,0.67494583,0.0010678602,0.00016682588,0.0002597034,0.0005005146,0.00029433754,0.0020933982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794,0.00077032816,0.00012096257,0.00037065093,0.00067381654,0.00012421088],"domain_scores_gemma":[0.99558324,0.0020475022,0.0005073901,0.00058086385,0.001036462,0.00024453425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00626494,0.0008855543,0.0010812897,0.0011842789,0.0004599581,0.0020637922,0.002324655,0.002006775,0.0012334002],"category_scores_gemma":[0.013725192,0.0007347287,0.0011793026,0.0005411578,0.0018307301,0.0022776816,0.0032937026,0.0034694984,0.00067735394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030123242,0.00016229974,0.0044377856,0.00019685426,0.0002556301,0.00021701561,0.00026167734,0.7465472,0.010730105,0.049022995,0.005397031,0.18247011],"study_design_scores_gemma":[0.0000062662707,0.000020181504,0.00008420533,0.000011660415,0.000007993935,0.000022394177,0.000006355639,0.98917514,0.0010551149,0.009078527,0.00052746484,0.0000046284804],"about_ca_topic_score_codex":0.004649828,"about_ca_topic_score_gemma":0.0056220912,"teacher_disagreement_score":0.00626494,"about_ca_system_score_codex":0.0015767253,"about_ca_system_score_gemma":0.0025342496,"threshold_uncertainty_score":0.033132553},"labels":[],"label_agreement":null},{"id":"W4387169745","doi":"10.3390/s23198150","title":"Comparison of Experienced and Novice Drivers’ Visual and Driving Behaviors during Warned or Unwarned Near–Forward Collisions","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Canadian Institutes of Health Research; Agence Nationale de la Recherche","keywords":"Collision; Driving simulator; Brake; License; Cognition; Poison control; Computer science; Simulation; Psychology; Computer security; Engineering; Medicine; Medical emergency; Automotive engineering","score_opus":0.04138585813063569,"score_gpt":0.43027153514882993,"score_spread":0.38888567701819426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387169745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99983585,0.000016525813,0.000053432588,0.0000016101303,0.0000012354225,0.0000041414496,0.0000091289885,0.0000011385685,0.00007698469],"genre_scores_gemma":[0.99924386,0.000047042562,0.00016312927,0.000007807186,0.0000028705945,0.000010394379,0.00006543857,9.779643e-7,0.00045852928],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997689,0.0000378325,0.00002408914,0.000058330173,0.000052171323,0.00005869607],"domain_scores_gemma":[0.9990804,0.00014147576,0.00021453707,0.00007292285,0.00018416213,0.00030650763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030484018,0.00034424898,0.00024037274,0.00034642662,0.00012062606,0.0002818908,0.00017308655,0.00029958607,0.00078541104],"category_scores_gemma":[0.0012968879,0.00011000468,0.00026682872,0.000079919555,0.00020029744,0.00022062635,0.00040608,0.00023862063,0.00018581869],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039533083,0.0044428366,0.77124393,0.00036120333,0.00032766155,0.00081213564,0.0054213633,0.00088055164,0.1387413,0.000054951295,0.00029673902,0.073463954],"study_design_scores_gemma":[0.000022816244,0.005652819,0.98699343,0.000007664674,0.000040931096,0.00024329065,0.0012938265,0.0004468431,0.0048673498,0.000028184028,0.00038222354,0.000020625896],"about_ca_topic_score_codex":0.0010571711,"about_ca_topic_score_gemma":0.0020083762,"teacher_disagreement_score":0.0010571711,"about_ca_system_score_codex":0.00008089409,"about_ca_system_score_gemma":0.00012967917,"threshold_uncertainty_score":0.0026274323},"labels":[],"label_agreement":null},{"id":"W4387264763","doi":"10.3390/s23198206","title":"Transient Thermal Analysis of Concrete Box Girders: Assessing Temperature Variations in Canadian Climate Zones","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Council Canada","keywords":"Temperature gradient; Thermal; Transient (computer programming); Environmental science; Climate zones; Mean radiant temperature; Maximum temperature; Finite element method; Climatology; Climate change; Meteorology; Materials science; Structural engineering; Atmospheric sciences; Geology; Geography; Engineering; Computer science","score_opus":0.014798256445365785,"score_gpt":0.28626005936971693,"score_spread":0.2714618029243511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387264763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960288,0.000026491938,0.002382536,0.000008438762,0.0000019587553,0.000010852691,0.00024041918,0.000042778884,0.0012577295],"genre_scores_gemma":[0.9983551,0.000022841576,0.0010630077,0.000002871895,4.1688725e-7,0.000006255901,0.0002187986,0.0000063540415,0.0003242926],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998286,0.0000073799642,0.0000037665068,0.00004029729,0.00008418451,0.000035787867],"domain_scores_gemma":[0.99984777,0.000020323638,0.000015744808,0.0000088915185,0.000091876114,0.000015334854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018874003,0.00040393183,0.00022728638,0.0007559161,0.0007053202,0.0003981256,0.00059316517,0.00026353842,0.000585032],"category_scores_gemma":[0.00041761145,0.00018638175,0.00030362865,0.00059359043,0.0003764444,0.00022203919,0.00021806319,0.00017672745,0.00011028175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058248214,0.00019726365,0.39820266,0.00017742548,0.00011477715,0.0003826354,0.001319598,0.3716263,0.16024476,0.0012007939,0.0011902341,0.06476108],"study_design_scores_gemma":[0.000011593991,0.00010291912,0.6845981,0.000013646671,0.000053645308,0.00009911329,0.0007215427,0.29045346,0.022637367,0.00015859224,0.0011009686,0.00004907396],"about_ca_topic_score_codex":0.7246336,"about_ca_topic_score_gemma":0.8797236,"teacher_disagreement_score":0.27536643,"about_ca_system_score_codex":0.0034527096,"about_ca_system_score_gemma":0.0022567809,"threshold_uncertainty_score":0.5539762},"labels":[],"label_agreement":null},{"id":"W4387265173","doi":"10.3390/s23198197","title":"Human Micro-Expressions in Multimodal Social Behavioral Biometrics","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Identification (biology); Computer science; Rank (graph theory); Benchmark (surveying); Expression (computer science); Trait; Machine learning; Artificial intelligence; Human–computer interaction; Mathematics","score_opus":0.060529109502687944,"score_gpt":0.3474126903523526,"score_spread":0.2868835808496647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387265173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55373156,0.0033842374,0.42533037,0.0007275311,0.00031379808,0.00017203957,0.0020311459,0.0016354236,0.012673858],"genre_scores_gemma":[0.961701,0.0005809341,0.032735948,0.00013301082,0.00009187874,0.00007505807,0.0005016826,0.000032981126,0.0041476153],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990157,0.00034954233,0.000041183615,0.00020852688,0.00029138918,0.00009360434],"domain_scores_gemma":[0.9995098,0.00011302082,0.00012240866,0.00007893075,0.0001405084,0.000035358236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006455332,0.00047682758,0.0006374424,0.00092600577,0.00022795546,0.0005855416,0.0002805272,0.00040081792,0.0014364006],"category_scores_gemma":[0.00181483,0.0001364682,0.00029658756,0.00084284507,0.00026018568,0.00075152767,0.00088806084,0.00036197272,0.0011352954],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001047565,0.00020922368,0.043019187,0.0004436981,0.00016352469,0.0004390909,0.00070756243,0.012560745,0.14956169,0.0045668157,0.0066073104,0.7806735],"study_design_scores_gemma":[0.000034891636,0.0007775664,0.25701973,0.0001781313,0.00022852082,0.0038341219,0.0015394537,0.5924286,0.10299872,0.012408151,0.028346742,0.0002053672],"about_ca_topic_score_codex":0.0009636686,"about_ca_topic_score_gemma":0.0016464823,"teacher_disagreement_score":0.0014364006,"about_ca_system_score_codex":0.00027094295,"about_ca_system_score_gemma":0.00018528508,"threshold_uncertainty_score":0.004805267},"labels":[],"label_agreement":null},{"id":"W4387302362","doi":"10.3390/s23198229","title":"Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reinforcement learning; Computer science; Graph; Autonomy; Field (mathematics); Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.013058540786218688,"score_gpt":0.2774711913940004,"score_spread":0.2644126506077817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387302362","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021283263,0.9170189,0.07075719,0.0013561237,0.0006870816,0.00003686945,0.000054517477,0.00012497873,0.007836085],"genre_scores_gemma":[0.049079742,0.9178517,0.027809558,0.00060536794,0.0014582468,0.00009331284,0.0001498522,0.00004439096,0.002907862],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996118,0.000078448444,0.00004805336,0.00011975256,0.00011363567,0.00002829128],"domain_scores_gemma":[0.9990988,0.0005984695,0.00006733948,0.000034385866,0.00016865313,0.000032442756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008261919,0.0009590206,0.0010375619,0.0011602383,0.0002509474,0.0013748228,0.001348465,0.0012726646,0.0019454549],"category_scores_gemma":[0.0016623732,0.00047690008,0.00091851293,0.0019197317,0.00077442324,0.0022725922,0.00070678303,0.0016926192,0.0006935083],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005796555,0.00014983545,0.000987201,0.011411939,0.00019101934,0.00019889406,0.00024339488,0.043234985,0.0018917896,0.10371344,0.012792056,0.82512736],"study_design_scores_gemma":[0.00004836496,0.0004937443,0.0022243937,0.0045387386,0.0004933804,0.0011138963,0.00035082604,0.19934218,0.003316308,0.1252309,0.66257924,0.00026806217],"about_ca_topic_score_codex":0.0028606048,"about_ca_topic_score_gemma":0.0016595395,"teacher_disagreement_score":0.0028606048,"about_ca_system_score_codex":0.0008725866,"about_ca_system_score_gemma":0.0011439333,"threshold_uncertainty_score":0.006508231},"labels":[],"label_agreement":null},{"id":"W4387458074","doi":"10.3390/s23198308","title":"Pile Damage Detection Using Machine Learning with the Multipoint Traveling Wave Decomposition Method","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Machine learning; Multilayer perceptron; Pile; Perceptron; Extreme learning machine; Artificial neural network; Signal processing; Boosting (machine learning); Pattern recognition (psychology); Algorithm","score_opus":0.029798762206273942,"score_gpt":0.3126908439255181,"score_spread":0.2828920817192442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387458074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058015913,0.00010416108,0.9408466,0.00004759028,0.0000122563015,0.000035312845,0.000041042524,0.00038491611,0.000512148],"genre_scores_gemma":[0.67908776,0.00016124416,0.31961533,0.00003279359,0.00001870061,0.00007695673,0.00014551173,0.00002512718,0.0008365421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997713,0.000054994987,0.0000116739975,0.000039414816,0.000099633784,0.000022957418],"domain_scores_gemma":[0.9996847,0.00011852078,0.00007127042,0.000030770298,0.00008198647,0.000012788739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035398285,0.00048396367,0.00045537882,0.00093890826,0.00014695876,0.00034089526,0.00034642877,0.00039836933,0.0004273476],"category_scores_gemma":[0.0009340344,0.00018128753,0.00042500693,0.00052354805,0.00020294846,0.0005134301,0.0003579045,0.00041743752,0.00016962881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020442104,0.00018042607,0.005029362,0.00014635433,0.00008932948,0.00015232919,0.000089060675,0.50038964,0.07287136,0.0019087621,0.0008941365,0.41804492],"study_design_scores_gemma":[0.000002862989,0.000034354314,0.001146694,0.0000036782267,0.0000051803686,0.00002558487,0.000007122171,0.99311817,0.0051644626,0.00032599218,0.00015977786,0.000006197482],"about_ca_topic_score_codex":0.0009760625,"about_ca_topic_score_gemma":0.000979969,"teacher_disagreement_score":0.0009760625,"about_ca_system_score_codex":0.00019313728,"about_ca_system_score_gemma":0.00028261307,"threshold_uncertainty_score":0.0019407868},"labels":[],"label_agreement":null},{"id":"W4387458188","doi":"10.3390/s23198296","title":"Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Computer science; Smart meter; Mixture model; Data mining; Feature (linguistics); Energy consumption; Feature selection; Granularity; Robustness (evolution); Cloud computing; Metering mode; Artificial intelligence; Smart grid; Engineering","score_opus":0.042135948910332365,"score_gpt":0.29287161676125245,"score_spread":0.2507356678509201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387458188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019269226,0.00038018695,0.9781677,0.0001787,0.00003192985,0.000034286124,0.00009788581,0.0010517153,0.0007884128],"genre_scores_gemma":[0.6333526,0.0005174179,0.35962114,0.00034863965,0.00012576752,0.00016289487,0.0012239869,0.00042736597,0.0042201458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994869,0.00015598167,0.000023650338,0.00015695757,0.00010355609,0.00007303007],"domain_scores_gemma":[0.999141,0.00051717,0.00007877215,0.000095472526,0.00012732856,0.000040345418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010711398,0.0010467313,0.0014021344,0.0011901752,0.0005904853,0.0012319572,0.0020034325,0.0012033994,0.0018494459],"category_scores_gemma":[0.0034729969,0.00066910614,0.001511394,0.0013084979,0.0007225629,0.001793203,0.0015965977,0.0015927327,0.001084727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046327905,0.00015561268,0.003607596,0.00011194303,0.00015716375,0.00016013824,0.00026180735,0.7339822,0.0064225895,0.017333966,0.003947302,0.23339643],"study_design_scores_gemma":[0.0000031723039,0.0000073460697,0.00017772286,0.0000028316626,0.0000052346254,0.000011128906,0.000007385984,0.99631065,0.00041375333,0.002736715,0.00031928427,0.0000047690637],"about_ca_topic_score_codex":0.008837571,"about_ca_topic_score_gemma":0.0105272485,"teacher_disagreement_score":0.008837571,"about_ca_system_score_codex":0.00083743944,"about_ca_system_score_gemma":0.0006992102,"threshold_uncertainty_score":0.017572284},"labels":[],"label_agreement":null},{"id":"W4387503020","doi":"10.3390/s23208352","title":"Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Demand response; Smart grid; Computer science; Robustness (evolution); Computer security; Stackelberg competition; Blockchain; Smart contract; Database transaction; Distributed computing; Electricity; Engineering; Database","score_opus":0.020756337878352866,"score_gpt":0.24066494424641766,"score_spread":0.21990860636806478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387503020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10921906,0.0002945612,0.8812398,0.0007979036,0.00007548734,0.0002005802,0.00030901926,0.0002720263,0.007591543],"genre_scores_gemma":[0.98032856,0.00018754708,0.015926616,0.00007413596,0.000021557598,0.00013751473,0.000110150875,0.000022209906,0.003191639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978398,0.00079461565,0.00010783519,0.00047401743,0.00039419203,0.00038942054],"domain_scores_gemma":[0.9952467,0.0030892624,0.00063816406,0.00029947152,0.0004918671,0.00023447488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023164395,0.0012567412,0.0016210743,0.000606414,0.0010781997,0.0018901565,0.0015840335,0.0018202048,0.00402096],"category_scores_gemma":[0.0061360532,0.00054678175,0.0009356617,0.00093314354,0.001720706,0.002820756,0.0017493056,0.0018615252,0.0004645304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015925143,0.00006346533,0.00077208393,0.00006181509,0.000042863536,0.00027918225,0.000097135155,0.94771636,0.0015251419,0.041498214,0.0006149505,0.007169494],"study_design_scores_gemma":[0.000021442786,0.00003108938,0.00006302764,0.000004078584,0.000007294671,0.000022591124,0.000016654063,0.9833919,0.00025876102,0.015977556,0.00019658841,0.000009079153],"about_ca_topic_score_codex":0.008611582,"about_ca_topic_score_gemma":0.007030902,"teacher_disagreement_score":0.008611582,"about_ca_system_score_codex":0.0016518397,"about_ca_system_score_gemma":0.0023016722,"threshold_uncertainty_score":0.017122924},"labels":[],"label_agreement":null},{"id":"W4387568427","doi":"10.3390/s23208428","title":"Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Synaptive (Canada); Western University","funders":"","keywords":"Echo time; Echo (communications protocol); Contrast (vision); Computer science; Pulse sequence; Metric (unit); Nuclear magnetic resonance; Artificial intelligence; Magnetic resonance imaging; Physics; Medicine","score_opus":0.0205716051833669,"score_gpt":0.3210211809172638,"score_spread":0.30044957573389686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387568427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17808393,0.0014702997,0.8156886,0.0002646455,0.000043232892,0.00016657126,0.00017061102,0.00083601446,0.0032759674],"genre_scores_gemma":[0.40330493,0.0011102322,0.59300494,0.00012220524,0.000018365196,0.00025226257,0.00028146434,0.0004656848,0.0014398588],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997639,0.00005951671,0.000013506531,0.000058493042,0.00007311735,0.00003160453],"domain_scores_gemma":[0.99979264,0.00007985718,0.00003596895,0.000013199771,0.000064083564,0.000014215953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073568535,0.0009780858,0.0003943938,0.0003558319,0.0002581868,0.00069275725,0.0005474475,0.0004856669,0.00061926764],"category_scores_gemma":[0.0013026107,0.0003302327,0.00027487235,0.0004035631,0.00032797834,0.00060048426,0.00040848623,0.0005964567,0.00048782388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035626948,0.00010733407,0.0010378851,0.00028329963,0.00006452231,0.00026237516,0.000110980895,0.045931593,0.89662427,0.003136435,0.000816168,0.051268913],"study_design_scores_gemma":[0.00005879301,0.0007495859,0.004901542,0.00004427065,0.00013478893,0.0005936092,0.00008478377,0.29507038,0.6862021,0.0032551151,0.0088004265,0.000104564875],"about_ca_topic_score_codex":0.000978043,"about_ca_topic_score_gemma":0.0013558678,"teacher_disagreement_score":0.0009780858,"about_ca_system_score_codex":0.00040830055,"about_ca_system_score_gemma":0.0006420421,"threshold_uncertainty_score":0.0038906932},"labels":[],"label_agreement":null},{"id":"W4387576250","doi":"10.3390/s23208395","title":"Development and Testing of a Soft Exoskeleton Robotic Hand Training Device","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Thumb; Actuator; Simulation; Rehabilitation; Pneumatic actuator; Reliability (semiconductor); Work (physics); Software; Accelerometer; Computer science; Engineering; Artificial intelligence; Physical therapy; Medicine; Mechanical engineering; Surgery","score_opus":0.07490464265215609,"score_gpt":0.30060088127867285,"score_spread":0.22569623862651678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387576250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5357327,0.00090303505,0.45424274,0.00031195453,0.00035489618,0.0023829034,0.00078046817,0.002044858,0.0032464678],"genre_scores_gemma":[0.8047934,0.00039116832,0.18735164,0.00020297087,0.00004071211,0.0009511057,0.00054812117,0.000072918665,0.0056478987],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871635,0.00020378744,0.00020141093,0.00023191876,0.00058544514,0.00006113069],"domain_scores_gemma":[0.99866724,0.00039470787,0.00017168553,0.00021698407,0.00045402418,0.00009539221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015166252,0.00050529616,0.00048294317,0.0005452571,0.00022474934,0.00048101082,0.0011471966,0.0008339527,0.0030471294],"category_scores_gemma":[0.002282056,0.00026456025,0.0004173494,0.00019752185,0.0003676968,0.00064374355,0.00066253985,0.00023053132,0.00072866504],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052176364,0.0005688634,0.006921087,0.0017014983,0.00009522497,0.00063462154,0.0004523492,0.00500146,0.8306819,0.0007806064,0.0009820296,0.15165856],"study_design_scores_gemma":[0.0005109452,0.028912751,0.06352853,0.00042282164,0.0003795484,0.0047983555,0.00051670044,0.050011136,0.80908775,0.0008406247,0.040800977,0.0001899443],"about_ca_topic_score_codex":0.00020550127,"about_ca_topic_score_gemma":0.00021524986,"teacher_disagreement_score":0.0030471294,"about_ca_system_score_codex":0.00013123061,"about_ca_system_score_gemma":0.000613781,"threshold_uncertainty_score":0.010193706},"labels":[],"label_agreement":null},{"id":"W4387581549","doi":"10.3390/s23208414","title":"Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Veterinary Equine Medical Research","field":"Veterinary","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Quarter (Canadian coin); Movement (music); Motion analysis; Symmetry (geometry); Video recording; Inertial measurement unit; Computer graphics (images); Computer vision; Computer science; Geodesy; Artificial intelligence; Physics; Mathematics; Acoustics; Geometry; Geography","score_opus":0.25173579653863537,"score_gpt":0.3956957164403986,"score_spread":0.1439599199017632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387581549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9549279,0.0015863865,0.039784182,0.00015860559,0.00013432495,0.0003358124,0.0004814414,0.00027465916,0.0023166493],"genre_scores_gemma":[0.9778301,0.00027708575,0.020894846,0.00008224367,0.000041906726,0.00017413775,0.00028875624,0.000023875886,0.0003870464],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960691,0.0018095046,0.00034003932,0.000572543,0.0010278036,0.00018102425],"domain_scores_gemma":[0.99080735,0.00342107,0.0017918048,0.00051723036,0.0032752,0.00018737614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004797029,0.0005937922,0.00041939496,0.0013324115,0.00027200702,0.0012065845,0.00064174325,0.00076351274,0.001737777],"category_scores_gemma":[0.017892366,0.00022598037,0.00037349924,0.0005789707,0.0004465871,0.00092610065,0.00095901283,0.0002730591,0.00048679727],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006557845,0.0005674543,0.52326256,0.0014510561,0.0007475219,0.00019213241,0.0017494324,0.004602036,0.093917154,0.00066236494,0.0017604257,0.36453006],"study_design_scores_gemma":[0.00012254503,0.006256684,0.907558,0.00036762893,0.0004051927,0.0006900785,0.0017050507,0.034395915,0.043730136,0.00077141233,0.003908556,0.000088692745],"about_ca_topic_score_codex":0.0017998737,"about_ca_topic_score_gemma":0.0028123937,"teacher_disagreement_score":0.004797029,"about_ca_system_score_codex":0.00041855683,"about_ca_system_score_gemma":0.00038680242,"threshold_uncertainty_score":0.025369406},"labels":[],"label_agreement":null},{"id":"W4387618024","doi":"10.3390/s23208438","title":"Quantifying the Effects of Network Latency for a Teleoperated Robot","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; Royal University Hospital Foundation; University of Saskatchewan","keywords":"Teleoperation; Latency (audio); Real-time computing; Simulation; Computer science; Network delay; Robot; Computer network; Telecommunications; Artificial intelligence","score_opus":0.025471852613483587,"score_gpt":0.24931207760991037,"score_spread":0.22384022499642678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387618024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92570233,0.0005918312,0.07197394,0.00009227432,0.00005770812,0.000071615585,0.00014440941,0.00034602924,0.0010198619],"genre_scores_gemma":[0.9946569,0.000129889,0.0048884377,0.000013521223,0.0000055822406,0.00001937319,0.000047008056,0.000021027432,0.00021832869],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870646,0.00024315139,0.000082772596,0.00017432454,0.00062544877,0.00016792212],"domain_scores_gemma":[0.9908494,0.005768191,0.0013757097,0.0005144342,0.0013476082,0.00014462421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009805458,0.00056839467,0.00029066144,0.0007142717,0.00030376413,0.0006028885,0.00047957257,0.00045505818,0.0008012601],"category_scores_gemma":[0.0067458334,0.0001879108,0.0001919892,0.00054996175,0.00034527163,0.0010256276,0.0004039378,0.00038753412,0.00011540275],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020893528,0.00047602723,0.049014065,0.0008150459,0.00018860282,0.0010122112,0.00054527057,0.34876963,0.47854283,0.0013017398,0.0004933598,0.116751894],"study_design_scores_gemma":[0.000041219893,0.0031131047,0.06432948,0.000061447754,0.00019055506,0.0010592416,0.000586529,0.6322697,0.2959655,0.0008938255,0.0013916785,0.000097695374],"about_ca_topic_score_codex":0.0024194783,"about_ca_topic_score_gemma":0.0026391777,"teacher_disagreement_score":0.0024194783,"about_ca_system_score_codex":0.000693582,"about_ca_system_score_gemma":0.00049740286,"threshold_uncertainty_score":0.0051856637},"labels":[],"label_agreement":null},{"id":"W4387706994","doi":"10.3390/s23208527","title":"Resonant Gas Sensing in the Terahertz Spectral Range Using Two-Wire Phase-Shifted Waveguide Bragg Gratings","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Natural Science Foundation of China","keywords":"Grating; Materials science; Terahertz radiation; Optics; Fiber Bragg grating; Waveguide; Refractive index; Optoelectronics; Wavelength; Physics","score_opus":0.02666888505120371,"score_gpt":0.27898164321697455,"score_spread":0.2523127581657708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387706994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87916666,0.0015255472,0.116992444,0.00016217698,0.0000633078,0.000026909307,0.0001280515,0.00047090423,0.0014640336],"genre_scores_gemma":[0.9376958,0.0007576836,0.0602018,0.00007727204,0.000016307205,0.000034415192,0.00009843887,0.000036608588,0.0010816072],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998448,0.000018307914,0.000008513954,0.00005018964,0.000055016866,0.000023104732],"domain_scores_gemma":[0.9999163,0.000022758788,0.000027901087,0.000013135396,0.0000134224865,0.0000064290907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017039882,0.0002987831,0.00025605527,0.00017537714,0.00007213249,0.00025502976,0.00033654028,0.00031871733,0.00019008988],"category_scores_gemma":[0.00018223799,0.00023245599,0.00018776966,0.00018474793,0.00031009683,0.0003698439,0.00025064658,0.0002615948,0.00018012193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021442125,0.0000073480333,0.00013955926,0.000030779578,0.0000037782793,0.000022739288,0.000012581888,0.00035757571,0.99672145,0.00031194615,0.000024235229,0.0023467066],"study_design_scores_gemma":[0.000007048523,0.000047118247,0.0004284954,0.0000019252616,0.000006079348,0.00008784901,0.000010954856,0.0084534865,0.99016434,0.00009941172,0.00068730884,0.000005851305],"about_ca_topic_score_codex":0.00030196353,"about_ca_topic_score_gemma":0.00050384057,"teacher_disagreement_score":0.00033654028,"about_ca_system_score_codex":0.00020221599,"about_ca_system_score_gemma":0.00012838641,"threshold_uncertainty_score":0.0014671087},"labels":[],"label_agreement":null},{"id":"W4387743566","doi":"10.3390/s23208541","title":"A Real-Time Inspection System for Industrial Helical Gears","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual inspection; Automotive industry; Automated optical inspection; Process (computing); Inspection time; Automated X-ray inspection; Computer science; Engineering; Machine vision; Work (physics); Engineering drawing; Automotive engineering; Artificial intelligence; Mechanical engineering; Image processing","score_opus":0.033038486348292186,"score_gpt":0.24369892958241404,"score_spread":0.21066044323412186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387743566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35453412,0.0005724279,0.5399105,0.000409817,0.00034966265,0.0006262721,0.0016689427,0.091725245,0.010202962],"genre_scores_gemma":[0.8534179,0.00009296738,0.13753805,0.00015720857,0.000028319504,0.00016224176,0.00090067723,0.0003381912,0.0073644845],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969125,0.00001944671,0.000015991289,0.000102869955,0.00013641658,0.000034079076],"domain_scores_gemma":[0.99968016,0.00005650622,0.000037015652,0.000057028425,0.000118912656,0.000050287108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038299485,0.00052525953,0.0005283124,0.0007352179,0.0003320761,0.00037550429,0.00094526366,0.00051370106,0.007689867],"category_scores_gemma":[0.0007996579,0.00024701635,0.00023122034,0.0002551677,0.00022439584,0.00050412805,0.0006732476,0.00034245386,0.0019948853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017473432,0.00045438576,0.013812701,0.00034019363,0.00008871591,0.0015899534,0.0006178188,0.018265847,0.3672012,0.0019574899,0.03645536,0.557469],"study_design_scores_gemma":[0.00025104982,0.0016223306,0.033087812,0.00008355922,0.00009864141,0.0017900608,0.00020254577,0.7706924,0.1546561,0.0015324699,0.03583642,0.00014666247],"about_ca_topic_score_codex":0.0030681153,"about_ca_topic_score_gemma":0.0047281715,"teacher_disagreement_score":0.007689867,"about_ca_system_score_codex":0.0005453905,"about_ca_system_score_gemma":0.0005564502,"threshold_uncertainty_score":0.025725126},"labels":[],"label_agreement":null},{"id":"W4387813157","doi":"10.3390/s23208596","title":"Interconnect for Dense Electronically Scanned Antenna Array Using High-Speed Vertical Connector","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Printed circuit board; Cable gland; Interconnection; Coplanar waveguide; Coaxial; Signal integrity; Electrical engineering; Electronic circuit; Transmission line; Electronics; Microwave; Engineering; Electronic engineering; Telecommunications","score_opus":0.021813711046711738,"score_gpt":0.2466857522424125,"score_spread":0.22487204119570076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387813157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.672227,0.0007228788,0.31429192,0.0002106345,0.00021166289,0.00010602132,0.0001762277,0.00277253,0.009281173],"genre_scores_gemma":[0.9294286,0.00016848418,0.06688889,0.000053707223,0.000028991617,0.000057626916,0.00019535507,0.00007609285,0.003102172],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962366,0.00007186038,0.000023106586,0.00006733136,0.00015671145,0.000057272748],"domain_scores_gemma":[0.9991835,0.000093708855,0.00034980147,0.00014270551,0.0001750363,0.000055227203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030044274,0.00062233343,0.00019086902,0.00041185872,0.00015194665,0.0006093784,0.0006822184,0.00041993643,0.001455722],"category_scores_gemma":[0.0006381168,0.00021721964,0.00019180513,0.00048578216,0.00022353142,0.0006265112,0.00047520472,0.00022407,0.00070739386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022988825,0.00014708751,0.0052169384,0.00024632242,0.000053635336,0.000450702,0.00017278104,0.021064263,0.9156228,0.0037288843,0.0023762074,0.050690588],"study_design_scores_gemma":[0.00007274182,0.005442687,0.009590445,0.000035663266,0.00007910114,0.0012515421,0.00015777384,0.15993023,0.7886915,0.000586073,0.034083005,0.00007922162],"about_ca_topic_score_codex":0.00022856442,"about_ca_topic_score_gemma":0.0006069815,"teacher_disagreement_score":0.001455722,"about_ca_system_score_codex":0.00042106342,"about_ca_system_score_gemma":0.00023024358,"threshold_uncertainty_score":0.0048698783},"labels":[],"label_agreement":null},{"id":"W4387908868","doi":"10.3390/s23218659","title":"Quantification of the Risk of Musculoskeletal Disorders of the Upper Limb Using Fuzzy Logic: A Study of Manual Wheelchair Propulsion","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut de Readaptation Gingras Lindsay de Montreal; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Kinematics; Wheelchair; Physical medicine and rehabilitation; Manual wheelchair; Elbow; Spinal cord injury; Fuzzy logic; Physical therapy; Upper limb; Medicine; Wrist; Propulsion; Treadmill; Engineering; Computer science; Surgery; Spinal cord; Artificial intelligence","score_opus":0.05242537512188207,"score_gpt":0.3824046246879906,"score_spread":0.3299792495661085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387908868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99948454,0.000034066245,0.00039094948,0.0000030086085,4.0909563e-7,0.000007840266,0.000012098634,7.3028843e-7,0.00006635435],"genre_scores_gemma":[0.99952674,0.000029620514,0.00036611894,0.0000033332233,0.000001178974,0.000007998923,0.000017356979,2.6643986e-7,0.00004737816],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997415,0.00010697259,0.000020954716,0.00003665727,0.00006486088,0.000029098876],"domain_scores_gemma":[0.9990338,0.00047047774,0.00023782697,0.000026701699,0.00014695448,0.00008427457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006851834,0.0003184832,0.00023749415,0.00086795207,0.00021603797,0.00028674278,0.00014974513,0.00034313722,0.00065197435],"category_scores_gemma":[0.002194848,0.000105455816,0.00030388957,0.0004145595,0.0002471049,0.00018069234,0.0002032203,0.00013666418,0.00007159863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018203733,0.0009636278,0.9521485,0.00022464487,0.00023958884,0.00062853296,0.0018694134,0.0029267587,0.014957706,0.00009926794,0.00006653916,0.024055101],"study_design_scores_gemma":[0.000025751526,0.0036095446,0.98441666,0.00003848066,0.00010541001,0.00064700324,0.0015662796,0.007987779,0.0013365974,0.00012520984,0.00012040245,0.00002091058],"about_ca_topic_score_codex":0.0037515818,"about_ca_topic_score_gemma":0.002292327,"teacher_disagreement_score":0.0037515818,"about_ca_system_score_codex":0.00021408933,"about_ca_system_score_gemma":0.0002019162,"threshold_uncertainty_score":0.0074594617},"labels":[],"label_agreement":null},{"id":"W4387908992","doi":"10.3390/s23218684","title":"Effects of Functional Electrical Stimulation on Gait Characteristics in Healthy Individuals: A Systematic Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Planarian Biology and Electrostimulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Conseil Régional de La Réunion; Agence Nationale de la Recherche","keywords":"Functional electrical stimulation; Physical medicine and rehabilitation; Gait; Cadence; Ankle; Kinematics; Ground reaction force; Cochrane Library; Physical therapy; Biomechanics; Medicine; Population; Range of motion; Meta-analysis; Stimulation; Surgery","score_opus":0.028365928710226397,"score_gpt":0.31533863694772485,"score_spread":0.28697270823749843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387908992","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007199123,0.9986148,0.00008316536,0.000103588056,0.000060442726,0.00009528883,0.00015858929,0.0000051418565,0.00015912771],"genre_scores_gemma":[0.009493254,0.9894455,0.0003264589,0.00024125668,0.000053465214,0.0002238312,0.00013070396,0.000002986114,0.000082429404],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.997224,0.0008573314,0.0010502625,0.00026839954,0.0004906832,0.00010926519],"domain_scores_gemma":[0.9864607,0.010778986,0.0017085676,0.00015667778,0.00077225745,0.0001228751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036718599,0.0014499698,0.0061424514,0.0073019206,0.00044407824,0.0019211008,0.0013374818,0.0017744319,0.004414858],"category_scores_gemma":[0.017443229,0.00070663216,0.0046849367,0.0067043398,0.00064780994,0.001483524,0.0009812377,0.0007268542,0.00028946594],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014372752,0.0000150716305,0.00032971826,0.95357,0.0039222077,0.000068380585,0.000081746686,0.00006742375,0.00015670246,0.00014121558,0.0009115877,0.040592212],"study_design_scores_gemma":[0.0002380016,0.0003661271,0.004717,0.91119224,0.058529884,0.0005906524,0.00024108212,0.00009179218,0.00030092418,0.00031078965,0.023383291,0.000038218797],"about_ca_topic_score_codex":0.0044752364,"about_ca_topic_score_gemma":0.012392811,"teacher_disagreement_score":0.0073019206,"about_ca_system_score_codex":0.0019055879,"about_ca_system_score_gemma":0.006507937,"threshold_uncertainty_score":0.019418836},"labels":[],"label_agreement":null},{"id":"W4387959484","doi":"10.3390/s23218719","title":"Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; American College of Sports Medicine","keywords":"Ground reaction force; Accelerometer; Repeatability; Acceleration; Force platform; Reliability (semiconductor); Simulation; Center of mass (relativistic); Geodesy; Computer science; Mathematics; Statistics; Mechanics; Geology; Kinematics; Physics","score_opus":0.05707705113625679,"score_gpt":0.35550255379830814,"score_spread":0.29842550266205137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387959484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75397474,0.004950032,0.23639871,0.00015048898,0.00023749867,0.00038327838,0.0008980407,0.0007102196,0.002297016],"genre_scores_gemma":[0.87779176,0.0016922547,0.117765546,0.00010660653,0.00007708516,0.00044356147,0.0007327914,0.00017815306,0.0012122139],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9962806,0.0014811691,0.000361205,0.00073433976,0.001008877,0.00013377413],"domain_scores_gemma":[0.9920515,0.0043642493,0.00078576757,0.00050354534,0.0021490809,0.0001458648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058419877,0.0010433715,0.0009370892,0.0018026291,0.00027040928,0.0008767295,0.00077356806,0.0009278975,0.0010214404],"category_scores_gemma":[0.013786999,0.00043022638,0.00072611135,0.0009012582,0.00026586856,0.00089160365,0.0007666247,0.00038653554,0.00047400093],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045091934,0.0003767448,0.26629105,0.0018695443,0.0027455976,0.000087228866,0.0015855605,0.0061923233,0.04082626,0.0003250355,0.0009293265,0.67426217],"study_design_scores_gemma":[0.00039132507,0.0033481594,0.89500207,0.000438789,0.0012714047,0.0011539556,0.0012946103,0.07215504,0.020134877,0.00089415855,0.0036329997,0.00028266117],"about_ca_topic_score_codex":0.0023052918,"about_ca_topic_score_gemma":0.0056408388,"teacher_disagreement_score":0.0058419877,"about_ca_system_score_codex":0.00020885501,"about_ca_system_score_gemma":0.00033361162,"threshold_uncertainty_score":0.03089577},"labels":[],"label_agreement":null},{"id":"W4388041751","doi":"10.3390/s23218874","title":"The Implementation of Precise Point Positioning (PPP): A Comprehensive Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Precise Point Positioning; Computer science; Global Positioning System; Satellite system; Real-time computing; Satellite; Differential (mechanical device); Satellite navigation; Kinematics; Telecommunications; Aerospace engineering; Engineering","score_opus":0.049185678059413866,"score_gpt":0.3581907259083824,"score_spread":0.30900504784896854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388041751","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002386707,0.9952988,0.0013674392,0.00020499433,0.00024945987,0.00001372087,0.00004112104,0.000019801153,0.0025658945],"genre_scores_gemma":[0.0015763941,0.99657935,0.0010210976,0.00009097099,0.00012999913,0.000011393252,0.000056931236,0.000005235458,0.0005285796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926215,0.00010721139,0.000103859544,0.00014439547,0.0003377875,0.00004452156],"domain_scores_gemma":[0.9986779,0.0007122364,0.00016552114,0.00005246556,0.00035679978,0.000035143617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001250304,0.0012771179,0.0013559143,0.0029609306,0.0003192593,0.0013892131,0.0014661826,0.001269625,0.0034379733],"category_scores_gemma":[0.0018574479,0.00059410156,0.0007354629,0.003955283,0.00061776827,0.00217833,0.0008724008,0.0010184753,0.002293841],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002883593,0.00005121296,0.00025674337,0.028444225,0.000064192755,0.000102869904,0.00007072269,0.0011092883,0.0016271775,0.0072778715,0.011453669,0.9495132],"study_design_scores_gemma":[0.0000042642837,0.00014198692,0.00068624027,0.004682913,0.00011867288,0.0005457185,0.000092894574,0.0004076631,0.0016049052,0.0020205483,0.989656,0.000038244754],"about_ca_topic_score_codex":0.0019631977,"about_ca_topic_score_gemma":0.0019662,"teacher_disagreement_score":0.0034379733,"about_ca_system_score_codex":0.0005998268,"about_ca_system_score_gemma":0.0017835287,"threshold_uncertainty_score":0.011501133},"labels":[],"label_agreement":null},{"id":"W4388070767","doi":"10.3390/s23218856","title":"Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Bootstrapping (finance); Computer science; Environmental data; Data mining; Statistical power; Statistics; Data science; Remote sensing; Geography; Econometrics; Mathematics; Ecology","score_opus":0.02898591357880775,"score_gpt":0.30453043660499574,"score_spread":0.275544523026188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388070767","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01724377,0.00060045347,0.98022103,0.0003828754,0.000079952806,0.000053199536,0.00034232257,0.0005881174,0.00048820057],"genre_scores_gemma":[0.40304106,0.00070100126,0.5932387,0.00039186957,0.000334422,0.00022382486,0.0013477238,0.00028850677,0.00043298124],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9916931,0.003933829,0.00064848585,0.0016064238,0.0018029094,0.00031525973],"domain_scores_gemma":[0.9471484,0.04139591,0.0030591323,0.0050152754,0.0026733028,0.00070798525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010818657,0.0013304314,0.0017308805,0.0055183363,0.0009113785,0.0028811644,0.001817905,0.001561037,0.0013620354],"category_scores_gemma":[0.074187666,0.0006524396,0.00147117,0.004968498,0.0016255351,0.0048538805,0.0030268822,0.0024248508,0.0006610372],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076525385,0.00031182895,0.054593213,0.0010115001,0.0007317698,0.0016964012,0.0020261214,0.17170021,0.0072005005,0.08175718,0.007999175,0.6702069],"study_design_scores_gemma":[0.000034098146,0.0001974885,0.0034364732,0.0001100646,0.000063598156,0.00054548244,0.00041094207,0.80360675,0.0019014349,0.18390587,0.005721876,0.00006596433],"about_ca_topic_score_codex":0.0014093522,"about_ca_topic_score_gemma":0.0013295783,"teacher_disagreement_score":0.010818657,"about_ca_system_score_codex":0.0006626974,"about_ca_system_score_gemma":0.0012850165,"threshold_uncertainty_score":0.057215214},"labels":[],"label_agreement":null},{"id":"W4388104924","doi":"10.3390/s23218800","title":"Effect of Robotic-Assisted Gait at Different Levels of Guidance and Body Weight Support on Lower Limb Joint Kinematics and Coordination","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Gait; Ankle; Kinematics; Sagittal plane; Physical medicine and rehabilitation; Pelvis; Treadmill; Coronal plane; Medicine; Gait analysis; Biomechanics; Physical therapy; Anatomy; Physics","score_opus":0.02081622075762209,"score_gpt":0.27723079101660764,"score_spread":0.25641457025898556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388104924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99960285,0.00007759283,0.00016101052,0.000007841837,0.000004145178,0.000007830994,0.000021075559,0.000008447667,0.00010917295],"genre_scores_gemma":[0.9988086,0.00010849461,0.0005761383,0.000017036018,0.000004070005,0.000028764474,0.0000779129,0.0000027077067,0.00037625356],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99989474,0.000027937815,0.000011003282,0.000016427459,0.000018461138,0.000031486223],"domain_scores_gemma":[0.9998857,0.000029315586,0.000025681315,0.000010514702,0.000014815936,0.0000340976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014924536,0.0003494799,0.00023372586,0.0002804392,0.000098851386,0.0001458545,0.00012410354,0.00021538085,0.00090730796],"category_scores_gemma":[0.0005097011,0.00010452216,0.00023564529,0.000109980974,0.00015718066,0.00014068978,0.0002492841,0.00015341969,0.00010538936],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.07854381,0.020099666,0.023434756,0.0010780804,0.0009862642,0.0006211825,0.00041644284,0.00699144,0.5956325,0.0002106117,0.000619773,0.27136552],"study_design_scores_gemma":[0.0031588657,0.21466349,0.6693719,0.0001353945,0.0009492362,0.0011485819,0.0008409309,0.01688628,0.088783875,0.0004437099,0.0035130335,0.00010463743],"about_ca_topic_score_codex":0.00083789835,"about_ca_topic_score_gemma":0.0014337131,"teacher_disagreement_score":0.00090730796,"about_ca_system_score_codex":0.00007601334,"about_ca_system_score_gemma":0.00011669936,"threshold_uncertainty_score":0.0030352473},"labels":[],"label_agreement":null},{"id":"W4388132972","doi":"10.3390/s23218881","title":"Clinical Static Balance Assessment: A Narrative Review of Traditional and IMU-Based Posturography in Older Adults and Individuals with Incomplete Spinal Cord Injury","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; Alberta Health Services; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Alberta","keywords":"Posturography; Balance (ability); Physical medicine and rehabilitation; Balance problems; Spinal cord injury; Psychological intervention; Poison control; Dynamic balance; Physical therapy; Medicine; Psychology; Spinal cord; Engineering","score_opus":0.09915714002012244,"score_gpt":0.4797294030100849,"score_spread":0.38057226298996244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388132972","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008672521,0.99956065,0.00003669488,0.000104552426,0.000048508336,0.000007213085,0.00002738213,0.0000019192007,0.00012619249],"genre_scores_gemma":[0.0009640885,0.99862146,0.000116311676,0.00014096886,0.000058162914,0.000017330314,0.000032159656,0.0000010898362,0.00004842409],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99873585,0.0002817309,0.00050224015,0.000164889,0.00027080413,0.000044520435],"domain_scores_gemma":[0.99292105,0.0055404874,0.00077012327,0.00007330295,0.0006172755,0.000077807665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019910566,0.000996686,0.0025856416,0.0053978506,0.00036384008,0.0015525785,0.0012855099,0.0014271302,0.003365093],"category_scores_gemma":[0.0098550655,0.00045237105,0.0020828736,0.0051002614,0.00055117346,0.0017139585,0.0008782056,0.0009902125,0.0005380859],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012931005,0.000040242216,0.00064981164,0.4291998,0.000761525,0.00016191343,0.00027336954,0.00017158022,0.00026406322,0.00092824234,0.009893682,0.55752635],"study_design_scores_gemma":[0.000078589066,0.00029909308,0.008347445,0.6063935,0.0070876153,0.0026065388,0.00056094525,0.00019391454,0.0003453689,0.0013449277,0.37266445,0.000077577904],"about_ca_topic_score_codex":0.004180054,"about_ca_topic_score_gemma":0.009824909,"teacher_disagreement_score":0.0053978506,"about_ca_system_score_codex":0.0010149727,"about_ca_system_score_gemma":0.0040459153,"threshold_uncertainty_score":0.01125735},"labels":[],"label_agreement":null},{"id":"W4388185309","doi":"10.3390/s23218765","title":"Mass Reduction Techniques for Short Backfire Antennas: Additive Manufacturing and Structural Perforations","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Research Manitoba","keywords":"Materials science; Reduction (mathematics); Surface roughness; Parametric statistics; Coating; Antenna (radio); 3D printing; Electrical conductor; Surface finish; Composite material; Mechanical engineering; Computer science; Engineering; Telecommunications; Geometry; Mathematics","score_opus":0.013652633497295404,"score_gpt":0.2383517027976758,"score_spread":0.22469906930038042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388185309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33013606,0.0027420113,0.66091746,0.00029681536,0.00021524879,0.00007510675,0.000040938314,0.0007295842,0.004846857],"genre_scores_gemma":[0.7938709,0.000962654,0.20323956,0.0001320964,0.00006418535,0.000047755122,0.000034493303,0.00007029442,0.0015781043],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995753,0.000046607296,0.00002231447,0.00005231915,0.00025662928,0.000046852536],"domain_scores_gemma":[0.9993569,0.00020980014,0.00022184008,0.00012401592,0.00007025977,0.000017119068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037068702,0.0006357,0.00038898707,0.00048081946,0.00015480972,0.00045131924,0.000569327,0.00070315547,0.0006832741],"category_scores_gemma":[0.0007808231,0.00031467786,0.0006481419,0.00029085606,0.0006139064,0.0006168755,0.0005966918,0.0005773899,0.00037824464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006905382,0.000027537297,0.0003923655,0.00017883822,0.000020904585,0.00030907485,0.00009406844,0.0047532404,0.95530784,0.0015775451,0.0001663468,0.037103165],"study_design_scores_gemma":[0.000023526814,0.00072105794,0.0013898936,0.00002892468,0.00003630598,0.001668636,0.00007264676,0.018927934,0.9696381,0.0010187592,0.006442946,0.000031179265],"about_ca_topic_score_codex":0.00004809613,"about_ca_topic_score_gemma":0.000118625765,"teacher_disagreement_score":0.00070315547,"about_ca_system_score_codex":0.00019490939,"about_ca_system_score_gemma":0.000102562306,"threshold_uncertainty_score":0.0022857785},"labels":[],"label_agreement":null},{"id":"W4388187734","doi":"10.3390/s23218760","title":"A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Beamforming; Convolutional neural network; Artificial intelligence; Pixel; Iterative reconstruction; Computer vision; Data set; Sensitivity (control systems); Image resolution; Pattern recognition (psychology); Engineering; Electronic engineering; Telecommunications","score_opus":0.006528604537920251,"score_gpt":0.20993471196853214,"score_spread":0.20340610743061188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388187734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033503827,0.00086715893,0.96031356,0.00031091788,0.00007609001,0.000047247613,0.00018028729,0.0020084027,0.0026924156],"genre_scores_gemma":[0.6111423,0.0009801092,0.37710327,0.00029775815,0.000053779433,0.00016664565,0.00086927233,0.00015256561,0.009234332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983335,0.00002482386,0.000008544098,0.000055916415,0.000046329642,0.00003095177],"domain_scores_gemma":[0.99978274,0.000082914135,0.000024762972,0.000023504841,0.0000747045,0.000011339402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004890726,0.0007112836,0.0003377802,0.0003074504,0.00022193042,0.00043959916,0.0010621905,0.00078832003,0.0015328965],"category_scores_gemma":[0.0010620144,0.00040130463,0.00045491656,0.00041089885,0.00031803473,0.00062564894,0.0005702056,0.000892491,0.0004936386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020186412,0.00010527588,0.001344057,0.0001307007,0.00009538115,0.00016426563,0.000047990758,0.6542704,0.03251006,0.005908588,0.0032720456,0.30194935],"study_design_scores_gemma":[0.0000025060858,0.000015086674,0.00013136568,0.0000039620454,0.0000068005183,0.00001572824,0.000002067802,0.9958917,0.0029283848,0.0004888714,0.0005104041,0.0000031215761],"about_ca_topic_score_codex":0.013712804,"about_ca_topic_score_gemma":0.013892826,"teacher_disagreement_score":0.013712804,"about_ca_system_score_codex":0.0008974292,"about_ca_system_score_gemma":0.0008734339,"threshold_uncertainty_score":0.027265966},"labels":[],"label_agreement":null},{"id":"W4388191027","doi":"10.3390/s23218772","title":"Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Jawaharlal Nehru University; Nottingham Trent University","keywords":"Metaheuristic; Intrusion detection system; Computer science; Intrusion; Artificial intelligence; Deep learning; Machine learning","score_opus":0.022444749054494555,"score_gpt":0.23234929061849408,"score_spread":0.2099045415639995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388191027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0904982,0.0010039392,0.9028434,0.0005254329,0.00013091831,0.000091806374,0.000093447794,0.00122544,0.0035874508],"genre_scores_gemma":[0.896863,0.00028522307,0.09884103,0.00025651444,0.000052383333,0.00012306606,0.00021026499,0.000053385746,0.003315239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997435,0.000048114725,0.000017539138,0.000069283225,0.0000533294,0.000068286536],"domain_scores_gemma":[0.99965096,0.00015303637,0.00004349121,0.000020581325,0.00010161941,0.000030278106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063498877,0.0010833978,0.0010292507,0.0008131558,0.00037511662,0.0008014754,0.0016975631,0.0013525956,0.0010166343],"category_scores_gemma":[0.0010803707,0.0006636702,0.0010777875,0.00056255783,0.0004785718,0.0007366128,0.00085174287,0.0013825868,0.00017299266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045294604,0.000066430344,0.0011508217,0.000023334773,0.00007319649,0.000047652353,0.000020616679,0.9428215,0.0009534779,0.0013906648,0.0005556684,0.05285146],"study_design_scores_gemma":[0.0000014167634,0.0000073002184,0.000032515654,9.495513e-7,0.0000029402474,0.0000024621806,0.0000011311162,0.9996062,0.000098359335,0.00020940106,0.00003632458,0.0000010284364],"about_ca_topic_score_codex":0.016825292,"about_ca_topic_score_gemma":0.01615457,"teacher_disagreement_score":0.016825292,"about_ca_system_score_codex":0.0012028797,"about_ca_system_score_gemma":0.0012334466,"threshold_uncertainty_score":0.033454716},"labels":[],"label_agreement":null},{"id":"W4388200316","doi":"10.3390/s23218729","title":"Is Running Power a Useful Metric? Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University; University of Calgary","funders":"Faculty of Kinesiology, University of Calgary; Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Running economy; Metric (unit); Intensity (physics); Anaerobic exercise; Steady state (chemistry); Mathematics; Aerobic exercise; Blood lactate; Power (physics); Running time; Simulation; Computer science; VO2 max; Physical therapy; Chemistry; Physics; Thermodynamics; Medicine; Algorithm; Heart rate; Engineering; Internal medicine","score_opus":0.11257774952161478,"score_gpt":0.3303454515952654,"score_spread":0.21776770207365062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388200316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99290293,0.00041580765,0.0057792715,0.000054344353,0.000008428404,0.0000071452596,0.000084857355,0.00002486071,0.0007224017],"genre_scores_gemma":[0.9969633,0.00014013238,0.002522531,0.000037706875,0.00001974643,0.000009457723,0.00008720115,0.000008151457,0.00021163536],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995534,0.00014536647,0.000055566274,0.00011487858,0.00010272618,0.000028112769],"domain_scores_gemma":[0.99831176,0.00059538527,0.0005989275,0.00015560375,0.000231034,0.00010726655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013296739,0.00037508388,0.00041427623,0.0007163221,0.000110857734,0.00047242147,0.00029805364,0.00038092257,0.0008052514],"category_scores_gemma":[0.0040250523,0.0002021836,0.00014111448,0.00035751847,0.00038273606,0.0007509062,0.0004310941,0.00025149592,0.0003718437],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046054393,0.000070261834,0.8846628,0.00011524914,0.0001544553,0.000106753025,0.0003314049,0.0012592851,0.044303298,0.00013669385,0.00018982007,0.068209454],"study_design_scores_gemma":[0.000008835167,0.00070786703,0.9882307,0.000033182805,0.000041561776,0.00029849718,0.00021306181,0.0034257814,0.006398844,0.00019959491,0.00042471598,0.000017418202],"about_ca_topic_score_codex":0.0003952461,"about_ca_topic_score_gemma":0.0011399622,"teacher_disagreement_score":0.0013296739,"about_ca_system_score_codex":0.00007201509,"about_ca_system_score_gemma":0.000056816058,"threshold_uncertainty_score":0.0070320964},"labels":[],"label_agreement":null},{"id":"W4388204951","doi":"10.3390/s23218726","title":"Analysis and Design of a Diplexing Power Divider for Ku-Band Satellite Applications","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Power dividers and directional couplers; Insertion loss; Ku band; Return loss; Wilkinson power divider; Cable gland; Electrical engineering; Electronic circuit; Cascade; Multi-band device; Communications satellite; Electronic engineering; Engineering; Printed circuit board; Power (physics); Frequency band; Computer science; Satellite; Frequency divider; Physics","score_opus":0.016882676803572725,"score_gpt":0.23294594141264408,"score_spread":0.21606326460907135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388204951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28791642,0.0019470012,0.69162935,0.00029636043,0.00005544332,0.00015271791,0.00014092695,0.0009235963,0.016938135],"genre_scores_gemma":[0.91069394,0.0009391951,0.08237681,0.000034117485,0.000025535179,0.00007753349,0.00009196168,0.000056289082,0.005704558],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986374,0.00002074673,0.000004336516,0.000028460883,0.000067145076,0.000015627524],"domain_scores_gemma":[0.999928,0.000019180075,0.000023065351,0.000008393614,0.000017306118,0.0000040546347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014316032,0.0003905335,0.00028215806,0.0002401414,0.00015095944,0.0005896737,0.00039243777,0.00041102132,0.0014190794],"category_scores_gemma":[0.00021889205,0.0002455134,0.00039746007,0.00019818175,0.00021342268,0.0004147751,0.00012784911,0.00022119525,0.00049663516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018995289,0.000065245265,0.0014029435,0.0004205083,0.00010722915,0.0004082532,0.00016420407,0.16676997,0.7566218,0.017455311,0.0008188797,0.055575725],"study_design_scores_gemma":[0.000042821215,0.0006110377,0.0024030001,0.000027345557,0.00008178487,0.0004542713,0.00006122868,0.78395194,0.1960643,0.0013732689,0.014900184,0.000028825894],"about_ca_topic_score_codex":0.00051266333,"about_ca_topic_score_gemma":0.0004598596,"teacher_disagreement_score":0.0014190794,"about_ca_system_score_codex":0.00049979053,"about_ca_system_score_gemma":0.0002840439,"threshold_uncertainty_score":0.0047472715},"labels":[],"label_agreement":null},{"id":"W4388288787","doi":"10.3390/s23218952","title":"Research on the Recognition and Tracking of Group-Housed Pigs’ Posture Based on Edge Computing","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Anhui Provincial Department of Education; Anhui Provincial Department of Science and Technology","keywords":"Tracking (education); Computer science; Enhanced Data Rates for GSM Evolution; Identification (biology); Porting; Edge computing; Pruning; Tracking system; Node (physics); Convolution (computer science); Artificial intelligence; Real-time computing; Algorithm; Computer vision; Pattern recognition (psychology); Engineering; Filter (signal processing); Artificial neural network; Software","score_opus":0.31819863351706584,"score_gpt":0.41248818162728446,"score_spread":0.09428954811021861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388288787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14996032,0.00056874077,0.8454522,0.00012251647,0.000089365,0.0000512818,0.00009344389,0.00082125486,0.0028409227],"genre_scores_gemma":[0.7556179,0.00062091614,0.23976947,0.00017313541,0.00004434078,0.00007307498,0.00028442132,0.000065322456,0.0033513985],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99977595,0.000030043084,0.000009882878,0.00009252986,0.000054513956,0.000037156715],"domain_scores_gemma":[0.99972516,0.00008021135,0.00004233748,0.00003433429,0.00009094937,0.000027035634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004002273,0.0004976058,0.0004584748,0.00046955261,0.00021165377,0.0004534681,0.00078935694,0.0004162841,0.0009691156],"category_scores_gemma":[0.0006306878,0.00022423052,0.00042720407,0.00044255963,0.00026946398,0.0009319236,0.00035940122,0.00040444446,0.00028852336],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049090903,0.00020166434,0.023403026,0.00027345607,0.00012248766,0.00029292933,0.00025949453,0.1846364,0.121068574,0.0056487415,0.0019126557,0.6616897],"study_design_scores_gemma":[0.000009055609,0.00023476002,0.009420162,0.000023719644,0.000052255655,0.00013928422,0.00006164813,0.9660691,0.020419326,0.001763034,0.0017850491,0.000022554988],"about_ca_topic_score_codex":0.003663895,"about_ca_topic_score_gemma":0.0051651136,"teacher_disagreement_score":0.003663895,"about_ca_system_score_codex":0.0003477424,"about_ca_system_score_gemma":0.0005592422,"threshold_uncertainty_score":0.007285118},"labels":[],"label_agreement":null},{"id":"W4388380350","doi":"10.3390/s23218977","title":"Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Environmental science; Aerosol; Air pollution; Mean squared error; Carbon fibers; Air quality index; Pollution; Reproducibility; Particulates; Calibration; Remote sensing; Meteorology; Materials science; Mathematics; Chemistry; Statistics; Geography","score_opus":0.033223743316813555,"score_gpt":0.31607234899967246,"score_spread":0.2828486056828589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388380350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99018085,0.00035593798,0.008028965,0.00007827507,0.000016472412,0.00008042361,0.00022896117,0.000041232724,0.0009889628],"genre_scores_gemma":[0.9779432,0.00022355682,0.020328272,0.00011521842,0.00000915744,0.000117888216,0.00035401204,0.000009572026,0.00089907023],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99939704,0.00020657468,0.000036941135,0.00020794029,0.00009990099,0.0000515005],"domain_scores_gemma":[0.9996902,0.000056393463,0.0000594294,0.00003547741,0.00014118823,0.000017349688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011787079,0.00035202625,0.00028761046,0.00034440603,0.0006488007,0.0006561689,0.00075272244,0.00056313403,0.00035412514],"category_scores_gemma":[0.0006036363,0.00024793114,0.00027637178,0.00041991478,0.00032595967,0.00041113928,0.0003956678,0.00021545048,0.00011710322],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033657553,0.00047861255,0.6030337,0.00072618824,0.0002280235,0.00044838895,0.006948335,0.0038098379,0.28569406,0.0004995461,0.00075789244,0.097038805],"study_design_scores_gemma":[0.000070157,0.00155432,0.861034,0.00016209998,0.0004029018,0.00065766834,0.004503907,0.03035359,0.08843053,0.0004181668,0.012324216,0.00008844891],"about_ca_topic_score_codex":0.034155417,"about_ca_topic_score_gemma":0.061478756,"teacher_disagreement_score":0.034155417,"about_ca_system_score_codex":0.00082033064,"about_ca_system_score_gemma":0.0012682761,"threshold_uncertainty_score":0.067913234},"labels":[],"label_agreement":null},{"id":"W4388446512","doi":"10.3390/s23229028","title":"Performance of Grating Couplers Used in the Optical Switch Configuration","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Lyon; Agence Nationale de la Recherche","keywords":"Grating; Optics; Optical switch; Optoelectronics; Materials science; Computer science; Telecommunications; Engineering; Electrical engineering; Physics","score_opus":0.017085907332622403,"score_gpt":0.23245731747217466,"score_spread":0.21537141013955224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388446512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99748826,0.00046678595,0.0007888881,0.00003752786,0.000028531129,0.000009846009,0.000112595124,0.00009721035,0.0009704391],"genre_scores_gemma":[0.99808264,0.00027457753,0.00073281594,0.000021758116,0.000012948094,0.0000071571867,0.00010258579,0.000023357881,0.00074225786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999413,0.00006707807,0.00003793922,0.00013796172,0.00021379036,0.00013037634],"domain_scores_gemma":[0.9993368,0.00020475274,0.00016579738,0.00006490738,0.00015235113,0.000075398784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039650168,0.0007248531,0.00040898225,0.0004558848,0.00021562126,0.0005849509,0.0005532704,0.0007935741,0.0013295745],"category_scores_gemma":[0.0006476911,0.00026181038,0.00031472708,0.0004449509,0.000256566,0.0003742094,0.00021757472,0.00026440594,0.00037926977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041376287,0.00005185835,0.0007156541,0.000051229406,0.000029857523,0.00008498393,0.000052052084,0.001051793,0.99480116,0.00007136926,0.00010232724,0.002573877],"study_design_scores_gemma":[0.000014284223,0.0005095092,0.0035706153,0.000006003679,0.000024815263,0.00006846071,0.000020333946,0.003076685,0.9922805,0.000013094034,0.00040414554,0.000011503689],"about_ca_topic_score_codex":0.0013871989,"about_ca_topic_score_gemma":0.0010756904,"teacher_disagreement_score":0.0013871989,"about_ca_system_score_codex":0.0004688764,"about_ca_system_score_gemma":0.00021817508,"threshold_uncertainty_score":0.0044478774},"labels":[],"label_agreement":null},{"id":"W4388555065","doi":"10.3390/s23229059","title":"The Impact of Sex, Body Mass Index, Age, Exercise Type and Exercise Duration on Interstitial Glucose Levels during Exercise","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetes Management and Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; York University","funders":"","keywords":"Medicine; Body mass index; Internal medicine; Underweight; Aerobic exercise; Endocrinology; Population; Overweight","score_opus":0.026344612271229997,"score_gpt":0.3209223788603193,"score_spread":0.29457776658908935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388555065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99855834,0.00025375234,0.0006434648,0.000030087558,0.000006013874,0.0000041382846,0.00021322572,0.000008392171,0.00028245634],"genre_scores_gemma":[0.99922585,0.000074545846,0.00024035759,0.00001529413,0.0000054279953,0.0000043255413,0.00023170737,0.000007429648,0.00019493971],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916244,0.00041542287,0.000051364135,0.00013454816,0.00011241878,0.0001238613],"domain_scores_gemma":[0.9977295,0.0010488316,0.00058804115,0.00035188562,0.00012405508,0.00015775421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013659525,0.00028222505,0.00039783743,0.0002862285,0.00016301441,0.0005167053,0.00028394535,0.0002146813,0.0009329035],"category_scores_gemma":[0.0042442526,0.00019710195,0.0008664833,0.0004872481,0.0002395932,0.00033068162,0.0004128239,0.00027018302,0.00014880043],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079835113,0.00004009244,0.9918502,0.000015739,0.00034246754,0.000066290464,0.000096168704,0.00026038627,0.0012028744,0.00002740656,0.00005561737,0.0052444898],"study_design_scores_gemma":[0.0000035563755,0.00013922254,0.99887305,0.0000020202722,0.00005998306,0.00006349264,0.000038353664,0.0006174173,0.00009971253,0.000023218985,0.00007747652,0.0000024121703],"about_ca_topic_score_codex":0.003305613,"about_ca_topic_score_gemma":0.005072348,"teacher_disagreement_score":0.003305613,"about_ca_system_score_codex":0.00011167119,"about_ca_system_score_gemma":0.0002427292,"threshold_uncertainty_score":0.0072239637},"labels":[],"label_agreement":null},{"id":"W4388630230","doi":"10.3390/s23229149","title":"Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; California HIV/AIDS Research Program","keywords":"Finger tapping; Task (project management); Machine learning; Artificial intelligence; Computer science; Rating scale; Decision tree; Parkinson's disease; Physical medicine and rehabilitation; Disease; Psychology; Medicine; Engineering; Audiology","score_opus":0.033665614286695754,"score_gpt":0.36102478517839476,"score_spread":0.327359170891699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388630230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6816772,0.0028412708,0.30653593,0.00040553123,0.00011804593,0.00038004457,0.0028723401,0.002002572,0.0031670502],"genre_scores_gemma":[0.91458166,0.00066233176,0.08281742,0.00009061194,0.00005872264,0.0000820251,0.0008681695,0.000032409007,0.0008065317],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99970216,0.00010105364,0.000026333577,0.00007852829,0.00006221229,0.000029691248],"domain_scores_gemma":[0.9992592,0.00027734754,0.00012739039,0.000051251325,0.00023796933,0.00004684129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048745336,0.0005278599,0.00039208136,0.0011985896,0.0001140346,0.00050074025,0.00032600967,0.00052143546,0.0006957057],"category_scores_gemma":[0.002076712,0.000115977724,0.0001556672,0.0004399941,0.00015837187,0.00035344635,0.00043940247,0.00022390066,0.0003159826],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011707336,0.00019328871,0.053816937,0.0005730195,0.000071778384,0.00079099985,0.000269171,0.014648914,0.16008705,0.0005144198,0.0035701322,0.76429355],"study_design_scores_gemma":[0.00008597575,0.0006604603,0.26794073,0.00020830911,0.00013024139,0.0031975156,0.0006747642,0.61091375,0.10783949,0.0029681385,0.0052727717,0.00010792017],"about_ca_topic_score_codex":0.0016427337,"about_ca_topic_score_gemma":0.004326797,"teacher_disagreement_score":0.0016427337,"about_ca_system_score_codex":0.00018590536,"about_ca_system_score_gemma":0.00024060662,"threshold_uncertainty_score":0.003266275},"labels":[],"label_agreement":null},{"id":"W4388630234","doi":"10.3390/s23229143","title":"Correction: Lagrois et al. Low-to-Mid-Frequency Monopole Source Levels of Underwater Noise from Small Recreational Vessels in the St. Lawrence Estuary Beluga Critical Habitat. Sensors 2023, 23, 1674","year":2023,"lang":"en","type":"erratum","venue":"Sensors","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Ministère des Forêts, de la Faune et des Parcs","keywords":"Underwater; Estuary; Noise (video); Oceanography; Acoustics; Sonar; Recreation; Habitat; Marine engineering; Beluga Whale; Environmental science; Fishery; Geography; Geology; Engineering; Physics; Computer science; Ecology; Arctic; Biology","score_opus":0.04059097010549997,"score_gpt":0.284356094547017,"score_spread":0.24376512444151702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388630234","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017099871,0.0004968567,0.0009472575,0.024339166,0.9679385,0.000035823246,0.0036120643,0.0005051426,0.0019542575],"genre_scores_gemma":[0.03305989,0.010640109,0.017144071,0.10125373,0.38941404,0.0007817274,0.02525908,0.008165753,0.4142815],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938958,0.00061568036,0.001327552,0.00072041724,0.0030515457,0.00038901638],"domain_scores_gemma":[0.9303302,0.008655725,0.0025596695,0.0037438115,0.052990552,0.0017200172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060707135,0.0029826474,0.002059299,0.004671364,0.003811089,0.0042147515,0.0039872075,0.005403616,0.0804245],"category_scores_gemma":[0.10064869,0.001377875,0.0019427423,0.0031686572,0.0023478656,0.0022363034,0.0027028942,0.009311011,0.047140677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017642227,0.0000028460047,0.00005296356,0.000096620955,0.000007410153,0.000093557726,0.000027706084,0.000020575084,0.000034152858,0.00019328992,0.9960671,0.003386101],"study_design_scores_gemma":[0.000058625188,0.000021669424,0.0012124511,0.0005623844,0.000056691682,0.00053543237,0.0001546484,0.00021790086,0.00040627018,0.0010076257,0.9957125,0.000053801443],"about_ca_topic_score_codex":0.043121394,"about_ca_topic_score_gemma":0.038076,"teacher_disagreement_score":0.9568786,"about_ca_system_score_codex":0.0038028935,"about_ca_system_score_gemma":0.008540102,"threshold_uncertainty_score":0.26904678},"labels":[],"label_agreement":null},{"id":"W4388654380","doi":"10.3390/s23229162","title":"3D Object Detection Using Multiple-Frame Proposal Features Fusion","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Merge (version control); Computer science; Artificial intelligence; Frame (networking); Fusion; Computer vision; Object detection; Point cloud; Object (grammar); Feature (linguistics); Sensor fusion; Pattern recognition (psychology); Data mining; Information retrieval","score_opus":0.02157973476433038,"score_gpt":0.2798126025572462,"score_spread":0.2582328677929158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388654380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018030616,0.0005065046,0.9787442,0.00007931789,0.00010027907,0.00009635131,0.00018336669,0.0015514672,0.00070791267],"genre_scores_gemma":[0.40360945,0.00064187427,0.5901996,0.0001585639,0.00016133103,0.00021294638,0.0018944966,0.00030274416,0.0028189889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983084,0.00016847551,0.000056723922,0.000498355,0.0007759029,0.00019199341],"domain_scores_gemma":[0.99896073,0.00022722568,0.00009671257,0.00021372786,0.0004367331,0.00006490721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014881642,0.0019325864,0.0019039814,0.003123714,0.00055562105,0.0014413375,0.0022304633,0.0014452181,0.001754232],"category_scores_gemma":[0.0032705923,0.00068795244,0.001827622,0.0020748165,0.000614164,0.001813419,0.0030880063,0.0012724596,0.0013486602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005021838,0.0001785452,0.0028534816,0.00017542778,0.00020592214,0.00031597185,0.0001582203,0.04424643,0.07212948,0.0030761373,0.0056030615,0.8705551],"study_design_scores_gemma":[0.00004301703,0.00023363241,0.0028109518,0.000024326653,0.000102390906,0.0004276957,0.00006655661,0.9397963,0.046491012,0.004215571,0.0057245824,0.00006400045],"about_ca_topic_score_codex":0.004340593,"about_ca_topic_score_gemma":0.003661544,"teacher_disagreement_score":0.004340593,"about_ca_system_score_codex":0.00072904816,"about_ca_system_score_gemma":0.0011054355,"threshold_uncertainty_score":0.008630633},"labels":[],"label_agreement":null},{"id":"W4388773013","doi":"10.3390/s23229234","title":"Design and Demonstration of a Microelectromechanical System Single-Ring Resonator with Inner Ring-Shaped Spring Supports for Inertial Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Resonator; Vibration; Resonance (particle physics); Materials science; Silicon on insulator; Voltage; Microelectromechanical systems; Ring (chemistry); Helical resonator; Silicon; Gyroscope; Wafer; Optical ring resonators; Optoelectronics; Electrical engineering; Acoustics; Physics; Engineering; Chemistry; Atomic physics","score_opus":0.014910726210654219,"score_gpt":0.21279945017620516,"score_spread":0.19788872396555093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388773013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69344157,0.00154924,0.29632828,0.0006656654,0.00059474225,0.00033142618,0.00033177214,0.0017455717,0.0050117415],"genre_scores_gemma":[0.6859572,0.00030614695,0.30990037,0.00006709871,0.00006567006,0.00011359936,0.00009352202,0.000058127098,0.0034382723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996215,0.00003658414,0.000026883617,0.000107972846,0.00017435035,0.000032642733],"domain_scores_gemma":[0.99958986,0.00006965973,0.0001029891,0.00007020795,0.00011215005,0.000055166794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043181606,0.00036536265,0.0003829461,0.00022268646,0.00023130391,0.00030177733,0.0012186349,0.0006543149,0.0010410254],"category_scores_gemma":[0.00043262797,0.0002579877,0.00034249632,0.00010051961,0.00023399612,0.0005280383,0.00033041692,0.00024330014,0.00064048666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043769378,0.0000241599,0.00023676016,0.000089267895,0.000009477796,0.00013124936,0.00004306241,0.0004190835,0.98939383,0.0004842404,0.00022793877,0.008897213],"study_design_scores_gemma":[0.00003859825,0.0008088353,0.0016548295,0.000010641896,0.000025219904,0.0007864875,0.00003408064,0.0194157,0.96804374,0.00009281597,0.009059942,0.00002919512],"about_ca_topic_score_codex":0.00018577157,"about_ca_topic_score_gemma":0.0003331571,"teacher_disagreement_score":0.0012186349,"about_ca_system_score_codex":0.00019124632,"about_ca_system_score_gemma":0.00026545266,"threshold_uncertainty_score":0.0034825802},"labels":[],"label_agreement":null},{"id":"W4388775093","doi":"10.3390/s23229248","title":"Towards the Augmentation of Digital Twin Performance","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Montréal","funders":"","keywords":"Contextualization; Computer science; Performance indicator; Process (computing); Cyber-physical system; Key (lock); Production (economics); Industrial production; Industry 4.0; Interface (matter); Systems engineering; Process management; Data science; Engineering; Data mining; Computer security","score_opus":0.02741404034027573,"score_gpt":0.23225174858125883,"score_spread":0.2048377082409831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388775093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028989807,0.0031615968,0.9292026,0.0011618793,0.00026943575,0.000105299856,0.00037478725,0.001794262,0.034940347],"genre_scores_gemma":[0.5389037,0.005326838,0.44564566,0.00037725206,0.00028729398,0.00014380687,0.0012645457,0.00062281505,0.007428151],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965217,0.00096807454,0.00025951117,0.0006614951,0.001407768,0.00018145867],"domain_scores_gemma":[0.99458355,0.0018765327,0.00048028975,0.0012881974,0.0014829788,0.00028843738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042990223,0.0010025124,0.0006184304,0.0032537514,0.00042675366,0.0053138505,0.0015282109,0.0008035478,0.0045026997],"category_scores_gemma":[0.010969893,0.0003733168,0.00055043766,0.0036032572,0.0021036572,0.010363183,0.0060566636,0.001756315,0.0010794983],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027199424,0.00016031646,0.0051162336,0.0012652249,0.00007642885,0.00029556782,0.0020405892,0.020729046,0.017561302,0.31520662,0.0043747006,0.6329019],"study_design_scores_gemma":[0.000059958027,0.00094106543,0.009614913,0.0012673998,0.00025425226,0.0014772945,0.0033086843,0.2338666,0.07187984,0.28360733,0.3934864,0.0002363263],"about_ca_topic_score_codex":0.0011230947,"about_ca_topic_score_gemma":0.0007181485,"teacher_disagreement_score":0.0053138505,"about_ca_system_score_codex":0.0010833375,"about_ca_system_score_gemma":0.0013814023,"threshold_uncertainty_score":0.022735715},"labels":[],"label_agreement":null},{"id":"W4388859038","doi":"10.3390/s23239315","title":"UAV-Based Image and LiDAR Fusion for Pavement Crack Segmentation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"RGB color model; Segmentation; Lidar; Artificial intelligence; Convolutional neural network; Computer science; Computer vision; Aerial image; Elevation (ballistics); Remote sensing; Image (mathematics); Engineering; Geology","score_opus":0.008499813957795036,"score_gpt":0.236720876954189,"score_spread":0.22822106299639397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388859038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42691258,0.0033119882,0.5527393,0.00030684387,0.00028689095,0.00019327187,0.0021836269,0.007916446,0.0061490545],"genre_scores_gemma":[0.87766594,0.0005606769,0.11681944,0.00011715759,0.00004618732,0.00006058889,0.0024224718,0.00008998115,0.0022175233],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996474,0.00003114402,0.00001644231,0.0001284182,0.00011029897,0.000066257904],"domain_scores_gemma":[0.9997696,0.00003303077,0.000028367296,0.00005141084,0.00010463374,0.000012937478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029023443,0.00087867305,0.00062557677,0.0013748712,0.00020161972,0.0004537591,0.00059819705,0.00067741756,0.0009426298],"category_scores_gemma":[0.0007616611,0.00028436148,0.0006017516,0.0008268585,0.00021177932,0.0008699251,0.00069096737,0.00055522926,0.0005404542],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005656905,0.00028688478,0.014946292,0.00043815805,0.00025650027,0.0005019085,0.0001955884,0.14053452,0.13828845,0.0010896153,0.0061882813,0.6967081],"study_design_scores_gemma":[0.00001380563,0.0001293646,0.015537293,0.000039402126,0.00009511116,0.00027905445,0.00013943778,0.9261785,0.05267514,0.00096530985,0.003915417,0.00003218043],"about_ca_topic_score_codex":0.00625773,"about_ca_topic_score_gemma":0.009217788,"teacher_disagreement_score":0.00625773,"about_ca_system_score_codex":0.0004188562,"about_ca_system_score_gemma":0.0004332082,"threshold_uncertainty_score":0.012442648},"labels":[],"label_agreement":null},{"id":"W4388859068","doi":"10.3390/s23239306","title":"A Spectrally Interrogated Polarimetric Optical Fiber Sensor for Current Measurement with Temperature Correction","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Magneto-Optical Properties and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Bulgarian National Science Fund","keywords":"Interrogation; Polarimetry; Current (fluid); Temperature measurement; Optics; Wavelength; Materials science; Spectral line; Physics; Computational physics; Optoelectronics; Scattering","score_opus":0.022596129939546676,"score_gpt":0.2259842621411594,"score_spread":0.20338813220161273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388859068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78214246,0.0020763227,0.20788431,0.00041690035,0.0001943566,0.00023547513,0.0005630979,0.00085635163,0.0056307134],"genre_scores_gemma":[0.84789973,0.00094782683,0.14677012,0.0001108357,0.00006859614,0.000081903796,0.00025167587,0.00006316709,0.0038060758],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942183,0.00006250965,0.0000137587085,0.000105904306,0.0003586987,0.00003731689],"domain_scores_gemma":[0.99957067,0.00010315295,0.00009584004,0.00003694058,0.0001584788,0.000035008008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003609653,0.00044228786,0.00036215046,0.0003226167,0.00027491612,0.0003934156,0.0007369737,0.0004313975,0.0007236807],"category_scores_gemma":[0.0006062314,0.00023584039,0.00013657937,0.00030589674,0.00047971754,0.0005982923,0.00029842896,0.000542681,0.0004986364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057279016,0.000018148694,0.00022362769,0.000044674383,0.0000031787265,0.00002324627,0.000021834685,0.00027961168,0.9941778,0.0001813851,0.00005386796,0.004915333],"study_design_scores_gemma":[0.0000047427147,0.00019590586,0.0010213931,0.0000032278322,0.000008789039,0.00020929881,0.000012517765,0.01046088,0.9860721,0.000055088043,0.0019407514,0.0000152694],"about_ca_topic_score_codex":0.0007695382,"about_ca_topic_score_gemma":0.0013009072,"teacher_disagreement_score":0.0007695382,"about_ca_system_score_codex":0.00036475796,"about_ca_system_score_gemma":0.00037064322,"threshold_uncertainty_score":0.0026464462},"labels":[],"label_agreement":null},{"id":"W4388942832","doi":"10.3390/s23239352","title":"EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Brain–computer interface; Electroencephalography; Computer science; Binary classification; Support vector machine; Artificial intelligence; Classifier (UML); Brain activity and meditation; Speech recognition; Machine learning; Pattern recognition (psychology); Psychology","score_opus":0.03369414577481611,"score_gpt":0.3261519026080044,"score_spread":0.2924577568331883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388942832","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58813083,0.004791502,0.39295593,0.0015675151,0.00024254643,0.0003107629,0.0017510422,0.0021772785,0.008072709],"genre_scores_gemma":[0.87701464,0.0012223942,0.118730634,0.0001705208,0.00016254939,0.000138804,0.0011198134,0.00007543096,0.0013652525],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993931,0.0001442096,0.000051092404,0.00012275031,0.00023162087,0.00005725773],"domain_scores_gemma":[0.9984113,0.00076583365,0.0001902631,0.00017706485,0.00038323484,0.00007218578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082778925,0.0009849041,0.00065616146,0.001415004,0.00026047014,0.0012179641,0.0004831278,0.00058606564,0.0012038231],"category_scores_gemma":[0.0059431084,0.00013426796,0.0004894163,0.0014421886,0.00028938564,0.0009531076,0.00073601597,0.0007432342,0.00057589635],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004107966,0.00034309106,0.0072913077,0.0004097154,0.0001136273,0.00010737702,0.00020826163,0.010584006,0.102406345,0.0010715019,0.0026315025,0.8744223],"study_design_scores_gemma":[0.00014487232,0.0018819069,0.2287891,0.00038832676,0.000543301,0.0016414517,0.00053702825,0.5829996,0.1519238,0.014126896,0.016842194,0.00018157529],"about_ca_topic_score_codex":0.0012316745,"about_ca_topic_score_gemma":0.0019834812,"teacher_disagreement_score":0.001415004,"about_ca_system_score_codex":0.00019622884,"about_ca_system_score_gemma":0.00038174636,"threshold_uncertainty_score":0.004377842},"labels":[],"label_agreement":null},{"id":"W4388972011","doi":"10.3390/s23239385","title":"Edge Computing for Effective and Efficient Traffic Characterization","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Higher Education Commission Mauritius; Higher Education Commission, Pakistan","keywords":"Headway; Edge computing; Computer science; Cloud computing; Real-time computing; Enhanced Data Rates for GSM Evolution; Node (physics); Centroid; Bandwidth (computing); Traffic flow (computer networking); Simulation; Engineering; Computer network; Artificial intelligence","score_opus":0.006353222316050135,"score_gpt":0.21435724809588194,"score_spread":0.20800402577983182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388972011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090083934,0.0009780966,0.8821343,0.00039935694,0.00021854446,0.000108928616,0.0009825102,0.0058184476,0.019275889],"genre_scores_gemma":[0.81513095,0.0010969008,0.17595199,0.00018560795,0.0001130944,0.00008668139,0.0016228206,0.00026902946,0.0055429554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986136,0.000016352853,0.000007363586,0.000032828593,0.00005523685,0.000026884683],"domain_scores_gemma":[0.9998498,0.000037248905,0.000016688684,0.00002890851,0.000058075726,0.000009301105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121047444,0.0005198321,0.00035003343,0.0008764305,0.00029917192,0.00077885453,0.0006076504,0.00027231788,0.0026522067],"category_scores_gemma":[0.0005737304,0.00015808552,0.00023651525,0.0011393374,0.00015848925,0.0011640065,0.00049448066,0.0003604897,0.0007516175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037428323,0.0001913463,0.006484431,0.00020979374,0.00005911959,0.00022504621,0.00013779606,0.16557641,0.041991577,0.022524934,0.02080806,0.7414171],"study_design_scores_gemma":[0.000011125782,0.000061785184,0.0024924423,0.000026474925,0.000021287142,0.00011474253,0.00009661387,0.94788224,0.01739755,0.016292393,0.015574519,0.00002888821],"about_ca_topic_score_codex":0.002664201,"about_ca_topic_score_gemma":0.0033071546,"teacher_disagreement_score":0.002664201,"about_ca_system_score_codex":0.0002817317,"about_ca_system_score_gemma":0.0002765004,"threshold_uncertainty_score":0.008872509},"labels":[],"label_agreement":null},{"id":"W4388977366","doi":"10.3390/s23239376","title":"Utility of Thermographic Imaging for Callus Identification in Wound and Foot Care","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Callus; Biomedical engineering; Epidermis (zoology); Materials science; Thermography; Anatomy; Medicine; Horticulture; Infrared; Biology; Optics","score_opus":0.01684094364325862,"score_gpt":0.29554835971038423,"score_spread":0.2787074160671256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388977366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4887899,0.020324338,0.4786336,0.0008705534,0.00018091251,0.00022883895,0.0001759827,0.00074274716,0.01005316],"genre_scores_gemma":[0.9414229,0.0023586666,0.055380177,0.0001128893,0.000037137936,0.00004094413,0.00003805709,0.000051946983,0.0005572128],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992756,0.00032113938,0.000028898987,0.00010669326,0.00023093178,0.000036573052],"domain_scores_gemma":[0.9982022,0.0011450923,0.00023100711,0.00010870799,0.00025619636,0.00005685452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010007842,0.00043899828,0.0002918908,0.0009900088,0.0001864411,0.0009054681,0.0005147483,0.0006294493,0.0016719159],"category_scores_gemma":[0.0034724772,0.00024911136,0.0002625603,0.00042212443,0.00082811667,0.00065273436,0.0003817951,0.00037151718,0.0003572854],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010455578,0.00021919164,0.019641856,0.0012965507,0.00006038927,0.0021106298,0.00043850427,0.027616898,0.78089935,0.0023952133,0.0007308081,0.16354503],"study_design_scores_gemma":[0.00004402979,0.001660179,0.028262071,0.00040806681,0.0002034535,0.011209533,0.00071266806,0.17124958,0.7751888,0.0031391566,0.007758532,0.00016392849],"about_ca_topic_score_codex":0.0005530498,"about_ca_topic_score_gemma":0.00090189354,"teacher_disagreement_score":0.0016719159,"about_ca_system_score_codex":0.0004603445,"about_ca_system_score_gemma":0.00042330855,"threshold_uncertainty_score":0.005593121},"labels":[],"label_agreement":null},{"id":"W4389088757","doi":"10.3390/s23239470","title":"Feasibility, Acceptability, and Usability of Physiology and Emotion Monitoring in Adults and Children Using the Novel Time2Feel Smartphone Application","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Ministry of Colleges and Universities","keywords":"Context (archaeology); Usability; Applied psychology; Psychology; Wearable computer; Arousal; Wearable technology; Experience sampling method; Computer science; Social psychology; Human–computer interaction","score_opus":0.04875717636726052,"score_gpt":0.37870221056543085,"score_spread":0.32994503419817034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389088757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99684805,0.000085604595,0.0015469703,0.000085540036,0.000014830589,0.0002474135,0.00013347164,0.00003562002,0.0010024913],"genre_scores_gemma":[0.99106044,0.00022558607,0.006829048,0.000084455176,0.000016534344,0.0007962056,0.00013370889,0.000016626023,0.00083739904],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99701995,0.0014703154,0.00030343726,0.00035604884,0.0006109944,0.0002391539],"domain_scores_gemma":[0.993148,0.004071065,0.0007871209,0.0003735782,0.0012386693,0.00038164598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005339851,0.00047720017,0.00035624532,0.00043989325,0.00030874283,0.00076028507,0.0003797616,0.00042224093,0.002116768],"category_scores_gemma":[0.014259142,0.00020690524,0.0007714604,0.00017460293,0.0003953028,0.00071475445,0.00088097964,0.00042150138,0.000326381],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028289368,0.0032140296,0.6328194,0.0012274105,0.00024694682,0.0014272524,0.033451762,0.0004974391,0.031695616,0.00032610755,0.0018521472,0.29041296],"study_design_scores_gemma":[0.00020850466,0.01820808,0.93096775,0.00031385475,0.00026204024,0.0028144708,0.020414125,0.0015965558,0.014044916,0.00022662859,0.010792911,0.00015017283],"about_ca_topic_score_codex":0.0010956721,"about_ca_topic_score_gemma":0.0023404933,"teacher_disagreement_score":0.005339851,"about_ca_system_score_codex":0.00028985614,"about_ca_system_score_gemma":0.0004181279,"threshold_uncertainty_score":0.028240144},"labels":[],"label_agreement":null},{"id":"W4389203101","doi":"10.3390/s23239506","title":"Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China; Hubei Provincial Department of Education","keywords":"Computer science; Heuristic; Relay; Weighting; Path (computing); Routing (electronic design automation); Transmission (telecommunications); Equal-cost multi-path routing; Computer network; Mathematical optimization; Static routing; Routing protocol; Artificial intelligence; Mathematics; Telecommunications","score_opus":0.017148155097884536,"score_gpt":0.29379339797300597,"score_spread":0.27664524287512143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389203101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013371282,0.00026522594,0.9843911,0.00014247655,0.000043552383,0.0000959498,0.000031999112,0.00015117871,0.0015072037],"genre_scores_gemma":[0.6349166,0.00041835528,0.361469,0.00014478587,0.000051989566,0.00048383896,0.00013879043,0.000049605147,0.0023270273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834216,0.0007166993,0.00010329878,0.0002981778,0.0003728236,0.0001668315],"domain_scores_gemma":[0.9982596,0.0010694589,0.00017609044,0.00006328166,0.00034571957,0.000085855485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027177911,0.0013048688,0.001516857,0.0014231191,0.00089148304,0.0013741532,0.0016858493,0.0012161471,0.002379253],"category_scores_gemma":[0.0036730815,0.00059526606,0.0013230345,0.0013795149,0.0006343941,0.0013504892,0.0011410879,0.0012646544,0.00021134524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008255725,0.00006811061,0.0008334776,0.00012488192,0.00008393799,0.00011202085,0.00015762362,0.92644763,0.0012412977,0.008881236,0.0007164858,0.06125078],"study_design_scores_gemma":[0.000012461258,0.000033437922,0.00006727356,0.0000055362398,0.000012802691,0.00001334769,0.000020108813,0.99700016,0.00028784815,0.0022978568,0.00024092301,0.000008251523],"about_ca_topic_score_codex":0.0056377635,"about_ca_topic_score_gemma":0.0035793788,"teacher_disagreement_score":0.0056377635,"about_ca_system_score_codex":0.0014320374,"about_ca_system_score_gemma":0.0021904306,"threshold_uncertainty_score":0.014373243},"labels":[],"label_agreement":null},{"id":"W4389203612","doi":"10.3390/s23239498","title":"The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":436,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wearable computer; Wearable technology; Computer science; Digital health; Health care; Body area network; Data science; Human–computer interaction; Embedded system; Artificial intelligence; Wireless sensor network","score_opus":0.052664885574800625,"score_gpt":0.3415563472156661,"score_spread":0.28889146164086543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389203612","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002782772,0.9956937,0.00133028,0.00035406993,0.0003917405,0.000011603554,0.000017466426,0.000017363836,0.0019054937],"genre_scores_gemma":[0.0013684354,0.9960097,0.0011447279,0.0002838379,0.00036071782,0.000016891912,0.000025298028,0.0000045383645,0.0007858885],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997217,0.000042402626,0.0000371105,0.000060302966,0.00011318642,0.000025303954],"domain_scores_gemma":[0.9993393,0.00036351595,0.00007706226,0.000019662582,0.00016620102,0.00003420299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068444066,0.0010293532,0.00097463833,0.0027500128,0.00031101378,0.0012377712,0.00086848025,0.0015367892,0.0024949352],"category_scores_gemma":[0.00082352856,0.00047311524,0.0005741122,0.0031394346,0.00055957545,0.0023381172,0.00078984903,0.0016952076,0.0018099551],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005134285,0.00014373254,0.0003327637,0.02344362,0.00008017854,0.00027190094,0.00014461346,0.0009903198,0.0075785117,0.014556031,0.026111653,0.9262954],"study_design_scores_gemma":[0.0000061793576,0.00016910883,0.00058889773,0.0031192855,0.00007791282,0.0010744481,0.00008648674,0.0006369684,0.0023460228,0.0043638707,0.9874894,0.000041219355],"about_ca_topic_score_codex":0.00054583955,"about_ca_topic_score_gemma":0.00073437026,"teacher_disagreement_score":0.0027500128,"about_ca_system_score_codex":0.0004273631,"about_ca_system_score_gemma":0.0007663786,"threshold_uncertainty_score":0.008346379},"labels":[],"label_agreement":null},{"id":"W4389223603","doi":"10.3390/s23239552","title":"Enhanced GNSS Reliability on High-Dynamic Platforms: A Comparative Study of Multi-Frequency, Multi-Constellation Signals in Jamming Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; GLONASS; Global Positioning System; Jamming; Computer science; Galileo (satellite navigation); GPS signals; GNSS augmentation; Satellite navigation; Constellation; Remote sensing; Electronic engineering; Real-time computing; Telecommunications; Assisted GPS; Engineering; Geography","score_opus":0.03245208131847757,"score_gpt":0.2782006174106527,"score_spread":0.24574853609217515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389223603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948978,0.000209946,0.0037426653,0.000020466905,0.000009690627,0.000010577038,0.000030240511,0.000042096013,0.0010364762],"genre_scores_gemma":[0.99896824,0.00009809616,0.00064906897,0.000004550391,0.0000049031337,0.0000024389449,0.000034107863,0.0000058842656,0.0002326987],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995307,0.000082953964,0.0000194465,0.0000621575,0.0002309449,0.000073924886],"domain_scores_gemma":[0.9987801,0.00034058196,0.00019222284,0.00011233911,0.0004951281,0.0000796277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004898634,0.00041183317,0.0003315848,0.0009066581,0.00024250968,0.00040588184,0.00036995078,0.00041924085,0.00051371084],"category_scores_gemma":[0.0014523718,0.000117489544,0.00024306895,0.00054156,0.00030004626,0.0005721287,0.00034196646,0.00022331074,0.00020795329],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030906613,0.0005315178,0.08674024,0.001036798,0.00029649062,0.004332041,0.0014776322,0.28410247,0.4495467,0.0013112386,0.0007878567,0.16674636],"study_design_scores_gemma":[0.00008310586,0.010247847,0.30289873,0.00011088239,0.00048838946,0.0032924772,0.0026102432,0.4580526,0.21695739,0.0007936277,0.0043090233,0.00015572025],"about_ca_topic_score_codex":0.0011558915,"about_ca_topic_score_gemma":0.0012958827,"teacher_disagreement_score":0.0011558915,"about_ca_system_score_codex":0.00021562332,"about_ca_system_score_gemma":0.00014773672,"threshold_uncertainty_score":0.0025906563},"labels":[],"label_agreement":null},{"id":"W4389346820","doi":"10.3390/s23249625","title":"A Method for Quantifying Back Flexion/Extension from Three Inertial Measurement Units Mounted on a Horse’s Withers, Thoracolumbar Region, and Pelvis","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Veterinary Equine Medical Research","field":"Veterinary","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Agence Nationale de la Recherche","keywords":"Withers; Pelvis; Inertial measurement unit; Motion capture; Horse; Orthodontics; Computer science; Anatomy; Computer vision; Motion (physics); Medicine; Geology","score_opus":0.4285849963557204,"score_gpt":0.4513657349742652,"score_spread":0.022780738618544794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389346820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17328812,0.002615328,0.81749874,0.00012982267,0.00047893391,0.00048139732,0.0010456146,0.0014089638,0.0030531804],"genre_scores_gemma":[0.49181858,0.0013622011,0.5020364,0.0001952646,0.00015603278,0.0008344716,0.00087892497,0.000105640414,0.0026125975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983999,0.00040831708,0.00010595899,0.000285876,0.0007425805,0.00005736111],"domain_scores_gemma":[0.9986059,0.0003790054,0.00029863566,0.00014791323,0.0005313439,0.000037268717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011478476,0.001159699,0.0005890078,0.001913008,0.0002429992,0.00061483425,0.00066067063,0.00089497794,0.0009562784],"category_scores_gemma":[0.0034141173,0.00031867938,0.0004195683,0.0013685192,0.0002703897,0.00048816673,0.00059060147,0.0004786204,0.0006210583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006191223,0.0002074071,0.060621653,0.0009689367,0.00038610233,0.00013671289,0.00040006757,0.002207199,0.29047713,0.00084488187,0.0024614094,0.64066947],"study_design_scores_gemma":[0.00021055748,0.0027987612,0.47611082,0.0005656595,0.0010466818,0.00471338,0.001027681,0.10920294,0.37362328,0.0015314826,0.02866938,0.0004993946],"about_ca_topic_score_codex":0.0010201484,"about_ca_topic_score_gemma":0.0029485715,"teacher_disagreement_score":0.001913008,"about_ca_system_score_codex":0.00022408816,"about_ca_system_score_gemma":0.00037088798,"threshold_uncertainty_score":0.0060704947},"labels":[],"label_agreement":null},{"id":"W4389440138","doi":"10.3390/s23249670","title":"Breaking the Fatigue Cycle: Investigating the Effect of Work-Rest Schedules on Muscle Fatigue in Material Handling Jobs","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Muscle fatigue; Work (physics); Electromyography; Physical medicine and rehabilitation; Kinematics; Musculoskeletal disorder; Physical therapy; Work-related musculoskeletal disorders; Rest (music); Medicine; Computer science; Human factors and ergonomics; Poison control; Engineering; Cardiology; Mechanical engineering; Emergency medicine","score_opus":0.025777653382874195,"score_gpt":0.3077012916231168,"score_spread":0.28192363824024264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389440138","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99902415,0.00015836375,0.0004141394,0.00001609111,0.0000131222505,0.00009500525,0.000058687707,0.0000038791372,0.00021659635],"genre_scores_gemma":[0.9971047,0.0001474948,0.0011933403,0.00004769827,0.000026794636,0.00041483113,0.00016690462,0.000005172478,0.00089308515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948895,0.00015241212,0.000049721413,0.00008875507,0.00012889352,0.000091300564],"domain_scores_gemma":[0.9984518,0.00060007244,0.0004364773,0.00010416152,0.00013542394,0.0002720126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094589475,0.00037436196,0.0004163587,0.00030658545,0.0002914448,0.00029449686,0.00031949708,0.0004186079,0.0021853913],"category_scores_gemma":[0.0028411923,0.00016987322,0.00055819843,0.0001752892,0.00021155631,0.00027169002,0.00039028376,0.00040446492,0.00029064593],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.08878811,0.04289103,0.41049066,0.001805921,0.0012398952,0.0003090004,0.0039787763,0.0029233983,0.19014968,0.00019879382,0.0006443292,0.25658044],"study_design_scores_gemma":[0.00015617654,0.04528993,0.9488039,0.00003826482,0.00011818056,0.000047763293,0.0003893439,0.0007207666,0.0036774243,0.00007181685,0.0006674226,0.000018934712],"about_ca_topic_score_codex":0.0012525241,"about_ca_topic_score_gemma":0.0018749009,"teacher_disagreement_score":0.0021853913,"about_ca_system_score_codex":0.00019013628,"about_ca_system_score_gemma":0.0003207707,"threshold_uncertainty_score":0.007310927},"labels":[],"label_agreement":null},{"id":"W4389440149","doi":"10.3390/s23249668","title":"Comparative Analysis of Resident Space Object (RSO) Detection Methods","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Remote sensing; Computer science; Frame (networking); Situation awareness; Spacecraft; Satellite; Streak; Identification (biology); Real-time computing; Artificial intelligence; Computer vision; Geography; Aerospace engineering; Engineering; Physics; Telecommunications; Optics","score_opus":0.022325081351230004,"score_gpt":0.3122758651642433,"score_spread":0.2899507838130133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389440149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.743775,0.03291037,0.18511496,0.00076925807,0.0014241172,0.00090447237,0.008328462,0.009778778,0.016994476],"genre_scores_gemma":[0.81576663,0.004495121,0.15503334,0.00019293204,0.00026776284,0.0003111573,0.018284436,0.00069090107,0.004957821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99328446,0.0010739426,0.00078927755,0.0016718386,0.0026046494,0.00057579146],"domain_scores_gemma":[0.9892278,0.005977649,0.0006066322,0.0008137044,0.0030739768,0.0003002884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006658424,0.0018230461,0.0016473879,0.008740024,0.0007856902,0.002270488,0.0014303904,0.0017798999,0.0017858938],"category_scores_gemma":[0.014387412,0.00027561132,0.0012762296,0.0029913366,0.00048227454,0.0020268827,0.0009979003,0.0006397177,0.0013444576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022013206,0.0006453744,0.050337482,0.0021558926,0.0013101246,0.00036025234,0.00043646945,0.04744351,0.01814575,0.001765437,0.016891535,0.85830694],"study_design_scores_gemma":[0.00019430669,0.0022300552,0.13734297,0.00041267826,0.000990846,0.0015264732,0.0016714158,0.75653845,0.0663828,0.002776741,0.029587653,0.00034564416],"about_ca_topic_score_codex":0.009496611,"about_ca_topic_score_gemma":0.009373461,"teacher_disagreement_score":0.009496611,"about_ca_system_score_codex":0.00094559026,"about_ca_system_score_gemma":0.0011052749,"threshold_uncertainty_score":0.03521353},"labels":[],"label_agreement":null},{"id":"W4389485987","doi":"10.3390/s23249692","title":"Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Scalability; Blockchain; Distributed computing; Big data; Low latency (capital markets); Publication; Computer network; Computer security; Database; Operating system","score_opus":0.042843312090916505,"score_gpt":0.3162643872431333,"score_spread":0.2734210751522168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389485987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020487675,0.00040184002,0.95923185,0.0006176169,0.0003436461,0.0006120049,0.0005239593,0.006219442,0.011562061],"genre_scores_gemma":[0.7130989,0.0006728828,0.2584666,0.00039176544,0.00029323384,0.0012065695,0.0015627488,0.00069527474,0.023612116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970119,0.00063948584,0.00039493057,0.0004897206,0.001104486,0.00035938233],"domain_scores_gemma":[0.99710876,0.0007588283,0.00021823824,0.0012260933,0.00045681943,0.00023130224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002338936,0.0004658787,0.00085092604,0.0010229172,0.00146877,0.002776017,0.0016777217,0.0012212413,0.011191147],"category_scores_gemma":[0.0048227594,0.00041463575,0.00071812654,0.0012944908,0.0009452565,0.0037754406,0.0029579338,0.0012905047,0.0035686095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015319448,0.0007762322,0.004500511,0.0010498237,0.0002099435,0.0035978567,0.0020644001,0.057335705,0.08028081,0.4186741,0.0363952,0.39358342],"study_design_scores_gemma":[0.00046611152,0.00042067518,0.00077151705,0.00014096755,0.000096333235,0.0013515556,0.0002628027,0.65013975,0.05890443,0.16948454,0.11777026,0.00019107645],"about_ca_topic_score_codex":0.0013865525,"about_ca_topic_score_gemma":0.0015862635,"teacher_disagreement_score":0.011191147,"about_ca_system_score_codex":0.00074795523,"about_ca_system_score_gemma":0.0019769205,"threshold_uncertainty_score":0.037438154},"labels":[],"label_agreement":null},{"id":"W4389487560","doi":"10.3390/s23249700","title":"Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Welding; Robot; Robot welding; Computer vision; Robotics; Computer science; Machine vision; Deep learning; Sensor fusion; Monocular vision; Adaptation (eye); Human–computer interaction; Engineering; Mechanical engineering","score_opus":0.031635356507202346,"score_gpt":0.26984986181391957,"score_spread":0.23821450530671723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389487560","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014589865,0.4975832,0.44221875,0.0029379476,0.001121778,0.00009452162,0.00021676417,0.0006446267,0.04059248],"genre_scores_gemma":[0.35463014,0.4317208,0.18760605,0.0015357771,0.0014945032,0.00019306161,0.00046762548,0.00013753382,0.022214524],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994832,0.00006419694,0.000028713603,0.00010470218,0.0002924322,0.000026774947],"domain_scores_gemma":[0.9995931,0.00016611851,0.00004870075,0.0000241137,0.0001512868,0.000016604034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004622188,0.0006218695,0.0004288126,0.0010460987,0.00022846767,0.0011239536,0.0007018533,0.0011403793,0.002566702],"category_scores_gemma":[0.00096137583,0.00034461328,0.0005752649,0.001028037,0.0008242257,0.0020314036,0.00070165936,0.0009980031,0.00055960746],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001021974,0.000052978674,0.0009752307,0.0056389216,0.00010462094,0.00021990822,0.00025411654,0.01072826,0.04152815,0.058588497,0.008514464,0.8732926],"study_design_scores_gemma":[0.000041457068,0.0006285817,0.011851009,0.0027824014,0.00035717458,0.0029781074,0.0007501841,0.11943526,0.08637048,0.12906805,0.6454439,0.00029340255],"about_ca_topic_score_codex":0.001248056,"about_ca_topic_score_gemma":0.0009264872,"teacher_disagreement_score":0.002566702,"about_ca_system_score_codex":0.00056260213,"about_ca_system_score_gemma":0.0007188815,"threshold_uncertainty_score":0.008586466},"labels":[],"label_agreement":null},{"id":"W4389543308","doi":"10.3390/s23249767","title":"Thermo-Optic Response and Optical Bistablility of Integrated High-Index Doped Silica Ring Resonators","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Swinburne University of Technology","keywords":"Materials science; Resonator; Photonics; Optoelectronics; Doping; Silicon photonics; Silicon; Characterization (materials science); Refractive index; Silicon nitride; Electronic engineering; Nanotechnology; Engineering","score_opus":0.010191642611835925,"score_gpt":0.2271484801447224,"score_spread":0.21695683753288647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389543308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969152,0.00024918918,0.0019802118,0.000022060174,0.00001030109,0.000005340665,0.000035898112,0.000030341162,0.00075125933],"genre_scores_gemma":[0.99861395,0.00008462848,0.0009866944,0.000006878275,0.0000034969476,0.0000044784306,0.000022805834,0.000005625375,0.00027139724],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997782,0.000019538884,0.000009576542,0.0000614461,0.000103185776,0.000028108154],"domain_scores_gemma":[0.99956006,0.0002162138,0.00012025655,0.000027928836,0.00005361577,0.00002197363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030905116,0.00028881372,0.00017748652,0.00020185653,0.00011859161,0.00024993665,0.00030667608,0.00030730007,0.0006860612],"category_scores_gemma":[0.0006001493,0.00019007828,0.00020701614,0.0001289605,0.00053381216,0.00032301556,0.00016445636,0.00030260737,0.00012666853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005675943,0.000016959017,0.00026131532,0.000019869234,0.0000074228915,0.000032185137,0.000033304168,0.0008006004,0.99804676,0.00017661002,0.000014551733,0.000533615],"study_design_scores_gemma":[0.000012973869,0.00013436216,0.0022375775,0.0000038439184,0.000008419295,0.00005305632,0.000025334297,0.020249689,0.9770379,0.00007065441,0.00015193492,0.000014360025],"about_ca_topic_score_codex":0.00043523667,"about_ca_topic_score_gemma":0.00075523177,"teacher_disagreement_score":0.0006860612,"about_ca_system_score_codex":0.00030497767,"about_ca_system_score_gemma":0.00015099153,"threshold_uncertainty_score":0.0022950768},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4389544701","doi":"10.3390/s23249724","title":"A Dual-Threshold Algorithm for Ice-Covered Lake Water Level Retrieval Using Sentinel-3 SAR Altimetry Waveforms","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; National Natural Science Foundation of China","keywords":"Altimeter; Remote sensing; Synthetic aperture radar; Satellite; Elevation (ballistics); Water level; Mean squared error; Geology; Waveform; Radar; Environmental science; Brightness temperature; Algorithm; Meteorology; Brightness; Computer science; Mathematics; Geography; Telecommunications","score_opus":0.03642718302702923,"score_gpt":0.2754262371635841,"score_spread":0.23899905413655487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389544701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087675124,0.00023391862,0.9095239,0.00007143264,0.00005577693,0.00006584199,0.00012404099,0.0011909553,0.0010590258],"genre_scores_gemma":[0.27369478,0.00014416632,0.7230814,0.0000688758,0.00003014228,0.00014093149,0.00086117,0.0001297683,0.0018487469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996456,0.000032708238,0.00003913727,0.00009525375,0.00013386164,0.000053517975],"domain_scores_gemma":[0.9997445,0.00003874631,0.000035139536,0.000025314403,0.00013443692,0.0000219029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005274772,0.00049253576,0.0005344287,0.0011464254,0.00036612205,0.0006997542,0.00083354005,0.00044726534,0.00081630156],"category_scores_gemma":[0.0010320703,0.00032887855,0.00058403926,0.0010010884,0.00023042454,0.0007065964,0.0007681648,0.00045217035,0.0006311914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003646006,0.00014182804,0.0121700205,0.00009928198,0.00013115248,0.00013116623,0.00026329304,0.05502551,0.10177441,0.0030366192,0.0038215483,0.82304054],"study_design_scores_gemma":[0.00006481121,0.0001060176,0.007701761,0.00000975954,0.00004743164,0.00012828142,0.0001031616,0.9708185,0.016425887,0.0013537989,0.0032055008,0.000035138317],"about_ca_topic_score_codex":0.005390023,"about_ca_topic_score_gemma":0.0060476223,"teacher_disagreement_score":0.005390023,"about_ca_system_score_codex":0.00032644137,"about_ca_system_score_gemma":0.0010775883,"threshold_uncertainty_score":0.010717273},"labels":[],"label_agreement":null},{"id":"W4389613576","doi":"10.3390/s23249788","title":"Monitoring of a Productive Blue-Green Roof Using Low-Cost Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Agricultural engineering; RGB color model; Environmental science; Roof; Computer science; Engineering; Artificial intelligence; Civil engineering","score_opus":0.028318950220698687,"score_gpt":0.2588525993777597,"score_spread":0.23053364915706098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389613576","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9598309,0.00017402049,0.037220016,0.000029298066,0.000012672345,0.000031883075,0.00038854187,0.0002699668,0.002042792],"genre_scores_gemma":[0.972562,0.000138699,0.026366962,0.000016137885,0.000004659325,0.000020992324,0.00015575174,0.00001101884,0.0007238444],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998894,0.00001328914,0.0000028535385,0.000029537054,0.000047316087,0.000017583603],"domain_scores_gemma":[0.99993587,0.00001160827,0.000015982865,0.000007127375,0.000024201814,0.000005223144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000933169,0.00022426769,0.00017463506,0.00036952956,0.00016283388,0.00021795864,0.00022515772,0.00020755532,0.0005579997],"category_scores_gemma":[0.00008454853,0.00009403554,0.00012100946,0.00032817194,0.00008990667,0.00024494732,0.00015527969,0.00010752168,0.00013947107],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011537093,0.00008193197,0.036729116,0.0001317394,0.000029873321,0.000135466,0.00014225353,0.0063568284,0.90176517,0.00014548414,0.0003774005,0.05398945],"study_design_scores_gemma":[0.000026416776,0.00060113525,0.3881609,0.000041047126,0.00011686291,0.000416751,0.0010351778,0.10700079,0.49646717,0.00036414654,0.0057251235,0.000044490207],"about_ca_topic_score_codex":0.0015832576,"about_ca_topic_score_gemma":0.0057554124,"teacher_disagreement_score":0.0015832576,"about_ca_system_score_codex":0.00014526809,"about_ca_system_score_gemma":0.0001178295,"threshold_uncertainty_score":0.0031481385},"labels":[],"label_agreement":null},{"id":"W4389615191","doi":"10.3390/s23249780","title":"Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Wearable computer; Wired glove; Robot; GRASP; Human–computer interaction; Computer science; Human–robot interaction; Artificial intelligence; Accelerometer; Sensor fusion; Computer vision; Gesture; Simulation; Embedded system","score_opus":0.04068626525969482,"score_gpt":0.2600413074629053,"score_spread":0.2193550422032105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389615191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076832026,0.00069733645,0.9175338,0.00023877624,0.000088810484,0.00022234223,0.00008931202,0.0014508294,0.002846685],"genre_scores_gemma":[0.67616814,0.0005419131,0.31948075,0.0001844718,0.000050521754,0.0002648306,0.00015253805,0.00005808024,0.0030986986],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994325,0.00016977909,0.000040717652,0.00013492611,0.00018461836,0.000037366648],"domain_scores_gemma":[0.99939847,0.00024878758,0.000099283396,0.000107134336,0.00009623758,0.00005004028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078007154,0.0006512332,0.0005980805,0.00035376745,0.00024493007,0.0005320576,0.0005912247,0.0009203303,0.0020496373],"category_scores_gemma":[0.0016230282,0.00035243592,0.00045806548,0.00030077345,0.00056345,0.0009694027,0.0012264391,0.0005608778,0.00051405525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006421444,0.0006743078,0.0030426686,0.0009536686,0.00014807329,0.00087267155,0.0006307045,0.056612927,0.45818368,0.0027739264,0.0023866887,0.47307855],"study_design_scores_gemma":[0.00011521075,0.0022493575,0.014897782,0.00017531609,0.00009180804,0.0021525577,0.00022889506,0.8085237,0.15903796,0.0038607777,0.008494743,0.00017195928],"about_ca_topic_score_codex":0.00073967903,"about_ca_topic_score_gemma":0.0011370492,"teacher_disagreement_score":0.0020496373,"about_ca_system_score_codex":0.00016244635,"about_ca_system_score_gemma":0.00030474865,"threshold_uncertainty_score":0.00685668},"labels":[],"label_agreement":null},{"id":"W4389670504","doi":"10.3390/s23249796","title":"Sensitivity Analysis of Intensity-Modulated Plastic Optical Fiber Sensors for Effective Aging Detection in Rapeseed Transformer Oil","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Materials science; Optical fiber; Repeatability; Sensitivity (control systems); Linearity; Fiber optic sensor; Wavelength; Transformer; Optics; Optoelectronics; Electronic engineering; Voltage; Electrical engineering; Engineering; Physics","score_opus":0.007295340809461281,"score_gpt":0.22471528170949037,"score_spread":0.21741994090002909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389670504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9780351,0.00089855725,0.020143142,0.00006448955,0.000021750106,0.000037369595,0.00007918434,0.00006458993,0.0006556983],"genre_scores_gemma":[0.9904799,0.0005492224,0.008382483,0.00004511066,0.0000063304274,0.000019472403,0.000049286024,0.000011712275,0.00045640257],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994672,0.00010245708,0.000032957352,0.000097406,0.0002630933,0.000036851216],"domain_scores_gemma":[0.9993992,0.0002898041,0.00010280373,0.000042460884,0.00015071718,0.000015038015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007448739,0.00032746128,0.00019164481,0.000300723,0.0001578138,0.000265378,0.00028923972,0.0003596209,0.00039778813],"category_scores_gemma":[0.0014408209,0.00015964698,0.00027578743,0.00022717977,0.00029747168,0.00027145803,0.00026080114,0.00028460883,0.00012136631],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010237184,0.000020226495,0.0009429749,0.00005872321,0.000010361438,0.000048251542,0.00006386611,0.0012396608,0.9928885,0.000063457584,0.000017980006,0.004543661],"study_design_scores_gemma":[0.0000011678212,0.00017653145,0.0020238638,0.0000057158504,0.000012678798,0.00006047822,0.000045580582,0.005748742,0.9916543,0.00003118227,0.00023297506,0.0000067577835],"about_ca_topic_score_codex":0.0009313218,"about_ca_topic_score_gemma":0.0016579311,"teacher_disagreement_score":0.0009313218,"about_ca_system_score_codex":0.0003013969,"about_ca_system_score_gemma":0.00017420731,"threshold_uncertainty_score":0.0039393306},"labels":[],"label_agreement":null},{"id":"W4389889720","doi":"10.3390/s23249900","title":"Particle Tracking and Micromixing Performance Characterization with a Mobile Device","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto Tecnológico y de Estudios Superiores de Monterrey; Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología; University of Ottawa","keywords":"Micromixer; Micromixing; Multiphysics; Mixing (physics); Homogeneity (statistics); Computer science; Reynolds number; Tracking (education); Characterization (materials science); Computational fluid dynamics; Materials science; Biological system; Simulation; Mechanics; Mechanical engineering; Microfluidics; Engineering; Nanotechnology; Physics; Finite element method; Structural engineering","score_opus":0.011475539998925654,"score_gpt":0.1915812560568361,"score_spread":0.18010571605791045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389889720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6566304,0.0007720621,0.33443215,0.00024275287,0.000120440156,0.00041604746,0.0006976676,0.002477565,0.004210814],"genre_scores_gemma":[0.76399475,0.00044814972,0.23112333,0.00008675384,0.000023308387,0.0003342782,0.00034387448,0.00010739909,0.0035381108],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996656,0.000021745042,0.000025733147,0.00007478535,0.00018086348,0.000031267475],"domain_scores_gemma":[0.9996259,0.0001365649,0.00006982653,0.000049592305,0.000095319214,0.000022731161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006157931,0.00048269835,0.000367612,0.0006632908,0.00030266016,0.000527757,0.0004067299,0.000517449,0.0016931865],"category_scores_gemma":[0.00074158114,0.00017079459,0.00029777727,0.00034252962,0.00022902405,0.0003156857,0.00034437122,0.0004381853,0.0004792164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001361319,0.00007206993,0.0011826763,0.000112718226,0.000013168868,0.0000641164,0.00012474044,0.0032634877,0.9670254,0.0006336287,0.0002966497,0.027075147],"study_design_scores_gemma":[0.000011449418,0.00038498078,0.001866958,0.000008907323,0.00001772695,0.00007213696,0.0000357188,0.029221576,0.9661053,0.00010459504,0.0021435258,0.00002720138],"about_ca_topic_score_codex":0.00091330736,"about_ca_topic_score_gemma":0.0011171971,"teacher_disagreement_score":0.0016931865,"about_ca_system_score_codex":0.00043422176,"about_ca_system_score_gemma":0.00041501145,"threshold_uncertainty_score":0.005664289},"labels":[],"label_agreement":null},{"id":"W4389890004","doi":"10.3390/s23249833","title":"Design of Experiments to Compare the Mechanical Properties of Polylactic Acid Using Material Extrusion Three-Dimensional-Printing Thermal Parameters Based on a Cyber–Physical Production System","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Extrusion; Polylactic acid; Fused deposition modeling; Ultimate tensile strength; 3D printing; Materials science; Composite material; Factorial experiment; Design of experiments; Deposition (geology); Taguchi methods; Mechanical engineering; Polymer; Computer science; Engineering; Geology","score_opus":0.06284317280642401,"score_gpt":0.2467665469659044,"score_spread":0.18392337415948037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389890004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8651299,0.0014398533,0.0921484,0.00016683446,0.0010715737,0.03428102,0.0018838412,0.00049801206,0.0033806544],"genre_scores_gemma":[0.63865083,0.0018357909,0.23680818,0.0005624885,0.00020885107,0.11445124,0.0015064102,0.00015078713,0.0058254055],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99230474,0.0016585209,0.0014104601,0.0016057546,0.0021529912,0.00086739316],"domain_scores_gemma":[0.9944877,0.0018731471,0.001510535,0.0005080009,0.0012474969,0.0003730904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006068614,0.0022565695,0.0026366787,0.0010360241,0.00083584024,0.0013204267,0.001830562,0.001671234,0.004414665],"category_scores_gemma":[0.0038617451,0.00083129335,0.0018937702,0.0006131422,0.0008673452,0.0005597587,0.0008541193,0.0025027203,0.0007257949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0180916,0.013509776,0.0018232276,0.0034623025,0.0003412521,0.00013902571,0.000351314,0.0058395583,0.9270549,0.0005507128,0.00025740545,0.028578917],"study_design_scores_gemma":[0.003388785,0.32000574,0.023684148,0.00021331727,0.0015429363,0.00010592574,0.00033949164,0.0172103,0.62193984,0.0007167496,0.010608208,0.00024453457],"about_ca_topic_score_codex":0.000511045,"about_ca_topic_score_gemma":0.0006393492,"teacher_disagreement_score":0.006068614,"about_ca_system_score_codex":0.00083252677,"about_ca_system_score_gemma":0.0013069721,"threshold_uncertainty_score":0.0320943},"labels":[],"label_agreement":null},{"id":"W4389922197","doi":"10.3390/s23249850","title":"Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Artificial intelligence; Matching (statistics); Computer science; Artificial neural network; Pattern recognition (psychology); Scheme (mathematics); Feature (linguistics); Computer vision; Feature extraction; Mathematics; Statistics","score_opus":0.020481423458244625,"score_gpt":0.2828008971203686,"score_spread":0.262319473662124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389922197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037089717,0.000092302944,0.96050304,0.00003427013,0.000023901808,0.00003105659,0.00007335734,0.00076092145,0.0013914068],"genre_scores_gemma":[0.67292225,0.00013619405,0.32404596,0.000087212655,0.000034178873,0.00006857285,0.00029100178,0.00009119234,0.002323391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996928,0.000033326374,0.000011509963,0.0000627079,0.00016522796,0.000034462893],"domain_scores_gemma":[0.99977905,0.000032487114,0.000040594605,0.00005228861,0.00008324025,0.000012375981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020529385,0.00043694148,0.00042040207,0.00062397675,0.00017573837,0.00024095004,0.0007749684,0.0003606727,0.001381297],"category_scores_gemma":[0.00063452905,0.00026838123,0.00030766532,0.00070581445,0.00021737316,0.00068505283,0.00074417755,0.00047325285,0.00030361416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020458747,0.00017105976,0.002454522,0.000099002544,0.000053277232,0.00006699026,0.00007722262,0.15397386,0.17972083,0.0049369573,0.0022983477,0.6559434],"study_design_scores_gemma":[0.000012172309,0.000029605026,0.0012035632,0.000003501622,0.000007510743,0.000058696718,0.000010523289,0.96854043,0.027819064,0.0013082654,0.0009957142,0.000010986429],"about_ca_topic_score_codex":0.0028841977,"about_ca_topic_score_gemma":0.00638107,"teacher_disagreement_score":0.0028841977,"about_ca_system_score_codex":0.00040681366,"about_ca_system_score_gemma":0.00074157864,"threshold_uncertainty_score":0.005734861},"labels":[],"label_agreement":null},{"id":"W4389925444","doi":"10.3390/s23249873","title":"Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Myofascial pain diagnosis and treatment","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Artificial intelligence; Texture (cosmology); Myofascial pain syndrome; Pattern recognition (psychology); Feature (linguistics); Computer science; Medicine; Pathology; Image (mathematics)","score_opus":0.01040900428920603,"score_gpt":0.26948066472594034,"score_spread":0.2590716604367343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389925444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.596189,0.0024267116,0.39795506,0.00030790066,0.00016878113,0.00019855214,0.0003327581,0.00056885165,0.0018523498],"genre_scores_gemma":[0.91177875,0.000577909,0.08656347,0.00009041965,0.0000783812,0.00009742091,0.00024272018,0.000027541902,0.0005435138],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914837,0.00024524826,0.0000711149,0.0001746077,0.0002481342,0.000112562346],"domain_scores_gemma":[0.99883777,0.0005958644,0.00016432186,0.000078692916,0.0002705388,0.000052798783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016126083,0.00062750163,0.0009802894,0.002094839,0.00022107676,0.00088636,0.00032480323,0.0006093325,0.00068162795],"category_scores_gemma":[0.003150828,0.000175731,0.00073180284,0.0012116544,0.0002665,0.00078947126,0.00038764573,0.0004622298,0.000432374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012037184,0.00056525104,0.056579467,0.00040160047,0.00041436803,0.00028495045,0.0002289606,0.015572336,0.17804395,0.000475039,0.0012034217,0.7450269],"study_design_scores_gemma":[0.00007925143,0.002018686,0.22856297,0.00012416253,0.0004907748,0.0011621699,0.0006501605,0.70749676,0.054384757,0.0026990492,0.0021821265,0.00014913251],"about_ca_topic_score_codex":0.0008139066,"about_ca_topic_score_gemma":0.0010396275,"teacher_disagreement_score":0.002094839,"about_ca_system_score_codex":0.00018328773,"about_ca_system_score_gemma":0.00021542337,"threshold_uncertainty_score":0.008528352},"labels":[],"label_agreement":null},{"id":"W4390025995","doi":"10.3390/s24010010","title":"Advances in IoMT for Healthcare Systems","year":2023,"lang":"en","type":"editorial","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Health care; Healthcare system; Disease management; Medicine; Computer science; Health management system; Pathology; Alternative medicine","score_opus":0.01737942391435145,"score_gpt":0.3098380286395213,"score_spread":0.2924586047251699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390025995","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000071750816,0.11025626,0.0012642771,0.154202,0.71848744,0.000020706231,0.00006942559,0.00016511965,0.015463079],"genre_scores_gemma":[0.0015764757,0.095795184,0.0010230996,0.06613031,0.812031,0.000033045468,0.000065872606,0.00013174277,0.023213247],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99527127,0.00082580344,0.000490597,0.00044139582,0.0026495734,0.00032138344],"domain_scores_gemma":[0.9809209,0.009885632,0.0006814266,0.00066826574,0.0062147137,0.0016290953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007214875,0.001486413,0.0013102125,0.002335469,0.0015502018,0.0076992526,0.0019655705,0.011005669,0.015858497],"category_scores_gemma":[0.02047019,0.00059772294,0.0014930996,0.0016576295,0.0034787203,0.0065408885,0.0025056228,0.019305147,0.012577912],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016101512,0.000008753281,0.000019917026,0.00040938237,0.000012040743,0.00007073418,0.000029856856,0.000042459033,0.00007440388,0.007318161,0.95572484,0.036273375],"study_design_scores_gemma":[0.0000055463843,0.0000075480834,0.000041017738,0.00026526154,0.0000066642738,0.00007292222,0.0000132012665,0.000037512025,0.000030447456,0.002179506,0.9973346,0.000005777622],"about_ca_topic_score_codex":0.0016747421,"about_ca_topic_score_gemma":0.004205176,"teacher_disagreement_score":0.015858497,"about_ca_system_score_codex":0.003264877,"about_ca_system_score_gemma":0.0036372237,"threshold_uncertainty_score":0.05305195},"labels":[],"label_agreement":null},{"id":"W4390050842","doi":"10.3390/s24010054","title":"Assessment of the Smartpill, a Wireless Sensor, as a Measurement Tool for Intra-Abdominal Pressure (IAP)","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Abdominal Surgery and Complications","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada; Aix-Marseille Université","keywords":"Insufflation; Pressure sensor; Pressure measurement; Medicine; Limits of agreement; Biomedical engineering; Anesthesia; Nuclear medicine; Physics","score_opus":0.04023547444924385,"score_gpt":0.3129655470767399,"score_spread":0.27273007262749605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390050842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9450156,0.002259058,0.050589666,0.00014979584,0.0001336626,0.00011201741,0.0003426986,0.0001935122,0.0012039258],"genre_scores_gemma":[0.975298,0.0010224323,0.022410564,0.00015091481,0.00006352257,0.00012849423,0.00031159475,0.00003171375,0.0005828578],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880457,0.00025676246,0.000049420734,0.00027714393,0.00054876535,0.00006332871],"domain_scores_gemma":[0.9978942,0.00049217755,0.0008482075,0.00015153796,0.0004825544,0.00013134482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015371521,0.00062732823,0.00033872118,0.00041040187,0.00013096909,0.00054852944,0.00035748523,0.0005463377,0.00072513113],"category_scores_gemma":[0.0021231791,0.00022824015,0.00030232043,0.00032049682,0.00056638423,0.000553011,0.00036670576,0.00041705134,0.00027404417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008553687,0.00013822746,0.035006985,0.0004713772,0.00011194568,0.000104230276,0.0001379346,0.0005125025,0.9294256,0.000076651166,0.0002297765,0.032929435],"study_design_scores_gemma":[0.000055676872,0.011093131,0.3197201,0.00009859098,0.00046678755,0.0013397661,0.00020245188,0.012338218,0.6512727,0.0001438197,0.00320061,0.00006820496],"about_ca_topic_score_codex":0.00023270701,"about_ca_topic_score_gemma":0.0003764494,"teacher_disagreement_score":0.0015371521,"about_ca_system_score_codex":0.00023268706,"about_ca_system_score_gemma":0.00029299184,"threshold_uncertainty_score":0.008129358},"labels":[],"label_agreement":null},{"id":"W4390109902","doi":"10.3390/s24010063","title":"Experimental Study of Fully Passive, Fully Active, and Active–Passive Upper-Limb Exoskeleton Efficiency: An Assessment of Lifting Tasks","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Exoskeleton; Kinematics; Wearable computer; Electromyography; Powered exoskeleton; Engineering; Simulation; Computer science; Physical medicine and rehabilitation; Medicine; Embedded system","score_opus":0.011943934380977123,"score_gpt":0.29821979322749137,"score_spread":0.28627585884651424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390109902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967127,0.000037491318,0.0027670397,0.000012555395,0.00001508561,0.00013164939,0.00009044118,0.000012203087,0.00022091765],"genre_scores_gemma":[0.9900169,0.00010282986,0.0065688994,0.00003575846,0.000026257934,0.0008552178,0.00029260886,0.000014592306,0.0020870822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928254,0.00014127826,0.00011296042,0.00018426492,0.0001649884,0.00011399588],"domain_scores_gemma":[0.9988362,0.00036917272,0.00016697583,0.00015644725,0.000231218,0.00024006209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007694447,0.00065522623,0.00048090628,0.00040258095,0.00024749065,0.00021402993,0.0003774004,0.00040240947,0.002964139],"category_scores_gemma":[0.0015805531,0.00024624506,0.00032770363,0.0001684236,0.0005237817,0.00043045165,0.00065231964,0.00037915134,0.00040212032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016728504,0.025475413,0.009634352,0.00060257065,0.0001578821,0.00017541676,0.0010394423,0.0014618769,0.90225905,0.00019665598,0.000263085,0.04200571],"study_design_scores_gemma":[0.0022717204,0.40333724,0.28185833,0.00013446803,0.00043073387,0.00081505923,0.0018914203,0.013959245,0.29082474,0.0006574798,0.0036750261,0.00014460253],"about_ca_topic_score_codex":0.00015986283,"about_ca_topic_score_gemma":0.000207473,"teacher_disagreement_score":0.002964139,"about_ca_system_score_codex":0.00007153448,"about_ca_system_score_gemma":0.00020248129,"threshold_uncertainty_score":0.0099160075},"labels":[],"label_agreement":null},{"id":"W4390114537","doi":"10.3390/s24010071","title":"A Dual-Purpose Camera for Attitude Determination and Resident Space Object Detection on a Stratospheric Balloon","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canadian Space Agency","keywords":"CubeSat; Payload (computing); Situation awareness; Computer science; International Space Station; Real-time computing; Remote sensing; Systems engineering; Satellite; Aerospace engineering; Computer vision; Simulation; Engineering; Computer security","score_opus":0.010243911126826157,"score_gpt":0.23799082495351,"score_spread":0.22774691382668386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390114537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53734386,0.00044944795,0.425818,0.0005923326,0.0003219369,0.0010458562,0.0012888953,0.008248446,0.024891194],"genre_scores_gemma":[0.7134005,0.00017113029,0.27648628,0.00028212788,0.000043920645,0.00020300857,0.0010135785,0.00010748432,0.008292014],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962234,0.00003812382,0.000009777066,0.000079243306,0.0001975484,0.00005286778],"domain_scores_gemma":[0.9996414,0.000040524697,0.000030849595,0.00005771163,0.00016319995,0.00006616027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000420376,0.00033651365,0.00030047479,0.0006940316,0.00025117362,0.00037750098,0.00052930816,0.00034546392,0.0019598075],"category_scores_gemma":[0.0005009099,0.00020252433,0.00020202616,0.00028936245,0.00021758674,0.0005020122,0.0006401663,0.00041025522,0.0006925259],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007862728,0.00023424604,0.014128872,0.00016726575,0.00008102263,0.0006391033,0.00048209325,0.003999351,0.73506635,0.0038886503,0.008751934,0.23177482],"study_design_scores_gemma":[0.00034574184,0.0039749867,0.071938865,0.00009949068,0.0002078902,0.003227023,0.00041686016,0.22777854,0.5933308,0.0010595825,0.09740411,0.00021610958],"about_ca_topic_score_codex":0.0024356735,"about_ca_topic_score_gemma":0.004170716,"teacher_disagreement_score":0.0024356735,"about_ca_system_score_codex":0.0003939027,"about_ca_system_score_gemma":0.00078020914,"threshold_uncertainty_score":0.006556213},"labels":[],"label_agreement":null},{"id":"W4390175233","doi":"10.3390/s24010080","title":"A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Brain–computer interface; Computer science; Human–computer interaction; Wearable computer; Proof of concept; Context (archaeology); Electroencephalography; Robot; Interface (matter); Artificial intelligence; Embedded system; Psychology","score_opus":0.03378557971222489,"score_gpt":0.2870595344391302,"score_spread":0.2532739547269053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390175233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50099885,0.0034902412,0.45457238,0.0037326366,0.0018023155,0.0032977508,0.0021957168,0.009593986,0.020316128],"genre_scores_gemma":[0.75272113,0.0012815674,0.22792828,0.00066469697,0.00013021565,0.001556268,0.00087580655,0.0002811896,0.014560924],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952924,0.00007183043,0.00002921584,0.000074964,0.00024329161,0.000051421717],"domain_scores_gemma":[0.9995759,0.00008401088,0.00003664019,0.00004011711,0.00014887982,0.0001145616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008097571,0.00066359964,0.00041205445,0.00027656657,0.00029512675,0.00047081834,0.0013476041,0.0011441306,0.0044156103],"category_scores_gemma":[0.0009960562,0.00020495353,0.00034014843,0.00009104812,0.0004162591,0.0007624025,0.0006910935,0.00077989127,0.0016269486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006497532,0.0012379807,0.0020047599,0.0023519276,0.00017383115,0.0033819925,0.0009966436,0.006832725,0.7741754,0.0047162096,0.02294172,0.18053703],"study_design_scores_gemma":[0.0009175098,0.026314627,0.011099673,0.00045648398,0.00020220414,0.008562834,0.0006742125,0.105254084,0.65461975,0.0027096586,0.18883826,0.00035077764],"about_ca_topic_score_codex":0.00076484756,"about_ca_topic_score_gemma":0.0007168904,"teacher_disagreement_score":0.0044156103,"about_ca_system_score_codex":0.00019273275,"about_ca_system_score_gemma":0.00050379033,"threshold_uncertainty_score":0.01477164},"labels":[],"label_agreement":null},{"id":"W4390236242","doi":"10.3390/s24010111","title":"Practical Applications of a Set-Based Camera Deployment Methodology","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Software deployment; Factory (object-oriented programming); Computer science; Set (abstract data type); Artificial intelligence; Computer vision; Visual inspection; Real-time computing; Software engineering","score_opus":0.08238951041840058,"score_gpt":0.3385939207696798,"score_spread":0.2562044103512792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390236242","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037595867,0.000025716403,0.99385047,0.00004199607,0.000010531579,0.000027537599,0.000022516639,0.0002065062,0.0020550836],"genre_scores_gemma":[0.15499489,0.00009513396,0.84233814,0.00005073545,0.000015131381,0.00016905028,0.00012937676,0.00022247278,0.0019851036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991872,0.00021372146,0.00004393952,0.00011992759,0.0003822867,0.000052990003],"domain_scores_gemma":[0.9990388,0.0004912387,0.0000790582,0.00014062178,0.000211733,0.000038535953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010037309,0.00087857724,0.0006744675,0.0006653557,0.00055284135,0.0009007019,0.0014689028,0.0009615798,0.006003735],"category_scores_gemma":[0.002632137,0.00075117854,0.0011296743,0.00045927876,0.00076297927,0.0008970637,0.00161023,0.0012595622,0.00072851265],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003968255,0.000044417684,0.000486051,0.00016287992,0.00003663713,0.000094335475,0.00020599221,0.881769,0.0069653047,0.03367094,0.001336114,0.0751886],"study_design_scores_gemma":[0.000013120072,0.000037965856,0.000079460064,0.000024180716,0.0000058730902,0.00006058993,0.0000333875,0.9841474,0.003060499,0.009261154,0.0032654617,0.000010810098],"about_ca_topic_score_codex":0.002742998,"about_ca_topic_score_gemma":0.0029484157,"teacher_disagreement_score":0.006003735,"about_ca_system_score_codex":0.00073353405,"about_ca_system_score_gemma":0.0009018028,"threshold_uncertainty_score":0.0200845},"labels":[],"label_agreement":null},{"id":"W4390270225","doi":"10.3390/s24010146","title":"Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Høgskulen på Vestlandet","keywords":"Bridging (networking); Resilience (materials science); Computer security; Process (computing); Emergency management; Domain (mathematical analysis); Grounded theory; Maritime security; Process management; Incident management; Engineering; Computer science; Knowledge management; Qualitative research","score_opus":0.010709902940653613,"score_gpt":0.23808246419772225,"score_spread":0.22737256125706864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390270225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8585506,0.0005405381,0.049535602,0.015678609,0.00016582865,0.00031153127,0.00007189977,0.00028568064,0.07485969],"genre_scores_gemma":[0.9877,0.00028232316,0.010300965,0.0003614377,0.00001758784,0.000047641257,0.000029400655,0.000025928275,0.0012348166],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99634993,0.002272273,0.00010916078,0.00023119808,0.00037562044,0.0006618887],"domain_scores_gemma":[0.9922661,0.0033156066,0.001225431,0.00059420126,0.0008293061,0.0017693228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050540147,0.0004127979,0.00018814925,0.0011580923,0.0043155323,0.004921513,0.0014488712,0.0012823603,0.0050818473],"category_scores_gemma":[0.009486529,0.00022815616,0.00029753207,0.0010032816,0.0033924053,0.008534011,0.008223258,0.0017139962,0.00062807754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042815492,0.001559688,0.06085955,0.0016986552,0.00011235913,0.002990664,0.28189027,0.009936381,0.012632377,0.23412667,0.020475104,0.37329012],"study_design_scores_gemma":[0.00005539496,0.0009849584,0.02914336,0.001494276,0.0000947288,0.0011288717,0.62149924,0.015719656,0.0070148245,0.070909016,0.25182402,0.00013159997],"about_ca_topic_score_codex":0.002213497,"about_ca_topic_score_gemma":0.0040314267,"teacher_disagreement_score":0.0050818473,"about_ca_system_score_codex":0.0023599805,"about_ca_system_score_gemma":0.006843129,"threshold_uncertainty_score":0.02672851},"labels":[],"label_agreement":null},{"id":"W4390466463","doi":"10.3390/s24010251","title":"Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Damper; Estimator; Magnetorheological fluid; Fault detection and isolation; Magnetorheological damper; Computer science; Fault (geology); Engineering; Structural engineering; Actuator; Artificial intelligence; Mathematics; Statistics; Geology; Seismology","score_opus":0.018623181419207737,"score_gpt":0.2512772409380035,"score_spread":0.23265405951879578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390466463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0091237025,0.00026045495,0.989787,0.00004091118,0.000036215857,0.00001126811,0.000011587875,0.0003106601,0.000418186],"genre_scores_gemma":[0.7394148,0.00045927177,0.25737447,0.000091492584,0.0000748182,0.00006706699,0.00013003431,0.00007186939,0.002316084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956566,0.00008636784,0.000026935646,0.00009405338,0.00018520819,0.00004177552],"domain_scores_gemma":[0.99906427,0.00046048552,0.00017679953,0.000097778015,0.00017748709,0.000023342736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085588556,0.00072250614,0.00092700333,0.00082218536,0.00029354406,0.0006029239,0.000755184,0.001042618,0.0008347327],"category_scores_gemma":[0.0032349005,0.00028493546,0.00077934936,0.00035985344,0.0002814128,0.001067912,0.00060806493,0.0010825543,0.00030995024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029196983,0.00017700643,0.003927406,0.00028541722,0.00018919987,0.00021292732,0.00020091282,0.603102,0.03663537,0.011185556,0.0010691093,0.34272313],"study_design_scores_gemma":[0.000008472569,0.00006405242,0.0006319523,0.00000871779,0.000016248314,0.000053472755,0.000011779903,0.9928671,0.0042204205,0.0011984955,0.00090336107,0.000015903346],"about_ca_topic_score_codex":0.0018445135,"about_ca_topic_score_gemma":0.0019200789,"teacher_disagreement_score":0.0018445135,"about_ca_system_score_codex":0.00037659105,"about_ca_system_score_gemma":0.00047154303,"threshold_uncertainty_score":0.0045264363},"labels":[],"label_agreement":null},{"id":"W4390614030","doi":"10.3390/s24020318","title":"Leveraging Generative Design and Point Cloud Data to Improve Conformance to Passing Lane Layout","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Signage; Process (computing); Computer science; Transport engineering; Cloud computing; Point (geometry); Building information modeling; Point cloud; Systems engineering; Software engineering; Engineering; Artificial intelligence","score_opus":0.07447289945482889,"score_gpt":0.26725719559126687,"score_spread":0.192784296136438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390614030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10043761,0.000089532245,0.88950306,0.00016020292,0.00002700255,0.00021150538,0.00060464017,0.0044688154,0.0044976254],"genre_scores_gemma":[0.5770824,0.00010335535,0.41844928,0.000062666244,0.0000056844624,0.0001558904,0.0021534879,0.00065526046,0.0013319544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984017,0.00038747134,0.000097656724,0.0002891782,0.00073865586,0.00008532487],"domain_scores_gemma":[0.99616635,0.001645995,0.0002793332,0.0011350733,0.0007028312,0.000070505426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022528255,0.00076715974,0.00047435166,0.0022084475,0.00046556862,0.0019134387,0.0014069752,0.0006775264,0.0021088608],"category_scores_gemma":[0.008127418,0.00060657586,0.0012984074,0.0015646048,0.0011218425,0.0011853301,0.0016630098,0.000813812,0.0007453862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017573098,0.00031258704,0.04785324,0.0004934734,0.00014078978,0.0004460227,0.0022204004,0.57698256,0.024041219,0.017570745,0.0029037732,0.32685944],"study_design_scores_gemma":[0.000018647512,0.00010542262,0.0065584583,0.000049825576,0.00004841679,0.00018502386,0.00043191542,0.9587167,0.015261064,0.010182711,0.008392881,0.000049029248],"about_ca_topic_score_codex":0.011307232,"about_ca_topic_score_gemma":0.029190669,"teacher_disagreement_score":0.011307232,"about_ca_system_score_codex":0.0009962213,"about_ca_system_score_gemma":0.001313558,"threshold_uncertainty_score":0.022482872},"labels":[],"label_agreement":null},{"id":"W4390616921","doi":"10.3390/s24020335","title":"Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Cluster analysis; Concatenation (mathematics); Dimensionality reduction; Principal component analysis; Constant false alarm rate; Computer science; Data mining; Power (physics); Real-time computing; Data validation; Engineering; Artificial intelligence; Electronic engineering","score_opus":0.08251819703629738,"score_gpt":0.2953072986545021,"score_spread":0.21278910161820475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390616921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89306796,0.00018437169,0.10397367,0.00010914674,0.00006887253,0.00009182062,0.00028589863,0.0012433506,0.0009748408],"genre_scores_gemma":[0.9826249,0.000040128645,0.016393034,0.000029754006,0.0000088364895,0.000053264328,0.00045494596,0.000020306325,0.00037482177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99777657,0.00087415683,0.00018486152,0.00042342595,0.0005734889,0.000167517],"domain_scores_gemma":[0.9928349,0.0037900328,0.00066382263,0.0010906542,0.0014651201,0.00015544794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003650998,0.0009829988,0.0005504584,0.00090026134,0.0003510465,0.0006005842,0.00095222035,0.0014034329,0.0007176443],"category_scores_gemma":[0.011933933,0.00018413994,0.00046747952,0.0004809127,0.00068047066,0.00075498497,0.00089988444,0.00069378427,0.00044621585],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015248097,0.00086571096,0.04489192,0.00036443915,0.0002441419,0.0003675566,0.00024069817,0.7955049,0.04906914,0.0014534118,0.0011272428,0.104346015],"study_design_scores_gemma":[0.00003060576,0.00024508234,0.0092970105,0.000016170376,0.00001475557,0.00008555761,0.00003110911,0.9575724,0.03216399,0.00022312236,0.00030524156,0.000015005442],"about_ca_topic_score_codex":0.003043762,"about_ca_topic_score_gemma":0.0022219222,"teacher_disagreement_score":0.003650998,"about_ca_system_score_codex":0.00049429527,"about_ca_system_score_gemma":0.00064088625,"threshold_uncertainty_score":0.019308507},"labels":[],"label_agreement":null},{"id":"W4390663391","doi":"10.3390/s24020354","title":"Effects of Manual Therapy on Parkinson’s Gait: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Gait; Physical medicine and rehabilitation; STRIDE; Physical therapy; Medicine; Confidence interval; Rehabilitation; Randomized controlled trial; Parkinson's disease; Range of motion; Strictly standardized mean difference; Disease; Surgery; Internal medicine","score_opus":0.028161534462702708,"score_gpt":0.3449631802150629,"score_spread":0.3168016457523602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390663391","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048954395,0.9989042,0.000063723266,0.000096750824,0.000057651636,0.00009539893,0.000116649244,0.0000052431196,0.00017071536],"genre_scores_gemma":[0.008360455,0.9906832,0.0002836954,0.0002357518,0.000048943777,0.00018384776,0.00010191862,0.000002933483,0.00009923746],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.997259,0.00096694153,0.00078475615,0.00023698283,0.0006522266,0.00010003927],"domain_scores_gemma":[0.9909746,0.0068822764,0.0013491352,0.000100680656,0.0005808532,0.0001125389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038590664,0.0013901998,0.0070434227,0.004360094,0.00049643964,0.0018502576,0.0013152375,0.0016659304,0.0050186682],"category_scores_gemma":[0.01737884,0.00070485787,0.0059762243,0.004796378,0.0006475877,0.0013247713,0.00096940686,0.0009434893,0.00034342226],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020560606,0.000015678163,0.00024231353,0.9470187,0.007188731,0.00007142842,0.000077898876,0.00008032628,0.0001288713,0.00016174706,0.0013008981,0.043507747],"study_design_scores_gemma":[0.0005002999,0.00034968392,0.0047138436,0.87464863,0.093271285,0.0005054311,0.00016378963,0.00011802153,0.00025002155,0.00038710228,0.025054721,0.000037238944],"about_ca_topic_score_codex":0.0067113508,"about_ca_topic_score_gemma":0.019176038,"teacher_disagreement_score":0.0070434227,"about_ca_system_score_codex":0.0024745138,"about_ca_system_score_gemma":0.005671005,"threshold_uncertainty_score":0.020408988},"labels":[],"label_agreement":null},{"id":"W4390669971","doi":"10.3390/s24020380","title":"Nonprehensile Manipulation for Rapid Object Spinning via Multisensory Learning from Demonstration","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feed forward; GRASP; Computer science; Kinematics; Task (project management); Object (grammar); Artificial intelligence; Haptic technology; Control engineering; Control (management); Control theory (sociology); Engineering","score_opus":0.029422701942634015,"score_gpt":0.24804501570622767,"score_spread":0.21862231376359365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390669971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3834228,0.0004478821,0.6128866,0.0000955876,0.00002950291,0.0000891964,0.000025231202,0.00053217664,0.0024710977],"genre_scores_gemma":[0.9453544,0.00011736602,0.053783536,0.000024662708,0.0000065793724,0.000051133127,0.00002367778,0.000009412372,0.00062913034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985254,0.000020615144,0.000012657839,0.00003720112,0.00006253211,0.000014390453],"domain_scores_gemma":[0.99965715,0.00013685392,0.000091042195,0.000053415493,0.000039932027,0.00002157893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023577361,0.00031722017,0.0002611744,0.00016010401,0.00012911948,0.00020139993,0.00034105175,0.00025820377,0.00075525086],"category_scores_gemma":[0.0006666826,0.00013045134,0.00019146106,0.00013214741,0.00029472858,0.00037579736,0.00047615744,0.00029756926,0.00010206366],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019832075,0.00017334435,0.00055050594,0.0002701998,0.00002171872,0.00021127524,0.00016844348,0.03392304,0.7848576,0.0017311563,0.00019549925,0.17769894],"study_design_scores_gemma":[0.00007295683,0.001666166,0.0064455257,0.00003786694,0.000033315788,0.0005224329,0.00005822253,0.71072274,0.27604377,0.0017116389,0.002623677,0.00006166144],"about_ca_topic_score_codex":0.0005707306,"about_ca_topic_score_gemma":0.00074884074,"teacher_disagreement_score":0.00075525086,"about_ca_system_score_codex":0.00011812774,"about_ca_system_score_gemma":0.00023297608,"threshold_uncertainty_score":0.0025265813},"labels":[],"label_agreement":null},{"id":"W4390695490","doi":"10.3390/s24020408","title":"Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Fuel efficiency; Particle swarm optimization; Computer science; Speedup; Trajectory; Swarm behaviour; Smoothing; Simulation; Real-time computing; Mathematical optimization; Automotive engineering; Engineering; Algorithm; Artificial intelligence; Parallel computing; Mathematics","score_opus":0.05263922027544007,"score_gpt":0.29994070944944484,"score_spread":0.24730148917400477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390695490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040708065,0.00019069106,0.95541745,0.00008574471,0.00004210916,0.000040502626,0.000031041767,0.00036316103,0.0031212252],"genre_scores_gemma":[0.63606966,0.00023392227,0.3605015,0.00005305176,0.000027483995,0.00011121076,0.000119939075,0.00010024334,0.0027829988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989176,0.00001845128,0.0000047682734,0.000024054953,0.000048091537,0.000012921375],"domain_scores_gemma":[0.99986994,0.00005084122,0.000022549855,0.000012099941,0.000037009686,0.000007467444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002424447,0.000633095,0.0004844296,0.00037806114,0.00033497723,0.00041926824,0.00044010382,0.0003567206,0.0006855602],"category_scores_gemma":[0.00052834663,0.00025450485,0.00043210492,0.0002989102,0.00022424097,0.00039755955,0.0002431809,0.00038897368,0.00012644878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034690278,0.000026249078,0.0005357708,0.000037232534,0.000025439973,0.00002955113,0.000025213754,0.9524525,0.0036285096,0.0013366936,0.00038707277,0.0414812],"study_design_scores_gemma":[0.0000046717387,0.000014131262,0.00012837068,0.000001596178,0.0000036777465,0.0000049905616,0.0000049112837,0.99822587,0.00086444,0.00041379724,0.000331822,0.0000016876082],"about_ca_topic_score_codex":0.0077988305,"about_ca_topic_score_gemma":0.0077958796,"teacher_disagreement_score":0.0077988305,"about_ca_system_score_codex":0.0004692761,"about_ca_system_score_gemma":0.00072615215,"threshold_uncertainty_score":0.015506864},"labels":[],"label_agreement":null},{"id":"W4390702735","doi":"10.3390/s24020430","title":"Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Flexibility (engineering); Exploit; Artificial neural network; Internet of Things; Deep learning; Artificial intelligence; Process (computing); Machine learning; The Internet; Distributed computing; Computer network; Real-time computing; Embedded system; Computer security","score_opus":0.026781096586081177,"score_gpt":0.2913563429271612,"score_spread":0.26457524634108004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390702735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14663734,0.0015262754,0.84273756,0.0013995368,0.0002287811,0.000042599488,0.00013732097,0.0018221599,0.0054684663],"genre_scores_gemma":[0.9131079,0.0006358527,0.084337875,0.00027586037,0.00003619712,0.000044455755,0.00025685295,0.00005874846,0.0012462619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996107,0.00009231221,0.000025673444,0.00009209704,0.00013510021,0.00004417314],"domain_scores_gemma":[0.9989184,0.0006329336,0.00008037809,0.0001305148,0.00019829064,0.000039543545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095049763,0.00082991377,0.00043774588,0.000324598,0.00028121917,0.00040731387,0.00069827423,0.00077454955,0.00080768904],"category_scores_gemma":[0.003733451,0.00026267223,0.00041743615,0.00032188153,0.0006527493,0.0015580263,0.0011030644,0.0010777843,0.00021158738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028980282,0.00014022185,0.0030585367,0.00019464012,0.000064428306,0.00019512781,0.00008740065,0.72660035,0.017958682,0.007654393,0.0023664944,0.24138995],"study_design_scores_gemma":[0.000006405205,0.00004479572,0.0003247471,0.000009390642,0.00000904866,0.000031923566,0.000012900464,0.9918596,0.0052388315,0.0020104807,0.0004463612,0.000005478474],"about_ca_topic_score_codex":0.0028480634,"about_ca_topic_score_gemma":0.0031349938,"teacher_disagreement_score":0.0028480634,"about_ca_system_score_codex":0.0005576416,"about_ca_system_score_gemma":0.00079327315,"threshold_uncertainty_score":0.0056630373},"labels":[],"label_agreement":null},{"id":"W4390725067","doi":"10.3390/s24020400","title":"The Development of a Wearable Biofeedback System to Elicit Temporal Gait Asymmetry using Rhythmic Auditory Stimulation and an Assessment of Immediate Effects","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Council of Ontario Universities","keywords":"Cadence; Gait; Biofeedback; Metronome; Physical medicine and rehabilitation; Rhythm; Wearable computer; Psychology; Computer science; Medicine","score_opus":0.023991551571968313,"score_gpt":0.39044093228949606,"score_spread":0.36644938071752775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390725067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4766258,0.0016625708,0.5137535,0.00046612986,0.00042778192,0.0012492248,0.000548864,0.0015531692,0.0037130576],"genre_scores_gemma":[0.77178097,0.0008448048,0.22168119,0.00042310392,0.000094806346,0.0008659443,0.0002740835,0.000054517575,0.0039805374],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977523,0.00004162638,0.000024980982,0.000052992833,0.00008830055,0.000016793829],"domain_scores_gemma":[0.99974746,0.00009087874,0.00003625841,0.000023274399,0.00008050474,0.000021755866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040818314,0.00035475055,0.00027647906,0.0003165365,0.00009996453,0.0002680437,0.0004010134,0.0005760023,0.001375889],"category_scores_gemma":[0.00067889696,0.00014900093,0.00029401123,0.00015834514,0.00015612342,0.000352338,0.00026678242,0.00022435881,0.00031839026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003087866,0.00023128283,0.001226457,0.00034029427,0.000028069513,0.00015401072,0.000064076376,0.00045174043,0.8781313,0.00021545537,0.00050184294,0.11834673],"study_design_scores_gemma":[0.00035517503,0.012084488,0.06156125,0.00018620111,0.00033478442,0.004853367,0.00017651154,0.039158914,0.8649823,0.0007661845,0.015392736,0.00014816097],"about_ca_topic_score_codex":0.00030082822,"about_ca_topic_score_gemma":0.0005398145,"teacher_disagreement_score":0.001375889,"about_ca_system_score_codex":0.00013801528,"about_ca_system_score_gemma":0.00019439435,"threshold_uncertainty_score":0.00460279},"labels":[],"label_agreement":null},{"id":"W4390748059","doi":"10.3390/s24020439","title":"A Flexible Printed Circuit Board Based Microelectromechanical Field Mill with a Vertical Movement Shutter Driven by an Electrostatic Actuator","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Manitoba Hydro","keywords":"Shutter; Actuator; Electric field; SIGNAL (programming language); Microelectromechanical systems; Electrical engineering; Amplifier; Acoustics; Materials science; Engineering; Optics; Optoelectronics; Computer science; Physics; CMOS","score_opus":0.007567991057734238,"score_gpt":0.23642235497336989,"score_spread":0.22885436391563566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390748059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64552146,0.0015910686,0.34194055,0.00047204856,0.00046510992,0.0003236778,0.00063506636,0.0035140328,0.005536979],"genre_scores_gemma":[0.7485297,0.00032215554,0.24552412,0.00019794915,0.00006799598,0.00010494908,0.00029261096,0.00006961842,0.004890995],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963355,0.000019252851,0.000020815429,0.00010431543,0.00019335108,0.000028582048],"domain_scores_gemma":[0.99968064,0.00006523421,0.0001042391,0.000057216395,0.0000535096,0.0000390988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023332042,0.00041846005,0.00034917318,0.00036951125,0.00013115893,0.0003300917,0.0009159209,0.00054925587,0.0011744725],"category_scores_gemma":[0.00025878023,0.0003499295,0.00027598866,0.00029251736,0.00026886552,0.0004844776,0.0002596061,0.00041294884,0.0004423888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038908638,0.000028119397,0.00035751238,0.000055768505,0.000006787136,0.00007113024,0.000010882998,0.00022273241,0.98707694,0.00024332006,0.00017680856,0.011711089],"study_design_scores_gemma":[0.000050466937,0.00085264706,0.004531859,0.000007918108,0.00002326782,0.00084666023,0.000015736905,0.0075724456,0.98029983,0.0000765617,0.0056818645,0.000040751875],"about_ca_topic_score_codex":0.00020242433,"about_ca_topic_score_gemma":0.00038604814,"teacher_disagreement_score":0.0011744725,"about_ca_system_score_codex":0.00017715937,"about_ca_system_score_gemma":0.0002490748,"threshold_uncertainty_score":0.003929019},"labels":[],"label_agreement":null},{"id":"W4390748312","doi":"10.3390/s24020440","title":"Calibration of Ring Oscillator-Based Integrated Temperature Sensors for Power Management Systems","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; Université du Québec à Montréal; Polytechnique Montréal","funders":"Mitacs","keywords":"Calibration; Ring oscillator; Ring (chemistry); Power (physics); Electrical engineering; Power management; Systems engineering; Computer science; Electronic engineering; Engineering; Remote sensing; Physics; Chemistry; Voltage; Geography","score_opus":0.006937378716024602,"score_gpt":0.21427330124919355,"score_spread":0.20733592253316896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390748312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32627678,0.002524945,0.6649288,0.00020744302,0.00029907114,0.00020608935,0.00020057327,0.0019504947,0.0034058408],"genre_scores_gemma":[0.81685627,0.000565663,0.18035819,0.00011451245,0.000051964143,0.000109672575,0.00011554395,0.000115166615,0.0017130687],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875677,0.0002016759,0.000051738665,0.00030145404,0.0006322867,0.000055989964],"domain_scores_gemma":[0.9992299,0.00019097597,0.00017306865,0.00014943971,0.00023905508,0.000017475848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012000215,0.0005614335,0.00035002307,0.00047318763,0.00019886924,0.000448549,0.0013624085,0.0005127078,0.0009981432],"category_scores_gemma":[0.0023097587,0.00023224465,0.00020747479,0.00036647898,0.0002910763,0.0006342303,0.00041332614,0.00044108724,0.00039311865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015223399,0.00009259366,0.0026079013,0.00022586547,0.000054307682,0.00007837829,0.00014653792,0.008529282,0.9022708,0.001156183,0.00045406504,0.08423189],"study_design_scores_gemma":[0.000016179612,0.00044573643,0.00421023,0.00002756319,0.000035870722,0.00023733272,0.000037770973,0.08225677,0.9068766,0.00039765652,0.0054235174,0.000034593588],"about_ca_topic_score_codex":0.0003550209,"about_ca_topic_score_gemma":0.00072492904,"teacher_disagreement_score":0.0013624085,"about_ca_system_score_codex":0.0004858852,"about_ca_system_score_gemma":0.00035860224,"threshold_uncertainty_score":0.0063464046},"labels":[],"label_agreement":null},{"id":"W4390749068","doi":"10.3390/s24020452","title":"Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in Rural Settings","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Replicate; Computer science; USable; Real-time computing; Simulation","score_opus":0.03671850367350208,"score_gpt":0.272172708301087,"score_spread":0.2354542046275849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390749068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89666927,0.00008224124,0.09673324,0.000104751685,0.000020632928,0.0000856763,0.00035365447,0.00029078798,0.0056597693],"genre_scores_gemma":[0.98308426,0.000057412606,0.016289089,0.00001078578,0.0000024043572,0.000028909812,0.0001378584,0.000018741493,0.00037046082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998241,0.000053797234,0.000007544832,0.000025641042,0.00006125718,0.000027655838],"domain_scores_gemma":[0.99957806,0.00022854438,0.00004525371,0.00004265162,0.000069537506,0.000035906916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021407873,0.000371461,0.00022664346,0.00045098658,0.00034297715,0.00062850513,0.00041022626,0.00041424623,0.0008054951],"category_scores_gemma":[0.0011023134,0.00022787965,0.0002491332,0.00039342293,0.0003067141,0.0007332989,0.0005601074,0.0003065118,0.00014696423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005481653,0.000078071396,0.00913233,0.00003853053,0.00002716779,0.00011900027,0.00017679273,0.9727975,0.0069828653,0.0009702813,0.00021460086,0.009408139],"study_design_scores_gemma":[0.0000071701447,0.00006117893,0.002768366,0.000005826363,0.00000681761,0.00003901507,0.00017070964,0.9937921,0.0018788791,0.00062182226,0.00063642336,0.0000116207475],"about_ca_topic_score_codex":0.0114292335,"about_ca_topic_score_gemma":0.02044645,"teacher_disagreement_score":0.0114292335,"about_ca_system_score_codex":0.0005096576,"about_ca_system_score_gemma":0.00064158946,"threshold_uncertainty_score":0.022725403},"labels":[],"label_agreement":null},{"id":"W4390789028","doi":"10.3390/s24020471","title":"Error Enhancement for Upper Limb Rehabilitation in the Chronic Phase after Stroke: A 5-Day Pre-Post Intervention Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Stiftelsen Promobilia","keywords":"Chronic stroke; Stroke (engine); Physical medicine and rehabilitation; Rehabilitation; Physical therapy; Medicine; Kinematics; Upper limb; Psychology","score_opus":0.012097535382733915,"score_gpt":0.35093017317967967,"score_spread":0.3388326377969458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390789028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988852,0.000108921166,0.00011079998,0.000023543771,0.000019553554,0.00043408852,0.00006635276,0.000013210802,0.0003382523],"genre_scores_gemma":[0.9968035,0.00020305408,0.0007205377,0.000059806687,0.00006921638,0.0010744492,0.00026142824,0.000004172582,0.0008039029],"study_design_codex":"nonrandomized_trial","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99931264,0.00023984333,0.000083739935,0.00008530097,0.00008947499,0.0001890679],"domain_scores_gemma":[0.99852246,0.0003183731,0.0002488259,0.00013839146,0.00018746877,0.0005845092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012780258,0.0007147981,0.0015595588,0.00049049256,0.0005864107,0.00036308038,0.00037383678,0.00070626085,0.0024028085],"category_scores_gemma":[0.0016166253,0.00020676306,0.00073665986,0.0004384761,0.00034989777,0.0003523741,0.0004983062,0.00079124386,0.0004484575],"study_design_candidate":"nonrandomized_trial","study_design_consensus":"nonrandomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.31963834,0.44788203,0.028296242,0.00085893826,0.0007593304,0.00040180035,0.0009321962,0.0006602505,0.013694758,0.00006963663,0.0008946775,0.18591185],"study_design_scores_gemma":[0.016854383,0.70520324,0.2744574,0.000041259067,0.00026661085,0.000065255525,0.00019309182,0.0005455817,0.0016955173,0.000038732553,0.0006114539,0.000027471362],"about_ca_topic_score_codex":0.0017780446,"about_ca_topic_score_gemma":0.0031746062,"teacher_disagreement_score":0.0024028085,"about_ca_system_score_codex":0.00036325437,"about_ca_system_score_gemma":0.000743991,"threshold_uncertainty_score":0.008038223},"labels":[],"label_agreement":null},{"id":"W4390831498","doi":"10.3390/s24020489","title":"Adaptive-Robust Controller for Smart Exoskeleton Robot","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College Ahuntsic","funders":"University of Wisconsin-Milwaukee","keywords":"Control theory (sociology); Exoskeleton; Robustness (evolution); Lyapunov function; Robust control; Robot; Controller (irrigation); Computer science; Control engineering; Sliding mode control; Robotics; Convergence (economics); Adaptive control; Rehabilitation robotics; Lyapunov stability; Control system; Engineering; Artificial intelligence; Simulation; Control (management); Nonlinear system","score_opus":0.01714392672059247,"score_gpt":0.22421417333964727,"score_spread":0.2070702466190548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390831498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015963465,0.0005956604,0.9749194,0.0001297761,0.00020585158,0.000092533264,0.000027552409,0.0016305514,0.006435259],"genre_scores_gemma":[0.93604815,0.00039500414,0.057999425,0.0001485559,0.00008774057,0.000282444,0.00006877699,0.000037244852,0.0049327346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997148,0.00003370851,0.000022997623,0.00008622328,0.00011578415,0.00002642009],"domain_scores_gemma":[0.9998217,0.000039547307,0.000040146802,0.000017456572,0.00007307445,0.000008086287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037102398,0.0005947908,0.00041432798,0.00035367877,0.000328253,0.00054234365,0.0008031023,0.0006521605,0.0017896089],"category_scores_gemma":[0.000556703,0.00015937487,0.00036413173,0.00018162337,0.00037988345,0.00032103935,0.00050859794,0.00049247965,0.00044855863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004087778,0.00014096468,0.0007918913,0.0006889684,0.00012278905,0.00074022607,0.00030875168,0.57687616,0.120797805,0.014430434,0.004211266,0.280482],"study_design_scores_gemma":[0.000043484968,0.00025935093,0.00040203737,0.000024563527,0.000019593055,0.0001128913,0.000013524217,0.9876529,0.006781526,0.0008987129,0.0037764676,0.000014998924],"about_ca_topic_score_codex":0.0021628242,"about_ca_topic_score_gemma":0.0016269486,"teacher_disagreement_score":0.0021628242,"about_ca_system_score_codex":0.0002427623,"about_ca_system_score_gemma":0.00040541668,"threshold_uncertainty_score":0.0059868097},"labels":[],"label_agreement":null},{"id":"W4390876248","doi":"10.3390/s24020540","title":"A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saccade; Electrooculography; Algorithm; Fusion; Constant (computer programming); Computer science; Sensor fusion; Artificial intelligence; Computer vision; Eye movement","score_opus":0.02238594253045921,"score_gpt":0.2319190399864109,"score_spread":0.20953309745595167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390876248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019207407,0.0002797633,0.9794037,0.000055839224,0.00003963485,0.000026699467,0.00001937375,0.00035234675,0.0006152654],"genre_scores_gemma":[0.46805513,0.00048656165,0.52812576,0.0001186935,0.00006384826,0.0001092501,0.00025248298,0.0001088443,0.0026795135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.000050667226,0.000042708176,0.00014941617,0.00015463194,0.00005167847],"domain_scores_gemma":[0.9995747,0.00010050757,0.00005560961,0.00005112601,0.00019288794,0.000025070736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000849327,0.00082164776,0.0009913439,0.00089168217,0.00047181998,0.00082048704,0.00089290267,0.0010312574,0.00078512554],"category_scores_gemma":[0.0019549988,0.00034514559,0.0011031049,0.00069851486,0.00033036116,0.0009804402,0.0008476683,0.0010500597,0.00044021814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038850194,0.00016299254,0.00239008,0.00012000016,0.00012816902,0.00018361483,0.00024564477,0.241326,0.07144658,0.007304029,0.0016372609,0.674667],"study_design_scores_gemma":[0.000010936373,0.00008755495,0.00086171,0.0000100885,0.000029578146,0.000057598845,0.000019076118,0.9905991,0.0063083386,0.0009809899,0.001018053,0.00001711408],"about_ca_topic_score_codex":0.00633283,"about_ca_topic_score_gemma":0.003923431,"teacher_disagreement_score":0.00633283,"about_ca_system_score_codex":0.0004318311,"about_ca_system_score_gemma":0.0011369771,"threshold_uncertainty_score":0.012591958},"labels":[],"label_agreement":null},{"id":"W4390898434","doi":"10.3390/s24020499","title":"Non-Invasive Estimation of Intracranial Pressure-Derived Cerebrovascular Reactivity Using Near-Infrared Spectroscopy Sensor Technology in Acute Neural Injury: A Time-Series Analysis","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canadian Institutes of Health Research; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Research Manitoba; National Institute of Neurological Disorders and Stroke; Health Sciences Centre Foundation","keywords":"Traumatic brain injury; Medicine; Internal medicine; Cardiology","score_opus":0.009009843804494875,"score_gpt":0.2688088415656837,"score_spread":0.2597989977611888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390898434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95583284,0.00045090204,0.042879764,0.000081271144,0.000019779802,0.000048572838,0.00022294805,0.00006410349,0.0003998668],"genre_scores_gemma":[0.9933442,0.00028205375,0.005757613,0.000010649563,0.000015156404,0.00004170574,0.00029995333,0.000008205395,0.00024050401],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99957365,0.00019496294,0.000035837686,0.00007091066,0.000098781406,0.000025852682],"domain_scores_gemma":[0.9990132,0.0005796394,0.0001747443,0.00011300324,0.000090109505,0.000029299837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020244217,0.00049540156,0.00040962,0.000803912,0.000129779,0.0004957046,0.00040709143,0.0002621266,0.00047023885],"category_scores_gemma":[0.0032950914,0.00011925033,0.00061622,0.00052290945,0.00018456904,0.0003875177,0.00030851478,0.0004185732,0.0001093334],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012918315,0.00090513594,0.71078193,0.00033037862,0.001607793,0.0006629851,0.0005544051,0.098408215,0.027478835,0.0016914215,0.000627506,0.1556595],"study_design_scores_gemma":[0.000012943105,0.0008944043,0.47891873,0.000037733833,0.00030683316,0.00043221167,0.00029922702,0.5118981,0.0055243797,0.0008642894,0.0007628796,0.000048332688],"about_ca_topic_score_codex":0.0023511269,"about_ca_topic_score_gemma":0.0024379338,"teacher_disagreement_score":0.0023511269,"about_ca_system_score_codex":0.00018677542,"about_ca_system_score_gemma":0.00037429482,"threshold_uncertainty_score":0.0107063055},"labels":[],"label_agreement":null},{"id":"W4390906497","doi":"10.3390/s24020574","title":"LoRaCELL-Driven IoT Smart Lighting Systems: Sustainability in Urban Infrastructure","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Agência Nacional de Energia Elétrica","keywords":"Smart city; Internet of Things; Sustainability; Urbanization; Architectural engineering; Computer science; Transformative learning; Telecommunications; Transport engineering; Engineering; Computer security","score_opus":0.004659312326964196,"score_gpt":0.22381912789494085,"score_spread":0.21915981556797665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390906497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13903518,0.0030112234,0.72283363,0.0025222548,0.0005102581,0.0003450426,0.00037031723,0.008563468,0.12280855],"genre_scores_gemma":[0.9277151,0.00092087605,0.048239276,0.00049261504,0.00006122752,0.0001116275,0.0002274245,0.00009943744,0.022132272],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997105,0.00005977866,0.0000090896665,0.000049701095,0.00013060085,0.00004036561],"domain_scores_gemma":[0.999835,0.000025752286,0.000023043509,0.000028079234,0.00006946185,0.00001859572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031136893,0.0002613931,0.00021535765,0.0002476344,0.00035557823,0.000929574,0.0006243001,0.00042645904,0.002411011],"category_scores_gemma":[0.00045628636,0.00010016682,0.0001510768,0.0003472609,0.00033379358,0.00097544154,0.0009660628,0.00044120097,0.0010543102],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005524378,0.00032037476,0.007454227,0.00060563465,0.0000624855,0.00078855746,0.00072721986,0.07824187,0.1380747,0.06610795,0.032877956,0.6741865],"study_design_scores_gemma":[0.00007650406,0.00074983464,0.003403738,0.00012386018,0.000058043024,0.0010661879,0.0007069834,0.5109999,0.14415373,0.020904388,0.31765172,0.00010511276],"about_ca_topic_score_codex":0.0015653766,"about_ca_topic_score_gemma":0.0023355538,"teacher_disagreement_score":0.002411011,"about_ca_system_score_codex":0.0007352361,"about_ca_system_score_gemma":0.0004357205,"threshold_uncertainty_score":0.008065641},"labels":[],"label_agreement":null},{"id":"W4391024105","doi":"10.3390/s24020653","title":"How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Research Council Canada; McGill University","keywords":"Kalman filter; Covariance matrix; Mathematics; Control theory (sociology); Covariance; Extended Kalman filter; Ensemble Kalman filter; Applied mathematics; Computer science; Statistics; Artificial intelligence","score_opus":0.013078330823385356,"score_gpt":0.20159904208053964,"score_spread":0.18852071125715428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391024105","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014575435,0.00066662475,0.97586274,0.004071552,0.00034993904,0.000049527604,0.00021500785,0.0014313047,0.002777876],"genre_scores_gemma":[0.67877007,0.0011746086,0.31203386,0.002066298,0.00033331223,0.00021832492,0.00078222225,0.00059767714,0.0040236963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99578696,0.0013626942,0.00040606578,0.0009887402,0.0010089537,0.00044647203],"domain_scores_gemma":[0.9783901,0.012071904,0.0014018773,0.0041603926,0.0035114025,0.00046436835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079830615,0.0015149104,0.0018803531,0.00075081177,0.0013382783,0.0025155293,0.0019693945,0.00419285,0.0038805117],"category_scores_gemma":[0.052453153,0.0012391702,0.0013513307,0.0004911684,0.0025748168,0.0073512355,0.0029974538,0.004796248,0.0022264218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039871506,0.00010692145,0.008013747,0.00055117236,0.0003854697,0.0006058299,0.0007378952,0.72128785,0.0058514695,0.07791317,0.015216156,0.16893165],"study_design_scores_gemma":[0.00008653994,0.000080551865,0.0013023096,0.00022008794,0.00006260219,0.0002162528,0.0002239223,0.88326293,0.005321372,0.101069205,0.007998311,0.00015589214],"about_ca_topic_score_codex":0.016233813,"about_ca_topic_score_gemma":0.011528312,"teacher_disagreement_score":0.016233813,"about_ca_system_score_codex":0.0009898451,"about_ca_system_score_gemma":0.0043904264,"threshold_uncertainty_score":0.042218983},"labels":[],"label_agreement":null},{"id":"W4391025210","doi":"10.3390/s24020656","title":"A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Sigma Xia; American College of Sports Medicine","keywords":"Inertial measurement unit; Time domain; Frequency domain; Units of measurement; Inertial frame of reference; Unit (ring theory); Computer science; Physics; Mathematics; Artificial intelligence; Classical mechanics; Computer vision","score_opus":0.06287457405577354,"score_gpt":0.3477574133348013,"score_spread":0.2848828392790278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391025210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914936,0.0001413808,0.007238462,0.00004688989,0.00006896325,0.00002411069,0.0002389801,0.00015125622,0.0005964161],"genre_scores_gemma":[0.9948744,0.00006143557,0.004319524,0.000041796364,0.000015643443,0.000036781817,0.00024273338,0.000048099522,0.00035961738],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999463,0.0001042336,0.00003937554,0.00020762724,0.00010710591,0.00007873707],"domain_scores_gemma":[0.99919945,0.0003147115,0.00017832845,0.000112009424,0.00012747162,0.00006813291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045309824,0.00042182996,0.0003394566,0.00027943196,0.00022169143,0.00046290463,0.0002702124,0.0005155192,0.0017251824],"category_scores_gemma":[0.0045418236,0.0002613136,0.00018149492,0.00029393952,0.00038772944,0.00046681488,0.00066499243,0.00030187835,0.00047626274],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050242273,0.00038560794,0.13074568,0.00054339,0.00021893135,0.0007505793,0.002762623,0.0079478,0.7010499,0.0004311542,0.0015281175,0.14861189],"study_design_scores_gemma":[0.00007345213,0.0039988933,0.906463,0.00008679706,0.00014980948,0.00090161455,0.0014395893,0.013468333,0.0683993,0.00085900584,0.004073928,0.00008632693],"about_ca_topic_score_codex":0.0012747365,"about_ca_topic_score_gemma":0.0023123743,"teacher_disagreement_score":0.0017251824,"about_ca_system_score_codex":0.00013307543,"about_ca_system_score_gemma":0.00016837704,"threshold_uncertainty_score":0.005771339},"labels":[],"label_agreement":null},{"id":"W4391025291","doi":"10.3390/s24020647","title":"Breaking Barriers: Exploring Neurotransmitters through In Vivo vs. In Vitro Rivalry","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Centre québécois sur les matériaux fonctionnels; CMC Microsystems","keywords":"Neurotransmitter; Neuroscience; Neurotransmission; Nanotechnology; Neurotransmitter Agents; Computer science; Biology; Materials science","score_opus":0.060504274856666186,"score_gpt":0.2754348112265287,"score_spread":0.2149305363698625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391025291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25187576,0.29863226,0.39417976,0.007693626,0.003351718,0.0005210121,0.0010979844,0.0014440292,0.041203897],"genre_scores_gemma":[0.658413,0.17897493,0.14655198,0.0036704957,0.0005865567,0.0006857182,0.00087360444,0.0003091308,0.009934658],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989009,0.000265671,0.00007936832,0.00025835983,0.00036945532,0.00012610537],"domain_scores_gemma":[0.9990765,0.0004671673,0.00016619042,0.000090096655,0.0001503311,0.000049742383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001280204,0.00088407256,0.0011118737,0.00040976843,0.0004561987,0.0019606033,0.0011527017,0.0015095227,0.0017042016],"category_scores_gemma":[0.0019053447,0.0004943388,0.0006628566,0.0003934246,0.0012896955,0.0025911606,0.001122323,0.0018119592,0.0011028018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008855058,0.000048762657,0.0003264213,0.0020243474,0.000050321345,0.00024743628,0.0002253969,0.0005855759,0.9577835,0.00899632,0.000797899,0.028825363],"study_design_scores_gemma":[0.00001602486,0.00052309583,0.0010519518,0.0002581373,0.000105144274,0.0008124963,0.00030898053,0.0033198567,0.93042505,0.0042497385,0.058871385,0.00005816678],"about_ca_topic_score_codex":0.0008190816,"about_ca_topic_score_gemma":0.0012631719,"teacher_disagreement_score":0.0019606033,"about_ca_system_score_codex":0.00092835736,"about_ca_system_score_gemma":0.00084112416,"threshold_uncertainty_score":0.006770432},"labels":[],"label_agreement":null},{"id":"W4391100539","doi":"10.3390/s24020669","title":"Extrinsic Calibration of Thermal Camera and 3D LiDAR Sensor via Human Matching in Both Modalities during Sensor Setup Movement","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Computer science; Computer vision; Adaptability; Artificial intelligence; Sensor fusion; Lidar; Modalities; Real-time computing; Matching (statistics); Remote sensing; Geography","score_opus":0.00770263184916821,"score_gpt":0.20871779600326,"score_spread":0.20101516415409176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391100539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.121854,0.0008234823,0.8661531,0.0002804118,0.00034088007,0.00015126864,0.0006904069,0.002820891,0.006885506],"genre_scores_gemma":[0.7077776,0.00044260736,0.28077096,0.0003875903,0.00010517469,0.00016765695,0.002449041,0.00042344152,0.0074758693],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981554,0.00033047472,0.000060933835,0.0006438914,0.0006285613,0.00018077789],"domain_scores_gemma":[0.9992268,0.00009427865,0.00010767288,0.00027901708,0.00024507017,0.00004722966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010911656,0.0011996283,0.00076914195,0.000892096,0.00038176437,0.0011354412,0.001011937,0.0010974063,0.0025811545],"category_scores_gemma":[0.004247119,0.00036211702,0.000743553,0.0010050371,0.0005982159,0.0014146287,0.002531475,0.0010766613,0.0020233027],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067787233,0.00025053235,0.012652577,0.00046472688,0.0002128265,0.00037012354,0.0004993957,0.07387936,0.13692419,0.0049416306,0.012775993,0.7563508],"study_design_scores_gemma":[0.000055891604,0.00046365187,0.025235936,0.00015745246,0.00013227467,0.002462211,0.00051529374,0.7715294,0.16156209,0.010321662,0.027434058,0.00013012077],"about_ca_topic_score_codex":0.0019620098,"about_ca_topic_score_gemma":0.0044392073,"teacher_disagreement_score":0.0025811545,"about_ca_system_score_codex":0.00034022072,"about_ca_system_score_gemma":0.0008861505,"threshold_uncertainty_score":0.008634806},"labels":[],"label_agreement":null},{"id":"W4391175471","doi":"10.3390/s24030744","title":"Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetes Management and Research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Glycemic; Medicine; Cohort; Plasma glucose; Meal; Continuous glucose monitoring; Diabetes mellitus; Internal medicine; Sleep (system call); Type 2 diabetes; Endocrinology; Gerontology","score_opus":0.009839081033429026,"score_gpt":0.30278485117907644,"score_spread":0.29294577014564743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391175471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99874675,0.0001947764,0.00016058965,0.000033956476,0.0000061548503,0.000010742248,0.00047580193,0.000005030289,0.00036609938],"genre_scores_gemma":[0.9986498,0.00016445066,0.0002216869,0.00006099088,0.000019614343,0.000023734081,0.00061731925,0.0000028716636,0.00023955443],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982184,0.000047157257,0.000019071414,0.000053159103,0.000034536537,0.000024220848],"domain_scores_gemma":[0.99969244,0.000049713326,0.000112216985,0.000039274193,0.000039785795,0.00006648991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002488825,0.00025926525,0.00029883554,0.00045883897,0.0003415219,0.00056555297,0.00020131086,0.00036106564,0.000855588],"category_scores_gemma":[0.0011160318,0.00017660398,0.00025135258,0.0005737137,0.00014774714,0.00034091913,0.00035011384,0.0003134619,0.00028768778],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000139753,0.000040369305,0.9965293,0.000012556988,0.000045851375,0.0001212271,0.00015004279,0.000021933636,0.0005796814,0.00000850755,0.00013066476,0.002220188],"study_design_scores_gemma":[0.0000025479972,0.00007405356,0.9992625,0.0000029664593,0.000012357135,0.00020603674,0.00017728597,0.0000706065,0.000025510935,0.000010148082,0.00015356726,0.0000024487122],"about_ca_topic_score_codex":0.00243586,"about_ca_topic_score_gemma":0.0034429238,"teacher_disagreement_score":0.00243586,"about_ca_system_score_codex":0.00009945176,"about_ca_system_score_gemma":0.00009120384,"threshold_uncertainty_score":0.004843354},"labels":[],"label_agreement":null},{"id":"W4391345032","doi":"10.3390/s24030897","title":"FPGA Implementation of Complex-Valued Neural Network for Polar-Represented Image Classification","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Field-programmable gate array; Computer science; MNIST database; Artificial neural network; Convolutional neural network; Artificial intelligence; Contextual image classification; Implementation; Machine learning; Inference; Computer engineering; Resource (disambiguation); Image (mathematics); Embedded system","score_opus":0.029967725838669267,"score_gpt":0.31221237331080925,"score_spread":0.28224464747214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391345032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056348287,0.0004322996,0.9286764,0.00022704352,0.00018347817,0.00006742123,0.00015965749,0.0033912242,0.010514227],"genre_scores_gemma":[0.71603215,0.00035687588,0.27772695,0.0001446121,0.000033537188,0.00008702693,0.0003265255,0.000071900446,0.005220384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998944,0.000019251484,0.000006615616,0.000024267742,0.00004091701,0.000014501713],"domain_scores_gemma":[0.9998472,0.000043293934,0.00001466768,0.000032075495,0.000056158427,0.000006647113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017812145,0.0003441187,0.00016333039,0.00024175293,0.00012997963,0.00046821873,0.00065229315,0.0002518028,0.0037095326],"category_scores_gemma":[0.0005732346,0.0001201532,0.00014630065,0.00026707,0.00014280964,0.00048727175,0.00019795926,0.00034836607,0.00090217585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039082093,0.00013692312,0.0029650542,0.0003027583,0.00008837874,0.00033314273,0.00010683818,0.25110158,0.07084739,0.019433325,0.009456547,0.6448372],"study_design_scores_gemma":[0.000018216586,0.0001039808,0.00073180144,0.000017965782,0.000014424022,0.0001418346,0.000019414641,0.96720785,0.022957051,0.001949575,0.0068256822,0.000012281055],"about_ca_topic_score_codex":0.0034686907,"about_ca_topic_score_gemma":0.004917881,"teacher_disagreement_score":0.0037095326,"about_ca_system_score_codex":0.00037010422,"about_ca_system_score_gemma":0.00041454725,"threshold_uncertainty_score":0.012409568},"labels":[],"label_agreement":null},{"id":"W4391345087","doi":"10.3390/s24030884","title":"Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Activity recognition; Artificial intelligence; Generalization; Machine learning; Transfer of learning; Deep learning; Focus (optics); Encoder; Pattern recognition (psychology)","score_opus":0.015560111803390543,"score_gpt":0.2478244522383195,"score_spread":0.23226434043492897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391345087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19229402,0.0016397511,0.7861227,0.00045345735,0.00021761087,0.0001496261,0.00068769907,0.014569798,0.0038654457],"genre_scores_gemma":[0.87908095,0.0003804863,0.113495424,0.00053433405,0.00009054681,0.00016313091,0.0015882001,0.00019899063,0.004467944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950063,0.00010467655,0.000019443614,0.00022509531,0.00008426139,0.000065949826],"domain_scores_gemma":[0.9995776,0.0001621843,0.000048078487,0.00007558327,0.00010483616,0.00003174462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074198906,0.0010876852,0.00093033334,0.00044971931,0.0001850074,0.00044940432,0.0014816774,0.00077402743,0.001051418],"category_scores_gemma":[0.0015412258,0.00031490385,0.0007950266,0.00037928656,0.00041855863,0.0009889747,0.0010256608,0.0009917344,0.00069107406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057595625,0.0008464745,0.0059031905,0.00022462274,0.00021624574,0.00022381358,0.0002572677,0.25108668,0.02436115,0.0010845505,0.009700668,0.7055193],"study_design_scores_gemma":[0.00001900088,0.0001293533,0.0018073841,0.000010097627,0.00002092073,0.000051201154,0.00002224048,0.98988336,0.006154777,0.001043908,0.00084569893,0.0000119579845],"about_ca_topic_score_codex":0.005334024,"about_ca_topic_score_gemma":0.009897918,"teacher_disagreement_score":0.005334024,"about_ca_system_score_codex":0.0005411343,"about_ca_system_score_gemma":0.00058063347,"threshold_uncertainty_score":0.010605931},"labels":[],"label_agreement":null},{"id":"W4391345203","doi":"10.3390/s24030864","title":"Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Agri-Food Innovation Alliance; University of Guelph","keywords":"Soil fertility; Soil quality; Environmental science; Soil test; Soil science; Soil pH; Soil organic matter; Soil health; Soil water","score_opus":0.24624315470271693,"score_gpt":0.4803599802389741,"score_spread":0.23411682553625715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391345203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9159847,0.00096941437,0.07566395,0.000077961704,0.000040610066,0.00039526308,0.0021241547,0.00043598062,0.004307984],"genre_scores_gemma":[0.94686764,0.00046283772,0.0497144,0.000038928723,0.000013244654,0.00032152148,0.001728063,0.000030606076,0.0008228395],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983047,0.0002932893,0.0001731964,0.0003236284,0.0008176847,0.00008750705],"domain_scores_gemma":[0.9977429,0.0006051353,0.0006429075,0.00014915914,0.0007471814,0.000112837806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025431172,0.0010625371,0.0008396803,0.003098667,0.00034611532,0.0014067884,0.0006357266,0.000823474,0.0005836022],"category_scores_gemma":[0.0032303233,0.00027660216,0.0012420559,0.0026870908,0.00042949515,0.0009865214,0.0007445017,0.00056797167,0.00036773292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004966595,0.00042289207,0.78091896,0.0005364354,0.0010008919,0.00018846968,0.00026717802,0.028901022,0.04735941,0.00046212212,0.0006520078,0.13879389],"study_design_scores_gemma":[0.00008003769,0.0012677785,0.7060953,0.00009042764,0.00055314484,0.0004360123,0.00054092135,0.23492496,0.05175362,0.0009163552,0.0031283665,0.0002130999],"about_ca_topic_score_codex":0.0054306225,"about_ca_topic_score_gemma":0.01046276,"teacher_disagreement_score":0.0054306225,"about_ca_system_score_codex":0.0007504923,"about_ca_system_score_gemma":0.00059534545,"threshold_uncertainty_score":0.01344949},"labels":[],"label_agreement":null},{"id":"W4391345344","doi":"10.3390/s24030876","title":"Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Serviceability (structure); Structural health monitoring; Bridge (graph theory); Identification (biology); Computer science; Vibration; Process (computing); Reliability engineering; Engineering; Systems engineering; Forensic engineering; Structural engineering","score_opus":0.0501816596214487,"score_gpt":0.38207586351683653,"score_spread":0.3318942038953878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391345344","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037282417,0.99441177,0.0034456078,0.00034698113,0.00035892465,0.000014536393,0.000022798922,0.000018869636,0.0010076313],"genre_scores_gemma":[0.0039189826,0.99018735,0.0041960827,0.00029583724,0.0006583929,0.000026099988,0.00007224618,0.000011806131,0.0006332733],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990828,0.00016116358,0.00013013963,0.00021982429,0.00035050287,0.000055531327],"domain_scores_gemma":[0.99540603,0.0029275988,0.00035589957,0.00014510636,0.0010694887,0.00009589464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002743242,0.0010838035,0.0015031283,0.0030889194,0.00032428466,0.0015511992,0.0020460286,0.0021112785,0.0025603375],"category_scores_gemma":[0.003352048,0.00069468736,0.0010284975,0.0032548169,0.0010329998,0.003349341,0.0008370038,0.0018809662,0.0017685917],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010368845,0.00014436382,0.000609233,0.025376508,0.00016633012,0.00020064658,0.0001842684,0.002072893,0.006357939,0.011297253,0.014623594,0.93886334],"study_design_scores_gemma":[0.000015140353,0.0005612514,0.0024397662,0.009681249,0.0005279888,0.0017814416,0.00037875312,0.0035339042,0.008139849,0.006688349,0.9660822,0.00017007673],"about_ca_topic_score_codex":0.0013384618,"about_ca_topic_score_gemma":0.0009995579,"teacher_disagreement_score":0.0030889194,"about_ca_system_score_codex":0.0006932615,"about_ca_system_score_gemma":0.0013497453,"threshold_uncertainty_score":0.01450783},"labels":[],"label_agreement":null},{"id":"W4391345536","doi":"10.3390/s24030842","title":"Two-Stage Atomic Decomposition of Multichannel EEG and the Previously Undetectable Sleep Spindles","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sleep spindle; Electroencephalography; Sleep (system call); Matching pursuit; Computer science; Sleep Stages; Pattern recognition (psychology); Pipeline (software); SIGNAL (programming language); Artificial intelligence; Noise (video); Matching (statistics); Slow-wave sleep; Psychology; Neuroscience; Polysomnography; Mathematics; Statistics","score_opus":0.015362845088612048,"score_gpt":0.27007139394538093,"score_spread":0.25470854885676886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391345536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041937687,0.0000990953,0.95702684,0.00006349445,0.000015444404,0.00003811602,0.00008326782,0.0003038551,0.00043214703],"genre_scores_gemma":[0.24276744,0.0001687649,0.7553295,0.000024793884,0.000036060235,0.00008349562,0.0002923991,0.000098166136,0.0011994092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998522,0.000030254207,0.000007768233,0.00003612366,0.000053811782,0.000019947993],"domain_scores_gemma":[0.9997181,0.00008599864,0.000049795366,0.00006102935,0.00006820287,0.000016797483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003545021,0.0004417938,0.00025217037,0.0007081443,0.00018617038,0.00042645785,0.0003776118,0.00026487498,0.0013484007],"category_scores_gemma":[0.0012493428,0.00021044878,0.00045738282,0.00052238663,0.00029348335,0.00033635777,0.00054397417,0.0003271112,0.00041813072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026548177,0.000076695,0.0057511413,0.00028169464,0.000108071494,0.00022733015,0.00033191487,0.043998294,0.22189578,0.010136826,0.0014838215,0.7154429],"study_design_scores_gemma":[0.000030768864,0.0001953742,0.02097151,0.000023683391,0.00006532231,0.0003875869,0.00011466567,0.90135103,0.06051599,0.010276385,0.0060231974,0.000044424025],"about_ca_topic_score_codex":0.0011888721,"about_ca_topic_score_gemma":0.002058474,"teacher_disagreement_score":0.0013484007,"about_ca_system_score_codex":0.000094676616,"about_ca_system_score_gemma":0.0004200205,"threshold_uncertainty_score":0.0045108795},"labels":[],"label_agreement":null},{"id":"W4391472114","doi":"10.3390/s24030971","title":"Detecting Multiple Damages in UHPFRC Beams through Modal Curvature Analysis","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Curvature; Structural engineering; Finite element method; Modal; Modal analysis; Beam (structure); Vibration; Acceleration; Materials science; Engineering; Acoustics; Mathematics; Geometry; Physics; Composite material","score_opus":0.016526485110050268,"score_gpt":0.2906914974059668,"score_spread":0.2741650122959165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391472114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46652594,0.00044929967,0.5303482,0.000064858876,0.000029458583,0.000059484486,0.00011320049,0.0009250066,0.0014846204],"genre_scores_gemma":[0.8839635,0.00017540791,0.11497029,0.00002343436,0.000010893916,0.000026423322,0.000094681614,0.000034412722,0.00070095446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994568,0.00007976171,0.00001941336,0.00010832943,0.00029282103,0.000042790667],"domain_scores_gemma":[0.9992906,0.00014972311,0.00020671132,0.00008867079,0.00023047473,0.000033808312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054854195,0.0006623732,0.00030813442,0.0017154956,0.00014452297,0.00029409226,0.00045821528,0.0005574488,0.00058242015],"category_scores_gemma":[0.0007915164,0.0002549177,0.00038941894,0.0006843771,0.00033551338,0.00064494356,0.00051645096,0.00028376837,0.00024338039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038143815,0.00011222264,0.024750547,0.0002863726,0.00010035205,0.00027286191,0.00044101206,0.037885204,0.6910232,0.0013113313,0.00045536293,0.24298024],"study_design_scores_gemma":[0.000018010978,0.00042648494,0.090349115,0.000045740824,0.00009145884,0.0009152572,0.00023154616,0.6829874,0.2217037,0.0008190077,0.0022707393,0.00014163503],"about_ca_topic_score_codex":0.0010197,"about_ca_topic_score_gemma":0.0021671732,"teacher_disagreement_score":0.0017154956,"about_ca_system_score_codex":0.00028387335,"about_ca_system_score_gemma":0.00021544946,"threshold_uncertainty_score":0.0029010177},"labels":[],"label_agreement":null},{"id":"W4391535653","doi":"10.3390/s24031020","title":"A Microwave Differential Dielectric Sensor Based on Mode Splitting of Coupled Resonators","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"King Saud University","keywords":"Resonator; Microstrip; Microwave; Coupling (piping); Planar; Dielectric; Optoelectronics; Materials science; Resonance (particle physics); Port (circuit theory); Coplanar waveguide; Electronic engineering; Acoustics; Physics; Optics; Engineering; Telecommunications; Computer science; Atomic physics","score_opus":0.011837064005559469,"score_gpt":0.2285373886988776,"score_spread":0.21670032469331812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391535653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76543975,0.00064117595,0.23047554,0.00013646268,0.00012256866,0.00008991018,0.00010481593,0.0006576385,0.002332237],"genre_scores_gemma":[0.8761213,0.00024098651,0.12259479,0.000059036745,0.000015805494,0.000035024044,0.000046196437,0.000020623984,0.00086633814],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956614,0.000060131766,0.000015891022,0.00014179827,0.00018973839,0.000026382402],"domain_scores_gemma":[0.99973375,0.000079097954,0.00008599637,0.000040543557,0.000040530413,0.000020013289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032280313,0.00035916298,0.00037709234,0.00018120061,0.00014256827,0.00037306335,0.0008694184,0.00048732542,0.00043931897],"category_scores_gemma":[0.00048126103,0.00031517626,0.00020840862,0.00016205925,0.00037285767,0.00082261243,0.0004267089,0.00035836129,0.00022750492],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041695435,0.000020532738,0.00027468568,0.000054881104,0.00000788368,0.00006465557,0.000024690846,0.0009210735,0.99394494,0.0006591448,0.0000357374,0.0039500156],"study_design_scores_gemma":[0.000014606406,0.00032703136,0.0007004957,0.0000036932609,0.000014351951,0.00025350557,0.000018183238,0.039532658,0.95763636,0.00016479132,0.0013158001,0.000018502054],"about_ca_topic_score_codex":0.00013106629,"about_ca_topic_score_gemma":0.00026785617,"teacher_disagreement_score":0.0008694184,"about_ca_system_score_codex":0.00027238514,"about_ca_system_score_gemma":0.00016856175,"threshold_uncertainty_score":0.0019762516},"labels":[],"label_agreement":null},{"id":"W4391574457","doi":"10.3390/s24041044","title":"Cryologger Ice Tracking Beacon: A Low-Cost, Open-Source Platform for Tracking Icebergs and Ice Islands","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Environment and Climate Change Canada; Transport Canada; Polar Knowledge Canada; ArcticNet; University of Ottawa","keywords":"Iceberg; Real-time computing; Global Positioning System; Remote sensing; Tracking (education); Data logger; Computer science; Arctic; Sea ice; Environmental science; Geology; Oceanography; Telecommunications; Operating system","score_opus":0.04442200384224423,"score_gpt":0.2695211349089006,"score_spread":0.22509913106665638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391574457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27901903,0.0021820732,0.5045757,0.00067529915,0.0006286571,0.001354304,0.04439533,0.10893952,0.058230076],"genre_scores_gemma":[0.64720184,0.0010299659,0.266818,0.00051430095,0.00016387788,0.0010150591,0.047319878,0.0053640073,0.030573146],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996798,0.000020190413,0.000008932916,0.000070604394,0.00018220638,0.0000382129],"domain_scores_gemma":[0.999699,0.000039083257,0.000055931996,0.00005200316,0.00010065408,0.000053328095],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00036448633,0.00058720796,0.00035160233,0.0010849207,0.00028181876,0.00043571298,0.00077500683,0.00032433597,0.005249167],"category_scores_gemma":[0.0006327143,0.00023603137,0.00016421333,0.0007705652,0.00026415926,0.0006511968,0.00071043876,0.00039008234,0.0027646285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012698617,0.00025230515,0.043849643,0.0009352436,0.00011796773,0.00061076053,0.0014143004,0.011135188,0.27437863,0.0039801337,0.15124176,0.51081425],"study_design_scores_gemma":[0.00036646033,0.0009705404,0.15375954,0.00029594672,0.00012252953,0.0010852016,0.00053972006,0.113695316,0.16628626,0.0027078956,0.55992293,0.00024769097],"about_ca_topic_score_codex":0.0064373035,"about_ca_topic_score_gemma":0.010330069,"teacher_disagreement_score":0.999225,"about_ca_system_score_codex":0.00040852945,"about_ca_system_score_gemma":0.00063818216,"threshold_uncertainty_score":0.017560244},"labels":[],"label_agreement":null},{"id":"W4391575482","doi":"10.3390/s24041055","title":"Instrumented Pre-Hospital Care Simulation Mannequin for Use in Spinal Motion Restrictions Scenarios: Validation of Cervical and Lumbar Motion Assessment","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Lumbar; Pelvis; Motion (physics); Computer science; Motion analysis; Medicine; Simulation; Nuclear medicine; Physical medicine and rehabilitation; Computer vision; Surgery","score_opus":0.0178994558458421,"score_gpt":0.32959619667530193,"score_spread":0.31169674082945986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391575482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.966978,0.000051387502,0.031624332,0.000037370835,0.00001327254,0.00053755275,0.00020862532,0.00012136186,0.0004280832],"genre_scores_gemma":[0.9747501,0.000038139005,0.024296686,0.00003142577,0.0000045464876,0.00034921884,0.00031555613,0.000008733831,0.00020553124],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978815,0.0011011458,0.0001934364,0.00016569905,0.0005502755,0.000107927975],"domain_scores_gemma":[0.9962739,0.0015078805,0.00042597542,0.00039992956,0.001224444,0.0001678145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00288719,0.0007124885,0.00029377674,0.0004365838,0.00023247594,0.00028564373,0.00085055316,0.0006375527,0.0008432089],"category_scores_gemma":[0.008898414,0.00017043247,0.0002727328,0.00016889512,0.0003822489,0.00031489483,0.0005594829,0.0002751933,0.0002707764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006305026,0.00409755,0.33973438,0.00093762163,0.00031208183,0.0007173756,0.0032483113,0.045087803,0.4412752,0.00048408372,0.0009158343,0.1568848],"study_design_scores_gemma":[0.0004593313,0.03147756,0.58100736,0.00022786151,0.00026490743,0.003278516,0.0013125901,0.13866192,0.23686746,0.00031291158,0.005968287,0.0001612978],"about_ca_topic_score_codex":0.0021700696,"about_ca_topic_score_gemma":0.0035577335,"teacher_disagreement_score":0.00288719,"about_ca_system_score_codex":0.00037531406,"about_ca_system_score_gemma":0.0008885915,"threshold_uncertainty_score":0.015269101},"labels":[],"label_agreement":null},{"id":"W4391576219","doi":"10.3390/s24041054","title":"Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Regression; Regression analysis; Support vector machine; Artificial intelligence; Feature selection; Convolutional neural network; Computer science; Pattern recognition (psychology); Mean squared error; Electroencephalography; Multivariate statistics; Statistics; Machine learning; Mathematics; Medicine","score_opus":0.11694992156826213,"score_gpt":0.34558576077979547,"score_spread":0.22863583921153335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391576219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38052964,0.0005885708,0.6150604,0.00012769882,0.000046510457,0.00008878415,0.0006286012,0.0018786207,0.0010513002],"genre_scores_gemma":[0.88800645,0.00033123104,0.10996336,0.000031624455,0.000033606255,0.000064616215,0.0006524367,0.00010837497,0.0008083404],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996991,0.000069449095,0.000027630738,0.000105845334,0.000073888215,0.000024115892],"domain_scores_gemma":[0.99921274,0.00035458672,0.00016675213,0.00006663304,0.00017665538,0.000022570737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008324807,0.0009103388,0.0005392537,0.0009036775,0.0001031994,0.00043873634,0.00026708044,0.000253694,0.00062438723],"category_scores_gemma":[0.0032827193,0.00013219014,0.00059376727,0.00074164657,0.00012415738,0.0004066171,0.00023638766,0.00041547022,0.00030074935],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007006271,0.00032240266,0.053036246,0.00029640528,0.0006144036,0.00055314257,0.00019860853,0.21017759,0.10638791,0.000868247,0.002369722,0.62447476],"study_design_scores_gemma":[0.000015199476,0.00018872025,0.04152555,0.000016054446,0.00008872285,0.00033954164,0.0000413588,0.9395047,0.016776415,0.00082971295,0.0006398691,0.00003427104],"about_ca_topic_score_codex":0.0019937921,"about_ca_topic_score_gemma":0.002491119,"teacher_disagreement_score":0.0019937921,"about_ca_system_score_codex":0.00013155838,"about_ca_system_score_gemma":0.00021752472,"threshold_uncertainty_score":0.0044026375},"labels":[],"label_agreement":null},{"id":"W4391576879","doi":"10.3390/s24041056","title":"Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hamilton Health Sciences; St. Peter's Hospital; McMaster University; University of Waterloo","funders":"","keywords":"Radar; Population; Gait; Computer science; Medicine; Artificial intelligence; Algorithm; Physical medicine and rehabilitation; Telecommunications","score_opus":0.03714924332324875,"score_gpt":0.3309178096100954,"score_spread":0.29376856628684667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391576879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78423953,0.0015552905,0.20756426,0.00015567946,0.00013765745,0.0003060401,0.0013183432,0.0018551442,0.002868147],"genre_scores_gemma":[0.8826545,0.0007683974,0.11427479,0.00016655425,0.0000719314,0.00024126802,0.00080467016,0.00004270281,0.00097522524],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99962306,0.0001267,0.000028372768,0.00008713246,0.00011502714,0.000019764344],"domain_scores_gemma":[0.99937844,0.00013899441,0.00011203312,0.0000694288,0.0002699361,0.00003121092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042501886,0.00048390075,0.00048107895,0.0008243191,0.00013647522,0.0003578646,0.0005206738,0.00042302874,0.0008332073],"category_scores_gemma":[0.0016309337,0.00014920266,0.00022966036,0.00044453036,0.000102752514,0.0003207218,0.0005231458,0.0002973044,0.00067122444],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001971503,0.0005386583,0.18183006,0.00085959595,0.00017277239,0.0005568195,0.0007086823,0.0049411026,0.11567426,0.00046387335,0.00479563,0.687487],"study_design_scores_gemma":[0.0004255435,0.002704927,0.6290881,0.00029439118,0.00048235944,0.0040500895,0.001071783,0.2634941,0.08978253,0.001529699,0.0068680095,0.00020845202],"about_ca_topic_score_codex":0.0011012612,"about_ca_topic_score_gemma":0.0021785735,"teacher_disagreement_score":0.0011012612,"about_ca_system_score_codex":0.00011563297,"about_ca_system_score_gemma":0.00020328542,"threshold_uncertainty_score":0.0027873516},"labels":[],"label_agreement":null},{"id":"W4391604400","doi":"10.3390/s24041085","title":"Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network packet; Schedule; Differential evolution; Scheduling (production processes); Wireless sensor network; Channel (broadcasting); Throughput; Real-time computing; Algorithm; Wireless; Computer network; Mathematical optimization; Mathematics","score_opus":0.01396996822067827,"score_gpt":0.25509411122625875,"score_spread":0.24112414300558047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391604400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66891235,0.0010698478,0.32258594,0.00018233224,0.000077255,0.000098611825,0.000055867484,0.00022997489,0.0067877867],"genre_scores_gemma":[0.98106605,0.00013666604,0.01845079,0.0000135275495,0.000004987317,0.000018137165,0.000030422565,0.00000950941,0.00026978913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994816,0.00017002708,0.00002259372,0.0000604686,0.00016334138,0.00010196804],"domain_scores_gemma":[0.9983498,0.0011006313,0.00011930188,0.00007015825,0.00029306268,0.00006718768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011966121,0.00041250838,0.00041982814,0.0007392668,0.00031067804,0.0004512627,0.0004448631,0.00026853508,0.0005167895],"category_scores_gemma":[0.002906811,0.0000991794,0.00023747899,0.00059820624,0.0002590298,0.00056897546,0.0003071624,0.00018951383,0.000034582732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015626586,0.00007184086,0.0014511405,0.00004778729,0.000030657306,0.00004569633,0.00003465199,0.95858073,0.003947909,0.0035555654,0.0002238606,0.03185393],"study_design_scores_gemma":[0.0000054848942,0.00008550493,0.00030252992,0.0000015253357,0.000007752879,0.00001537615,0.000014666094,0.99820614,0.00088181306,0.00036975404,0.000106645464,0.0000027227443],"about_ca_topic_score_codex":0.0034266228,"about_ca_topic_score_gemma":0.0027856617,"teacher_disagreement_score":0.0034266228,"about_ca_system_score_codex":0.0008521415,"about_ca_system_score_gemma":0.0010755489,"threshold_uncertainty_score":0.0068133473},"labels":[],"label_agreement":null},{"id":"W4391604574","doi":"10.3390/s24041082","title":"Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Air Force Research Laboratory; National Research Council Canada; U.S. Air Force; Florida Institute of Technology","keywords":"Cognition; Human multitasking; Electroencephalography; Psychology; Task (project management); Effects of sleep deprivation on cognitive performance; Attentional blink; Audiology; Workload; Elementary cognitive task; Cognitive load; Cognitive psychology; Computer science; Neuroscience; Engineering; Medicine","score_opus":0.01625445734422184,"score_gpt":0.2728574846668834,"score_spread":0.2566030273226616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391604574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875968,0.00029881968,0.00966405,0.000046448993,0.000014397421,0.000052502106,0.00041049795,0.00009079218,0.0018256746],"genre_scores_gemma":[0.99472564,0.00020054358,0.0043222625,0.000032614687,0.000021157972,0.000050789426,0.00024134365,0.000018835059,0.00038694864],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99985504,0.000028892866,0.0000150140295,0.00003933205,0.000044390974,0.00001730997],"domain_scores_gemma":[0.99889755,0.00039178127,0.00036130584,0.00007180954,0.00016187286,0.000115701085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003686494,0.00039097713,0.00014608598,0.000457847,0.0001030067,0.00043320883,0.00018370626,0.00031008563,0.0025814592],"category_scores_gemma":[0.0021517917,0.0000957108,0.00010649759,0.0002437257,0.00023438598,0.00042677057,0.00031247162,0.00023059729,0.0003455853],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002073052,0.0007467373,0.2354539,0.00095626776,0.00018344332,0.00045099994,0.0012290712,0.0017039943,0.5761561,0.00040002458,0.0012957978,0.1793506],"study_design_scores_gemma":[0.000044025983,0.0011911681,0.97005874,0.000050812283,0.000045628534,0.00068991183,0.000340375,0.002633918,0.02299289,0.00048230874,0.0014435309,0.000026696056],"about_ca_topic_score_codex":0.00039052605,"about_ca_topic_score_gemma":0.0010023826,"teacher_disagreement_score":0.0025814592,"about_ca_system_score_codex":0.00009770841,"about_ca_system_score_gemma":0.00009967668,"threshold_uncertainty_score":0.008635819},"labels":[],"label_agreement":null},{"id":"W4391711278","doi":"10.3390/s24041100","title":"Optimizing Epoch Length and Activity Count Threshold Parameters in Accelerometry: Enhancing Upper Extremity Use Quantification in Cerebral Palsy","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Université Laval","keywords":"Epoch (astronomy); Accelerometer; Cerebral palsy; Wrist; Medicine; Physical medicine and rehabilitation; Computer science; Surgery; Computer vision","score_opus":0.046672195974459915,"score_gpt":0.29750210739033645,"score_spread":0.2508299114158765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391711278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8336704,0.00312111,0.15982296,0.00018590639,0.00013147482,0.0003975504,0.0004319579,0.0005243513,0.0017142823],"genre_scores_gemma":[0.78285676,0.0024848673,0.21243949,0.0001153147,0.000071731134,0.00066511915,0.00054002914,0.00016588064,0.0006607977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994134,0.0002246774,0.000084084415,0.00011261593,0.00012776218,0.000037547972],"domain_scores_gemma":[0.9989692,0.0004983623,0.00020200388,0.00007574056,0.00021539429,0.000039305898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017160256,0.0008012013,0.0005302277,0.0008549698,0.00018029202,0.0007504645,0.000302702,0.00050176866,0.00071514456],"category_scores_gemma":[0.0058270493,0.00024972824,0.00030478934,0.0008651501,0.00021323758,0.0005756283,0.0003814057,0.0002802655,0.0003556425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036383693,0.00072118733,0.17714591,0.0016655206,0.00030265012,0.000283005,0.00087626226,0.010465106,0.28147626,0.00038447406,0.0013187873,0.52172244],"study_design_scores_gemma":[0.00019861791,0.0044560153,0.8589867,0.00055180857,0.00065570266,0.0015985195,0.0009559849,0.035129715,0.09150289,0.0010222975,0.004770555,0.00017116351],"about_ca_topic_score_codex":0.0010415323,"about_ca_topic_score_gemma":0.0033237291,"teacher_disagreement_score":0.0017160256,"about_ca_system_score_codex":0.00017177699,"about_ca_system_score_gemma":0.000439196,"threshold_uncertainty_score":0.009075344},"labels":[],"label_agreement":null},{"id":"W4391752375","doi":"10.3390/s24041190","title":"A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Pile; Computer science; Convolutional neural network; Artificial neural network; Identification (biology); Task (project management); Algorithm; Set (abstract data type); Structural health monitoring; Series (stratigraphy); Artificial intelligence; Data mining; Pattern recognition (psychology); Engineering; Structural engineering; Geology","score_opus":0.018572200611959737,"score_gpt":0.2668367836707498,"score_spread":0.24826458305879007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391752375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062213894,0.00078606326,0.93336254,0.00018463329,0.00008138012,0.000040271603,0.00012899934,0.0012667853,0.0019354986],"genre_scores_gemma":[0.8525936,0.00038251057,0.14261298,0.00011511432,0.000052147967,0.00009100233,0.00031267424,0.00004742212,0.0037925506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998062,0.000031268857,0.000012882007,0.000057334146,0.00006379407,0.000028596174],"domain_scores_gemma":[0.99979883,0.00005921498,0.000031409785,0.000026275338,0.000072543895,0.000011615328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005202246,0.0006735656,0.00046351904,0.00038354218,0.00021614593,0.00041054105,0.0009787584,0.0006230978,0.0007153824],"category_scores_gemma":[0.000734442,0.00028803851,0.00056189013,0.00041308746,0.00023861883,0.0007497029,0.0004442046,0.0005480975,0.00025631537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024084558,0.00016641666,0.0022644938,0.000114445895,0.00017846309,0.00017357749,0.000056598772,0.7077007,0.03577434,0.0037243906,0.0016437162,0.24796201],"study_design_scores_gemma":[0.0000015494224,0.000022386526,0.00018264828,0.0000016481471,0.000008198188,0.0000130248,0.0000013947468,0.99786997,0.0015440998,0.00021136289,0.00013971247,0.000003885549],"about_ca_topic_score_codex":0.006941645,"about_ca_topic_score_gemma":0.008601448,"teacher_disagreement_score":0.006941645,"about_ca_system_score_codex":0.0005393017,"about_ca_system_score_gemma":0.00048092028,"threshold_uncertainty_score":0.013802469},"labels":[],"label_agreement":null},{"id":"W4391813416","doi":"10.3390/s24041210","title":"An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Node (physics); Fading; Computer science; Multipath propagation; Context (archaeology); Log-distance path loss model; RSS; Path loss; Key distribution in wireless sensor networks; Signal strength; Wireless network; Wireless; Computer network; Real-time computing; Telecommunications; Engineering; Geography","score_opus":0.011388155615084716,"score_gpt":0.22790380674389077,"score_spread":0.21651565112880605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391813416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009585599,0.00022457511,0.98924094,0.000034431792,0.00001709156,0.000012898495,0.0000150225815,0.00020758515,0.0006619232],"genre_scores_gemma":[0.6534084,0.0010201597,0.34399968,0.00006473312,0.00007027571,0.00008300894,0.00007894239,0.000054079323,0.0012207221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947923,0.00017097546,0.000020929929,0.0000923131,0.00020908115,0.000027402235],"domain_scores_gemma":[0.99955577,0.00015500684,0.00006735781,0.00010984097,0.00009910064,0.000012866257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056197157,0.00058584084,0.00038725158,0.0005747979,0.00020897831,0.0004641792,0.00066260743,0.00052025117,0.00046586187],"category_scores_gemma":[0.0019677915,0.00026699336,0.00039508037,0.00055887614,0.0005079115,0.0010208937,0.00074032677,0.0004548168,0.00030382266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005300019,0.000028226548,0.0010867084,0.00012568933,0.000042090574,0.00016868084,0.00009516526,0.8820037,0.032485835,0.018146716,0.0005370332,0.06522708],"study_design_scores_gemma":[0.000004333766,0.00007445738,0.00036098345,0.0000080886375,0.000015858532,0.00009793932,0.00001695392,0.9912464,0.0040362542,0.002728985,0.0013958657,0.000013933951],"about_ca_topic_score_codex":0.0013765496,"about_ca_topic_score_gemma":0.0019498104,"teacher_disagreement_score":0.0013765496,"about_ca_system_score_codex":0.00030654308,"about_ca_system_score_gemma":0.0005209518,"threshold_uncertainty_score":0.0029720068},"labels":[],"label_agreement":null},{"id":"W4391821022","doi":"10.3390/s24041207","title":"FFT-Based Simultaneous Calculations of Very Long Signal Multi-Resolution Spectra for Ultra-Wideband Digital Radio Frequency Receiver and Other Digital Sensor Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"Ministère de la Défense Nationale; Government of Canada","keywords":"Fast Fourier transform; Bandwidth (computing); Bin; Computer science; Wideband; Resolution (logic); Digital signal processing; Electronic engineering; Algorithm; Telecommunications; Engineering; Computer hardware","score_opus":0.019762130608538406,"score_gpt":0.27455272832490407,"score_spread":0.25479059771636564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391821022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019780288,0.0005040182,0.9725697,0.00010886519,0.00017729458,0.00005055956,0.00022483531,0.0036834995,0.002901025],"genre_scores_gemma":[0.14204144,0.00046937476,0.8528222,0.00006926758,0.00006488039,0.00010872884,0.0006236464,0.00052758225,0.0032729318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979764,0.000015675314,0.000017463693,0.000027450023,0.00012911198,0.000012710112],"domain_scores_gemma":[0.9996238,0.00010851681,0.000038202234,0.000057353864,0.00015335799,0.00001871489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028427906,0.0007296692,0.00041568352,0.00085095887,0.00046590515,0.00051774626,0.0007010006,0.0004194715,0.00580448],"category_scores_gemma":[0.0015036896,0.00027186002,0.00038542558,0.0010245347,0.00019788851,0.0015655837,0.00039532126,0.0005279442,0.0016700902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046057085,0.00014362948,0.0023898627,0.0005661586,0.0001243019,0.00062188186,0.0004106995,0.047992934,0.23985165,0.017739194,0.013195827,0.67650336],"study_design_scores_gemma":[0.00004858856,0.00014337509,0.0023409817,0.00006124225,0.000056298235,0.00059142144,0.00011679625,0.8395961,0.12174161,0.006542588,0.02869113,0.0000698378],"about_ca_topic_score_codex":0.0012614387,"about_ca_topic_score_gemma":0.0021378216,"teacher_disagreement_score":0.00580448,"about_ca_system_score_codex":0.00028115508,"about_ca_system_score_gemma":0.0004508228,"threshold_uncertainty_score":0.019417942},"labels":[],"label_agreement":null},{"id":"W4391840798","doi":"10.3390/s24041230","title":"Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Electricity Theft Detection Techniques","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Hyperparameter; Hyperparameter optimization; Computer science; Machine learning; Genetic algorithm; Precision and recall; Artificial intelligence; Algorithm; Metaheuristic; Grid; Smart grid; Data mining; Support vector machine; Engineering; Mathematics","score_opus":0.0041796830002364,"score_gpt":0.18967106045191665,"score_spread":0.18549137745168023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391840798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06379095,0.0033684736,0.92633885,0.0016090401,0.00015062052,0.00013637388,0.000050049293,0.00097602705,0.0035796212],"genre_scores_gemma":[0.58913136,0.0014278543,0.40639612,0.0005911969,0.000115843475,0.00015623835,0.00009979144,0.00018054736,0.0019010236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841416,0.00090072694,0.000051720228,0.00014074656,0.0003900384,0.00010254864],"domain_scores_gemma":[0.9980902,0.0011855704,0.0002688564,0.00013175693,0.0002692865,0.00005432217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003699415,0.0011635227,0.0014611398,0.0017895935,0.0004330744,0.0017707701,0.0010992603,0.0018230833,0.00080321444],"category_scores_gemma":[0.006753006,0.0005489769,0.00078760664,0.0018590159,0.00092242623,0.0018519942,0.000828659,0.001411986,0.0002516948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011487298,0.00010615425,0.0030761736,0.0000717682,0.00015199196,0.00006558893,0.00005331554,0.8633138,0.0009628484,0.0068585714,0.0011437685,0.12408108],"study_design_scores_gemma":[0.0000081370845,0.00003128851,0.00028915226,0.000020732297,0.000011836725,0.000020488982,0.000019454694,0.9950205,0.00047142257,0.0033314642,0.00076748885,0.000007979706],"about_ca_topic_score_codex":0.0056685884,"about_ca_topic_score_gemma":0.0033294098,"teacher_disagreement_score":0.0056685884,"about_ca_system_score_codex":0.0010852328,"about_ca_system_score_gemma":0.0012762545,"threshold_uncertainty_score":0.019564629},"labels":[],"label_agreement":null},{"id":"W4391883269","doi":"10.3390/s24041264","title":"Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Computer security; Data integrity; Blockchain; Confidentiality; Robustness (evolution); Information sensitivity; Cryptography; Data exchange; World Wide Web","score_opus":0.027907150806785002,"score_gpt":0.35644463417793576,"score_spread":0.32853748337115074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391883269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02529039,0.00027421705,0.9635726,0.0005170138,0.00007313825,0.000100422876,0.00006108104,0.0001622595,0.009948804],"genre_scores_gemma":[0.87941957,0.00077981636,0.10596651,0.00022307939,0.00009737646,0.00024455262,0.000090682646,0.00007375612,0.0131046735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998382,0.0005255308,0.00009629734,0.00028954886,0.0005151111,0.0001915609],"domain_scores_gemma":[0.99675024,0.0017067855,0.00042321798,0.00066475634,0.000361992,0.00009296927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018664472,0.00075819413,0.0006221348,0.0011100268,0.00074607274,0.0018670443,0.002194851,0.0025432985,0.0028699136],"category_scores_gemma":[0.0055869464,0.00041918491,0.0010920084,0.00085874065,0.0034929947,0.006500607,0.003207306,0.0018157267,0.0006049048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003285414,0.00008523833,0.0008261043,0.00017452931,0.00005799936,0.00093558076,0.00088879105,0.26622874,0.018877184,0.6784313,0.001161373,0.032004643],"study_design_scores_gemma":[0.00003321519,0.00008489022,0.00012788274,0.000049700873,0.000023464292,0.00028786573,0.00008148832,0.886522,0.0046681142,0.10439977,0.0036866195,0.00003508394],"about_ca_topic_score_codex":0.002301829,"about_ca_topic_score_gemma":0.001182985,"teacher_disagreement_score":0.0028699136,"about_ca_system_score_codex":0.0014672274,"about_ca_system_score_gemma":0.0009200067,"threshold_uncertainty_score":0.010645509},"labels":[],"label_agreement":null},{"id":"W4391928247","doi":"10.3390/s24041269","title":"Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Anhui Provincial Department of Science and Technology","keywords":"Hidden Markov model; Speech recognition; Pattern recognition (psychology); Mel-frequency cepstrum; Computer science; Robustness (evolution); Artificial intelligence; Artificial neural network; Hilbert–Huang transform; Feature extraction; White noise","score_opus":0.19338918072489306,"score_gpt":0.3946094726716947,"score_spread":0.20122029194680166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391928247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08505968,0.010912211,0.8969026,0.0006842667,0.00034079998,0.000053824897,0.00025241965,0.0014002299,0.0043939883],"genre_scores_gemma":[0.8327125,0.009535424,0.1478405,0.0004874479,0.00020692662,0.000099618715,0.0010064798,0.0000942369,0.008016831],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996147,0.000068182475,0.000029798937,0.00012885097,0.0001029785,0.000055447985],"domain_scores_gemma":[0.9994978,0.00022040836,0.000051538955,0.000044930403,0.00015880128,0.000026453152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008352725,0.0006783191,0.00058971834,0.0006803939,0.00017851386,0.0006613453,0.0007759458,0.0007726549,0.00104602],"category_scores_gemma":[0.0014080382,0.00032095148,0.00073875336,0.0006241121,0.00032302973,0.0013334893,0.00041236507,0.0008438951,0.00036282104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024774412,0.00022656407,0.010581642,0.00044942624,0.00026750128,0.00019519757,0.00014820194,0.1733979,0.03165631,0.0056347493,0.0028117674,0.774383],"study_design_scores_gemma":[0.0000056704066,0.00010847043,0.0033923527,0.000035382094,0.000058697162,0.00006327947,0.000036134246,0.9853833,0.0066599683,0.0022379262,0.0019985212,0.000020300775],"about_ca_topic_score_codex":0.0075544985,"about_ca_topic_score_gemma":0.0061641294,"teacher_disagreement_score":0.0075544985,"about_ca_system_score_codex":0.0005494829,"about_ca_system_score_gemma":0.00073361694,"threshold_uncertainty_score":0.015021086},"labels":[],"label_agreement":null},{"id":"W4391948695","doi":"10.3390/s24041298","title":"The Application of High-Resolution, Embedded Fibre Optic (FO) Sensing for Large-Diameter Composite Steel/Plastic Pipeline Performance under Dynamic Transport Loads","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Innovate UK; Engineering and Physical Sciences Research Council; New Brunswick Innovation Foundation","keywords":"Composite number; Pipeline (software); Materials science; Composite material; High resolution; Structural engineering; Mechanical engineering; Engineering; Remote sensing; Geology","score_opus":0.006063659018087426,"score_gpt":0.2264844814902437,"score_spread":0.22042082247215627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391948695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95368797,0.00032509307,0.04402426,0.00007901533,0.00002638841,0.00003243559,0.00011615198,0.00024173848,0.0014668852],"genre_scores_gemma":[0.97954625,0.00015548049,0.019424725,0.0000144703945,0.000005154906,0.000008789244,0.000033843007,0.000009735637,0.00080154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999734,0.000032418306,0.000009213629,0.00006429479,0.00014056459,0.000019548977],"domain_scores_gemma":[0.9997278,0.000073145544,0.000083077764,0.000032623353,0.00007013358,0.000013319877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003010084,0.00043061844,0.00014304652,0.0002922765,0.00018178363,0.0003207059,0.00032368975,0.0005220894,0.0005235775],"category_scores_gemma":[0.00053146784,0.00014802834,0.00012936756,0.0001993682,0.00035843987,0.00037567524,0.00025360257,0.00022714598,0.00017425217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004502246,0.000017127053,0.0012582451,0.00005753745,0.00000425336,0.00005333685,0.000044966462,0.0024230988,0.98636705,0.000052917898,0.000055775505,0.00962067],"study_design_scores_gemma":[0.0000053876165,0.0004921638,0.013630451,0.000007867831,0.000013750861,0.00025124854,0.00009521061,0.01896746,0.96517044,0.000055305576,0.0012865164,0.000024056419],"about_ca_topic_score_codex":0.000986622,"about_ca_topic_score_gemma":0.0031445036,"teacher_disagreement_score":0.000986622,"about_ca_system_score_codex":0.00025880465,"about_ca_system_score_gemma":0.00015948327,"threshold_uncertainty_score":0.0019617677},"labels":[],"label_agreement":null},{"id":"W4392058346","doi":"10.3390/s24051413","title":"Advanced Dielectric Resonator Antenna Technology for 5G and 6G Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Extremely high frequency; Flexibility (engineering); Antenna (radio); Context (archaeology); Printed circuit board; Computer science; Dielectric resonator; Electronic engineering; Dielectric resonator antenna; Resonator; Materials science; Electrical engineering; Telecommunications; Engineering","score_opus":0.005127484024567617,"score_gpt":0.21994152926034782,"score_spread":0.2148140452357802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392058346","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027213145,0.63506645,0.22380973,0.0024761262,0.0038544629,0.00015152394,0.0004080727,0.0011011361,0.10591941],"genre_scores_gemma":[0.20143117,0.56199574,0.15638511,0.0024058076,0.00222742,0.0001893204,0.00067906536,0.00025796398,0.07442838],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976426,0.00002974252,0.000016591504,0.000059094244,0.00010359283,0.000026715363],"domain_scores_gemma":[0.99987555,0.00003587033,0.000028117378,0.000012538321,0.00003898183,0.00000893085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033871763,0.0006470861,0.00041656318,0.0005860145,0.00013932951,0.00080341764,0.00048517916,0.0009510004,0.0055106953],"category_scores_gemma":[0.00029277377,0.00032505105,0.00050512975,0.00065639504,0.00020576044,0.0010314048,0.0002889426,0.0007841614,0.0054222485],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011710853,0.0000674294,0.0005287441,0.003916692,0.00009295509,0.00047004825,0.00013390435,0.002821947,0.2861563,0.027304657,0.022144472,0.6562457],"study_design_scores_gemma":[0.000014287794,0.00048152058,0.0008963657,0.00028917808,0.00008675292,0.0025058542,0.00007960122,0.004250361,0.07296351,0.0039728726,0.9144038,0.00005583308],"about_ca_topic_score_codex":0.00013328536,"about_ca_topic_score_gemma":0.00022965841,"teacher_disagreement_score":0.0055106953,"about_ca_system_score_codex":0.00031763111,"about_ca_system_score_gemma":0.00017593872,"threshold_uncertainty_score":0.018435061},"labels":[],"label_agreement":null},{"id":"W4392097083","doi":"10.3390/s24051453","title":"Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Research Manitoba; Health Sciences Centre Foundation","keywords":"Cerebral perfusion pressure; Computer science; Cerebral blood flow; Machine learning; Artificial intelligence; Cerebral autoregulation; Support vector machine; Medicine; Blood pressure; Internal medicine","score_opus":0.02301663951503149,"score_gpt":0.26569190476943044,"score_spread":0.24267526525439895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392097083","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042765788,0.99775094,0.0009339398,0.0003014484,0.00010566274,0.00004663715,0.00020773227,0.00001119614,0.00021474622],"genre_scores_gemma":[0.0030664867,0.9952631,0.0011189609,0.00015718321,0.0000815742,0.00008695063,0.00015310588,0.0000050884682,0.00006750038],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99721485,0.0007155651,0.0011048191,0.0003333881,0.0005698131,0.00006163755],"domain_scores_gemma":[0.9663158,0.028967472,0.0022614754,0.00048990623,0.0018417534,0.00012361049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061822985,0.0013889782,0.0030206246,0.008636192,0.00039334042,0.0022115242,0.001551638,0.0018535648,0.0028266339],"category_scores_gemma":[0.030364407,0.00074346515,0.004654984,0.008447884,0.00065481826,0.0021537256,0.0009342667,0.0012565929,0.0004991324],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009875441,0.000065418004,0.0014496542,0.55938244,0.002668095,0.00021495087,0.00040210917,0.0024317203,0.00046826748,0.002380256,0.005777437,0.42466092],"study_design_scores_gemma":[0.00004469716,0.00032894054,0.006151795,0.80165327,0.014817829,0.000757167,0.0005745155,0.0023136232,0.00064491376,0.0035636008,0.16903035,0.0001193913],"about_ca_topic_score_codex":0.008474927,"about_ca_topic_score_gemma":0.014435392,"teacher_disagreement_score":0.008636192,"about_ca_system_score_codex":0.0014243855,"about_ca_system_score_gemma":0.009445846,"threshold_uncertainty_score":0.03269553},"labels":[],"label_agreement":null},{"id":"W4392110289","doi":"10.3390/s24051445","title":"Biomechanical Effects of Using a Passive Exoskeleton for the Upper Limb in Industrial Manufacturing Activities: A Pilot Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electromyography; Exoskeleton; Usability; Deltoid curve; Physical medicine and rehabilitation; Work (physics); Engineering; Physical therapy; Medicine; Computer science; Human–computer interaction; Surgery","score_opus":0.027025442990321936,"score_gpt":0.26334137516399797,"score_spread":0.23631593217367602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392110289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99926525,0.000021512125,0.00055092195,0.000009263074,0.000004073377,0.00004646438,0.000017558976,0.0000054750476,0.000079545585],"genre_scores_gemma":[0.9971276,0.00006656574,0.0019397859,0.000022064367,0.000014316035,0.00012255803,0.00006323378,0.000003482252,0.00064049766],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99951255,0.00018917408,0.000038751547,0.00007393708,0.00009699605,0.00008862209],"domain_scores_gemma":[0.99886346,0.0005408877,0.00011270929,0.00014048131,0.00017745464,0.00016495396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088250166,0.0006447198,0.00039535068,0.0002880671,0.00040348634,0.00018455161,0.00035491836,0.00044124832,0.0023423913],"category_scores_gemma":[0.0016408508,0.00021824478,0.00042985933,0.00010711663,0.00042750916,0.00025411593,0.00054481975,0.00033929403,0.0003020482],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.024600448,0.057958852,0.06263442,0.0012954356,0.00022557113,0.0019566726,0.005147921,0.0020351463,0.7238099,0.00009214507,0.00034670578,0.11989676],"study_design_scores_gemma":[0.0010378791,0.61356235,0.31579363,0.00006607449,0.00024789505,0.0014220388,0.0035580376,0.0026366508,0.059755024,0.00012566519,0.0017436066,0.000051150964],"about_ca_topic_score_codex":0.00046568623,"about_ca_topic_score_gemma":0.0008051052,"teacher_disagreement_score":0.0023423913,"about_ca_system_score_codex":0.000074662734,"about_ca_system_score_gemma":0.00022838861,"threshold_uncertainty_score":0.007836103},"labels":[],"label_agreement":null},{"id":"W4392155141","doi":"10.3390/s24051518","title":"Quantifying the Impact of Motions on Human Aiming Performance: Evidence from Eye Tracking and Bio-Signals","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Eye tracking; Tracking (education); Computer science; Human eye; Artificial intelligence; Eye movement; Computer vision; Engineering; Psychology","score_opus":0.18372257847858675,"score_gpt":0.38902145313720604,"score_spread":0.2052988746586193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392155141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99530125,0.00042103726,0.00358567,0.000019203004,0.0000034127556,0.000014730758,0.00009522314,0.000018300234,0.00054117915],"genre_scores_gemma":[0.99687564,0.00035055412,0.0024671822,0.000019698258,0.000006349758,0.00001876374,0.00008875733,0.0000067748356,0.00016624553],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996511,0.00010479608,0.000033050826,0.00008533669,0.00009736772,0.000028397655],"domain_scores_gemma":[0.99835783,0.00070020143,0.0006177595,0.00012042549,0.00013068873,0.000073021554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005454713,0.00038437376,0.00022676472,0.00045743558,0.00012910902,0.00037487943,0.00017271527,0.00040872052,0.00057459064],"category_scores_gemma":[0.0032602903,0.00018468927,0.00015260688,0.00028295544,0.0003364381,0.00031025655,0.00043720752,0.00014799334,0.00013184696],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016890374,0.00031894972,0.1662102,0.0006449847,0.00031385038,0.00025843256,0.0011294014,0.002037854,0.7295297,0.00017248484,0.00023823777,0.097456865],"study_design_scores_gemma":[0.000017580369,0.0009435786,0.9719036,0.000027374843,0.000073303105,0.00037569096,0.00020797508,0.0018033012,0.024070222,0.00021436605,0.00034076878,0.00002231946],"about_ca_topic_score_codex":0.00093912217,"about_ca_topic_score_gemma":0.0015064284,"teacher_disagreement_score":0.00093912217,"about_ca_system_score_codex":0.00010432628,"about_ca_system_score_gemma":0.00013824024,"threshold_uncertainty_score":0.0028847456},"labels":[],"label_agreement":null},{"id":"W4392166152","doi":"10.3390/s24051485","title":"An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hyperspectral imaging; Partial least squares regression; Cluster analysis; Thresholding; Mathematics; Statistics; Pattern recognition (psychology); Artificial intelligence; Regression; Range (aeronautics); Data set; Computer science; Image (mathematics); Engineering","score_opus":0.028660267425214513,"score_gpt":0.3117697280785099,"score_spread":0.2831094606532954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392166152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069098644,0.00031432774,0.9283373,0.00008153822,0.000026214382,0.000108666376,0.00012413948,0.0009841472,0.00092501333],"genre_scores_gemma":[0.44946018,0.0005047516,0.5451209,0.00009544921,0.000050960894,0.00032916872,0.00048092293,0.00013017342,0.003827402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996598,0.000072114795,0.00001758178,0.00012445972,0.000099880956,0.000026099598],"domain_scores_gemma":[0.99974877,0.00007931987,0.00003942073,0.000016740809,0.000108371816,0.000007361451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007547418,0.00080906827,0.0005176589,0.0007106907,0.00021034328,0.00039146084,0.00073876645,0.000473452,0.00067367894],"category_scores_gemma":[0.000963696,0.00034123403,0.0010141341,0.0006556301,0.00024109508,0.00039417372,0.00034350887,0.0006940388,0.00038655396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002856247,0.00029544684,0.0069292886,0.00032804158,0.00027558047,0.00027805913,0.00028264767,0.21457238,0.18748425,0.0017752323,0.0018808233,0.58561254],"study_design_scores_gemma":[0.00001162441,0.00016647871,0.005173494,0.00001007249,0.000052466694,0.000121803656,0.000043653377,0.9755824,0.017023757,0.0005065083,0.0012719035,0.000035848887],"about_ca_topic_score_codex":0.0023844377,"about_ca_topic_score_gemma":0.003305995,"teacher_disagreement_score":0.0023844377,"about_ca_system_score_codex":0.00024667155,"about_ca_system_score_gemma":0.0004422347,"threshold_uncertainty_score":0.0047410727},"labels":[],"label_agreement":null},{"id":"W4392169428","doi":"10.3390/s24051515","title":"B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Netaji Subhas University of Technology; Nottingham Trent University","keywords":"Computer science; Blockchain; Architecture; Cloud computing; Computer security; Context (archaeology); Vehicular ad hoc network; Computer network; Fog computing; The Internet; Vehicle-to-vehicle; Internet of Things; Wireless ad hoc network; Telecommunications; Wireless; Operating system","score_opus":0.007015796544599026,"score_gpt":0.21900568580514967,"score_spread":0.21198988926055065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392169428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076508425,0.00058967964,0.90012586,0.0006910207,0.0001688056,0.00049202994,0.00030669614,0.0043134936,0.016804008],"genre_scores_gemma":[0.9209725,0.00039352963,0.071921855,0.00018472,0.000023791112,0.00019601351,0.00037618016,0.000095816584,0.005835579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951756,0.000080332706,0.000036500744,0.000080381746,0.00016100002,0.00012428558],"domain_scores_gemma":[0.9994941,0.000085148684,0.00004915725,0.00012066366,0.0001533419,0.000097539225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061793375,0.00041739433,0.00037635397,0.00051055127,0.0010890367,0.0014449058,0.0011603356,0.00087752636,0.003327401],"category_scores_gemma":[0.0010440154,0.00022397774,0.00035311625,0.00036736412,0.0009713906,0.0019074361,0.0020527616,0.00089520955,0.0008807103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009839515,0.00036390778,0.006612881,0.00049331755,0.00015780664,0.001383511,0.001082115,0.5396572,0.039814416,0.22437581,0.013104554,0.17197056],"study_design_scores_gemma":[0.000049565115,0.00016889199,0.00047286547,0.000051717947,0.000033005424,0.0001873033,0.00012311539,0.9252507,0.011361549,0.04258409,0.019676622,0.000040546678],"about_ca_topic_score_codex":0.00891649,"about_ca_topic_score_gemma":0.009339685,"teacher_disagreement_score":0.00891649,"about_ca_system_score_codex":0.0010972813,"about_ca_system_score_gemma":0.002467753,"threshold_uncertainty_score":0.017729223},"labels":[],"label_agreement":null},{"id":"W4392354560","doi":"10.3390/s24051609","title":"Augmented Reality-Based Real-Time Visualization for Structural Modal Identification","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Visualization; Computer science; Identification (biology); Python (programming language); Augmented reality; Structural health monitoring; Modal; Data visualization; Real-time computing; Human–computer interaction; Data mining; Engineering","score_opus":0.024409299522962474,"score_gpt":0.2847506118278713,"score_spread":0.26034131230490887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392354560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025462683,0.0005997381,0.96539414,0.0001653939,0.00011524716,0.000054761163,0.00036441494,0.0038534987,0.003989967],"genre_scores_gemma":[0.4190601,0.0011713806,0.5740604,0.0001470417,0.00009392298,0.00013813119,0.000866587,0.00035461597,0.0041078213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995222,0.00010621679,0.000024084346,0.00009028404,0.00021973856,0.00003745499],"domain_scores_gemma":[0.99950075,0.00017278387,0.000058531154,0.00012191822,0.0001229028,0.000023215858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037861013,0.0007816107,0.0003509324,0.00090633344,0.00019887608,0.0012958334,0.00062622014,0.0006383105,0.0058556516],"category_scores_gemma":[0.0011740777,0.00039707794,0.0005811689,0.0005813368,0.00030054472,0.0011441523,0.0011318361,0.0007402763,0.001246359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065066345,0.00019308428,0.0019311799,0.0007342389,0.0001291727,0.00062300375,0.0012902527,0.0492631,0.3265363,0.011710334,0.009912963,0.59702575],"study_design_scores_gemma":[0.000088353234,0.0006796859,0.009685275,0.0001829692,0.00016952558,0.0018621023,0.0006547191,0.72491616,0.17910172,0.012718529,0.06968774,0.00025315344],"about_ca_topic_score_codex":0.000648618,"about_ca_topic_score_gemma":0.0011096436,"teacher_disagreement_score":0.0058556516,"about_ca_system_score_codex":0.00019629604,"about_ca_system_score_gemma":0.00027900655,"threshold_uncertainty_score":0.019589126},"labels":[],"label_agreement":null},{"id":"W4392356957","doi":"10.3390/s24051623","title":"Temporal Stability of Management Zone Patterns: Case Study with Contact and Non-Contact Soil Electrical Conductivity Sensors in Dryland Pastures","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Precision agriculture; Soil science; Remote sensing; Spatial variability; Soil water; Stability (learning theory); Geographic information system; Hydrology (agriculture); Agriculture; Geography; Mathematics; Engineering; Computer science; Statistics; Geotechnical engineering","score_opus":0.011946645780709635,"score_gpt":0.2414606065296508,"score_spread":0.22951396074894115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392356957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991798,0.000034059045,0.0004134178,0.000011906488,0.0000010423695,0.000007995064,0.000090972084,0.0000056842805,0.00025496774],"genre_scores_gemma":[0.9987596,0.000027428478,0.000895541,0.0000036343465,0.0000020530672,0.000005383945,0.00012760836,0.0000021758747,0.0001766841],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994332,0.00011709677,0.00005176408,0.0001986454,0.00011990611,0.00007947138],"domain_scores_gemma":[0.99840945,0.00051219837,0.000514277,0.00017537239,0.00027787228,0.000110756504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084116025,0.00022510321,0.00025734038,0.0010010898,0.00050217105,0.00068389205,0.0005056356,0.0005861991,0.0003188225],"category_scores_gemma":[0.0023568606,0.00015905152,0.0002708226,0.0015898243,0.0005054503,0.00043863538,0.00052368833,0.00024135725,0.000069892165],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023161065,0.00024284466,0.9716508,0.000062818646,0.00008392975,0.002571597,0.0020959012,0.0045602694,0.004503439,0.00015962987,0.00019758954,0.013639525],"study_design_scores_gemma":[0.000008254608,0.0001926136,0.97867155,0.000015723355,0.000044098124,0.0007395996,0.002787793,0.014813344,0.0018972284,0.00012753488,0.0006799467,0.000022331267],"about_ca_topic_score_codex":0.025051847,"about_ca_topic_score_gemma":0.047643594,"teacher_disagreement_score":0.025051847,"about_ca_system_score_codex":0.00069683173,"about_ca_system_score_gemma":0.0002965695,"threshold_uncertainty_score":0.04981202},"labels":[],"label_agreement":null},{"id":"W4392370301","doi":"10.3390/s24051651","title":"Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Remote sensing; Wetland; Lidar; Terrain; Environmental science; Earth observation; Synthetic aperture radar; Vegetation (pathology); Satellite imagery; Digital elevation model; Shuttle Radar Topography Mission; Environmental resource management; Geography; Satellite; Cartography; Ecology; Engineering","score_opus":0.014041859473467557,"score_gpt":0.24053577210391316,"score_spread":0.2264939126304456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392370301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7866383,0.00055836246,0.12530302,0.0010095462,0.0001738061,0.0005668531,0.037518434,0.034724757,0.013506873],"genre_scores_gemma":[0.69748867,0.00032157253,0.27364784,0.00010924919,0.00002503391,0.00010970083,0.026157958,0.0006649471,0.001475154],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99974924,0.000040944637,0.000011483815,0.000048350328,0.00010101687,0.000048954666],"domain_scores_gemma":[0.9996841,0.00005681108,0.00003090256,0.000057497862,0.00013771726,0.00003294609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078618573,0.0005946686,0.00024984492,0.0019082823,0.00023044474,0.0007600895,0.00073933264,0.00023365814,0.00075281423],"category_scores_gemma":[0.0012670695,0.00023740655,0.0005836461,0.0016394397,0.00016493502,0.0010134832,0.0010239991,0.0003661608,0.00041052594],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066505256,0.0007567296,0.15252908,0.00068062067,0.00062854745,0.0008614845,0.0014239047,0.17656913,0.043001343,0.003089093,0.04543008,0.574365],"study_design_scores_gemma":[0.00010503941,0.0001374605,0.14522722,0.00008768206,0.00019120627,0.00018885055,0.0011621088,0.8031563,0.019586371,0.0017707628,0.028247375,0.0001396316],"about_ca_topic_score_codex":0.14736074,"about_ca_topic_score_gemma":0.2848424,"teacher_disagreement_score":0.14736074,"about_ca_system_score_codex":0.0005724587,"about_ca_system_score_gemma":0.0010398112,"threshold_uncertainty_score":0.293006},"labels":[],"label_agreement":null},{"id":"W4392376857","doi":"10.3390/s24051664","title":"COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada; University of Waterloo","funders":"National Research Council Canada","keywords":"Convolutional neural network; Workflow; Artificial intelligence; Deep learning; Ultrasound; Computer science; Coronavirus disease 2019 (COVID-19); Artificial neural network; Pandemic; Machine learning; Medicine; Radiology; Pathology; Database","score_opus":0.03672456628615496,"score_gpt":0.4032123233713369,"score_spread":0.36648775708518194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392376857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016344782,0.0004952,0.97907364,0.0006930158,0.00006011687,0.00006140526,0.00023647855,0.001517697,0.0015177178],"genre_scores_gemma":[0.6971797,0.0006127991,0.29005557,0.0011956828,0.0001720283,0.00040848771,0.0014642206,0.00041130753,0.008500196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943084,0.00017675008,0.000025031819,0.00014675486,0.00013784844,0.00008278967],"domain_scores_gemma":[0.9988167,0.00059209304,0.00012699237,0.00010109228,0.0002867431,0.00007633939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013331281,0.0012654804,0.0008952526,0.00058209803,0.00038649776,0.001045874,0.0022904247,0.0019808062,0.0025247098],"category_scores_gemma":[0.0041579157,0.0006044478,0.00094108866,0.00041288155,0.0010142525,0.0011692859,0.0020029624,0.0027517697,0.0006417631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016375205,0.00009630353,0.0017265662,0.00009668792,0.000074765165,0.00016181002,0.000066725705,0.8885633,0.0034609826,0.007734292,0.0037746753,0.09408011],"study_design_scores_gemma":[0.0000025565164,0.0000146973625,0.00005831409,0.0000037763698,0.000003508428,0.000007988494,0.0000018046721,0.9981958,0.0003116877,0.0011883109,0.00020838933,0.0000031773152],"about_ca_topic_score_codex":0.011463441,"about_ca_topic_score_gemma":0.013037912,"teacher_disagreement_score":0.011463441,"about_ca_system_score_codex":0.0012404149,"about_ca_system_score_gemma":0.0018997054,"threshold_uncertainty_score":0.022793412},"labels":[],"label_agreement":null},{"id":"W4392553987","doi":"10.3390/s24051711","title":"The Rise of Passive RFID RTLS Solutions in Industry 5.0","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"RFID technology advancements","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vanier College; Université du Québec à Montréal","funders":"","keywords":"Real-time locating system; Internet of Things; Software deployment; Computer science; Risk analysis (engineering); Systems engineering; Data science; Engineering; Telecommunications; Business; Embedded system; Software engineering","score_opus":0.00908730753659002,"score_gpt":0.23415704963680073,"score_spread":0.22506974210021072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392553987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22343525,0.12425485,0.3718815,0.031622246,0.0038474407,0.00032962757,0.00035303895,0.0020400698,0.24223594],"genre_scores_gemma":[0.70975995,0.061737735,0.16729714,0.006715153,0.0013978798,0.00012099378,0.00061918073,0.00031877647,0.052033275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960252,0.0008350327,0.00021479226,0.0005243072,0.0020785201,0.00032223543],"domain_scores_gemma":[0.994069,0.0016978858,0.0007827671,0.0005284797,0.0026557273,0.00026607074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051700776,0.0006000664,0.000306534,0.0017375561,0.0006940111,0.0046385843,0.0014109719,0.0027038415,0.0039440333],"category_scores_gemma":[0.005595157,0.00036900945,0.0004776908,0.0022850665,0.0014808542,0.007879808,0.0022746052,0.002122656,0.0020244487],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002367026,0.0001500106,0.007901864,0.0021333569,0.000030251793,0.00077672664,0.0023890594,0.0030449484,0.047750454,0.19255655,0.012414145,0.730616],"study_design_scores_gemma":[0.000014795097,0.0010275235,0.005951517,0.0011698763,0.0000681706,0.0022029227,0.003575186,0.008528313,0.051984463,0.025550622,0.8997972,0.00012932917],"about_ca_topic_score_codex":0.0007688722,"about_ca_topic_score_gemma":0.00079868734,"teacher_disagreement_score":0.0051700776,"about_ca_system_score_codex":0.0020169606,"about_ca_system_score_gemma":0.0013322523,"threshold_uncertainty_score":0.02734232},"labels":[],"label_agreement":null},{"id":"W4392659747","doi":"10.3390/s24061797","title":"Demonstration of a Transparent and Adhesive Sealing Top for Microfluidic Lab-Chip Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microfluidics; Materials science; Polyester; Cleanroom; Wetting; Adhesive; Fabrication; Nanotechnology; Lab-on-a-chip; Compatibility (geochemistry); Seal (emblem); Polymer; Composite material","score_opus":0.01898242034271017,"score_gpt":0.26485016670700406,"score_spread":0.2458677463642939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392659747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46182013,0.011132672,0.5110767,0.0010457304,0.0018024964,0.00042678334,0.0008768834,0.0043985704,0.0074199587],"genre_scores_gemma":[0.655763,0.004015106,0.33463353,0.00032951933,0.0001838166,0.00023083093,0.00033970535,0.00011741689,0.00438717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974054,0.000019796531,0.000019086347,0.0000616705,0.000119401106,0.00003952261],"domain_scores_gemma":[0.9997634,0.000063762374,0.000052473122,0.00003440313,0.000037369144,0.000048556278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026074643,0.00063181313,0.0003067685,0.00035339498,0.00036922828,0.0005196309,0.0007795688,0.00075732614,0.0008754944],"category_scores_gemma":[0.0004189656,0.00031956594,0.00041707276,0.00015418303,0.00030137884,0.0003923001,0.0005775176,0.0007980144,0.0005548714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012656537,0.000017587787,0.00006536694,0.00009695275,0.0000057110624,0.00017570495,0.00002840097,0.00011448157,0.991926,0.0002754942,0.00022744114,0.007054263],"study_design_scores_gemma":[0.00000402935,0.00011001363,0.00059767615,0.000007677827,0.000009661871,0.00038205917,0.0000107652195,0.0016425705,0.9922369,0.00007436919,0.004909903,0.000014411337],"about_ca_topic_score_codex":0.00025573611,"about_ca_topic_score_gemma":0.00036536844,"teacher_disagreement_score":0.0008754944,"about_ca_system_score_codex":0.00018481647,"about_ca_system_score_gemma":0.00035583225,"threshold_uncertainty_score":0.002928853},"labels":[],"label_agreement":null},{"id":"W4392698163","doi":"10.3390/s24061821","title":"Influence of a Three-Month Mixed Reality Training on Gait Speed and Cognitive Functions in Adults with Intellectual Disability: A Pilot Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Virtual reality; Cognition; Gait; Physical medicine and rehabilitation; Augmented reality; Psychology; Perception; Rehabilitation; Intellectual disability; Mixed reality; Gait analysis; Computer science; Human–computer interaction; Medicine","score_opus":0.044626481066842805,"score_gpt":0.29226686155189513,"score_spread":0.24764038048505232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392698163","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9996691,0.000037844093,0.00004430947,0.000010124133,0.0000059349277,0.0000871206,0.000038058468,0.0000034269412,0.000104168736],"genre_scores_gemma":[0.9986089,0.00009870546,0.00036731103,0.000026498476,0.00001744794,0.0003320126,0.000091815586,0.000001181701,0.00045602283],"study_design_codex":"nonrandomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9997166,0.000072008515,0.000034534518,0.000053773707,0.000038231257,0.000084811974],"domain_scores_gemma":[0.99924564,0.00014290043,0.00007263116,0.00006605239,0.000101230835,0.00037154666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006608258,0.0006559385,0.0007511628,0.0003816381,0.0005644013,0.0003152932,0.00029769752,0.0006302798,0.0017105769],"category_scores_gemma":[0.0010621328,0.00021028251,0.00087406574,0.00018119182,0.00028966885,0.00025376244,0.0004962017,0.0005385977,0.0003439716],"study_design_candidate":"randomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.12439551,0.4652343,0.13635999,0.0015886462,0.0011255674,0.002046685,0.01288317,0.0013645485,0.061444227,0.00011869051,0.0011016857,0.192337],"study_design_scores_gemma":[0.0031138828,0.5675884,0.42055634,0.00003899805,0.00043311794,0.00030835895,0.0022492353,0.00065250834,0.0038261584,0.000046354064,0.0011274086,0.000059350037],"about_ca_topic_score_codex":0.0019095979,"about_ca_topic_score_gemma":0.0023725282,"teacher_disagreement_score":0.0019095979,"about_ca_system_score_codex":0.00017984596,"about_ca_system_score_gemma":0.00034414232,"threshold_uncertainty_score":0.0057224035},"labels":[],"label_agreement":null},{"id":"W4392763212","doi":"10.3390/s24061847","title":"Development of a Small-Footprint 50 MHz Linear Array: Fabrication and Micro-Ultrasound Imaging Demonstration","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Research Institute; Sunnybrook Research Institute","keywords":"Footprint; Materials science; Fabrication; Interconnection; Coaxial; Coaxial cable; Electrode array; Electroplating; Antenna array; Bandwidth (computing); Printed circuit board; Optoelectronics; Acoustics; Antenna (radio); Computer science; Electrical engineering; Voltage; Nanotechnology; Telecommunications; Physics; Engineering","score_opus":0.011383389305955172,"score_gpt":0.21370436668422538,"score_spread":0.2023209773782702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392763212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45739663,0.0011949798,0.52852964,0.00080431846,0.00024437488,0.00057242904,0.0005261743,0.0023991102,0.008332376],"genre_scores_gemma":[0.5594514,0.0005513046,0.43442035,0.00017678252,0.000044384182,0.00028101713,0.00022480467,0.00012174141,0.004728237],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997961,0.000025210064,0.000013088628,0.000060033963,0.00007490439,0.000030719482],"domain_scores_gemma":[0.9997515,0.0000641307,0.000046623158,0.00004166812,0.000058940062,0.00003704771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034091523,0.00040471696,0.00023803892,0.00021240156,0.00014700601,0.00026350372,0.0005015807,0.00057608634,0.0014507641],"category_scores_gemma":[0.0004295869,0.00027601523,0.00018174549,0.00016563303,0.0002951999,0.0004456938,0.00033104976,0.00035118355,0.0010911552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016711367,0.00003055786,0.0002010706,0.000050064085,0.0000039959223,0.0000839527,0.000055932498,0.00051886594,0.9897105,0.00029035046,0.00020863338,0.008829301],"study_design_scores_gemma":[0.000014153055,0.00038375624,0.0013625472,0.000006630836,0.000009971128,0.00032764158,0.00003876176,0.005149185,0.9867889,0.00013034955,0.005768645,0.000019417497],"about_ca_topic_score_codex":0.00051078055,"about_ca_topic_score_gemma":0.0010128224,"teacher_disagreement_score":0.0014507641,"about_ca_system_score_codex":0.00022987815,"about_ca_system_score_gemma":0.0003103651,"threshold_uncertainty_score":0.004853308},"labels":[],"label_agreement":null},{"id":"W4392852494","doi":"10.3390/s24061871","title":"Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Northern British Columbia","funders":"","keywords":"Gradient boosting; Computer science; Gradient descent; Boosting (machine learning); The Internet; Data mining; Overfitting; Provisioning; Outlier; Adaptability; Machine learning; Artificial intelligence; Random forest; Artificial neural network; Computer network","score_opus":0.0272530891144793,"score_gpt":0.21697120812051776,"score_spread":0.18971811900603847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392852494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37135145,0.00078185555,0.62140286,0.0010531044,0.00020188437,0.00009807862,0.0005068775,0.0009496126,0.0036542753],"genre_scores_gemma":[0.9702966,0.00017383712,0.027854959,0.00006269575,0.000045019216,0.000054742806,0.00026805667,0.000020767982,0.0012233541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997346,0.000092957904,0.000013823932,0.000060116494,0.00005417607,0.000044327757],"domain_scores_gemma":[0.999119,0.00047678017,0.00011509201,0.00006339143,0.00017638279,0.000049340775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012836184,0.00058983226,0.00061130826,0.00046533725,0.00030412347,0.0006022471,0.0009620782,0.00075306353,0.0006437003],"category_scores_gemma":[0.0026570521,0.00032557052,0.000537523,0.00046783258,0.00025598862,0.00069689675,0.00042395812,0.0013730212,0.00018173737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025662875,0.000027871429,0.0016162591,0.000009534074,0.000014763873,0.0000143919715,0.00000986667,0.98898137,0.00022792025,0.00082700857,0.00030686174,0.007938563],"study_design_scores_gemma":[7.0168215e-7,0.0000041675253,0.00012222416,8.589863e-7,0.0000016211044,0.0000013026834,0.0000010929324,0.9995722,0.00004394492,0.00020367673,0.0000471502,0.0000010798677],"about_ca_topic_score_codex":0.011691412,"about_ca_topic_score_gemma":0.010077307,"teacher_disagreement_score":0.011691412,"about_ca_system_score_codex":0.0005356707,"about_ca_system_score_gemma":0.00073427433,"threshold_uncertainty_score":0.023246765},"labels":[],"label_agreement":null},{"id":"W4392852836","doi":"10.3390/s24061863","title":"Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Edge computing; Mobile edge computing; Server; Node (physics); Task (project management); Enhanced Data Rates for GSM Evolution; Computer network; Distributed computing; Wireless network; Wireless; Application layer; Process (computing); Real-time computing; Artificial intelligence; Operating system; Engineering","score_opus":0.012704366999072758,"score_gpt":0.2275888235405298,"score_spread":0.21488445654145705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392852836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10948308,0.00037930705,0.88647103,0.0001745702,0.00005429181,0.000042002062,0.000025771462,0.00029277807,0.0030772067],"genre_scores_gemma":[0.9800781,0.000073806805,0.019003455,0.00003969533,0.000010054687,0.000018742292,0.000018646055,0.0000097351,0.0007478287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996395,0.00007087652,0.0000147668725,0.00007341329,0.00006703929,0.00013435833],"domain_scores_gemma":[0.99975115,0.00010220002,0.000030381312,0.000029253582,0.00005244686,0.00003462237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042708556,0.0006471944,0.0005739765,0.0002756794,0.00056221936,0.0006249106,0.0010785384,0.00045842584,0.0006268633],"category_scores_gemma":[0.00092248066,0.00021685522,0.00024556936,0.00032735418,0.0005145484,0.0010037611,0.0010609592,0.0004674129,0.00010237558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016470885,0.000075059135,0.001026977,0.000051119692,0.000024083136,0.00011743134,0.00011033905,0.9262772,0.007490791,0.009837532,0.0009870197,0.05383766],"study_design_scores_gemma":[0.0000019872093,0.000017892922,0.000062763385,0.000001064661,0.0000022128577,0.000006781187,0.000012162016,0.9979206,0.00063263037,0.0012305527,0.000109197914,0.000002103406],"about_ca_topic_score_codex":0.0053833956,"about_ca_topic_score_gemma":0.007608389,"teacher_disagreement_score":0.0053833956,"about_ca_system_score_codex":0.00054195535,"about_ca_system_score_gemma":0.00078041136,"threshold_uncertainty_score":0.01070416},"labels":[],"label_agreement":null},{"id":"W4392919103","doi":"10.3390/s24061917","title":"Leveraging the Sensitivity of Plants with Deep Learning to Recognize Human Emotions","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Plant and Biological Electrophysiology Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Software AG – Stiftung; Narodowe Centrum Nauki","keywords":"Computer science; Anger; Happiness; Artificial intelligence; Random forest; Set (abstract data type); Recall; Mel-frequency cepstrum; Enhanced Data Rates for GSM Evolution; Emotion classification; Parameterized complexity; Pattern recognition (psychology); Machine learning; Feature extraction; Psychology; Cognitive psychology","score_opus":0.0263095331195455,"score_gpt":0.2261838134871135,"score_spread":0.199874280367568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392919103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35462478,0.00089743827,0.6360321,0.0003853271,0.00014022274,0.000072493596,0.00022995242,0.002427066,0.0051905448],"genre_scores_gemma":[0.9650604,0.00017304895,0.0327842,0.00008512793,0.000024303978,0.000026180958,0.00019629486,0.000035037003,0.0016154653],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997832,0.00004037147,0.000008325738,0.00007499163,0.00004755488,0.00004555201],"domain_scores_gemma":[0.99965966,0.00016675593,0.00003836531,0.00003757915,0.00007912875,0.000018548659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005680174,0.00075553195,0.00029245656,0.0004328951,0.00015288754,0.0005099523,0.00042205662,0.00047367025,0.0011444755],"category_scores_gemma":[0.0015591276,0.00022466299,0.00043893757,0.0002332846,0.0003525107,0.0008193229,0.00067472813,0.0007139372,0.0005220402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047509282,0.00025510602,0.009215924,0.0002090513,0.00015642875,0.00028181486,0.00029108112,0.3185647,0.1363479,0.0025548425,0.0029241776,0.5287239],"study_design_scores_gemma":[0.0000047085564,0.000109876,0.0044665383,0.000014921197,0.000027816819,0.0000627625,0.0000342802,0.9802576,0.01227715,0.0021494734,0.00058126147,0.000013596235],"about_ca_topic_score_codex":0.0026037127,"about_ca_topic_score_gemma":0.0037252975,"teacher_disagreement_score":0.0026037127,"about_ca_system_score_codex":0.00035482127,"about_ca_system_score_gemma":0.00029045754,"threshold_uncertainty_score":0.0051770806},"labels":[],"label_agreement":null},{"id":"W4392986349","doi":"10.3390/s24061985","title":"A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"GNSS applications; Computer science; Inertial navigation system; Satellite system; Navigation system; Adaptive neuro fuzzy inference system; GNSS augmentation; Multipath propagation; Air navigation; GLONASS; Global Positioning System; Real-time computing; Artificial intelligence; Fuzzy logic; Telecommunications; Fuzzy control system; Channel (broadcasting)","score_opus":0.007910536479813815,"score_gpt":0.2155934532815918,"score_spread":0.20768291680177797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392986349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084976465,0.0005211968,0.9060985,0.00019173835,0.00014804526,0.000087240725,0.00010506635,0.0016252329,0.006246545],"genre_scores_gemma":[0.912396,0.0002237314,0.08409525,0.0000810473,0.00002877219,0.00009352743,0.00015253547,0.000021614931,0.0029073923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981314,0.000021898473,0.000015053343,0.000046047633,0.000090809335,0.000013116017],"domain_scores_gemma":[0.9998878,0.000026551548,0.000023160881,0.000008294036,0.000049288927,0.0000047985723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023061884,0.0005672538,0.00035463492,0.0002974938,0.00026769916,0.00032113423,0.0005832023,0.00056017254,0.00074645213],"category_scores_gemma":[0.00045302935,0.00023588874,0.00038037865,0.00022406614,0.00019657885,0.00039393033,0.00028930922,0.00042631212,0.0002026694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002112634,0.00008544601,0.0030777114,0.00025272096,0.00010122995,0.00039254516,0.00018473789,0.6787346,0.085867286,0.002380815,0.0015555327,0.22715606],"study_design_scores_gemma":[0.000013241207,0.00009510023,0.00096594164,0.000016717508,0.00002591017,0.000052348918,0.000012159902,0.9899713,0.0073166997,0.00025712175,0.0012617377,0.000011742365],"about_ca_topic_score_codex":0.0060248263,"about_ca_topic_score_gemma":0.0067299632,"teacher_disagreement_score":0.0060248263,"about_ca_system_score_codex":0.0003028518,"about_ca_system_score_gemma":0.00043130657,"threshold_uncertainty_score":0.01197952},"labels":[],"label_agreement":null},{"id":"W4393006261","doi":"10.3390/s24061970","title":"Comparing a Portable Motion Analysis System against the Gold Standard for Potential Anterior Cruciate Ligament Injury Prevention and Screening","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; Shriners Hospitals for Children - Canada; Concordia University; McGill University Health Centre; McGill University","funders":"","keywords":"Sagittal plane; Coronal plane; Anterior cruciate ligament; ACL injury; Motion analysis; Kinematics; Orthodontics; Gold standard (test); Computer science; Medicine; Anatomy; Computer vision; Physics; Radiology","score_opus":0.012285776419879373,"score_gpt":0.2834924437770046,"score_spread":0.27120666735712523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393006261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8708854,0.0034670273,0.10310062,0.0004569487,0.0005077152,0.002874941,0.006570455,0.0014195351,0.010717544],"genre_scores_gemma":[0.88947517,0.0010667233,0.096577786,0.00042956963,0.00012332566,0.0026934478,0.004988437,0.00016337684,0.004482207],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99576676,0.0012319997,0.00051754294,0.0011156189,0.0011829132,0.00018512717],"domain_scores_gemma":[0.9969668,0.00075097155,0.00061367376,0.0002491061,0.0012717993,0.00014763861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039790636,0.0011679311,0.00093586673,0.0021498655,0.00040244803,0.00093060243,0.00097027974,0.001033943,0.003920393],"category_scores_gemma":[0.0076717497,0.0003315497,0.00083908765,0.0010793101,0.0004992235,0.0006186279,0.0011396512,0.00046083514,0.0012423588],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0064514535,0.0014711317,0.5057641,0.003182609,0.0014241058,0.00061812246,0.001293383,0.005982516,0.13028425,0.0015461043,0.011610795,0.33037135],"study_design_scores_gemma":[0.00044162865,0.0041911094,0.9356684,0.00042800364,0.00049354683,0.0014157281,0.0005688496,0.023945084,0.025260538,0.00064007455,0.0068149096,0.00013211128],"about_ca_topic_score_codex":0.0028272602,"about_ca_topic_score_gemma":0.0054468615,"teacher_disagreement_score":0.0039790636,"about_ca_system_score_codex":0.00048459982,"about_ca_system_score_gemma":0.0005170558,"threshold_uncertainty_score":0.021043539},"labels":[],"label_agreement":null},{"id":"W4393043884","doi":"10.3390/s24061996","title":"Self-Supervised Open-Set Speaker Recognition with Laguerre–Voronoi Descriptors","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Speaker recognition; Pattern recognition (psychology); Speech recognition; Artificial intelligence; Open set; Feature extraction; Cluster analysis; Set (abstract data type); Feature (linguistics); Artificial neural network; Biometrics; Speaker diarisation; Voronoi diagram; Neural gas; Time delay neural network; Mathematics","score_opus":0.046830119155900594,"score_gpt":0.2578130349466004,"score_spread":0.2109829157906998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393043884","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03179589,0.00017852537,0.96581876,0.00007181874,0.000048504327,0.00005243733,0.000100445526,0.00084116723,0.001092472],"genre_scores_gemma":[0.7708655,0.00013837848,0.22405934,0.00012381734,0.00008693877,0.00010268167,0.00065410987,0.00015300488,0.003816186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895644,0.0002340913,0.000053049916,0.0003405117,0.00030809484,0.000107927866],"domain_scores_gemma":[0.99870384,0.00050998334,0.00017520909,0.00024914654,0.00029998834,0.0000617615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088794157,0.0004978115,0.0011022178,0.0005785815,0.00042002692,0.0010263831,0.0017604153,0.00067886844,0.001749414],"category_scores_gemma":[0.002743699,0.00031352197,0.0006051052,0.00048787825,0.00072259223,0.0014015784,0.0016423551,0.0011349292,0.0008582515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006165543,0.00021542373,0.002378254,0.0001356845,0.0001200612,0.00017275431,0.00035688083,0.17103182,0.05197056,0.018809171,0.00405945,0.7501334],"study_design_scores_gemma":[0.000007251569,0.00003318078,0.00037587222,0.0000050614167,0.000006701995,0.00008214005,0.00003113828,0.9812867,0.012641554,0.0047154585,0.0007995771,0.000015460928],"about_ca_topic_score_codex":0.0024241866,"about_ca_topic_score_gemma":0.0037678417,"teacher_disagreement_score":0.0024241866,"about_ca_system_score_codex":0.00061739626,"about_ca_system_score_gemma":0.0006808885,"threshold_uncertainty_score":0.0058524013},"labels":[],"label_agreement":null},{"id":"W4393045065","doi":"10.3390/s24061994","title":"Concurrent Supra-Postural Auditory–Hand Coordination Task Affects Postural Control: Using Sonification to Explore Environmental Unpredictability in Factors Affecting Fall Risk","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Institute of General Medical Sciences; McMaster Institute for Research on Aging, McMaster University","keywords":"Stimulus (psychology); Psychology; Physical medicine and rehabilitation; Sensory system; Cognition; Balance (ability); Motor coordination; Audiology; Cognitive psychology; Neuroscience; Medicine","score_opus":0.03481226590388323,"score_gpt":0.3466921337621016,"score_spread":0.3118798678582184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393045065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992962,0.00010369453,0.00046020138,0.00001145604,0.000004944843,0.000015705795,0.0000113875685,0.0000028736101,0.000093423165],"genre_scores_gemma":[0.9981433,0.00011905775,0.0013966213,0.000031660522,0.000013413787,0.000066649955,0.000026824182,0.0000027591013,0.00019966374],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99985456,0.00004048887,0.000008789731,0.000041969233,0.000028461598,0.000025629672],"domain_scores_gemma":[0.9997459,0.0001076787,0.000049907518,0.000012271443,0.000021608319,0.00006257544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030616194,0.00034789578,0.0002861155,0.00023464402,0.00011150679,0.00021559864,0.00013429792,0.00031817664,0.0006907399],"category_scores_gemma":[0.0009649318,0.00020078752,0.00013214917,0.00010429028,0.0003348872,0.00023415257,0.0002851784,0.00025841902,0.000062546314],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02179496,0.007258494,0.09965619,0.0003393519,0.00015379146,0.0002457187,0.0011175628,0.0004193141,0.7950904,0.000098236545,0.00015682534,0.073669106],"study_design_scores_gemma":[0.0005987044,0.041713696,0.8902352,0.00003335756,0.000310461,0.0005004877,0.00062711554,0.0034969326,0.061710916,0.0002633936,0.00048277847,0.00002697881],"about_ca_topic_score_codex":0.000714545,"about_ca_topic_score_gemma":0.0015299945,"teacher_disagreement_score":0.000714545,"about_ca_system_score_codex":0.0001216111,"about_ca_system_score_gemma":0.0002236619,"threshold_uncertainty_score":0.0023106933},"labels":[],"label_agreement":null},{"id":"W4393150239","doi":"10.3390/s24072087","title":"A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Observability; Control theory (sociology); Observer (physics); Kalman filter; Monte Carlo method; Extended Kalman filter; Iterated function; Alpha beta filter; Filter (signal processing); Computer science; Position (finance); Nonlinear system; Algorithm; Engineering; Mathematics; Artificial intelligence; Computer vision; Moving horizon estimation; Physics; Applied mathematics","score_opus":0.02149536594404253,"score_gpt":0.27008433890195804,"score_spread":0.24858897295791552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393150239","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026326925,0.00010595438,0.99668497,0.000022782846,0.000019095565,0.00001031249,0.000010264138,0.00015722863,0.00035669736],"genre_scores_gemma":[0.5055146,0.00060446974,0.49030265,0.00011988481,0.00011554603,0.00018373477,0.0002568934,0.00010932587,0.002792904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949527,0.00008479806,0.000034891844,0.00012092069,0.00021691766,0.000047135603],"domain_scores_gemma":[0.99906784,0.0004639364,0.00011642872,0.000076628574,0.00024571636,0.000029479086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084660243,0.0005657786,0.00092616596,0.000503184,0.00045696384,0.0005763052,0.0011702335,0.00083952316,0.0010165006],"category_scores_gemma":[0.0031132025,0.0004266611,0.0007501617,0.00048748453,0.0004922051,0.0010204449,0.000601667,0.0011429482,0.00033701851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102248814,0.000044131357,0.0016149764,0.00009270682,0.00007672542,0.00006909962,0.0000998987,0.843711,0.007493172,0.010122705,0.0007579055,0.13581543],"study_design_scores_gemma":[0.0000038022617,0.0000177249,0.00016551602,0.000004642325,0.0000064919172,0.00001827951,0.0000024172105,0.9976884,0.00082525593,0.0006850406,0.0005747226,0.0000076185224],"about_ca_topic_score_codex":0.0131443655,"about_ca_topic_score_gemma":0.012551993,"teacher_disagreement_score":0.0131443655,"about_ca_system_score_codex":0.0006729089,"about_ca_system_score_gemma":0.0016966817,"threshold_uncertainty_score":0.026135743},"labels":[],"label_agreement":null},{"id":"W4393162741","doi":"10.3390/s24072048","title":"Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Calgary","keywords":"Heart rate variability; Sampling (signal processing); Pulse (music); Pulse rate; Heart rate; Environmental science; Cardiology; Medicine; Computer science; Internal medicine; Telecommunications; Blood pressure","score_opus":0.01621667164589083,"score_gpt":0.2790845298557851,"score_spread":0.2628678582098942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393162741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6410655,0.22376068,0.0840015,0.018292468,0.00713859,0.0006125083,0.0025963606,0.00045954707,0.022072906],"genre_scores_gemma":[0.9660086,0.01381231,0.010281008,0.0031835868,0.0035864383,0.0003262263,0.0007080688,0.00039408988,0.0016995864],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9814386,0.007838314,0.0016812432,0.0056276526,0.0029288954,0.00048532113],"domain_scores_gemma":[0.8439501,0.12459824,0.017041026,0.0070876298,0.005971201,0.001351823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03253822,0.0007113971,0.0014808401,0.0008844778,0.0004618484,0.0022017027,0.0010690388,0.0019098086,0.004029013],"category_scores_gemma":[0.12081635,0.00048305548,0.0014509998,0.0026611013,0.002022132,0.003631353,0.00076651736,0.0016235459,0.0008319772],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028901638,0.00019857656,0.6585223,0.0020753765,0.0056699775,0.00030487502,0.0029079285,0.0012002025,0.0019920983,0.005238124,0.005618561,0.31338176],"study_design_scores_gemma":[0.000100668556,0.0006508921,0.9762299,0.0009064561,0.0014401656,0.00050457893,0.00078057835,0.0023573458,0.00055216637,0.009308438,0.0070768446,0.000091981485],"about_ca_topic_score_codex":0.0018616733,"about_ca_topic_score_gemma":0.0023018585,"teacher_disagreement_score":0.03253822,"about_ca_system_score_codex":0.0003942395,"about_ca_system_score_gemma":0.0008086802,"threshold_uncertainty_score":0.17208064},"labels":[],"label_agreement":null},{"id":"W4393181685","doi":"10.3390/s24072107","title":"Object Detection and Tracking with YOLO and the Sliding Innovation Filter","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Oil Sands Technology and Research Authority; McMaster University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Kalman filter; Computer vision; Artificial intelligence; Video tracking; Object detection; Filter (signal processing); Object (grammar); Tracking system; Trajectory; Tracking (education); Extended Kalman filter; Pattern recognition (psychology)","score_opus":0.021967897245596498,"score_gpt":0.26690382086501724,"score_spread":0.24493592361942074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393181685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037597893,0.00029942265,0.99461657,0.00005360254,0.000050701005,0.000016494338,0.000009706458,0.00027710665,0.0009165055],"genre_scores_gemma":[0.45564112,0.0013057857,0.5352629,0.00030799498,0.00018842904,0.00021036851,0.00013615542,0.00010362465,0.0068435892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877566,0.00017523172,0.00007097416,0.00037270313,0.0004934149,0.00011200475],"domain_scores_gemma":[0.9989672,0.00040085224,0.00017778882,0.00015699059,0.0002510538,0.00004610504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013375431,0.00082083856,0.000969442,0.00097119063,0.00048016995,0.0012078448,0.001171077,0.0015213426,0.0012101497],"category_scores_gemma":[0.003612445,0.0005267524,0.0010931559,0.0007925795,0.0009866313,0.0017058711,0.0012533803,0.0013890015,0.0006778005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048242632,0.00014292804,0.0029714352,0.00032620286,0.00023138912,0.00028569272,0.0004760355,0.31462538,0.04330015,0.072939724,0.0025632773,0.56165534],"study_design_scores_gemma":[0.00001300562,0.00013157615,0.0005360906,0.0000135808305,0.000032209762,0.00008270333,0.000013607602,0.98871905,0.004630648,0.0032043795,0.0025947285,0.000028393939],"about_ca_topic_score_codex":0.006050148,"about_ca_topic_score_gemma":0.0038503618,"teacher_disagreement_score":0.006050148,"about_ca_system_score_codex":0.00086232997,"about_ca_system_score_gemma":0.0012247995,"threshold_uncertainty_score":0.012029827},"labels":[],"label_agreement":null},{"id":"W4393190295","doi":"10.3390/s24072119","title":"Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Wearable computer; Wavelet packet decomposition; Activity recognition; Support vector machine; Pattern recognition (psychology); Computer science; Artificial intelligence; Discrete wavelet transform; Wavelet transform; Selection (genetic algorithm); Entropy (arrow of time); Machine learning; Data mining","score_opus":0.0634569301542508,"score_gpt":0.31802343856195875,"score_spread":0.25456650840770795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393190295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2451322,0.0012582559,0.7510349,0.00021454405,0.00009214446,0.00009838264,0.00040203368,0.00046274238,0.0013047494],"genre_scores_gemma":[0.84416467,0.0007293101,0.15330087,0.000048980633,0.00005172695,0.00013384604,0.0008530363,0.000071683906,0.00064584095],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990879,0.0002855148,0.00007002228,0.00013372395,0.00035828937,0.0000645446],"domain_scores_gemma":[0.9977957,0.0013131463,0.00021069472,0.00015931777,0.0004801991,0.00004097976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002523338,0.0005128911,0.00055337825,0.0011357674,0.00017188658,0.00057223695,0.00033540995,0.00034820073,0.00056416285],"category_scores_gemma":[0.007733313,0.0001261317,0.00039292345,0.0009776992,0.00023515377,0.00077576574,0.00032521685,0.00035332725,0.00023124987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008987525,0.00029254175,0.020225134,0.000518534,0.000153319,0.00027472974,0.00018108258,0.20250717,0.06309431,0.0052780644,0.0033991993,0.70317715],"study_design_scores_gemma":[0.000012683926,0.00015478586,0.014651486,0.000028757018,0.000035070807,0.00011896807,0.000075714815,0.966398,0.015088667,0.0022921606,0.0011275806,0.000016228343],"about_ca_topic_score_codex":0.0008159439,"about_ca_topic_score_gemma":0.0006967404,"teacher_disagreement_score":0.002523338,"about_ca_system_score_codex":0.00027706352,"about_ca_system_score_gemma":0.0003566589,"threshold_uncertainty_score":0.013344824},"labels":[],"label_agreement":null},{"id":"W4393201061","doi":"10.3390/s24072118","title":"SPR and Double Resonance LPG Biosensors for Helicobacter pylori BabA Antigen Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Helicobacter pylori-related gastroenterology studies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Bulgarian National Science Fund","keywords":"Surface plasmon resonance; Bacterial adhesin; Helicobacter pylori; Biosensor; Antigen; Bacterial outer membrane; Receptor; Microbiology; Chemistry; Molecular biology; Biology; Medicine; Immunology; Materials science; Nanotechnology; Biochemistry; Nanoparticle; Internal medicine; Gene; Escherichia coli","score_opus":0.021516705314213038,"score_gpt":0.2771220564588653,"score_spread":0.25560535114465227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393201061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71608794,0.013789461,0.26289824,0.0010558392,0.00045370543,0.00019473082,0.00036746587,0.0011685321,0.0039841584],"genre_scores_gemma":[0.8127814,0.0030282482,0.18001053,0.00048341753,0.000087325105,0.00017640206,0.00017695405,0.000058236947,0.003197519],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866176,0.000500043,0.000058356833,0.00025315763,0.00042992656,0.00009679267],"domain_scores_gemma":[0.99929833,0.00031725026,0.00012782875,0.00006125423,0.0001366762,0.000058640588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014365496,0.0007364215,0.0005719071,0.0008110084,0.00017504902,0.00040670644,0.0006487865,0.0013544416,0.00066026585],"category_scores_gemma":[0.0013512656,0.00044539265,0.0004448012,0.00039911928,0.00047468298,0.0006061187,0.00052361446,0.0008986228,0.0005442695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009078218,0.000032133357,0.00022260429,0.00010687171,0.000008598877,0.000065273874,0.000045153134,0.00029850536,0.9936435,0.00019843975,0.00007733477,0.0052107917],"study_design_scores_gemma":[0.000029207544,0.0010102949,0.0022157335,0.000021012822,0.00003491414,0.00081444794,0.00010944408,0.020142416,0.9728601,0.00031262962,0.0024055666,0.000044213964],"about_ca_topic_score_codex":0.00034462378,"about_ca_topic_score_gemma":0.0005031425,"teacher_disagreement_score":0.0014365496,"about_ca_system_score_codex":0.00030250123,"about_ca_system_score_gemma":0.00020154536,"threshold_uncertainty_score":0.0075972676},"labels":[],"label_agreement":null},{"id":"W4393356390","doi":"10.3390/s24072238","title":"Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Canada Research Chairs","keywords":"Accelerometer; Wearable computer; Wearable technology; Computer science; Heart rate monitor; Heart rate; Pedometer; Activity tracker; Noise (video); Real-time computing; Computer hardware; Simulation; Artificial intelligence; Embedded system; Physical activity; Medicine","score_opus":0.07449754517901784,"score_gpt":0.27958318788597947,"score_spread":0.2050856427069616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393356390","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8240401,0.0011564437,0.16123565,0.0001771868,0.00051035633,0.00052446174,0.0025131872,0.0024454866,0.0073971185],"genre_scores_gemma":[0.9146295,0.0008151901,0.077009164,0.0001533948,0.000097993354,0.00036159303,0.0012334244,0.00007996177,0.0056196945],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993855,0.000118528595,0.00006347889,0.00015870335,0.00025193067,0.000021841293],"domain_scores_gemma":[0.9994978,0.00008828193,0.00008376703,0.00009587429,0.00020782842,0.00002645613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031033464,0.0007627611,0.00049113936,0.0005822198,0.00014682655,0.0003985602,0.0003736045,0.0005343054,0.0024672272],"category_scores_gemma":[0.0013884201,0.00015767182,0.00019939919,0.00062367774,0.00014967775,0.00024980682,0.0002826745,0.00021835236,0.0011143384],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084520027,0.00031293128,0.027477423,0.000630326,0.00008669765,0.0004034069,0.0002488656,0.00065279315,0.7838564,0.00015460598,0.0017836853,0.18354765],"study_design_scores_gemma":[0.00031329447,0.006326231,0.29242772,0.00014899693,0.00037299548,0.0054360577,0.00041828904,0.03554989,0.64739937,0.00022187839,0.011256074,0.00012929036],"about_ca_topic_score_codex":0.00033776104,"about_ca_topic_score_gemma":0.00068428286,"teacher_disagreement_score":0.0024672272,"about_ca_system_score_codex":0.00007141902,"about_ca_system_score_gemma":0.00015897723,"threshold_uncertainty_score":0.008253634},"labels":[],"label_agreement":null},{"id":"W4393356419","doi":"10.3390/s24072231","title":"Innovative Metaheuristic Optimization Approach with a Bi-Triad for Rehabilitation Exoskeletons","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Kinematics; Robot; Exoskeleton; Scalability; Computer science; Kinematic chain; Interface (matter); Simulation; Control engineering; Engineering; Artificial intelligence","score_opus":0.007547114917143808,"score_gpt":0.2233102770864823,"score_spread":0.2157631621693385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393356419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01844321,0.00047498016,0.97569263,0.00019370754,0.00007461414,0.00011094744,0.00006000625,0.00023761173,0.00471227],"genre_scores_gemma":[0.32416245,0.0005213379,0.6703238,0.000240131,0.000073522,0.00070440845,0.00020974613,0.00012732511,0.003637247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963164,0.00017085743,0.00002199659,0.00004444103,0.00009097998,0.00004004137],"domain_scores_gemma":[0.9996655,0.00016568307,0.000042741514,0.000029970603,0.00007390821,0.000022203696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009658834,0.0010949242,0.0009508133,0.0012902109,0.00040381378,0.0009473179,0.0011241469,0.0015439314,0.0023095652],"category_scores_gemma":[0.0013581029,0.00047649382,0.0012564702,0.00089365634,0.0004669217,0.00053407904,0.0009364108,0.0010698752,0.00038842604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036750316,0.00008509866,0.0003313935,0.00008655508,0.00008295637,0.000038324026,0.000025963502,0.96057343,0.0013976492,0.0056749713,0.00045714728,0.03120973],"study_design_scores_gemma":[0.000007542252,0.000034174263,0.00004867311,0.000007397204,0.000008195466,0.000008847487,0.00000899393,0.9982496,0.00020840335,0.0009664368,0.0004486698,0.0000030817437],"about_ca_topic_score_codex":0.0026309683,"about_ca_topic_score_gemma":0.0023521741,"teacher_disagreement_score":0.0026309683,"about_ca_system_score_codex":0.00047808053,"about_ca_system_score_gemma":0.0010094033,"threshold_uncertainty_score":0.007726252},"labels":[],"label_agreement":null},{"id":"W4393356935","doi":"10.3390/s24072218","title":"Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"","keywords":"Polysomnography; Bedtime; Sleep (system call); Physical therapy; Medicine; Audiology; Psychology; Physical medicine and rehabilitation; Apnea; Anesthesia; Internal medicine; Computer science","score_opus":0.03247426269679022,"score_gpt":0.32498705902529756,"score_spread":0.29251279632850735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393356935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986266,0.00029138848,0.00043857403,0.000013281359,0.000022248201,0.00004909983,0.00012821626,0.000015655714,0.0004149764],"genre_scores_gemma":[0.99733704,0.0002677583,0.0015670804,0.000035365487,0.000030309597,0.000053043073,0.00028720245,0.000005242428,0.00041692355],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999418,0.00017182279,0.00008513367,0.00014341853,0.00014616258,0.000035368208],"domain_scores_gemma":[0.99841,0.000570437,0.00038712035,0.000078956466,0.00036535395,0.00018807617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013955329,0.00053709355,0.00042416912,0.00042844977,0.00017431415,0.00041518317,0.00031490566,0.00052051875,0.0010506986],"category_scores_gemma":[0.0031117806,0.00019375444,0.00040426003,0.0001805957,0.00013974264,0.00033670347,0.00031904085,0.0001544286,0.00029653808],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008677561,0.0009606782,0.87471724,0.0005866972,0.0004363607,0.000121019984,0.0008716422,0.00043509083,0.0185564,0.00003326114,0.0002997379,0.09430426],"study_design_scores_gemma":[0.000102866245,0.009522764,0.986159,0.00003772329,0.0001805443,0.0002561021,0.0002762946,0.0013246839,0.0017575404,0.000015198942,0.00035194616,0.000015332063],"about_ca_topic_score_codex":0.0015900234,"about_ca_topic_score_gemma":0.00386242,"teacher_disagreement_score":0.0015900234,"about_ca_system_score_codex":0.00017715109,"about_ca_system_score_gemma":0.0001393068,"threshold_uncertainty_score":0.007380426},"labels":[],"label_agreement":null},{"id":"W4393935428","doi":"10.3390/s24072297","title":"Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Naive Bayes classifier; Pattern recognition (psychology); Support vector machine; Computer science; Artificial intelligence; Principal component analysis; Decision tree; Linear discriminant analysis; Fault detection and isolation; Dimensionality reduction; Ultrasonic sensor; Random forest; Classifier (UML)","score_opus":0.03238142587340873,"score_gpt":0.23132694546737825,"score_spread":0.19894551959396953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393935428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17437865,0.00093438267,0.8205136,0.0002477937,0.000090788955,0.000056098626,0.00023313118,0.0012718843,0.002273667],"genre_scores_gemma":[0.9565282,0.0003023507,0.040970843,0.00006190067,0.000047989022,0.00009127132,0.00034210493,0.000035318204,0.0016199867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969745,0.000052360185,0.000021048687,0.00010309433,0.000080639475,0.00004538779],"domain_scores_gemma":[0.9995295,0.00017470328,0.000062352585,0.000045348108,0.00016029595,0.00002794498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006458262,0.00081452035,0.0012291231,0.0008531103,0.0003323247,0.0007867013,0.0008487283,0.0006529649,0.0008931775],"category_scores_gemma":[0.0011501741,0.0002815741,0.0011755096,0.0005759872,0.00020718222,0.0007695367,0.00054589857,0.00068052293,0.00027316716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015591859,0.00009775391,0.006006639,0.000038840608,0.00020438351,0.000105000334,0.0000548265,0.89364386,0.0044014924,0.0013625004,0.00058361003,0.093345165],"study_design_scores_gemma":[9.0566726e-7,0.000018876726,0.00023401804,0.0000013176325,0.0000093987655,0.0000058238056,0.000002118413,0.99923444,0.0002472117,0.00018786688,0.00005580932,0.000002104836],"about_ca_topic_score_codex":0.007711751,"about_ca_topic_score_gemma":0.0062988973,"teacher_disagreement_score":0.007711751,"about_ca_system_score_codex":0.0005713647,"about_ca_system_score_gemma":0.0004988033,"threshold_uncertainty_score":0.015333712},"labels":[],"label_agreement":null},{"id":"W4393992991","doi":"10.3390/s24072312","title":"Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"AI in cancer detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Support vector machine; Artificial intelligence; Naive Bayes classifier; Machine learning; Decision tree; Breast cancer; Computer science; Classifier (UML); Cross-validation; Computer-aided diagnosis; Cancer; Pattern recognition (psychology); Medicine; Internal medicine","score_opus":0.009837236044524375,"score_gpt":0.26904943620935157,"score_spread":0.2592122001648272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393992991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067157656,0.00883868,0.9163249,0.0012662834,0.0002177578,0.00016302169,0.00054684974,0.0025486755,0.0029361863],"genre_scores_gemma":[0.56147265,0.0068390304,0.42467192,0.00033745784,0.00035947523,0.00015990822,0.0011872728,0.00016367972,0.004808652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933356,0.00018009543,0.000043031487,0.00016097743,0.00022391745,0.00005837137],"domain_scores_gemma":[0.99880743,0.0005902685,0.00015723577,0.00008212236,0.00033244997,0.000030468465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015100877,0.0009136351,0.00093184854,0.0020000155,0.00021067464,0.0012289071,0.0009113185,0.00082444603,0.0011691226],"category_scores_gemma":[0.004565362,0.0004320409,0.00085123413,0.0016211176,0.00028347713,0.001508532,0.00043930803,0.0009788871,0.00086965284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022496562,0.00027301023,0.018508058,0.00043706736,0.00022969165,0.0002380084,0.00011384601,0.39356464,0.00995821,0.0051662317,0.007325905,0.5639605],"study_design_scores_gemma":[0.0000046177447,0.000027456059,0.0016349545,0.000020276246,0.000029974179,0.000065459324,0.00002042452,0.9931404,0.0017502997,0.0014962042,0.001793696,0.000016221862],"about_ca_topic_score_codex":0.007815083,"about_ca_topic_score_gemma":0.0063336645,"teacher_disagreement_score":0.007815083,"about_ca_system_score_codex":0.00071563234,"about_ca_system_score_gemma":0.0005934906,"threshold_uncertainty_score":0.015539229},"labels":[],"label_agreement":null},{"id":"W4394568483","doi":"10.3390/s24072366","title":"Next Generation Computing and Communication Hub for First Responders in Smart Cities","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Workload; Computer science; Standardization; Interoperability; Cognitive computing; Cognition; Computer security; Medicine; World Wide Web","score_opus":0.05989695689202528,"score_gpt":0.2827937469701239,"score_spread":0.2228967900780986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394568483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07844857,0.000492062,0.87488633,0.0033145207,0.00027968362,0.00029279612,0.00024131975,0.00487906,0.037165664],"genre_scores_gemma":[0.74025065,0.00067741756,0.24398962,0.00052182237,0.00013439494,0.00030154342,0.0004961001,0.0003498032,0.013278627],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990637,0.0003386533,0.000047894602,0.00016358666,0.00021494347,0.00017129762],"domain_scores_gemma":[0.998978,0.00021145155,0.000111869114,0.00032885274,0.00020156767,0.00016825156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00143836,0.00043190026,0.0003100939,0.0008766677,0.0010673312,0.0031783052,0.0012548772,0.0012213978,0.0049389573],"category_scores_gemma":[0.0020747979,0.00024422584,0.00043273976,0.0006497431,0.0008161355,0.005781225,0.0036211363,0.0013179053,0.0015322807],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027380453,0.00020531511,0.0041218,0.0003446245,0.000045800374,0.0006280441,0.004657752,0.04841409,0.020534443,0.69871086,0.019007115,0.20305645],"study_design_scores_gemma":[0.000057526646,0.0003320108,0.0037294535,0.00036355172,0.00006714754,0.0005650609,0.0044243312,0.37916872,0.022132656,0.17334679,0.41567132,0.00014145942],"about_ca_topic_score_codex":0.0020158975,"about_ca_topic_score_gemma":0.002013398,"teacher_disagreement_score":0.0049389573,"about_ca_system_score_codex":0.0012669795,"about_ca_system_score_gemma":0.0015030281,"threshold_uncertainty_score":0.016522467},"labels":[],"label_agreement":null},{"id":"W4394570857","doi":"10.3390/s24072368","title":"MSK-TIM: A Telerobotic Ultrasound System for Assessing the Musculoskeletal System","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Telerobotics; Zoom; Software; Wrist; Ultrasound; Simulation; Engineering; Medicine; Medical physics; Robot; Artificial intelligence; Computer science; Radiology; Operating system","score_opus":0.011436664829784973,"score_gpt":0.26148020003239936,"score_spread":0.2500435352026144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394570857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6506802,0.005107066,0.32387343,0.0006626757,0.00036688067,0.0016483995,0.0012893147,0.0050651776,0.011306838],"genre_scores_gemma":[0.6912675,0.0013657697,0.29725406,0.0003358371,0.00015097471,0.000807291,0.0005998711,0.00013842386,0.008080178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994785,0.0001486032,0.00003133221,0.00009209927,0.0002089088,0.0000405672],"domain_scores_gemma":[0.99943525,0.0001970845,0.00008758225,0.00006961701,0.00015426616,0.000056222456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000993113,0.00046000697,0.0004542488,0.0005278964,0.00018561647,0.00045902995,0.00073632924,0.00070511794,0.0050510163],"category_scores_gemma":[0.0013403146,0.00019805801,0.00029026406,0.00026612473,0.00036098095,0.0006458883,0.0005492684,0.00029947612,0.0011724723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021289142,0.00047449302,0.009639006,0.0020891726,0.00009439832,0.00036114053,0.0004990605,0.0030295814,0.49113572,0.0011871278,0.003752967,0.4856085],"study_design_scores_gemma":[0.0010373077,0.03737051,0.291307,0.0007211265,0.00090628525,0.023846988,0.001250162,0.088560194,0.43129918,0.0017632785,0.12135869,0.00057921436],"about_ca_topic_score_codex":0.00034856074,"about_ca_topic_score_gemma":0.00068770396,"teacher_disagreement_score":0.0050510163,"about_ca_system_score_codex":0.0002635133,"about_ca_system_score_gemma":0.0004453969,"threshold_uncertainty_score":0.01689732},"labels":[],"label_agreement":null},{"id":"W4394577314","doi":"10.3390/s24072324","title":"Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; McGill University","keywords":"Cyber-physical system; Machining; Process (computing); Computer science; Engineering; Risk analysis (engineering); Manufacturing engineering; Reliability engineering; Systems engineering; Mechanical engineering","score_opus":0.007516103061707233,"score_gpt":0.25063802387990136,"score_spread":0.24312192081819411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394577314","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006251643,0.9886341,0.0068684476,0.0003630928,0.0002935714,0.000015762482,0.000023477904,0.000031556327,0.0031447783],"genre_scores_gemma":[0.006664115,0.98956513,0.0022434779,0.00017630593,0.00046353135,0.000019535444,0.00004503686,0.00000832832,0.00081450294],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997538,0.000041719195,0.000028287423,0.000050221603,0.00010778208,0.000018295495],"domain_scores_gemma":[0.99913377,0.00058555015,0.00008342057,0.00002473062,0.0001460877,0.000026425569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004923501,0.0013193488,0.0012065149,0.0016124946,0.0002975471,0.0014429842,0.00084801414,0.0015114493,0.003116891],"category_scores_gemma":[0.00094947213,0.00046210643,0.00077147916,0.0019139241,0.0005963774,0.0022146166,0.00071066595,0.0012845247,0.0011413231],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052107138,0.000115258845,0.0006379848,0.027379172,0.00017883965,0.00023059826,0.00019247999,0.0123920925,0.0029882432,0.040152397,0.0135365,0.9021443],"study_design_scores_gemma":[0.000015282432,0.00035281683,0.0023089787,0.009590157,0.00032386326,0.001179036,0.00031041188,0.013189078,0.0026135577,0.027061613,0.94292945,0.00012572395],"about_ca_topic_score_codex":0.0009775115,"about_ca_topic_score_gemma":0.000852468,"teacher_disagreement_score":0.003116891,"about_ca_system_score_codex":0.0004571919,"about_ca_system_score_gemma":0.0010150486,"threshold_uncertainty_score":0.010427117},"labels":[],"label_agreement":null},{"id":"W4394605432","doi":"10.3390/s24082390","title":"IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Gait; Artificial neural network; Computer science; Artificial intelligence; Phase (matter); Estimation; Gait analysis; Computer vision; Physical medicine and rehabilitation; Engineering; Medicine; Physics","score_opus":0.015353254030340819,"score_gpt":0.27548917851928845,"score_spread":0.2601359244889476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394605432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16282573,0.0010697473,0.83165514,0.000096819116,0.0001713312,0.00009077463,0.00057844527,0.001749601,0.0017623515],"genre_scores_gemma":[0.8586974,0.00041588288,0.1375094,0.00008348488,0.00007091624,0.00013149003,0.0007633272,0.00006822505,0.0022599022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997912,0.000028900999,0.000013182882,0.00007589344,0.0000604,0.000030332962],"domain_scores_gemma":[0.9998055,0.000048214493,0.000039494204,0.000026592948,0.00006823661,0.000011945049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043083113,0.00085479754,0.00057758123,0.0007371015,0.00014398954,0.00037102535,0.0005498653,0.00052313803,0.000990507],"category_scores_gemma":[0.0011778717,0.00031796924,0.0003600516,0.00060488,0.00013472722,0.00043099158,0.00046375595,0.000406032,0.00049372524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006245183,0.0002970527,0.014297507,0.00031153264,0.00027631773,0.00025911446,0.00012118963,0.16466357,0.0647979,0.00075235474,0.002534098,0.75106484],"study_design_scores_gemma":[0.000015737176,0.000115740644,0.011645262,0.000025674082,0.000042425792,0.00018074442,0.00001749497,0.97398746,0.012528938,0.000410677,0.001008322,0.000021425389],"about_ca_topic_score_codex":0.0026118536,"about_ca_topic_score_gemma":0.0047046756,"teacher_disagreement_score":0.0026118536,"about_ca_system_score_codex":0.00021191222,"about_ca_system_score_gemma":0.00024402587,"threshold_uncertainty_score":0.0051933527},"labels":[],"label_agreement":null},{"id":"W4394726414","doi":"10.3390/s24082441","title":"Dirt Track Surface Preparation and Associated Differences in Speed, Stride Length, and Stride Frequency in Galloping Horses","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Veterinary Equine Medical Research","field":"Veterinary","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"STRIDE; Environmental science; Animal science; Track (disk drive); Dirt; Simulation; Mathematics; Medicine; Physical medicine and rehabilitation; Engineering; Ecology; Biology","score_opus":0.09067503464096756,"score_gpt":0.3764678955884262,"score_spread":0.28579286094745865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394726414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997688,0.000030410663,0.0001243473,0.0000047047056,9.464165e-7,0.000002200816,0.000026545704,0.0000025039506,0.00003953624],"genre_scores_gemma":[0.999361,0.000024710967,0.00022036581,0.00000553869,0.00000216099,0.000008227649,0.00013259647,0.0000028068662,0.00024255848],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996917,0.00008351994,0.000018655075,0.000101680555,0.000029484672,0.000074927855],"domain_scores_gemma":[0.9992968,0.0001671733,0.00029902873,0.000065043205,0.000054672295,0.00011726539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004342648,0.00026195543,0.0003469546,0.00042091793,0.00021059594,0.00041863322,0.0001941602,0.00043294794,0.00096407626],"category_scores_gemma":[0.0015249723,0.00027747592,0.00029028233,0.00025710618,0.0002901387,0.0002521711,0.00030861894,0.00020559496,0.00016822842],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014751772,0.00022838656,0.97123754,0.000043714394,0.00014753749,0.00016961408,0.00037958432,0.0009657197,0.01942934,0.000028624612,0.000069321104,0.0058254455],"study_design_scores_gemma":[0.0000016151726,0.00031012425,0.99856526,0.000002231533,0.000010889359,0.000026884345,0.00008164809,0.00069642765,0.0002551584,0.000010898886,0.000036679074,0.0000021182484],"about_ca_topic_score_codex":0.00453566,"about_ca_topic_score_gemma":0.01081826,"teacher_disagreement_score":0.00453566,"about_ca_system_score_codex":0.00024577172,"about_ca_system_score_gemma":0.00017667252,"threshold_uncertainty_score":0.00901854},"labels":[],"label_agreement":null},{"id":"W4394748604","doi":"10.3390/s24082486","title":"Tolerance Considerations for MHMIC Manufacturing Process at Millimeter-Wave Band","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"3D IC and TSV technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Resistor; Microstrip; Fabrication; Flexibility (engineering); Electronic circuit; Compensation (psychology); Electronic engineering; Materials science; Integrated circuit; Extremely high frequency; Microwave; Optoelectronics; Electrical engineering; Engineering; Telecommunications; Voltage","score_opus":0.02421128381625582,"score_gpt":0.23428296258568038,"score_spread":0.21007167876942456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394748604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53569806,0.0054811304,0.43139714,0.001119068,0.00036510825,0.00008570639,0.00023040985,0.0007840945,0.024839373],"genre_scores_gemma":[0.9714121,0.0005428921,0.026658757,0.00008031582,0.0000522784,0.000029077257,0.00005817462,0.000067609806,0.0010988289],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998755,0.00013876538,0.00005385453,0.00018714352,0.0007655587,0.000099687066],"domain_scores_gemma":[0.9976125,0.0010093279,0.00056998385,0.0003611571,0.00041118736,0.000035805017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011730783,0.00040859604,0.00029228095,0.0004003658,0.0004167568,0.0008389254,0.0005944131,0.0005141836,0.0011450306],"category_scores_gemma":[0.003576188,0.00021179022,0.00028074766,0.00022919924,0.0004397631,0.0006029012,0.000549623,0.00041787943,0.00028823028],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040798663,0.00005912492,0.004962166,0.00067187444,0.00006244384,0.0006504803,0.0005394654,0.12839533,0.7722134,0.024302693,0.0008860999,0.066849045],"study_design_scores_gemma":[0.000031284475,0.0014462601,0.018164529,0.00014949676,0.00014449317,0.0014049433,0.0005041173,0.17611177,0.76433,0.01041792,0.027186353,0.000108723136],"about_ca_topic_score_codex":0.0006502759,"about_ca_topic_score_gemma":0.00078180135,"teacher_disagreement_score":0.0011730783,"about_ca_system_score_codex":0.00063463906,"about_ca_system_score_gemma":0.00034326187,"threshold_uncertainty_score":0.00620389},"labels":[],"label_agreement":null},{"id":"W4394755344","doi":"10.3390/s24082470","title":"Smart Sensor-Based Monitoring Technology for Machinery Fault Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Bearing (navigation); Fault (geology); Condition monitoring; Data acquisition; Engineering; SIGNAL (programming language); Hilbert–Huang transform; Fault detection and isolation; Signal processing; Rolling-element bearing; Vibration; Real-time computing; Computer science; Control engineering; Electronic engineering; Artificial intelligence; Actuator; White noise; Digital signal processing; Acoustics; Electrical engineering","score_opus":0.009449119203829301,"score_gpt":0.27569147827546775,"score_spread":0.26624235907163846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394755344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049300615,0.0026371935,0.9392195,0.00032148647,0.00020761439,0.00011458068,0.00029204425,0.0020224103,0.005884557],"genre_scores_gemma":[0.7080058,0.0021191249,0.2822633,0.0004282405,0.00013535979,0.00014890784,0.0003951419,0.0000685953,0.0064355345],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995639,0.00006368111,0.000022811706,0.00008534178,0.0002475138,0.00001684436],"domain_scores_gemma":[0.9997004,0.00008174269,0.00006452875,0.000048510174,0.000089835026,0.000015005195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026814287,0.0003404029,0.0003520756,0.0006080492,0.00013846993,0.00034024488,0.0005098828,0.00054381735,0.0018729478],"category_scores_gemma":[0.00064139895,0.0002034452,0.00019158359,0.00055342575,0.00023825509,0.0009938419,0.00035753244,0.00036979915,0.00082927896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021134736,0.0000771094,0.003944341,0.00048207256,0.000039296847,0.00015552761,0.00012106636,0.0071387175,0.5559287,0.006012664,0.0055079865,0.4203812],"study_design_scores_gemma":[0.00006957851,0.0011139147,0.019863928,0.000116860836,0.00013603961,0.0021348638,0.00014399251,0.3597339,0.52080363,0.006907028,0.08883478,0.00014149227],"about_ca_topic_score_codex":0.00021742622,"about_ca_topic_score_gemma":0.00037353378,"teacher_disagreement_score":0.0018729478,"about_ca_system_score_codex":0.00022236144,"about_ca_system_score_gemma":0.00016856016,"threshold_uncertainty_score":0.0062656403},"labels":[],"label_agreement":null},{"id":"W4394809261","doi":"10.3390/s24082512","title":"A Phosphenotron Device for Sensoric Spatial Resolution of Phosphenes within the Visual Field Using Non-Invasive Transcranial Alternating Current Stimulation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phosphene; Transcranial alternating current stimulation; Transcranial magnetic stimulation; Neuroscience; Visual field; Stimulation; Current (fluid); Alternating current; Computer science; Medicine; Biomedical engineering; Psychology; Electrical engineering; Engineering","score_opus":0.04686584150399671,"score_gpt":0.32942719193163794,"score_spread":0.28256135042764124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394809261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49653423,0.0073516853,0.48472762,0.0004325059,0.0003360144,0.0005775185,0.0003466638,0.001111714,0.008581984],"genre_scores_gemma":[0.8670139,0.0016961412,0.12665485,0.00035673723,0.00010548512,0.00022641069,0.0001585204,0.00004822911,0.0037397703],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998242,0.000030160429,0.000014800663,0.000050039256,0.00007165544,0.000009210121],"domain_scores_gemma":[0.99976426,0.00012387542,0.000038362665,0.000026511581,0.00003299064,0.00001400333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021874016,0.00027949244,0.00014822325,0.00027474764,0.00011126197,0.00018936084,0.00041395132,0.00028814372,0.0016315496],"category_scores_gemma":[0.0005987424,0.000117421,0.0001621322,0.00010838029,0.0002312208,0.00037573883,0.00031663338,0.0003096974,0.00019335285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031092507,0.0000474131,0.0006916283,0.00024827744,0.000017840739,0.00027000066,0.00007695487,0.00019354573,0.8926481,0.00045658374,0.00043742754,0.104601234],"study_design_scores_gemma":[0.0002995071,0.0069251955,0.04439332,0.00015875408,0.0002527841,0.017974032,0.00015687836,0.013849234,0.8864965,0.0012897233,0.028079532,0.0001245816],"about_ca_topic_score_codex":0.00015690664,"about_ca_topic_score_gemma":0.00031188052,"teacher_disagreement_score":0.0016315496,"about_ca_system_score_codex":0.00010084643,"about_ca_system_score_gemma":0.00011010479,"threshold_uncertainty_score":0.0054581165},"labels":[],"label_agreement":null},{"id":"W4394846989","doi":"10.3390/s24082545","title":"Pupil Response in Visual Tracking Tasks: The Impacts of Task Load, Familiarity, and Gaze Position","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Gaze; Eye tracking; Task (project management); Pupil; Position (finance); Tracking (education); Computer science; Pupillary response; Human–computer interaction; Computer vision; Cognitive psychology; Psychology; Pupil diameter; Artificial intelligence; Engineering; Neuroscience","score_opus":0.011865502866184839,"score_gpt":0.279179671472612,"score_spread":0.2673141686064272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394846989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99659,0.0001399939,0.0026784458,0.000021709795,0.000006374656,0.00003632414,0.00005750074,0.000021267284,0.00044832582],"genre_scores_gemma":[0.9977181,0.000075175856,0.0015502227,0.000022314298,0.000009013943,0.00007536651,0.0000870152,0.000013817076,0.00044887865],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991811,0.00022290563,0.00008262025,0.0001923886,0.00025189357,0.000069020476],"domain_scores_gemma":[0.994726,0.003152396,0.0011656538,0.00030185696,0.00041631833,0.00023778134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009950853,0.00041009742,0.000361552,0.00025703904,0.00021208242,0.0004485195,0.00016434035,0.00035735234,0.0015857054],"category_scores_gemma":[0.007091901,0.0001800644,0.00027132608,0.00016536808,0.00023166653,0.0004603037,0.00047272237,0.00026954006,0.00024873306],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005886469,0.0011106422,0.21477279,0.0005013292,0.00023754097,0.00024310531,0.0019801047,0.001526563,0.68745583,0.00013253582,0.00023481091,0.08591824],"study_design_scores_gemma":[0.000037101294,0.0034486041,0.9664714,0.0000142140325,0.00006291696,0.0002174075,0.00024065228,0.00213308,0.02693786,0.00012949672,0.00028334063,0.000023903367],"about_ca_topic_score_codex":0.0008240992,"about_ca_topic_score_gemma":0.0011218394,"teacher_disagreement_score":0.0015857054,"about_ca_system_score_codex":0.00018360643,"about_ca_system_score_gemma":0.00021707201,"threshold_uncertainty_score":0.005304694},"labels":[],"label_agreement":null},{"id":"W4394923616","doi":"10.3390/s24082584","title":"Design of Bio-Optical Transceiver for In Vivo Biomedical Sensor Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Ottawa","funders":"","keywords":"Transceiver; Nanodevice; Nanosensor; Computer science; Nanotechnology; Wireless; Materials science; Telecommunications","score_opus":0.016014873901085837,"score_gpt":0.24566563786212225,"score_spread":0.22965076396103642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394923616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17466058,0.00094883156,0.808803,0.0004514803,0.00031317564,0.00042345596,0.0001904794,0.0010626187,0.01314632],"genre_scores_gemma":[0.632123,0.0011081825,0.35003915,0.0003088674,0.00013466601,0.0004792837,0.00025193256,0.00012930181,0.015425579],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998487,0.000020488684,0.0000092672635,0.00003536746,0.00006736567,0.00001876314],"domain_scores_gemma":[0.99986696,0.00002024371,0.00003789388,0.000010715322,0.000049542803,0.000014582013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020002776,0.00030631988,0.00020672126,0.00016773718,0.00015613057,0.00032063865,0.0005754895,0.00039299016,0.0016181605],"category_scores_gemma":[0.00023615923,0.00019482335,0.00015867403,0.0001160265,0.00011657112,0.0003239463,0.00026337613,0.0002790704,0.0013252433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006168607,0.000058738566,0.0003179019,0.00014243263,0.000017351382,0.00013186429,0.000054148895,0.005850957,0.9717159,0.002580315,0.00063612557,0.018432505],"study_design_scores_gemma":[0.000034783145,0.00090046634,0.0011769821,0.00002471479,0.000045322056,0.00081623363,0.00003928766,0.10273179,0.8669023,0.00063170114,0.026661862,0.000034382763],"about_ca_topic_score_codex":0.000103841136,"about_ca_topic_score_gemma":0.00019590357,"teacher_disagreement_score":0.0016181605,"about_ca_system_score_codex":0.00022377346,"about_ca_system_score_gemma":0.0003202302,"threshold_uncertainty_score":0.0054132342},"labels":[],"label_agreement":null},{"id":"W4394923647","doi":"10.3390/s24082585","title":"Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robotic hand; Impedance control; Haptic technology; Stiffness; Mechanical impedance; Electrical impedance; Robot hand; Engineering; Computer science; Simulation; Robot; Artificial intelligence; Electrical engineering; Structural engineering","score_opus":0.014089088934037286,"score_gpt":0.22214615952662334,"score_spread":0.20805707059258605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394923647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16388111,0.0012028281,0.8252562,0.00029186526,0.00014208865,0.00032386652,0.000097550925,0.0024630511,0.006341352],"genre_scores_gemma":[0.48427618,0.00041338912,0.5074707,0.00014792573,0.000026125064,0.00022047912,0.000071973984,0.000054728924,0.007318422],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996026,0.000026905678,0.000034444507,0.000120569544,0.00018318086,0.000032386393],"domain_scores_gemma":[0.9996927,0.000061200604,0.00007377761,0.00006162099,0.000067602785,0.000043239503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041013595,0.0004936944,0.00040580082,0.0003336256,0.00017207026,0.00039262747,0.0010368979,0.0007523875,0.001303106],"category_scores_gemma":[0.00046702434,0.00038418584,0.00035716928,0.00015176769,0.0003948554,0.0008249611,0.00058521435,0.00032177995,0.0005006419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007432999,0.00006232552,0.00035940704,0.00022578717,0.000015080444,0.00033903978,0.00007918864,0.0033393074,0.9248314,0.0016723085,0.00029077398,0.06871102],"study_design_scores_gemma":[0.0001999247,0.0029491063,0.006559942,0.00007501117,0.000084672916,0.0069039413,0.000050570536,0.13679102,0.8125346,0.0010880468,0.032506153,0.00025708938],"about_ca_topic_score_codex":0.00037252725,"about_ca_topic_score_gemma":0.0003292343,"teacher_disagreement_score":0.001303106,"about_ca_system_score_codex":0.00020197396,"about_ca_system_score_gemma":0.0004025234,"threshold_uncertainty_score":0.004359305},"labels":[],"label_agreement":null},{"id":"W4395002414","doi":"10.3390/s24082649","title":"Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Computer science; Kinematics; Gait; Motion capture; Trajectory; Wearable computer; Artificial intelligence; Simulation; Motion (physics); Embedded system","score_opus":0.07428746450926299,"score_gpt":0.37311268170887174,"score_spread":0.29882521719960875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395002414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15333627,0.00016697579,0.83924377,0.00019475575,0.00010650738,0.00007213278,0.0002332075,0.004492257,0.0021541137],"genre_scores_gemma":[0.8974299,0.00011298482,0.1001341,0.00006499877,0.000016841634,0.00008869202,0.00026088377,0.00006090315,0.0018308045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987876,0.00001736738,0.000010246931,0.000041154108,0.00003517423,0.0000172744],"domain_scores_gemma":[0.9996915,0.000112812835,0.000027416081,0.00004962683,0.00010118748,0.000017490158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036582674,0.00048739254,0.0003598989,0.00022309387,0.00016664945,0.00037851502,0.00071026344,0.0004377563,0.0018538586],"category_scores_gemma":[0.0014439657,0.00021667541,0.00025933151,0.00019767371,0.00017857872,0.00075168523,0.00040836242,0.0005116491,0.00046831067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014197793,0.000087514956,0.0028033108,0.00006475857,0.00005067122,0.00013091312,0.000077263125,0.84439045,0.01516997,0.0013797847,0.0010791396,0.13462426],"study_design_scores_gemma":[0.000003383672,0.000034468947,0.00037811865,0.000004018578,0.000006278633,0.000013381508,0.000008064361,0.995939,0.0028527486,0.0003727704,0.00038342815,0.0000043368677],"about_ca_topic_score_codex":0.00780401,"about_ca_topic_score_gemma":0.009530999,"teacher_disagreement_score":0.00780401,"about_ca_system_score_codex":0.0003317726,"about_ca_system_score_gemma":0.00058843323,"threshold_uncertainty_score":0.015517175},"labels":[],"label_agreement":null},{"id":"W4395002453","doi":"10.3390/s24082654","title":"Gain and Bandwidth Enhancement of 3D-Printed Short Backfire Antennas Using Rim Flaring and Iris Matching","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"Bandwidth (computing); Impedance matching; Antenna gain; Materials science; Electrical impedance; Optics; Optoelectronics; Antenna factor; Antenna measurement; Acoustics; Computer science; Engineering; Antenna (radio); Electrical engineering; Physics; Telecommunications","score_opus":0.015857070574592347,"score_gpt":0.24155773151782833,"score_spread":0.225700660943236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395002453","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7394258,0.0014090159,0.2497897,0.00021928859,0.0001720717,0.00003093534,0.000061684834,0.001221873,0.0076696156],"genre_scores_gemma":[0.9231096,0.00042924008,0.07446369,0.000079504025,0.000032038948,0.000020135574,0.00005129486,0.0000624164,0.0017521331],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996855,0.000035882465,0.000023715369,0.000057007524,0.00015613397,0.00004174118],"domain_scores_gemma":[0.9994894,0.0001409995,0.00018009517,0.00007585838,0.00009189216,0.000021678972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003711329,0.00046658266,0.0002914398,0.00041668353,0.00008381593,0.0004416864,0.0004578101,0.00069679884,0.0006565993],"category_scores_gemma":[0.0006544767,0.00024294198,0.0005053407,0.00029863534,0.00031783708,0.000563148,0.00031752666,0.00041556387,0.00048761023],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069174464,0.000018250776,0.00025830138,0.00005534321,0.000010580382,0.0000997583,0.000054960678,0.0012733965,0.9827139,0.00037797613,0.00010542697,0.014962968],"study_design_scores_gemma":[0.000008382299,0.00018723017,0.0013222179,0.000007555809,0.000015500173,0.00033317436,0.00002416591,0.009469222,0.9860939,0.00015343109,0.0023671577,0.00001804896],"about_ca_topic_score_codex":0.000081024256,"about_ca_topic_score_gemma":0.00017413008,"teacher_disagreement_score":0.00069679884,"about_ca_system_score_codex":0.0002654724,"about_ca_system_score_gemma":0.0000640484,"threshold_uncertainty_score":0.0021965504},"labels":[],"label_agreement":null},{"id":"W4395002556","doi":"10.3390/s24082635","title":"Assessing the Impact of COVID-19 on Amateur Runners’ Performance: An Analysis through Monitoring Devices","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Amateur; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Engineering; Aeronautics; Computer science; Medicine; Virology; History; Internal medicine; Outbreak","score_opus":0.042441368001595245,"score_gpt":0.4190840459514234,"score_spread":0.37664267794982814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395002556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967248,0.001262443,0.0004671534,0.00003424947,0.000019266854,0.000048028494,0.0004768704,0.000007086088,0.00096012617],"genre_scores_gemma":[0.99764997,0.00050076057,0.0004623921,0.000041896943,0.000028862874,0.00007238881,0.00060147734,0.000005286221,0.00063698174],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990314,0.00021769383,0.00013271515,0.00019138066,0.00025554816,0.0001712191],"domain_scores_gemma":[0.99853444,0.00020418059,0.00061415066,0.00008661065,0.0003700616,0.00019057078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000880238,0.00049821736,0.00055122445,0.0008168626,0.0004633156,0.0006640169,0.00036547607,0.00040512165,0.0012105907],"category_scores_gemma":[0.0021789395,0.00016837237,0.00046287925,0.0005191883,0.00030501085,0.0003227644,0.00081670494,0.0003821436,0.00034604027],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005139594,0.00018009849,0.9823767,0.00025885357,0.0001595741,0.00028019914,0.00053848635,0.00007519381,0.0015576041,0.000028248494,0.0001726866,0.013858342],"study_design_scores_gemma":[0.0000034319194,0.0009590495,0.9969025,0.00006391697,0.00007388255,0.00040388716,0.00049437553,0.00009738917,0.00035805427,0.00001898612,0.0006139713,0.00001056654],"about_ca_topic_score_codex":0.0032517663,"about_ca_topic_score_gemma":0.00670093,"teacher_disagreement_score":0.0032517663,"about_ca_system_score_codex":0.00024490998,"about_ca_system_score_gemma":0.00032170236,"threshold_uncertainty_score":0.0064656734},"labels":[],"label_agreement":null},{"id":"W4395010193","doi":"10.3390/s24082644","title":"Characterization of Running Intensity in Canadian Football Based on Tactical Position","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Hôpital du Sacré-Cœur de Montréal; University of Waterloo","funders":"U.S. Department of Veterans Affairs","keywords":"Football; Aeronautics; Position (finance); Engineering; Intensity (physics); Computer science; Simulation; Business; Political science; Physics; Optics","score_opus":0.014420809633447156,"score_gpt":0.26527457518443104,"score_spread":0.2508537655509839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395010193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930078,0.00010121256,0.0037267238,0.000020856913,0.0000063378984,0.000036852987,0.0010302712,0.000054123593,0.0020156521],"genre_scores_gemma":[0.99448955,0.0001242819,0.0033434362,0.0000176883,0.0000043799864,0.000033794582,0.0010580566,0.000010458894,0.00091832544],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99962246,0.000018529212,0.00001337203,0.00008986768,0.00017806636,0.000077753604],"domain_scores_gemma":[0.9996574,0.000038303875,0.00006700509,0.000012887018,0.00018000831,0.000044389606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002255563,0.0005177497,0.00034415213,0.0013075604,0.00047872803,0.0007337505,0.00051625527,0.0003129692,0.0010093133],"category_scores_gemma":[0.0009212457,0.00021808792,0.00019056606,0.0012990023,0.0002575257,0.00017087671,0.000451059,0.00019351259,0.00026376793],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006008489,0.0001417916,0.84864587,0.00025760988,0.00015799302,0.00017460005,0.0019142071,0.005120864,0.05686757,0.00024216948,0.000788481,0.085088044],"study_design_scores_gemma":[0.0000026799032,0.000055849745,0.9940205,0.000010027718,0.000020515763,0.000058666836,0.00044972546,0.0033835743,0.001613769,0.000026303422,0.00034447416,0.000013922534],"about_ca_topic_score_codex":0.44732967,"about_ca_topic_score_gemma":0.6879991,"teacher_disagreement_score":0.55267036,"about_ca_system_score_codex":0.0011531536,"about_ca_system_score_gemma":0.0014891004,"threshold_uncertainty_score":0.88945186},"labels":[],"label_agreement":null},{"id":"W4395010561","doi":"10.3390/s24082630","title":"Maximizing the Reliability and Precision of Measures of Prefrontal Cortical Oxygenation Using Frequency-Domain Near-Infrared Spectroscopy","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Branch Out Neurological Foundation; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; International Olympic Committee; Fondation Brain Canada","keywords":"Reliability (semiconductor); Functional near-infrared spectroscopy; Frequency domain; Prefrontal cortex; Spectroscopy; Neuroscience; Computer science; Psychology; Cognition; Physics; Computer vision","score_opus":0.024050039213128302,"score_gpt":0.31352734521195014,"score_spread":0.2894773059988218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395010561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7853287,0.0041110287,0.20390783,0.00025365758,0.0001922011,0.0007880693,0.00062843214,0.00043263132,0.004357429],"genre_scores_gemma":[0.91724086,0.0007819062,0.07985226,0.00015002453,0.00011130029,0.00066188426,0.0004442075,0.0001075297,0.00064995507],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9840009,0.0073522152,0.0012186245,0.0040113055,0.003004959,0.00041198675],"domain_scores_gemma":[0.9780211,0.010238488,0.0028386754,0.0032739483,0.0054192417,0.00020850559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024669405,0.000969922,0.0011441044,0.0011956494,0.000710582,0.0012889649,0.0008915072,0.00095022423,0.00072292594],"category_scores_gemma":[0.03828347,0.0005494059,0.0007877906,0.0010501501,0.0010877454,0.0008279723,0.0012139174,0.00070552155,0.00045639445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035946749,0.0007689349,0.48872826,0.0019954261,0.0029025362,0.00035502634,0.0048235143,0.005727188,0.19638819,0.0018726176,0.0023046718,0.29053897],"study_design_scores_gemma":[0.00022041958,0.0029512916,0.8962828,0.00031534393,0.0012718794,0.0009500053,0.00063851697,0.022612385,0.0643947,0.003932437,0.0062914547,0.00013878137],"about_ca_topic_score_codex":0.0016770764,"about_ca_topic_score_gemma":0.0035004201,"teacher_disagreement_score":0.024669405,"about_ca_system_score_codex":0.0003096419,"about_ca_system_score_gemma":0.00089705084,"threshold_uncertainty_score":0.13046587},"labels":[],"label_agreement":null},{"id":"W4395011868","doi":"10.3390/s24082662","title":"Using a Slit to Suppress Optical Aberrations in Laser Triangulation Sensors","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Université du Québec à Rimouski","keywords":"Triangulation; Lens (geology); Slit; Position (finance); Optics; Laser; Diffraction; Computer science; Computer vision; Artificial intelligence; Measure (data warehouse); Object (grammar); Image sensor; Table (database); Physics; Mathematics","score_opus":0.08362354508502286,"score_gpt":0.34085204647269496,"score_spread":0.2572285013876721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395011868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07929525,0.00078468875,0.9161925,0.0001754593,0.00017395431,0.000084901934,0.00016858653,0.0014385048,0.0016861474],"genre_scores_gemma":[0.27477914,0.0003644916,0.7226236,0.00021715731,0.000050615243,0.00009796003,0.00021847972,0.0001524058,0.0014961809],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977574,0.00026184466,0.00018971988,0.00039294493,0.0012997723,0.0000983039],"domain_scores_gemma":[0.99324554,0.0023404951,0.0011911565,0.0011676083,0.0018924917,0.00016273532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012243254,0.00047605415,0.0006526576,0.0007714498,0.00058069575,0.0010512973,0.0014386161,0.0013882192,0.0014354112],"category_scores_gemma":[0.0053440086,0.0006013901,0.00046940276,0.0011471774,0.00080146856,0.001922393,0.0009680571,0.0007980586,0.00073275086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052170537,0.00015896457,0.007978321,0.0006462012,0.00009232578,0.00045510635,0.00047405667,0.011471571,0.82120657,0.0070226365,0.0013476457,0.1486249],"study_design_scores_gemma":[0.0000674637,0.0012362367,0.010439187,0.00007872802,0.000097439995,0.0021545757,0.00019346719,0.13317116,0.8343617,0.0025889599,0.015466604,0.00014454332],"about_ca_topic_score_codex":0.0015326454,"about_ca_topic_score_gemma":0.0024937382,"teacher_disagreement_score":0.0015326454,"about_ca_system_score_codex":0.0008097374,"about_ca_system_score_gemma":0.0010603267,"threshold_uncertainty_score":0.006474912},"labels":[],"label_agreement":null},{"id":"W4395035038","doi":"10.3390/s24092675","title":"A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Series (stratigraphy); Diabetic foot; Diabetic foot ulcer; Foot (prosody); Computer science; Medicine; Diabetes mellitus; Biology","score_opus":0.0235973989435556,"score_gpt":0.3113078360327985,"score_spread":0.2877104370892429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395035038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9412538,0.013063901,0.018757798,0.00734422,0.00086135406,0.0006864691,0.00033928928,0.00023628295,0.017456898],"genre_scores_gemma":[0.98450214,0.0037390029,0.006449066,0.0018651135,0.00053984625,0.00010284386,0.00011466257,0.00004121048,0.0026461235],"study_design_codex":"case_report","study_design_gemma":"case_report","domain_scores_codex":[0.99854183,0.00024984442,0.00022788762,0.00021558956,0.00040192978,0.00036288204],"domain_scores_gemma":[0.9979691,0.00057511934,0.00044459346,0.00013009316,0.00015795123,0.00072307384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005867117,0.0019991638,0.0010306161,0.0026252912,0.003261772,0.0021911727,0.0016679126,0.0052037123,0.002201161],"category_scores_gemma":[0.004298373,0.00075541926,0.0015788459,0.0023493515,0.0013995323,0.0018840879,0.0018129548,0.004098625,0.0008245387],"study_design_candidate":"case_report","study_design_consensus":"case_report","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026992082,0.00022106599,0.008795481,0.000071180206,0.000019112089,0.983424,0.0007655583,0.00012881993,0.0004026429,0.00037957,0.0006396381,0.005125916],"study_design_scores_gemma":[0.0000066382877,0.000093792376,0.0025446464,0.000059013582,0.000014668894,0.9945642,0.0006123096,0.00040342755,0.00033850843,0.00015984077,0.0011891004,0.000013978935],"about_ca_topic_score_codex":0.002884343,"about_ca_topic_score_gemma":0.0043021017,"teacher_disagreement_score":0.0052037123,"about_ca_system_score_codex":0.0016232932,"about_ca_system_score_gemma":0.0010146295,"threshold_uncertainty_score":0.011777818},"labels":[],"label_agreement":null},{"id":"W4395112635","doi":"10.3390/s24092691","title":"The Impact of Dual-Tasks and Disease Severity on Posture, Gait, and Functional Mobility among People Living with Dementia in Residential Care Facilities: A Pilot Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dementia; Gerontology; Physical medicine and rehabilitation; Gait; Medicine; Disease; Physical therapy; Dual (grammatical number); Activities of daily living; Psychology","score_opus":0.014726626665090552,"score_gpt":0.2943786272545417,"score_spread":0.27965200058945117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395112635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99986947,0.000021301747,0.000015108528,0.0000032541357,0.0000010872157,0.000006660562,0.000026759253,7.052921e-7,0.00005572147],"genre_scores_gemma":[0.99974364,0.000015979535,0.00007096717,0.00001201881,0.000003153009,0.000011882208,0.000064811305,4.2011223e-7,0.00007723916],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99945945,0.0001585682,0.0000554205,0.00007943611,0.00012638448,0.000120770455],"domain_scores_gemma":[0.9987004,0.00022751944,0.00034286265,0.00009301561,0.00024101578,0.00039516386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077258423,0.00048913626,0.0003949465,0.0005530058,0.0005527515,0.0004639359,0.00026183634,0.00042682484,0.0008622138],"category_scores_gemma":[0.0023766523,0.00034970368,0.0005727941,0.00031911477,0.00030964662,0.00043174406,0.0005906491,0.0004779625,0.00022523223],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010938975,0.0014214775,0.9912888,0.000024298988,0.000117536365,0.00015233314,0.0004912837,0.00004489324,0.00072224607,0.000004309087,0.000070673545,0.0045683957],"study_design_scores_gemma":[0.000020766465,0.0020008117,0.997382,0.0000024603623,0.000029278428,0.00011102675,0.00023272325,0.000091512506,0.00008342279,0.0000049993514,0.000036860318,0.0000040771542],"about_ca_topic_score_codex":0.0060533523,"about_ca_topic_score_gemma":0.0139683215,"teacher_disagreement_score":0.0060533523,"about_ca_system_score_codex":0.0003118954,"about_ca_system_score_gemma":0.00026384715,"threshold_uncertainty_score":0.012036264},"labels":[],"label_agreement":null},{"id":"W4395673881","doi":"10.3390/s24092743","title":"Impact of Solid Materials in the Gap Space between Driving Electrodes in a MEMS Tri-Electrode Electrostatic Actuator","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; National Research Council Canada; National Institute for Nanotechnology","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Voltage; Materials science; Electrode; Actuator; Microelectromechanical systems; Dielectric; Space charge; Voltage reduction; Optoelectronics; Displacement (psychology); Electrical engineering; Electronic engineering; Nanotechnology; Engineering; Chemistry; Physics","score_opus":0.01242245571458947,"score_gpt":0.2922692556388322,"score_spread":0.2798467999242428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395673881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923637,0.0010506688,0.004757962,0.00006454218,0.000055059685,0.000011150246,0.000050119364,0.0000613336,0.0015855919],"genre_scores_gemma":[0.99442756,0.00042744895,0.0046503358,0.000021160811,0.000006001741,0.000008344848,0.000031690888,0.000011437425,0.00041598288],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998053,0.000026561158,0.000019552795,0.000033377808,0.00008869515,0.000026597001],"domain_scores_gemma":[0.9994609,0.00023489176,0.00014037201,0.000048413982,0.000083728635,0.000031655545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017926455,0.00025125098,0.0002436755,0.0002226937,0.00019895213,0.000482209,0.0003039139,0.00042562748,0.0009300015],"category_scores_gemma":[0.00055348326,0.00021469913,0.00016584987,0.00023004017,0.0003785401,0.0006531046,0.0003359442,0.00027842674,0.00026909352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018917916,0.000047054727,0.00028137496,0.0002107138,0.0000161794,0.00019648384,0.000035755005,0.0020677363,0.9919993,0.000550109,0.00004316708,0.0043628677],"study_design_scores_gemma":[0.000016511925,0.00064914586,0.0015365921,0.000027229122,0.00003833365,0.00022735437,0.000099198434,0.008832026,0.98667645,0.000136809,0.0017491515,0.000011234133],"about_ca_topic_score_codex":0.00013967429,"about_ca_topic_score_gemma":0.00044135915,"teacher_disagreement_score":0.0009300015,"about_ca_system_score_codex":0.00012082187,"about_ca_system_score_gemma":0.00017739397,"threshold_uncertainty_score":0.0031111836},"labels":[],"label_agreement":null},{"id":"W4395673998","doi":"10.3390/s24092746","title":"DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Benchmarking; Intrusion detection system; Artificial intelligence; Machine learning; Generative adversarial network; Data mining; Deep learning","score_opus":0.011603605341227043,"score_gpt":0.22522101096467811,"score_spread":0.21361740562345108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395673998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023407567,0.00054546277,0.96800196,0.0005503305,0.000116700954,0.00011483898,0.00022858703,0.004419296,0.002615308],"genre_scores_gemma":[0.6081275,0.00031167717,0.38139698,0.0010906394,0.00016974786,0.00029095673,0.0012619122,0.00044906596,0.006901535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989286,0.00048234704,0.000033750654,0.00023418973,0.00022887385,0.00009225054],"domain_scores_gemma":[0.9985682,0.00067842583,0.00010784714,0.00026153613,0.00031399904,0.00006990341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023044148,0.0011409471,0.0010705481,0.0008715155,0.00026858883,0.0007753655,0.0026830544,0.0011482369,0.0015204803],"category_scores_gemma":[0.0033949905,0.00045432826,0.00084787357,0.0005388118,0.00066082936,0.0012184944,0.0013220293,0.0021876965,0.00081494264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021599911,0.0002380355,0.0028316255,0.00008174223,0.00014108,0.000106774874,0.000087510896,0.70356166,0.0056325058,0.009192315,0.008888671,0.26902205],"study_design_scores_gemma":[0.000003006018,0.000015548214,0.00007937962,0.0000027613303,0.000002897268,0.000014647444,0.0000026130142,0.9975702,0.0005300644,0.001448505,0.00032701044,0.000003451143],"about_ca_topic_score_codex":0.0028933506,"about_ca_topic_score_gemma":0.004223957,"teacher_disagreement_score":0.0028933506,"about_ca_system_score_codex":0.0009890731,"about_ca_system_score_gemma":0.0006043435,"threshold_uncertainty_score":0.012187064},"labels":[],"label_agreement":null},{"id":"W4396219644","doi":"10.3390/s24092826","title":"3D-Printed Conformal Meta-Lens with Multiple Beam-Shaping Functionalities for Mm-Wave Sensing Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Antenna and Metasurface Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Lens (geology); Conformal map; 3d printed; Optics; Materials science; Beam (structure); Computer science; Optoelectronics; Engineering; Physics; Biomedical engineering; Mathematics; Geometry","score_opus":0.0667951971044005,"score_gpt":0.2508902442144097,"score_spread":0.18409504711000918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396219644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35107628,0.005170169,0.62053984,0.00037324746,0.00039825804,0.00014611354,0.00062679884,0.0025677606,0.019101543],"genre_scores_gemma":[0.6747932,0.0010306134,0.3201418,0.00015045436,0.000048938124,0.00007194296,0.00021855622,0.00008877688,0.0034557907],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979883,0.00001678867,0.000009406271,0.000030910996,0.0001239064,0.00002006501],"domain_scores_gemma":[0.9997501,0.000043129963,0.00008291704,0.000059114034,0.000043638698,0.00002115408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000130208,0.00053648755,0.00026062192,0.00036208783,0.0001141362,0.000539735,0.0005174998,0.0005606905,0.0009186539],"category_scores_gemma":[0.000248496,0.0002465387,0.0004948277,0.0003646975,0.00022609631,0.00045478906,0.00035818576,0.00037242423,0.0005374293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037874404,0.000017650866,0.00038440563,0.00012221589,0.000021209216,0.00017693614,0.000031577205,0.00340084,0.9721839,0.0022857348,0.00037725992,0.02096033],"study_design_scores_gemma":[0.000014681335,0.00013809206,0.0010603289,0.000008403049,0.00001889035,0.00074929494,0.00002500797,0.026287286,0.9589847,0.0003663756,0.012311151,0.000035982157],"about_ca_topic_score_codex":0.0003535306,"about_ca_topic_score_gemma":0.0008293169,"teacher_disagreement_score":0.0009186539,"about_ca_system_score_codex":0.00045747182,"about_ca_system_score_gemma":0.00021139965,"threshold_uncertainty_score":0.0033191442},"labels":[],"label_agreement":null},{"id":"W4396219790","doi":"10.3390/s24092828","title":"Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; Korea Institute for Advancement of Technology; Ministry of Education, Science and Technology; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Occupancy grid mapping; Occupancy; Lidar; Computer science; Grid; Artificial intelligence; Fusion; Computer vision; Deep learning; Sensor fusion; Information fusion; Remote sensing; Geography; Engineering; Robot; Mobile robot; Civil engineering","score_opus":0.008680686673007,"score_gpt":0.2754471658296445,"score_spread":0.26676647915663754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396219790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023759983,0.0003126544,0.9734152,0.00018007372,0.00006552711,0.000048915394,0.00014871043,0.0011483702,0.0009204628],"genre_scores_gemma":[0.7135231,0.0003567496,0.28161007,0.00031624956,0.000077307464,0.0001240199,0.0010398919,0.0001639272,0.0027887558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953794,0.000050792456,0.00002609863,0.00013624692,0.00015982334,0.00008914116],"domain_scores_gemma":[0.99955994,0.000115857,0.00005646441,0.000071435854,0.00015668936,0.00003962037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006366968,0.0008355173,0.001184944,0.0010310939,0.00036658713,0.00096775324,0.0019058099,0.0008559549,0.0013766933],"category_scores_gemma":[0.0020004257,0.00054066104,0.00084377686,0.0011659879,0.0004749301,0.0016621083,0.0017375603,0.0013727777,0.00035620865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025573737,0.00016386792,0.003264002,0.00009184848,0.00009542291,0.00014354884,0.00015881698,0.42704436,0.0054634153,0.0065168478,0.0046229637,0.5521792],"study_design_scores_gemma":[0.000005661787,0.000013669243,0.0001617424,0.0000041103463,0.0000056714766,0.000018555684,0.000009592204,0.9968046,0.00089795206,0.0017015218,0.0003723028,0.0000046853825],"about_ca_topic_score_codex":0.013783666,"about_ca_topic_score_gemma":0.010018426,"teacher_disagreement_score":0.013783666,"about_ca_system_score_codex":0.0009559828,"about_ca_system_score_gemma":0.0011847279,"threshold_uncertainty_score":0.027406871},"labels":[],"label_agreement":null},{"id":"W4396229955","doi":"10.3390/s24092796","title":"An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Mixed reality; Presentation (obstetrics); Augmented reality; Computer science; Human–computer interaction; Audio visual; Computer graphics (images); Multimedia; Medicine","score_opus":0.01738087038839651,"score_gpt":0.2584178942633221,"score_spread":0.24103702387492562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396229955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080796145,0.0003498943,0.9870242,0.00015006348,0.00016717402,0.000033116605,0.000031784304,0.0011978643,0.002966271],"genre_scores_gemma":[0.34948274,0.0006315028,0.6418821,0.0002127985,0.00019110441,0.00012509493,0.00014855234,0.00016375532,0.0071623367],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994734,0.000113202026,0.000017893575,0.00010514101,0.0002451274,0.00004529521],"domain_scores_gemma":[0.9997693,0.00005633997,0.00002479719,0.00005487836,0.00006564798,0.000029058721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004280083,0.0007939129,0.00049698725,0.0006707208,0.0004242781,0.0010601414,0.0011755998,0.00093410106,0.0031341428],"category_scores_gemma":[0.001003642,0.00035966566,0.0005109907,0.00039414683,0.00046249072,0.0013118184,0.0019400109,0.0007838727,0.0010720132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045189034,0.00018145969,0.0006086198,0.0003541856,0.00010691094,0.00055461045,0.0006466213,0.053918872,0.14506678,0.014745559,0.0063388175,0.7770257],"study_design_scores_gemma":[0.000105162595,0.0010077375,0.0017492466,0.000112465896,0.000090582274,0.0014842371,0.00052330515,0.8430775,0.08715647,0.011747421,0.0527999,0.00014601521],"about_ca_topic_score_codex":0.000833602,"about_ca_topic_score_gemma":0.00136303,"teacher_disagreement_score":0.0031341428,"about_ca_system_score_codex":0.00022203392,"about_ca_system_score_gemma":0.00044775123,"threshold_uncertainty_score":0.010484695},"labels":[],"label_agreement":null},{"id":"W4396615558","doi":"10.3390/s24092923","title":"A Computer Vision Framework for Structural Analysis of Hand-Drawn Engineering Sketches","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Computer science; Feature (linguistics); Sketch; Truss; Artificial intelligence; Software; Image (mathematics); Mirroring; Engineering drawing; Computer vision; Engineering; Algorithm; Programming language","score_opus":0.013366911924876147,"score_gpt":0.24812170035807288,"score_spread":0.23475478843319672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396615558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041799704,0.000075085176,0.99751675,0.000019195892,0.000011520123,0.000026705839,0.00004344167,0.0014140591,0.00047524628],"genre_scores_gemma":[0.046063934,0.00039053333,0.94980925,0.000069814065,0.000043341264,0.0001790058,0.00056869944,0.00029218037,0.0025832392],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903715,0.00011088143,0.00005109116,0.00029327953,0.0004297333,0.000077962985],"domain_scores_gemma":[0.9994373,0.00012451832,0.000049989176,0.00012434686,0.00022845183,0.000035357018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008784056,0.0012389447,0.0008364796,0.0023354094,0.0004932917,0.0018211893,0.002495549,0.0011804862,0.005451909],"category_scores_gemma":[0.0018684693,0.0006388942,0.0022493557,0.0011277276,0.0008409014,0.0015701215,0.0011748894,0.0014400986,0.0027304501],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010214929,0.000124083,0.0005157154,0.00037119168,0.000115346455,0.00030665676,0.00020919618,0.1661637,0.065090254,0.074862055,0.010713438,0.68142617],"study_design_scores_gemma":[0.000014201356,0.000066126864,0.0005722644,0.000045503708,0.00002539534,0.00027552492,0.000042879023,0.94077873,0.013754375,0.024550144,0.019837497,0.000037220914],"about_ca_topic_score_codex":0.0078013325,"about_ca_topic_score_gemma":0.008661251,"teacher_disagreement_score":0.0078013325,"about_ca_system_score_codex":0.0011286353,"about_ca_system_score_gemma":0.0010577857,"threshold_uncertainty_score":0.018238485},"labels":[],"label_agreement":null},{"id":"W4396666689","doi":"10.3390/s24092944","title":"Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pressure sensor; Gait analysis; Gait; Wireless; Computer science; Engineering; Physical medicine and rehabilitation; Medicine; Mechanical engineering; Telecommunications","score_opus":0.011481588210252046,"score_gpt":0.23389484465382512,"score_spread":0.22241325644357307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396666689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7752956,0.00095844775,0.22043912,0.00025397263,0.0002854554,0.000099687284,0.00038466242,0.0006274203,0.0016555487],"genre_scores_gemma":[0.9051076,0.00047808274,0.09207679,0.00015724143,0.00006272737,0.000068166584,0.00016990911,0.000033019463,0.0018464648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998042,0.000018580538,0.000017298533,0.000060305876,0.00008196422,0.000017669887],"domain_scores_gemma":[0.9998159,0.00004018177,0.00004369379,0.000025625284,0.00005509976,0.000019571062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014828445,0.00032826304,0.00025778217,0.00037989125,0.000083659266,0.0002707609,0.00048348372,0.0004045474,0.00047284676],"category_scores_gemma":[0.00046479507,0.00019616919,0.0001853952,0.000317838,0.00016213034,0.0004035852,0.00029622784,0.00019559392,0.00019880458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048358823,0.000037193095,0.0009383387,0.00007358343,0.0000123182,0.00020819064,0.00002175226,0.0003499344,0.9757878,0.00007383438,0.00015479075,0.02229388],"study_design_scores_gemma":[0.000019333342,0.00070324045,0.015196341,0.000013638425,0.000057417234,0.0013941056,0.000046619374,0.029378474,0.9503862,0.00017773676,0.002587008,0.00003970687],"about_ca_topic_score_codex":0.00016240637,"about_ca_topic_score_gemma":0.0004204173,"teacher_disagreement_score":0.00048348372,"about_ca_system_score_codex":0.00008652786,"about_ca_system_score_gemma":0.00011935477,"threshold_uncertainty_score":0.0015818477},"labels":[],"label_agreement":null},{"id":"W4396666805","doi":"10.3390/s24092927","title":"Consensus-Based Information Filtering in Distributed LiDAR Sensor Network for Tracking Mobile Robots","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Tracking (education); Mobile robot; Computer science; Wireless sensor network; Real-time computing; Robot; Artificial intelligence; Remote sensing; Computer network; Geography","score_opus":0.012851334837778013,"score_gpt":0.24629234829499524,"score_spread":0.23344101345721724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396666805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008723873,0.00007046001,0.99042046,0.000041268748,0.000020450409,0.0000147629125,0.000009617282,0.00017484883,0.0005243242],"genre_scores_gemma":[0.8664555,0.00018726058,0.1312033,0.00008160339,0.00004603066,0.00015363847,0.0000847927,0.000029486744,0.0017583262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948955,0.00008173745,0.000029317835,0.00014513526,0.00020547285,0.000048762533],"domain_scores_gemma":[0.99942756,0.00021349541,0.000101142825,0.00005535561,0.00017833839,0.000024137109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065991393,0.00040037802,0.0006550324,0.0004096725,0.00043789882,0.00046852004,0.0009948607,0.00057527726,0.0005383416],"category_scores_gemma":[0.0015411628,0.0002523053,0.0004293556,0.00041693426,0.00044636006,0.00083644094,0.0007364517,0.0005579008,0.00016210438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017930627,0.00011623579,0.0018328087,0.0001386354,0.0000751564,0.00021022187,0.00025204234,0.7653299,0.032103263,0.020472812,0.0019333246,0.17735632],"study_design_scores_gemma":[0.0000052462015,0.00003557069,0.000099755816,0.0000022340798,0.0000048727825,0.000014024964,0.000006829318,0.99670815,0.001745877,0.0010820829,0.0002911351,0.0000041769044],"about_ca_topic_score_codex":0.0040783994,"about_ca_topic_score_gemma":0.0035222785,"teacher_disagreement_score":0.0040783994,"about_ca_system_score_codex":0.0006200354,"about_ca_system_score_gemma":0.00088147016,"threshold_uncertainty_score":0.0081092715},"labels":[],"label_agreement":null},{"id":"W4396697905","doi":"10.3390/s24102957","title":"A Machine Learning-Based Tropospheric Prediction Approach for High-Precision Real-Time GNSS Positioning","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Troposphere; Quasi-Zenith Satellite System; Computer science; Satellite system; Zenith; Global Positioning System; Satellite; Remote sensing; Real-time computing; Environmental science; Meteorology; Telecommunications; Geography; Engineering; Aerospace engineering","score_opus":0.006403972594898163,"score_gpt":0.20460836175535346,"score_spread":0.1982043891604553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396697905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021988323,0.00027274573,0.9751017,0.00008532698,0.00009177474,0.000034292145,0.00009138339,0.0012740926,0.0010603681],"genre_scores_gemma":[0.6122505,0.00036915852,0.381594,0.00015338839,0.0001865095,0.0001616385,0.0005828897,0.00014841073,0.0045535374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997009,0.00003563516,0.000020944675,0.000108519795,0.00009425576,0.000039757306],"domain_scores_gemma":[0.9995844,0.00012428808,0.000058670856,0.000034915658,0.00018044082,0.000017341468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004795296,0.0007942035,0.00048962556,0.00075398665,0.0004487034,0.0004441013,0.00094971055,0.0006113464,0.0012608754],"category_scores_gemma":[0.0010904642,0.0002908516,0.00052722933,0.0007372929,0.0002254475,0.0006269846,0.0003365481,0.0011293085,0.00062277424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106364074,0.00017647164,0.0031849483,0.00006713098,0.000096968135,0.00010571383,0.00005730758,0.52373093,0.014512875,0.0015194619,0.0032307582,0.45321104],"study_design_scores_gemma":[0.0000019938393,0.000010835381,0.0003779434,0.0000018890546,0.0000043082746,0.000007604807,0.0000032112914,0.99819905,0.00091807364,0.00020709944,0.00026366036,0.0000043582945],"about_ca_topic_score_codex":0.012001254,"about_ca_topic_score_gemma":0.011548593,"teacher_disagreement_score":0.012001254,"about_ca_system_score_codex":0.00045782386,"about_ca_system_score_gemma":0.000786128,"threshold_uncertainty_score":0.02386278},"labels":[],"label_agreement":null},{"id":"W4396707470","doi":"10.3390/s24102967","title":"Porcine Model of Cerebral Ischemic Stroke Utilizing Intracortical Recordings for the Continuous Monitoring of the Ischemic Area","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Bevica Fonden; Aalborg Universitetshospital; Aalborg Universitet","keywords":"Ischemic stroke; Medicine; Stroke (engine); Cardiology; Neuroscience; Ischemia; Biomedical engineering; Psychology; Engineering; Mechanical engineering","score_opus":0.046108301031840014,"score_gpt":0.2815187726412913,"score_spread":0.2354104716094513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396707470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7324929,0.0070438613,0.23863816,0.0010061432,0.0015980913,0.0012912138,0.0019439897,0.0009684044,0.015017234],"genre_scores_gemma":[0.8770645,0.00771661,0.09885358,0.00044040353,0.00020449984,0.0017774533,0.0024083806,0.00012913298,0.011405546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997422,0.000052729247,0.000024688023,0.000084002495,0.000051950487,0.000044398326],"domain_scores_gemma":[0.99973327,0.000051154755,0.00007006324,0.00008163444,0.00004056767,0.000023307073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061779656,0.0008197926,0.00047342925,0.00043775776,0.00018514576,0.00046499938,0.0004090481,0.000620679,0.0028946248],"category_scores_gemma":[0.00030496577,0.0002870839,0.00042520833,0.0002883743,0.0006632272,0.00047445495,0.00031709645,0.0008876934,0.001095596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077278604,0.00028179967,0.00055115536,0.0002571614,0.000036222,0.00038792973,0.000046949062,0.00027644832,0.9856985,0.0004087151,0.00048990856,0.0107923895],"study_design_scores_gemma":[0.00024011412,0.016649367,0.013492709,0.00014745731,0.0003304461,0.0042971927,0.00016405575,0.0042123483,0.9354439,0.0008111901,0.024156941,0.000054365944],"about_ca_topic_score_codex":0.0003284928,"about_ca_topic_score_gemma":0.00073115627,"teacher_disagreement_score":0.0028946248,"about_ca_system_score_codex":0.00016779028,"about_ca_system_score_gemma":0.0003793806,"threshold_uncertainty_score":0.00968343},"labels":[],"label_agreement":null},{"id":"W4396732117","doi":"10.3390/s24102984","title":"Early Eye Disengagement Is Regulated by Task Complexity and Task Repetition in Visual Tracking Task","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Recall; Disengagement theory; Eye movement; Task (project management); Cognition; Eye tracking; Cognitive psychology; Fixation (population genetics); Cognitive load; Repetition (rhetorical device); Psychology; Computer science; Artificial intelligence; Neuroscience; Engineering; Medicine","score_opus":0.018153130192636514,"score_gpt":0.27459644234174413,"score_spread":0.2564433121491076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396732117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989897,0.00037043524,0.008666954,0.00002876517,0.00001054992,0.00005185998,0.000091318194,0.00007210201,0.00081110094],"genre_scores_gemma":[0.9949104,0.00015473853,0.0039678267,0.000028856202,0.000009105771,0.00009168392,0.00017695036,0.00003580075,0.0006245486],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99900204,0.0001619815,0.00013066149,0.00024079607,0.00037672571,0.00008786388],"domain_scores_gemma":[0.99454063,0.00222899,0.0019330409,0.0005542225,0.00048265042,0.00026040475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081148965,0.0005526219,0.00043828558,0.00048167427,0.00019430374,0.00065977906,0.0002772596,0.00034455038,0.0008566643],"category_scores_gemma":[0.008498741,0.0002799042,0.00035280097,0.00022941962,0.00025178742,0.000496148,0.0007267715,0.00040651302,0.00019016492],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018112146,0.0003653149,0.036397826,0.00026109,0.00014505899,0.000133041,0.00067638943,0.0012224782,0.9192732,0.00015497672,0.00017620852,0.039383076],"study_design_scores_gemma":[0.000060684433,0.0016638694,0.8943034,0.000044860528,0.00010149044,0.00030117293,0.00015990673,0.011628705,0.09027258,0.0006646804,0.00073530094,0.00006339697],"about_ca_topic_score_codex":0.0018224336,"about_ca_topic_score_gemma":0.0018697262,"teacher_disagreement_score":0.0018224336,"about_ca_system_score_codex":0.00026406054,"about_ca_system_score_gemma":0.00031156014,"threshold_uncertainty_score":0.004291594},"labels":[],"label_agreement":null},{"id":"W4396774136","doi":"10.3390/s24102997","title":"Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Magna International (Canada); Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Ontario Institute of Technology","keywords":"Lidar; Automotive industry; Remote sensing; Environmental science; Computer vision; Computer science; Engineering; Geology; Aerospace engineering","score_opus":0.008129972621859098,"score_gpt":0.2533899052452722,"score_spread":0.2452599326234131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396774136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98328084,0.0018525608,0.011303485,0.000076647535,0.000022137903,0.000023597562,0.000076418575,0.000084310836,0.0032798988],"genre_scores_gemma":[0.99346703,0.00078350154,0.004900643,0.00003970986,0.000011529479,0.000010936783,0.000059020036,0.000010370284,0.0007173647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997466,0.000019803902,0.0000049855153,0.000034020173,0.00015555405,0.000039018756],"domain_scores_gemma":[0.9997737,0.00006238726,0.000050752522,0.000013900264,0.00008849995,0.000010740196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025133413,0.00021037033,0.00018684097,0.00039049497,0.0001524139,0.00035788707,0.00036886946,0.0003782095,0.0007165277],"category_scores_gemma":[0.00043409737,0.00017068925,0.00015646471,0.00022268393,0.00027684914,0.00042687758,0.0002677474,0.00020011952,0.00018981512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013312546,0.00004594568,0.002518079,0.00016306627,0.00001273784,0.00034220683,0.00011773394,0.0029278782,0.9810509,0.0005788804,0.00015997054,0.011949451],"study_design_scores_gemma":[0.000013278223,0.0007146941,0.01797194,0.00002911846,0.00003705587,0.0006931179,0.00048630746,0.06810831,0.9081726,0.00036470572,0.0033794935,0.000029364612],"about_ca_topic_score_codex":0.0007459863,"about_ca_topic_score_gemma":0.0005520331,"teacher_disagreement_score":0.0007459863,"about_ca_system_score_codex":0.00035303348,"about_ca_system_score_gemma":0.00016010462,"threshold_uncertainty_score":0.0025615096},"labels":[],"label_agreement":null},{"id":"W4396805984","doi":"10.3390/s24103019","title":"Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Physical Activity and Health","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Children's Hospital Research Institute","keywords":"Accelerometer; Count data; Metric (unit); Intensity (physics); Statistics; Mathematics; Computer science; Physics; Optics; Engineering","score_opus":0.07742212618285452,"score_gpt":0.3197416293049583,"score_spread":0.2423195031221038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396805984","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7074291,0.0009394197,0.27701056,0.00033560648,0.00023430886,0.0009424199,0.006191107,0.0018626213,0.005054858],"genre_scores_gemma":[0.80469376,0.00031179466,0.18861192,0.00018889709,0.00007021575,0.0013762123,0.0035321447,0.00046399643,0.0007511367],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971998,0.0012358659,0.00032193528,0.00053840945,0.0005964374,0.00010761249],"domain_scores_gemma":[0.99586475,0.0014905153,0.000806097,0.00051354093,0.0011991995,0.00012593855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043271505,0.00063401187,0.00060741615,0.0018271914,0.0002471419,0.0010365986,0.0007474828,0.0005490164,0.0011387347],"category_scores_gemma":[0.018024227,0.0003726091,0.00068407744,0.0017520665,0.00034572883,0.0007616654,0.0010969813,0.0006224037,0.00050140196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001076793,0.00023449743,0.6522059,0.0007650997,0.0005544576,0.00022259334,0.0021022751,0.0114206225,0.012303166,0.0016544793,0.0060194987,0.31144068],"study_design_scores_gemma":[0.00016347614,0.00091217144,0.8969877,0.00031227397,0.00024365772,0.0006872246,0.0011451554,0.07158675,0.0111328205,0.0041143387,0.012557637,0.00015688666],"about_ca_topic_score_codex":0.005816771,"about_ca_topic_score_gemma":0.010712702,"teacher_disagreement_score":0.005816771,"about_ca_system_score_codex":0.0003443558,"about_ca_system_score_gemma":0.00043771393,"threshold_uncertainty_score":0.022884429},"labels":[],"label_agreement":null},{"id":"W4396806464","doi":"10.3390/s24103020","title":"CMOS Compatible Hydrogen Sensor Using Platinum Gate and ALD–Aluminum Oxide","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Centre National de la Recherche Scientifique; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada; École Centrale de Lyon; Indian National Science Academy","keywords":"CMOS; Materials science; Fabrication; Capacitance; Hydrogen; Back end of line; Optoelectronics; Stack (abstract data type); Oxide; Aluminium; Electronic engineering; Nanotechnology; Electrical engineering; Chemistry; Engineering; Computer science; Metallurgy; Electrode; Dielectric","score_opus":0.015657654976423987,"score_gpt":0.2198438264656409,"score_spread":0.20418617148921692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396806464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9326993,0.0029194197,0.05592875,0.00029434552,0.00036644784,0.00012717994,0.0010100182,0.0008443936,0.0058100503],"genre_scores_gemma":[0.9431833,0.0011676335,0.051206063,0.00012902806,0.000045465345,0.000055920562,0.0003560259,0.000025751428,0.0038307633],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999819,0.000009683678,0.000010186767,0.000071350594,0.00006961825,0.000020269823],"domain_scores_gemma":[0.9999285,0.000012706552,0.000019674324,0.000008827732,0.00002301307,0.0000073021333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012338269,0.00030709477,0.00021703145,0.00012995819,0.00014385012,0.00027527334,0.0005391239,0.00037955766,0.00071409333],"category_scores_gemma":[0.00015654664,0.00016442375,0.00017110005,0.00018743589,0.00012378796,0.00032816414,0.00018588729,0.0002387326,0.00041266278],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013872851,0.000004721994,0.0000646744,0.000036925503,0.0000035373089,0.00003600431,0.0000062489785,0.000030257896,0.99852365,0.000045508725,0.000038539376,0.0011959695],"study_design_scores_gemma":[0.0000030534761,0.000080206184,0.0004970506,0.0000017692332,0.000008978733,0.00012712672,0.000008943591,0.00065456517,0.99722713,0.000020634694,0.001367168,0.0000032749522],"about_ca_topic_score_codex":0.00034765227,"about_ca_topic_score_gemma":0.00097285624,"teacher_disagreement_score":0.00071409333,"about_ca_system_score_codex":0.0002301685,"about_ca_system_score_gemma":0.00015702068,"threshold_uncertainty_score":0.0023888946},"labels":[],"label_agreement":null},{"id":"W4396855513","doi":"10.3390/s24103100","title":"Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shanghai","keywords":"Obstacle avoidance; Iterative closest point; Computer science; Robot; Computer vision; Point cloud; Obstacle; Artificial intelligence; Motion planning; Mobile robot; Overshoot (microwave communication); Path (computing); Collision avoidance; Trajectory; Collision; Geography","score_opus":0.013617329699053273,"score_gpt":0.2458808329737931,"score_spread":0.23226350327473982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396855513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070883404,0.00007304513,0.9916864,0.000023356779,0.0000138098785,0.000017007123,0.000012574144,0.00032780512,0.00075774296],"genre_scores_gemma":[0.2766028,0.00025586525,0.7199551,0.000042388758,0.00001832903,0.00013054066,0.00012624147,0.00009169316,0.0027769355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.00004606874,0.000014720972,0.00008343944,0.000175906,0.000034048],"domain_scores_gemma":[0.9997069,0.00008049526,0.0000490244,0.000061657964,0.00008634588,0.000015572752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025283484,0.0007205204,0.0006076901,0.0005910923,0.00038670117,0.000499919,0.00079729455,0.0005485854,0.001264012],"category_scores_gemma":[0.00073531875,0.00038982436,0.00035344603,0.00084291055,0.00041542563,0.0009770739,0.0009646143,0.000719233,0.00037979853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014824381,0.00008868494,0.0009845729,0.00020767446,0.000049337545,0.00020884325,0.00025924153,0.44682187,0.0944172,0.01567263,0.0019035491,0.43923825],"study_design_scores_gemma":[0.000017942253,0.0000776724,0.0008807402,0.000010331697,0.000013569418,0.00019688494,0.000029650944,0.97347903,0.01780592,0.003483656,0.0039784145,0.000026250096],"about_ca_topic_score_codex":0.002444133,"about_ca_topic_score_gemma":0.0027208482,"teacher_disagreement_score":0.002444133,"about_ca_system_score_codex":0.00031867088,"about_ca_system_score_gemma":0.0008327009,"threshold_uncertainty_score":0.0048598647},"labels":[],"label_agreement":null},{"id":"W4396958108","doi":"10.3390/s24103167","title":"Efficient IoT-Assisted Waste Collection for Urban Smart Cities","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Al-Imam Muhammad Ibn Saud Islamic University","keywords":"Waste collection; Truck; Dispose pattern; Knapsack problem; Waste management; Data collection; Population; Dumping; Municipal solid waste; Engineering; Computer science; Business; Mathematics","score_opus":0.020372915344223065,"score_gpt":0.2567272563173554,"score_spread":0.23635434097313232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396958108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09286225,0.0012596673,0.88935983,0.00037412508,0.00021272378,0.0001346101,0.0002328271,0.0013152699,0.014248726],"genre_scores_gemma":[0.9427525,0.0008793596,0.051663447,0.00009005728,0.000023147333,0.000102243284,0.00019054911,0.000045310815,0.0042533535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998048,0.000041555817,0.000014051161,0.00003097309,0.00006499502,0.000043660613],"domain_scores_gemma":[0.99988544,0.000032217322,0.000019458372,0.00001814611,0.000034559725,0.000010162196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024461708,0.0006473063,0.00055269804,0.0004615198,0.00053238636,0.000793184,0.0008518293,0.0006700279,0.0014603379],"category_scores_gemma":[0.0002939025,0.00028991638,0.00074375956,0.001002991,0.0003539454,0.0013800537,0.000898744,0.0004109931,0.00034647863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024097932,0.00014864313,0.002056096,0.00035828032,0.00006327124,0.00041517193,0.000111604204,0.84374505,0.035898533,0.010878554,0.0025614484,0.10352235],"study_design_scores_gemma":[0.000013142103,0.000079751,0.00046646624,0.000019725003,0.000027008131,0.000071433475,0.000084314684,0.9810104,0.009908744,0.003995186,0.004302804,0.000020944519],"about_ca_topic_score_codex":0.0023248042,"about_ca_topic_score_gemma":0.003484035,"teacher_disagreement_score":0.0023248042,"about_ca_system_score_codex":0.0005169777,"about_ca_system_score_gemma":0.00064615096,"threshold_uncertainty_score":0.004885316},"labels":[],"label_agreement":null},{"id":"W4398139689","doi":"10.3390/s24103217","title":"Development of an NO2 Gas Sensor Based on Laser-Induced Graphene Operating at Room Temperature","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Graphene; Materials science; Laser; Optoelectronics; Nanotechnology; Optics; Physics","score_opus":0.011767689668816169,"score_gpt":0.21585134450408053,"score_spread":0.20408365483526436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398139689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94939333,0.0017521073,0.044119824,0.0002812916,0.00016319688,0.00011126651,0.00040524497,0.0004768914,0.0032968295],"genre_scores_gemma":[0.9397128,0.0010181256,0.056422513,0.000092656795,0.000015368429,0.00006131568,0.00025126655,0.000025470026,0.002400494],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998722,0.000013474272,0.000006129206,0.000030023377,0.00006210194,0.000016142272],"domain_scores_gemma":[0.99993753,0.000010213988,0.0000150763935,0.000008700461,0.000016263391,0.00001232094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015350427,0.0004088838,0.00022119588,0.000204582,0.00014264566,0.0002102723,0.0004719187,0.00046480814,0.00024106313],"category_scores_gemma":[0.00015981041,0.00017545155,0.00018646938,0.00013603763,0.00021628819,0.0003412526,0.00024014323,0.00031279586,0.00013054404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105048575,0.000006794488,0.0000616744,0.000021116046,0.00000270773,0.000026400676,0.000005347294,0.00006642397,0.9986286,0.00005087393,0.00001955338,0.001100021],"study_design_scores_gemma":[0.0000026967316,0.00008960113,0.00045939806,0.000001697322,0.00000477131,0.00007743926,0.000006998226,0.0017204991,0.9968219,0.000015762942,0.00079275644,0.0000064675564],"about_ca_topic_score_codex":0.00047450047,"about_ca_topic_score_gemma":0.0018143334,"teacher_disagreement_score":0.00047450047,"about_ca_system_score_codex":0.0001961963,"about_ca_system_score_gemma":0.00023620493,"threshold_uncertainty_score":0.0014235377},"labels":[],"label_agreement":null},{"id":"W4398139762","doi":"10.3390/s24103206","title":"Efficacy of a Single-Bout of Auditory Feedback Training on Gait Performance and Kinematics in Healthy Young Adults","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Treadmill; Kinematics; Ankle; Gait; Physical medicine and rehabilitation; Physical therapy; Physics; Surgery","score_opus":0.036992435261873687,"score_gpt":0.33503695942951217,"score_spread":0.2980445241676385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398139762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993005,0.00025071905,0.00014914571,0.000017259783,0.000019036128,0.000022853244,0.000023198105,0.000012296474,0.00020486563],"genre_scores_gemma":[0.9990085,0.00013393807,0.0002445642,0.000029922068,0.000032329226,0.000033249835,0.000045026514,0.0000022170686,0.00047024016],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997812,0.0000364383,0.000020868714,0.00006982126,0.00003930065,0.000052331576],"domain_scores_gemma":[0.99965215,0.00009559071,0.000041567106,0.000017188262,0.000049879764,0.00014352768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043811154,0.0005514422,0.00071612076,0.00021084766,0.00027967797,0.00018557347,0.00021835009,0.0006088568,0.0013481786],"category_scores_gemma":[0.00086937734,0.00018237862,0.00026354042,0.00007186143,0.00023053572,0.00019803799,0.00036951495,0.00024027376,0.00029878426],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.10871162,0.04824943,0.1388135,0.0014759137,0.0010643734,0.0014055767,0.0015733675,0.0022637667,0.31037858,0.000063752595,0.000818956,0.38518116],"study_design_scores_gemma":[0.0012042212,0.13211365,0.85736644,0.00005722309,0.00031388324,0.00039037404,0.00035582864,0.0009069382,0.0067040604,0.000042079177,0.00052236277,0.000022948501],"about_ca_topic_score_codex":0.0013106775,"about_ca_topic_score_gemma":0.002314742,"teacher_disagreement_score":0.0013481786,"about_ca_system_score_codex":0.0001387082,"about_ca_system_score_gemma":0.0002071846,"threshold_uncertainty_score":0.004510045},"labels":[],"label_agreement":null},{"id":"W4398170657","doi":"10.3390/s24113261","title":"Revisiting the Role of Sensors for Shaping Plant Research: Applications and Future Perspectives","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Engineering; Computer science; Systems engineering; Data science","score_opus":0.03828452479490251,"score_gpt":0.3078761827874516,"score_spread":0.2695916579925491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398170657","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008727898,0.97874373,0.0066537107,0.007135442,0.0020597496,0.000020692896,0.000096255004,0.00008926123,0.004328471],"genre_scores_gemma":[0.0069903587,0.97312015,0.008733587,0.004209284,0.0025366137,0.000056519373,0.00016582242,0.000059410173,0.0041281763],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889946,0.00028132892,0.00009910813,0.0002540044,0.00034541864,0.00012069047],"domain_scores_gemma":[0.9970331,0.0017020734,0.00014650698,0.0001305864,0.00082947727,0.00015815941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003703852,0.0009887848,0.0013300863,0.0020578953,0.00062729506,0.0031680248,0.00151529,0.0027225928,0.00539729],"category_scores_gemma":[0.003104107,0.0004924571,0.0009628353,0.0019088219,0.001979243,0.007396079,0.0016307723,0.0045447783,0.0032691425],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017905475,0.00015053598,0.0012595939,0.025042884,0.00013574318,0.0006626641,0.00096797565,0.0012792324,0.034090076,0.09680057,0.0712938,0.7681379],"study_design_scores_gemma":[0.000007775465,0.0001567825,0.00063971034,0.0023589693,0.00005447755,0.00079963723,0.00040698954,0.00053293136,0.0038216468,0.02297745,0.96818984,0.000053720338],"about_ca_topic_score_codex":0.00091255584,"about_ca_topic_score_gemma":0.0015721875,"teacher_disagreement_score":0.00539729,"about_ca_system_score_codex":0.0013563139,"about_ca_system_score_gemma":0.0019477394,"threshold_uncertainty_score":0.019588053},"labels":[],"label_agreement":null},{"id":"W4398222772","doi":"10.3390/s24113287","title":"Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Bridge (graph theory); Structural health monitoring; Engineering; Computer science; Systems engineering; Forensic engineering; Medicine; Structural engineering; Surgery","score_opus":0.04146903739289193,"score_gpt":0.3823358856127362,"score_spread":0.34086684821984425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398222772","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016742475,0.99803203,0.00014012335,0.00034310555,0.00018436018,0.0000115021785,0.000045467277,0.0000056538843,0.0010703383],"genre_scores_gemma":[0.0007739805,0.99824536,0.000222651,0.00020440452,0.00014128191,0.000012239733,0.000046849895,0.0000014317499,0.00035175914],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99962664,0.0000802544,0.000079210404,0.00007553049,0.00011214733,0.00002615871],"domain_scores_gemma":[0.9984523,0.0010086634,0.00013859359,0.000023132305,0.00033153975,0.00004589751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009026461,0.0008677569,0.0011562009,0.0042791083,0.0004099018,0.0014026753,0.0007795525,0.001359843,0.006790614],"category_scores_gemma":[0.0021444305,0.00038730548,0.0010024561,0.0038931207,0.00038674526,0.0021021124,0.00079735124,0.0011216908,0.0017745806],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006209571,0.00006926339,0.0004375605,0.093896225,0.00014540518,0.00027723514,0.00025125965,0.000364389,0.0012201924,0.004802496,0.029926637,0.8685473],"study_design_scores_gemma":[0.000012778759,0.00015254441,0.0019618114,0.03685535,0.00047578497,0.0013760121,0.000278599,0.00017192002,0.0004924825,0.0015443936,0.9566401,0.000038061797],"about_ca_topic_score_codex":0.002484547,"about_ca_topic_score_gemma":0.003786855,"teacher_disagreement_score":0.006790614,"about_ca_system_score_codex":0.0006161072,"about_ca_system_score_gemma":0.0021787612,"threshold_uncertainty_score":0.02271682},"labels":[],"label_agreement":null},{"id":"W4399054882","doi":"10.3390/s24113404","title":"Innovative Non-Invasive and Non-Intrusive Precision Thermometry in Stainless-Steel Tanks Using Ultrasound Transducers","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor Technologies Research","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transducer; Homogeneous; Distilled water; Materials science; Process (computing); Ultrasound; Ultrasonic sensor; Temperature measurement; Range (aeronautics); Temperature control; Acoustics; Computer science; Process engineering; Environmental science; Mechanical engineering; Engineering; Composite material; Chemistry; Physics","score_opus":0.014358213574426498,"score_gpt":0.28029362137124897,"score_spread":0.26593540779682245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399054882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4167581,0.0045571197,0.5720938,0.0006480108,0.00042055987,0.0002687146,0.00025780124,0.0011668201,0.0038291556],"genre_scores_gemma":[0.75097245,0.0021639443,0.24246362,0.00021499074,0.000101518985,0.00017666079,0.00014988516,0.00006200289,0.003694898],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915564,0.0001190196,0.0000483597,0.00016703815,0.00046365248,0.00004621839],"domain_scores_gemma":[0.99947745,0.00015832383,0.00014827319,0.000057588695,0.0001308766,0.000027644854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006397,0.000492904,0.00042000128,0.00029338765,0.00021698428,0.00055124174,0.0009531561,0.00072750635,0.0006018101],"category_scores_gemma":[0.00093373575,0.00032748,0.00028508843,0.0003973252,0.00062742,0.0010481317,0.00057551,0.0005565961,0.00040789164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043423726,0.000018592275,0.00022466388,0.000104276245,0.0000039231168,0.00004130478,0.0000536785,0.00046805048,0.99079925,0.00043218766,0.00009926717,0.007711371],"study_design_scores_gemma":[0.000011644489,0.0004351805,0.0009498239,0.000016752656,0.00001653055,0.00015365181,0.000059479025,0.012017284,0.98260015,0.0002652089,0.0034395684,0.000034779012],"about_ca_topic_score_codex":0.0005628558,"about_ca_topic_score_gemma":0.0013216183,"teacher_disagreement_score":0.0009531561,"about_ca_system_score_codex":0.0003967257,"about_ca_system_score_gemma":0.0005177838,"threshold_uncertainty_score":0.0033831596},"labels":[],"label_agreement":null},{"id":"W4399139654","doi":"10.3390/s24113503","title":"Noise Reduction and Localization Accuracy in a Mobile Magnetoencephalography System","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Magnetoencephalography; Noise (video); Reduction (mathematics); Noise reduction; Computer science; Artificial intelligence; Psychology; Mathematics; Neuroscience","score_opus":0.008516720449770672,"score_gpt":0.26940759882514026,"score_spread":0.26089087837536956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399139654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8994812,0.0001724228,0.09875551,0.00011296468,0.00001985569,0.000020059055,0.000033776825,0.00045913638,0.0009451329],"genre_scores_gemma":[0.985494,0.000036202604,0.014072634,0.000018797235,0.0000048335132,0.00000884942,0.000040382223,0.000019999765,0.00030421597],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996495,0.0001066646,0.000022017173,0.00007105089,0.000096706186,0.000053943597],"domain_scores_gemma":[0.9988991,0.0006042307,0.00012476975,0.00013077192,0.00020706865,0.00003402396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006282596,0.00042105498,0.00028268446,0.00029723297,0.00021778536,0.00045850297,0.00035078084,0.00042349703,0.00055349804],"category_scores_gemma":[0.0036331357,0.00019332631,0.00020887134,0.00016359569,0.000385611,0.00049357157,0.0004289798,0.00022218384,0.00023129354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022651034,0.00017596211,0.013700955,0.00023688567,0.00014428575,0.0005121935,0.0005282243,0.3319666,0.52521145,0.0017282665,0.000482564,0.123047516],"study_design_scores_gemma":[0.00007527995,0.0028701818,0.03273297,0.00004975369,0.0001847039,0.0005542166,0.00021851168,0.600194,0.36020204,0.0011137201,0.0017414602,0.000063203675],"about_ca_topic_score_codex":0.0011002744,"about_ca_topic_score_gemma":0.00091792597,"teacher_disagreement_score":0.0011002744,"about_ca_system_score_codex":0.00037421085,"about_ca_system_score_gemma":0.0002908616,"threshold_uncertainty_score":0.0033226013},"labels":[],"label_agreement":null},{"id":"W4399283828","doi":"10.3390/s24113618","title":"Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Université Mohammed VI Polytechnique","keywords":"Remote sensing; Synthetic aperture radar; Context (archaeology); Radar; Computer science; Cloud computing; Data science; Environmental science; Geography; Telecommunications","score_opus":0.10654071773728356,"score_gpt":0.32070976139413937,"score_spread":0.2141690436568558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399283828","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087206316,0.9963451,0.0010476621,0.0002565358,0.00013966615,0.000017403885,0.00006743973,0.000013919019,0.0012402172],"genre_scores_gemma":[0.0031765653,0.9948453,0.0014242041,0.000105433224,0.00011522413,0.000009253907,0.00008198065,0.0000040893297,0.00023791079],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996618,0.00006506076,0.00006385332,0.00007213454,0.00011375456,0.000023377934],"domain_scores_gemma":[0.9986926,0.00081972545,0.00016482102,0.000027439562,0.00026741612,0.000028090111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010750623,0.0008314905,0.00083838997,0.003472129,0.0002273139,0.0011449526,0.0007616379,0.00075012556,0.0016738813],"category_scores_gemma":[0.0016990183,0.00035527517,0.00092789985,0.0035272946,0.0003747188,0.0016208297,0.0004146674,0.0007064709,0.000714108],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038573387,0.00005020168,0.0013486928,0.02196666,0.00018265919,0.00017782264,0.00013316587,0.00083381165,0.002047797,0.0013249688,0.0076613068,0.9642343],"study_design_scores_gemma":[0.00001539522,0.00032727997,0.013311737,0.022432966,0.0009872984,0.0025821908,0.0005900868,0.0019312327,0.0035184452,0.0029098156,0.95127183,0.000121675876],"about_ca_topic_score_codex":0.003123589,"about_ca_topic_score_gemma":0.0036246825,"teacher_disagreement_score":0.003472129,"about_ca_system_score_codex":0.000382903,"about_ca_system_score_gemma":0.0009890005,"threshold_uncertainty_score":0.006210804},"labels":[],"label_agreement":null},{"id":"W4399292676","doi":"10.3390/s24113599","title":"Using Physiological Markers to Assess Comfort during Neuromuscular Electrical Stimulation Induced Muscle Contraction in a Virtually Guided Environment: Pilot Study for a Path toward Combating ICU-Acquired Weakness","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Intensive Care Unit Cognitive Disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber Polytechnic; Toronto Metropolitan University; Queen's University; University of Toronto","funders":"","keywords":"Physical medicine and rehabilitation; Tibialis anterior muscle; Heart rate variability; Stimulation; Medicine; Cardiology; Heart rate; Internal medicine; Biomedical engineering; Skeletal muscle","score_opus":0.130058496920317,"score_gpt":0.3517547018094543,"score_spread":0.22169620488913727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399292676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999188,0.000013233976,0.0004956869,0.000014736924,0.0000035670841,0.00017924645,0.00001496924,0.0000030497824,0.00008758489],"genre_scores_gemma":[0.99507636,0.00003680536,0.0038714418,0.000075256336,0.000011636443,0.0005621671,0.000057416746,0.0000030668132,0.00030580544],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995328,0.0002754547,0.00002450661,0.000059856964,0.00006096139,0.000046331705],"domain_scores_gemma":[0.99924123,0.00023826164,0.00010307667,0.00007175825,0.00010985825,0.00023590591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010209671,0.00044861314,0.00032551744,0.00015767243,0.00028629752,0.00027480946,0.00030796055,0.00043023072,0.0010415541],"category_scores_gemma":[0.0015945763,0.0001770133,0.00025872028,0.0000837069,0.0004153049,0.00029263675,0.00033447973,0.0004762568,0.00016812001],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.030538041,0.1169094,0.30891767,0.0006562955,0.0003535799,0.0013608191,0.0062469756,0.0017336226,0.4089041,0.00023315131,0.000607988,0.123538375],"study_design_scores_gemma":[0.0012580123,0.55404544,0.42265216,0.000033277913,0.00012530359,0.0004280259,0.0016598257,0.0025970233,0.016304702,0.00008968787,0.0007681364,0.000038322665],"about_ca_topic_score_codex":0.00065048353,"about_ca_topic_score_gemma":0.0011026363,"teacher_disagreement_score":0.0010415541,"about_ca_system_score_codex":0.00013518741,"about_ca_system_score_gemma":0.00040820593,"threshold_uncertainty_score":0.0053994656},"labels":[],"label_agreement":null},{"id":"W4399390261","doi":"10.3390/s24113691","title":"Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Preprocessor; Artificial intelligence; Computational complexity theory; Feature extraction; Artificial neural network; Feature (linguistics); Data mining; Machine learning; Pattern recognition (psychology); Algorithm","score_opus":0.013655352605634604,"score_gpt":0.31015702709077414,"score_spread":0.29650167448513953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399390261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052177515,0.0006370591,0.9428967,0.00027643188,0.00011534994,0.000053373482,0.00015687877,0.0021818131,0.0015047628],"genre_scores_gemma":[0.8335982,0.00026243425,0.16064078,0.00025348194,0.00008621912,0.00006341377,0.00051632774,0.000115002185,0.00446408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997814,0.00002410346,0.000010663763,0.00007259184,0.00006248951,0.00004879343],"domain_scores_gemma":[0.9997454,0.00007803627,0.000037398775,0.000030092375,0.00008879698,0.000020265355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045768006,0.0009384494,0.00068618485,0.0008176959,0.00031445295,0.00048748267,0.0011320938,0.0008311852,0.001595837],"category_scores_gemma":[0.0011026443,0.0002779541,0.0006192063,0.00045589134,0.00033853957,0.0009409196,0.0009535858,0.00078398135,0.0003582532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004114616,0.00012994962,0.0037764844,0.00010747199,0.00008754994,0.00033948367,0.00011028987,0.21781929,0.03123943,0.0028147271,0.0058054193,0.73735845],"study_design_scores_gemma":[0.000007895384,0.000045617453,0.00070074724,0.00000436697,0.00001953502,0.000065137916,0.000009619963,0.9922909,0.0048002354,0.0014239206,0.00062428485,0.000007682127],"about_ca_topic_score_codex":0.005077666,"about_ca_topic_score_gemma":0.006868834,"teacher_disagreement_score":0.005077666,"about_ca_system_score_codex":0.0006006589,"about_ca_system_score_gemma":0.00069899333,"threshold_uncertainty_score":0.010096192},"labels":[],"label_agreement":null},{"id":"W4399495168","doi":"10.3390/s24123737","title":"Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Yuhan","keywords":"Fiducial marker; Artificial intelligence; Computer vision; Computer science; Motion capture; Ground truth; Convolutional neural network; Pose; Calibration; Tracking (education); Match moving; Tracking system; Deep learning; Artificial neural network; Head (geology); Motion (physics); Physics; Kalman filter","score_opus":0.022259907476486707,"score_gpt":0.3304371246463424,"score_spread":0.3081772171698557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399495168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1443188,0.0010797476,0.82939935,0.0017304679,0.00085794117,0.0011186781,0.0023214947,0.009164359,0.01000915],"genre_scores_gemma":[0.40239793,0.00082418235,0.5781016,0.0010921276,0.00011036926,0.001358738,0.0037939984,0.00048635315,0.011834687],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994986,0.00008570706,0.000031227297,0.000104691906,0.00022444685,0.00005528663],"domain_scores_gemma":[0.9991391,0.00013976867,0.00012144146,0.00016334114,0.00033505008,0.000101291786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016921923,0.0006115218,0.00030643,0.0004714048,0.00023206823,0.0006415081,0.0013333026,0.0008370872,0.003770791],"category_scores_gemma":[0.0025620111,0.00024138627,0.00029738093,0.0003088111,0.00040351885,0.0010845099,0.0010105626,0.00069127104,0.0015990478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068499416,0.0005002744,0.0050365003,0.00061691843,0.00009719063,0.00054376957,0.0002457063,0.013038229,0.6662542,0.00735541,0.017587604,0.28803912],"study_design_scores_gemma":[0.00016853296,0.0028847153,0.007958102,0.00016695006,0.00009281808,0.000921515,0.000093684524,0.13522859,0.78600425,0.0022737416,0.06412293,0.000084148385],"about_ca_topic_score_codex":0.0016360335,"about_ca_topic_score_gemma":0.002223973,"teacher_disagreement_score":0.003770791,"about_ca_system_score_codex":0.0006912124,"about_ca_system_score_gemma":0.0010042513,"threshold_uncertainty_score":0.012614548},"labels":[],"label_agreement":null},{"id":"W4399495271","doi":"10.3390/s24123751","title":"Nitrophenyl Thiourea-Modified Polyethylenimine Colorimetric Sensor for Sulfate, Fluorine, and Acetate","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Molecular Sensors and Ion Detection","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Thiourea; Chemistry; Titration; Selectivity; Colorimetry; Hydrogen bond; Polyethylenimine; Sulfate; Fourier transform infrared spectroscopy; Spectroscopy; Nuclear chemistry; Inorganic chemistry; Organic chemistry; Chromatography; Molecule; Chemical engineering","score_opus":0.014031971978328863,"score_gpt":0.24798586552938962,"score_spread":0.23395389355106075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399495271","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82800114,0.0046938895,0.15938234,0.0001944835,0.00020477024,0.00017494262,0.00037271614,0.0012662435,0.0057094623],"genre_scores_gemma":[0.8380835,0.002124155,0.15015018,0.00013944344,0.000038389942,0.00020634344,0.00052252435,0.000034686196,0.008700866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956733,0.00006474947,0.000028379141,0.00011751705,0.00018814753,0.000033837645],"domain_scores_gemma":[0.9998142,0.000032882068,0.0000419205,0.00001827801,0.00007447666,0.000018180515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033682643,0.000571022,0.00029284524,0.00036907595,0.00016079815,0.00020403635,0.00067584676,0.0005302952,0.00064410164],"category_scores_gemma":[0.00035431658,0.00026368897,0.00025911132,0.00026657165,0.00019229166,0.00044676723,0.00023632727,0.00036384477,0.0002975125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018869181,0.0000086789005,0.000101182864,0.0000342674,0.0000026192156,0.000022912242,0.000009330648,0.000038375358,0.9971666,0.000043838034,0.000033070744,0.0025202509],"study_design_scores_gemma":[0.0000022908605,0.00007243057,0.00060807884,0.0000022463369,0.000004857121,0.00009880024,0.000004946834,0.0017775322,0.99641377,0.000009346429,0.0009994605,0.000006282986],"about_ca_topic_score_codex":0.00057237607,"about_ca_topic_score_gemma":0.001553801,"teacher_disagreement_score":0.00067584676,"about_ca_system_score_codex":0.00030933265,"about_ca_system_score_gemma":0.00021555528,"threshold_uncertainty_score":0.0022443533},"labels":[],"label_agreement":null},{"id":"W4399533760","doi":"10.3390/s24123778","title":"Advanced Image Stitching Method for Dual-Sensor Inspection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Québec Metro High Tech Park (Canada); Université Laval","funders":"Canada Foundation for Innovation","keywords":"Image stitching; Computer science; Artificial intelligence; Visualization; Computer vision; Feature (linguistics); Image fusion; Automated X-ray inspection; Distortion (music); Image processing; Image (mathematics)","score_opus":0.005351742052159477,"score_gpt":0.2668898548295949,"score_spread":0.2615381127774354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399533760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03311845,0.00052264536,0.96337414,0.00008874679,0.00008674347,0.00004761049,0.000046547302,0.0006495674,0.002065662],"genre_scores_gemma":[0.33523756,0.0005847965,0.659997,0.00008642974,0.00007137356,0.00007188713,0.00014910837,0.00007640561,0.0037254686],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994873,0.00003464114,0.000018953253,0.00010759213,0.0003217516,0.000029810799],"domain_scores_gemma":[0.9996438,0.00006726925,0.00005319528,0.00005883532,0.00015746846,0.000019551278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025794122,0.00048206077,0.0003708832,0.00094636803,0.00017272087,0.00036293367,0.0007286848,0.00076279347,0.0016263764],"category_scores_gemma":[0.00063203933,0.00027784705,0.00043280763,0.0006967647,0.00032005532,0.0008382434,0.000472964,0.0006740238,0.0006092974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018127635,0.00009700543,0.0008782926,0.00020139251,0.000031028405,0.00015946188,0.0001538019,0.020622013,0.4882432,0.002765889,0.0015502216,0.48511642],"study_design_scores_gemma":[0.000017259694,0.0002455596,0.0022877827,0.000018746912,0.00003536117,0.0006844826,0.000041940883,0.80547667,0.18285039,0.0012124225,0.007076789,0.000052491392],"about_ca_topic_score_codex":0.0006790296,"about_ca_topic_score_gemma":0.00082097854,"teacher_disagreement_score":0.0016263764,"about_ca_system_score_codex":0.00027613103,"about_ca_system_score_gemma":0.0003375603,"threshold_uncertainty_score":0.0054407716},"labels":[],"label_agreement":null},{"id":"W4399639829","doi":"10.3390/s24123814","title":"Comparing the Drop Vertical Jump Tracking Performance of the Azure Kinect to the Kinect V2","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University Health Centre; McGill University","funders":"","keywords":"Sagittal plane; Coronal plane; Anterior cruciate ligament; Jump; Computer science; Orthodontics; Medicine; Anatomy; Physics","score_opus":0.01902057622290617,"score_gpt":0.2757747483172521,"score_spread":0.256754172094346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399639829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92696226,0.0014133494,0.056592137,0.00032260967,0.00022840624,0.00094929285,0.004233569,0.00055586646,0.0087425355],"genre_scores_gemma":[0.9455039,0.00055815716,0.04750011,0.0002731234,0.00004086602,0.0008030483,0.0025209056,0.00011709914,0.002682666],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979995,0.0005404464,0.00023772127,0.0005453411,0.00056343287,0.00011353248],"domain_scores_gemma":[0.9978502,0.0009680066,0.0002465537,0.00015779828,0.00064206176,0.00013533782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003161334,0.0006694867,0.0005835132,0.0006408359,0.0002069522,0.0007836605,0.0007752529,0.00069621275,0.004308821],"category_scores_gemma":[0.008783903,0.00028175404,0.0006291002,0.00042710957,0.00034274164,0.00085145276,0.0009903399,0.00045568257,0.0009109153],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020674028,0.0015463476,0.3628628,0.0029295855,0.001513515,0.00027257722,0.0019502401,0.012625106,0.1345134,0.0014533937,0.0058161644,0.4538429],"study_design_scores_gemma":[0.0004437694,0.0055408655,0.91383415,0.00032041932,0.0006119371,0.0007019874,0.00058695406,0.039335225,0.0318555,0.00050467596,0.0060908934,0.00017357338],"about_ca_topic_score_codex":0.004543443,"about_ca_topic_score_gemma":0.009795981,"teacher_disagreement_score":0.004543443,"about_ca_system_score_codex":0.000306449,"about_ca_system_score_gemma":0.0004403044,"threshold_uncertainty_score":0.016718984},"labels":[],"label_agreement":null},{"id":"W4399672849","doi":"10.3390/s24123859","title":"Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an Explainable Classifier","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Autonomy; Collision; Artificial intelligence; Machine learning; Simulation; Transport engineering; Engineering; Computer security","score_opus":0.025402008759786376,"score_gpt":0.28831996749348604,"score_spread":0.26291795873369966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399672849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9479641,0.00077566673,0.048306685,0.0004955542,0.00009803752,0.00012460678,0.0003022433,0.00026656044,0.0016664541],"genre_scores_gemma":[0.987092,0.0002270087,0.011686952,0.00004295956,0.000028479204,0.000042624924,0.00045445774,0.000015068436,0.00041042938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911636,0.00031158858,0.000047810645,0.00024279763,0.00014623013,0.00013524076],"domain_scores_gemma":[0.9938619,0.004640228,0.00026252226,0.0002889007,0.00074579095,0.00020056196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003259455,0.0007356293,0.0007685639,0.00089894823,0.00045827185,0.001499872,0.000906729,0.0013195995,0.0009848366],"category_scores_gemma":[0.008025417,0.00019983904,0.00087739096,0.00050415786,0.00042945464,0.0015556832,0.0005478014,0.0014487526,0.00032807718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029409726,0.0018877016,0.11327009,0.0003724702,0.0004822785,0.00028336002,0.00087943685,0.5489412,0.0036569994,0.002308998,0.002375705,0.32260078],"study_design_scores_gemma":[0.000021374071,0.00041404893,0.011425576,0.000021571455,0.00007373242,0.000035836638,0.0001969463,0.9852856,0.0010374469,0.0009968434,0.00046748816,0.00002366237],"about_ca_topic_score_codex":0.016583687,"about_ca_topic_score_gemma":0.011099673,"teacher_disagreement_score":0.016583687,"about_ca_system_score_codex":0.001471654,"about_ca_system_score_gemma":0.0011982449,"threshold_uncertainty_score":0.032974303},"labels":[],"label_agreement":null},{"id":"W4399675623","doi":"10.3390/s24123849","title":"Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Nyquist–Shannon sampling theorem; Sampling (signal processing); Iterative reconstruction; Computer science; Synthetic aperture radar; Computer vision; Nyquist frequency; Image quality; Radar imaging; Artificial intelligence; Image (mathematics); Radar; Algorithm; Filter (signal processing)","score_opus":0.008233065367689238,"score_gpt":0.23107431866871095,"score_spread":0.22284125330102172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399675623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065162973,0.00013347741,0.9928155,0.000034419125,0.000018251902,0.00001393146,0.0000108862005,0.0001305453,0.0003267016],"genre_scores_gemma":[0.13786887,0.0003208988,0.86064124,0.00003906515,0.000035174784,0.000055602577,0.00006846536,0.000035021247,0.0009356927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996425,0.000105485844,0.000015503632,0.00004356112,0.00017618059,0.000016822023],"domain_scores_gemma":[0.99958986,0.00019217862,0.00006695763,0.00006229589,0.00007269652,0.00001603557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005773222,0.0005309691,0.0004899828,0.0002198454,0.00011196388,0.00041661048,0.0005598571,0.00045411155,0.00047888554],"category_scores_gemma":[0.0010264032,0.00021713288,0.00035313398,0.00044381391,0.00037080844,0.0007176689,0.00043773794,0.0004266536,0.0002519649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037305852,0.00011218726,0.001837651,0.0005700187,0.00012659117,0.0004796703,0.00020878563,0.20868832,0.37724715,0.041260324,0.0018093687,0.3672869],"study_design_scores_gemma":[0.00001882215,0.00012058491,0.00029963834,0.000010288596,0.000012921342,0.0004852794,0.000012755394,0.9556071,0.038494118,0.0020022844,0.0029136585,0.000022494318],"about_ca_topic_score_codex":0.00022493178,"about_ca_topic_score_gemma":0.0003412939,"teacher_disagreement_score":0.0005773222,"about_ca_system_score_codex":0.00015637372,"about_ca_system_score_gemma":0.00028113255,"threshold_uncertainty_score":0.0030531883},"labels":[],"label_agreement":null},{"id":"W4399745660","doi":"10.3390/s24123873","title":"An Innovative EEG-Based Pain Identification and Quantification: A Pilot Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Pain Mechanisms and Treatments","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Université du Québec à Chicoutimi","keywords":"Electroencephalography; Chronic pain; Physical medicine and rehabilitation; Neurophysiology; Population; Medicine; Physical therapy; Audiology; Psychiatry","score_opus":0.0446653670390781,"score_gpt":0.3371883632660732,"score_spread":0.2925229962269951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399745660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9637924,0.0004921407,0.03294945,0.00007019907,0.000060024977,0.0016215178,0.0001894016,0.00004417193,0.0007807804],"genre_scores_gemma":[0.9668297,0.00038501786,0.02987429,0.00008917883,0.00010215759,0.0019179966,0.0002015042,0.000011606441,0.0005886258],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99904567,0.0005205854,0.00005372163,0.00012441019,0.00020354071,0.000052047144],"domain_scores_gemma":[0.9990933,0.00035543184,0.000100538615,0.00007701803,0.00028863628,0.000085179665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016470372,0.0006807781,0.00043473952,0.00039143345,0.00017337907,0.00036592962,0.00051971833,0.0004406592,0.0019852633],"category_scores_gemma":[0.0026474332,0.00014598879,0.00034584352,0.00022183839,0.00044658856,0.0004447743,0.0005326543,0.00031277438,0.00028045152],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011516639,0.011797494,0.07893272,0.002022413,0.00043926024,0.0009790837,0.0016762044,0.0015050583,0.6046431,0.0006051619,0.00066433166,0.28521848],"study_design_scores_gemma":[0.0044637625,0.26981953,0.6101654,0.00021149938,0.0011878741,0.0043773465,0.0021161186,0.01961908,0.08280854,0.000861972,0.00421344,0.0001554385],"about_ca_topic_score_codex":0.0003079723,"about_ca_topic_score_gemma":0.00038642075,"teacher_disagreement_score":0.0019852633,"about_ca_system_score_codex":0.00010786138,"about_ca_system_score_gemma":0.0003138031,"threshold_uncertainty_score":0.008710504},"labels":[],"label_agreement":null},{"id":"W4399779618","doi":"10.3390/s24123939","title":"Development of a Sensitive Colorimetric Indicator for Detecting Beef Spoilage in Smart Packaging","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Food spoilage; Food packaging; Chemistry; Ammonia; pH indicator; Ammonia gas; Detection limit; Active packaging; Bacterial growth; Food science; Chromatography; Bacteria; Biochemistry; Biology","score_opus":0.012446016640528865,"score_gpt":0.24029170620048432,"score_spread":0.22784568955995546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399779618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7499244,0.007678086,0.23653644,0.00038640713,0.00039868936,0.00034116313,0.00042182294,0.0010978758,0.0032150918],"genre_scores_gemma":[0.81248313,0.0025775596,0.18057254,0.0002975203,0.00008088401,0.00022626403,0.0003327148,0.00005093313,0.0033785321],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953055,0.0000699926,0.000024973178,0.00011196685,0.00022268268,0.000039765055],"domain_scores_gemma":[0.9997675,0.000047360067,0.0000752566,0.000014488648,0.00007176743,0.000023743089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005621923,0.0006665228,0.00036396584,0.00040783876,0.00013300477,0.00043774088,0.0005137775,0.00086161704,0.0004594581],"category_scores_gemma":[0.0004598794,0.00030653723,0.0003398034,0.00024055823,0.0002215714,0.00076212647,0.00045928886,0.0004524733,0.00034094628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036690606,0.000019704054,0.00038382827,0.00013869861,0.0000072661096,0.00006884478,0.000027807211,0.00015840604,0.9931251,0.00008556815,0.000067713736,0.00588043],"study_design_scores_gemma":[0.000007077759,0.00040593327,0.0015381475,0.000014124751,0.000024697516,0.000279932,0.000042619024,0.0050612576,0.99002105,0.000053500396,0.00253034,0.000021327107],"about_ca_topic_score_codex":0.00020701713,"about_ca_topic_score_gemma":0.0003289171,"teacher_disagreement_score":0.00086161704,"about_ca_system_score_codex":0.00022250968,"about_ca_system_score_gemma":0.00015210583,"threshold_uncertainty_score":0.002973199},"labels":[],"label_agreement":null},{"id":"W4399807674","doi":"10.3390/s24123965","title":"Enhancing the Efficiency of Resilient Multipath-Routed Elastic Optical Networks: A Novel Approach for Coexisting Protected and Unprotected Services with Idle Slot Reuse","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Universidade de Pernambuco; Universidade Federal de Pernambuco; University of Waterloo; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Reuse; Computer network; Computer science; Bandwidth (computing); Network topology; Idle; Call blocking; Multipath propagation; Topology (electrical circuits); Frequency reuse; Routing (electronic design automation); Distributed computing; Handover; Engineering; Channel (broadcasting); Electrical engineering","score_opus":0.009696866825819111,"score_gpt":0.2171298394977404,"score_spread":0.2074329726719213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399807674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19050395,0.00044819704,0.8053216,0.00017751362,0.000030532872,0.000054144635,0.000016483948,0.0003256585,0.0031219404],"genre_scores_gemma":[0.91956717,0.00011697378,0.07967036,0.000030169316,0.000017959394,0.000018406605,0.000009415188,0.000019629899,0.00055000966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996377,0.00011268419,0.000012543735,0.000053533688,0.00009902033,0.00008454317],"domain_scores_gemma":[0.99930084,0.00033587127,0.000116920026,0.00014134862,0.00006473665,0.00004043011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006756844,0.0005446177,0.00044428444,0.000594131,0.00037966695,0.0005317549,0.0011213404,0.00039987336,0.00080357806],"category_scores_gemma":[0.0012474688,0.00020216971,0.00031723164,0.0004476714,0.0006456047,0.0011281247,0.00084673637,0.00035204185,0.00010966628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042697982,0.0001924002,0.0014557041,0.00016357846,0.00009181584,0.00020389396,0.00013265495,0.70275307,0.09396444,0.03129176,0.0006038996,0.1687198],"study_design_scores_gemma":[0.0000140540105,0.00011473487,0.00019241174,0.0000056072945,0.00001804297,0.000114781265,0.000028914366,0.9831744,0.010901726,0.0047841244,0.0006406874,0.000010554112],"about_ca_topic_score_codex":0.0005311058,"about_ca_topic_score_gemma":0.00082308176,"teacher_disagreement_score":0.0011213404,"about_ca_system_score_codex":0.00048193723,"about_ca_system_score_gemma":0.0006197376,"threshold_uncertainty_score":0.003573358},"labels":[],"label_agreement":null},{"id":"W4399837285","doi":"10.3390/s24217036","title":"MEMS and ECM Sensor Technologies for Cardiorespiratory Sound Monitoring—A Comprehensive Review","year":2024,"lang":"en","type":"preprint","venue":"Sensors","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sound (geography); Acoustics; Lung; Medicine; Environmental science; Computer science; Cardiology; Internal medicine; Physics","score_opus":0.04873533654948566,"score_gpt":0.34543585275904776,"score_spread":0.2967005162095621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399837285","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008382383,0.9920873,0.003600322,0.00032736425,0.00048889505,0.000019233146,0.00006010281,0.000044352084,0.002534195],"genre_scores_gemma":[0.004088861,0.9883823,0.0044702636,0.00030796687,0.0007075673,0.000033063363,0.00012305177,0.000015735866,0.0018712602],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993685,0.000079226804,0.00007818797,0.0001296645,0.00030353744,0.000040958803],"domain_scores_gemma":[0.99928147,0.00041017812,0.00006721281,0.000028681072,0.00018700963,0.000025472451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093982613,0.0010715271,0.0009835252,0.002229416,0.00027740977,0.0010682595,0.00092433277,0.0012808077,0.0033058785],"category_scores_gemma":[0.0012977521,0.00043973836,0.0006471108,0.0019452624,0.00032611165,0.0018289874,0.000778669,0.0010748012,0.002200824],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006199424,0.000089162444,0.00053958903,0.016553134,0.00007948369,0.00019696825,0.00009608909,0.00079634145,0.0125697125,0.0043425933,0.013101952,0.951573],"study_design_scores_gemma":[0.000009373666,0.00035751006,0.0020212906,0.0043322197,0.0002179847,0.002136826,0.00014780619,0.0014685952,0.013360508,0.0030357074,0.97283566,0.000076468255],"about_ca_topic_score_codex":0.000413041,"about_ca_topic_score_gemma":0.00047776892,"teacher_disagreement_score":0.0033058785,"about_ca_system_score_codex":0.00027621083,"about_ca_system_score_gemma":0.00071445416,"threshold_uncertainty_score":0.011059284},"labels":[],"label_agreement":null},{"id":"W4399862206","doi":"10.3390/s24123994","title":"Robust Multi-Modal Image Registration for Image Fusion Enhancement in Infrastructure Inspection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Québec Metro High Tech Park (Canada); Université Laval","funders":"Canada Foundation for Innovation","keywords":"Robustness (evolution); Computer vision; Image fusion; Artificial intelligence; Modal; Computer science; Image registration; Fusion; Sensor fusion; Image (mathematics); Data mining; Materials science","score_opus":0.010473377853620316,"score_gpt":0.23976471946335176,"score_spread":0.22929134160973144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399862206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028852053,0.00033860444,0.96942174,0.0000885573,0.00002846598,0.000032450935,0.00001825461,0.00034119928,0.00087858806],"genre_scores_gemma":[0.5352995,0.00044108788,0.46255207,0.00006907668,0.000040177198,0.00006964788,0.00007727277,0.00013278045,0.0013184593],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999318,0.00022628068,0.000032818207,0.00011831167,0.00025181207,0.000052838026],"domain_scores_gemma":[0.9995449,0.00016606582,0.00009053338,0.00007481519,0.00010714533,0.000016525717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00099119,0.00047144463,0.00048306034,0.0006205264,0.00025650926,0.000531399,0.0006179358,0.0006132892,0.0009553605],"category_scores_gemma":[0.0018439578,0.00023582442,0.0006439102,0.00067634543,0.00048025537,0.0010989467,0.0008719437,0.00064332807,0.00036034037],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005958313,0.00022778822,0.001232325,0.00036095595,0.000101642334,0.0001882095,0.00032339102,0.221504,0.38452032,0.012700859,0.001474518,0.37677002],"study_design_scores_gemma":[0.000008386836,0.00013198292,0.0012317399,0.000012164234,0.000031064625,0.00017660558,0.000036782516,0.90630823,0.08813939,0.002413781,0.001478078,0.000031717373],"about_ca_topic_score_codex":0.00046133663,"about_ca_topic_score_gemma":0.00062954344,"teacher_disagreement_score":0.00099119,"about_ca_system_score_codex":0.0002989045,"about_ca_system_score_gemma":0.00033619878,"threshold_uncertainty_score":0.00524199},"labels":[],"label_agreement":null},{"id":"W4399871288","doi":"10.3390/s24123990","title":"Acceleration for Efficient Automated Generation of Operational Amplifiers","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advancements in Semiconductor Devices and Circuit Design","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Sizing; Boosting (machine learning); Computer science; Operational amplifier; Electronic engineering; Amplifier; Engineering; Artificial intelligence; CMOS","score_opus":0.04686842986397545,"score_gpt":0.289202190340479,"score_spread":0.24233376047650357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399871288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079937786,0.0001763603,0.90741235,0.00012260706,0.000060716087,0.000069882255,0.000061716644,0.0035939144,0.008564558],"genre_scores_gemma":[0.5986683,0.00009168611,0.39731964,0.000049210266,0.00001898919,0.00006537194,0.0001247339,0.00024928193,0.0034128593],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997137,0.000028793285,0.000009569205,0.00003585895,0.00018429784,0.000027795215],"domain_scores_gemma":[0.9996859,0.00011988394,0.000045244433,0.00005907181,0.0000788082,0.000011168492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021004907,0.00045238528,0.00022730372,0.00035390255,0.00018559754,0.00036721127,0.00058138306,0.00030018244,0.0027655961],"category_scores_gemma":[0.00066747604,0.00019815245,0.00018073128,0.00028347468,0.00026424465,0.00045978228,0.00038859088,0.00045526316,0.00071809476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012511578,0.000080793376,0.0010937512,0.00017586985,0.000017655033,0.00014592864,0.00020378546,0.12397286,0.5611589,0.022193165,0.0030969938,0.28773525],"study_design_scores_gemma":[0.00003090411,0.00023523941,0.0006899566,0.000010383456,0.000011695227,0.00015689299,0.00002441054,0.77916723,0.1977647,0.0033732306,0.018517895,0.00001752724],"about_ca_topic_score_codex":0.0005050948,"about_ca_topic_score_gemma":0.00089443836,"teacher_disagreement_score":0.0027655961,"about_ca_system_score_codex":0.00031116357,"about_ca_system_score_gemma":0.0004146407,"threshold_uncertainty_score":0.009251833},"labels":[],"label_agreement":null},{"id":"W4400037333","doi":"10.3390/s24134164","title":"A Review of Subsidence Monitoring Techniques in Offshore Environments","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Submarine pipeline; Sustainability; Subsidence; Environmental science; Interferometric synthetic aperture radar; Environmental resource management; Risk analysis (engineering); Civil engineering; Engineering; Environmental planning; Remote sensing; Geology; Synthetic aperture radar; Business","score_opus":0.025520727018252788,"score_gpt":0.3091276238070117,"score_spread":0.2836068967887589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400037333","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002452209,0.99747086,0.00053081114,0.00014773467,0.00013400883,0.000012740584,0.000050042643,0.000011800849,0.001396739],"genre_scores_gemma":[0.0011824213,0.9973552,0.00073961756,0.000102732796,0.000093445204,0.000011941758,0.000059860053,0.0000039760343,0.0004508335],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993349,0.00010391694,0.00014332321,0.0001343347,0.00024799953,0.00003550321],"domain_scores_gemma":[0.9984097,0.0009633226,0.00022077332,0.000043437867,0.0003258434,0.000036933652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011660813,0.0011878484,0.0014315064,0.005464043,0.0003909369,0.0012328258,0.0011236244,0.0012664727,0.0046308213],"category_scores_gemma":[0.0023283185,0.0005770367,0.0012331811,0.005593823,0.0004935069,0.0021270823,0.0006818501,0.0009733379,0.001798982],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055680535,0.00006462746,0.0004232449,0.07030494,0.00014341183,0.00017852469,0.00013179464,0.00062959193,0.0023932306,0.0022287152,0.012424642,0.91102153],"study_design_scores_gemma":[0.000009641668,0.00015823498,0.0025531158,0.019464916,0.00044789247,0.0014578437,0.00018814897,0.00024924547,0.0018026911,0.0014508582,0.9721671,0.000050451756],"about_ca_topic_score_codex":0.0023486854,"about_ca_topic_score_gemma":0.0030409778,"teacher_disagreement_score":0.005464043,"about_ca_system_score_codex":0.0005371768,"about_ca_system_score_gemma":0.0017587361,"threshold_uncertainty_score":0.015491664},"labels":[],"label_agreement":null},{"id":"W4400086853","doi":"10.3390/s24134183","title":"Detecting Near-Surface Sub-Millimeter Voids in Additively Manufactured Ti-5V-5Al-5Mo-3Cr Alloy Using a Transmit-Receive Eddy Current Probe","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Alloy; Materials science; Millimeter; Titanium alloy; Eddy current; Extremely high frequency; Current (fluid); Metallurgy; Electrical engineering; Optoelectronics; Optics; Engineering; Physics","score_opus":0.020387002628821514,"score_gpt":0.26119862497053553,"score_spread":0.24081162234171402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400086853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95842993,0.00015744989,0.040499836,0.000040860683,0.000011056688,0.000029188057,0.00003417406,0.00027829417,0.0005192184],"genre_scores_gemma":[0.9767364,0.000036431826,0.022839878,0.000020842295,0.0000025186764,0.0000105091085,0.000019274188,0.000010578894,0.0003235029],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997967,0.000023318493,0.0000071643244,0.00004285889,0.00010935648,0.000020562273],"domain_scores_gemma":[0.9995272,0.00016785045,0.00012661524,0.000039781575,0.00011637411,0.000022134349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032263086,0.00019886337,0.00022562426,0.00027915958,0.0000919888,0.00024851877,0.00040338575,0.00042284373,0.0002929751],"category_scores_gemma":[0.00078850973,0.00016525725,0.00013998924,0.00011941465,0.00039182787,0.00028765076,0.00025896137,0.00014676986,0.00008547894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114418544,0.000012160007,0.00124549,0.000036693717,0.0000032881605,0.00004519904,0.00007463684,0.00070973224,0.9912599,0.000098665994,0.00003456022,0.006365321],"study_design_scores_gemma":[0.000017637401,0.0006332641,0.011419433,0.00000638986,0.000021888956,0.00034213744,0.00010613652,0.016179489,0.97049594,0.00007018139,0.00068950135,0.000017986606],"about_ca_topic_score_codex":0.00059235725,"about_ca_topic_score_gemma":0.0014085739,"teacher_disagreement_score":0.00059235725,"about_ca_system_score_codex":0.0002683751,"about_ca_system_score_gemma":0.00019000327,"threshold_uncertainty_score":0.0019471645},"labels":[],"label_agreement":null},{"id":"W4400293757","doi":"10.3390/s24134317","title":"Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Douglas Mental Health University Institute; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Canadian Institutes of Health Research; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Polysomnography; Sleep (system call); Heart rate variability; Algorithm; Computer science; Offset (computer science); Feature (linguistics); Machine learning; Electroencephalography; Heart rate; Medicine; Audiology; Artificial intelligence; Psychiatry; Internal medicine","score_opus":0.0559449354999699,"score_gpt":0.3729646608128763,"score_spread":0.31701972531290645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400293757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015966993,0.00030309346,0.98161155,0.000069276204,0.000043731307,0.000041106843,0.00007993889,0.001348179,0.0005360359],"genre_scores_gemma":[0.5171726,0.000375914,0.47831145,0.0001097455,0.000059868114,0.0001961254,0.0004311709,0.000208285,0.0031347927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995956,0.00010480946,0.000034992318,0.00012157391,0.000115824456,0.000027196667],"domain_scores_gemma":[0.9992236,0.00047811147,0.00006556386,0.00007819916,0.00013440609,0.000020205734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011315359,0.0008076418,0.00071346987,0.00093786314,0.0002559459,0.00056622986,0.0007297307,0.0007637048,0.0015809534],"category_scores_gemma":[0.003139421,0.00029057276,0.0007281312,0.0006825608,0.0002524338,0.00051414536,0.00038780167,0.0010463807,0.000773839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001952626,0.0002350286,0.005228292,0.00012418607,0.00023442498,0.00012499905,0.00008553127,0.44306856,0.014896646,0.004099841,0.0022779047,0.5294293],"study_design_scores_gemma":[0.0000037864677,0.000022418584,0.0007760557,0.000004114427,0.0000069609273,0.00002211444,0.0000026001164,0.99723876,0.00097051094,0.00060344563,0.00034245345,0.0000067123115],"about_ca_topic_score_codex":0.005798443,"about_ca_topic_score_gemma":0.003334125,"teacher_disagreement_score":0.005798443,"about_ca_system_score_codex":0.00038065363,"about_ca_system_score_gemma":0.00065508013,"threshold_uncertainty_score":0.011529386},"labels":[],"label_agreement":null},{"id":"W4400366544","doi":"10.3390/s24134349","title":"Probabilistic Analysis of Critical Speed Values of a Rotating Machine as a Function of the Change of Dynamic Parameters","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR","keywords":"Probabilistic logic; Monte Carlo method; Rotor (electric); Vibration; Bearing (navigation); Helicopter rotor; Engineering; Critical speed; Stability (learning theory); Control theory (sociology); Computer science; Mechanical engineering; Mathematics; Physics; Artificial intelligence; Machine learning","score_opus":0.07927064364646644,"score_gpt":0.35570069218278483,"score_spread":0.27643004853631836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400366544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4529535,0.00028440746,0.5433049,0.00019361371,0.000015331896,0.000046213045,0.0001726713,0.00037140222,0.0026579332],"genre_scores_gemma":[0.99349505,0.000050479797,0.006185448,0.0000069988573,0.0000037665986,0.000012451743,0.000046241734,0.000012022497,0.00018763893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949586,0.000117874435,0.000018809254,0.000107425934,0.00020977088,0.000050154853],"domain_scores_gemma":[0.9940785,0.004275773,0.000890722,0.00030552826,0.0003849042,0.00006462776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015051235,0.00036952127,0.0003653578,0.0010630863,0.00033825534,0.0005307951,0.00044335515,0.00051224494,0.0007302201],"category_scores_gemma":[0.007456111,0.00040685304,0.00042974745,0.00048564185,0.0007671933,0.00074951147,0.000386593,0.00058690965,0.00008870448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004709909,0.000017702832,0.0025387502,0.000023204164,0.000018194789,0.0000496457,0.000034504137,0.9842036,0.003601332,0.004267935,0.00007626848,0.0051217764],"study_design_scores_gemma":[0.0000013407354,0.000026130101,0.0014234803,0.0000027507692,0.0000056020936,0.000035161356,0.0000073734823,0.9953673,0.0015744731,0.0014439974,0.00010332073,0.000009139443],"about_ca_topic_score_codex":0.0017297823,"about_ca_topic_score_gemma":0.0017462298,"teacher_disagreement_score":0.0017297823,"about_ca_system_score_codex":0.000586995,"about_ca_system_score_gemma":0.00046190497,"threshold_uncertainty_score":0.007959962},"labels":[],"label_agreement":null},{"id":"W4400410345","doi":"10.3390/s24134423","title":"Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; Beijing Nova Program; National Natural Science Foundation of China","keywords":"Greenhouse gas; Hazardous waste; Environmental science; Production (economics); Fugitive emissions; Instrumentation (computer programming); Methane; Waste management; Biochemical engineering; Process engineering; Environmental engineering; Computer science; Engineering; Ecology","score_opus":0.052827424245112005,"score_gpt":0.3790168532979217,"score_spread":0.3261894290528097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400410345","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038468261,0.9970804,0.0009304434,0.00012448178,0.00013529506,0.000011993334,0.00003709401,0.000018023109,0.0012775571],"genre_scores_gemma":[0.0018753298,0.99605393,0.0011377949,0.00009678282,0.00009523974,0.000015247383,0.00005262367,0.000004349227,0.000668686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995307,0.000049030252,0.00005314616,0.00011150802,0.00022330432,0.00003231961],"domain_scores_gemma":[0.9991148,0.00045840404,0.00013834448,0.000030057165,0.00022830845,0.000029999144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010958026,0.0012425896,0.0015399005,0.0034878564,0.0003046531,0.0010806235,0.0012018873,0.0012795348,0.0028284907],"category_scores_gemma":[0.001152768,0.00053788227,0.00096735964,0.0031159858,0.00048854656,0.0020765243,0.0006148955,0.0011846139,0.0016673743],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054311055,0.00013182913,0.00045957146,0.035591185,0.00014577396,0.00016240223,0.00007499029,0.0011315888,0.008854592,0.0041522863,0.009343704,0.93989784],"study_design_scores_gemma":[0.000010687842,0.0002927092,0.0017810191,0.0047240243,0.00034161616,0.0012028841,0.00012851214,0.000698321,0.008008168,0.0020037354,0.9807357,0.000072642535],"about_ca_topic_score_codex":0.0013134374,"about_ca_topic_score_gemma":0.0015145823,"teacher_disagreement_score":0.0034878564,"about_ca_system_score_codex":0.00045161918,"about_ca_system_score_gemma":0.0010530099,"threshold_uncertainty_score":0.009462178},"labels":[],"label_agreement":null},{"id":"W4400418791","doi":"10.3390/s24134398","title":"Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Valence (chemistry); Arousal; Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Feature extraction; Cognition; Feature (linguistics); Pattern recognition (psychology); Machine learning; Psychology","score_opus":0.03111234844892857,"score_gpt":0.3379729311824622,"score_spread":0.3068605827335336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400418791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69039583,0.000774703,0.3020409,0.00028282826,0.00014485308,0.00009642571,0.0010535045,0.0016236544,0.003587378],"genre_scores_gemma":[0.96498024,0.0001813245,0.032517344,0.00006425186,0.000024351026,0.000040867464,0.0006975518,0.00003498274,0.0014590892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998652,0.000022443506,0.0000057042867,0.000045561475,0.00002704427,0.000034052453],"domain_scores_gemma":[0.9997862,0.0000795495,0.000029928973,0.000020053965,0.00006799377,0.000016313636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028180346,0.0006758883,0.0002682321,0.0004191925,0.00009127771,0.00043992326,0.00028398895,0.00030281543,0.0008504206],"category_scores_gemma":[0.0011654883,0.00014871081,0.0003594473,0.0002021435,0.00015163328,0.00042098097,0.00029770005,0.0006310804,0.00026787762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089304877,0.0005703,0.037491538,0.00016835863,0.0001733456,0.00027078722,0.00020862685,0.16920975,0.08194514,0.0010158236,0.0056306883,0.7024226],"study_design_scores_gemma":[0.000013885402,0.0001872005,0.023899375,0.00002536162,0.0000400995,0.000105215666,0.000075053256,0.9547437,0.019006774,0.0010796748,0.0008014893,0.000022043088],"about_ca_topic_score_codex":0.0023964755,"about_ca_topic_score_gemma":0.0039939093,"teacher_disagreement_score":0.0023964755,"about_ca_system_score_codex":0.0003275562,"about_ca_system_score_gemma":0.00017888822,"threshold_uncertainty_score":0.0047650337},"labels":[],"label_agreement":null},{"id":"W4400419606","doi":"10.3390/s24134404","title":"Sensor-Assisted Analysis of Autonomic and Cerebrovascular Dysregulation following Concussion in an Individual with a History of Ten Concussions: A Case Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Concussion; Medicine; Physical medicine and rehabilitation; Psychology; Poison control; Injury prevention; Medical emergency","score_opus":0.06369099288825508,"score_gpt":0.34214818386374984,"score_spread":0.27845719097549476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400419606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995577,0.00067669107,0.0009831736,0.0005479914,0.000045840552,0.000082287384,0.000089569316,0.000019482028,0.0019780071],"genre_scores_gemma":[0.9972186,0.0007165085,0.00087538816,0.00026961384,0.00009446211,0.000015791866,0.000079390775,0.00001001182,0.0007201279],"study_design_codex":"case_report","study_design_gemma":"case_report","domain_scores_codex":[0.99940467,0.00007902231,0.00008326441,0.00013895685,0.00015203911,0.00014208128],"domain_scores_gemma":[0.9991742,0.00014233668,0.00022829327,0.000058096175,0.00015638141,0.00024066858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043693813,0.0008777346,0.0005295353,0.0020248313,0.0022394774,0.00097607786,0.0009214419,0.0018543131,0.001247198],"category_scores_gemma":[0.0022093283,0.0005988545,0.0007177055,0.0009021337,0.0009761308,0.00077928364,0.0010668996,0.0014251887,0.0004371312],"study_design_candidate":"case_report","study_design_consensus":"case_report","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001037901,0.0003380552,0.15492098,0.00008313929,0.00004712666,0.83391666,0.003474682,0.00011519962,0.0011302129,0.00012544826,0.00055428914,0.005190441],"study_design_scores_gemma":[0.000008578748,0.00045149634,0.08479915,0.000058936443,0.000039888968,0.909312,0.0029636633,0.00053020066,0.0005729991,0.0001340752,0.0010947332,0.000034285255],"about_ca_topic_score_codex":0.0060428376,"about_ca_topic_score_gemma":0.0116098905,"teacher_disagreement_score":0.0060428376,"about_ca_system_score_codex":0.0011183737,"about_ca_system_score_gemma":0.00073824316,"threshold_uncertainty_score":0.012015343},"labels":[],"label_agreement":null},{"id":"W4400487586","doi":"10.3390/s24144456","title":"Natural Frequency Transmissibility for Detection of Cracks in Horizontal Axis Wind Turbine Blades","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Transmissibility (structural dynamics); Turbine blade; Structural engineering; Vibration; Acceleration; Turbine; Natural frequency; Transverse plane; Amplitude; Acoustics; Engineering; Marine engineering; Mechanical engineering; Physics; Optics; Vibration isolation","score_opus":0.011411729224062273,"score_gpt":0.2750000237030785,"score_spread":0.26358829447901627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400487586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87207574,0.00047665343,0.12580301,0.0000547778,0.000024811854,0.000048343132,0.00007095443,0.00033630087,0.0011093125],"genre_scores_gemma":[0.9714362,0.00014135362,0.027917089,0.000008286599,0.0000035023545,0.000013207805,0.000024486417,0.000009620353,0.00044625302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997012,0.000039642673,0.000011442269,0.00004418371,0.00018818764,0.000015372696],"domain_scores_gemma":[0.9986644,0.0006336679,0.000305417,0.00011620669,0.00024403784,0.000036366822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036665687,0.00025848753,0.0001785372,0.00060544093,0.000094751216,0.00021781487,0.00029242563,0.00036954347,0.0007248448],"category_scores_gemma":[0.0013319681,0.0002207768,0.00017797628,0.00019157007,0.0002642963,0.00033593507,0.00021422574,0.0002120523,0.0001246719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018290678,0.00006307227,0.0122805415,0.00016517508,0.000011960565,0.00013050184,0.00018071225,0.011906729,0.9144665,0.00021983126,0.00010644883,0.060285605],"study_design_scores_gemma":[0.000019967478,0.0013317262,0.11539022,0.000056403493,0.00003667051,0.0011725016,0.00025474903,0.31155407,0.56847566,0.00037358145,0.0012588347,0.000075564014],"about_ca_topic_score_codex":0.00046370534,"about_ca_topic_score_gemma":0.0017036201,"teacher_disagreement_score":0.0007248448,"about_ca_system_score_codex":0.00020626507,"about_ca_system_score_gemma":0.00011357111,"threshold_uncertainty_score":0.0024248958},"labels":[],"label_agreement":null},{"id":"W4400522974","doi":"10.3390/s24144499","title":"Design, Fabrication, and Dynamic Analysis of a MEMS Ring Resonator Supported by Twin Circular Curve Beams","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; CMC Microsystems","keywords":"Resonator; Microelectromechanical systems; Finite element method; Silicon on insulator; Stiffness; Voltage; Fabrication; Materials science; Electronic engineering; Acoustics; Silicon; Engineering; Mechanical engineering; Structural engineering; Optoelectronics; Electrical engineering; Physics","score_opus":0.00863305240439353,"score_gpt":0.23583098286986087,"score_spread":0.22719793046546735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400522974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62739027,0.000986684,0.3653726,0.00028694212,0.00012519289,0.0002515899,0.00012323103,0.00039588442,0.005067661],"genre_scores_gemma":[0.73703146,0.00045700057,0.25943765,0.00003773114,0.000019725117,0.0001247747,0.000069723115,0.00005019621,0.002771707],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959904,0.000033673023,0.000014262517,0.00007487478,0.00024750078,0.000030633684],"domain_scores_gemma":[0.99965227,0.00006592448,0.00007780161,0.00006649015,0.00011188218,0.00002564879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051350327,0.00033397152,0.0003745181,0.00023579002,0.00030881554,0.0003655278,0.00059286336,0.00051204505,0.00059767964],"category_scores_gemma":[0.0004900542,0.00028034297,0.00047137876,0.0001428241,0.0002967598,0.0004727298,0.0002705627,0.0002367912,0.00027793928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004618813,0.00003696182,0.0006667568,0.00011744896,0.00002256102,0.00016494122,0.00010489282,0.0155263,0.9691265,0.0010626452,0.00012469463,0.013000006],"study_design_scores_gemma":[0.0000321231,0.00157681,0.004691441,0.000018747784,0.000053084717,0.0006746423,0.00009888659,0.2328072,0.74967057,0.00028782454,0.010032091,0.000056668556],"about_ca_topic_score_codex":0.0005753266,"about_ca_topic_score_gemma":0.0007743864,"teacher_disagreement_score":0.00059767964,"about_ca_system_score_codex":0.0003717457,"about_ca_system_score_gemma":0.000619448,"threshold_uncertainty_score":0.0027157068},"labels":[],"label_agreement":null},{"id":"W4400581866","doi":"10.3390/s24144510","title":"Potential of a New, Flexible Electrode sEMG System in Detecting Electromyographic Activation in Low Back Muscles during Clinical Tests: A Pilot Study on Wearables for Pain Management","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Réseau québécois de recherche sur la douleur; Canada First Research Excellence Fund; Centre for Interdisciplinary Research in Rehabilitation; Université Laval","keywords":"Wearable computer; Electromyography; Physical medicine and rehabilitation; Medicine; Biomedical engineering; Computer science; Embedded system","score_opus":0.020987569744793752,"score_gpt":0.2663541198652911,"score_spread":0.24536655012049735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400581866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98577523,0.00036314543,0.013091663,0.00006119386,0.00004237979,0.000159114,0.000076794204,0.000042623185,0.0003878116],"genre_scores_gemma":[0.98627,0.0002152125,0.012728157,0.00008802645,0.000031267744,0.00010252963,0.000060638322,0.0000068763447,0.00049722166],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99947816,0.00023899821,0.00003115164,0.00009877074,0.000112003494,0.000040942476],"domain_scores_gemma":[0.9992561,0.0003118052,0.00007857167,0.00007134852,0.0002148668,0.00006725136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000835749,0.0005471149,0.0003673058,0.00036321903,0.00011929856,0.00027824388,0.00042483967,0.0006167049,0.0013717853],"category_scores_gemma":[0.0013741212,0.00014585882,0.0003064034,0.00016595631,0.0002781195,0.00044334057,0.00030248938,0.00020490254,0.0002063597],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004608932,0.0021079653,0.04654523,0.00086986047,0.00019652279,0.00080434367,0.00061320345,0.001277031,0.8021212,0.00016986506,0.0003032017,0.1403827],"study_design_scores_gemma":[0.0010691544,0.15210076,0.5237474,0.00019476986,0.0009309514,0.0073780445,0.0015019033,0.030776622,0.27632514,0.00050392235,0.0053369594,0.00013442512],"about_ca_topic_score_codex":0.00022208304,"about_ca_topic_score_gemma":0.000450027,"teacher_disagreement_score":0.0013717853,"about_ca_system_score_codex":0.000097829245,"about_ca_system_score_gemma":0.00012471853,"threshold_uncertainty_score":0.004589021},"labels":[],"label_agreement":null},{"id":"W4400724777","doi":"10.3390/s24144631","title":"Sensor-Enhanced Smart Gripper Development for Automated Meat Processing","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Magyar Tudományos Akadémia; European Commission","keywords":"Grippers; Automation; Mechatronics; Software deployment; Robot; Engineering; Process (computing); Smart material; Systems engineering; Computer science; Artificial intelligence; Embedded system; Control engineering; Mechanical engineering; Software engineering","score_opus":0.041614925328348665,"score_gpt":0.3171161586620753,"score_spread":0.27550123333372667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400724777","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00219617,0.9813193,0.010548866,0.00014161241,0.00035266846,0.00004686648,0.000058856433,0.00008525929,0.0052503464],"genre_scores_gemma":[0.01055806,0.9694792,0.0123079065,0.0002775021,0.0001783143,0.00006823746,0.00018404111,0.000021901858,0.0069248388],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996911,0.000026489224,0.000032901356,0.00007000752,0.00015566636,0.000023899272],"domain_scores_gemma":[0.99981993,0.000065307664,0.000036894082,0.000011962914,0.00005500695,0.000010793482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046461212,0.0009225111,0.0008986656,0.0018173003,0.00017242238,0.00069682207,0.00095355173,0.0012040323,0.002694811],"category_scores_gemma":[0.00049322215,0.00053549465,0.0006769536,0.0014984165,0.00024799068,0.0013351135,0.0005964254,0.0010044201,0.0023122323],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004263462,0.00010167413,0.00014977233,0.016094727,0.00006765075,0.00023971443,0.000051030755,0.0015355806,0.02966106,0.005587163,0.0066817915,0.9397871],"study_design_scores_gemma":[0.000015666785,0.0004931675,0.0013304106,0.0024950064,0.00014257488,0.0019114286,0.000057238336,0.0019060703,0.029482247,0.0021327036,0.9599689,0.000064545944],"about_ca_topic_score_codex":0.00038962223,"about_ca_topic_score_gemma":0.00045989163,"teacher_disagreement_score":0.002694811,"about_ca_system_score_codex":0.00027540643,"about_ca_system_score_gemma":0.0004871956,"threshold_uncertainty_score":0.009015083},"labels":[],"label_agreement":null},{"id":"W4400876023","doi":"10.3390/s24144744","title":"The Overlay, a New Solution for Volume Variations in the Residual Limb for Individuals with a Transtibial Amputation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Institut de Réadaptation en Déficience Physique de Québec; Mitacs","keywords":"Amputation; Overlay; Residual; Volume (thermodynamics); Residual volume; Physical medicine and rehabilitation; Computer science; Artificial limbs; Biomedical engineering; Medicine; Surgery; Prosthesis; Artificial intelligence; Physics; Internal medicine; Algorithm; Operating system","score_opus":0.010487061751772178,"score_gpt":0.23878752252967408,"score_spread":0.2283004607779019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400876023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8851662,0.009348076,0.098406605,0.00063342793,0.00042701475,0.00032741574,0.0004924739,0.0014361752,0.0037625479],"genre_scores_gemma":[0.937037,0.0032874583,0.054746903,0.00031931294,0.00026282453,0.00026503572,0.00047653183,0.00014293448,0.0034620045],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995096,0.000083272746,0.000049492515,0.000063791194,0.0002502046,0.000043663],"domain_scores_gemma":[0.9995977,0.00013008446,0.000100970836,0.000057576184,0.0000630905,0.000050638057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007051908,0.00057351193,0.00041704535,0.0011400806,0.00019787475,0.0004535812,0.0006161404,0.00039757337,0.0030057488],"category_scores_gemma":[0.0017211253,0.00015049506,0.0006490218,0.00038473395,0.00037282298,0.00065097347,0.0009019395,0.0004582124,0.00042400218],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023132707,0.0009550827,0.010398287,0.0015840493,0.00021151954,0.0019106343,0.00050832675,0.0022438422,0.24895637,0.0005847553,0.0039879833,0.7263459],"study_design_scores_gemma":[0.0023175972,0.069963746,0.3211914,0.0016634021,0.0027389475,0.12918407,0.0022787782,0.04168257,0.27888772,0.0036321545,0.14587015,0.00058948435],"about_ca_topic_score_codex":0.00035377554,"about_ca_topic_score_gemma":0.0006153786,"teacher_disagreement_score":0.0030057488,"about_ca_system_score_codex":0.0001267988,"about_ca_system_score_gemma":0.00024435206,"threshold_uncertainty_score":0.010055184},"labels":[],"label_agreement":null},{"id":"W4400876131","doi":"10.3390/s24144707","title":"Deformation Estimation of Textureless Objects from a Single Image","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Polygon mesh; Artificial intelligence; Computer science; Vertex (graph theory); Computer vision; Deformation (meteorology); Graph; Image (mathematics); RGB color model; Chamfer (geometry); Pattern recognition (psychology); Mathematics; Geometry; Computer graphics (images); Theoretical computer science","score_opus":0.006796845941283476,"score_gpt":0.20521395347641092,"score_spread":0.19841710753512745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400876131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23235935,0.0002531159,0.7627118,0.00010477759,0.00006542897,0.00005865865,0.00021353825,0.002003283,0.0022300365],"genre_scores_gemma":[0.8316783,0.00026018772,0.16571647,0.000048581678,0.000021892469,0.000035380886,0.00043759908,0.00012937817,0.001672269],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973637,0.00001926839,0.000008324903,0.00007884165,0.00012527082,0.000031856423],"domain_scores_gemma":[0.9997315,0.000043363176,0.00005308049,0.0000903573,0.00006617153,0.000015569542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022485909,0.0005377852,0.0004515555,0.00079167786,0.000168108,0.00054028345,0.0004979465,0.0005265418,0.0010215524],"category_scores_gemma":[0.00081888185,0.00030366916,0.00044620136,0.0004951816,0.00033520855,0.0005906927,0.0005576597,0.0003839916,0.00057300186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000337043,0.000108058055,0.0049944296,0.0001450511,0.000065402855,0.00019330437,0.00014641897,0.14917028,0.39968845,0.0013872437,0.0015115753,0.44225273],"study_design_scores_gemma":[0.000008609465,0.00009912246,0.011970725,0.00001788911,0.00002045369,0.00034392372,0.000076448996,0.84144616,0.14276753,0.0010594309,0.0021573282,0.000032316315],"about_ca_topic_score_codex":0.0016978476,"about_ca_topic_score_gemma":0.0023652709,"teacher_disagreement_score":0.0016978476,"about_ca_system_score_codex":0.00039226215,"about_ca_system_score_gemma":0.00038402987,"threshold_uncertainty_score":0.003417492},"labels":[],"label_agreement":null},{"id":"W4400876177","doi":"10.3390/s24144706","title":"Automated Detection of In-Home Activities with Ultra-Wideband Sensors","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Computer science; Wideband; Embedded system; Real-time computing; Telecommunications; Engineering; Electrical engineering","score_opus":0.013041109121908851,"score_gpt":0.23766839263428075,"score_spread":0.2246272835123719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400876177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7059235,0.0007876192,0.2871481,0.00017360985,0.00009674037,0.00007437146,0.00054955063,0.0020661626,0.0031803201],"genre_scores_gemma":[0.9507055,0.00026863496,0.047443073,0.000057683104,0.000026732558,0.00003709746,0.00026937536,0.000019626923,0.0011722248],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997664,0.00005649988,0.0000136543895,0.000061783656,0.00007165806,0.000029921563],"domain_scores_gemma":[0.99969923,0.00010670307,0.00006299541,0.000031233863,0.000081863516,0.000017992823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020733023,0.00047589355,0.00036455062,0.000656078,0.00012447056,0.00046052685,0.00040279442,0.00038393796,0.00047042582],"category_scores_gemma":[0.0008706159,0.00014283907,0.00019950625,0.00043372845,0.000115333656,0.0004085941,0.00038270248,0.00021089452,0.0004129479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077988615,0.0005207295,0.10094054,0.00025388462,0.00015807441,0.0004731449,0.00038297317,0.045050517,0.12982883,0.0006639893,0.0035743935,0.71737295],"study_design_scores_gemma":[0.000042343403,0.0005131959,0.13235241,0.00006768548,0.0000964771,0.00063425134,0.0004241502,0.78982323,0.07131049,0.0011797653,0.0034991244,0.000056913243],"about_ca_topic_score_codex":0.0020135946,"about_ca_topic_score_gemma":0.0037712154,"teacher_disagreement_score":0.0020135946,"about_ca_system_score_codex":0.00017584246,"about_ca_system_score_gemma":0.0001888783,"threshold_uncertainty_score":0.004003823},"labels":[],"label_agreement":null},{"id":"W4400876601","doi":"10.3390/s24144702","title":"Design and Performance Analysis of Compact Printed Ridge Gap Waveguide Phase Shifters for Millimeter-Wave Systems","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Apollo Microwaves (Canada)","funders":"King Saud University","keywords":"Extremely high frequency; Ridge; Millimeter; Phase (matter); Waveguide; Materials science; Optoelectronics; Electronic engineering; Optics; Engineering; Electrical engineering; Computer science; Physics; Geology","score_opus":0.037234818441674776,"score_gpt":0.2567991239704797,"score_spread":0.21956430552880493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400876601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38236567,0.002056459,0.599812,0.00031330826,0.00008131429,0.00011477239,0.00012595355,0.0007313001,0.014399167],"genre_scores_gemma":[0.9285685,0.00047135592,0.067054115,0.000027518356,0.000022278413,0.00006168732,0.00005603843,0.000038741426,0.003699819],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975735,0.00004084969,0.000008725671,0.00003726472,0.00013564627,0.00002021566],"domain_scores_gemma":[0.9997594,0.00007953417,0.00008036732,0.000029014656,0.000044811757,0.000006977896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030477074,0.0003893224,0.00029527844,0.00017612318,0.00012931453,0.00054494204,0.0003933565,0.00051623245,0.0011812306],"category_scores_gemma":[0.0004898487,0.00019275094,0.0003016171,0.00017418739,0.0001831108,0.000540633,0.00017209715,0.0002168422,0.0005017665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030978673,0.00009040094,0.00082028384,0.00037007098,0.00009350847,0.00020949308,0.00013858154,0.20659935,0.7179822,0.014573631,0.0007197323,0.058092915],"study_design_scores_gemma":[0.000036757883,0.0007170096,0.0011435904,0.000022122951,0.00003721335,0.00021736383,0.00003789925,0.75521195,0.23525101,0.0011563955,0.006145722,0.000022967817],"about_ca_topic_score_codex":0.00022285343,"about_ca_topic_score_gemma":0.0003083966,"teacher_disagreement_score":0.0011812306,"about_ca_system_score_codex":0.00042188747,"about_ca_system_score_gemma":0.00019775458,"threshold_uncertainty_score":0.0039516687},"labels":[],"label_agreement":null},{"id":"W4400974019","doi":"10.3390/s24154848","title":"Inertial Sensor-Based Quantification of Movement Symmetry in Trotting Warmblood Show-Jumping Horses after “Limb-by-Limb” Re-Shoeing of Forelimbs with Rolled Rocker Shoes","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Veterinary Equine Medical Research","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Warmblood; Jumping; Medicine; Physical medicine and rehabilitation; Anatomy; Movement (music); Geology; Physics; Acoustics; Horse","score_opus":0.04643866973985964,"score_gpt":0.3409883582560182,"score_spread":0.29454968851615854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400974019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981962,0.000043550062,0.0015095678,0.0000036225101,0.0000029539995,0.00002114349,0.00007805389,0.000015638121,0.00012917418],"genre_scores_gemma":[0.997244,0.000042591044,0.0021734352,0.0000069518355,0.000005714815,0.000042143605,0.00023149891,0.0000069866023,0.0002467176],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996929,0.00006870938,0.000023571214,0.00007002454,0.000092796465,0.000051969826],"domain_scores_gemma":[0.9995747,0.000077320656,0.00015182603,0.000034773293,0.000108665365,0.00005271493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049627264,0.00026543142,0.00031231495,0.00072319404,0.00012579454,0.00031114786,0.00020532578,0.00028741307,0.0007758692],"category_scores_gemma":[0.0011338606,0.00018964814,0.00019310671,0.0003238126,0.00027805957,0.00022516352,0.00023844189,0.00016736923,0.0001773314],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023780307,0.0003807139,0.32864642,0.0001595226,0.00015471948,0.00021988132,0.00077655714,0.001000741,0.6244989,0.00006767456,0.00022718598,0.04148973],"study_design_scores_gemma":[0.000011945377,0.0009906513,0.97527945,0.0000044988287,0.000030452315,0.000119250886,0.0001266023,0.0026509373,0.02066096,0.000015382437,0.000100051,0.000009835974],"about_ca_topic_score_codex":0.001985201,"about_ca_topic_score_gemma":0.0040966556,"teacher_disagreement_score":0.001985201,"about_ca_system_score_codex":0.00014315308,"about_ca_system_score_gemma":0.00012889289,"threshold_uncertainty_score":0.003947258},"labels":[],"label_agreement":null},{"id":"W4400984810","doi":"10.3390/s24154818","title":"Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Routing protocol; Computer science; Computer network; Zone Routing Protocol; Reinforcement learning; Network packet; Smart grid; Distributed computing; IPv6; Enhanced Interior Gateway Routing Protocol; Link-state routing protocol; Engineering; The Internet; Machine learning","score_opus":0.0281007632149584,"score_gpt":0.3279905415279558,"score_spread":0.2998897783129974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400984810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008925632,0.00038408636,0.9841309,0.00067310716,0.00016785196,0.00020960878,0.000090213536,0.002145882,0.0032727004],"genre_scores_gemma":[0.7114638,0.00069425063,0.2798194,0.001191786,0.00016384643,0.00074327353,0.00047954623,0.0002489208,0.0051952065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989503,0.0003978469,0.00007921451,0.00014375734,0.00033652844,0.00009244277],"domain_scores_gemma":[0.9978682,0.0008821692,0.00027277137,0.000353756,0.0005349744,0.00008817078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002450931,0.00045735275,0.00057507906,0.0007559259,0.0007719086,0.0011535464,0.0017127506,0.0011120178,0.0020862462],"category_scores_gemma":[0.005545177,0.00020180573,0.00036654394,0.00070084305,0.0012554963,0.0016760245,0.0015258155,0.001551042,0.0006864802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005472798,0.00033975203,0.0017915613,0.00045051528,0.00010859888,0.00035601054,0.00042547612,0.3506055,0.02551697,0.118840165,0.023688765,0.4773294],"study_design_scores_gemma":[0.000055553042,0.0002430946,0.00023949929,0.00003234243,0.000022677184,0.0001617382,0.000032484433,0.95006406,0.008996465,0.026662799,0.013450301,0.000038970804],"about_ca_topic_score_codex":0.0017697731,"about_ca_topic_score_gemma":0.0021018072,"teacher_disagreement_score":0.002450931,"about_ca_system_score_codex":0.0009952873,"about_ca_system_score_gemma":0.001452454,"threshold_uncertainty_score":0.012961924},"labels":[],"label_agreement":null},{"id":"W4401108328","doi":"10.3390/s24154944","title":"Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Nitrogen fixation; Rhizobia; Raman spectroscopy; Bacteria; Nitrogen; Sustainable agriculture; Chemistry; Materials science; Environmental chemistry; Nanotechnology; Biology; Agriculture; Ecology; Physics","score_opus":0.035687281708951836,"score_gpt":0.30872860263168284,"score_spread":0.273041320922731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401108328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93728083,0.0011469442,0.06006486,0.000079622325,0.000014938393,0.000018341485,0.00020613185,0.00015114549,0.0010370518],"genre_scores_gemma":[0.9717243,0.000602766,0.027028915,0.000023903907,0.0000046222435,0.000015236371,0.00015549357,0.000012730708,0.00043202296],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984753,0.000029372226,0.0000046117934,0.000054377295,0.00004893334,0.000015079148],"domain_scores_gemma":[0.99992514,0.000035631667,0.000018154742,0.0000047352646,0.000012912272,0.0000034531788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040570679,0.0004296221,0.0001674754,0.00027415418,0.00013387586,0.00020234694,0.0002074457,0.00023701596,0.00023140958],"category_scores_gemma":[0.00032852945,0.00014199007,0.00033533733,0.0002464569,0.00015577694,0.00023027278,0.0001949131,0.00024972632,0.00007968433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052748026,0.000027423208,0.0035481756,0.00008345731,0.00003535325,0.000037067522,0.000038025526,0.0081446245,0.9747478,0.00027329466,0.000045448578,0.01296665],"study_design_scores_gemma":[0.000012834306,0.00028164842,0.034372818,0.00001849369,0.000060678565,0.00020902495,0.00013142994,0.33702633,0.62491924,0.0010444224,0.0018549826,0.000068177804],"about_ca_topic_score_codex":0.002476492,"about_ca_topic_score_gemma":0.0049072145,"teacher_disagreement_score":0.002476492,"about_ca_system_score_codex":0.00024528388,"about_ca_system_score_gemma":0.00014592198,"threshold_uncertainty_score":0.004924178},"labels":[],"label_agreement":null},{"id":"W4401123146","doi":"10.3390/s24154928","title":"Chipless RFID Sensor for Measuring Time-Varying Electric Fields Using a Contactless Air-Filled Substrate-Integrated Waveguide Resonator","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Manitoba Hydro","keywords":"Resonator; Chipless RFID; Substrate (aquarium); Materials science; Waveguide; Optoelectronics; Acoustics; Electric field; Electrical engineering; Electronic engineering; Engineering; Physics","score_opus":0.03191756848443391,"score_gpt":0.245437644451334,"score_spread":0.2135200759669001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401123146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.361643,0.0040211426,0.6291893,0.00023621444,0.0005095548,0.00012410467,0.00023298603,0.0014116098,0.0026320885],"genre_scores_gemma":[0.66132134,0.0012685367,0.33269426,0.00023907732,0.000110929424,0.000080120815,0.00019931374,0.00006602914,0.0040204143],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994517,0.00008560216,0.000029859671,0.00017163041,0.00023536228,0.00002586225],"domain_scores_gemma":[0.99949384,0.00015478025,0.0001265695,0.00006605128,0.00013143547,0.00002733772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041068273,0.0005464692,0.00049099285,0.00029752555,0.00009850501,0.0004385152,0.0010912888,0.0006576612,0.00050264184],"category_scores_gemma":[0.00067312794,0.00024180573,0.00036281432,0.0002471499,0.0002588086,0.0009411572,0.00036936157,0.0003712734,0.0005118545],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049432583,0.000021275515,0.00047213415,0.000121306184,0.000018920651,0.000056845733,0.000024234872,0.00027511345,0.9841727,0.00023126965,0.00013257237,0.014424273],"study_design_scores_gemma":[0.000018627363,0.00038995693,0.0014499177,0.000008917319,0.000041276075,0.0006716306,0.000027109969,0.011613089,0.98233634,0.00007392334,0.0033398108,0.000029503548],"about_ca_topic_score_codex":0.0001113851,"about_ca_topic_score_gemma":0.00028335635,"teacher_disagreement_score":0.0010912888,"about_ca_system_score_codex":0.00017845142,"about_ca_system_score_gemma":0.00017327446,"threshold_uncertainty_score":0.002171874},"labels":[],"label_agreement":null},{"id":"W4401178773","doi":"10.3390/s24154953","title":"L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Test (biology); Random forest; Lower limb; Segmentation; Physical medicine and rehabilitation; Computer science; Inertial measurement unit; Machine learning; Artificial intelligence; Psychology; Algorithm; Medicine; Surgery","score_opus":0.03322604443660923,"score_gpt":0.37496870065576254,"score_spread":0.34174265621915334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401178773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23889789,0.0006666195,0.75052345,0.00034439622,0.00009262777,0.00066483486,0.0005088483,0.006679298,0.0016220127],"genre_scores_gemma":[0.73340833,0.00016849874,0.26029617,0.00029356364,0.000046822774,0.0006843376,0.001880739,0.0002050122,0.003016483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992906,0.00021215226,0.00006277958,0.00022615041,0.00008359669,0.00012469171],"domain_scores_gemma":[0.9976804,0.0014858348,0.00015421605,0.000108772256,0.0004750488,0.00009563381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029018247,0.001167773,0.001043377,0.0012021804,0.00059047964,0.0006152821,0.000988612,0.0013210451,0.0019186803],"category_scores_gemma":[0.0053133694,0.00037967347,0.0010393308,0.00048571828,0.00031008504,0.0005145435,0.00046069152,0.0011304406,0.0010268791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014644136,0.0010083079,0.019863734,0.00011693977,0.00021489695,0.00037978025,0.0002082211,0.24724919,0.014522279,0.00056052406,0.006812268,0.7075995],"study_design_scores_gemma":[0.00006847795,0.00016654645,0.0036731185,0.00001961464,0.000034063214,0.00009200911,0.00003393142,0.9920487,0.0026621905,0.00071373425,0.00046658132,0.000020926103],"about_ca_topic_score_codex":0.0182366,"about_ca_topic_score_gemma":0.022921642,"teacher_disagreement_score":0.0182366,"about_ca_system_score_codex":0.0005825762,"about_ca_system_score_gemma":0.0015385491,"threshold_uncertainty_score":0.036260903},"labels":[],"label_agreement":null},{"id":"W4401214088","doi":"10.3390/s24154984","title":"Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Capacitive sensing; Nondestructive testing; Extant taxon; Computer science; Systems engineering; Engineering; Electrical engineering; Physics","score_opus":0.13986255740993753,"score_gpt":0.4300823836170437,"score_spread":0.2902198262071062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401214088","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027506685,0.997271,0.00058002886,0.0002883902,0.00016650776,0.000008690452,0.00002594846,0.000011227013,0.0013732143],"genre_scores_gemma":[0.0011864116,0.997696,0.0005196409,0.00013595655,0.000102925704,0.000007714813,0.000027306978,0.0000026444563,0.0003214811],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934834,0.00009551856,0.00009475951,0.00013343278,0.00028126242,0.000046742967],"domain_scores_gemma":[0.9976084,0.0015892182,0.00024036638,0.000054368178,0.00044803432,0.000059700713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017348182,0.0010304148,0.0014271934,0.0045163804,0.00045100597,0.0017601515,0.000996714,0.0012508723,0.004467421],"category_scores_gemma":[0.0024498946,0.0005550052,0.0009382469,0.004231204,0.0007479061,0.0021062493,0.0008170697,0.0015145232,0.0015885601],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051573326,0.000088449975,0.00045091962,0.057114013,0.00012501837,0.00021642518,0.00022497706,0.0006758226,0.005077654,0.008976908,0.013824093,0.9131742],"study_design_scores_gemma":[0.000007115889,0.0001615507,0.0010880003,0.011239206,0.0003131202,0.001007474,0.00024320143,0.0002859973,0.002574425,0.0031412842,0.97989035,0.000048296482],"about_ca_topic_score_codex":0.0012314146,"about_ca_topic_score_gemma":0.002198397,"teacher_disagreement_score":0.0045163804,"about_ca_system_score_codex":0.0006420152,"about_ca_system_score_gemma":0.002229816,"threshold_uncertainty_score":0.01494503},"labels":[],"label_agreement":null},{"id":"W4401356469","doi":"10.3390/s24165095","title":"PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Federated learning; Stroke (engine); Consensus conference; Artificial intelligence; Engineering; Library science; Aerospace engineering","score_opus":0.02608148196771698,"score_gpt":0.3090426644041122,"score_spread":0.28296118243639523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401356469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022255674,0.00016726524,0.97447896,0.000444841,0.000053386593,0.0000682929,0.000093829265,0.0005939015,0.001843761],"genre_scores_gemma":[0.87956136,0.00013633334,0.114934325,0.00036036118,0.000040428935,0.00018648851,0.00023048009,0.000056136283,0.0044940477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.00018826318,0.0000468625,0.00027344446,0.00020745242,0.00012717694],"domain_scores_gemma":[0.9986022,0.0005364107,0.00013058801,0.00013343287,0.00049191137,0.000105444225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016160212,0.0006745427,0.0010883036,0.00056417414,0.00073964236,0.0011836297,0.0022577913,0.0014217679,0.0023001025],"category_scores_gemma":[0.0040434008,0.00028392207,0.00080737163,0.0005617188,0.0006907096,0.0016576785,0.0017009194,0.0016644892,0.00047395844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012322707,0.000090766596,0.0014222191,0.000040691244,0.00004817269,0.000092749964,0.00009476719,0.91948724,0.0012697529,0.0056226877,0.0014761297,0.07023159],"study_design_scores_gemma":[0.000005025124,0.000019987772,0.00008641361,0.0000029138541,0.000004443784,0.000010102635,0.000010606257,0.9969709,0.00023596123,0.0024322576,0.00021777108,0.0000036082738],"about_ca_topic_score_codex":0.012914576,"about_ca_topic_score_gemma":0.011490848,"teacher_disagreement_score":0.012914576,"about_ca_system_score_codex":0.001400567,"about_ca_system_score_gemma":0.0021158052,"threshold_uncertainty_score":0.025678813},"labels":[],"label_agreement":null},{"id":"W4401426379","doi":"10.3390/s24165117","title":"Dual-Band Antenna Array Fed by Ridge Gap Waveguide with Dual-Periodic Interdigital-Pin Bed of Nails","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Key Research and Development Program of China","keywords":"Multi-band device; Dual (grammatical number); Antenna (radio); Ridge; Materials science; Waveguide; Acoustics; Optics; Optoelectronics; Electrical engineering; Physics; Engineering; Geology","score_opus":0.00821576946174072,"score_gpt":0.20626323039542738,"score_spread":0.19804746093368666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401426379","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84540856,0.00037040937,0.14562409,0.0001684412,0.00017544923,0.000035394107,0.00017473304,0.0014286718,0.006614208],"genre_scores_gemma":[0.8987478,0.00018095513,0.096047066,0.00005453062,0.000030544714,0.000059514612,0.00016390411,0.000041227322,0.0046744333],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997836,0.000024376894,0.0000117423415,0.00007974564,0.00006115413,0.0000393193],"domain_scores_gemma":[0.9997924,0.000021275884,0.00006785873,0.000040693874,0.00004986046,0.000028069362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098109784,0.00044969463,0.0004845841,0.00029511942,0.00010551406,0.00038187503,0.0006784529,0.00042007476,0.000796429],"category_scores_gemma":[0.00017179598,0.0003909505,0.0003330558,0.00036255913,0.00016824575,0.00043866405,0.00044451636,0.00023440522,0.00062169466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103500555,0.000034169552,0.00089852075,0.00006398603,0.000030156993,0.00010679223,0.00003681083,0.00219695,0.9823828,0.00049985776,0.00032975324,0.013316524],"study_design_scores_gemma":[0.000043145214,0.0006032347,0.0027059042,0.0000110713645,0.0000509343,0.00055001804,0.0000628031,0.042836897,0.94802386,0.00018961984,0.004877907,0.00004460152],"about_ca_topic_score_codex":0.00023559519,"about_ca_topic_score_gemma":0.00044485845,"teacher_disagreement_score":0.000796429,"about_ca_system_score_codex":0.00022988253,"about_ca_system_score_gemma":0.00019173216,"threshold_uncertainty_score":0.0026643276},"labels":[],"label_agreement":null},{"id":"W4401510550","doi":"10.3390/s24165186","title":"A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hilbert–Huang transform; Fault (geology); Rotor (electric); Fault detection and isolation; Signature (topology); Computer science; Noise (video); Induction motor; Data acquisition; Bar (unit); Engineering; Artificial intelligence; Control engineering; Pattern recognition (psychology); White noise; Voltage; Electrical engineering; Actuator","score_opus":0.011311622405145846,"score_gpt":0.27979999341972583,"score_spread":0.26848837101458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401510550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049421243,0.00070468965,0.94738156,0.00012722854,0.00011551989,0.000040345923,0.00009826624,0.00046632852,0.0016447507],"genre_scores_gemma":[0.39796552,0.0006854527,0.598334,0.00010921527,0.000045605222,0.00005538045,0.00025574054,0.000042355736,0.0025067844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976414,0.00003611166,0.000018065366,0.00004651086,0.0001218319,0.000013359188],"domain_scores_gemma":[0.99975175,0.00007211923,0.000042218366,0.000035875466,0.00008728033,0.000010788239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027110628,0.000435374,0.00029169387,0.0007812056,0.00012908637,0.0002809375,0.00033736712,0.00041735615,0.0009823177],"category_scores_gemma":[0.00096200174,0.00014595217,0.00024359467,0.0005765763,0.00018117798,0.00052450935,0.0003042334,0.0003623682,0.00038759748],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030356203,0.000077832585,0.002222771,0.0002470914,0.000030873747,0.00020912617,0.0000861423,0.0074141566,0.2987076,0.0018577394,0.0013502421,0.6874929],"study_design_scores_gemma":[0.000060655784,0.000662836,0.015647003,0.000074935895,0.00006178178,0.002966258,0.00013526774,0.55724144,0.39652166,0.0017172854,0.02483096,0.000080036014],"about_ca_topic_score_codex":0.00023824372,"about_ca_topic_score_gemma":0.00043809446,"teacher_disagreement_score":0.0009823177,"about_ca_system_score_codex":0.00017219762,"about_ca_system_score_gemma":0.00016579342,"threshold_uncertainty_score":0.0032861829},"labels":[],"label_agreement":null},{"id":"W4401511223","doi":"10.3390/s24165168","title":"SmartVR Pointer: Using Smartphones and Gaze Orientation for Selection and Navigation in Virtual Reality","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Pointer (user interface); Computer science; Gaze; Laser pointer; Virtual reality; Computer vision; Human–computer interaction; Phone; Mobile phone; Optical head-mounted display; Artificial intelligence; Computer graphics (images)","score_opus":0.02127685964199727,"score_gpt":0.3094027750414207,"score_spread":0.28812591539942345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401511223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10035748,0.0033829182,0.8502094,0.0005453446,0.0003935878,0.0005384753,0.000881639,0.025162477,0.018528666],"genre_scores_gemma":[0.53020024,0.002985075,0.43891293,0.0008359415,0.00018748191,0.00068979716,0.0012301435,0.0011065616,0.023851758],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994331,0.00011163854,0.000037224967,0.0001236445,0.00022279393,0.00007159961],"domain_scores_gemma":[0.99930215,0.00016583528,0.000067991714,0.00013045914,0.00025713674,0.00007636134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055845064,0.0008299075,0.00043190995,0.0007056934,0.0002519959,0.0008648583,0.0011281902,0.00096642,0.008383433],"category_scores_gemma":[0.0017830144,0.00042744886,0.0006368413,0.00035771925,0.00035954156,0.0016290142,0.0020086074,0.0005449701,0.0024891696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008199639,0.00020992245,0.006014924,0.0011457454,0.00015535101,0.0013860076,0.002295813,0.0033193023,0.25893453,0.008824401,0.023060212,0.6938339],"study_design_scores_gemma":[0.0008005613,0.0056704315,0.06406699,0.0011662125,0.00096228416,0.015434543,0.0019242727,0.19176492,0.25789413,0.009253361,0.44945922,0.001603082],"about_ca_topic_score_codex":0.0036490993,"about_ca_topic_score_gemma":0.004179147,"teacher_disagreement_score":0.008383433,"about_ca_system_score_codex":0.00020997458,"about_ca_system_score_gemma":0.00041302288,"threshold_uncertainty_score":0.028045356},"labels":[],"label_agreement":null},{"id":"W4401511719","doi":"10.3390/s24165210","title":"IoT Forensics: Current Perspectives and Future Directions","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digital forensics; Network forensics; Computer security; Computer science; Internet of Things; Data science; Field (mathematics); Identification (biology); Cloud computing; Computer forensics; Variety (cybernetics); Digital evidence; Internet privacy","score_opus":0.00814556354847022,"score_gpt":0.23602974202910995,"score_spread":0.22788417848063974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401511719","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013508258,0.86531925,0.0051374375,0.11171165,0.0034491762,0.000051814306,0.00013666922,0.00011986619,0.012723346],"genre_scores_gemma":[0.019660262,0.95049363,0.009680357,0.012820635,0.004496438,0.00013980827,0.00022187231,0.00005374653,0.0024332365],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949569,0.002498553,0.000405259,0.00052301417,0.0011468915,0.00046935183],"domain_scores_gemma":[0.94828147,0.03840086,0.0018925912,0.0012072531,0.008305638,0.0019120984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016350938,0.0010975557,0.0012022365,0.006297059,0.0034788472,0.010138697,0.0028029096,0.009161542,0.015376672],"category_scores_gemma":[0.025889628,0.00045966945,0.0012118332,0.004662108,0.008525604,0.021299819,0.0053615565,0.005736863,0.0041873846],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011370659,0.00019729354,0.0023876685,0.013153681,0.000048796373,0.00078422803,0.00429044,0.0010927113,0.000532066,0.1744361,0.080789104,0.72217417],"study_design_scores_gemma":[0.000017487948,0.00009511302,0.0015776452,0.03537629,0.00006065537,0.0018863574,0.019218031,0.00096773444,0.000534709,0.1921757,0.7479647,0.00012557075],"about_ca_topic_score_codex":0.0032148731,"about_ca_topic_score_gemma":0.0051178816,"teacher_disagreement_score":0.016350938,"about_ca_system_score_codex":0.003990606,"about_ca_system_score_gemma":0.013252296,"threshold_uncertainty_score":0.08647305},"labels":[],"label_agreement":null},{"id":"W4401618373","doi":"10.3390/s24165280","title":"Wearable Multi-Sensor Positioning Prototype for Rowing Technique Evaluation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rowing; Inertial measurement unit; GNSS applications; Wearable computer; Computer science; Simulation; Satellite system; Positioning system; Real-time computing; Engineering; Global Positioning System; Artificial intelligence; Embedded system; Telecommunications","score_opus":0.022539880036570228,"score_gpt":0.2910392863909901,"score_spread":0.2684994063544199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401618373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4448794,0.00081306114,0.5471091,0.00019747374,0.00044905505,0.00067904976,0.00058187515,0.0021270209,0.003163872],"genre_scores_gemma":[0.8423258,0.00040513207,0.15164845,0.00011002312,0.00004517157,0.00038763907,0.00030425907,0.000051947158,0.0047216066],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995565,0.00011590788,0.00003434317,0.00010081859,0.0001629222,0.000029513118],"domain_scores_gemma":[0.99939954,0.00013248376,0.00006115326,0.00011867526,0.00024191705,0.000046156852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055593177,0.0006764237,0.0005555075,0.0004158754,0.00014195757,0.00038842697,0.0009754322,0.0006401644,0.003231993],"category_scores_gemma":[0.0012782363,0.00024874657,0.00041683967,0.00027199794,0.00016166568,0.00048130506,0.00034604885,0.00021452397,0.00075160747],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097200397,0.00028947185,0.0067253304,0.0013652274,0.00010191192,0.00067281694,0.00039084532,0.007021854,0.7786574,0.0006825199,0.0018154404,0.20130517],"study_design_scores_gemma":[0.0003699324,0.017875971,0.08269851,0.0003408728,0.0005567794,0.005398841,0.000804703,0.12364456,0.7425107,0.00077475375,0.024763007,0.0002614118],"about_ca_topic_score_codex":0.0002772004,"about_ca_topic_score_gemma":0.0004668576,"teacher_disagreement_score":0.003231993,"about_ca_system_score_codex":0.00012471947,"about_ca_system_score_gemma":0.00019164408,"threshold_uncertainty_score":0.010812104},"labels":[],"label_agreement":null},{"id":"W4401618971","doi":"10.3390/s24165274","title":"Correction: Golovko et al. Ambient Dose and Dose Rate Measurement in SNOLAB Underground Laboratory at Sudbury, Ontario, Canada. Sensors 2023, 23, 1945","year":2024,"lang":"en","type":"erratum","venue":"Sensors","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Snolab; Canadian Nuclear Laboratories","funders":"","keywords":"Dose rate; Environmental science; Remote sensing; Medical physics; Geology; Physics","score_opus":0.012959016835357745,"score_gpt":0.22659459292172165,"score_spread":0.2136355760863639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401618971","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024995208,0.0013064481,0.0010936956,0.027267996,0.95590234,0.000057459707,0.008132478,0.0010978705,0.004891804],"genre_scores_gemma":[0.032188065,0.014233399,0.020223046,0.09224022,0.2593267,0.0006441821,0.033046998,0.011502334,0.5365951],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939809,0.00035302844,0.0007983626,0.00071189326,0.0036946204,0.00046121355],"domain_scores_gemma":[0.9448289,0.0037455384,0.001683497,0.0030236985,0.045104146,0.0016142279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040161065,0.0031791045,0.0021480303,0.006091139,0.005054401,0.004295444,0.0045444095,0.0060762404,0.065832265],"category_scores_gemma":[0.05744028,0.001556983,0.0020377075,0.0041667866,0.0027164177,0.0022433794,0.00281028,0.008999694,0.041766096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013623631,0.0000022897304,0.000058891063,0.00008699087,0.00000624412,0.00005163154,0.00002617111,0.00002468884,0.00003899049,0.00019144172,0.9966994,0.0027996968],"study_design_scores_gemma":[0.000024198056,0.0000062122344,0.0010330403,0.00023214931,0.00003089511,0.00017102539,0.0000723712,0.00012750684,0.00032008628,0.00042513348,0.99752444,0.00003297096],"about_ca_topic_score_codex":0.32416287,"about_ca_topic_score_gemma":0.3181874,"teacher_disagreement_score":0.67583716,"about_ca_system_score_codex":0.00959975,"about_ca_system_score_gemma":0.018525753,"threshold_uncertainty_score":0.64455205},"labels":[],"label_agreement":null},{"id":"W4401698507","doi":"10.3390/s24165355","title":"Eddy Current Sensor Probe Design for Subsurface Defect Detection in Additive Manufacturing","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Eddy current; Surface roughness; Surface finish; Composite material; RADIUS; Fusion; Engineering; Computer science; Electrical engineering","score_opus":0.019636454252904754,"score_gpt":0.24456126630498196,"score_spread":0.2249248120520772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401698507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13001917,0.0011911864,0.8643397,0.00028930095,0.00019507881,0.00026732858,0.00011419983,0.0013308821,0.0022531813],"genre_scores_gemma":[0.62131244,0.00035939206,0.37499765,0.00020881106,0.00006073035,0.00021228188,0.0000970682,0.000049080805,0.002702524],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999329,0.000121652454,0.000034980454,0.00015055606,0.0003286042,0.00003529306],"domain_scores_gemma":[0.9990963,0.00020604962,0.00019909155,0.00008909123,0.00037077756,0.00003875167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000491459,0.00055802125,0.0005329364,0.0004125772,0.00014510847,0.00046672233,0.0012678977,0.000947402,0.0008447796],"category_scores_gemma":[0.0009445961,0.00034895123,0.0002761608,0.00025342588,0.0004009215,0.00083923514,0.0003194776,0.00031903808,0.00040961098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001299851,0.00003826464,0.00044333906,0.0001267201,0.000012458778,0.00005194743,0.000041828604,0.001258984,0.971684,0.0007281022,0.00033578125,0.025148591],"study_design_scores_gemma":[0.000050957922,0.0012781844,0.0023576461,0.000019756486,0.000044726716,0.00066400116,0.00004629981,0.06802011,0.9179954,0.00040982428,0.009056279,0.000056752277],"about_ca_topic_score_codex":0.00023973851,"about_ca_topic_score_gemma":0.00043965076,"teacher_disagreement_score":0.0012678977,"about_ca_system_score_codex":0.00051914057,"about_ca_system_score_gemma":0.00028844498,"threshold_uncertainty_score":0.003766656},"labels":[],"label_agreement":null},{"id":"W4401811731","doi":"10.3390/s24165387","title":"An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Ontario Agri-Food Innovation Alliance; Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Fire blight; PEAR; Orchard; Computer science; Channel (broadcasting); Pipeline (software); Artificial intelligence; Blight; Machine learning; Agricultural engineering; Engineering; Horticulture; Biology","score_opus":0.03769860287125405,"score_gpt":0.3418619954199564,"score_spread":0.30416339254870234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401811731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89444387,0.00116974,0.099094175,0.0003877328,0.00012238753,0.000095593525,0.00028377207,0.0021765963,0.0022261832],"genre_scores_gemma":[0.9690878,0.00016878427,0.029246015,0.00006456402,0.00001576074,0.000024876934,0.00030218513,0.00003042804,0.0010596013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996147,0.000096548734,0.000019920766,0.00012901574,0.00009240974,0.000047491627],"domain_scores_gemma":[0.9989693,0.00055472803,0.000066377535,0.00007306328,0.00027246133,0.00006411664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014622533,0.0011337132,0.00060528057,0.00047555706,0.0002559376,0.0005030274,0.0009206464,0.0009556841,0.0007051582],"category_scores_gemma":[0.00257745,0.00032586674,0.00055098935,0.0002772649,0.00025020848,0.00085603975,0.00045245385,0.00065613084,0.00021518607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012251426,0.0011198597,0.02953463,0.00020436877,0.00031960377,0.00019597319,0.000081745595,0.7550528,0.02350324,0.0005107366,0.0017186623,0.18653326],"study_design_scores_gemma":[0.000011177124,0.00014449048,0.002820327,0.0000040155346,0.000014748669,0.000016590522,0.000009295298,0.99416214,0.0026467724,0.00007728134,0.00008767025,0.000005514114],"about_ca_topic_score_codex":0.016107611,"about_ca_topic_score_gemma":0.014629961,"teacher_disagreement_score":0.016107611,"about_ca_system_score_codex":0.000728871,"about_ca_system_score_gemma":0.000469998,"threshold_uncertainty_score":0.03202772},"labels":[],"label_agreement":null},{"id":"W4401889312","doi":"10.3390/s24175488","title":"Electrochemical Aptasensor with Antifouling Properties for Label-Free Detection of Oxytetracycline","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Oxytetracycline; Aptamer; Detection limit; Maximum Residue Limit; European union; Food and drug administration; Chemistry; Electrochemistry; Amine gas treating; Residue (chemistry); Analyte; Chromatography; Combinatorial chemistry; Antibiotics; Electrode; Pharmacology; Biology; Biochemistry; Organic chemistry","score_opus":0.011542631303548967,"score_gpt":0.25817042737905715,"score_spread":0.2466277960755082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401889312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6088042,0.013106106,0.3715297,0.0007582078,0.0003889046,0.00030693877,0.0004568335,0.000990645,0.0036583627],"genre_scores_gemma":[0.7102745,0.007533826,0.2707666,0.0006719348,0.000100927624,0.00022102761,0.0004919786,0.00008755917,0.009851629],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992442,0.00015637791,0.00006150659,0.00016985786,0.00031697875,0.0000511588],"domain_scores_gemma":[0.9995752,0.00012587883,0.0000837462,0.000033052198,0.00014095702,0.000041140243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007036361,0.000789942,0.0004507562,0.00035911935,0.00018364952,0.0002848279,0.00053043547,0.00096711173,0.000747568],"category_scores_gemma":[0.00072755036,0.00032608368,0.00034766019,0.00022310982,0.00023457823,0.00051056466,0.00038313505,0.00087107637,0.00056266406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007846316,0.0000063216708,0.000020347525,0.000033008022,0.0000024534847,0.000014575681,0.000007294137,0.00003317506,0.9989421,0.000023268427,0.000014905746,0.0008947342],"study_design_scores_gemma":[0.0000031085865,0.00009696192,0.00030393546,0.000004368564,0.000008982734,0.00014529857,0.000007764322,0.0009872532,0.99728453,0.000025411558,0.0011254455,0.000006940068],"about_ca_topic_score_codex":0.00041616982,"about_ca_topic_score_gemma":0.0011567792,"teacher_disagreement_score":0.00096711173,"about_ca_system_score_codex":0.00034670616,"about_ca_system_score_gemma":0.00027997943,"threshold_uncertainty_score":0.0037212372},"labels":[],"label_agreement":null},{"id":"W4401891206","doi":"10.3390/s24175486","title":"Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Institut Universitaire de Gériatrie de Montréal; Université de Sherbrooke; Université du Québec à Montréal; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"","keywords":"Activities of daily living; Sitting; Physical medicine and rehabilitation; Ankle; Smartwatch; Computer science; Task (project management); Medicine; Kinematics; Segmentation; Physical therapy; Artificial intelligence; Wearable computer; Engineering; Embedded system; Surgery","score_opus":0.020368338838800588,"score_gpt":0.31239831994862044,"score_spread":0.29202998110981987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401891206","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9787637,0.000407443,0.019222872,0.000059390033,0.0000345077,0.00010761307,0.00048969407,0.0002712851,0.0006434643],"genre_scores_gemma":[0.9754047,0.00023179634,0.023121625,0.000057117682,0.000027048884,0.00011568697,0.0005258885,0.000011652602,0.00050437986],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980086,0.000050156,0.0000224359,0.00006853904,0.00003609192,0.000021979133],"domain_scores_gemma":[0.9996331,0.00013231797,0.00006104913,0.000019427842,0.00010979039,0.000044406774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003021101,0.0005531747,0.0005012082,0.001024646,0.00013752763,0.00034455856,0.00022507658,0.00048686343,0.00084065745],"category_scores_gemma":[0.0008870642,0.00016003884,0.00033290978,0.00033530185,0.00010918306,0.00024223399,0.00039398065,0.0001443072,0.00041388898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026841022,0.00050302513,0.5177326,0.00053033506,0.0002699583,0.0009169557,0.00063658354,0.0026309043,0.121209666,0.00008515484,0.0013788037,0.35142195],"study_design_scores_gemma":[0.00019662824,0.002507364,0.9226458,0.00007531061,0.0002646182,0.0029372582,0.00057289924,0.044500455,0.024696978,0.00020924999,0.0013280585,0.00006529161],"about_ca_topic_score_codex":0.001068146,"about_ca_topic_score_gemma":0.003404162,"teacher_disagreement_score":0.001068146,"about_ca_system_score_codex":0.00009221162,"about_ca_system_score_gemma":0.00012116337,"threshold_uncertainty_score":0.002812326},"labels":[],"label_agreement":null},{"id":"W4401891958","doi":"10.3390/s24175511","title":"Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canadian Institutes of Health Research","keywords":"Microphone; Breathing; Respiratory sounds; Medicine; Acoustics; Audiology; Auscultation; Noise (video); Computer science; Sound pressure; Anesthesia; Asthma; Physics; Cardiology; Internal medicine; Artificial intelligence","score_opus":0.025731294828065618,"score_gpt":0.30316597733929723,"score_spread":0.2774346825112316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401891958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17871861,0.000966524,0.81008893,0.00052152714,0.00061633124,0.0021913322,0.0006313176,0.0012503039,0.0050151288],"genre_scores_gemma":[0.50443965,0.0009220169,0.48558012,0.00036037934,0.00013872373,0.0027319395,0.00032403364,0.00007019016,0.0054329373],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875987,0.00022509908,0.00010210589,0.00031905918,0.0005132315,0.000080610494],"domain_scores_gemma":[0.99910164,0.00020902268,0.00011351531,0.000086523585,0.0004101677,0.00007909153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011139192,0.0008715193,0.0006789846,0.00059419445,0.00035323328,0.00065394613,0.0013837616,0.0014648346,0.0023620932],"category_scores_gemma":[0.0013723974,0.00042890353,0.0006351498,0.00029304996,0.00045046798,0.0005155597,0.00059691194,0.0003748203,0.000995588],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069435727,0.00027620653,0.005562177,0.001523987,0.000074958196,0.0009995879,0.0004468836,0.0052039865,0.88440955,0.0017359671,0.0013512248,0.09772113],"study_design_scores_gemma":[0.00040404883,0.010145453,0.036137152,0.00018014519,0.00038194263,0.0045332266,0.0006780068,0.12758218,0.7771912,0.0011309374,0.04129704,0.00033874193],"about_ca_topic_score_codex":0.000684291,"about_ca_topic_score_gemma":0.00075022713,"teacher_disagreement_score":0.0023620932,"about_ca_system_score_codex":0.00031369898,"about_ca_system_score_gemma":0.0008345354,"threshold_uncertainty_score":0.007902026},"labels":[],"label_agreement":null},{"id":"W4401978251","doi":"10.3390/s24165378","title":"Targeted FT-NIR and SERS Detection of Breast Cancer HER-II Biomarkers in Blood Serum Using PCB-Based Plasmonic Active Nanostructured Thin Film Label-Free Immunosensor Immobilized with Directional GNU-Conjugated Antibody","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Conjugated system; Plasmon; Materials science; Breast cancer; Nanotechnology; Chemistry; Optoelectronics; Cancer; Polymer; Medicine","score_opus":0.007650713576976944,"score_gpt":0.23995928558310034,"score_spread":0.2323085720061234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401978251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93736273,0.002084064,0.05722976,0.00023345811,0.00007144795,0.000055681194,0.0001791085,0.0003507356,0.0024331184],"genre_scores_gemma":[0.9468315,0.001297636,0.04749991,0.00013073876,0.000019442943,0.000036763668,0.0002323768,0.000025120447,0.0039265286],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985814,0.00002139016,0.000007497719,0.000037586324,0.00005358263,0.000021739255],"domain_scores_gemma":[0.999946,0.000008815386,0.000014357224,0.000006580505,0.00001705597,0.000007195868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016170145,0.0003137265,0.00014627498,0.00015802644,0.00007452232,0.00016797053,0.00026971023,0.0003330107,0.00040137427],"category_scores_gemma":[0.00017058903,0.00018384347,0.00021337018,0.000111604226,0.00014690735,0.00020626273,0.00014370643,0.00021176458,0.00026874818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018707118,0.0000057051743,0.00008794921,0.000016460395,0.0000016640635,0.000020846282,0.0000055544324,0.00006933774,0.99850935,0.000030073577,0.000021076266,0.0012132812],"study_design_scores_gemma":[0.0000034139607,0.000062338615,0.00054226624,9.576968e-7,0.0000040453883,0.000102423815,0.0000049166147,0.0011872139,0.9976088,0.00001222661,0.00046865724,0.0000026609769],"about_ca_topic_score_codex":0.000623975,"about_ca_topic_score_gemma":0.00082564575,"teacher_disagreement_score":0.000623975,"about_ca_system_score_codex":0.00030289486,"about_ca_system_score_gemma":0.0001509888,"threshold_uncertainty_score":0.0021976829},"labels":[],"label_agreement":null},{"id":"W4402049907","doi":"10.3390/s24175652","title":"A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Mitacs","keywords":"Pothole (geology); Artificial intelligence; Machine learning; Computer science; Road surface; Point cloud; Deep learning; Machine vision; Segmentation; Image processing; Object detection; Engineering; Image (mathematics)","score_opus":0.0254875856791774,"score_gpt":0.35882378522651315,"score_spread":0.33333619954733573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402049907","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015350556,0.9550177,0.035069246,0.00064261555,0.00070959,0.00006721258,0.00021153806,0.00025647494,0.00649071],"genre_scores_gemma":[0.008925154,0.96687126,0.01965038,0.00055246835,0.00071961235,0.00007001136,0.00045173793,0.000052842304,0.0027065147],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931884,0.00008094535,0.00010569443,0.00015000947,0.0003084583,0.0000361648],"domain_scores_gemma":[0.9985752,0.00073937734,0.00012510241,0.00005459344,0.0004666551,0.000039033184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010011189,0.0013851679,0.0013699874,0.0037797166,0.00033531434,0.0013545706,0.0014013175,0.0013767115,0.0038536715],"category_scores_gemma":[0.002301612,0.0007225333,0.0012358745,0.0035413827,0.00047444887,0.0026161328,0.00060503336,0.0010720147,0.002180108],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005584497,0.00007618274,0.00067421177,0.021253554,0.00016326697,0.0001880154,0.00009582624,0.0026888696,0.004531139,0.0046778154,0.023316788,0.9422785],"study_design_scores_gemma":[0.000020536465,0.00042045745,0.004041389,0.008012129,0.0005911914,0.0022525897,0.00018250197,0.012678506,0.007937288,0.0065517602,0.9571102,0.00020129951],"about_ca_topic_score_codex":0.0017939789,"about_ca_topic_score_gemma":0.0020310935,"teacher_disagreement_score":0.0038536715,"about_ca_system_score_codex":0.00049561507,"about_ca_system_score_gemma":0.001273075,"threshold_uncertainty_score":0.012891829},"labels":[],"label_agreement":null},{"id":"W4402054216","doi":"10.3390/s24175619","title":"HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Computer science; Cluster analysis; Dynamic time warping; Image warping; Synthetic data; Algorithm; Data mining; Artificial intelligence","score_opus":0.023029295719829958,"score_gpt":0.2302490800865179,"score_spread":0.20721978436668795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402054216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06559836,0.00011158997,0.92716974,0.0003131832,0.000085334505,0.00012848938,0.0017982404,0.0020637868,0.0027312706],"genre_scores_gemma":[0.8722772,0.00014787726,0.12155384,0.00012369826,0.000040136547,0.00029995665,0.002834482,0.0002098493,0.0025130084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966836,0.0000920336,0.000022019289,0.00007518767,0.00011409308,0.000028393435],"domain_scores_gemma":[0.9989942,0.0005708587,0.00011361724,0.000107739426,0.0001653673,0.00004822598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007001147,0.0005527652,0.00041145625,0.0005130569,0.00025211796,0.00072404667,0.0013188682,0.0007266603,0.001599765],"category_scores_gemma":[0.0033534183,0.00033522065,0.000611711,0.00059739343,0.00040114493,0.00073839794,0.0006931383,0.00072600617,0.00033711395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041485197,0.000024931684,0.0009749553,0.000022545111,0.000013211161,0.00003339019,0.000018307277,0.98750824,0.00071906723,0.0026687267,0.0007403634,0.0072347126],"study_design_scores_gemma":[0.0000031988463,0.0000067514834,0.00011392672,0.0000012014427,0.0000011683492,0.000007414007,0.000002652144,0.99862874,0.00022047393,0.0007821811,0.00022928984,0.000003134783],"about_ca_topic_score_codex":0.008721503,"about_ca_topic_score_gemma":0.008352062,"teacher_disagreement_score":0.008721503,"about_ca_system_score_codex":0.00057847705,"about_ca_system_score_gemma":0.0007259861,"threshold_uncertainty_score":0.017341495},"labels":[],"label_agreement":null},{"id":"W4402125847","doi":"10.3390/s24175687","title":"Optimal Sensor Placement for Enhanced Efficiency in Structural Health Monitoring of Medium-Rise Buildings","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Modal; Structural health monitoring; Vulnerability (computing); Vibration; Parametric statistics; Structural engineering; Computer science; Scale (ratio); Engineering; Civil engineering","score_opus":0.016496890815274615,"score_gpt":0.32142614130525443,"score_spread":0.3049292504899798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402125847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28262204,0.00019976478,0.7145013,0.000062518244,0.000029828503,0.00005540574,0.00006657236,0.0007144409,0.00174802],"genre_scores_gemma":[0.8747737,0.000054749966,0.12475875,0.000011937407,0.000004726744,0.000023694587,0.000037137157,0.000019893752,0.00031559882],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974495,0.00006537156,0.000010580935,0.00007433668,0.000069028356,0.000035675665],"domain_scores_gemma":[0.9996456,0.000112771944,0.00008175645,0.000060359536,0.00007491612,0.000024613986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023149519,0.0006970367,0.00037686844,0.00034753047,0.00018555966,0.00033550232,0.0006479779,0.00044352337,0.00083889137],"category_scores_gemma":[0.0008151453,0.0002148609,0.00020241275,0.00024000519,0.00028247054,0.0005424737,0.0004844132,0.0002271068,0.00034727826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000657578,0.00021876716,0.006558917,0.00023919348,0.00003832154,0.00042463574,0.0002989303,0.21998237,0.54072976,0.0024718004,0.0008285481,0.22755113],"study_design_scores_gemma":[0.00004888179,0.0008174173,0.007875042,0.000028111546,0.000045745488,0.00033835074,0.00020909276,0.7618831,0.22469051,0.002091497,0.0019235314,0.000048667775],"about_ca_topic_score_codex":0.00036800225,"about_ca_topic_score_gemma":0.0011651431,"teacher_disagreement_score":0.00083889137,"about_ca_system_score_codex":0.00019268443,"about_ca_system_score_gemma":0.00029926747,"threshold_uncertainty_score":0.002806425},"labels":[],"label_agreement":null},{"id":"W4402277325","doi":"10.3390/s24175770","title":"Energy Performance of LR-FHSS: Analysis and Evaluation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Direcció General de Recerca, Generalitat de Catalunya; Ministerio de Ciencia e Innovación; Agència de Gestió d'Ajuts Universitaris i de Recerca; Ministerio de Ciencia, Innovación y Universidades; Federation for the Humanities and Social Sciences","keywords":"Energy consumption; Spread spectrum; Payload (computing); Robustness (evolution); Frequency-hopping spread spectrum; Transmission (telecommunications); Energy (signal processing); Engineering; Computer network; Electronic engineering; Computer science; Electrical engineering; Real-time computing; Mathematics; Statistics","score_opus":0.009726243293749732,"score_gpt":0.2449280373744626,"score_spread":0.23520179408071284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402277325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94464093,0.00087712234,0.043036114,0.0002102331,0.0000405989,0.000080249556,0.00053548254,0.0008646742,0.009714551],"genre_scores_gemma":[0.9968581,0.00008391904,0.0021304556,0.000014564095,0.0000039384067,0.000013640169,0.00015355059,0.00001928182,0.00072251033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939525,0.00009847513,0.000025503743,0.000078387326,0.00027728983,0.00012517208],"domain_scores_gemma":[0.99869823,0.0006214126,0.00009950189,0.000138596,0.0003929623,0.00004920508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006807593,0.0007146332,0.00044016194,0.0008490248,0.0002720485,0.00043052182,0.00073858374,0.0006594292,0.0018711106],"category_scores_gemma":[0.0022844796,0.00009935385,0.00029855192,0.00073372386,0.00035549555,0.00060097873,0.00036343155,0.00021064853,0.00043657032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010860309,0.00020275288,0.012278571,0.00023325521,0.000074182986,0.00028402687,0.000105160136,0.8782154,0.027436672,0.0019121027,0.0014458002,0.07672607],"study_design_scores_gemma":[0.000022548204,0.00089801225,0.0075930874,0.000020463303,0.00002965757,0.00024327314,0.00011425407,0.9715549,0.017767752,0.00065602054,0.00107626,0.000023738097],"about_ca_topic_score_codex":0.004401339,"about_ca_topic_score_gemma":0.0036126457,"teacher_disagreement_score":0.004401339,"about_ca_system_score_codex":0.00083666015,"about_ca_system_score_gemma":0.0002927456,"threshold_uncertainty_score":0.008751392},"labels":[],"label_agreement":null},{"id":"W4402313467","doi":"10.3390/s24175802","title":"Affinity-Driven Transfer Learning for Load Forecasting","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Operationalization; Transfer of learning; Artificial intelligence; Robustness (evolution); Task (project management); Similarity (geometry); Data mining; Engineering","score_opus":0.023751216942040163,"score_gpt":0.22417751458937696,"score_spread":0.2004262976473368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402313467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055937734,0.0005608085,0.9381389,0.00048113774,0.00014251066,0.00015124476,0.00019823232,0.001435713,0.0029536884],"genre_scores_gemma":[0.89003754,0.00024237881,0.10459691,0.00033323845,0.00016736654,0.00024786175,0.00056212355,0.00011641758,0.0036961876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987437,0.0004647666,0.000070764465,0.00029299606,0.000299855,0.00012795998],"domain_scores_gemma":[0.9960609,0.0021957979,0.0002576695,0.00051277777,0.00079341274,0.0001794714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029092736,0.0012316874,0.0011501242,0.00140304,0.0007001975,0.0010769136,0.002267524,0.0014808261,0.002484014],"category_scores_gemma":[0.011120292,0.00034392782,0.00062311924,0.0018645729,0.0007902763,0.002933434,0.0016758738,0.0021938444,0.0011218858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027323916,0.00043268225,0.005059039,0.0001078767,0.00013184673,0.000111669804,0.00015944162,0.6499917,0.0024453232,0.0074738907,0.004359168,0.32945418],"study_design_scores_gemma":[0.0000051968946,0.000033890497,0.0002239896,0.000003239084,0.000004129617,0.000008714264,0.000012564431,0.9931379,0.00044456846,0.0058612176,0.0002592864,0.0000052570963],"about_ca_topic_score_codex":0.0045140036,"about_ca_topic_score_gemma":0.0034258966,"teacher_disagreement_score":0.0045140036,"about_ca_system_score_codex":0.0011966121,"about_ca_system_score_gemma":0.0012239674,"threshold_uncertainty_score":0.015385866},"labels":[],"label_agreement":null},{"id":"W4402318539","doi":"10.3390/s24175794","title":"Forage Height and Above-Ground Biomass Estimation by Comparing UAV-Based Multispectral and RGB Imagery","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Beef Cattle Research Council","keywords":"Remote sensing; Multispectral image; RGB color model; Normalized Difference Vegetation Index; Environmental science; Image resolution; Forage; Canopy; Biomass (ecology); Population; Leaf area index; Computer science; Geography; Artificial intelligence; Agronomy","score_opus":0.005898268857814111,"score_gpt":0.21423449580945417,"score_spread":0.20833622695164006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402318539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9888938,0.00020074092,0.009291826,0.0000133479625,0.000009798593,0.000011291726,0.00037781647,0.0001276004,0.0010738255],"genre_scores_gemma":[0.9876408,0.00010037602,0.011416346,0.000012365025,0.0000034474574,0.0000074603045,0.0005267904,0.0000116159445,0.0002807565],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998604,0.000016491125,0.000007457437,0.000038578863,0.00005448389,0.000022474627],"domain_scores_gemma":[0.9998553,0.000033585176,0.000026535548,0.000019157475,0.000055324876,0.00001006999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002304641,0.00031139387,0.00019221871,0.0007735695,0.000100019264,0.00039287872,0.00018779025,0.00019214411,0.00048227175],"category_scores_gemma":[0.0004277876,0.00012211635,0.0002549121,0.00051764137,0.000093815834,0.00036169312,0.00019519913,0.000098941164,0.0001713131],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007119068,0.00022004222,0.31962752,0.0003462929,0.00047861392,0.00023299945,0.00039279624,0.052046504,0.38001207,0.0004446226,0.00087082846,0.24461591],"study_design_scores_gemma":[0.000026887361,0.00020902201,0.752671,0.000027272881,0.00014279383,0.00018701266,0.0004448299,0.19023764,0.05459079,0.000143137,0.0012719686,0.00004763116],"about_ca_topic_score_codex":0.008519902,"about_ca_topic_score_gemma":0.02049311,"teacher_disagreement_score":0.008519902,"about_ca_system_score_codex":0.00021993787,"about_ca_system_score_gemma":0.00012604777,"threshold_uncertainty_score":0.016940594},"labels":[],"label_agreement":null},{"id":"W4402356135","doi":"10.3390/s24175821","title":"Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cramér–Rao bound; Initialization; Estimator; Algorithm; Embedding; Computer science; Convergence (economics); Mathematical optimization; Mathematics; Control theory (sociology); Statistics","score_opus":0.05427382651610916,"score_gpt":0.3031959870810878,"score_spread":0.24892216056497865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402356135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007656828,0.00009765574,0.991695,0.000040942767,0.0000116338215,0.000010585908,0.0000145677195,0.000076437944,0.00039635837],"genre_scores_gemma":[0.47983393,0.0002888419,0.51683736,0.0001051047,0.000067586654,0.00011776287,0.0001731372,0.000066407345,0.002509873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995888,0.00013362052,0.000018432323,0.000071960254,0.00015062666,0.000036605],"domain_scores_gemma":[0.9990978,0.000525285,0.000102565835,0.00009144117,0.00014462831,0.00003827853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081259693,0.00050643244,0.0006398253,0.00034092937,0.00018360783,0.000623064,0.0007469438,0.00046599976,0.0008042162],"category_scores_gemma":[0.0029711397,0.00029196515,0.0003408283,0.0004580013,0.00046058046,0.0011163354,0.00085903285,0.0008389505,0.00025765487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002292841,0.00007601656,0.0035780214,0.00014728567,0.00007944775,0.00010805544,0.0001114079,0.7636091,0.019563453,0.038020484,0.001117975,0.17335951],"study_design_scores_gemma":[0.000006484046,0.000015461304,0.00015547159,0.0000031558554,0.0000037734137,0.000019714413,0.000003105832,0.99558216,0.0018530074,0.0020090376,0.00034349345,0.000005157714],"about_ca_topic_score_codex":0.00099471,"about_ca_topic_score_gemma":0.0010477918,"teacher_disagreement_score":0.00099471,"about_ca_system_score_codex":0.00037029976,"about_ca_system_score_gemma":0.00079176686,"threshold_uncertainty_score":0.0042974353},"labels":[],"label_agreement":null},{"id":"W4402374990","doi":"10.3390/s24175823","title":"Radiation Impedance of Rectangular CMUTs","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institutes of Health; Alberta Innovates; Alberta Innovates - Technology Futures; CMC Microsystems","keywords":"Radiation impedance; Acoustics; Electrical impedance; Finite element method; Transducer; Capacitive sensing; Radiation; Admittance; Computer science; Materials science; Optics; Engineering; Physics; Structural engineering; Electrical engineering","score_opus":0.005946868599004499,"score_gpt":0.2603034163674065,"score_spread":0.254356547768402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402374990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43943927,0.0008442174,0.5501618,0.00034758425,0.00016321191,0.000079897196,0.00020833258,0.0014800286,0.0072755516],"genre_scores_gemma":[0.9361618,0.00035161228,0.060384735,0.00010549968,0.000015381245,0.000052664316,0.00008775021,0.00007631261,0.0027641866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965656,0.00006677627,0.000020937794,0.000094606934,0.00012610511,0.000034947596],"domain_scores_gemma":[0.9993641,0.0003334935,0.00012436359,0.00006513164,0.00008840997,0.00002453488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004847567,0.00032377863,0.0002751907,0.00025334663,0.00016079324,0.0005596875,0.00051445054,0.00069268927,0.0014469044],"category_scores_gemma":[0.0029150208,0.00029264568,0.00032816757,0.0003401192,0.00042614248,0.00085469615,0.0005749993,0.00032225027,0.00050820544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005096127,0.000049119142,0.0048966585,0.00031848974,0.000038638573,0.0006413081,0.0006644702,0.16235015,0.7461673,0.010180491,0.00090236653,0.07328157],"study_design_scores_gemma":[0.00003621455,0.0006240392,0.0031951736,0.00008361903,0.000048506197,0.00082367513,0.0002945681,0.53297025,0.44688803,0.00236838,0.012590879,0.00007661314],"about_ca_topic_score_codex":0.00059585663,"about_ca_topic_score_gemma":0.00052780256,"teacher_disagreement_score":0.0014469044,"about_ca_system_score_codex":0.00062251877,"about_ca_system_score_gemma":0.0004456509,"threshold_uncertainty_score":0.004840374},"labels":[],"label_agreement":null},{"id":"W4402417618","doi":"10.3390/s24185864","title":"Microfibrous Carbon Paper Decorated with High-Density Manganese Dioxide Nanorods: An Electrochemical Nonenzymatic Platform of Glucose Sensing","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Bureau for International Education","keywords":"Nanorod; Ascorbic acid; Electrode; Materials science; Electrochemistry; Nanotechnology; Selectivity; Linear range; Chemical engineering; Biosensor; Detection limit; Inorganic chemistry; Catalysis; Chemistry; Chromatography; Organic chemistry","score_opus":0.004020362931481802,"score_gpt":0.18331213678903813,"score_spread":0.17929177385755632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402417618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9831911,0.0016251762,0.013306531,0.000084209445,0.00008428706,0.000027299724,0.00015103287,0.00013803074,0.0013923683],"genre_scores_gemma":[0.9836258,0.00046327198,0.014679115,0.000029219518,0.000011649032,0.000018804754,0.00010940076,0.00001359819,0.0010492582],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988973,0.000011231531,0.000007513245,0.00003839881,0.000036018722,0.00001706181],"domain_scores_gemma":[0.99989355,0.000019665327,0.00003382426,0.000011762599,0.000022831442,0.000018291317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099856494,0.0004288692,0.0001219474,0.00018953445,0.00008784318,0.00020443223,0.00034188823,0.00046871597,0.00035316963],"category_scores_gemma":[0.00017750633,0.00015836659,0.00016652522,0.00011717448,0.00012502406,0.00022278714,0.00014383983,0.00022803742,0.00015049979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009786321,0.000004604357,0.000052175375,0.000017027312,0.0000018817997,0.00002314374,0.0000026252205,0.000036255227,0.9990773,0.000021458745,0.000009334415,0.0007442932],"study_design_scores_gemma":[0.0000026531045,0.00005077979,0.0011082207,0.000001790972,0.000004281438,0.00010760923,0.000007622189,0.0009625666,0.9971705,0.000014707266,0.0005655047,0.000003867157],"about_ca_topic_score_codex":0.00042398457,"about_ca_topic_score_gemma":0.0013244742,"teacher_disagreement_score":0.00046871597,"about_ca_system_score_codex":0.00017280885,"about_ca_system_score_gemma":0.00009065057,"threshold_uncertainty_score":0.0012538433},"labels":[],"label_agreement":null},{"id":"W4402419701","doi":"10.3390/s24185862","title":"AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Chittagong University of Engineering and Technology","keywords":"Bengali; Sadness; Computer science; Modalities; Anger; Speech recognition; Emotion recognition; Artificial intelligence; Robustness (evolution); Affective computing; Psychology","score_opus":0.075440798700261,"score_gpt":0.38742081317777677,"score_spread":0.3119800144775158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402419701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27571002,0.007285898,0.67262787,0.0009545706,0.0010697154,0.0009662585,0.009483008,0.012760009,0.019142646],"genre_scores_gemma":[0.70842755,0.0017971472,0.25857237,0.0007022574,0.00035884802,0.0007497696,0.014728955,0.0003762069,0.014286961],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940693,0.00011654857,0.00003081419,0.00024405215,0.0001164739,0.000085059866],"domain_scores_gemma":[0.99966085,0.00011536151,0.00002945313,0.000051539897,0.00011450459,0.000028251661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010460827,0.0013254074,0.0007502707,0.001360597,0.00034913776,0.000930876,0.0008102468,0.00077214703,0.0031997554],"category_scores_gemma":[0.0014258545,0.00021714778,0.0010719263,0.00071510975,0.00026433537,0.0011456797,0.0014995936,0.00089110003,0.0018536003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013622917,0.0005279645,0.007060622,0.0006336462,0.0003840833,0.000346386,0.000364361,0.010039632,0.14005958,0.0012720941,0.022678062,0.81527126],"study_design_scores_gemma":[0.00016402859,0.0011951666,0.05723141,0.00021735171,0.0007317342,0.001358318,0.0010190554,0.77217925,0.11921176,0.008054936,0.03842983,0.00020720826],"about_ca_topic_score_codex":0.0029089763,"about_ca_topic_score_gemma":0.004724643,"teacher_disagreement_score":0.0031997554,"about_ca_system_score_codex":0.00046760074,"about_ca_system_score_gemma":0.00026139859,"threshold_uncertainty_score":0.010704279},"labels":[],"label_agreement":null},{"id":"W4402455460","doi":"10.3390/s24185901","title":"Enhancing Reliability and Stability of BLE Mesh Networks: A Multipath Optimized AODV Approach","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Bluetooth and Wireless Communication Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Sains Malaysia; Trent University; Nottingham Trent University; Centre International de Recherche sur le Cancer","keywords":"Computer network; Computer science; Ad hoc On-Demand Distance Vector Routing; Distance-vector routing protocol; Wireless ad hoc network; Network packet; Goodput; Wireless mesh network; Flooding (psychology); Mobile ad hoc network; Routing protocol; Wireless network; Optimized Link State Routing Protocol; Wireless; Throughput; Telecommunications","score_opus":0.01824181468749848,"score_gpt":0.2418135071077274,"score_spread":0.22357169242022892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402455460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08807121,0.0015641175,0.903551,0.0003899823,0.00015751157,0.00011561199,0.00009426585,0.0010015784,0.005054771],"genre_scores_gemma":[0.8932278,0.0006289777,0.10372825,0.00006770181,0.000075094744,0.00006776331,0.00012559703,0.00004868302,0.0020301393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967957,0.00008860858,0.000021814305,0.000044591754,0.00010877734,0.000056664456],"domain_scores_gemma":[0.9994197,0.00018701714,0.00009679558,0.00006514048,0.00019420963,0.00003723178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004992171,0.00043264034,0.00034936913,0.0009773341,0.00048498408,0.0005187515,0.00086802436,0.00040186854,0.0004778755],"category_scores_gemma":[0.001410431,0.0002016703,0.00033467144,0.0004581148,0.00026541267,0.00078049203,0.00074108486,0.00033546044,0.00018388554],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034081572,0.00011523442,0.004794276,0.00027228618,0.00017056533,0.00034556992,0.00032566022,0.48669806,0.11267294,0.022234848,0.0036682787,0.36836144],"study_design_scores_gemma":[0.000024540206,0.00022282646,0.0010155146,0.000015961064,0.000070521106,0.00020401619,0.0001283043,0.96950275,0.017020479,0.005431907,0.0063212155,0.00004197007],"about_ca_topic_score_codex":0.0017015565,"about_ca_topic_score_gemma":0.0023389815,"teacher_disagreement_score":0.0017015565,"about_ca_system_score_codex":0.00054651895,"about_ca_system_score_gemma":0.00045974288,"threshold_uncertainty_score":0.0039652586},"labels":[],"label_agreement":null},{"id":"W4402462228","doi":"10.3390/s24185885","title":"Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Geospatial analysis; Computer science; Interference (communication); Ranging; Frequency allocation; Internet of Things; Data sharing; Spectrum management; Distributed computing; Telecommunications; Computer network; Wireless; Computer security; Cognitive radio; Remote sensing; Geography","score_opus":0.02686075253289324,"score_gpt":0.26887241405932755,"score_spread":0.2420116615264343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402462228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09261097,0.000596592,0.8983923,0.00036184612,0.00006270813,0.00006694518,0.00013848628,0.0007088685,0.0070612817],"genre_scores_gemma":[0.8511966,0.00028478532,0.14742519,0.000080293146,0.00003889487,0.000039528102,0.00014845369,0.000041758954,0.0007444481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995228,0.00012491376,0.000021893327,0.000093745606,0.00016963806,0.00006697627],"domain_scores_gemma":[0.99958247,0.00011959646,0.000055583052,0.0001161313,0.00008453737,0.000041740375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009581868,0.00047539052,0.00051900424,0.0010638909,0.00044303553,0.0014148402,0.0013754854,0.00047701466,0.0008660569],"category_scores_gemma":[0.0012767568,0.00021532767,0.0003933976,0.0011399237,0.00045601345,0.0024358556,0.0022391544,0.00049382803,0.00021841067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000416448,0.00081875006,0.012365599,0.00019614278,0.00021146292,0.0006893311,0.0008489999,0.3576511,0.066715784,0.05805384,0.0028818944,0.49915063],"study_design_scores_gemma":[0.000019910793,0.000089507994,0.0035529695,0.000028585022,0.00004709877,0.0003640283,0.00037103627,0.95550776,0.011782274,0.020884063,0.00730003,0.00005273745],"about_ca_topic_score_codex":0.0036879932,"about_ca_topic_score_gemma":0.0045672976,"teacher_disagreement_score":0.0036879932,"about_ca_system_score_codex":0.00037800297,"about_ca_system_score_gemma":0.00067654334,"threshold_uncertainty_score":0.0073331},"labels":[],"label_agreement":null},{"id":"W4402488152","doi":"10.3390/s24185918","title":"Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Australian Research Council","keywords":"Electronic nose; Decomposition; Starvation; Chemistry; Computer science; Chromatography; Organic chemistry; Artificial intelligence; Biology","score_opus":0.010576166373941822,"score_gpt":0.2523632162855999,"score_spread":0.24178704991165806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402488152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9618382,0.001635736,0.033368822,0.00018709002,0.00019336202,0.00013364626,0.00028862664,0.0002169111,0.0021377408],"genre_scores_gemma":[0.9644902,0.00075758324,0.031900622,0.0002952879,0.000022668954,0.000068753696,0.00019912879,0.000011691846,0.0022541138],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971825,0.000046754918,0.000019116671,0.00007004395,0.00012126412,0.000024576531],"domain_scores_gemma":[0.99982196,0.000056898192,0.000022690803,0.000011007151,0.00007547324,0.0000121016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005283517,0.00031569114,0.00025975588,0.00021826244,0.00015428936,0.00026453505,0.0003959874,0.0007136437,0.00042561773],"category_scores_gemma":[0.00061946246,0.00013880346,0.00032060622,0.0001116812,0.00021205391,0.00032012243,0.0002935606,0.000220821,0.00018169856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003546885,0.00007174731,0.0019347875,0.0001123461,0.000015338055,0.00010306831,0.000038442147,0.0003563825,0.9815482,0.00007073899,0.00013449143,0.015259831],"study_design_scores_gemma":[0.000017706501,0.0016668615,0.010804966,0.000021388243,0.000042276777,0.0007611137,0.00010374177,0.01106515,0.9740417,0.00007673696,0.001365718,0.00003278228],"about_ca_topic_score_codex":0.00057459006,"about_ca_topic_score_gemma":0.0012435225,"teacher_disagreement_score":0.0007136437,"about_ca_system_score_codex":0.00015223176,"about_ca_system_score_gemma":0.00024887727,"threshold_uncertainty_score":0.0027942061},"labels":[],"label_agreement":null},{"id":"W4402524567","doi":"10.3390/s24185939","title":"Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Internet of Things; Tracking (education); Computer science; Health management system; Data science; Medicine; World Wide Web; Psychology; Alternative medicine","score_opus":0.1357441953565138,"score_gpt":0.48413128161189883,"score_spread":0.34838708625538506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402524567","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020034605,0.9987281,0.00014144476,0.00019310007,0.00009563305,0.00011774705,0.00022281051,0.000006819737,0.00029398903],"genre_scores_gemma":[0.0014358212,0.9976413,0.00031771528,0.00020826444,0.0000344177,0.00014006034,0.00012220813,0.0000027504827,0.00009744676],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99681634,0.00091504754,0.0011545379,0.0002663701,0.0007349988,0.00011274828],"domain_scores_gemma":[0.9896712,0.007245164,0.0015389835,0.0001641138,0.0012310941,0.00014942801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041732197,0.0015040032,0.004792742,0.009321274,0.0006727537,0.00221378,0.0018337762,0.0015490303,0.007288567],"category_scores_gemma":[0.01900638,0.00072141254,0.005801193,0.009852953,0.0006353693,0.0020200815,0.0015093172,0.0011246771,0.0007570469],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009018438,0.00001834553,0.00024758204,0.89306444,0.0022935043,0.00008321179,0.00013945576,0.00009526531,0.000177531,0.000406457,0.0036038542,0.09978018],"study_design_scores_gemma":[0.000096474076,0.00014386552,0.0020367096,0.89213055,0.022572994,0.00040784702,0.00021639472,0.00006897176,0.00020681064,0.00055745814,0.08152922,0.000032796204],"about_ca_topic_score_codex":0.007999323,"about_ca_topic_score_gemma":0.023071542,"teacher_disagreement_score":0.009321274,"about_ca_system_score_codex":0.0022378901,"about_ca_system_score_gemma":0.011762873,"threshold_uncertainty_score":0.02438271},"labels":[],"label_agreement":null},{"id":"W4402601120","doi":"10.3390/s24186031","title":"A Novel Size-Based Centrifugal Microfluidic Design to Enrich and Magnetically Isolate Circulating Tumor Cells from Blood Cells through Biocompatible Magnetite–Arginine Nanoparticles","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Circulating tumor cell; Microfluidics; Microchannel; Centrifugal force; Materials science; Biomedical engineering; Drag; Nanotechnology; Fictitious force; Flow (mathematics); Cancer; Engineering; Mechanics; Physics; Biology","score_opus":0.012505654286837038,"score_gpt":0.20012966715783148,"score_spread":0.18762401287099445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402601120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42511848,0.0068150028,0.55229914,0.0015183713,0.0010796069,0.000647616,0.0004335851,0.0022494614,0.009838675],"genre_scores_gemma":[0.72022593,0.0017830036,0.26927763,0.0005343056,0.00016134977,0.00044636388,0.00023321321,0.00007990288,0.0072583207],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997428,0.000026948952,0.000024567971,0.000102287115,0.00006939665,0.000034005512],"domain_scores_gemma":[0.99981076,0.000034790595,0.00006402242,0.000017651104,0.00004590511,0.00002696909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036109798,0.0006105487,0.00028352469,0.00042805704,0.0002925193,0.00039568322,0.0009025069,0.0005817999,0.00055369514],"category_scores_gemma":[0.00029979064,0.0002475992,0.0004304539,0.00015456752,0.000284649,0.0004263255,0.00031743708,0.00037358812,0.00032275147],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007834461,0.0000648727,0.00027259946,0.00014026221,0.000012873597,0.0000656172,0.00003117582,0.0007510525,0.98091,0.0016230224,0.0004567263,0.015593411],"study_design_scores_gemma":[0.000057659036,0.00043038113,0.0010439795,0.000009986096,0.00004628551,0.00038997704,0.000013308466,0.018783052,0.96070963,0.00017144343,0.018292915,0.00005143082],"about_ca_topic_score_codex":0.00053455605,"about_ca_topic_score_gemma":0.0006848325,"teacher_disagreement_score":0.0009025069,"about_ca_system_score_codex":0.0005823844,"about_ca_system_score_gemma":0.00053073966,"threshold_uncertainty_score":0.0042254925},"labels":[],"label_agreement":null},{"id":"W4402616274","doi":"10.3390/s24186006","title":"Accurate Low Complexity Quadrature Angular Diversity Aperture Receiver for Visible Light Positioning","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Research Council; Monash University; Ministère des relations internationales et de la Francophonie; Impact Fund","keywords":"Photodiode; Beacon; PSoC; Angle of arrival; Bluetooth; Computer science; Chip; Visible light communication; Wireless; Optics; Electronic engineering; Engineering; Telecommunications; Physics; Embedded system; Light-emitting diode; System on a chip","score_opus":0.020751861620466758,"score_gpt":0.24650677299454307,"score_spread":0.22575491137407633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402616274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020674227,0.0010983673,0.97294015,0.0001821719,0.00019161258,0.000057302324,0.00008981609,0.0011668077,0.0035996172],"genre_scores_gemma":[0.44922462,0.00074073806,0.54192686,0.00037572157,0.00019312078,0.00010759251,0.00020542569,0.00008759965,0.007138403],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991761,0.00013801531,0.000035162455,0.00016975512,0.00042584873,0.000055188888],"domain_scores_gemma":[0.99917847,0.000198126,0.00015864278,0.000114945506,0.00031547868,0.000034245622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006607632,0.0004665513,0.00047216014,0.000549711,0.00023640119,0.000847567,0.0011210691,0.0008194576,0.002067727],"category_scores_gemma":[0.0011636163,0.0003314445,0.00030573577,0.0005474083,0.00025054792,0.000814215,0.00055289874,0.0007429021,0.0016140959],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003358994,0.000107724445,0.0028940933,0.0003317479,0.00006501397,0.00015445758,0.00017911619,0.008621943,0.677209,0.011625473,0.0037212353,0.29475427],"study_design_scores_gemma":[0.00016417562,0.0015397883,0.0045178044,0.00012272943,0.00015472993,0.0020236794,0.00008654584,0.31496313,0.5926178,0.003383897,0.08023606,0.00018966822],"about_ca_topic_score_codex":0.00042910947,"about_ca_topic_score_gemma":0.0007398267,"teacher_disagreement_score":0.002067727,"about_ca_system_score_codex":0.0005055262,"about_ca_system_score_gemma":0.000570401,"threshold_uncertainty_score":0.0069171786},"labels":[],"label_agreement":null},{"id":"W4402681949","doi":"10.3390/s24186085","title":"The Determination of On-Water Rowing Stroke Kinematics Using an Undecimated Wavelet Transform of a Rowing Hull-Mounted Accelerometer Signal","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"Rowing; Accelerometer; Kinematics; Hull; SIGNAL (programming language); Wavelet transform; Stroke (engine); Marine engineering; Acoustics; Computer science; Engineering; Wavelet; Artificial intelligence; Mechanical engineering; Physics","score_opus":0.027606764108597453,"score_gpt":0.273233987654576,"score_spread":0.24562722354597857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402681949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81198317,0.00021704007,0.18242343,0.000048597936,0.00006567147,0.00014655404,0.0010234902,0.00043272506,0.0036594127],"genre_scores_gemma":[0.93383956,0.00031664432,0.06330545,0.000031180403,0.000016531636,0.00009254842,0.0007648954,0.000054683245,0.0015783975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979657,0.000022879205,0.000020067795,0.000050130726,0.0000854501,0.000025005804],"domain_scores_gemma":[0.9994276,0.00015718133,0.00012738598,0.000055639986,0.00021091034,0.00002129751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040905515,0.00043957322,0.00027399347,0.00076509285,0.000116863055,0.0004883208,0.0001998356,0.000215493,0.0012750383],"category_scores_gemma":[0.0019394183,0.00015429583,0.0002464569,0.0006631321,0.00018094164,0.0003793325,0.00027582556,0.0002662003,0.00067900924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043954112,0.0002249268,0.14975524,0.00058009004,0.00015273012,0.00027516275,0.0010475498,0.005464982,0.31866398,0.00070739316,0.0016270663,0.5210614],"study_design_scores_gemma":[0.000016666305,0.00067430176,0.8456024,0.00011699553,0.00009142948,0.0005930784,0.0011118603,0.0726509,0.074773386,0.00074745686,0.0035520557,0.000069535316],"about_ca_topic_score_codex":0.0017091241,"about_ca_topic_score_gemma":0.003949597,"teacher_disagreement_score":0.0017091241,"about_ca_system_score_codex":0.00009492594,"about_ca_system_score_gemma":0.00022348561,"threshold_uncertainty_score":0.0042654276},"labels":[],"label_agreement":null},{"id":"W4402764960","doi":"10.3390/s24196172","title":"Sensors in Bone: Technologies, Applications, and Future Directions","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health; International Fibrodysplasia Ossificans Progressiva Association","keywords":"Osteoporosis; Resilience (materials science); Risk analysis (engineering); Population; Bone health; Medicine; Bone mineral; Bone remodeling; Intensive care medicine; Computer science; Pathology; Environmental health","score_opus":0.03582545850585619,"score_gpt":0.3835296849827476,"score_spread":0.3477042264768914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402764960","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014446907,0.9966472,0.00042555307,0.00052396365,0.00046847234,0.000009396448,0.000026812853,0.000017855848,0.0017363194],"genre_scores_gemma":[0.0008125281,0.99700636,0.00053233677,0.0002947233,0.00028821692,0.0000100788675,0.000036969846,0.0000032257096,0.001015542],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993742,0.000098130884,0.00006766515,0.00009473087,0.00029838868,0.00006685884],"domain_scores_gemma":[0.9990382,0.00050442247,0.00008286535,0.000026608574,0.00030058602,0.00004732755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012065822,0.0010326416,0.0015117229,0.0021980614,0.00041350705,0.0017882227,0.0010912863,0.0020224631,0.006542035],"category_scores_gemma":[0.00149532,0.00046333484,0.0009492818,0.0024282755,0.0006655339,0.0026150357,0.000985787,0.0026282845,0.0036916058],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008041097,0.00010172085,0.00027151234,0.027787982,0.000090353584,0.00020762456,0.0001302543,0.0005411411,0.0057282904,0.011908512,0.029455416,0.92369694],"study_design_scores_gemma":[0.00000814332,0.00012747265,0.00039656876,0.0041016545,0.000094397925,0.000720908,0.00010565514,0.0001945135,0.0012616441,0.0043216464,0.98864067,0.000026769147],"about_ca_topic_score_codex":0.0011868706,"about_ca_topic_score_gemma":0.0017772407,"teacher_disagreement_score":0.006542035,"about_ca_system_score_codex":0.000653086,"about_ca_system_score_gemma":0.001543162,"threshold_uncertainty_score":0.021885276},"labels":[],"label_agreement":null},{"id":"W4402769654","doi":"10.3390/s24186103","title":"Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Aperiodic graph; Electroencephalography; Major depressive disorder; Alpha (finance); Psychology; Anhedonia; Audiology; Pattern recognition (psychology); Neuroscience; Clinical psychology; Mathematics; Medicine; Cognitive psychology; Amygdala; Combinatorics","score_opus":0.01966331823281184,"score_gpt":0.25347564819400237,"score_spread":0.23381232996119053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402769654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9798238,0.0017881289,0.016223466,0.00028462667,0.000046147397,0.000056062625,0.00026149867,0.0000726121,0.0014436683],"genre_scores_gemma":[0.9958067,0.00020546268,0.0037188116,0.000026147769,0.000027024242,0.000008900285,0.00011842061,0.0000063681396,0.00008200488],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983955,0.0009270264,0.00021393129,0.00021909016,0.00018023996,0.000064237734],"domain_scores_gemma":[0.98834723,0.009103065,0.0010260862,0.000568794,0.0007376725,0.00021710349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046260697,0.00043692667,0.000431189,0.0011761424,0.00018531566,0.0008271864,0.00021749051,0.00032623697,0.00059348805],"category_scores_gemma":[0.01455614,0.00013123649,0.0003729972,0.0006653721,0.00034370675,0.00057873095,0.00050279585,0.0003641446,0.00018025559],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013011214,0.00009098298,0.8057926,0.00017937298,0.00037557425,0.00035649212,0.00033074274,0.004696019,0.016023379,0.00041146768,0.00039652683,0.17004567],"study_design_scores_gemma":[0.000032784177,0.00038178885,0.9644008,0.000053125896,0.00016010091,0.00067244424,0.00029959378,0.030205326,0.0018131433,0.0015869526,0.0003743221,0.000019553676],"about_ca_topic_score_codex":0.0008609027,"about_ca_topic_score_gemma":0.0018299303,"teacher_disagreement_score":0.0046260697,"about_ca_system_score_codex":0.00012649134,"about_ca_system_score_gemma":0.00017587063,"threshold_uncertainty_score":0.024465263},"labels":[],"label_agreement":null},{"id":"W4402789500","doi":"10.3390/s24185987","title":"Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quality of service; Computer science; Scheduling (production processes); Network packet; Computer network; Differential evolution; Packet loss; Schedule; Distributed computing; Engineering","score_opus":0.017007650107127188,"score_gpt":0.2586748440222478,"score_spread":0.2416671939151206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402789500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16766876,0.00033600235,0.8283201,0.00023317726,0.000056107947,0.000055834815,0.000026422382,0.00011802703,0.0031855449],"genre_scores_gemma":[0.95984924,0.00008495758,0.03921922,0.000043434273,0.000008329842,0.000056055454,0.000021539863,0.000016988453,0.00070023333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997389,0.00012356845,0.000007007228,0.000028177472,0.000069316346,0.000032941418],"domain_scores_gemma":[0.99957854,0.0002793046,0.000051809686,0.000012748829,0.000054465,0.000023117973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009596975,0.00033483194,0.0004055338,0.00028446017,0.00023822077,0.00033694107,0.00043467857,0.00032894063,0.0003747338],"category_scores_gemma":[0.0014435971,0.00018975665,0.00029892445,0.0002586056,0.00039253262,0.00027785474,0.00045166156,0.0003802684,0.000032048734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009218227,0.000009199012,0.00013119554,0.000005827424,0.0000068701893,0.000009983024,0.000007819887,0.99620795,0.0006031666,0.0006013502,0.000039966042,0.0023673994],"study_design_scores_gemma":[0.0000018805911,0.000008863858,0.0000353682,4.1109135e-7,0.0000010425115,0.0000012362067,0.0000017221695,0.99966466,0.000088492416,0.00016461336,0.000031116448,6.0453084e-7],"about_ca_topic_score_codex":0.0038161632,"about_ca_topic_score_gemma":0.0032919247,"teacher_disagreement_score":0.0038161632,"about_ca_system_score_codex":0.000675174,"about_ca_system_score_gemma":0.00076082064,"threshold_uncertainty_score":0.0075879097},"labels":[],"label_agreement":null},{"id":"W4402793888","doi":"10.3390/s24185963","title":"An Adaptive RF Front-End Architecture for Multi-Band SDR in Avionics","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Software-defined radio; RF front end; Avionics; Radio frequency; Computer science; Engineering; Embedded system; Electronic engineering; Telecommunications","score_opus":0.03134415291574377,"score_gpt":0.26383462433997285,"score_spread":0.23249047142422907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402793888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07416902,0.00074096833,0.9141798,0.00013831069,0.00009960664,0.000058418955,0.00004160594,0.0017804053,0.008791756],"genre_scores_gemma":[0.64335585,0.00038530983,0.34760138,0.00025012167,0.00007967751,0.000048249218,0.00014447383,0.000091645496,0.008043354],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997671,0.00003197264,0.000010738333,0.000054233722,0.00011243726,0.000023529661],"domain_scores_gemma":[0.9998628,0.000019055124,0.000019891193,0.000033226293,0.000055448363,0.000009561801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002672213,0.00040947867,0.00016689878,0.0004243224,0.00023649316,0.00044043554,0.00085373246,0.00047030314,0.001342119],"category_scores_gemma":[0.00022599027,0.00012661974,0.0002730911,0.00019188906,0.00018737029,0.00048586272,0.00031815938,0.0005125788,0.0009307616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024228606,0.00011574425,0.0014363794,0.00012883641,0.000045049645,0.00030764975,0.00013551659,0.035627164,0.64416534,0.014976715,0.0019483697,0.30087095],"study_design_scores_gemma":[0.000064167085,0.0013103731,0.0041872733,0.000065183034,0.0000897424,0.0015987416,0.00006184379,0.51007545,0.41244313,0.0050941547,0.06491865,0.00009135894],"about_ca_topic_score_codex":0.00047120132,"about_ca_topic_score_gemma":0.0009776298,"teacher_disagreement_score":0.001342119,"about_ca_system_score_codex":0.00034838248,"about_ca_system_score_gemma":0.0001813759,"threshold_uncertainty_score":0.004489839},"labels":[],"label_agreement":null},{"id":"W4402860563","doi":"10.3390/s24196246","title":"Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Vector Institute; Centre for Addiction and Mental Health","funders":"Krembil Foundation; University of Washington","keywords":"Android (operating system); Computer science; Accelerometer; Global Positioning System; Data quality; Gyroscope; Correctness; Real-time computing; Data collection; Scalability; Embedded system; Database; Engineering; Operating system","score_opus":0.10534491367951483,"score_gpt":0.44393878070947873,"score_spread":0.3385938670299639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402860563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98506826,0.0012364545,0.007824395,0.00028515607,0.00005919007,0.00013115734,0.0036879247,0.00015063098,0.0015569245],"genre_scores_gemma":[0.9926991,0.000357898,0.003858237,0.00010628766,0.00003857521,0.00009168312,0.0026166968,0.00003191313,0.00019961539],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955996,0.0015077017,0.0006559943,0.00066310185,0.0013319608,0.00024154232],"domain_scores_gemma":[0.9817452,0.0071087508,0.0036321485,0.0013955106,0.0056716017,0.00044673312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004553303,0.00065994414,0.0007213985,0.0020912923,0.0002780471,0.0014957436,0.00057663646,0.00067203,0.000751614],"category_scores_gemma":[0.03074136,0.00025257052,0.0007426143,0.0017102072,0.0005255285,0.0011298497,0.0013860374,0.00041325917,0.00027821312],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001109036,0.000099914665,0.9259335,0.0009224955,0.0009690274,0.00035216112,0.0025071984,0.0024449863,0.0075291446,0.000328019,0.0014007341,0.056403846],"study_design_scores_gemma":[0.000015841704,0.00030278802,0.9871661,0.00014426904,0.0002260281,0.0005123208,0.0015338137,0.006354638,0.0019363977,0.00029412674,0.0014606037,0.00005306147],"about_ca_topic_score_codex":0.0040231775,"about_ca_topic_score_gemma":0.0054350747,"teacher_disagreement_score":0.004553303,"about_ca_system_score_codex":0.00031563814,"about_ca_system_score_gemma":0.000355463,"threshold_uncertainty_score":0.024080455},"labels":[],"label_agreement":null},{"id":"W4402924050","doi":"10.3390/s24196264","title":"Complementary Metal–Oxide–Semiconductor-Based Magnetic and Optical Sensors for Life Science Applications","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Electrowetting and Microfluidic Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Miniaturization; CMOS; Scalability; Computer science; Nanotechnology; Power consumption; Electrical engineering; Engineering; Materials science; Power (physics); Physics","score_opus":0.034936281369375294,"score_gpt":0.30474842778882777,"score_spread":0.2698121464194525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402924050","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044566716,0.99440855,0.0010460102,0.00028161242,0.00031135703,0.000015032949,0.000031737767,0.000022725695,0.0034373342],"genre_scores_gemma":[0.002034742,0.9950015,0.00094046886,0.00016519261,0.00014648282,0.000015106517,0.000037404185,0.0000027084338,0.0016564222],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974865,0.000025485137,0.000020766409,0.000046644906,0.0001323757,0.000026054144],"domain_scores_gemma":[0.9997842,0.00008981128,0.000034935896,0.000007899666,0.00006766876,0.000015484062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592645,0.0010282334,0.00080856343,0.0029814069,0.0002893255,0.000745927,0.0007639253,0.0011193409,0.0031783232],"category_scores_gemma":[0.00047044348,0.00037977428,0.00050414726,0.0028219405,0.00039421034,0.0013966131,0.00064263254,0.0015039244,0.0024641133],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026826321,0.0001332055,0.00015797514,0.014824818,0.00005155359,0.00018721979,0.0000659129,0.00055655104,0.018476212,0.009233931,0.015633859,0.94065195],"study_design_scores_gemma":[0.000004750411,0.000116227035,0.00041569065,0.0014311303,0.00005644727,0.00077888725,0.000053412827,0.00025596673,0.005506195,0.002150353,0.989205,0.000025993604],"about_ca_topic_score_codex":0.00067795167,"about_ca_topic_score_gemma":0.0014618691,"teacher_disagreement_score":0.0031783232,"about_ca_system_score_codex":0.00041658146,"about_ca_system_score_gemma":0.0007954369,"threshold_uncertainty_score":0.010632575},"labels":[],"label_agreement":null},{"id":"W4402964300","doi":"10.3390/s24196300","title":"SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Computer science; Scalability; Data science; Deception; Flexibility (engineering); Analytics; Field (mathematics); Big data; Computer security; Trustworthiness; Benchmark (surveying); Data mining; Database","score_opus":0.031050174410516287,"score_gpt":0.24915293231636038,"score_spread":0.2181027579058441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402964300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054690197,0.001465824,0.90914726,0.0018456851,0.00035447595,0.00031264083,0.0012695411,0.022901649,0.008012665],"genre_scores_gemma":[0.6360203,0.0006778873,0.350332,0.0014530191,0.00016030831,0.00025504892,0.002944566,0.0006180557,0.0075388188],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909186,0.00016155421,0.000040018294,0.00017557362,0.0004121994,0.0001187329],"domain_scores_gemma":[0.9985373,0.00040425523,0.0002081785,0.0004355223,0.00028271828,0.00013204316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014226333,0.0011207552,0.0005879807,0.0017438451,0.000534445,0.0017719319,0.0017979695,0.0015769257,0.002296607],"category_scores_gemma":[0.0045188977,0.0004837513,0.0005413727,0.0007254301,0.0010996615,0.0033816933,0.00393154,0.0024996581,0.0010972003],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009526595,0.00088179734,0.019578153,0.00050901965,0.00052163017,0.000766148,0.00047258215,0.09346902,0.04346082,0.03897348,0.070840396,0.72957426],"study_design_scores_gemma":[0.000048840197,0.0002094938,0.0021893207,0.000053434036,0.00003258669,0.0003432215,0.00008370618,0.90920645,0.034636065,0.038982403,0.014161029,0.000053449377],"about_ca_topic_score_codex":0.0013637149,"about_ca_topic_score_gemma":0.0025244781,"teacher_disagreement_score":0.002296607,"about_ca_system_score_codex":0.0008744602,"about_ca_system_score_gemma":0.0012138671,"threshold_uncertainty_score":0.00768286},"labels":[],"label_agreement":null},{"id":"W4402989097","doi":"10.3390/s24196307","title":"Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Propulsion; Kinematics; Inverse dynamics; Sagittal plane; Moment (physics); Control theory (sociology); Simulation; Acceleration; Computer science; Biomechanics; Mathematics; Engineering; Physics; Artificial intelligence; Classical mechanics","score_opus":0.05219372780606492,"score_gpt":0.38346250363758855,"score_spread":0.33126877583152364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402989097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79399824,0.0003124452,0.20352629,0.00004018774,0.000024381443,0.00007522773,0.00019662853,0.0005463258,0.0012802588],"genre_scores_gemma":[0.9875858,0.000090256784,0.011570516,0.000007884415,0.000007489072,0.00003858268,0.00011231611,0.000012203099,0.0005749241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998646,0.000036009365,0.000009612297,0.00004045666,0.00002967914,0.000019607438],"domain_scores_gemma":[0.9998437,0.000046935966,0.00002917819,0.000022035756,0.000044154287,0.000013972865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002203543,0.0006378852,0.0004155722,0.00043081166,0.000126086,0.00027563891,0.00024285329,0.00044140837,0.0006928078],"category_scores_gemma":[0.0009001605,0.00020470766,0.0002642963,0.00034779048,0.000096209515,0.00020781491,0.0003231409,0.00016724257,0.00033500057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001850373,0.0004767553,0.048337594,0.0009185869,0.0003417591,0.0013404139,0.0008871178,0.20140362,0.29039705,0.00051962666,0.0010385077,0.45248863],"study_design_scores_gemma":[0.00002850353,0.00093809166,0.10677606,0.000045383833,0.00009330105,0.0005013601,0.00019815074,0.8701776,0.020436577,0.00018273383,0.0005755442,0.000046740992],"about_ca_topic_score_codex":0.0038974427,"about_ca_topic_score_gemma":0.004215746,"teacher_disagreement_score":0.0038974427,"about_ca_system_score_codex":0.00008398387,"about_ca_system_score_gemma":0.00018140771,"threshold_uncertainty_score":0.007749498},"labels":[],"label_agreement":null},{"id":"W4402991087","doi":"10.3390/s24196314","title":"Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Computer science; Wireless sensor network; Scalability; Artificial neural network; Machine learning; Artificial intelligence; Data mining; Distributed computing; Computer network","score_opus":0.020671650928869714,"score_gpt":0.2516529711425559,"score_spread":0.2309813202136862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402991087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048918582,0.00047999024,0.9482443,0.00024281415,0.000075371754,0.000023338462,0.000080808204,0.0009741861,0.0009605377],"genre_scores_gemma":[0.7817895,0.0006351203,0.21515085,0.00021599754,0.00004660448,0.000084032115,0.0004109293,0.00008114234,0.0015858086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997695,0.00004485236,0.000016804146,0.000067040986,0.00007556161,0.00002619071],"domain_scores_gemma":[0.99950635,0.00020004567,0.00007289002,0.00007322843,0.00012909506,0.000018378041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006047324,0.00061237044,0.00044666414,0.00037847477,0.00029809654,0.00043165084,0.000966757,0.0005105574,0.0004564636],"category_scores_gemma":[0.002204152,0.00028905144,0.00042605633,0.0006655206,0.00047029334,0.0014255301,0.0010526933,0.0009098419,0.0001675313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011560798,0.00008425142,0.0019761443,0.00012114781,0.000053551317,0.00009973314,0.000098808865,0.7012361,0.019467069,0.003439232,0.0017893467,0.27151904],"study_design_scores_gemma":[0.0000029576559,0.000025269368,0.00023001431,0.000005596636,0.0000068413283,0.000017545177,0.000011920035,0.99393964,0.004093308,0.0012181619,0.00044423685,0.00000458324],"about_ca_topic_score_codex":0.003947819,"about_ca_topic_score_gemma":0.006146475,"teacher_disagreement_score":0.003947819,"about_ca_system_score_codex":0.0005877782,"about_ca_system_score_gemma":0.0005618832,"threshold_uncertainty_score":0.007849693},"labels":[],"label_agreement":null},{"id":"W4403048049","doi":"10.3390/s24196385","title":"A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China","keywords":"Artificial intelligence; Computer vision; Visible spectrum; Infrared; Computer science; Scale (ratio); Pattern recognition (psychology); Optics; Materials science; Geography; Optoelectronics; Cartography; Physics","score_opus":0.022953538698725698,"score_gpt":0.29036449187573155,"score_spread":0.26741095317700586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403048049","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18596105,0.006102017,0.020041186,0.0009093448,0.0009294491,0.0008148466,0.7694035,0.006249231,0.009589334],"genre_scores_gemma":[0.09618023,0.0011430037,0.02309515,0.0002578594,0.00009493013,0.0006149118,0.87492716,0.00014198406,0.0035448538],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991547,0.00007882907,0.00008029618,0.00032868353,0.00022959666,0.00012783306],"domain_scores_gemma":[0.9994293,0.000103566126,0.000077403616,0.00012161966,0.00020206493,0.000065941465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046549962,0.0017601891,0.0009902498,0.0021617336,0.00057887635,0.00075934414,0.001576501,0.0017384869,0.0031144798],"category_scores_gemma":[0.0010217495,0.0003479873,0.0012192874,0.0017773989,0.00043948187,0.0006530664,0.0011135845,0.0009476629,0.003442557],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001556571,0.0020162745,0.071526855,0.006548995,0.0008029209,0.0033636366,0.00048108568,0.023925958,0.068692975,0.0014337465,0.54282236,0.27682856],"study_design_scores_gemma":[0.0003817727,0.001173693,0.4082195,0.0019583304,0.0005221853,0.0038251125,0.0021309112,0.103867985,0.04415575,0.0025143279,0.4307734,0.000477038],"about_ca_topic_score_codex":0.0171877,"about_ca_topic_score_gemma":0.0424887,"teacher_disagreement_score":0.0171877,"about_ca_system_score_codex":0.0008439413,"about_ca_system_score_gemma":0.001007571,"threshold_uncertainty_score":0.034175336},"labels":[],"label_agreement":null},{"id":"W4403075504","doi":"10.3390/s24196386","title":"Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Convolutional neural network; Frame rate; Respiratory rate; Pattern recognition (psychology); Computer vision; Speech recognition; Heart rate; Medicine","score_opus":0.04056049848373663,"score_gpt":0.2724685071027976,"score_spread":0.23190800861906094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403075504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04978716,0.00069192203,0.946846,0.00013888655,0.00007919255,0.000037693968,0.00041689395,0.00093220413,0.0010701057],"genre_scores_gemma":[0.7883918,0.0008586221,0.2062953,0.00017672234,0.00012658886,0.00009705331,0.0011043104,0.0000951186,0.0028544643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979335,0.00003898052,0.000013757078,0.000070407186,0.000058768303,0.000024797715],"domain_scores_gemma":[0.99974257,0.00009375674,0.00004566045,0.00003287739,0.00006901956,0.00001608836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039375215,0.0006263428,0.000414249,0.0004892687,0.000113274335,0.0004196829,0.00055558264,0.00043459074,0.00084790384],"category_scores_gemma":[0.0013819193,0.00020410834,0.0003373972,0.00040724725,0.00015418671,0.00055330974,0.0004916382,0.0006882623,0.0004039526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005136686,0.00026841232,0.007364629,0.00030495846,0.00017328118,0.00027258956,0.000110912435,0.1506255,0.12002493,0.0024819053,0.0040244055,0.71383476],"study_design_scores_gemma":[0.000008098883,0.0000656197,0.0035146247,0.000021528214,0.000022308908,0.00014904306,0.000018068373,0.9742628,0.019389765,0.0014797567,0.0010539249,0.000014482735],"about_ca_topic_score_codex":0.0020056951,"about_ca_topic_score_gemma":0.004116567,"teacher_disagreement_score":0.0020056951,"about_ca_system_score_codex":0.0002663623,"about_ca_system_score_gemma":0.0003425022,"threshold_uncertainty_score":0.0039880276},"labels":[],"label_agreement":null},{"id":"W4403128721","doi":"10.3390/s24196440","title":"A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Cadence; Ground reaction force; Biomechanics; Kinematics; Crank; Simulation; Gait; Cycling; Force platform; Gait analysis; Computer science; Physical medicine and rehabilitation; Artificial intelligence; Physics; Medicine","score_opus":0.027569294076625523,"score_gpt":0.2988246419331102,"score_spread":0.2712553478564847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403128721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16566476,0.0013346976,0.82944554,0.00024516502,0.00008110755,0.00011966155,0.00027043765,0.0009398431,0.0018988999],"genre_scores_gemma":[0.923066,0.00041691848,0.07405978,0.00009365832,0.000046418558,0.0001784711,0.00033566798,0.00002781465,0.0017752104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979645,0.000035787743,0.000024414943,0.00007937662,0.00004178835,0.000022098755],"domain_scores_gemma":[0.9995505,0.00025293193,0.000044734894,0.00002414183,0.00011116706,0.00001651163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005676321,0.0007380202,0.0005680026,0.00086260244,0.00032063376,0.0004949211,0.0005191739,0.0008171257,0.0007119946],"category_scores_gemma":[0.0021303124,0.00034316568,0.00048925116,0.0005790402,0.00018519485,0.00034515286,0.00032299984,0.0006450711,0.00025886265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009333916,0.00013404689,0.005962724,0.00006273174,0.0000713971,0.00008311278,0.000046035617,0.79171824,0.0033412671,0.00042648628,0.0005682182,0.19749235],"study_design_scores_gemma":[0.0000013963605,0.000018012568,0.00074412185,0.000004400757,0.0000038259495,0.0000072519683,0.0000037079653,0.99876726,0.00018363638,0.00019028774,0.00007315231,0.000002970831],"about_ca_topic_score_codex":0.010925506,"about_ca_topic_score_gemma":0.007676259,"teacher_disagreement_score":0.010925506,"about_ca_system_score_codex":0.00036480406,"about_ca_system_score_gemma":0.0005830259,"threshold_uncertainty_score":0.021723807},"labels":[],"label_agreement":null},{"id":"W4403136855","doi":"10.3390/s24196431","title":"Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Hidden Markov model; Gait; Inertial measurement unit; Wearable computer; Accelerometer; Computer science; Measure (data warehouse); Artificial intelligence; Similarity (geometry); Gait analysis; Similarity measure; Pattern recognition (psychology); Gyroscope; Speech recognition; Engineering; Physical medicine and rehabilitation; Data mining; Medicine","score_opus":0.12332159322544387,"score_gpt":0.39012407853877684,"score_spread":0.26680248531333295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403136855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5123902,0.00018348449,0.48487726,0.000072873525,0.000046524445,0.000102623795,0.00037115434,0.00051920506,0.0014366355],"genre_scores_gemma":[0.95155066,0.000069553476,0.047564037,0.000027521673,0.000016963213,0.00006360565,0.0003212546,0.000022163054,0.0003640899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990102,0.00027599864,0.00011468691,0.00022903994,0.00031153677,0.000058532172],"domain_scores_gemma":[0.9977385,0.0012438322,0.0004186262,0.00018839222,0.00033413593,0.000076503624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00148362,0.00047915563,0.00060296035,0.0015256084,0.00021387184,0.0005169937,0.00035231162,0.000539585,0.0007973201],"category_scores_gemma":[0.0059940196,0.00020968288,0.0007220911,0.0009805667,0.00032354784,0.00081256486,0.00075453904,0.00035919982,0.00029876916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014223796,0.0005650394,0.16629851,0.00057498366,0.00079425,0.00041777288,0.0007561813,0.14885338,0.09029854,0.0028413201,0.0015642075,0.5856134],"study_design_scores_gemma":[0.000023927814,0.00063873356,0.134528,0.00004809297,0.00010505221,0.0004012415,0.00015614001,0.84993935,0.0115321735,0.0020328618,0.00052345684,0.000071002956],"about_ca_topic_score_codex":0.002159848,"about_ca_topic_score_gemma":0.002906025,"teacher_disagreement_score":0.002159848,"about_ca_system_score_codex":0.00027835454,"about_ca_system_score_gemma":0.00034599673,"threshold_uncertainty_score":0.007846236},"labels":[],"label_agreement":null},{"id":"W4403138452","doi":"10.3390/s24196427","title":"Effect of Unanticipated Tasks on Side-Cutting Stability of Lower Extremity with Patellofemoral Pain Syndrome","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Ningbo","keywords":"Patellofemoral pain syndrome; Physical medicine and rehabilitation; Ankle; Medicine; Rehabilitation; Physical therapy; Knee Joint; Patellofemoral joint; Joint stability; Orthodontics; Patella; Surgery","score_opus":0.013067896556615773,"score_gpt":0.22882043588586634,"score_spread":0.21575253932925056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403138452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99981385,0.000031591826,0.000036706693,0.0000058626624,0.0000015118706,0.00000233452,0.000008765926,9.126467e-7,0.00009844801],"genre_scores_gemma":[0.99983466,0.000017278579,0.00006548926,0.000007711712,0.0000019538077,0.0000028990246,0.000025333473,5.596717e-7,0.00004409094],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998048,0.00004859525,0.00002318749,0.000031150623,0.00004197995,0.000050317027],"domain_scores_gemma":[0.999126,0.00021254194,0.0003127998,0.000030122414,0.000083897816,0.00023467404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026136095,0.00033363164,0.00019063005,0.00024043623,0.00020621592,0.00017922063,0.000121455465,0.00024378378,0.0022094522],"category_scores_gemma":[0.0015332351,0.000112553804,0.00015583147,0.000081466555,0.00022719537,0.00014555927,0.00031503537,0.00019952547,0.00015448191],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046355333,0.0011944844,0.9230037,0.00011219242,0.00014341804,0.0044538863,0.00040711582,0.00041364052,0.041652445,0.000031536023,0.00014047732,0.023811584],"study_design_scores_gemma":[0.000025877225,0.0028374644,0.99192834,0.000013009938,0.00002341541,0.003335358,0.00020743549,0.00025334966,0.0012858632,0.00002027005,0.0000653051,0.0000044606486],"about_ca_topic_score_codex":0.00059620145,"about_ca_topic_score_gemma":0.001250141,"teacher_disagreement_score":0.0022094522,"about_ca_system_score_codex":0.000116881,"about_ca_system_score_gemma":0.00013559937,"threshold_uncertainty_score":0.0073913336},"labels":[],"label_agreement":null},{"id":"W4403250768","doi":"10.3390/s24196483","title":"Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Supine position; Position (finance); Pelvis; Artificial intelligence; Lying; Bin; Simulation; Prone position; Inertial measurement unit; Computer science; Physical medicine and rehabilitation; Engineering; Medicine; Surgery; Mechanical engineering","score_opus":0.03521455438625876,"score_gpt":0.39504062412347696,"score_spread":0.3598260697372182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403250768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7889231,0.0015433773,0.19482051,0.00096872385,0.0003511841,0.00079157244,0.0030020582,0.0025031746,0.007096428],"genre_scores_gemma":[0.95080227,0.00057595904,0.046329778,0.00014093924,0.00008481679,0.00020904274,0.0006677076,0.000042820015,0.0011466882],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992711,0.00020332159,0.000059379905,0.00014053647,0.0002651247,0.00006051828],"domain_scores_gemma":[0.99800044,0.0007860162,0.00040318992,0.000109765475,0.00058521796,0.00011537524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093197194,0.0010702774,0.00066126534,0.001337952,0.00033308755,0.0008707651,0.00047615697,0.00061093835,0.0029313636],"category_scores_gemma":[0.0056654364,0.00027831818,0.00044272357,0.00063409196,0.00020082983,0.00049770996,0.0005224977,0.0005059808,0.0014632228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001201643,0.00054351264,0.415865,0.00070430164,0.00020069027,0.0002979303,0.00084920286,0.0076866187,0.03337495,0.00022942992,0.0045645144,0.53448224],"study_design_scores_gemma":[0.00011400035,0.0033841822,0.7509541,0.00036286254,0.0002419116,0.001389646,0.0012703943,0.20203266,0.03234182,0.0010012805,0.0067421636,0.0001649999],"about_ca_topic_score_codex":0.0040021786,"about_ca_topic_score_gemma":0.008009413,"teacher_disagreement_score":0.0040021786,"about_ca_system_score_codex":0.00025476608,"about_ca_system_score_gemma":0.00043886396,"threshold_uncertainty_score":0.009806335},"labels":[],"label_agreement":null},{"id":"W4403296325","doi":"10.3390/s24206544","title":"Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; Tongji University; Henan Provincial Department of Transportation; U.S. Department of Transportation","keywords":"Point cloud; Robustness (evolution); Computer science; Segmentation; Cluster analysis; Data mining; Artificial intelligence; Feature extraction; Frame (networking); Computer vision; Precision and recall; Pattern recognition (psychology)","score_opus":0.01404814255733621,"score_gpt":0.2649224974138785,"score_spread":0.2508743548565423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403296325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09649783,0.0012240547,0.88463694,0.00018453914,0.00020107518,0.0005113829,0.004078391,0.009985125,0.00268056],"genre_scores_gemma":[0.39745235,0.0013308607,0.57878417,0.00015483274,0.00010246984,0.0004673306,0.018192837,0.0007708026,0.0027442824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880004,0.000044633576,0.00006200246,0.00031910362,0.0005151347,0.00025911542],"domain_scores_gemma":[0.99898285,0.00008355581,0.000113700065,0.00018919395,0.00058339897,0.00004719796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046743275,0.0017110442,0.0015474727,0.00655344,0.00082705554,0.0012892988,0.0016500932,0.0011497879,0.001034277],"category_scores_gemma":[0.001836213,0.00061437447,0.0017141047,0.00430897,0.0004556211,0.0013535216,0.0014087041,0.0011539524,0.0018725145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041842178,0.00031820408,0.012774869,0.0008272168,0.00026114643,0.0009332999,0.0005595008,0.08245173,0.10747234,0.002501374,0.017412726,0.77406913],"study_design_scores_gemma":[0.000036956095,0.00010816281,0.022293577,0.00013956479,0.00011910772,0.00069258956,0.0005296711,0.89805436,0.059443258,0.002714216,0.01575736,0.000111158115],"about_ca_topic_score_codex":0.029807162,"about_ca_topic_score_gemma":0.046056543,"teacher_disagreement_score":0.029807162,"about_ca_system_score_codex":0.0007159908,"about_ca_system_score_gemma":0.001764254,"threshold_uncertainty_score":0.059267282},"labels":[],"label_agreement":null},{"id":"W4403487562","doi":"10.3390/s24206679","title":"Classification of Breathing Phase and Path with In-Ear Microphones","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Music Media and Technology; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Classifier (UML); Computer science; Binary classification; Microphone; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Support vector machine; Sound pressure; Telecommunications","score_opus":0.01263141108056486,"score_gpt":0.2601623488788712,"score_spread":0.24753093779830632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403487562","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74123424,0.0012063519,0.24647422,0.00030172805,0.00038557302,0.0002722328,0.0027198563,0.0026201918,0.0047854707],"genre_scores_gemma":[0.89454126,0.00056174607,0.09736381,0.00015265493,0.00015354807,0.00013312642,0.002969179,0.00010700865,0.004017709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951863,0.00008694723,0.0000334394,0.00013508195,0.0001562734,0.00006953318],"domain_scores_gemma":[0.99944323,0.00020933956,0.00008183521,0.000049209113,0.00017035182,0.000046002864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047001836,0.0007517257,0.0006198348,0.0013762324,0.00020536786,0.00053401466,0.00053295016,0.00088154565,0.0020002583],"category_scores_gemma":[0.001664517,0.0001454742,0.00041963332,0.0007093739,0.00018578008,0.00072579895,0.0006167667,0.00037707257,0.001302666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015589312,0.0007409058,0.061311767,0.000551081,0.00018272386,0.00073406124,0.00023973743,0.013932468,0.21715708,0.0006183986,0.0056109754,0.6973619],"study_design_scores_gemma":[0.000198408,0.0017256294,0.2214136,0.00012986407,0.00021050396,0.003019671,0.0008574574,0.58339286,0.17513517,0.0025669858,0.011218774,0.0001311131],"about_ca_topic_score_codex":0.0011967915,"about_ca_topic_score_gemma":0.0021503821,"teacher_disagreement_score":0.0020002583,"about_ca_system_score_codex":0.00017272335,"about_ca_system_score_gemma":0.00028211152,"threshold_uncertainty_score":0.006691575},"labels":[],"label_agreement":null},{"id":"W4403516292","doi":"10.3390/s24206699","title":"Predicting Athlete Workload in Women’s Rugby Sevens Using GNSS Sensor Data, Contact Count and Mass","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"GNSS applications; Workload; Count data; Aeronautics; Computer science; Engineering; Telecommunications; Statistics; Global Positioning System; Operating system; Mathematics","score_opus":0.03764463390628911,"score_gpt":0.3002823979014681,"score_spread":0.262637763995179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403516292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991935,0.00004675128,0.00034424482,0.000014825376,0.0000017174502,0.000008486956,0.00012447358,0.0000057238626,0.0002604073],"genre_scores_gemma":[0.99869376,0.000073330135,0.00042955836,0.000008963948,0.0000050266553,0.000019353247,0.00043407548,0.0000023002733,0.00033374436],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997882,0.00005885168,0.000017240382,0.000046866717,0.00004633285,0.00004242859],"domain_scores_gemma":[0.99960035,0.00014953462,0.00010503149,0.000022266731,0.00007480479,0.000047999318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042980356,0.00054314546,0.00036551143,0.000955396,0.0002198888,0.0006025241,0.00024561642,0.00039917164,0.00094067],"category_scores_gemma":[0.0015589286,0.00017446297,0.0003198787,0.00065892003,0.00018487251,0.00022172673,0.00047449322,0.00017311361,0.00037625103],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001460132,0.00006798617,0.98673403,0.000018038094,0.000060118324,0.000073263735,0.00022431577,0.0016456366,0.0014708161,0.000033050495,0.00014935512,0.009377423],"study_design_scores_gemma":[0.0000032698372,0.00015696199,0.9921881,0.000012230468,0.000019660849,0.000070060974,0.0005609814,0.0063827424,0.00030754827,0.000034762106,0.0002582432,0.000005465163],"about_ca_topic_score_codex":0.019314665,"about_ca_topic_score_gemma":0.030515654,"teacher_disagreement_score":0.019314665,"about_ca_system_score_codex":0.00029533755,"about_ca_system_score_gemma":0.000249204,"threshold_uncertainty_score":0.038404524},"labels":[],"label_agreement":null},{"id":"W4403601768","doi":"10.3390/s24206764","title":"Piezo-VFETs: Vacuum Field Emission Transistors Controlled by Piezoelectric MEMS Sensors as an Artificial Mechanoreceptor with High Sensitivity and Low Power Consumption","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Piezoelectricity; Microelectromechanical systems; Transistor; Materials science; Sensitivity (control systems); Optoelectronics; Field-effect transistor; Electrical engineering; Piezoelectric sensor; Power (physics); Voltage; Electronic engineering; Engineering; Physics","score_opus":0.00553464478116495,"score_gpt":0.2269336002269588,"score_spread":0.22139895544579385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403601768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8213834,0.008055205,0.1492245,0.00083640224,0.0006424751,0.00013081479,0.0005176105,0.00080245535,0.018407274],"genre_scores_gemma":[0.9644221,0.0009891599,0.031244827,0.0001164135,0.00005897198,0.00004525874,0.0000837822,0.000021681031,0.0030177576],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991584,0.00000809268,0.0000041518497,0.000021992479,0.00003555737,0.000014373355],"domain_scores_gemma":[0.9999596,0.00000985212,0.000013640758,0.000002773612,0.000009200955,0.000005062753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077390956,0.00020786843,0.00016493959,0.000115732575,0.00008848364,0.00020090013,0.0003913629,0.0003859659,0.00076937216],"category_scores_gemma":[0.0001498597,0.00009788224,0.00014125179,0.00015596957,0.00013913926,0.00040768445,0.00017767125,0.00014725166,0.00022818359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002997581,0.000009566692,0.00019907206,0.00009653129,0.000007811302,0.00011368396,0.000015384012,0.0009519503,0.9864826,0.0014715202,0.00043273627,0.010189214],"study_design_scores_gemma":[0.000028690602,0.00036779695,0.0020835546,0.000033353648,0.0000205327,0.000751448,0.000049249094,0.03872141,0.9370648,0.0008033271,0.020049622,0.000026225884],"about_ca_topic_score_codex":0.00022654558,"about_ca_topic_score_gemma":0.0004341715,"teacher_disagreement_score":0.00076937216,"about_ca_system_score_codex":0.00016816892,"about_ca_system_score_gemma":0.000104287894,"threshold_uncertainty_score":0.0025738478},"labels":[],"label_agreement":null},{"id":"W4403601968","doi":"10.3390/s24206738","title":"An Overview of Software Sensor Applications in Biosystem Monitoring and Control","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Software; Monitoring and control; Computer science; Control (management); Engineering; Systems engineering; Embedded system; Control engineering; Operating system; Artificial intelligence","score_opus":0.03700925363584988,"score_gpt":0.32099567798668766,"score_spread":0.28398642435083776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403601968","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026718178,0.78176564,0.16891846,0.0017388737,0.0019265739,0.00024439482,0.0005545906,0.0017365542,0.04044306],"genre_scores_gemma":[0.031349555,0.8610356,0.08824834,0.0015640936,0.0019492183,0.00046208344,0.0012323816,0.00036305538,0.013795664],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984548,0.00025093343,0.0001988061,0.00023693143,0.000776772,0.0000818244],"domain_scores_gemma":[0.99808323,0.0011082252,0.00014095455,0.000115703275,0.0004894798,0.00006236006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330913,0.0017118092,0.0014023407,0.0027517395,0.0004225834,0.0021850492,0.0018352667,0.002491476,0.008193988],"category_scores_gemma":[0.0028096295,0.0007978422,0.0013492133,0.0038186526,0.0006999009,0.0030311216,0.0013144999,0.0019710455,0.0043559107],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010197977,0.0000946638,0.0007672284,0.016907599,0.000107043925,0.00038288764,0.00027459278,0.010114926,0.013129307,0.035466507,0.027630132,0.89502305],"study_design_scores_gemma":[0.000013168119,0.00023690671,0.0007691301,0.003794379,0.00009877889,0.00088121183,0.00010418215,0.010206148,0.0073971287,0.014179283,0.9622329,0.000086852255],"about_ca_topic_score_codex":0.0011167725,"about_ca_topic_score_gemma":0.0008013998,"teacher_disagreement_score":0.008193988,"about_ca_system_score_codex":0.0007333761,"about_ca_system_score_gemma":0.0011249862,"threshold_uncertainty_score":0.02741164},"labels":[],"label_agreement":null},{"id":"W4403680335","doi":"10.3390/s24216810","title":"Multi-Objective Design and Optimization of Hardware-Friendly Grid-Based Sparse MIMO Arrays","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"U.S. Department of Veterans Affairs","keywords":"Beamwidth; Computer science; Beamforming; Sparse array; MIMO; Computer engineering; Grid; Key (lock); Electronic engineering; Antenna (radio); Algorithm; Engineering; Mathematics; Telecommunications","score_opus":0.016348015388317087,"score_gpt":0.21907417585009767,"score_spread":0.20272616046178057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403680335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008893115,0.00010668258,0.9871149,0.000058133504,0.000013884691,0.00003223697,0.00004164182,0.00015676388,0.0035825605],"genre_scores_gemma":[0.5375451,0.00030881347,0.45785108,0.00011777172,0.00002854858,0.00038313304,0.0001865963,0.00011163969,0.0034673398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997491,0.00007104469,0.0000090315,0.000039661973,0.0000972428,0.000033861586],"domain_scores_gemma":[0.99970067,0.00013332436,0.000052065974,0.000020407671,0.000077589095,0.000015926267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046721514,0.00080442964,0.0005638306,0.00036394017,0.00015533033,0.0006328877,0.00062191865,0.00053235615,0.0017793044],"category_scores_gemma":[0.0008949489,0.00035060517,0.00041459344,0.0004239807,0.00033637413,0.00045153266,0.00066939,0.00047302575,0.0003977394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019802002,0.00001770234,0.00025387836,0.00004890239,0.00001411927,0.000025364152,0.000016330072,0.97419363,0.004572899,0.0034779678,0.00041252718,0.016946949],"study_design_scores_gemma":[0.000003980889,0.000034450855,0.00005928626,0.0000031249604,0.0000028048746,0.000009766587,0.000007418048,0.9979588,0.0006719298,0.0008014725,0.00044449666,0.0000025111726],"about_ca_topic_score_codex":0.0009956444,"about_ca_topic_score_gemma":0.001418984,"teacher_disagreement_score":0.0017793044,"about_ca_system_score_codex":0.00035251942,"about_ca_system_score_gemma":0.00069634017,"threshold_uncertainty_score":0.005952418},"labels":[],"label_agreement":null},{"id":"W4403689695","doi":"10.3390/s24216803","title":"Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Vancouver Coastal Health; University of British Columbia Hospital","funders":"Vancouver Coastal Health; Vancouver Coastal Health Research Institute","keywords":"Geriatric care; Engineering; Computer science; Psychology; Nursing; Medicine","score_opus":0.0149824201363047,"score_gpt":0.35054644947712826,"score_spread":0.3355640293408236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403689695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92782634,0.0044677425,0.008577872,0.050502658,0.0008114534,0.00021077157,0.00006430506,0.00004760061,0.007491324],"genre_scores_gemma":[0.9797895,0.003047217,0.0046103825,0.010911125,0.00019541972,0.00021044181,0.00003330597,0.000038028127,0.0011644454],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9321034,0.055088762,0.0020052306,0.0018643558,0.004105815,0.0048324657],"domain_scores_gemma":[0.9338636,0.037323575,0.0049195425,0.0011500325,0.009031311,0.013711975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04471302,0.0008500192,0.0009314509,0.0015321494,0.010551245,0.00901207,0.0026193848,0.0041151107,0.0018534033],"category_scores_gemma":[0.050891373,0.0008089688,0.0011393565,0.00103025,0.009122158,0.007061277,0.011965962,0.0075209136,0.00031379648],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058932626,0.0002253442,0.012957787,0.0004748504,0.000042778334,0.0012844778,0.96399087,0.00014232831,0.001004417,0.0017045299,0.0020195919,0.016094124],"study_design_scores_gemma":[0.000011054116,0.00018516195,0.0019576708,0.00045493312,0.000017680472,0.00034487646,0.98667395,0.00018266718,0.00026638896,0.00068074773,0.009196346,0.000028661012],"about_ca_topic_score_codex":0.004589829,"about_ca_topic_score_gemma":0.0063983505,"teacher_disagreement_score":0.04471302,"about_ca_system_score_codex":0.0049515977,"about_ca_system_score_gemma":0.011375874,"threshold_uncertainty_score":0.2364679},"labels":[],"label_agreement":null},{"id":"W4403765302","doi":"10.3390/s24216845","title":"An Exploratory Study of a Choreographic Approach to Golf Swing Dynamics: Bridging Biomechanics and Laban Movement Analysis","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Dynamics and Biomechanics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Bridging (networking); Biomechanics; Swing; Movement (music); Sports biomechanics; Dynamics (music); Computer science; Physical medicine and rehabilitation; Engineering; Human–computer interaction; Simulation; Mechanical engineering; Physics; Medicine; Acoustics; Anatomy","score_opus":0.007450829825114534,"score_gpt":0.21249481108651755,"score_spread":0.20504398126140302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403765302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941103,0.000044876,0.0040472248,0.000059089347,0.0000035378728,0.00008844598,0.00008922429,0.000013685038,0.0015435575],"genre_scores_gemma":[0.9930218,0.000077507604,0.0054132,0.000030049507,0.000005777241,0.000107460255,0.00010922696,0.000013755764,0.001221162],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99933076,0.00041666738,0.000015568527,0.00008742898,0.00007940187,0.000070158036],"domain_scores_gemma":[0.99905425,0.00048218144,0.00012039141,0.00008020064,0.00011932597,0.00014359705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008247434,0.00037079252,0.00029041103,0.001776033,0.0012919873,0.000859733,0.0003488922,0.00041001162,0.0021873114],"category_scores_gemma":[0.0031585987,0.00015079773,0.00018441617,0.0010098303,0.001193376,0.0006033993,0.0010936346,0.0003586549,0.00029626623],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072728464,0.0017634373,0.3563049,0.00097769,0.0000972685,0.0058992566,0.38247353,0.003946777,0.055902418,0.008975285,0.0030924797,0.17983967],"study_design_scores_gemma":[0.000037382408,0.001445464,0.62975323,0.00020528781,0.00003652947,0.0022308563,0.3225595,0.0123424465,0.0064465227,0.0045186155,0.020305125,0.000118988704],"about_ca_topic_score_codex":0.0041509992,"about_ca_topic_score_gemma":0.015468272,"teacher_disagreement_score":0.0041509992,"about_ca_system_score_codex":0.00048576292,"about_ca_system_score_gemma":0.00047802948,"threshold_uncertainty_score":0.008253634},"labels":[],"label_agreement":null},{"id":"W4403766025","doi":"10.3390/s24216856","title":"Onsite Seismic Monitoring Behavior of Undamaged Dams During the 2023 Kahramanmaraş Earthquakes (M7.7 and M7.6)","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Seismology; Aftershock; Geology; Foreshock; Fault (geology); Crest; Geotechnical engineering","score_opus":0.006383209655164119,"score_gpt":0.21434641092247023,"score_spread":0.2079632012673061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403766025","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993105,0.000011780261,0.000104695966,0.000007252191,0.0000010480363,0.0000030599838,0.00019476372,0.000017418186,0.0003494253],"genre_scores_gemma":[0.99939406,0.00002019296,0.00017602344,0.0000029250148,0.0000012734549,0.000006683247,0.00025794317,0.0000015033228,0.00013931906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988866,0.000014183949,0.000005581617,0.000022564196,0.00003563995,0.000033356468],"domain_scores_gemma":[0.9998653,0.000011398053,0.000044551656,0.000009919133,0.00004400562,0.00002483808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001301267,0.0002537847,0.00016817855,0.0005443383,0.00021324957,0.000195527,0.00024981325,0.0002788494,0.0004377419],"category_scores_gemma":[0.00029427485,0.000086744316,0.00008995976,0.00053583644,0.000116228584,0.00015034962,0.0003497694,0.00013347676,0.0001580207],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031915065,0.0001979568,0.9014989,0.00006215482,0.0000838992,0.0019499811,0.0021909,0.006176542,0.05424674,0.00015459617,0.001171058,0.03194805],"study_design_scores_gemma":[0.0000049260434,0.000108917025,0.9900152,0.0000059818044,0.000021177348,0.00013542177,0.0012506838,0.004252462,0.0032403918,0.000022852772,0.0009295316,0.0000123919635],"about_ca_topic_score_codex":0.011802222,"about_ca_topic_score_gemma":0.03531315,"teacher_disagreement_score":0.011802222,"about_ca_system_score_codex":0.00031890426,"about_ca_system_score_gemma":0.00020365325,"threshold_uncertainty_score":0.023467064},"labels":[],"label_agreement":null},{"id":"W4403835624","doi":"10.3390/s24216875","title":"Adaptation of a Model Spike Aptamer for Isothermal Amplification-Based Sensing","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aptamer; Loop-mediated isothermal amplification; RNA; DNA; Rolling circle replication; Nucleic acid; Computational biology; Nanotechnology; Biosensor; Biophysics; Chemistry; Biology; Polymerase; Genetics; Materials science; Gene","score_opus":0.025017660099031145,"score_gpt":0.3007966696221766,"score_spread":0.27577900952314544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403835624","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8478276,0.0005473367,0.14681827,0.00028147406,0.00022841207,0.00027345793,0.00035641872,0.00087293156,0.0027941857],"genre_scores_gemma":[0.9440727,0.00021672694,0.052457705,0.00014767011,0.000027015905,0.00012818183,0.0003874235,0.00006798485,0.002494613],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995975,0.00004930954,0.000026504531,0.0001023101,0.00017041384,0.000054028522],"domain_scores_gemma":[0.9997876,0.00004236888,0.00003640859,0.00004044965,0.00006154884,0.000031743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027231371,0.00035273813,0.00031803412,0.00016578835,0.00012569442,0.00042986873,0.0005204027,0.00059501873,0.00045839458],"category_scores_gemma":[0.00041333915,0.00017884678,0.0003957481,0.00016564268,0.00022823885,0.0003210314,0.00028468238,0.00066057744,0.00034911762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017475357,0.000022434331,0.000074453485,0.000016065296,0.0000024804906,0.000018325514,0.000008824877,0.00028465508,0.9981153,0.00011204443,0.000029434863,0.0012985396],"study_design_scores_gemma":[0.0000044375906,0.00010461988,0.00022942205,0.0000010026578,0.0000039001743,0.000070778144,0.00000502299,0.0060545886,0.9925298,0.000040400544,0.0009497212,0.0000062142876],"about_ca_topic_score_codex":0.0004091138,"about_ca_topic_score_gemma":0.00052481116,"teacher_disagreement_score":0.00059501873,"about_ca_system_score_codex":0.0004609441,"about_ca_system_score_gemma":0.00029532742,"threshold_uncertainty_score":0.003344357},"labels":[],"label_agreement":null},{"id":"W4403871345","doi":"10.3390/s24216937","title":"Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Pelvic floor disorders treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Classifier (UML); Random forest; Convolutional neural network; Artificial intelligence; Rating scale; Extractor; Machine learning; Mathematics; Engineering; Statistics","score_opus":0.0406424680452315,"score_gpt":0.31690814743897894,"score_spread":0.27626567939374747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403871345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2594968,0.0022174816,0.7042209,0.0008277479,0.00049184595,0.0009992415,0.0030221017,0.021815497,0.0069083837],"genre_scores_gemma":[0.758108,0.0007011419,0.23149651,0.00074405153,0.00019317465,0.00067373994,0.00245673,0.0001972742,0.005429317],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999501,0.000050777788,0.00004767494,0.00015621119,0.0001953029,0.000049011775],"domain_scores_gemma":[0.9991825,0.00018375498,0.00012181677,0.000051844214,0.00039287176,0.00006721243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076252053,0.00062179123,0.0005636976,0.001109541,0.00019508564,0.0005849598,0.0006521161,0.0005963518,0.0023979158],"category_scores_gemma":[0.0022725218,0.00016133829,0.0003908415,0.0006115218,0.00012223262,0.00059962954,0.000617484,0.0004733469,0.0009405033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006917819,0.0005418375,0.02400315,0.00035293025,0.00014207695,0.0002186361,0.00015554922,0.0076362714,0.057386894,0.00043455153,0.008006019,0.9004303],"study_design_scores_gemma":[0.00017531672,0.0015243195,0.11738397,0.00015788146,0.00029114546,0.0010976488,0.00014894112,0.80857056,0.057103343,0.0016866709,0.011699585,0.00016068999],"about_ca_topic_score_codex":0.002734231,"about_ca_topic_score_gemma":0.0029877126,"teacher_disagreement_score":0.002734231,"about_ca_system_score_codex":0.00044047966,"about_ca_system_score_gemma":0.0003670195,"threshold_uncertainty_score":0.0080218315},"labels":[],"label_agreement":null},{"id":"W4403874106","doi":"10.3390/s24216928","title":"Smart Compression Sock for Early Detection of Diabetic Foot Ulcers","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Diabetic foot; Compression (physics); Foot (prosody); Medicine; Computer science; Biomedical engineering; Diabetes mellitus; Materials science; Composite material; Endocrinology; Art","score_opus":0.016536947238033617,"score_gpt":0.28540545175072973,"score_spread":0.2688685045126961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403874106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97259843,0.0055580526,0.016484732,0.00025403663,0.0002481242,0.00035375557,0.0006949749,0.000563022,0.0032449341],"genre_scores_gemma":[0.9778733,0.002123876,0.017722791,0.00025875066,0.00007924626,0.00012121157,0.000289771,0.000018312498,0.0015127124],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997205,0.00006469389,0.00001877912,0.000041318064,0.00012587455,0.000028798919],"domain_scores_gemma":[0.999556,0.00015979788,0.000096887285,0.000018887467,0.000119050834,0.000049418217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032539334,0.000612818,0.0004764348,0.00066207687,0.00009516285,0.000383165,0.00031319394,0.00042305054,0.0028625382],"category_scores_gemma":[0.0011951084,0.00017405124,0.0002405323,0.00027343814,0.000120705794,0.00032998028,0.00030145585,0.00020216047,0.00040399958],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007526285,0.0018132828,0.14545727,0.002215872,0.00029941747,0.0008573625,0.00032481225,0.0012933263,0.18651591,0.00014721163,0.0035286057,0.6500206],"study_design_scores_gemma":[0.00094626157,0.02683042,0.81194186,0.0007346886,0.0011481142,0.008897587,0.00096817646,0.041733284,0.09476194,0.00042005465,0.011350716,0.00026687517],"about_ca_topic_score_codex":0.0006704233,"about_ca_topic_score_gemma":0.0016836397,"teacher_disagreement_score":0.0028625382,"about_ca_system_score_codex":0.0001328509,"about_ca_system_score_gemma":0.00014913852,"threshold_uncertainty_score":0.009576142},"labels":[],"label_agreement":null},{"id":"W4403895353","doi":"10.3390/s24216963","title":"Photoacoustic Resonators for Non-Invasive Blood Glucose Detection Through Photoacoustic Spectroscopy: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Photoacoustic imaging in biomedicine; Photoacoustic spectroscopy; Glycemic; Blood glucose monitoring; Medicine; Diabetes mellitus; Biomedical engineering; Materials science; Nanotechnology; Computer science; Optics; Physics","score_opus":0.01592989794794417,"score_gpt":0.27847179275312645,"score_spread":0.26254189480518225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403895353","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016395719,0.9990507,0.00015187543,0.00011852483,0.00008626574,0.00001909651,0.000049793496,0.0000050131534,0.0003548213],"genre_scores_gemma":[0.00087318843,0.9984498,0.00028019177,0.00009334662,0.000040628573,0.000022274216,0.000040878982,0.0000015355876,0.00019817323],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9994203,0.00010988359,0.00015086189,0.000094165436,0.00018892567,0.000035948495],"domain_scores_gemma":[0.9977062,0.0014385646,0.00034581704,0.00004365277,0.00041867472,0.000047034362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014428138,0.0010250718,0.0020715306,0.0047420594,0.0003272101,0.0013742488,0.0010145511,0.0011255806,0.006344664],"category_scores_gemma":[0.0041719936,0.00044890147,0.0020165567,0.0041277222,0.00035462275,0.0013380361,0.00064947625,0.0008116021,0.0011613325],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011458238,0.00008079734,0.00041384395,0.2865644,0.00081888307,0.00018579622,0.000120699115,0.00036566055,0.0016798839,0.0019301611,0.0134527525,0.6942726],"study_design_scores_gemma":[0.00006937574,0.00045401088,0.0034671142,0.14181463,0.0063926377,0.0018741788,0.00028773688,0.00036181026,0.0021986726,0.0020903351,0.84089696,0.00009263504],"about_ca_topic_score_codex":0.0020289884,"about_ca_topic_score_gemma":0.0040693837,"teacher_disagreement_score":0.006344664,"about_ca_system_score_codex":0.0005283089,"about_ca_system_score_gemma":0.0032273508,"threshold_uncertainty_score":0.021225035},"labels":[],"label_agreement":null},{"id":"W4404106335","doi":"10.3390/s24227144","title":"A Fabrication Method for Realizing Vertically Aligned Silicon Nanowires Featuring Precise Dimension Control","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Nanowire Synthesis and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Deep reactive-ion etching; Fabrication; Materials science; Nanowire; Etching (microfabrication); Silicon nanowires; Nanotechnology; Silicon; Lithography; Surface roughness; Photovoltaics; Electron-beam lithography; Optoelectronics; Reactive-ion etching; Resist; Photovoltaic system; Electrical engineering; Engineering","score_opus":0.009924171668860659,"score_gpt":0.2606164014058471,"score_spread":0.2506922297369864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404106335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36842555,0.012730014,0.59534365,0.0011278156,0.0010636882,0.00056775066,0.0011044081,0.0016791131,0.01795804],"genre_scores_gemma":[0.46009788,0.0055061956,0.5248906,0.0002481534,0.00014160154,0.00038728584,0.00050176,0.00011469692,0.008111859],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998404,0.000008961496,0.00001288134,0.000040745883,0.00008618643,0.000010809051],"domain_scores_gemma":[0.9999163,0.00001675561,0.000027291433,0.000019847539,0.00001322387,0.0000065719937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013231335,0.00050384126,0.00026214533,0.00035940754,0.00040146688,0.0002659185,0.00034150304,0.00046154446,0.00096244446],"category_scores_gemma":[0.0001430948,0.00057011924,0.0002450284,0.00025852007,0.00028630026,0.00036249554,0.0002418585,0.00064450386,0.00045057086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000351957,0.000006750557,0.00003021921,0.00006970089,0.0000024789806,0.000048987553,0.00001277288,0.00008101306,0.9939855,0.0007351088,0.00012359381,0.0049004],"study_design_scores_gemma":[0.000019696181,0.00015334086,0.00082691025,0.0000118076105,0.000010775997,0.000974036,0.000014439254,0.003008257,0.97530216,0.00033985754,0.019315472,0.000023187731],"about_ca_topic_score_codex":0.00036101448,"about_ca_topic_score_gemma":0.0009820239,"teacher_disagreement_score":0.00096244446,"about_ca_system_score_codex":0.00028746927,"about_ca_system_score_gemma":0.00041123512,"threshold_uncertainty_score":0.003219664},"labels":[],"label_agreement":null},{"id":"W4404252228","doi":"10.3390/s24227175","title":"Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"York University","keywords":"Accelerometer; Gait; Physical medicine and rehabilitation; Smartphone application; Computer science; Real-time computing; Medicine; Operating system; Multimedia","score_opus":0.07933138274658867,"score_gpt":0.35593724464886256,"score_spread":0.2766058619022739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404252228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948567,0.00007682056,0.004037953,0.00001798684,0.000010872502,0.00003344909,0.0006081436,0.000037772304,0.00032018212],"genre_scores_gemma":[0.987668,0.00017460785,0.010568709,0.000041653468,0.000022163824,0.000111460686,0.0008070348,0.000009398896,0.00059710274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979717,0.000041445233,0.000018195418,0.00006282267,0.000062251085,0.000018143035],"domain_scores_gemma":[0.9996517,0.000042278123,0.00009596452,0.000031076062,0.00013644618,0.000042566662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023125012,0.00030526335,0.00033358458,0.00054904254,0.00013517529,0.0002894447,0.00019375797,0.000327172,0.0007815489],"category_scores_gemma":[0.00064408383,0.00015453494,0.00022128936,0.00032922457,0.00014931012,0.00029078173,0.00030635056,0.00016676611,0.0002714575],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007907342,0.00034512335,0.64678156,0.00017748983,0.00018727095,0.00022828017,0.0006813812,0.00065509806,0.27850583,0.00007975078,0.0007746976,0.0707928],"study_design_scores_gemma":[0.000015350604,0.0007214489,0.98989826,0.000011551848,0.000042543976,0.00028815,0.00023439815,0.0031739105,0.0051582623,0.000046919387,0.00039194172,0.00001731327],"about_ca_topic_score_codex":0.0017827199,"about_ca_topic_score_gemma":0.0054178066,"teacher_disagreement_score":0.0017827199,"about_ca_system_score_codex":0.00010288046,"about_ca_system_score_gemma":0.00009640733,"threshold_uncertainty_score":0.0035446882},"labels":[],"label_agreement":null},{"id":"W4404252250","doi":"10.3390/s24227176","title":"Correction: Ebrahimi, A.; Czarnuch, S. Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC. Sensors 2021, 21, 3724","year":2024,"lang":"en","type":"erratum","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"RANSAC; Computer science; Artificial intelligence","score_opus":0.030840397937985046,"score_gpt":0.23871429946356632,"score_spread":0.20787390152558127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404252250","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028263745,0.00085189904,0.0022115975,0.018790234,0.9619789,0.000056357076,0.007639677,0.0017264263,0.0064621596],"genre_scores_gemma":[0.025997933,0.010129172,0.025233578,0.03843724,0.20201688,0.0005901305,0.038706053,0.010074558,0.64881444],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943557,0.000537643,0.000848028,0.0008948551,0.002895117,0.00046875674],"domain_scores_gemma":[0.96182925,0.0038192677,0.0017559553,0.003077866,0.027949505,0.0015681213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032506676,0.0038580622,0.00258134,0.0068536084,0.004246833,0.0048438557,0.00459936,0.0048362236,0.16089569],"category_scores_gemma":[0.05813244,0.001617245,0.0017459466,0.005579917,0.002669806,0.0032935631,0.0036919073,0.0070319534,0.11137407],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023408755,0.0000047050703,0.00005446915,0.00012708061,0.0000079819465,0.00011104046,0.000022706812,0.000040605624,0.00007478638,0.0003709975,0.99312943,0.006032757],"study_design_scores_gemma":[0.000021168065,0.000015026805,0.0004983042,0.0001831511,0.000023338162,0.00028079725,0.00008007362,0.000220207,0.00055516686,0.0006731943,0.99741876,0.000030957457],"about_ca_topic_score_codex":0.026254088,"about_ca_topic_score_gemma":0.03093365,"teacher_disagreement_score":0.16089569,"about_ca_system_score_codex":0.0030294647,"about_ca_system_score_gemma":0.0059940647,"threshold_uncertainty_score":0.53824973},"labels":[],"label_agreement":null},{"id":"W4404276110","doi":"10.3390/s24227231","title":"Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Skin cancer; Deep learning; Computer science; Artificial intelligence; Leverage (statistics); Artificial neural network; Deep neural networks; Architecture; Test set; Cancer; Machine learning; Medicine","score_opus":0.02110727035228062,"score_gpt":0.28845751395012764,"score_spread":0.26735024359784704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404276110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17208818,0.0018590302,0.8093544,0.00095845415,0.0002822566,0.00021757885,0.00042860166,0.008636538,0.0061749783],"genre_scores_gemma":[0.81365615,0.0004689667,0.17620178,0.0007292407,0.0000997634,0.00012141003,0.0008607605,0.00025361133,0.0076082493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983275,0.000025153664,0.0000063837965,0.000052345,0.00004309381,0.000040124047],"domain_scores_gemma":[0.9997255,0.000092964954,0.00002089627,0.00004402453,0.000083807376,0.000032808457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049896614,0.00078786106,0.00047575994,0.0004869173,0.00026337715,0.00051682297,0.0013241444,0.00060402707,0.0022679295],"category_scores_gemma":[0.0012503226,0.00025627651,0.00045304358,0.00034961588,0.00038417085,0.0012329984,0.0009484419,0.0011161343,0.0007163901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041142578,0.00033399207,0.0038788584,0.00016749823,0.00018749594,0.00028085633,0.00016224997,0.30026585,0.05818843,0.005724518,0.011820852,0.618578],"study_design_scores_gemma":[0.000016971391,0.00008837363,0.00046739838,0.000008119808,0.000026070806,0.00004028993,0.000012580787,0.9831761,0.012558641,0.0022287315,0.001367588,0.000009078052],"about_ca_topic_score_codex":0.0076659033,"about_ca_topic_score_gemma":0.017977461,"teacher_disagreement_score":0.0076659033,"about_ca_system_score_codex":0.00085589086,"about_ca_system_score_gemma":0.00086804223,"threshold_uncertainty_score":0.015242577},"labels":[],"label_agreement":null},{"id":"W4404304241","doi":"10.3390/s24227265","title":"Strategy for Accurate Detection of Six Tropane Alkaloids in Honey Using Lateral Flow Immunosensors","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Insect and Pesticide Research","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Tropane; Chromatography; Chemistry; Detection limit; Immunoassay; Biology; Organic chemistry; Antibody","score_opus":0.05816122916808514,"score_gpt":0.30813483139413933,"score_spread":0.2499736022260542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404304241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39504343,0.0063060047,0.59108853,0.00080415746,0.00044857923,0.0007874902,0.00049753307,0.0015814916,0.0034428204],"genre_scores_gemma":[0.54728377,0.0031743872,0.44395772,0.00064508006,0.00010396995,0.0009994532,0.00048660662,0.000046828485,0.0033021693],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940073,0.00012493902,0.00004371849,0.0001767835,0.00019821447,0.000055569133],"domain_scores_gemma":[0.99977773,0.000067591325,0.00005376151,0.0000137628995,0.0000720259,0.000015225222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007182361,0.0012481612,0.00038705472,0.0005772876,0.00024719682,0.00035113504,0.00066926976,0.00083539233,0.0005146058],"category_scores_gemma":[0.00065184414,0.0004321865,0.0004192015,0.0002130693,0.0003669072,0.0005619729,0.0004481799,0.00085453165,0.00041738022],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020848945,0.000025357138,0.00012959518,0.00010239706,0.000007598651,0.000043016866,0.000028324592,0.00012855478,0.99449414,0.00016827545,0.00008835416,0.004763548],"study_design_scores_gemma":[0.000009561899,0.00014431504,0.00044712258,0.000009650983,0.000018443561,0.00015794302,0.000024650619,0.007719076,0.9894623,0.000123532,0.0018653726,0.000018111687],"about_ca_topic_score_codex":0.0003638113,"about_ca_topic_score_gemma":0.00090153137,"teacher_disagreement_score":0.0012481612,"about_ca_system_score_codex":0.0003580166,"about_ca_system_score_gemma":0.00031582388,"threshold_uncertainty_score":0.0037984252},"labels":[],"label_agreement":null},{"id":"W4404330441","doi":"10.3390/s24227238","title":"A Systematic Review of Event-Matching Methods for Complex Event Detection in Video Streams","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Event (particle physics); STREAMS; Computer science; Matching (statistics); Data mining; Real-time computing; Computer network; Statistics; Mathematics","score_opus":0.051741160579949486,"score_gpt":0.41390458165425353,"score_spread":0.36216342107430405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404330441","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002818887,0.99728453,0.0012690831,0.000195264,0.00013587963,0.00011548705,0.00027205163,0.00002732202,0.00041861873],"genre_scores_gemma":[0.0023134004,0.99310607,0.003541518,0.00024797017,0.00008839962,0.00020380825,0.00028536402,0.000012259274,0.00020112263],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9971666,0.00075877097,0.0009425876,0.00036781488,0.0006980426,0.00006634304],"domain_scores_gemma":[0.98137015,0.015090369,0.0014941838,0.00032637763,0.0015876875,0.00013125596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046353075,0.0015727034,0.0029584863,0.007483375,0.00044437443,0.0018207497,0.0016391722,0.0013703919,0.0055810814],"category_scores_gemma":[0.02731337,0.0006898146,0.0041810763,0.006190801,0.00056930474,0.002484598,0.0010051491,0.0009722697,0.0010655395],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014505685,0.000042180873,0.0004483201,0.39216056,0.0015268759,0.00008731599,0.00015761923,0.0003902751,0.0005066128,0.0010428239,0.0073796352,0.5961127],"study_design_scores_gemma":[0.00017211295,0.00064742565,0.006205079,0.5340662,0.019822462,0.0012015428,0.00044397195,0.0009738531,0.0018393047,0.0030189645,0.4314431,0.00016599255],"about_ca_topic_score_codex":0.004205663,"about_ca_topic_score_gemma":0.0083757,"teacher_disagreement_score":0.007483375,"about_ca_system_score_codex":0.0011041841,"about_ca_system_score_gemma":0.0064451704,"threshold_uncertainty_score":0.024514139},"labels":[],"label_agreement":null},{"id":"W4404330453","doi":"10.3390/s24227235","title":"Facial Movements Extracted from Video for the Kinematic Classification of Speech","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Department of Health, Government of Western Australia; State Health Research Advisory Council","keywords":"Kinematics; Computer science; Speech recognition; Video recording; Artificial intelligence; Computer vision; Multimedia; Physics","score_opus":0.02566197339632642,"score_gpt":0.2557691831590249,"score_spread":0.2301072097626985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404330453","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8615169,0.0005441356,0.13210343,0.000064822525,0.00008537614,0.0002437885,0.0022536817,0.00068458164,0.002503365],"genre_scores_gemma":[0.9051148,0.000414567,0.09098851,0.000028548,0.000032459662,0.00021956657,0.0019567132,0.00004337316,0.0012013828],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972516,0.000043825203,0.000024904233,0.000078387784,0.00010205682,0.000025691226],"domain_scores_gemma":[0.9995732,0.00013958132,0.00006821736,0.000037122263,0.00015910006,0.00002283327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003222595,0.00038603434,0.0002554934,0.0011366149,0.00013018592,0.0002725288,0.00024792756,0.00029397872,0.0011644203],"category_scores_gemma":[0.0013827804,0.00010034952,0.00020411186,0.0003935855,0.00011608216,0.00024025427,0.00026437544,0.00015619639,0.00050334446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050615845,0.00016983865,0.060639415,0.000261948,0.000060596027,0.00029379147,0.0003214288,0.0038470428,0.35997358,0.0004020809,0.0015442432,0.57197994],"study_design_scores_gemma":[0.000049210936,0.0010506451,0.6341165,0.00012233594,0.00016637909,0.0020530375,0.00080715166,0.17921326,0.17595991,0.00053342804,0.005853861,0.00007427718],"about_ca_topic_score_codex":0.0023268014,"about_ca_topic_score_gemma":0.0050315782,"teacher_disagreement_score":0.0023268014,"about_ca_system_score_codex":0.00017589093,"about_ca_system_score_gemma":0.00024016772,"threshold_uncertainty_score":0.0046265125},"labels":[],"label_agreement":null},{"id":"W4404418548","doi":"10.3390/s24227296","title":"Development of a Real-Time Wearable Humming Detector Device","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Fonds de recherche du Québec","keywords":"Hum; Wearable computer; Computer science; Accelerometer; Software portability; Fast Fourier transform; Bluetooth Low Energy; Detector; Smartwatch; Energy (signal processing); Computer hardware; Bluetooth; Embedded system; Real-time computing; Human–computer interaction; Wireless; Telecommunications","score_opus":0.017502713602200546,"score_gpt":0.2556808455034341,"score_spread":0.23817813190123358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404418548","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1257136,0.0022030396,0.85769325,0.00044276356,0.00069946516,0.0013050854,0.0006598151,0.0038050937,0.0074778544],"genre_scores_gemma":[0.37715903,0.0014198304,0.6017362,0.0006979638,0.00018639199,0.001087511,0.0007963224,0.0001645586,0.016752295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995577,0.000075719785,0.000036526308,0.00012588585,0.00017127254,0.000032893986],"domain_scores_gemma":[0.999519,0.00013493498,0.000041298466,0.000055722074,0.00018796556,0.000060955314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062354753,0.00051124813,0.0006153255,0.00055302074,0.00014740168,0.0005048994,0.0010049873,0.00075225777,0.0034631505],"category_scores_gemma":[0.0010126401,0.00025203865,0.00029627528,0.00022619808,0.00019237015,0.0006106956,0.0005478246,0.00037798707,0.001713544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003808248,0.0002204395,0.0023681766,0.0007872814,0.000050095823,0.00049946154,0.00021117675,0.0007699284,0.7188842,0.0011236824,0.0030515036,0.27165315],"study_design_scores_gemma":[0.00031327904,0.007952001,0.021592693,0.0002800546,0.00028533512,0.0074405335,0.00033658018,0.04167722,0.8107357,0.0006893776,0.10843907,0.00025819827],"about_ca_topic_score_codex":0.0001657972,"about_ca_topic_score_gemma":0.0001823141,"teacher_disagreement_score":0.0034631505,"about_ca_system_score_codex":0.00012498282,"about_ca_system_score_gemma":0.0003052491,"threshold_uncertainty_score":0.011585414},"labels":[],"label_agreement":null},{"id":"W4404526808","doi":"10.3390/s24227346","title":"Correction: Kaur, M.; Menon, C. Submillimeter Sized 2D Electrothermal Optical Fiber Scanner. Sensors 2023, 23, 404","year":2024,"lang":"en","type":"erratum","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Scanner; Materials science; Optoelectronics; Computer science; Optics; Physics","score_opus":0.007160136576535153,"score_gpt":0.22341207383808234,"score_spread":0.21625193726154718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404526808","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010008635,0.0010106657,0.00059812464,0.032805257,0.96275616,0.00002016582,0.0005294971,0.0002897777,0.0018902672],"genre_scores_gemma":[0.012523533,0.014068094,0.0070359926,0.11425794,0.53315485,0.00027784132,0.0027485827,0.0018363701,0.31409684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950598,0.00044099006,0.00068578625,0.00075798755,0.0027156759,0.0003398106],"domain_scores_gemma":[0.96887,0.0049425415,0.0016360941,0.0016236715,0.02138136,0.0015463261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037012852,0.002901262,0.002150422,0.0043737236,0.0041775005,0.0040826956,0.003939868,0.008811763,0.035005223],"category_scores_gemma":[0.051603444,0.0015205344,0.001607172,0.0021875394,0.0036407316,0.0029527154,0.0023222156,0.01283312,0.03609743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013392691,0.0000040911905,0.00002653721,0.00008298412,0.000005276842,0.00016564733,0.00002008346,0.00001729075,0.000063061016,0.000388697,0.99496484,0.004248167],"study_design_scores_gemma":[0.000012751482,0.000010228471,0.0002777065,0.00013746446,0.000018039913,0.000474117,0.000043024185,0.000105766594,0.00040426274,0.0005044732,0.9979918,0.000020341919],"about_ca_topic_score_codex":0.017706467,"about_ca_topic_score_gemma":0.018165635,"teacher_disagreement_score":0.035005223,"about_ca_system_score_codex":0.0039898576,"about_ca_system_score_gemma":0.005798954,"threshold_uncertainty_score":0.11710411},"labels":[],"label_agreement":null},{"id":"W4404593237","doi":"10.3390/s24237426","title":"Eddy Current Measurement of Electrical Resistivity in Heat-Treated Zr-2.5%Nb Pressure Tubes","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Nuclear Materials and Properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Electrical resistivity and conductivity; Eddy current; Materials science; Current (fluid); Electrical current; Electrical engineering; Mechanics; Mechanical engineering; Engineering; Physics","score_opus":0.029935153642018406,"score_gpt":0.25842848000397045,"score_spread":0.22849332636195205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404593237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9849193,0.00029969562,0.013767756,0.000022197177,0.00001427649,0.00003238269,0.00017461547,0.00013725189,0.0006325393],"genre_scores_gemma":[0.9929676,0.00013949556,0.006309874,0.000011469684,0.000005018462,0.00002201523,0.00008515831,0.000018136992,0.00044124707],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972886,0.000063645486,0.000018639266,0.00005648149,0.000110565146,0.000021822725],"domain_scores_gemma":[0.9994678,0.00017842262,0.00014597399,0.000047485864,0.00014476718,0.000015657444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033323653,0.00020567376,0.00021398261,0.00019410781,0.0001156226,0.0002433869,0.00022744919,0.00018555507,0.00048419906],"category_scores_gemma":[0.0009685091,0.00015662023,0.0001493809,0.00024958517,0.00024751024,0.00015763214,0.00012523704,0.00022111955,0.000105493134],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006788753,0.000010820308,0.0009856658,0.00002800518,0.0000055241117,0.00002147281,0.000058307673,0.00024505547,0.9963864,0.000022227738,0.00001685875,0.0021516443],"study_design_scores_gemma":[0.000007795898,0.00031583133,0.016204821,0.00000308062,0.000019080026,0.00006799369,0.00003940528,0.002233028,0.98074484,0.0000138452,0.0003449307,0.0000053414815],"about_ca_topic_score_codex":0.000784774,"about_ca_topic_score_gemma":0.0008817057,"teacher_disagreement_score":0.000784774,"about_ca_system_score_codex":0.00021582058,"about_ca_system_score_gemma":0.000092257615,"threshold_uncertainty_score":0.0017623305},"labels":[],"label_agreement":null},{"id":"W4404636187","doi":"10.3390/s24237458","title":"An Automated Feature-Based Image Registration Strategy for Tool Condition Monitoring in CNC Machine Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Tool wear; Artificial intelligence; Computer science; Computer vision; Machine tool; Feature (linguistics); Machine vision; Subpixel rendering; Image processing; Machining; Process (computing); Enhanced Data Rates for GSM Evolution; Engineering; Image (mathematics); Pixel; Mechanical engineering","score_opus":0.0234904686020716,"score_gpt":0.3030937157512211,"score_spread":0.2796032471491495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404636187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07611985,0.00044626,0.9156898,0.000111485926,0.00007216824,0.0002390115,0.0001626755,0.0046757036,0.00248309],"genre_scores_gemma":[0.49587607,0.00019912134,0.5009113,0.00010890812,0.000033585227,0.00018833831,0.00043224278,0.00025641223,0.0019939402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871373,0.00014115826,0.000059230533,0.00028673623,0.00070257054,0.00009659676],"domain_scores_gemma":[0.9987882,0.00017084763,0.00018648413,0.0003117995,0.00050321623,0.00003955091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089248206,0.00057089305,0.0006702058,0.0017329323,0.00029898944,0.00081619626,0.0011952012,0.00072525104,0.0015142766],"category_scores_gemma":[0.0026570093,0.00028734596,0.00032488685,0.0014369246,0.0003997308,0.00095044606,0.00087406364,0.000676338,0.0011934567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003035252,0.00016266966,0.0026555813,0.00021986924,0.00003186559,0.00018187759,0.00020350218,0.00991355,0.3128905,0.0017589178,0.0032178033,0.66846025],"study_design_scores_gemma":[0.00004850224,0.0008521681,0.027356723,0.000050620398,0.000054798413,0.0013007276,0.00018175502,0.5817174,0.3680234,0.0019402924,0.018304601,0.00016903471],"about_ca_topic_score_codex":0.0016216262,"about_ca_topic_score_gemma":0.0027526426,"teacher_disagreement_score":0.0017329323,"about_ca_system_score_codex":0.00048456056,"about_ca_system_score_gemma":0.00087757985,"threshold_uncertainty_score":0.0050657988},"labels":[],"label_agreement":null},{"id":"W4404691502","doi":"10.3390/s24237503","title":"A Large-Scale Building Unsupervised Extraction Method Leveraging Airborne LiDAR Point Clouds and Remote Sensing Images Based on a Dual P-Snake Model","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Science Foundation of Heilongjiang Province","keywords":"Point cloud; Computer science; Lidar; Remote sensing; Artificial intelligence; Scale (ratio); Metric (unit); Computer vision; Boundary (topology); Extraction (chemistry); Building model; Pixel; Pattern recognition (psychology); Geography; Mathematics; Simulation; Engineering; Cartography","score_opus":0.014416947755836353,"score_gpt":0.2791655514871607,"score_spread":0.26474860373132436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404691502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01714403,0.0002519084,0.9781883,0.00009362107,0.00003758906,0.000095047515,0.00023270892,0.003193567,0.00076319935],"genre_scores_gemma":[0.12843162,0.00041180686,0.86538386,0.00016992113,0.000032162272,0.00017085824,0.0023807304,0.00055935665,0.0024596502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993742,0.00004597758,0.00003578464,0.00022440607,0.0002522892,0.00006736857],"domain_scores_gemma":[0.9994947,0.0000877356,0.000056595603,0.00012598097,0.0001982959,0.000036775342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006378386,0.0013079896,0.0011772522,0.0025934575,0.000555243,0.0012368024,0.0020361159,0.0011606733,0.0010937294],"category_scores_gemma":[0.0012845602,0.0009543683,0.0022355139,0.0021803032,0.0005697026,0.0017642506,0.0017339893,0.0015100957,0.0012459987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021069405,0.00017051528,0.0048543587,0.00028012137,0.00027964008,0.00034879573,0.000295818,0.19127189,0.0722825,0.004733867,0.0103472825,0.71492463],"study_design_scores_gemma":[0.000019494253,0.000049181406,0.0015356503,0.000022554328,0.000036348858,0.0003152346,0.000060736427,0.97631675,0.015327396,0.0024031682,0.003879035,0.000034529075],"about_ca_topic_score_codex":0.005570445,"about_ca_topic_score_gemma":0.010601499,"teacher_disagreement_score":0.005570445,"about_ca_system_score_codex":0.0004013978,"about_ca_system_score_gemma":0.0011437949,"threshold_uncertainty_score":0.011076033},"labels":[],"label_agreement":null},{"id":"W4404692828","doi":"10.3390/s24237483","title":"A Quantitative Method to Guide the Integration of Textile Inductive Electrodes in Automotive Applications for Respiratory Monitoring","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; CTT Group (Canada); Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Automotive industry; Textile; Electrode; Automotive engineering; Engineering; Computer science; Manufacturing engineering; Materials science; Chemistry; Aerospace engineering; Composite material","score_opus":0.04821157146762702,"score_gpt":0.368884413721684,"score_spread":0.320672842254057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404692828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019311069,0.00036469032,0.97729445,0.00010047719,0.00005374644,0.0001624236,0.000071705916,0.0011113154,0.001530172],"genre_scores_gemma":[0.19334967,0.00034595552,0.80412424,0.000121600235,0.0000251353,0.0001954041,0.0000869181,0.00019188982,0.0015591013],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983424,0.0004010792,0.00014810398,0.00019772945,0.00085170986,0.00005892365],"domain_scores_gemma":[0.9952625,0.0019127798,0.00063039403,0.00045912317,0.0016610279,0.00007422723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027617838,0.0010555775,0.00042633733,0.0015046971,0.0003032997,0.0010157509,0.0009821922,0.00096276635,0.0016372357],"category_scores_gemma":[0.0075413147,0.00045515056,0.00035981517,0.0005252423,0.00054909935,0.00096211623,0.00059482426,0.0006448289,0.0007912272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019190586,0.0000961689,0.0021540155,0.0005091637,0.000024055917,0.0001935217,0.00029314717,0.010048601,0.85075253,0.002229137,0.00068237126,0.13282539],"study_design_scores_gemma":[0.000046843023,0.001071559,0.005157263,0.00013968494,0.00007628023,0.0007496059,0.00020690374,0.11097474,0.8656867,0.0018342645,0.013947896,0.000108299704],"about_ca_topic_score_codex":0.0004904332,"about_ca_topic_score_gemma":0.0011656082,"teacher_disagreement_score":0.0027617838,"about_ca_system_score_codex":0.00042338096,"about_ca_system_score_gemma":0.0004074321,"threshold_uncertainty_score":0.01460588},"labels":[],"label_agreement":null},{"id":"W4404693793","doi":"10.3390/s24237473","title":"MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shanghai","keywords":"Artificial intelligence; Segmentation; Computer science; Convolutional neural network; Pattern recognition (psychology); Inference; Image segmentation; Transformer; Artificial neural network; Deep learning; Machine learning; Engineering","score_opus":0.00857722584169689,"score_gpt":0.24897657791845276,"score_spread":0.24039935207675586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404693793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11676129,0.0032367902,0.8642397,0.00065405946,0.00030295955,0.00020174605,0.00035272658,0.007912252,0.006338476],"genre_scores_gemma":[0.69792086,0.001121712,0.29219455,0.00055537035,0.00010672592,0.00010747869,0.00074901857,0.00028277235,0.0069614723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976856,0.000037618636,0.000011339771,0.0000670206,0.00007363777,0.000041859777],"domain_scores_gemma":[0.9997862,0.00006847617,0.000023687917,0.000029214098,0.00006705975,0.000025268257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005929487,0.0008693931,0.00061646494,0.0009046657,0.00023196138,0.00057924667,0.0011626829,0.0009051895,0.0012764472],"category_scores_gemma":[0.0010762884,0.000390979,0.0005047219,0.0006774689,0.0003374358,0.0011646193,0.00078526884,0.0006040725,0.0003413432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005527839,0.00023690866,0.0055109845,0.00024038482,0.0002688408,0.00045056775,0.00007872978,0.30657363,0.040230654,0.0049914788,0.008120555,0.6327445],"study_design_scores_gemma":[0.000012279029,0.00009777012,0.00045908702,0.0000071706736,0.000027151775,0.00012820729,0.000009540928,0.98871315,0.008024684,0.0009759047,0.0015339847,0.0000110490655],"about_ca_topic_score_codex":0.007665889,"about_ca_topic_score_gemma":0.009980384,"teacher_disagreement_score":0.007665889,"about_ca_system_score_codex":0.0008916593,"about_ca_system_score_gemma":0.0008929354,"threshold_uncertainty_score":0.015242577},"labels":[],"label_agreement":null},{"id":"W4404721399","doi":"10.3390/s24237535","title":"A Ku-Band Compact Offset Cylindrical Reflector Antenna with High Gain for Low-Earth Orbit Sensing Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Canadian Space Agency","keywords":"Cassegrain antenna; Offset dish antenna; Periscope antenna; Reflector (photography); Fan-beam antenna; Offset (computer science); CubeSat; Optics; Feed horn; Ku band; Antenna measurement; Engineering; Computer science; Antenna (radio); Physics; Aerospace engineering; Electrical engineering; Satellite","score_opus":0.012913146429034633,"score_gpt":0.2400922122091247,"score_spread":0.22717906578009006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404721399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30271563,0.0009573776,0.6651974,0.00046182337,0.00023080215,0.00011714691,0.0004249379,0.0022982783,0.027596585],"genre_scores_gemma":[0.7091969,0.0007253841,0.27839664,0.00022237423,0.000079655736,0.00009695152,0.00068169594,0.00016561356,0.010434763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972147,0.00003140418,0.000011262459,0.000058343594,0.00014402042,0.000033430435],"domain_scores_gemma":[0.9997154,0.000031102034,0.00006937423,0.000056403973,0.000105116735,0.000022607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016718134,0.00052302337,0.00034093,0.00024751094,0.000111223046,0.00049213076,0.0006030802,0.0005127068,0.0013751574],"category_scores_gemma":[0.00036194167,0.00019271787,0.00040785316,0.00047708055,0.00016731051,0.0004984178,0.00038860994,0.0003658731,0.001683542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002293393,0.000058725833,0.0026884442,0.00026170097,0.000040353127,0.0005675274,0.0001364629,0.009747808,0.87569624,0.0050936905,0.0040992196,0.10138035],"study_design_scores_gemma":[0.00006623576,0.0022040447,0.008587492,0.000051818224,0.00009864225,0.0050163562,0.0001949356,0.09896678,0.8127537,0.0011003504,0.07080437,0.00015522291],"about_ca_topic_score_codex":0.0002859628,"about_ca_topic_score_gemma":0.0006174242,"teacher_disagreement_score":0.0013751574,"about_ca_system_score_codex":0.0002531559,"about_ca_system_score_gemma":0.00027762624,"threshold_uncertainty_score":0.0046004057},"labels":[],"label_agreement":null},{"id":"W4404721453","doi":"10.3390/s24237532","title":"Technologies and Sensors for Artificial Muscles in Rehabilitation","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Wearable computer; Computer science; Artificial muscle; Rehabilitation; Physical medicine and rehabilitation; Artificial intelligence; Simulation; Medicine; Physical therapy; Embedded system","score_opus":0.03595909546050039,"score_gpt":0.3002282448630323,"score_spread":0.2642691494025319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404721453","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039975744,0.9914614,0.0013434704,0.0003995812,0.0005058844,0.000021182588,0.000038238304,0.000023412536,0.0058071306],"genre_scores_gemma":[0.0031580748,0.98907554,0.0020145352,0.0003720179,0.00021451182,0.00003629511,0.00006382036,0.0000078685325,0.0050573233],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99956137,0.00006948219,0.000045720753,0.00008091019,0.00020232632,0.00004018951],"domain_scores_gemma":[0.9996855,0.00014892314,0.000044995748,0.000016376258,0.0000890706,0.00001516652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067130214,0.0011191315,0.0010971554,0.0027055058,0.00039949937,0.0011547821,0.0010149267,0.0021510536,0.0077688503],"category_scores_gemma":[0.0007549285,0.00047954769,0.0010160499,0.0021532194,0.00053407194,0.0019741622,0.00092249166,0.0021106396,0.004707416],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050094808,0.00014504803,0.00021989418,0.026526194,0.00009471412,0.00029698893,0.00010750525,0.0006666021,0.013208967,0.013612572,0.021806037,0.92326534],"study_design_scores_gemma":[0.000006919975,0.000117112846,0.00057531986,0.0042225635,0.00007864152,0.0011048537,0.00006815007,0.00021993156,0.0031610474,0.0033598957,0.98706144,0.000024052428],"about_ca_topic_score_codex":0.00069697987,"about_ca_topic_score_gemma":0.0011858812,"teacher_disagreement_score":0.0077688503,"about_ca_system_score_codex":0.00047470813,"about_ca_system_score_gemma":0.0007185045,"threshold_uncertainty_score":0.025989354},"labels":[],"label_agreement":null},{"id":"W4404757890","doi":"10.3390/s24237556","title":"Rolling Resistance Evaluation of Pavements Using Embedded Transducers on a Semi-Trailer Suspension","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Trailer; Suspension (topology); Asphalt; Environmental science; Energy consumption; Slab; Rolling resistance; Automotive engineering; Engineering; Subgrade; Cement; Geotechnical engineering; Road surface; Structural engineering; Civil engineering; Materials science; Composite material","score_opus":0.05400492374635027,"score_gpt":0.31395714722409507,"score_spread":0.2599522234777448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404757890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99369556,0.000047308342,0.00580005,0.0000073437236,0.000007784931,0.000011795288,0.00009992871,0.0000946903,0.00023554853],"genre_scores_gemma":[0.99717253,0.00003972951,0.0023552275,0.0000056832096,0.0000028471206,0.000007586402,0.00008133826,0.000008229277,0.0003269278],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994261,0.00006319251,0.000029979488,0.00014846635,0.00027220536,0.000059976395],"domain_scores_gemma":[0.99963987,0.00009416466,0.000079248704,0.000039258986,0.0001268155,0.000020689551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027516394,0.0004649495,0.00041432958,0.00051426917,0.00015886524,0.0003418022,0.00047267936,0.0004020291,0.0007804913],"category_scores_gemma":[0.00050251273,0.0001533824,0.0002480212,0.00036521067,0.0002717351,0.00032240115,0.00028195477,0.000247309,0.00028937517],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023335921,0.000103122744,0.016451254,0.00010674492,0.000034637556,0.00013498856,0.00023327355,0.0055317017,0.95752275,0.00004459557,0.000091081216,0.019512529],"study_design_scores_gemma":[0.000016859767,0.0020022388,0.157214,0.000019969164,0.0001340516,0.00033410834,0.00071488187,0.055776644,0.781964,0.000084971594,0.0016677624,0.00007055042],"about_ca_topic_score_codex":0.0015332133,"about_ca_topic_score_gemma":0.0037916284,"teacher_disagreement_score":0.0015332133,"about_ca_system_score_codex":0.00021673492,"about_ca_system_score_gemma":0.00015691381,"threshold_uncertainty_score":0.0030485988},"labels":[],"label_agreement":null},{"id":"W4404960182","doi":"10.3390/s24237632","title":"Radar Sensor Data Fitting for Accurate Linear Sprint Modelling","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"Sprint; Radar; Computer science; Linear model; Remote sensing; Data mining; Telecommunications; Machine learning; Geography","score_opus":0.4893269840653866,"score_gpt":0.5202498308642206,"score_spread":0.030922846798834003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404960182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21671006,0.00052472576,0.7781234,0.00016561268,0.00008840218,0.0003038244,0.0005146434,0.0012382666,0.002331054],"genre_scores_gemma":[0.7673204,0.00040834642,0.22997732,0.00006455682,0.000015859545,0.00033825322,0.0007008262,0.00023871828,0.0009357499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824643,0.0006404114,0.00018862299,0.0002708266,0.00055318227,0.00010055633],"domain_scores_gemma":[0.99453753,0.0032678286,0.0004979487,0.00044283262,0.0011945487,0.00005924438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00448651,0.0012137566,0.0008233112,0.0009843952,0.00038559778,0.001156648,0.0008921441,0.000843311,0.0021428769],"category_scores_gemma":[0.018397031,0.00042967885,0.0011158772,0.0011983543,0.0003147373,0.0011599499,0.0008191051,0.0009955278,0.00089220155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010538867,0.0005710966,0.06130021,0.0021279263,0.0004249207,0.00032164526,0.0016315384,0.44911858,0.08959707,0.0022962017,0.0024585407,0.38909844],"study_design_scores_gemma":[0.000031982363,0.00059814,0.040586937,0.00024158008,0.00012168151,0.00027391608,0.0003740301,0.9155061,0.03486111,0.0012880161,0.005999456,0.000117071315],"about_ca_topic_score_codex":0.005002873,"about_ca_topic_score_gemma":0.006081219,"teacher_disagreement_score":0.005002873,"about_ca_system_score_codex":0.00048470372,"about_ca_system_score_gemma":0.0012029107,"threshold_uncertainty_score":0.023727238},"labels":[],"label_agreement":null},{"id":"W4404994101","doi":"10.3390/s24237754","title":"Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Inertial measurement unit; Computer science; Systems engineering; Engineering; Real-time computing; Artificial intelligence","score_opus":0.0372584709111057,"score_gpt":0.2657031642411769,"score_spread":0.2284446933300712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404994101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063244468,0.00010601979,0.9920859,0.000045486577,0.000017883285,0.000017569833,0.00003784321,0.00014387647,0.0012210202],"genre_scores_gemma":[0.7957605,0.0005164015,0.19882101,0.00010533227,0.00007112669,0.00017982948,0.000269077,0.00011504214,0.0041616145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940705,0.00014143364,0.000033195156,0.00017283623,0.00019786313,0.000047559843],"domain_scores_gemma":[0.9996371,0.000093533774,0.00009436352,0.00006981393,0.000091119415,0.000014020095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006778047,0.000949507,0.0005805843,0.0004555197,0.00026223183,0.00088471634,0.0011190424,0.00087875006,0.0006762501],"category_scores_gemma":[0.0016384114,0.0005145658,0.0008539822,0.00055685773,0.0005467846,0.0014171745,0.0009725365,0.0007663443,0.000317049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018224222,0.000010609444,0.00085630984,0.000035006073,0.0000293153,0.000050122322,0.000052580217,0.96434104,0.0032639352,0.011235952,0.00023544849,0.019871397],"study_design_scores_gemma":[0.0000010980527,0.000010402636,0.00019035087,0.000002573863,0.0000042647166,0.000012644713,0.0000045260585,0.9972899,0.00060487434,0.0014658222,0.00040958772,0.0000040384916],"about_ca_topic_score_codex":0.004303072,"about_ca_topic_score_gemma":0.0023213185,"teacher_disagreement_score":0.004303072,"about_ca_system_score_codex":0.0005373668,"about_ca_system_score_gemma":0.0005011709,"threshold_uncertainty_score":0.008556068},"labels":[],"label_agreement":null},{"id":"W4404994594","doi":"10.3390/s24237753","title":"Depth-Based Intervention Detection in the Neonatal Intensive Care Unit Using Vision Transformers","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neonatal intensive care unit; Transformer; Computer science; Artificial intelligence; Computer vision; Real-time computing; Video monitoring; Intensive care; Psychological intervention; Medicine; Engineering; Intensive care medicine; Pediatrics; Nursing","score_opus":0.016864359594172817,"score_gpt":0.26662827383328214,"score_spread":0.24976391423910932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404994594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.254291,0.00034547618,0.73977464,0.00016359327,0.00006852387,0.00013450682,0.00023065157,0.0027623682,0.0022293413],"genre_scores_gemma":[0.937386,0.00018703435,0.06123612,0.000048920825,0.0000077851355,0.000041236104,0.00015149018,0.000029773912,0.00091163657],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997733,0.00004174015,0.000009847288,0.000060349805,0.00008011407,0.00003455756],"domain_scores_gemma":[0.9997799,0.000071452596,0.000027182052,0.0000200004,0.00008287342,0.000018724706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003355313,0.0004852888,0.0003736704,0.00045158368,0.000109251894,0.0004585677,0.0006026964,0.00034717002,0.0008416545],"category_scores_gemma":[0.0012534437,0.0002007347,0.00036222505,0.0003218594,0.00019236226,0.00042544695,0.0005284068,0.000326047,0.00034673506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010230915,0.00027133708,0.02175763,0.00030566653,0.00011279622,0.00027060538,0.00016747836,0.2318543,0.1209922,0.0017007707,0.00194795,0.61959624],"study_design_scores_gemma":[0.000027531309,0.0003314748,0.005556791,0.000014512644,0.000025905927,0.00018362812,0.00004605889,0.95606154,0.03638697,0.000537552,0.0008066141,0.000021546808],"about_ca_topic_score_codex":0.0068016862,"about_ca_topic_score_gemma":0.0068078996,"teacher_disagreement_score":0.0068016862,"about_ca_system_score_codex":0.0005911362,"about_ca_system_score_gemma":0.00079882285,"threshold_uncertainty_score":0.013524234},"labels":[],"label_agreement":null},{"id":"W4405002098","doi":"10.3390/s24237693","title":"Adjoint-Assisted Shape Optimization of Microlenses for CMOS Image Sensors","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ansys (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"CMOS; Image sensor; Computer vision; Microlens; Artificial intelligence; Image (mathematics); Computer science; Computer graphics (images); Engineering; Optics; Electronic engineering; Lens (geology); Physics","score_opus":0.00961120173622991,"score_gpt":0.2280206760307326,"score_spread":0.2184094742945027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405002098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05296249,0.00034156235,0.9430242,0.00015807281,0.000026019694,0.00003184584,0.000028401259,0.00023793624,0.0031893833],"genre_scores_gemma":[0.62352973,0.00026196457,0.37461066,0.00008769159,0.000016435926,0.000074525095,0.000046708403,0.00008757893,0.0012846191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986136,0.000026952403,0.0000043682176,0.000015164645,0.0000781165,0.0000139665435],"domain_scores_gemma":[0.9997875,0.00011692666,0.000028837321,0.000014207802,0.00003948641,0.000013125942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044319752,0.00037698954,0.00032250045,0.0002574982,0.00017566417,0.0004164413,0.00034477562,0.0004951655,0.000530283],"category_scores_gemma":[0.00072495284,0.0002688412,0.00035324914,0.00018464332,0.000435468,0.0003231449,0.00044463377,0.00049385865,0.0001109799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037912883,0.000037083017,0.0003194994,0.000078040444,0.000017097593,0.000050685216,0.000045224144,0.8882158,0.07581001,0.014957844,0.00023754449,0.020193217],"study_design_scores_gemma":[0.000003011393,0.00001341925,0.000044561333,0.0000021825083,0.0000015855054,0.000011013474,0.0000036656254,0.9930078,0.0053687803,0.0012128389,0.00032756862,0.0000036660938],"about_ca_topic_score_codex":0.0007727429,"about_ca_topic_score_gemma":0.0011478158,"teacher_disagreement_score":0.0007727429,"about_ca_system_score_codex":0.00057212735,"about_ca_system_score_gemma":0.00056149106,"threshold_uncertainty_score":0.004151106},"labels":[],"label_agreement":null},{"id":"W4405002180","doi":"10.3390/s24237664","title":"Multimodal Material Classification Using Visual Attention","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton; Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.030582760571114788,"score_gpt":0.28287387229844135,"score_spread":0.25229111172732654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405002180","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5373039,0.007906088,0.41810256,0.0012294175,0.0005587401,0.0005118896,0.0053255185,0.013563622,0.015498269],"genre_scores_gemma":[0.907786,0.0009782466,0.07713948,0.00047208962,0.00028650183,0.00015099284,0.007044696,0.00017241902,0.0059696026],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932814,0.00009536445,0.000022914504,0.0003164508,0.00012751637,0.00010971033],"domain_scores_gemma":[0.99934953,0.00022654023,0.00007326165,0.000121425,0.00017059593,0.000058592606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093345967,0.0017470929,0.0016412032,0.0030804893,0.0004447268,0.0012739246,0.001788403,0.001232665,0.0025350342],"category_scores_gemma":[0.0019422116,0.00027889488,0.0015917396,0.0013068103,0.00040353579,0.0014293927,0.0016195147,0.0008511024,0.0011727448],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011774828,0.0005818487,0.010360993,0.00034655852,0.00029608418,0.00026867428,0.0001917605,0.041912433,0.04355026,0.0011489248,0.015102541,0.8850624],"study_design_scores_gemma":[0.000047166355,0.00038570855,0.012976818,0.00005694326,0.00015181582,0.0003104912,0.00019569533,0.96227455,0.015623406,0.0036963283,0.00423926,0.000041862382],"about_ca_topic_score_codex":0.0131777655,"about_ca_topic_score_gemma":0.01256941,"teacher_disagreement_score":0.0131777655,"about_ca_system_score_codex":0.0011343273,"about_ca_system_score_gemma":0.0006159299,"threshold_uncertainty_score":0.026202083},"labels":[],"label_agreement":null},{"id":"W4405002186","doi":"10.3390/s24237666","title":"The Minimum Number of Strides Required for Reliable Gait Measurements in Older Adult Fallers and Non-Fallers","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; GF Strong Rehabilitation Centre; Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"Gait; Physical medicine and rehabilitation; Computer science; Simulation; Medicine","score_opus":0.03883273681917743,"score_gpt":0.359770050098779,"score_spread":0.32093731327960157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405002186","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860023,0.00038152124,0.011062258,0.00014937836,0.00003745227,0.00014052927,0.00084321504,0.000101079764,0.0012822058],"genre_scores_gemma":[0.9749921,0.00025434367,0.022089178,0.000070025126,0.000020550531,0.00054631155,0.0013140002,0.000026722395,0.0006867115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964484,0.0011163347,0.0007952978,0.00046003793,0.0010172693,0.00016262327],"domain_scores_gemma":[0.9897301,0.0038816344,0.0019273894,0.00087207195,0.0031623293,0.000426477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005729027,0.00036989187,0.0007507298,0.0008305672,0.00059372175,0.0007648975,0.00057917734,0.0007910659,0.00126989],"category_scores_gemma":[0.028193988,0.0002919039,0.00049663516,0.0004612386,0.0003544617,0.00076085835,0.0008356232,0.00042771074,0.00062673783],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038437445,0.0006879825,0.70547366,0.0011603523,0.00028752495,0.00044011432,0.0049757287,0.0028717266,0.023592664,0.0005038425,0.003143264,0.25301942],"study_design_scores_gemma":[0.00005100946,0.0017058247,0.98457515,0.00023346631,0.000080044614,0.00063151796,0.0018717104,0.0038794298,0.004588493,0.00052015856,0.0018126686,0.000050455397],"about_ca_topic_score_codex":0.0021750096,"about_ca_topic_score_gemma":0.007679594,"teacher_disagreement_score":0.005729027,"about_ca_system_score_codex":0.00024328759,"about_ca_system_score_gemma":0.00069368543,"threshold_uncertainty_score":0.030298412},"labels":[],"label_agreement":null},{"id":"W4405002387","doi":"10.3390/s24237650","title":"Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Reputation; Routing protocol; Network packet; Latency (audio); Context (archaeology); Reputation system; Low latency (capital markets); Routing (electronic design automation); Protocol (science); Packet loss; Computer security; Distributed computing; Telecommunications","score_opus":0.04170181233166667,"score_gpt":0.30947528337528846,"score_spread":0.2677734710436218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405002387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073660456,0.0010701631,0.9190435,0.0004192534,0.00016088002,0.00017837131,0.00010516083,0.00091491005,0.0044473135],"genre_scores_gemma":[0.94693327,0.00035240458,0.051173214,0.000088879475,0.000036834936,0.00009812078,0.00011237561,0.000024165602,0.0011807531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994543,0.00014170226,0.00005347479,0.000097687174,0.00017194149,0.00008088849],"domain_scores_gemma":[0.9990355,0.00030219942,0.00022820063,0.00015150596,0.00019259004,0.000090093294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008119282,0.00045164893,0.0006035411,0.00049350667,0.0006897023,0.0007022748,0.00093793776,0.00043551723,0.00030232331],"category_scores_gemma":[0.002402119,0.00021049194,0.0003389967,0.00052537397,0.0003966994,0.0014262749,0.0014041328,0.0006694532,0.00010323621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072909,0.00030010112,0.0075940755,0.0004453702,0.00030552244,0.0020409788,0.0013257944,0.46325958,0.08353156,0.115522586,0.010918627,0.31402677],"study_design_scores_gemma":[0.000037127575,0.00016394598,0.00093498774,0.000018400458,0.00006512539,0.0005756135,0.000118476724,0.9705119,0.0069429334,0.014418939,0.006153178,0.00005934294],"about_ca_topic_score_codex":0.0023115857,"about_ca_topic_score_gemma":0.0029805552,"teacher_disagreement_score":0.0023115857,"about_ca_system_score_codex":0.00065500836,"about_ca_system_score_gemma":0.0010665664,"threshold_uncertainty_score":0.004752457},"labels":[],"label_agreement":null},{"id":"W4405074613","doi":"10.3390/s24237784","title":"Discrete Event System Specification for IoT Applications","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Petri Nets in System Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Event (particle physics); Computer science; Internet of Things; Embedded system; Physics","score_opus":0.025491267145254803,"score_gpt":0.28832545179164426,"score_spread":0.26283418464638947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405074613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017594531,0.00008312788,0.99345905,0.00019089614,0.000049637118,0.0001479912,0.00020205499,0.00041432193,0.0036935154],"genre_scores_gemma":[0.16368316,0.0006720498,0.8246348,0.00041057827,0.00009366854,0.0015717294,0.0018963721,0.0002664038,0.006771354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974057,0.0008637446,0.00048624244,0.00023500506,0.00088344206,0.0001258423],"domain_scores_gemma":[0.99741095,0.0011727952,0.00027583973,0.00041621333,0.00064897776,0.00007523267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002897464,0.00073292793,0.00041875915,0.0006852412,0.00044344415,0.0016689237,0.0014468617,0.0011878619,0.0023414544],"category_scores_gemma":[0.005120917,0.00047451674,0.0010309868,0.00069100363,0.0009525515,0.0012861051,0.0013026015,0.0022633485,0.0010293056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050787174,0.00008085267,0.0008218171,0.0003594552,0.000032062544,0.0006161996,0.0006439584,0.12014143,0.008740366,0.83115566,0.004443862,0.032913618],"study_design_scores_gemma":[0.0000670876,0.00012848852,0.00033410272,0.00023663508,0.0000422904,0.0005091031,0.00020670112,0.6682033,0.012164894,0.184239,0.13382015,0.00004832378],"about_ca_topic_score_codex":0.002300562,"about_ca_topic_score_gemma":0.0025967343,"teacher_disagreement_score":0.002897464,"about_ca_system_score_codex":0.00087061507,"about_ca_system_score_gemma":0.002152454,"threshold_uncertainty_score":0.01532346},"labels":[],"label_agreement":null},{"id":"W4405185278","doi":"10.3390/s24237841","title":"A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"License; Robustness (evolution); Convolutional neural network; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Deep learning; Adverse weather; Data mining; Meteorology","score_opus":0.015139535469468744,"score_gpt":0.22541454807586458,"score_spread":0.21027501260639583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405185278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2422638,0.0011063091,0.73729867,0.00056382385,0.0003446548,0.00013034855,0.0014529114,0.0076072393,0.009232392],"genre_scores_gemma":[0.88645726,0.00040749754,0.10238352,0.00017842796,0.000081529935,0.000046287198,0.0023623463,0.0001544345,0.007928596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998561,0.00001601436,0.0000058497517,0.0000500189,0.000038819733,0.000033100292],"domain_scores_gemma":[0.99985456,0.000023923292,0.00002128766,0.000024567154,0.00006584904,0.000009756069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029659888,0.0008578377,0.00027172966,0.0006761086,0.0002042822,0.0004090064,0.00076070795,0.00043156033,0.0014464587],"category_scores_gemma":[0.0006887557,0.00020908873,0.00039903753,0.00039287886,0.00022138389,0.0007948808,0.00051395135,0.0007218148,0.00062453246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023073428,0.00024429875,0.0077327006,0.000094210896,0.0001705072,0.00019234204,0.000056992187,0.568032,0.05144135,0.0030378501,0.008774539,0.35999244],"study_design_scores_gemma":[0.000003016631,0.000020183741,0.0017948018,0.0000046608075,0.000014824154,0.000026275877,0.000007981686,0.98837286,0.008192233,0.0005350873,0.0010196799,0.000008451303],"about_ca_topic_score_codex":0.027412424,"about_ca_topic_score_gemma":0.042121414,"teacher_disagreement_score":0.027412424,"about_ca_system_score_codex":0.00068984524,"about_ca_system_score_gemma":0.00071026816,"threshold_uncertainty_score":0.054505706},"labels":[],"label_agreement":null},{"id":"W4405237342","doi":"10.3390/s24247884","title":"Acoustic Wave Sensor Detection of an Ovarian Cancer Biomarker with Antifouling Surface Chemistry","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Acoustic Wave Resonator Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Biofouling; Biomarker; Ovarian cancer; Cancer detection; Cancer; Chemistry; Medicine; Internal medicine; Biochemistry","score_opus":0.011786313958470773,"score_gpt":0.22671118480390157,"score_spread":0.2149248708454308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405237342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9230562,0.005474024,0.06777154,0.0004534424,0.00023919133,0.000109828135,0.00020848514,0.00030346075,0.002383837],"genre_scores_gemma":[0.9141328,0.0032407409,0.07655475,0.000420133,0.000110718334,0.00016153975,0.00023841171,0.000024633879,0.005116286],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996649,0.00006260905,0.000018369568,0.00007102529,0.00015814466,0.000024946827],"domain_scores_gemma":[0.9997322,0.0000875924,0.00006322186,0.000014771072,0.00008353599,0.00001873528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003196418,0.00039996332,0.00028080522,0.00028036008,0.00011701883,0.00026340928,0.00039844017,0.00077444094,0.00058267784],"category_scores_gemma":[0.00048986526,0.00020731025,0.00022630337,0.00020196164,0.00020782898,0.00034722115,0.0002756258,0.0004391033,0.00031162373],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021641834,0.000008201601,0.00014974907,0.000033165015,0.0000036668391,0.000013706257,0.000011019948,0.000043743476,0.99794847,0.000025131365,0.000023664363,0.0017178832],"study_design_scores_gemma":[0.0000049656123,0.0002732959,0.0013787366,0.0000046610035,0.000012677632,0.00018607006,0.000024568264,0.0020370062,0.9946554,0.00002645228,0.0013891347,0.000006957921],"about_ca_topic_score_codex":0.00021103895,"about_ca_topic_score_gemma":0.00029312234,"teacher_disagreement_score":0.00077444094,"about_ca_system_score_codex":0.00022085154,"about_ca_system_score_gemma":0.00014505327,"threshold_uncertainty_score":0.0019491911},"labels":[],"label_agreement":null},{"id":"W4405284509","doi":"10.3390/s24247920","title":"A Rapidly Tunable Laser System for Measurements of NH2 at 597 nm Behind Reflected Shock Waves","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation Graduate Research Fellowship Program; Natural Sciences and Engineering Research Council of Canada; Total; National Science Foundation","keywords":"Materials science; Laser; Optics; Wavelength; Lithium niobate; Optoelectronics; Diode; Spectroscopy; Second-harmonic generation; Physics","score_opus":0.029763396390219368,"score_gpt":0.2904130915870715,"score_spread":0.26064969519685216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405284509","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.879221,0.00066183554,0.11556505,0.00014856768,0.00008439706,0.0002468217,0.00044640576,0.0010508526,0.002575102],"genre_scores_gemma":[0.8692203,0.00044357055,0.12668008,0.00012805594,0.00002893321,0.00039181157,0.000297627,0.000073683834,0.0027359587],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937797,0.0000891626,0.00002684233,0.00018193002,0.0002737777,0.000050340415],"domain_scores_gemma":[0.99964,0.00013851201,0.000071494265,0.00003626406,0.0000820907,0.000031563115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005736464,0.00051686977,0.00038359847,0.0006678209,0.0004576096,0.0003042148,0.0007159874,0.00067726104,0.0013360052],"category_scores_gemma":[0.000515789,0.00024766958,0.00021742498,0.00039812885,0.00041587278,0.00048682466,0.00044971803,0.0008412315,0.00036065208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003383432,0.000010968642,0.00018935825,0.000017244134,0.0000022870195,0.00001647955,0.000020312047,0.000065343615,0.99758744,0.00008923725,0.0000317908,0.0019356973],"study_design_scores_gemma":[0.0000250125,0.00023931744,0.0013334071,0.0000049042546,0.000010608871,0.00015390918,0.000023526214,0.0033246586,0.99380255,0.0000584087,0.0010071907,0.000016451537],"about_ca_topic_score_codex":0.0007243114,"about_ca_topic_score_gemma":0.0012565253,"teacher_disagreement_score":0.0013360052,"about_ca_system_score_codex":0.00052023825,"about_ca_system_score_gemma":0.00070413685,"threshold_uncertainty_score":0.0044693947},"labels":[],"label_agreement":null},{"id":"W4405326891","doi":"10.3390/s24247948","title":"Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Shanghai","keywords":"Transformer; Computer science; Feature extraction; Convolutional neural network; Artificial intelligence; Deep learning; Feature learning; Long short term memory; Artificial neural network; Pattern recognition (psychology); Recurrent neural network; Machine learning; Engineering; Voltage","score_opus":0.014409603119226845,"score_gpt":0.2984925133935247,"score_spread":0.2840829102742979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405326891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25914398,0.0015373316,0.72238207,0.00048786317,0.000262907,0.00023175424,0.0022699637,0.007170718,0.006513492],"genre_scores_gemma":[0.90099186,0.00046656505,0.087454356,0.00030281197,0.00009883367,0.00013322398,0.00432547,0.00014366425,0.0060832896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999858,0.000018201106,0.0000064638502,0.000051391118,0.000032422264,0.000033472457],"domain_scores_gemma":[0.9998098,0.000057022164,0.00001866034,0.000028511424,0.00007119097,0.000014850472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000284563,0.0010696643,0.00038147677,0.0006048278,0.00018566215,0.00034724647,0.00068101723,0.00044969784,0.0018454936],"category_scores_gemma":[0.0010134818,0.00018776716,0.00062093785,0.00042904506,0.0002411691,0.0008977191,0.00070657395,0.00085884245,0.000725836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008767088,0.0004665775,0.007851579,0.0002286002,0.0001825517,0.00036049468,0.00014549808,0.11237187,0.055211104,0.003289027,0.015564989,0.803451],"study_design_scores_gemma":[0.000022031081,0.0002736717,0.003196242,0.000013881226,0.00006902257,0.00012621493,0.000052027975,0.9729753,0.017800467,0.0031752181,0.0022765493,0.000019426683],"about_ca_topic_score_codex":0.0065396926,"about_ca_topic_score_gemma":0.012546284,"teacher_disagreement_score":0.0065396926,"about_ca_system_score_codex":0.000542596,"about_ca_system_score_gemma":0.0006292444,"threshold_uncertainty_score":0.01300329},"labels":[],"label_agreement":null},{"id":"W4405374406","doi":"10.3390/s24247960","title":"On the Feasibility of Detecting Faults and Irregularities in On-Load Tap Changers (OLTCs) by Vibroacoustic Signal Analysis","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Transformer; Robustness (evolution); Reliability engineering; Computer science; Engineering; Voltage; Electronic engineering; Electrical engineering","score_opus":0.015023141417520116,"score_gpt":0.23256459156771692,"score_spread":0.2175414501501968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405374406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95286673,0.0010333266,0.032790236,0.0005941855,0.00021832726,0.0001608463,0.0062239855,0.0006421669,0.0054702037],"genre_scores_gemma":[0.97350615,0.0004575618,0.015153904,0.000099019155,0.00008958151,0.00004801725,0.009644905,0.000022483993,0.0009782547],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991748,0.00014183688,0.00006541645,0.0002352356,0.00028043558,0.00010238247],"domain_scores_gemma":[0.9968972,0.0015959892,0.00033228318,0.00032566453,0.00073691277,0.00011190919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011422662,0.00073941716,0.00046492158,0.0016962319,0.00034325075,0.0008793448,0.00079611706,0.0010118426,0.000879763],"category_scores_gemma":[0.00522371,0.00013425491,0.00044709953,0.0009980344,0.00044123147,0.0011282461,0.00066562684,0.00066471426,0.00062070414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002509245,0.0013710841,0.31146353,0.0009846283,0.00031220255,0.00089186546,0.00029346356,0.1514357,0.04200564,0.0015472041,0.013366906,0.4738185],"study_design_scores_gemma":[0.00009438289,0.00073242444,0.27106407,0.00014794563,0.00010803749,0.0005027146,0.0006373806,0.69641805,0.021929897,0.001530859,0.0067577683,0.00007657339],"about_ca_topic_score_codex":0.012323046,"about_ca_topic_score_gemma":0.017823603,"teacher_disagreement_score":0.012323046,"about_ca_system_score_codex":0.00039926678,"about_ca_system_score_gemma":0.0005335832,"threshold_uncertainty_score":0.024502635},"labels":[],"label_agreement":null},{"id":"W4405374849","doi":"10.3390/s24247963","title":"A 6.7 μW Low-Noise, Compact PLL with an Input MEMS-Based Reference Oscillator Featuring a High-Resolution Dead/Blind Zone-Free PFD","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Phase-locked loop; Phase noise; dBc; CMOS; Phase frequency detector; Linearity; Voltage-controlled oscillator; Electronic engineering; Digitally controlled oscillator; Frequency synthesizer; Materials science; Electrical engineering; Voltage; Engineering; Capacitor; Variable-frequency oscillator; Charge pump","score_opus":0.018451072675265846,"score_gpt":0.24799838198302157,"score_spread":0.22954730930775571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405374849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31460235,0.003517662,0.6580887,0.0008573848,0.0008719953,0.00038905194,0.0008036565,0.008301457,0.0125677],"genre_scores_gemma":[0.69229954,0.0008279002,0.29118606,0.0005790916,0.00035669282,0.00016870955,0.0006072857,0.000281028,0.013693751],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993604,0.000045396853,0.000036064597,0.00018905096,0.00032525536,0.000043798016],"domain_scores_gemma":[0.99978846,0.000042407,0.000047063986,0.000029585099,0.00006766215,0.000024726112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039899393,0.00045255394,0.00065822084,0.00046894074,0.00025142232,0.00055824366,0.001310091,0.00061452,0.002218921],"category_scores_gemma":[0.0005454877,0.00028189184,0.00026158284,0.00047377686,0.00019943151,0.00080298097,0.0004081077,0.00045975926,0.0008469163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017757753,0.000063186024,0.0007271491,0.00021806319,0.000022860355,0.00021262979,0.00007189823,0.00076644646,0.93223715,0.0010315188,0.0012967552,0.063174695],"study_design_scores_gemma":[0.00013679413,0.0012013634,0.0028317454,0.000027209782,0.00008177377,0.0024291733,0.000026163349,0.024584208,0.9398812,0.00020826019,0.02852679,0.00006528595],"about_ca_topic_score_codex":0.00035094042,"about_ca_topic_score_gemma":0.0005576701,"teacher_disagreement_score":0.002218921,"about_ca_system_score_codex":0.00033146786,"about_ca_system_score_gemma":0.00033837903,"threshold_uncertainty_score":0.0074230433},"labels":[],"label_agreement":null},{"id":"W4405374959","doi":"10.3390/s24247959","title":"Design and Evaluation of Augmented Reality-Enhanced Robotic System for Epidural Interventions","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Augmented reality; Robotics; Computer science; Virtual reality; Human–computer interaction; Artificial intelligence; Rendering (computer graphics); Robot; Simulation","score_opus":0.1981595906429901,"score_gpt":0.4218156484345445,"score_spread":0.2236560577915544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405374959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4292113,0.0008277902,0.559481,0.00033810624,0.00036614528,0.0013326122,0.00024359197,0.0020827868,0.006116647],"genre_scores_gemma":[0.8731397,0.00033619875,0.12220029,0.00013441827,0.000027649716,0.00063724996,0.00016365622,0.000051018735,0.0033098552],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929607,0.00018596911,0.00007070394,0.00011636772,0.00024454412,0.00008623519],"domain_scores_gemma":[0.99921596,0.00017907204,0.00008142274,0.000094577146,0.0003398842,0.00008901934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091187574,0.0006215029,0.00047966652,0.00045878146,0.00023918075,0.0008015625,0.0010976441,0.0008336131,0.003701899],"category_scores_gemma":[0.0019553367,0.00032306052,0.00047754467,0.00012703624,0.00030277186,0.00048707693,0.00066519907,0.000269266,0.000851807],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036705192,0.001779235,0.010757476,0.0023143913,0.0003183273,0.002232115,0.0014797109,0.0952533,0.500778,0.003705702,0.004014278,0.37369692],"study_design_scores_gemma":[0.00077772356,0.022643227,0.03767813,0.0002468613,0.0008090655,0.0032030505,0.0007830459,0.6960608,0.20247228,0.0013942829,0.033587694,0.00034388775],"about_ca_topic_score_codex":0.00070538203,"about_ca_topic_score_gemma":0.00060499675,"teacher_disagreement_score":0.003701899,"about_ca_system_score_codex":0.0002702294,"about_ca_system_score_gemma":0.0007090032,"threshold_uncertainty_score":0.012384057},"labels":[],"label_agreement":null},{"id":"W4405458571","doi":"10.3390/s24248039","title":"XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Computer science; Spoofing attack; SIGNAL (programming language); Multipath propagation; Identification (biology); Interference (communication); Jamming; Feature (linguistics); Artificial intelligence; Signal processing; Quality (philosophy); Data mining; Machine learning; Pattern recognition (psychology); Global Positioning System; Telecommunications; Computer security; Radar; Channel (broadcasting)","score_opus":0.07780400552907932,"score_gpt":0.410662196717968,"score_spread":0.3328581911888887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405458571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71882385,0.003128959,0.26599342,0.0006824609,0.000046471687,0.00028887406,0.0015894787,0.00088845415,0.0085581085],"genre_scores_gemma":[0.96506137,0.0013955829,0.030292151,0.000042586606,0.000028969538,0.00007737095,0.0015160926,0.000048960774,0.0015369328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.000115408824,0.000030117304,0.00007159032,0.00018985012,0.000032247397],"domain_scores_gemma":[0.9985598,0.000723019,0.00025480954,0.00014754095,0.00027893134,0.000035893092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012036024,0.0008034376,0.0003441333,0.0025349318,0.00030936484,0.00073314796,0.0005641341,0.00041826823,0.000806147],"category_scores_gemma":[0.0029767046,0.00018835477,0.0008526536,0.0017415814,0.00035093926,0.0009002944,0.0005046858,0.0005189234,0.00016256163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021977938,0.00026497032,0.20451522,0.0008539971,0.0008388637,0.0009109827,0.0013396876,0.3123322,0.017627146,0.014826753,0.0033029923,0.44296744],"study_design_scores_gemma":[0.000020399046,0.00063279964,0.16812785,0.00014359197,0.00032940324,0.0005477553,0.0011671579,0.7977584,0.011738903,0.008448057,0.010995161,0.00009056491],"about_ca_topic_score_codex":0.007865555,"about_ca_topic_score_gemma":0.004418657,"teacher_disagreement_score":0.007865555,"about_ca_system_score_codex":0.00072102086,"about_ca_system_score_gemma":0.0005593158,"threshold_uncertainty_score":0.015639544},"labels":[],"label_agreement":null},{"id":"W4405458861","doi":"10.3390/s24247999","title":"Are Junior Tennis Players Less Exposed to Shocks and Vibrations than Adults? A Pilot Study","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Racket; Vibration; Accelerometer; Physical medicine and rehabilitation; Statistical parametric mapping; Statistical analysis; Physical therapy; Simulation; Mathematics; Engineering; Medicine; Computer science; Acoustics; Statistics; Physics","score_opus":0.025652752438811634,"score_gpt":0.2349636613261026,"score_spread":0.20931090888729098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405458861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99951124,0.000060593295,0.00004902789,0.000013248904,0.000005414308,0.0000070484757,0.000044112378,0.0000012519077,0.00030807257],"genre_scores_gemma":[0.99935,0.00006235345,0.000059566777,0.00002236487,0.00000870251,0.000006523593,0.000081684666,8.902525e-7,0.0004079556],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996362,0.00005474712,0.000057553036,0.00008584852,0.00007110105,0.00009447382],"domain_scores_gemma":[0.998833,0.00010681099,0.00045301323,0.000045630917,0.00012407578,0.0004374471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043834926,0.00035744824,0.00041000595,0.00074667693,0.00034150563,0.0006077468,0.00025981633,0.00058100163,0.0029188693],"category_scores_gemma":[0.0012375442,0.00023617927,0.00031375402,0.0004666247,0.00035049955,0.0005043396,0.00037279955,0.00032304172,0.00071934925],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000879037,0.0004586466,0.98635685,0.000049109145,0.00004862044,0.0005577089,0.0012371201,0.00002316228,0.0021697727,0.000031845673,0.000103500584,0.008084619],"study_design_scores_gemma":[0.000008545299,0.0009240082,0.99709046,0.000006544396,0.000013920017,0.00043550468,0.0012463159,0.000036382018,0.00007676026,0.000012512869,0.00014460957,0.0000043955392],"about_ca_topic_score_codex":0.002686305,"about_ca_topic_score_gemma":0.003688058,"teacher_disagreement_score":0.0029188693,"about_ca_system_score_codex":0.00015396443,"about_ca_system_score_gemma":0.00016380579,"threshold_uncertainty_score":0.009764612},"labels":[],"label_agreement":null},{"id":"W4405473314","doi":"10.3390/s24248040","title":"Advanced Monocular Outdoor Pose Estimation in Autonomous Systems: Leveraging Optical Flow, Depth Estimation, and Semantic Segmentation with Dynamic Object Removal","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Taipei Medical University","keywords":"Pose; Visual odometry; Computer science; Global Positioning System; Artificial intelligence; Computer vision; Odometry; Monocular; Segmentation; Drone; Real-time computing; Mobile robot; Robot","score_opus":0.0057036031217701,"score_gpt":0.22338072162096176,"score_spread":0.21767711849919166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405473314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016526142,0.0004216614,0.9815683,0.00005828485,0.000041615913,0.000032154232,0.0000482452,0.0005328696,0.00077087485],"genre_scores_gemma":[0.4648048,0.0010408438,0.53055537,0.00014840224,0.00013683675,0.000084808285,0.0004843204,0.00019351424,0.0025509773],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996506,0.000035903566,0.00001171376,0.00010947457,0.00013271169,0.00005964519],"domain_scores_gemma":[0.9997533,0.000042870764,0.00005418215,0.000049880106,0.00007752387,0.000022216767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030044556,0.001064773,0.0007094831,0.001165541,0.00027331043,0.00070111064,0.0008185706,0.0004563446,0.0007015133],"category_scores_gemma":[0.00079507165,0.0004914449,0.00064661977,0.0009164451,0.00045666116,0.001039631,0.0011141166,0.0006074645,0.00038536644],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099709076,0.00010686006,0.0021528043,0.00011534862,0.000087944965,0.000090231995,0.00019526671,0.13903156,0.06495144,0.0037217403,0.0017567695,0.7876904],"study_design_scores_gemma":[0.0000063679604,0.000067851106,0.0024768044,0.000016651764,0.000023804738,0.00007969936,0.000047352663,0.9772956,0.014079686,0.0028762287,0.0030048708,0.000025020012],"about_ca_topic_score_codex":0.007618423,"about_ca_topic_score_gemma":0.010355703,"teacher_disagreement_score":0.007618423,"about_ca_system_score_codex":0.00040138557,"about_ca_system_score_gemma":0.000913448,"threshold_uncertainty_score":0.015148163},"labels":[],"label_agreement":null},{"id":"W4405534176","doi":"10.3390/s24248071","title":"Light-Emitting Diode Array with Optical Linear Detector Enables High-Throughput Differential Single-Cell Dielectrophoretic Analysis","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Chinese hamster ovary cell; Dielectrophoresis; Microfluidics; Optoelectronics; Materials science; Diode; Throughput; Dielectric; Light-emitting diode; Electrode; Optics; Nanotechnology; Chemistry; Physics; Computer science","score_opus":0.006102997293157084,"score_gpt":0.18065899154167064,"score_spread":0.17455599424851356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405534176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24112809,0.0033457992,0.7434501,0.00045890838,0.00020440225,0.00023684588,0.001493138,0.0039364863,0.00574615],"genre_scores_gemma":[0.37964875,0.0015913621,0.60994834,0.0003105289,0.00008801945,0.0003639426,0.0009136742,0.00015565133,0.0069796657],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995158,0.000052646767,0.00002309931,0.00016829268,0.00020446976,0.000035741923],"domain_scores_gemma":[0.9997222,0.000113756876,0.000039623927,0.000038991766,0.00005852486,0.000026995081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037359542,0.00043826652,0.0005203228,0.00059711194,0.00021656697,0.0005875312,0.00081947475,0.00063716114,0.0010268365],"category_scores_gemma":[0.00036469087,0.00039493386,0.00022993294,0.0004042186,0.00027262417,0.000547263,0.0005422013,0.0006030204,0.0010359919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016541062,0.00002272265,0.00025260274,0.000051768253,0.000005728854,0.000027844702,0.00001101243,0.00014640957,0.98865414,0.00035726294,0.00022879071,0.010225179],"study_design_scores_gemma":[0.000008032728,0.00006270712,0.0012242708,0.000004774773,0.000012648975,0.00026860996,0.0000111786185,0.010917017,0.9814037,0.00026626553,0.0057989075,0.00002175814],"about_ca_topic_score_codex":0.00030348162,"about_ca_topic_score_gemma":0.0010044838,"teacher_disagreement_score":0.0010268365,"about_ca_system_score_codex":0.00040688054,"about_ca_system_score_gemma":0.00033742792,"threshold_uncertainty_score":0.003435135},"labels":[],"label_agreement":null},{"id":"W4405574717","doi":"10.3390/s24248089","title":"A Comprehensive Study of Recent Path-Planning Techniques in Dynamic Environments for Autonomous Robots","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College Ahuntsic","funders":"","keywords":"Motion planning; Obstacle; Key (lock); Computer science; Obstacle avoidance; Path (computing); Process (computing); Robot; Resource (disambiguation); Distributed computing; Systems engineering; Dynamic decision-making; Risk analysis (engineering); Human–computer interaction; Mobile robot; Engineering; Artificial intelligence; Computer security; Computer network","score_opus":0.03465704014129595,"score_gpt":0.3136110168898205,"score_spread":0.27895397674852457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405574717","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039223493,0.32777613,0.64993894,0.00083554775,0.00070869084,0.00009291264,0.00018529201,0.0003771322,0.016162943],"genre_scores_gemma":[0.050118435,0.544211,0.3955559,0.00048982643,0.0009812986,0.00023353877,0.00088582374,0.00028505636,0.0072391075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994336,0.00008336637,0.000050289567,0.0001386076,0.0002598828,0.000034268807],"domain_scores_gemma":[0.9988913,0.00062250433,0.00008050064,0.00006851444,0.00030327533,0.000033924745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007276998,0.0010832079,0.0008082823,0.0016817517,0.00060358335,0.0012499427,0.0013142927,0.0010169622,0.0029074992],"category_scores_gemma":[0.002372877,0.0007619823,0.0008645744,0.0039764396,0.0005435554,0.002525176,0.0010243151,0.0015296311,0.0015670701],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035290537,0.000056692912,0.00044356938,0.0044923853,0.00008043384,0.00009268885,0.00015548775,0.05512796,0.003114838,0.028271472,0.009463086,0.89866614],"study_design_scores_gemma":[0.000021011705,0.0004291912,0.002175756,0.0025291848,0.00023753964,0.0015886885,0.0003630871,0.14855531,0.007070362,0.059702087,0.77716017,0.00016753933],"about_ca_topic_score_codex":0.0017740409,"about_ca_topic_score_gemma":0.001918494,"teacher_disagreement_score":0.0029074992,"about_ca_system_score_codex":0.00069150695,"about_ca_system_score_gemma":0.0016467042,"threshold_uncertainty_score":0.009726524},"labels":[],"label_agreement":null},{"id":"W4405641139","doi":"10.3390/s24248148","title":"Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pan Am Clinic; University of Manitoba","funders":"Canadian Institutes of Health Research; Svenska Kulturfonden; Finska Läkaresällskapet; Medicinska Understödsföreningen Liv och Hälsa; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Research Manitoba; Health Sciences Centre Foundation","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Machine learning; Electroencephalography; Modalities; Cerebral perfusion pressure; Neuroscience; Cerebral blood flow; Medicine; Psychology; Biology","score_opus":0.12258290673407095,"score_gpt":0.4127355449305296,"score_spread":0.29015263819645865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405641139","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012562859,0.9518331,0.043437194,0.0013077866,0.00035818183,0.000022511715,0.000113205344,0.00005384091,0.0016179223],"genre_scores_gemma":[0.012408607,0.97298753,0.012672508,0.0002675048,0.000854394,0.000049593917,0.0001578128,0.000019372532,0.0005826353],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995271,0.00013281063,0.000064673484,0.000097739205,0.00015716354,0.000020451682],"domain_scores_gemma":[0.99826556,0.0013356986,0.0001309325,0.00003895631,0.00020190184,0.000026982778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016212033,0.0011453115,0.0012232868,0.0015441403,0.00016679666,0.001536675,0.0010533439,0.0009870586,0.0013290885],"category_scores_gemma":[0.0031621603,0.0004148477,0.0011316641,0.001732689,0.0005401009,0.0015576725,0.0006642324,0.0013947238,0.0005994524],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079333884,0.000115282804,0.0019833567,0.020607697,0.0005753464,0.00035932945,0.00020589815,0.03845112,0.0022617544,0.042179987,0.01691115,0.8762698],"study_design_scores_gemma":[0.00003453458,0.0005371138,0.007992577,0.019212306,0.0011951256,0.002456112,0.00035649654,0.1397265,0.0054243393,0.114525914,0.7082058,0.0003330915],"about_ca_topic_score_codex":0.0016418917,"about_ca_topic_score_gemma":0.0011527636,"teacher_disagreement_score":0.0016418917,"about_ca_system_score_codex":0.00056454283,"about_ca_system_score_gemma":0.0010521149,"threshold_uncertainty_score":0.00857383},"labels":[],"label_agreement":null},{"id":"W4405706181","doi":"10.3390/s24248211","title":"Real-Time Freezing of Gait Prediction and Detection in Parkinson’s Disease","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Weston Family Foundation","keywords":"Generalizability theory; Decision tree; Gait; Artificial intelligence; Computer science; Postural instability; Machine learning; Sensitivity (control systems); Parkinson's disease; Statistics; Physical medicine and rehabilitation; Medicine; Disease; Mathematics; Engineering","score_opus":0.015405531154223379,"score_gpt":0.3091363075947174,"score_spread":0.293730776440494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405706181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97118515,0.0010999967,0.025022332,0.00017643944,0.000094977106,0.00004615053,0.0012551352,0.00027322758,0.0008464991],"genre_scores_gemma":[0.9931283,0.00023363219,0.005533078,0.0000312327,0.000021683607,0.00002027016,0.0008449679,0.000004234092,0.00018263844],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999713,0.000065518514,0.000037124482,0.00007381142,0.00006865954,0.00004184516],"domain_scores_gemma":[0.99916327,0.0003119035,0.00014468415,0.000055119646,0.0002702084,0.000054710265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009445228,0.0006310254,0.00056595844,0.0009884227,0.00017278396,0.00048686852,0.00033232724,0.000506883,0.00030564863],"category_scores_gemma":[0.002892473,0.00015363972,0.00053119264,0.0005243106,0.000090895264,0.00038824687,0.00029242702,0.00043920524,0.00016467432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021882185,0.0007323192,0.5085088,0.00031847754,0.0005965137,0.00048703162,0.00024590356,0.09826294,0.012832083,0.0002139079,0.0033294696,0.37228423],"study_design_scores_gemma":[0.000042082258,0.00086555944,0.23376276,0.00011422705,0.00021556874,0.0004229137,0.00017125088,0.7560511,0.006795698,0.0005957632,0.00090881524,0.000054212094],"about_ca_topic_score_codex":0.009367481,"about_ca_topic_score_gemma":0.010112138,"teacher_disagreement_score":0.009367481,"about_ca_system_score_codex":0.00026208186,"about_ca_system_score_gemma":0.00027291125,"threshold_uncertainty_score":0.018625855},"labels":[],"label_agreement":null},{"id":"W4405721079","doi":"10.3390/s24248177","title":"Game-Theoretic Motion Planning with Perception Uncertainty and Right-of-Way Constraints","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stackelberg competition; Adaptability; Probabilistic logic; Computer science; Bayesian probability; Motion (physics); Motion planning; Reliability (semiconductor); Perception; Game theory; Trajectory; Perspective (graphical); Nash equilibrium; Bayesian game; Artificial intelligence; Machine learning; Operations research; Mathematical optimization; Engineering; Sequential game; Mathematics; Robot; Economics; Psychology; Mathematical economics","score_opus":0.005125450936100932,"score_gpt":0.19784443876852398,"score_spread":0.19271898783242306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405721079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013546323,0.0000906417,0.9819109,0.00019817216,0.000022694358,0.00005910736,0.00004506241,0.000060044007,0.0040671267],"genre_scores_gemma":[0.86033255,0.00021057921,0.13610853,0.00010042719,0.000033015644,0.00018661196,0.00007762203,0.000038428403,0.002912144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870896,0.00053358957,0.00006143836,0.00021457761,0.00030329978,0.00017803384],"domain_scores_gemma":[0.99803144,0.0013126131,0.00020139165,0.000114568975,0.00019751316,0.00014244762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016792046,0.0010952548,0.0009751284,0.000574532,0.0006067152,0.0014098905,0.001627501,0.0011924634,0.0015557848],"category_scores_gemma":[0.0051511903,0.00068154576,0.000700069,0.0005719447,0.001587974,0.0021046435,0.0019775962,0.0014663663,0.00019598167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004520067,0.000020549236,0.00023831063,0.000042820935,0.000024317469,0.000075306685,0.00014388721,0.92203224,0.0007089449,0.06836432,0.00028205785,0.008021961],"study_design_scores_gemma":[0.000009686876,0.00002573344,0.00006609685,0.0000052067644,0.000006554412,0.000015757967,0.000024658251,0.96413356,0.00018832293,0.03504968,0.00046724887,0.000007492737],"about_ca_topic_score_codex":0.013116351,"about_ca_topic_score_gemma":0.009601763,"teacher_disagreement_score":0.013116351,"about_ca_system_score_codex":0.0014653438,"about_ca_system_score_gemma":0.0024202461,"threshold_uncertainty_score":0.026080012},"labels":[],"label_agreement":null},{"id":"W4405721100","doi":"10.3390/s24248183","title":"High-Performance CP Magneto-Electric Dipole Antenna Fed by Printed Ridge Gap Waveguide at Millimeter-Wave","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Extremely high frequency; Wideband; Optoelectronics; Bandwidth (computing); Dipole antenna; Optics; Materials science; Axial ratio; Antenna (radio); Physics; Electrical engineering; Circular polarization; Engineering; Telecommunications; Microstrip","score_opus":0.010245403322513707,"score_gpt":0.19370795880981043,"score_spread":0.18346255548729673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405721100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72216874,0.00094216526,0.25750387,0.00046784768,0.00020112823,0.00002441275,0.00019456833,0.0013222005,0.017175056],"genre_scores_gemma":[0.9625396,0.00027598935,0.032644156,0.0000589531,0.000031776675,0.000016184777,0.00011314478,0.000048707443,0.0042714425],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990964,0.000013027548,0.0000035780379,0.000020375626,0.000036389876,0.000016995533],"domain_scores_gemma":[0.99987984,0.000018827968,0.000047995618,0.000017497288,0.00002703479,0.000008762677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008229326,0.0002808269,0.00019427908,0.00013827307,0.000053749194,0.0003291894,0.00025044428,0.00030020077,0.0005147868],"category_scores_gemma":[0.00011276007,0.00010159273,0.00019523376,0.00022779012,0.00010724593,0.0002321822,0.00016668139,0.00021802721,0.0006157268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000852315,0.000021245378,0.00079742196,0.00007674497,0.000020019319,0.0001585999,0.000028390576,0.002268218,0.9764631,0.0011387361,0.00091003376,0.018032365],"study_design_scores_gemma":[0.00002190759,0.00041685338,0.0032312574,0.000009404615,0.00003128639,0.0010371519,0.000042338797,0.034867477,0.9459998,0.00024623863,0.01407401,0.000022327858],"about_ca_topic_score_codex":0.0000701576,"about_ca_topic_score_gemma":0.000098583754,"teacher_disagreement_score":0.0005147868,"about_ca_system_score_codex":0.00014614422,"about_ca_system_score_gemma":0.00006911801,"threshold_uncertainty_score":0.0017220974},"labels":[],"label_agreement":null},{"id":"W4405721131","doi":"10.3390/s24248186","title":"UAV Trajectory Control and Power Optimization for Low-Latency C-V2X Communications in a Federated Learning Environment","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Network packet; Real-time computing; Latency (audio); Low latency (capital markets); Quality of service; Throughput; Wireless; Channel (broadcasting); Computer network; Telecommunications","score_opus":0.006465106436751751,"score_gpt":0.20644323911325643,"score_spread":0.1999781326765047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405721131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15873425,0.000345943,0.8342358,0.00027254949,0.000058553796,0.00005689627,0.000042945638,0.00033600142,0.0059171063],"genre_scores_gemma":[0.9880211,0.0000589255,0.0106396945,0.000018325452,0.000008211923,0.000018130153,0.00001794718,0.000009272144,0.0012082901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999803,0.0000322984,0.000007378241,0.000046739664,0.00005416707,0.00005651602],"domain_scores_gemma":[0.9997489,0.000088254266,0.00005517652,0.000013720567,0.000070136,0.000023875364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026448639,0.00058839115,0.000364297,0.00022935905,0.0004170986,0.0005651669,0.0005070038,0.00033641612,0.0010359836],"category_scores_gemma":[0.00071499357,0.00013141106,0.00017990221,0.00029361545,0.00033399987,0.00039440734,0.0005287164,0.00038050194,0.00014377729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013826304,0.000055918914,0.0008357659,0.000036674235,0.000012099243,0.00011761389,0.000078353725,0.9450301,0.006447852,0.004003992,0.00080653693,0.042436816],"study_design_scores_gemma":[0.0000062470053,0.00004636719,0.00016683078,0.0000015860156,0.000002401158,0.000012456029,0.000018421832,0.9982503,0.0007284976,0.00059651886,0.00016820147,0.0000020761652],"about_ca_topic_score_codex":0.006497402,"about_ca_topic_score_gemma":0.005709574,"teacher_disagreement_score":0.006497402,"about_ca_system_score_codex":0.0006322969,"about_ca_system_score_gemma":0.0008466871,"threshold_uncertainty_score":0.012919188},"labels":[],"label_agreement":null},{"id":"W4405721151","doi":"10.3390/s24248163","title":"Influence of Sudden Changes in Foot Strikes on Loading Rate Variability in Runners","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre Hospitalier Universitaire Sainte-Justine; Université Laval; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"Fonds de Recherche du Québec - Santé; Réseau Provincial de Recherche en Adaptation-Réadaptation","keywords":"Forefoot; Acceleration; Foot (prosody); Amplitude; Geology; Accelerometer; Geodesy; Physical medicine and rehabilitation; Kinematics; Medicine; Physics","score_opus":0.013402173345968129,"score_gpt":0.23056125249910625,"score_spread":0.21715907915313812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405721151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99935204,0.00007171982,0.00041949758,0.000003808751,0.0000032968753,0.0000036080514,0.000023751814,0.000005601193,0.00011657885],"genre_scores_gemma":[0.99962306,0.000034632234,0.0002047118,0.0000041834032,0.0000054557336,0.000005337057,0.000042730248,0.0000033332583,0.000076615164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997054,0.00008693213,0.000034576507,0.00006626427,0.00007335591,0.000033485478],"domain_scores_gemma":[0.9989085,0.0004773881,0.0003197891,0.00006713186,0.00009018534,0.0001369605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032196866,0.0003026463,0.00023164379,0.00031584888,0.00012944061,0.0002885049,0.000118452146,0.00027391434,0.0010787505],"category_scores_gemma":[0.0023707536,0.00014789459,0.00015363518,0.00012519071,0.00018203618,0.00015550654,0.00028210424,0.000199585,0.00017920423],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037612552,0.00047762613,0.7387422,0.00025968603,0.00028258038,0.00077126635,0.0010184353,0.00083652826,0.19388255,0.000040630715,0.00010087713,0.059826426],"study_design_scores_gemma":[0.0000061963046,0.00063738413,0.9962645,0.0000068534273,0.000020319245,0.00025614735,0.00011644589,0.00033820453,0.0022784292,0.00002199401,0.00004566279,0.000007901323],"about_ca_topic_score_codex":0.00036494338,"about_ca_topic_score_gemma":0.0008011841,"teacher_disagreement_score":0.0010787505,"about_ca_system_score_codex":0.000046301342,"about_ca_system_score_gemma":0.00007407518,"threshold_uncertainty_score":0.0036088228},"labels":[],"label_agreement":null},{"id":"W4405721377","doi":"10.3390/s24248173","title":"Optimized Synthetic Correlated Diffusion Imaging for Improving Breast Cancer Tumor Delineation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Breast cancer; Medicine; Receiver operating characteristic; Cancer; Prostate cancer; Diffusion MRI; Mammography; Magnetic resonance imaging; Gold standard (test); Modality (human–computer interaction); Medical imaging; Radiology; Medical physics; Computer science; Internal medicine; Artificial intelligence","score_opus":0.024322844281075114,"score_gpt":0.3334430636174504,"score_spread":0.3091202193363753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405721377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47496134,0.0021379336,0.5165678,0.00070361194,0.00008542759,0.000119843346,0.00027522875,0.0010892861,0.004059663],"genre_scores_gemma":[0.826267,0.00074328773,0.17156492,0.00019696142,0.000022067543,0.00009043611,0.00022773287,0.0001681952,0.0007193222],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998035,0.00006021417,0.0000110723395,0.000046585894,0.000058231424,0.000020339083],"domain_scores_gemma":[0.9995509,0.00018542513,0.00011100098,0.00004269474,0.00008207164,0.000027857768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006175098,0.00047948823,0.00024431478,0.00034504032,0.00014325726,0.00048229264,0.00029509046,0.0003902685,0.00045672365],"category_scores_gemma":[0.0021303368,0.00018651062,0.00020592139,0.00027986921,0.00034219932,0.00048741416,0.00039239172,0.00040275021,0.000155612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057279423,0.00017638535,0.0024380102,0.0003268388,0.000067841385,0.00021026957,0.00015720478,0.17870596,0.7519427,0.0036869529,0.0014902434,0.06022479],"study_design_scores_gemma":[0.000043224245,0.00038615364,0.0022132578,0.000023172819,0.00006484614,0.0003438195,0.000047853868,0.54430723,0.44670767,0.0015921085,0.0042044255,0.00006620462],"about_ca_topic_score_codex":0.00067683234,"about_ca_topic_score_gemma":0.00094991736,"teacher_disagreement_score":0.00067683234,"about_ca_system_score_codex":0.00039384767,"about_ca_system_score_gemma":0.00056458806,"threshold_uncertainty_score":0.0032657385},"labels":[],"label_agreement":null},{"id":"W4405758269","doi":"10.3390/s25010017","title":"Impact of Pathway Shape and Length on the Validity of the 6-Minute Walking Test: A Systematic Review and Meta-Analysis","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Test (biology); Criterion validity; Physical medicine and rehabilitation; Path (computing); Treadmill; Internal validity; Physical therapy; Computer science; Simulation; Medicine; Mathematics; Statistics; Construct validity; Psychometrics","score_opus":0.12281182108851989,"score_gpt":0.3172265045844635,"score_spread":0.1944146834959436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405758269","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019733317,0.99683243,0.00027970612,0.00013060845,0.00009659013,0.00019811827,0.00025707527,0.00000928562,0.00022288124],"genre_scores_gemma":[0.051844478,0.94468284,0.0011857956,0.0005815413,0.00017597416,0.00072862295,0.0005353875,0.000017321594,0.00024795302],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9921829,0.0026325653,0.0030665286,0.0007424967,0.001151209,0.00022427863],"domain_scores_gemma":[0.9686686,0.024256798,0.003813935,0.0006210481,0.0024441225,0.00019559484],"candidate_categories":["metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.012092658,0.0022471927,0.012147387,0.0051114685,0.0006747057,0.0035493204,0.0016456601,0.0021629084,0.003945026],"category_scores_gemma":[0.04451683,0.0010462507,0.02275441,0.006622366,0.00071395625,0.0019176021,0.001334347,0.0013368572,0.00037067695],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013512798,0.000039867395,0.0037837862,0.792424,0.16831045,0.0001404371,0.00011352073,0.00024160383,0.00022996533,0.00017119049,0.0007329408,0.032460846],"study_design_scores_gemma":[0.00055814185,0.00031724537,0.007043819,0.13239382,0.85373646,0.00022332156,0.000112437796,0.00012813482,0.00024687545,0.00029102247,0.004908473,0.000040246116],"about_ca_topic_score_codex":0.005776937,"about_ca_topic_score_gemma":0.013754974,"teacher_disagreement_score":0.98785263,"about_ca_system_score_codex":0.0021048668,"about_ca_system_score_gemma":0.0048671747,"threshold_uncertainty_score":0.06395286},"labels":[],"label_agreement":null},{"id":"W4405781962","doi":"10.3390/s25010061","title":"Event-Based Visual/Inertial Odometry for UAV Indoor Navigation","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Computer science; Odometry; Unavailability; Computer vision; Artificial intelligence; Real-time computing; Inertial measurement unit; GNSS applications; Event (particle physics); Simultaneous localization and mapping; Global Positioning System; Robot; Engineering; Mobile robot; Telecommunications","score_opus":0.00819064815250665,"score_gpt":0.2590645730135708,"score_spread":0.25087392486106413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405781962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03184602,0.0004766906,0.96474,0.00004766917,0.0001147542,0.000028742279,0.0002056277,0.0012085134,0.0013319441],"genre_scores_gemma":[0.8068778,0.00053642027,0.18995737,0.000072484516,0.00008650575,0.000054363205,0.0007956244,0.000067932175,0.0015516352],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980146,0.000024810855,0.00001096758,0.000063753745,0.00006776972,0.000031223],"domain_scores_gemma":[0.9998709,0.00002081128,0.000026801508,0.000032458323,0.000038845083,0.000010094397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015646261,0.00054278376,0.00042264463,0.0005730551,0.0002212332,0.0003420395,0.0006684092,0.0002698975,0.0007414455],"category_scores_gemma":[0.0005060681,0.0001853738,0.0003468428,0.00082171115,0.00017773717,0.00055378117,0.0006691779,0.00042787896,0.00033145805],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035181522,0.000113395115,0.0054086302,0.000236114,0.000106581654,0.00030273292,0.00020049405,0.2835848,0.046524834,0.0058455477,0.0047291284,0.6525959],"study_design_scores_gemma":[0.000019349083,0.00008394557,0.0039592376,0.000018629387,0.00002675539,0.00013380847,0.00008645614,0.9745439,0.012066474,0.0030141296,0.006025489,0.00002176908],"about_ca_topic_score_codex":0.006996122,"about_ca_topic_score_gemma":0.009451653,"teacher_disagreement_score":0.006996122,"about_ca_system_score_codex":0.0002325706,"about_ca_system_score_gemma":0.00044589778,"threshold_uncertainty_score":0.01391083},"labels":[],"label_agreement":null},{"id":"W4405965525","doi":"10.3390/s25010203","title":"Table Extraction with Table Data Using VGG-19 Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Table (database); Computer science; Row; Column (typography); Identification (biology); Data mining; Artificial intelligence; Machine learning; Task (project management); Database; Engineering; Frame (networking)","score_opus":0.04168891096115095,"score_gpt":0.3087875127823747,"score_spread":0.26709860182122375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405965525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13274948,0.0052426835,0.59194386,0.0012985081,0.0013701951,0.0010370006,0.10753273,0.12549232,0.0333332],"genre_scores_gemma":[0.3228249,0.0017954248,0.45195046,0.0009191869,0.00011458966,0.0005538613,0.19006176,0.0014327213,0.030347114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997454,0.000011174102,0.000018234585,0.00011155879,0.000070713206,0.000042905478],"domain_scores_gemma":[0.9998355,0.00002543005,0.000017140012,0.000052233696,0.000057381607,0.000012276522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020186865,0.001497096,0.0007124236,0.0017996029,0.0003083354,0.00144927,0.001745379,0.0010658808,0.010043831],"category_scores_gemma":[0.0007242327,0.00043424105,0.0010945747,0.002252832,0.00030207672,0.0017421169,0.0008386328,0.00096176256,0.0071458705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004016514,0.00032619602,0.0037253024,0.00055168034,0.00016740256,0.00042783265,0.00008083703,0.06525517,0.019229628,0.003872272,0.13707876,0.7688833],"study_design_scores_gemma":[0.000064946056,0.0001929001,0.0035142202,0.00014288806,0.00007093901,0.00035570964,0.00015683087,0.8921922,0.042438615,0.008422044,0.052388187,0.000060521892],"about_ca_topic_score_codex":0.017031135,"about_ca_topic_score_gemma":0.031579055,"teacher_disagreement_score":0.017031135,"about_ca_system_score_codex":0.001186931,"about_ca_system_score_gemma":0.0011916828,"threshold_uncertainty_score":0.03386402},"labels":[],"label_agreement":null},{"id":"W4406051893","doi":"10.3390/s25010239","title":"Parallelized Field-Programmable Gate Array Data Processing for High-Throughput Pulsed-Radar Systems","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Defence and Security Accelerator; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Field-programmable gate array; Throughput; Radar; Ethernet; Computer hardware; Electronic engineering; Real-time computing; Waveform; FPGA prototype; Gate array; Embedded system; Engineering; Telecommunications; Wireless","score_opus":0.01763203360667843,"score_gpt":0.2635049151842805,"score_spread":0.24587288157760206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406051893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069564834,0.00060454925,0.91880894,0.00021358319,0.00014140837,0.00015383014,0.00016635573,0.0037154434,0.006631063],"genre_scores_gemma":[0.62290233,0.00037505457,0.37258247,0.0001512366,0.000069222675,0.00014599452,0.0002945244,0.000079599704,0.0033994764],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997738,0.000032691605,0.0000129300315,0.000044229833,0.00010868659,0.000027774307],"domain_scores_gemma":[0.9997868,0.000059189428,0.000032958334,0.000035884183,0.00007397656,0.000011151313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021805549,0.00042543054,0.0002656367,0.00030691037,0.00019999954,0.0004998866,0.0006770445,0.00019547925,0.0020925037],"category_scores_gemma":[0.00037823274,0.00016449949,0.0001312211,0.00035168327,0.00017345563,0.0005573369,0.0002272957,0.0004378366,0.0004952557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076034333,0.00029410815,0.0017439771,0.00037722662,0.00009858955,0.0004952074,0.0001392221,0.08961145,0.42492986,0.01472415,0.008042227,0.45878372],"study_design_scores_gemma":[0.00012843711,0.0008691513,0.0012791229,0.000027776206,0.00004122563,0.00055203825,0.00003569632,0.7355154,0.2390861,0.003993165,0.018424714,0.000047153757],"about_ca_topic_score_codex":0.00068795413,"about_ca_topic_score_gemma":0.0014250957,"teacher_disagreement_score":0.0020925037,"about_ca_system_score_codex":0.00037248532,"about_ca_system_score_gemma":0.00052303786,"threshold_uncertainty_score":0.0070001483},"labels":[],"label_agreement":null},{"id":"W4406154665","doi":"10.3390/s25020329","title":"Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Synthetic aperture radar; Computer science; Artificial intelligence; Computer vision; Remote sensing; Satellite imagery","score_opus":0.02041853383306106,"score_gpt":0.2490951901359924,"score_spread":0.22867665630293135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406154665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022660818,0.00023449807,0.9750737,0.00007775847,0.000032347853,0.00003123842,0.000048564132,0.00054859376,0.0012923545],"genre_scores_gemma":[0.41344804,0.00031864352,0.5831835,0.00015461231,0.00007709107,0.000057670914,0.00026502396,0.00008671642,0.0024087413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925953,0.0001095161,0.00003329459,0.00017818855,0.0003389512,0.00008049845],"domain_scores_gemma":[0.99952316,0.00011799619,0.00006734804,0.000082050574,0.00017474516,0.000034680164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009777513,0.0007158506,0.00054802274,0.0016412586,0.00027280382,0.0007536537,0.00091524835,0.0006448452,0.0009692019],"category_scores_gemma":[0.0015967063,0.0003520714,0.00070686894,0.00076598465,0.00041916873,0.0012112245,0.0016029098,0.00056762615,0.00052809616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037597772,0.00029425716,0.0036247158,0.00020854559,0.00018272517,0.00025572212,0.0002642546,0.098382756,0.1381249,0.0070879646,0.0020273542,0.7491708],"study_design_scores_gemma":[0.000008353816,0.00011291363,0.0017621208,0.000017374125,0.00005688716,0.00028236283,0.00007557333,0.9474448,0.042513337,0.0048815873,0.0028148205,0.000029813717],"about_ca_topic_score_codex":0.0011211813,"about_ca_topic_score_gemma":0.0019867131,"teacher_disagreement_score":0.0016412586,"about_ca_system_score_codex":0.00030884644,"about_ca_system_score_gemma":0.00051969086,"threshold_uncertainty_score":0.0051709414},"labels":[],"label_agreement":null},{"id":"W4406175600","doi":"10.3390/s25020326","title":"Performance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management Architecture","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Mitacs; U.S. Department of Veterans Affairs","keywords":"Architecture; Deep learning; Artificial intelligence; Computer architecture; Computer science; Engineering; Geography; Archaeology","score_opus":0.009179012689318177,"score_gpt":0.24358047369014013,"score_spread":0.23440146100082196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406175600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94734955,0.0007394779,0.03786551,0.00025838145,0.00014018646,0.00018040494,0.00073542027,0.005636126,0.0070950044],"genre_scores_gemma":[0.9631697,0.00017367318,0.030209089,0.00013010336,0.0000137563975,0.00008632363,0.0021868744,0.000080167505,0.0039502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996922,0.000042659256,0.000022254,0.00008727222,0.000077294426,0.00007821763],"domain_scores_gemma":[0.9996226,0.000097087155,0.000031414103,0.00004044737,0.00016080117,0.00004763847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074076076,0.0011570802,0.00038959438,0.0005086976,0.00027839516,0.0004580404,0.0011394997,0.00062526384,0.0022009474],"category_scores_gemma":[0.0015711997,0.000266624,0.00035442656,0.00032264652,0.0002859023,0.0007953456,0.0006988618,0.0006172612,0.00068688364],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021831088,0.00081709604,0.018174114,0.00034454543,0.0003247897,0.0002490781,0.0001314469,0.6308228,0.026752802,0.0011109655,0.008039703,0.3110496],"study_design_scores_gemma":[0.000025367006,0.00019071288,0.0022070643,0.000010001153,0.00002900478,0.00001952272,0.000025037998,0.986118,0.010541752,0.0001497184,0.0006744213,0.000009379941],"about_ca_topic_score_codex":0.037819542,"about_ca_topic_score_gemma":0.03237025,"teacher_disagreement_score":0.037819542,"about_ca_system_score_codex":0.0011866105,"about_ca_system_score_gemma":0.0011448817,"threshold_uncertainty_score":0.07519883},"labels":[],"label_agreement":null},{"id":"W4406327655","doi":"10.3390/s25020439","title":"Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Terrain; Robot; Computer science; Artificial intelligence; Remote sensing; Geology; Geography; Cartography","score_opus":0.02777160890793751,"score_gpt":0.28908107470258365,"score_spread":0.26130946579464615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406327655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57549256,0.00016044122,0.42169777,0.000072612784,0.000022243692,0.0000849511,0.00036006325,0.000472869,0.0016364817],"genre_scores_gemma":[0.9271843,0.00007560062,0.0722649,0.0000072248395,0.0000037660052,0.00003873676,0.00024678782,0.000023094144,0.00015560958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997726,0.00004420145,0.000009536087,0.00003632259,0.00011642721,0.000020943262],"domain_scores_gemma":[0.99942195,0.0002775621,0.00010286157,0.00007989116,0.00008909367,0.00002872499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028572435,0.00040633752,0.00030518393,0.0008801663,0.00030922034,0.0005520649,0.00040161193,0.00037251908,0.0005203037],"category_scores_gemma":[0.0020802103,0.00021261105,0.00025648822,0.00064370554,0.00038213632,0.0008955675,0.0005548185,0.00026477323,0.00009387086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037074025,0.00016193229,0.017643424,0.0002726593,0.00008891539,0.00037381408,0.0003069502,0.81107706,0.060799625,0.0025862015,0.00048572046,0.10583293],"study_design_scores_gemma":[0.0000069140833,0.00011842214,0.0095272325,0.000011923291,0.000012217888,0.00010276594,0.000151955,0.97535056,0.012389344,0.0018706378,0.00044218748,0.000015853337],"about_ca_topic_score_codex":0.0028058887,"about_ca_topic_score_gemma":0.0061198855,"teacher_disagreement_score":0.0028058887,"about_ca_system_score_codex":0.00031215657,"about_ca_system_score_gemma":0.00040388387,"threshold_uncertainty_score":0.005579114},"labels":[],"label_agreement":null},{"id":"W4406330970","doi":"10.3390/s25020430","title":"In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Robustness (evolution); Inertial measurement unit; Artificial intelligence; Computer science; Machine learning; Variance (accounting); Wearable computer; Statistics; Mathematics","score_opus":0.025315440202924127,"score_gpt":0.2764825822121709,"score_spread":0.2511671420092468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406330970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76263636,0.0020797295,0.22882214,0.0008330755,0.000300995,0.00010727278,0.001023104,0.0017840015,0.0024133213],"genre_scores_gemma":[0.98386407,0.00017205115,0.013512367,0.00025989604,0.00005025564,0.00005189743,0.0012196078,0.00012933904,0.00074059947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982141,0.00063033553,0.00015275257,0.00054056186,0.00027379833,0.0001883566],"domain_scores_gemma":[0.9918858,0.0050967215,0.0005897703,0.0014101422,0.00072753575,0.0002900621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069167865,0.0014269189,0.0008326842,0.00085772865,0.0004985,0.0012474677,0.0011802426,0.0013454605,0.0009773178],"category_scores_gemma":[0.020826383,0.00043561167,0.00091443944,0.000576167,0.0010908138,0.001453159,0.0020803618,0.0019898156,0.0003987584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014604038,0.00040629998,0.05045409,0.00022178532,0.0006358126,0.00023862992,0.0002770905,0.7943018,0.008403002,0.0020011722,0.003471752,0.1381282],"study_design_scores_gemma":[0.000024127938,0.00030126303,0.008535501,0.00003802455,0.000054148408,0.0000845023,0.00007109953,0.9839371,0.0039234124,0.0023999403,0.0006011162,0.00002973881],"about_ca_topic_score_codex":0.0055667353,"about_ca_topic_score_gemma":0.0047047026,"teacher_disagreement_score":0.0069167865,"about_ca_system_score_codex":0.00069060136,"about_ca_system_score_gemma":0.000988635,"threshold_uncertainty_score":0.036579907},"labels":[],"label_agreement":null},{"id":"W4406401517","doi":"10.3390/s25020466","title":"Channeled Polarimetry for Magnetic Field/Current Detection","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Polarization and Ellipsometry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Bulgarian National Science Fund","keywords":"Faraday effect; Polarimetry; Polarization (electrochemistry); Magnetic field; Faraday cage; Magneto-optic effect; Optics; Faraday rotator; Physics; Chemistry; Scattering","score_opus":0.005408965716966225,"score_gpt":0.232922633885881,"score_spread":0.22751366816891477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406401517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056193117,0.0041615437,0.9284656,0.00028872734,0.00061580923,0.00017933219,0.0007963621,0.0016770614,0.007622417],"genre_scores_gemma":[0.43567577,0.005851569,0.55026245,0.00033260125,0.00024745625,0.0004219945,0.0009430511,0.00032747848,0.0059375535],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867177,0.00031244836,0.000049788523,0.00035270906,0.0005011797,0.000112155234],"domain_scores_gemma":[0.9985556,0.0006846657,0.00015483845,0.00021116629,0.0003380496,0.00005558805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007939303,0.00076516176,0.0005798189,0.0010544222,0.00044078438,0.0008698774,0.0005056086,0.0005866977,0.0042520585],"category_scores_gemma":[0.0012276856,0.00037408437,0.00028356307,0.0010209286,0.00068955997,0.0012230689,0.000770516,0.0012769573,0.0011778056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002200772,0.000121125086,0.0007600325,0.00032676372,0.000034781973,0.00008858912,0.000104093226,0.0023851993,0.9061113,0.007889515,0.0035047857,0.07845372],"study_design_scores_gemma":[0.000013203552,0.00010182943,0.0008671993,0.000022202843,0.000017769336,0.00026872166,0.000037205005,0.021785323,0.96075636,0.0018537675,0.014216305,0.000060078342],"about_ca_topic_score_codex":0.0004067351,"about_ca_topic_score_gemma":0.00078785664,"teacher_disagreement_score":0.0042520585,"about_ca_system_score_codex":0.00047262709,"about_ca_system_score_gemma":0.0004932976,"threshold_uncertainty_score":0.014224529},"labels":[],"label_agreement":null},{"id":"W4406549050","doi":"10.3390/s25020518","title":"LoRa Resource Allocation Algorithm for Higher Data Rates","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Resource allocation; Algorithm; Resource (disambiguation); Data mining; Computer network","score_opus":0.028392584590427906,"score_gpt":0.2971185884786924,"score_spread":0.26872600388826445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406549050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008108655,0.00042318192,0.98465514,0.00027765962,0.00009042495,0.000104215345,0.000071581904,0.0012930671,0.0049760574],"genre_scores_gemma":[0.32816976,0.00044670276,0.6612202,0.00043325283,0.00014458553,0.00059801573,0.00031301216,0.0002875698,0.008386947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905103,0.00030196097,0.00006395572,0.00015864194,0.00026623905,0.00015822233],"domain_scores_gemma":[0.99836403,0.0006492015,0.00020880943,0.00029483912,0.00041168433,0.00007148524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011669608,0.00080593344,0.0008256948,0.0009140317,0.00078770344,0.0012346732,0.0011701175,0.0005985783,0.0048376857],"category_scores_gemma":[0.004291581,0.00024677246,0.00045984428,0.0008387495,0.0005762176,0.0011675667,0.0012144968,0.0012045113,0.0026929702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045623188,0.00025452577,0.0017736002,0.000238794,0.000117986456,0.00022954107,0.00037268354,0.35591957,0.031888384,0.047259193,0.021495393,0.539994],"study_design_scores_gemma":[0.000079227066,0.0001226757,0.00035605137,0.000026881511,0.000023293625,0.0002532289,0.000072997704,0.9572555,0.012756085,0.011417481,0.017595014,0.000041502495],"about_ca_topic_score_codex":0.002039836,"about_ca_topic_score_gemma":0.0023381694,"teacher_disagreement_score":0.0048376857,"about_ca_system_score_codex":0.00076833065,"about_ca_system_score_gemma":0.0013964461,"threshold_uncertainty_score":0.016183674},"labels":[],"label_agreement":null},{"id":"W4406616228","doi":"10.3390/s25020572","title":"Identifying the Primary Kinetic Factors Influencing the Anterior–Posterior Center of Mass Displacement in Barbell Squats: A Factor Regression Analysis","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Sports injuries and prevention","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Ningbo; Ningbo University","keywords":"Squat; Orthodontics; Ankle; Physical medicine and rehabilitation; Squatting position; Medicine; Internal rotation; External rotation; Physical therapy; Displacement (psychology); ACL injury; Anterior cruciate ligament; Engineering; Surgery; Psychology","score_opus":0.013959766503872473,"score_gpt":0.30281289153592045,"score_spread":0.28885312503204796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406616228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913687,0.00020378937,0.007001107,0.000111069414,0.000024958,0.00011187288,0.0004527161,0.000054897577,0.0006709554],"genre_scores_gemma":[0.99606955,0.00007640997,0.002834923,0.000011498352,0.000013693621,0.00007299808,0.000424741,0.000011349024,0.00048486431],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990761,0.00027013614,0.00009792522,0.00019630625,0.00022850958,0.00013094838],"domain_scores_gemma":[0.9967179,0.001511341,0.0007291506,0.00017333815,0.0005780915,0.0002902231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022922985,0.0010834287,0.0005531365,0.0011987162,0.00048038925,0.0005335507,0.00046173765,0.00037131057,0.0039973096],"category_scores_gemma":[0.00740755,0.00027890017,0.0016592906,0.0008742715,0.00030711028,0.00039532376,0.00045354056,0.000612676,0.0006516404],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035892113,0.00013221864,0.98273623,0.0000540519,0.00035211194,0.00008429083,0.00026438897,0.0007242013,0.00092667,0.00007392931,0.0003596038,0.013933398],"study_design_scores_gemma":[0.00002115021,0.00042147125,0.9904936,0.00003922926,0.00021361872,0.00014942432,0.0004911668,0.007161633,0.0004115807,0.00010236557,0.00047784235,0.00001691099],"about_ca_topic_score_codex":0.012900826,"about_ca_topic_score_gemma":0.010637109,"teacher_disagreement_score":0.012900826,"about_ca_system_score_codex":0.0003698175,"about_ca_system_score_gemma":0.0010804655,"threshold_uncertainty_score":0.025651455},"labels":[],"label_agreement":null},{"id":"W4406616645","doi":"10.3390/s25020587","title":"Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Quantum cascade laser; Spectrometer; Benchmark (surveying); Detector; Computer science; Fourier transform infrared spectroscopy; Laser; Data processing; Optics; Materials science; Physics; Telecommunications","score_opus":0.030327138035132564,"score_gpt":0.2979880228833293,"score_spread":0.2676608848481968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406616645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43288946,0.0041314084,0.5446589,0.0012307311,0.00076096936,0.0005437391,0.0009983779,0.0045862463,0.010200138],"genre_scores_gemma":[0.62123907,0.0008067078,0.3694515,0.0006301756,0.00018721062,0.000302927,0.0004586479,0.00011642854,0.006807276],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989969,0.0000869938,0.000051290823,0.00027013844,0.00055583427,0.00003888945],"domain_scores_gemma":[0.9993327,0.00010542601,0.00016623939,0.000101939346,0.00024570347,0.00004800029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005191799,0.00042887108,0.0006166732,0.00057526684,0.00028452554,0.0006447392,0.0015125854,0.0009451187,0.0015424283],"category_scores_gemma":[0.0008851856,0.00034417678,0.00035074828,0.0006356887,0.00033235198,0.0013133216,0.0005814872,0.0005174211,0.00077368016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033847382,0.00020901966,0.001418866,0.0002757715,0.000041168027,0.00020776661,0.00014134862,0.0009970249,0.8909613,0.0018982182,0.003055546,0.10045554],"study_design_scores_gemma":[0.00013107008,0.0011687983,0.0043607918,0.000032869455,0.000089125984,0.0013975856,0.00005010345,0.1125669,0.86018884,0.00071398943,0.019148499,0.00015148036],"about_ca_topic_score_codex":0.000713696,"about_ca_topic_score_gemma":0.0013336082,"teacher_disagreement_score":0.0015424283,"about_ca_system_score_codex":0.00060933636,"about_ca_system_score_gemma":0.0004060142,"threshold_uncertainty_score":0.0051599145},"labels":[],"label_agreement":null},{"id":"W4406616933","doi":"10.3390/s25020586","title":"Characterization of RAP Signal Patterns, Temporal Relationships, and Artifact Profiles Derived from Intracranial Pressure Sensors in Acute Traumatic Neural Injury","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pan Am Clinic; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Research Manitoba; Health Sciences Centre Foundation","keywords":"Autoregressive integrated moving average; Traumatic brain injury; Artifact (error); Autoregressive model; Intracranial pressure; SIGNAL (programming language); Computer science; Pattern recognition (psychology); Time series; Artificial intelligence; Statistics; Mathematics; Medicine; Anesthesia","score_opus":0.01907997498149977,"score_gpt":0.2578717696000501,"score_spread":0.23879179461855032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406616933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9599951,0.000860435,0.037174024,0.000082425606,0.000023243378,0.00009946845,0.0005121765,0.00016580439,0.001087263],"genre_scores_gemma":[0.98529935,0.0005742468,0.012734483,0.000034002092,0.00003796045,0.00011811001,0.0006507916,0.000031594573,0.0005195111],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996123,0.00007247566,0.00005388205,0.00008019717,0.00014042943,0.000040798717],"domain_scores_gemma":[0.99874014,0.0003742354,0.0004281407,0.00008868012,0.000305615,0.000063227104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078057346,0.0003527946,0.0003089772,0.0012389614,0.00015914527,0.0004315328,0.00024229867,0.000313383,0.0007345736],"category_scores_gemma":[0.0035996176,0.00011318084,0.0002637556,0.001047427,0.00031068898,0.00043238184,0.000337467,0.00034344615,0.0002670229],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027593067,0.00039840734,0.3329562,0.0013463713,0.00031671423,0.0014112002,0.0029191657,0.00792204,0.2065356,0.0012540766,0.0018399572,0.440341],"study_design_scores_gemma":[0.000012924176,0.0011267348,0.93420166,0.000114541566,0.00017780025,0.0024393369,0.0009531931,0.026840825,0.03106176,0.001029891,0.001969871,0.00007146668],"about_ca_topic_score_codex":0.0010090105,"about_ca_topic_score_gemma":0.0014290062,"teacher_disagreement_score":0.0012389614,"about_ca_system_score_codex":0.0001713876,"about_ca_system_score_gemma":0.0003239355,"threshold_uncertainty_score":0.0041280985},"labels":[],"label_agreement":null},{"id":"W4406738882","doi":"10.3390/s25030686","title":"The Application of Integrated Force and Temperature Sensors to Enhance Orthotic Treatment Monitoring in Adolescent Idiopathic Scoliosis: A Pilot Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Idiopathic scoliosis; Medicine; Compliance (psychology); Physical therapy; Patient compliance; Scoliosis; Lumbar; Physical medicine and rehabilitation; Surgery; Psychology; Emergency medicine","score_opus":0.020493112490400308,"score_gpt":0.3290094205361783,"score_spread":0.308516308045778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406738882","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987674,0.00008860129,0.0008728808,0.000011804271,0.0000053608487,0.00013941214,0.000018979561,0.0000059975023,0.0000895342],"genre_scores_gemma":[0.9957131,0.0001606024,0.003730827,0.000022529597,0.00001882966,0.000173067,0.00004284906,0.0000039928,0.00013414776],"study_design_codex":"bench_or_experimental","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9993179,0.00030870698,0.000057221,0.00008422244,0.00015643344,0.000075637035],"domain_scores_gemma":[0.9992028,0.00026348841,0.00013924234,0.00007305722,0.00017168868,0.00014980051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085857365,0.00060281676,0.0006324831,0.00047535042,0.0002527418,0.0002778717,0.00033559542,0.0004639359,0.0008118524],"category_scores_gemma":[0.001432373,0.00022830676,0.0004715156,0.0002840786,0.00055880024,0.0003425443,0.00041147938,0.00042663937,0.0001349983],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.022493958,0.072437204,0.28256905,0.0012599025,0.000543321,0.0026976157,0.004084746,0.0026996166,0.40506887,0.00018099762,0.00040147008,0.20556325],"study_design_scores_gemma":[0.0024102442,0.4284264,0.49783292,0.000056840305,0.0004970847,0.003305149,0.0017448424,0.008777836,0.055238623,0.00006662616,0.0015523605,0.00009111069],"about_ca_topic_score_codex":0.0008571012,"about_ca_topic_score_gemma":0.0012900924,"teacher_disagreement_score":0.00085857365,"about_ca_system_score_codex":0.00014315867,"about_ca_system_score_gemma":0.0003824031,"threshold_uncertainty_score":0.0045406222},"labels":[],"label_agreement":null},{"id":"W4406767249","doi":"10.3390/s25030660","title":"Exploring the Link Between Motor Functions and the Relative Use of the More Affected Arm in Adults with Cerebral Palsy","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Cerebral palsy; Physical medicine and rehabilitation; Task (project management); Activities of daily living; Accelerometer; Psychology; Physical therapy; Medicine; Computer science; Engineering","score_opus":0.03356309086174905,"score_gpt":0.24728779220604458,"score_spread":0.21372470134429553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406767249","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995239,0.00007254697,0.00008981753,0.000011963228,8.8722624e-7,0.0000037099426,0.000060375212,0.0000016934551,0.00023510559],"genre_scores_gemma":[0.9995441,0.000059324342,0.00015573952,0.000005833115,0.0000025822458,0.000006359461,0.00011052183,7.568786e-7,0.00011464046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997782,0.000052935862,0.00004062339,0.000046229154,0.000052237378,0.00002981504],"domain_scores_gemma":[0.99884856,0.00031878636,0.00058785186,0.000039874813,0.00012027804,0.000084589774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048739716,0.00041735018,0.000210794,0.0007655862,0.00021174701,0.00036792192,0.00016588513,0.00033006366,0.0016067221],"category_scores_gemma":[0.0030887227,0.00013544211,0.00016561392,0.0005181002,0.0002310575,0.0003522824,0.00047196817,0.00029885434,0.00024880943],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063725034,0.00003060338,0.99635553,0.000013075459,0.00002072771,0.000050396564,0.00015563866,0.000034750607,0.00039206728,0.0000056872136,0.000019972538,0.0028579251],"study_design_scores_gemma":[7.110878e-7,0.00008577758,0.9995152,0.0000023510956,0.000005393015,0.00012536379,0.00013045323,0.000057815883,0.000044959987,0.0000068630593,0.000024116973,0.0000010516746],"about_ca_topic_score_codex":0.0031885945,"about_ca_topic_score_gemma":0.006926108,"teacher_disagreement_score":0.0031885945,"about_ca_system_score_codex":0.00013203695,"about_ca_system_score_gemma":0.00016906332,"threshold_uncertainty_score":0.006340027},"labels":[],"label_agreement":null},{"id":"W4406908679","doi":"10.3390/s25030792","title":"Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Cistel Technology (Canada); University of Waterloo; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Dalhousie University","keywords":"Multivariate statistics; Robustness (evolution); Sampling (signal processing); Computer science; Time series; Regression; Data mining; Environmental science; Statistics; Ecology; Machine learning; Mathematics","score_opus":0.02156962333526319,"score_gpt":0.2837955376717727,"score_spread":0.2622259143365095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406908679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93139416,0.00047931913,0.06694045,0.0003100235,0.00004782696,0.00005170795,0.00025288906,0.000096649615,0.00042707787],"genre_scores_gemma":[0.9851451,0.000264639,0.013636597,0.000045008426,0.00007010022,0.00001881913,0.000595452,0.000012413882,0.00021187581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984231,0.000640958,0.000121136014,0.00035508283,0.00032955894,0.00013019009],"domain_scores_gemma":[0.99253064,0.0049183867,0.000883835,0.00090186764,0.0005256558,0.00023961914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00458658,0.00053808227,0.00061635737,0.0009664444,0.0004881949,0.00052649016,0.00054196693,0.00045810913,0.00022475875],"category_scores_gemma":[0.0090759285,0.00019630989,0.0009072994,0.0009910049,0.0004895959,0.0013325048,0.0005753088,0.0007960291,0.00008021382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015659719,0.001284602,0.31800115,0.00028531876,0.00050898845,0.00063668715,0.0006529498,0.38360044,0.009348969,0.0040483563,0.0019768346,0.2780897],"study_design_scores_gemma":[0.0000121129015,0.00015356315,0.05856746,0.000012501222,0.000037438072,0.00011319247,0.00018481485,0.9377135,0.0014529309,0.001270472,0.00046426395,0.00001766875],"about_ca_topic_score_codex":0.00615161,"about_ca_topic_score_gemma":0.006213517,"teacher_disagreement_score":0.00615161,"about_ca_system_score_codex":0.0006418386,"about_ca_system_score_gemma":0.0005218357,"threshold_uncertainty_score":0.024256408},"labels":[],"label_agreement":null},{"id":"W4407143218","doi":"10.3390/s25030908","title":"Relationship Between Signals from Cerebral near Infrared Spectroscopy Sensor Technology and Objectively Measured Cerebral Blood Volume: A Systematic Scoping Review","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pan Am Clinic; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Research Manitoba","keywords":"Cerebral blood volume; Metric (unit); Systematic review; Association (psychology); Meta-analysis; Inclusion and exclusion criteria; Cerebral blood flow; Medicine; Computer science; Psychology; MEDLINE; Cardiology; Internal medicine; Pathology; Engineering; Biology","score_opus":0.022703152352013627,"score_gpt":0.3211213392350397,"score_spread":0.29841818688302607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407143218","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009187529,0.9965559,0.00047292895,0.000358804,0.00017982004,0.0006395001,0.0004742869,0.0000122457795,0.00038780578],"genre_scores_gemma":[0.00898414,0.98743314,0.001175223,0.0005415027,0.00010572636,0.0012347768,0.00034573858,0.000009898791,0.00016992477],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9865036,0.0039640963,0.0059071076,0.0008918739,0.0024416693,0.00029180612],"domain_scores_gemma":[0.9206257,0.06261902,0.009601819,0.0011875998,0.005650026,0.0003159167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01647893,0.0018277378,0.008241899,0.017176146,0.0010860271,0.00429988,0.0022159226,0.002737498,0.0053298133],"category_scores_gemma":[0.09954888,0.0013377265,0.009505783,0.01596727,0.0013546577,0.0032150582,0.0022964878,0.001498052,0.0006121137],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001028588,0.000013418125,0.0005942568,0.95904595,0.0063706683,0.00008302399,0.0002078665,0.00009346083,0.0001398933,0.00029978514,0.0011226891,0.03192622],"study_design_scores_gemma":[0.000058448477,0.00008640497,0.001645126,0.94186664,0.04485936,0.0001989663,0.00021378054,0.00007132807,0.00015281382,0.00036221,0.010455837,0.000029083318],"about_ca_topic_score_codex":0.008903353,"about_ca_topic_score_gemma":0.02528838,"teacher_disagreement_score":0.017176146,"about_ca_system_score_codex":0.0045746677,"about_ca_system_score_gemma":0.01920501,"threshold_uncertainty_score":0.08714992},"labels":[],"label_agreement":null},{"id":"W4407174085","doi":"10.3390/s25030960","title":"Spatial Perception and Navigation in the Absence of Vision","year":2025,"lang":"en","type":"editorial","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Perception; Blindness; Sensory substitution; Neuroscience; Psychology; Neuroplasticity; Spatial memory; Visual perception; Computer vision; Impaired Vision; Spatial analysis; Communication; Computer science; Cognitive psychology; Artificial intelligence; Human–computer interaction; Geography; Optometry; Medicine; Cognition","score_opus":0.013918422758643585,"score_gpt":0.3101476544092897,"score_spread":0.29622923165064613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407174085","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000065505374,0.03469711,0.00021317175,0.03970318,0.92423224,0.000014885128,0.000040230494,0.000042502696,0.0009911965],"genre_scores_gemma":[0.0005634292,0.016540498,0.00008760905,0.025055017,0.9538686,0.00001910353,0.000022405651,0.000018642053,0.0038246089],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971591,0.00061209756,0.00043203673,0.00043496,0.001141551,0.00022021288],"domain_scores_gemma":[0.9903362,0.0056891833,0.00043298196,0.00025893867,0.00222868,0.0010539765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005778356,0.0039049233,0.003298659,0.0021568707,0.0022394762,0.004590337,0.0039406987,0.019377623,0.0039538145],"category_scores_gemma":[0.011939129,0.0012486118,0.0022515291,0.0009039208,0.0039669117,0.0044059553,0.002250835,0.030914178,0.0042723683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017121766,0.000019089135,0.000046377245,0.000639128,0.000049646507,0.0003231911,0.000032067,0.000045616365,0.00018112184,0.0011907092,0.9802965,0.0170053],"study_design_scores_gemma":[0.0001253188,0.00008694374,0.00045493114,0.00076231407,0.00011244095,0.00076732493,0.000048518326,0.00021146008,0.00021272301,0.0032551913,0.99393034,0.000032514257],"about_ca_topic_score_codex":0.0026592568,"about_ca_topic_score_gemma":0.0052741417,"teacher_disagreement_score":0.019377623,"about_ca_system_score_codex":0.0025538113,"about_ca_system_score_gemma":0.0024996973,"threshold_uncertainty_score":0.030559242},"labels":[],"label_agreement":null},{"id":"W4407193038","doi":"10.3390/s25030983","title":"Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley; Simon Fraser University","funders":"","keywords":"Breast cancer; Diffuse optical imaging; Mammography; Cancer detection; Optical tomography; Computer science; Near-infrared spectroscopy; Biomedical engineering; Cancer; Medicine; Artificial intelligence; Iterative reconstruction; Optics; Physics; Internal medicine","score_opus":0.006223141264518629,"score_gpt":0.3615031102663519,"score_spread":0.3552799690018333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407193038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12548701,0.068321414,0.7804851,0.0029301743,0.00069065706,0.00038811116,0.00031445085,0.002586468,0.018796561],"genre_scores_gemma":[0.3640359,0.019230684,0.60147935,0.0023252403,0.0002781697,0.00032795008,0.00019067578,0.00018501122,0.011946957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994856,0.0001887178,0.000013663188,0.00010243708,0.00018449387,0.000025105634],"domain_scores_gemma":[0.9996691,0.0001679042,0.00004884912,0.000030163572,0.00006325749,0.000020744747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007731384,0.00047685963,0.00031173008,0.0005684911,0.00014245209,0.0005883432,0.0006005129,0.0010135479,0.0031646567],"category_scores_gemma":[0.001205697,0.00030231726,0.00029152568,0.00034050544,0.00034576212,0.00089599885,0.00069061783,0.0007756737,0.0010870213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017560663,0.000093847484,0.0011235143,0.00031949495,0.00003170603,0.00017396343,0.00010960347,0.0009662243,0.83799267,0.0032197926,0.0017664544,0.15402724],"study_design_scores_gemma":[0.000083104,0.0012137764,0.00524539,0.00021037618,0.00012042077,0.0047919843,0.00020674795,0.046168543,0.8303336,0.003987296,0.10748816,0.00015059863],"about_ca_topic_score_codex":0.000280188,"about_ca_topic_score_gemma":0.00064664654,"teacher_disagreement_score":0.0031646567,"about_ca_system_score_codex":0.0003578294,"about_ca_system_score_gemma":0.0002748223,"threshold_uncertainty_score":0.010586798},"labels":[],"label_agreement":null},{"id":"W4407293267","doi":"10.3390/s25041006","title":"A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Cegep de Sept Iles","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Predictive maintenance; Computer science; Cloud computing; Software deployment; Metric (unit); Data mining; Machine learning; Warning system; Data collection; Data acquisition; Real-time computing; Reliability engineering; Engineering; Artificial intelligence","score_opus":0.013610189841415921,"score_gpt":0.2671491560154782,"score_spread":0.25353896617406224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407293267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051654432,0.00015387405,0.9146504,0.0002850226,0.000121946556,0.00021997027,0.0003696305,0.029002551,0.003542166],"genre_scores_gemma":[0.7241651,0.00015350996,0.27086625,0.00019701182,0.000055481305,0.00022007643,0.00068819383,0.00021881114,0.003435533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999658,0.000045428656,0.000026598407,0.00011657406,0.00012186556,0.00003157555],"domain_scores_gemma":[0.99955696,0.00014648629,0.00003833884,0.00008864877,0.00014701053,0.000022428929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046057496,0.00055624155,0.00040775305,0.00049509713,0.00034495743,0.0005303728,0.001259585,0.0005718379,0.0025736992],"category_scores_gemma":[0.0018382425,0.00028635075,0.00033324573,0.0005209725,0.00024254715,0.0007054698,0.00047999906,0.00075052853,0.0007245291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005760958,0.0007684025,0.010330459,0.0002482773,0.00018463569,0.00064096897,0.00026380454,0.2515997,0.0382924,0.008455339,0.01183637,0.67680365],"study_design_scores_gemma":[0.000022345832,0.00006626144,0.0010046384,0.000010934051,0.000012040586,0.000063070474,0.000017709775,0.9832234,0.011156142,0.0013501403,0.0030628613,0.00001052342],"about_ca_topic_score_codex":0.005094281,"about_ca_topic_score_gemma":0.0039632656,"teacher_disagreement_score":0.005094281,"about_ca_system_score_codex":0.00044784747,"about_ca_system_score_gemma":0.00070655637,"threshold_uncertainty_score":0.010129273},"labels":[],"label_agreement":null},{"id":"W4407369415","doi":"10.3390/s25041083","title":"High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Squatting position; Inertial measurement unit; Dynamic time warping; Artificial intelligence; Computer science; Gait; Activity recognition; Knee flexion; STRIDE; Scale (ratio); Gait analysis; Accelerometer; Computer vision; Physical medicine and rehabilitation; Pattern recognition (psychology); Physical therapy; Medicine; Geography","score_opus":0.05168691636735369,"score_gpt":0.3308532742620001,"score_spread":0.27916635789464644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407369415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24630141,0.00028775068,0.75133884,0.00004337882,0.00004739095,0.00010945841,0.00038369303,0.0005418271,0.00094618974],"genre_scores_gemma":[0.8555106,0.00030739527,0.14248557,0.000031481144,0.000023239432,0.00013737672,0.0005291682,0.00003740893,0.00093776773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996908,0.0000488322,0.000024902807,0.00011600883,0.00009157688,0.00002792471],"domain_scores_gemma":[0.9996499,0.00010902336,0.00007736461,0.000050715946,0.00009541089,0.000017560526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044488054,0.0005670678,0.00041145075,0.00080725545,0.00012247355,0.0004927595,0.00033548556,0.00030253263,0.0007893983],"category_scores_gemma":[0.001534503,0.00018617162,0.0004467086,0.0007301825,0.0001776821,0.00048599657,0.00044315954,0.0003161426,0.00058767205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049237075,0.00037475044,0.038452093,0.00042271288,0.00022732798,0.00043548123,0.00038213006,0.0909938,0.14392301,0.0009219981,0.001042096,0.7223323],"study_design_scores_gemma":[0.000017363956,0.00047143872,0.11415378,0.0000583396,0.000057384103,0.0007696712,0.0001798771,0.84778786,0.033453375,0.0013963967,0.0016039436,0.000050610484],"about_ca_topic_score_codex":0.0015810739,"about_ca_topic_score_gemma":0.0028604348,"teacher_disagreement_score":0.0015810739,"about_ca_system_score_codex":0.0001473664,"about_ca_system_score_gemma":0.0002378903,"threshold_uncertainty_score":0.0031437278},"labels":[],"label_agreement":null},{"id":"W4407426458","doi":"10.3390/s25041126","title":"A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Universidade de Macau","keywords":"Gesture; Computer science; Feature selection; Identification (biology); Selection (genetic algorithm); Feature (linguistics); Gesture recognition; Artificial intelligence; Machine learning; Human–computer interaction; Pattern recognition (psychology)","score_opus":0.01029037630390018,"score_gpt":0.25989645963501795,"score_spread":0.24960608333111778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407426458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013150729,0.00018626182,0.9851002,0.00011908508,0.000054514672,0.000053008178,0.00006629006,0.0008222992,0.00044769395],"genre_scores_gemma":[0.49584976,0.00023870917,0.4997458,0.00025690024,0.00012863726,0.00034162577,0.0007227312,0.0002038935,0.0025119209],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854505,0.0003098055,0.00011852429,0.00038489513,0.00049106573,0.0001506506],"domain_scores_gemma":[0.9981634,0.00074945035,0.0001680584,0.0002726411,0.0005705009,0.00007597262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018094504,0.0011875315,0.0015446917,0.00187001,0.00055198156,0.0008962042,0.0013733584,0.00090393284,0.0022130536],"category_scores_gemma":[0.004746533,0.0004539222,0.0012330202,0.0014261235,0.0005488335,0.001833833,0.0012568054,0.0012277676,0.0007071903],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031985255,0.0003454923,0.009217122,0.00016404709,0.00020895399,0.0002844922,0.0001349421,0.10071175,0.020132232,0.0065312576,0.0040901336,0.85785973],"study_design_scores_gemma":[0.000018742763,0.000106703,0.0017759785,0.000011168124,0.00003023805,0.000120866884,0.000017709368,0.98969555,0.0041330187,0.0027172016,0.0013528783,0.000019907187],"about_ca_topic_score_codex":0.002392382,"about_ca_topic_score_gemma":0.0023772009,"teacher_disagreement_score":0.002392382,"about_ca_system_score_codex":0.00047826057,"about_ca_system_score_gemma":0.0010980315,"threshold_uncertainty_score":0.0095694065},"labels":[],"label_agreement":null},{"id":"W4407578834","doi":"10.3390/s25041175","title":"Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regenerative brake; Automotive engineering; Engineering; Engine braking; Computer science; Brake","score_opus":0.016694027544506117,"score_gpt":0.23644564331322526,"score_spread":0.21975161576871916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407578834","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6396495,0.0004532535,0.35168597,0.00008910267,0.000078312376,0.000074440686,0.00009705379,0.001285747,0.006586588],"genre_scores_gemma":[0.9914888,0.000046198737,0.0078035276,0.000010779389,0.0000051127313,0.0000092057435,0.000033018696,0.00001014474,0.00059321226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999385,0.00000676344,0.0000034013449,0.000016705386,0.000020464393,0.00001412227],"domain_scores_gemma":[0.99992263,0.000011448757,0.000017584744,0.000012827405,0.000025533558,0.000009979575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007481882,0.0003522611,0.00019365638,0.00024185068,0.00011966816,0.00021880252,0.00030340554,0.00013281681,0.00042564535],"category_scores_gemma":[0.00024301356,0.00012342165,0.00019203442,0.00011839048,0.00009111448,0.00028217956,0.00016881188,0.00014433032,0.00020089764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047840382,0.00024811487,0.017397597,0.00012430377,0.00008032079,0.00019185462,0.00017671619,0.2789519,0.22838552,0.0014107355,0.0012214332,0.47133315],"study_design_scores_gemma":[0.00001454791,0.00019333001,0.01006981,0.0000055152173,0.000034172386,0.00007112132,0.00006088825,0.96060926,0.026694952,0.0006430116,0.0015811485,0.000022181433],"about_ca_topic_score_codex":0.0027520785,"about_ca_topic_score_gemma":0.0049265926,"teacher_disagreement_score":0.0027520785,"about_ca_system_score_codex":0.00016263046,"about_ca_system_score_gemma":0.00019662872,"threshold_uncertainty_score":0.005472064},"labels":[],"label_agreement":null},{"id":"W4407585395","doi":"10.3390/s25041165","title":"Design and Optimization of a Gold and Silver Nanoparticle-Based SERS Biosensing Platform","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Biosensor; Nanotechnology; Biomolecule; Materials science; Raman scattering; Biochip; Colloidal gold; Nanoparticle; Silver nanoparticle; Multiphysics; Raman spectroscopy; Optics; Engineering","score_opus":0.018434453437025145,"score_gpt":0.2334929372029118,"score_spread":0.21505848376588665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407585395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5666083,0.0010143343,0.41718474,0.000429008,0.00017191065,0.00067634246,0.00026400652,0.0015796968,0.012071669],"genre_scores_gemma":[0.71344745,0.00044249615,0.2812564,0.00011534156,0.000017210616,0.00046910747,0.00021651968,0.0001316201,0.0039038216],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997508,0.0000207895,0.00001479458,0.00006714633,0.00011125202,0.00003526377],"domain_scores_gemma":[0.999892,0.000018450966,0.000027601407,0.000010344883,0.000034233984,0.000017390697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030517578,0.0007462455,0.00048058506,0.00032669262,0.00028148634,0.0005652328,0.00085721636,0.0006637495,0.00070632313],"category_scores_gemma":[0.00033534662,0.00047031595,0.00038361107,0.00018846705,0.00028289502,0.00043514496,0.00041899533,0.00034675887,0.00063501875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008595864,0.00009881633,0.00032013378,0.00017677645,0.000028725426,0.0001429893,0.000033412227,0.082382835,0.90401983,0.0017395988,0.00019301042,0.010777815],"study_design_scores_gemma":[0.000051396368,0.00030101268,0.00044024913,0.000008186821,0.000025505338,0.000105912055,0.000020435915,0.31326708,0.6811707,0.00043435788,0.0041460674,0.000029028843],"about_ca_topic_score_codex":0.00082183565,"about_ca_topic_score_gemma":0.0012105681,"teacher_disagreement_score":0.00085721636,"about_ca_system_score_codex":0.00068378705,"about_ca_system_score_gemma":0.00089051476,"threshold_uncertainty_score":0.004961252},"labels":[],"label_agreement":null},{"id":"W4407590981","doi":"10.3390/s25041183","title":"Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Bootstrapping (finance); Parametric statistics; Interpretation (philosophy); Computer science; Data mining; Statistics; Econometrics; Mathematics","score_opus":0.021916503469353362,"score_gpt":0.27647297161840795,"score_spread":0.2545564681490546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407590981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025666796,0.00005595786,0.9728579,0.00014077533,0.000017208582,0.000045531917,0.00010527781,0.00061811093,0.00049240846],"genre_scores_gemma":[0.3965633,0.00008511599,0.60154766,0.00013320052,0.000041975083,0.00019897951,0.0007271011,0.00020382021,0.0004989128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975182,0.001383506,0.00015114088,0.00037941049,0.00044406005,0.00012374205],"domain_scores_gemma":[0.9908236,0.0062952405,0.0005035753,0.0010466484,0.0012272203,0.000103751874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00582408,0.00091329834,0.000874124,0.0009860825,0.0005830817,0.0011714346,0.0017384993,0.0010832616,0.0014039436],"category_scores_gemma":[0.027722418,0.00048050354,0.0010591071,0.0011409667,0.00078098715,0.0012503917,0.0014971123,0.0012774909,0.00060387363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000397494,0.00019349148,0.0061891265,0.00017186294,0.00017944802,0.00022911544,0.00026700203,0.6744207,0.011247416,0.010126217,0.0022666915,0.29431134],"study_design_scores_gemma":[0.000011558265,0.000025425601,0.0006477135,0.000006482016,0.000006466019,0.000023270579,0.000029535111,0.991222,0.001601782,0.005986476,0.00042951232,0.000009660063],"about_ca_topic_score_codex":0.0052521904,"about_ca_topic_score_gemma":0.0064213295,"teacher_disagreement_score":0.00582408,"about_ca_system_score_codex":0.000514923,"about_ca_system_score_gemma":0.0014903083,"threshold_uncertainty_score":0.030800998},"labels":[],"label_agreement":null},{"id":"W4407881104","doi":"10.3390/s25051360","title":"Novel Robotic Balloon-Based Device for Wrist-Extension Therapy of Hemiparesis Stroke Patients","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Narodowe Centrum Badań i Rozwoju; Uniwersytet Medyczny w Lodzi","keywords":"Wrist; Physical medicine and rehabilitation; Hemiparesis; Stroke (engine); Medicine; Balloon; Rehabilitation; Physical therapy; Modified Rankin Scale; Paresis; Upper limb; Simulation; Computer science; Surgery; Engineering; Mechanical engineering","score_opus":0.021876734763553674,"score_gpt":0.2931297445339209,"score_spread":0.2712530097703672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407881104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8742942,0.0054313913,0.11107874,0.00043454295,0.00039923086,0.00069825625,0.00057318935,0.001440258,0.005650218],"genre_scores_gemma":[0.954096,0.0013886812,0.03824311,0.00020749534,0.00007071303,0.0007150146,0.0002984706,0.000023350862,0.0049571064],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998853,0.000024901121,0.000013247367,0.000023582788,0.000038174057,0.000014857232],"domain_scores_gemma":[0.9999404,0.000017864451,0.000011254563,0.000006307124,0.000013328505,0.0000109088905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020177262,0.00033490954,0.00042855655,0.00033400592,0.00014247763,0.0001758282,0.00051094004,0.00037008445,0.00213947],"category_scores_gemma":[0.00032567515,0.00014139598,0.0003758952,0.00013342776,0.00011199302,0.00021989593,0.0003322157,0.00015141182,0.00049017475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027945703,0.0016536223,0.006783495,0.0018574204,0.00015669249,0.0022165084,0.00059943,0.0025823452,0.5409477,0.00060875993,0.004277673,0.43552172],"study_design_scores_gemma":[0.0031332118,0.082726575,0.23359269,0.000823971,0.0013928958,0.04188275,0.00096319657,0.111841425,0.37325543,0.0015470326,0.1481952,0.00064566685],"about_ca_topic_score_codex":0.00031944044,"about_ca_topic_score_gemma":0.00055173086,"teacher_disagreement_score":0.00213947,"about_ca_system_score_codex":0.00008518843,"about_ca_system_score_gemma":0.0002292541,"threshold_uncertainty_score":0.007157266},"labels":[],"label_agreement":null},{"id":"W4407986554","doi":"10.3390/s25051421","title":"TapFix: Cursorless Typographical Error Correction for Touch-Sensor Displays","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SwIPe; Cursor (databases); Computer science; Gesture; Usability; Input method; Text entry; Character (mathematics); Human–computer interaction; Virtual keyboard; Artificial intelligence; Computer vision; Speech recognition; Computer hardware; Mathematics","score_opus":0.012256953495634004,"score_gpt":0.2925739681157354,"score_spread":0.2803170146201014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407986554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1378146,0.0036119397,0.80761164,0.00030785648,0.0007641774,0.00071884284,0.0019874952,0.03970419,0.0074792863],"genre_scores_gemma":[0.52643675,0.001615061,0.4500952,0.0003737984,0.0002787794,0.0005911477,0.0016270358,0.0022825564,0.016699689],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894553,0.00013790796,0.00008007148,0.00015615147,0.00061819796,0.000062217994],"domain_scores_gemma":[0.99720484,0.0010373165,0.0003568572,0.0006097711,0.0006280849,0.00016307579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005148621,0.0009385148,0.00048613144,0.0009722817,0.00024002462,0.0007269955,0.0013165227,0.00080136623,0.013169198],"category_scores_gemma":[0.004467506,0.0003455382,0.00037655747,0.00059414376,0.00030068457,0.0013650527,0.0011746393,0.00046157485,0.002071502],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000967342,0.00014291568,0.0029487386,0.0012139257,0.00010574909,0.00074339623,0.00058530306,0.0009308268,0.30752227,0.00094695564,0.01769524,0.66619736],"study_design_scores_gemma":[0.000541483,0.004796105,0.04928885,0.0005230126,0.00033862787,0.01568352,0.00056277675,0.064801075,0.6355347,0.00205759,0.22528146,0.00059075776],"about_ca_topic_score_codex":0.0006183844,"about_ca_topic_score_gemma":0.0010395581,"teacher_disagreement_score":0.013169198,"about_ca_system_score_codex":0.00017082441,"about_ca_system_score_gemma":0.00025720647,"threshold_uncertainty_score":0.044055343},"labels":[],"label_agreement":null},{"id":"W4408094097","doi":"10.3390/s25051537","title":"Visual-Inertial-Wheel Odometry with Slip Compensation and Dynamic Feature Elimination","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Odometry; Visual odometry; Artificial intelligence; Computer vision; Computer science; Robustness (evolution); Sensor fusion; Kalman filter; Inertial measurement unit; Extended Kalman filter; Feature (linguistics); Robot; Mobile robot","score_opus":0.002420613420623353,"score_gpt":0.20902987062780715,"score_spread":0.2066092572071838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408094097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019960307,0.0001631156,0.9777139,0.000052900363,0.00007430247,0.000029626175,0.00012455707,0.000983532,0.0008977376],"genre_scores_gemma":[0.70320654,0.00026290803,0.29224077,0.00011078425,0.0000651968,0.00010238757,0.000645268,0.00013178836,0.003234336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996057,0.00003080151,0.000025101515,0.00012157502,0.0001601986,0.000056600325],"domain_scores_gemma":[0.999559,0.000053068554,0.00009650138,0.00010646967,0.00016635125,0.000018614357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038961685,0.00094801,0.0007261874,0.00080262334,0.0003212325,0.00060909044,0.0012471011,0.00065420853,0.0011243139],"category_scores_gemma":[0.0017930894,0.00054111547,0.00052104297,0.0010841587,0.00041181117,0.001140343,0.0017022492,0.0009153975,0.0007401165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002481568,0.0001346543,0.007975376,0.00030573717,0.00014614385,0.00020320757,0.00025614921,0.1932917,0.06700599,0.0049855704,0.0032820038,0.7221653],"study_design_scores_gemma":[0.000020618538,0.000121734614,0.004862343,0.000031526586,0.00003225326,0.00019556783,0.000047799567,0.96297365,0.024482949,0.0025871429,0.0046084495,0.000035942052],"about_ca_topic_score_codex":0.006373748,"about_ca_topic_score_gemma":0.00978207,"teacher_disagreement_score":0.006373748,"about_ca_system_score_codex":0.00031732753,"about_ca_system_score_gemma":0.0010484648,"threshold_uncertainty_score":0.012673318},"labels":[],"label_agreement":null},{"id":"W4408200913","doi":"10.3390/s25051622","title":"Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada; Queen's University","funders":"Environment Canada; Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Remote sensing; Computer science; Land cover; Time series; Series (stratigraphy); Artificial neural network; Deep learning; Satellite; Artificial intelligence; Geography; Land use; Machine learning; Geology; Engineering","score_opus":0.007113219587184034,"score_gpt":0.19918715702728637,"score_spread":0.19207393744010234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408200913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.946452,0.00064001273,0.046186026,0.00039113662,0.00006223081,0.00007156932,0.0019310038,0.0007033631,0.0035624232],"genre_scores_gemma":[0.96633965,0.00021058357,0.029226832,0.000040740804,0.000008297434,0.000016086853,0.0022506325,0.0000361126,0.0018710241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997274,0.000023269582,0.000010519516,0.0000893338,0.00009373958,0.000055660716],"domain_scores_gemma":[0.9995474,0.000072086885,0.00003802971,0.00003775644,0.00027724585,0.000027470645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073885865,0.0005728258,0.00029338038,0.0006668272,0.0004968593,0.00077736226,0.0010779097,0.00047483435,0.000802095],"category_scores_gemma":[0.002140746,0.00027212713,0.00040353445,0.0010057334,0.00041712864,0.00046886565,0.00035899458,0.00059800956,0.00020784087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000283217,0.00013660679,0.028933201,0.00012089931,0.000118697906,0.0003482629,0.00014926396,0.8128944,0.0077510457,0.0016384788,0.0034463499,0.14417946],"study_design_scores_gemma":[0.000005325975,0.000009204725,0.010853768,0.000005566355,0.000011930375,0.00001407495,0.00004313466,0.9875951,0.0009816608,0.00010886547,0.00035857066,0.000012919325],"about_ca_topic_score_codex":0.8670783,"about_ca_topic_score_gemma":0.8614962,"teacher_disagreement_score":0.1329217,"about_ca_system_score_codex":0.003942606,"about_ca_system_score_gemma":0.004795727,"threshold_uncertainty_score":0.26740897},"labels":[],"label_agreement":null},{"id":"W4408201348","doi":"10.3390/s25051619","title":"Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Children's Hospital of Eastern Ontario","funders":"Centre Hospitalier pour Enfants de l'est de l'Ontario","keywords":"Ground truth; Computer science; Artificial intelligence; Algorithm","score_opus":0.02471551732618265,"score_gpt":0.29013019582867877,"score_spread":0.2654146785024961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408201348","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8187076,0.0011751765,0.16889116,0.00013666957,0.00018785312,0.00068486476,0.0028972435,0.0017115634,0.005607881],"genre_scores_gemma":[0.92436033,0.0005024935,0.07093817,0.00009076457,0.000091660186,0.00032564855,0.0011874664,0.00007860002,0.0024249405],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999238,0.00017827292,0.00007114421,0.00021696692,0.00024395526,0.000051642794],"domain_scores_gemma":[0.99898094,0.00023520947,0.00016387618,0.000084099825,0.0004859651,0.00004986743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006454675,0.0008494107,0.00050744985,0.0015508698,0.00012468676,0.0006674662,0.0004230181,0.00050184637,0.0022141289],"category_scores_gemma":[0.0026283218,0.00014963109,0.00034733093,0.00046722448,0.00018801796,0.00055457925,0.0005850909,0.00018305947,0.0009555867],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020801832,0.0002979053,0.10825718,0.00093834463,0.00032765177,0.00043351535,0.00041831713,0.005661207,0.13507938,0.0003788217,0.00378648,0.74234104],"study_design_scores_gemma":[0.00025348514,0.0027183124,0.6821164,0.00030923885,0.0004245869,0.0034365081,0.0009883179,0.20529485,0.09612871,0.0012042307,0.006892847,0.00023256868],"about_ca_topic_score_codex":0.0015591736,"about_ca_topic_score_gemma":0.0038158987,"teacher_disagreement_score":0.0022141289,"about_ca_system_score_codex":0.00016507284,"about_ca_system_score_gemma":0.00019922206,"threshold_uncertainty_score":0.0074070096},"labels":[],"label_agreement":null},{"id":"W4408236995","doi":"10.3390/s25061643","title":"Cost-Effective Photoacoustic Imaging Using High-Power Light-Emitting Diodes Driven by an Avalanche Oscillator","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"CMC Microsystems","keywords":"Laser; Optoelectronics; Light-emitting diode; Materials science; Transducer; Diode; Optics; High voltage; Computer science; Voltage; Electrical engineering; Acoustics; Physics; Engineering","score_opus":0.006733980268961283,"score_gpt":0.24197005614401704,"score_spread":0.23523607587505577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408236995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8067019,0.0023372394,0.18495405,0.00029396007,0.000085914566,0.00010698217,0.000082790604,0.00044687893,0.004990375],"genre_scores_gemma":[0.8856837,0.0008738624,0.11032872,0.00006163651,0.000019288358,0.000048355847,0.000049616006,0.000027583936,0.0029071965],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998716,0.000014470809,0.0000046367904,0.000028567323,0.00006697587,0.000013739116],"domain_scores_gemma":[0.99986136,0.00005478965,0.000030717885,0.000010735689,0.000029146137,0.0000131593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002067877,0.00021993283,0.0001344081,0.00017909694,0.00008749157,0.0003379936,0.00037996963,0.0003210464,0.00087470183],"category_scores_gemma":[0.00029946555,0.00017484433,0.00016457292,0.00016190557,0.00019046507,0.0006532425,0.000311646,0.0003675037,0.00025007158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002218633,0.000021499505,0.00016000154,0.000036169644,0.0000026712928,0.000026819233,0.000014067927,0.00023230584,0.99005604,0.00031246518,0.00005857468,0.009057209],"study_design_scores_gemma":[0.000013496885,0.0002461231,0.00085971615,0.0000065905906,0.000007778013,0.00018301219,0.000019879702,0.009884342,0.9857584,0.00014913334,0.0028588115,0.000012733498],"about_ca_topic_score_codex":0.0001623494,"about_ca_topic_score_gemma":0.00035999736,"teacher_disagreement_score":0.00087470183,"about_ca_system_score_codex":0.0002214542,"about_ca_system_score_gemma":0.00019238373,"threshold_uncertainty_score":0.0029261708},"labels":[],"label_agreement":null},{"id":"W4408275746","doi":"10.3390/s25061694","title":"Sensitive Electrochemical Determination of Vanillin Using a Bimetallic Hydroxide and Reduced Graphene Oxide Nanocomposite","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Electrochemical sensors and biosensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Vanillin; Bimetallic strip; Nanocomposite; Oxide; Hydroxide; Materials science; Electrochemical gas sensor; Detection limit; Cyclic voltammetry; Chemical engineering; Electrochemistry; Electrode; Nanotechnology; Nuclear chemistry; Inorganic chemistry; Catalysis; Chemistry; Organic chemistry; Metallurgy; Chromatography; Physical chemistry","score_opus":0.005142669153817036,"score_gpt":0.2144649823917927,"score_spread":0.20932231323797568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408275746","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89988554,0.009977476,0.08527854,0.000537143,0.00044686926,0.00019528512,0.00041836544,0.00071018666,0.0025506164],"genre_scores_gemma":[0.9038417,0.0027534345,0.09002864,0.0002507194,0.000060263108,0.00012798292,0.00029009586,0.000019070589,0.002627992],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989473,0.00021212712,0.00006880338,0.00032333093,0.00037599623,0.0000724537],"domain_scores_gemma":[0.99978644,0.000059380447,0.000048328893,0.000015863316,0.000067901514,0.000022093434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005577347,0.00079887913,0.00046440095,0.00078664167,0.00023847984,0.00041180666,0.0011017764,0.0016255094,0.00037868464],"category_scores_gemma":[0.0006799573,0.00048977096,0.00041715094,0.00048232672,0.00029895979,0.0006473468,0.00052024174,0.0006325545,0.00018743196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042487685,0.000030147508,0.0001314464,0.000074187235,0.0000140799075,0.00003694694,0.000011999515,0.000101549995,0.9964514,0.00004891268,0.00002616567,0.0030305448],"study_design_scores_gemma":[0.000008750571,0.00027470454,0.0007747529,0.000007311697,0.000027809258,0.00021921081,0.000027640515,0.0044149566,0.993274,0.0000407711,0.0009100678,0.000020089486],"about_ca_topic_score_codex":0.00074778777,"about_ca_topic_score_gemma":0.0015936093,"teacher_disagreement_score":0.0016255094,"about_ca_system_score_codex":0.0004606483,"about_ca_system_score_gemma":0.00024612393,"threshold_uncertainty_score":0.0033422709},"labels":[],"label_agreement":null},{"id":"W4408275946","doi":"10.3390/s25061680","title":"Validity and Reliability of Inertial Motion Unit-Based Performance Metrics During Wheelchair Racing Propulsion","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Canadian Institutes of Health Research","keywords":"Wheelchair; Inertial measurement unit; Accelerometer; Reliability (semiconductor); Simulation; Kinematics; Motion analysis; Acceleration; Athletes; Engineering; Physical medicine and rehabilitation; Computer science; Physical therapy; Medicine; Artificial intelligence","score_opus":0.038294183760753275,"score_gpt":0.3441200928532203,"score_spread":0.30582590909246704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408275946","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9933482,0.0004328204,0.0043267002,0.000016441507,0.000046357956,0.00008157977,0.00022655774,0.000060455422,0.0014610449],"genre_scores_gemma":[0.9975598,0.00008643208,0.0017821528,0.000009150136,0.000012763357,0.000047438854,0.00025577523,0.000015683034,0.00023080957],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965418,0.0008881748,0.0004998459,0.0007670358,0.0011097988,0.00019322905],"domain_scores_gemma":[0.99058795,0.0030336522,0.002033131,0.0010735735,0.00303645,0.00023518459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049281567,0.0006487534,0.0005541972,0.0016256084,0.0003653879,0.0009380459,0.0005986468,0.00063730875,0.00055680645],"category_scores_gemma":[0.01801723,0.00028009372,0.0005068726,0.00086189545,0.00063460885,0.00072939973,0.00093400595,0.00030147596,0.00038145733],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027890212,0.0001101275,0.9492264,0.00010998622,0.00039344482,0.000038440598,0.0011605057,0.0008055329,0.004356792,0.00011041564,0.00020717924,0.04320218],"study_design_scores_gemma":[0.000007794084,0.00042853344,0.9953745,0.000031961958,0.00006704977,0.00014679552,0.00038155343,0.001785516,0.0011939248,0.00009520264,0.00047019255,0.000016837263],"about_ca_topic_score_codex":0.0027221811,"about_ca_topic_score_gemma":0.00470619,"teacher_disagreement_score":0.0049281567,"about_ca_system_score_codex":0.000261428,"about_ca_system_score_gemma":0.00026940004,"threshold_uncertainty_score":0.026062906},"labels":[],"label_agreement":null},{"id":"W4408276043","doi":"10.3390/s25061667","title":"Obstacle Circumvention Strategies During Omnidirectional Treadmill Walking in Virtual Reality","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré de Santé et de Services Sociaux des Laurentides; Jewish Rehabilitation Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Obstacle; Virtual reality; Computer science; Treadmill; Preferred walking speed; Generalizability theory; Omnidirectional antenna; Simulation; Task (project management); Physical medicine and rehabilitation; Replicate; Obstacle avoidance; Block (permutation group theory); Human–computer interaction; Computer vision; Artificial intelligence; Psychology; Mathematics; Engineering; Medicine; Physical therapy","score_opus":0.026776842241694045,"score_gpt":0.3653507183268066,"score_spread":0.3385738760851126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408276043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978934,0.0000654576,0.0017403782,0.000006303217,0.000002249587,0.000020607109,0.000038003294,0.000012399118,0.00022114898],"genre_scores_gemma":[0.9968495,0.00007816476,0.002701713,0.000007297373,0.000001794296,0.000022422937,0.000082036124,0.0000058900014,0.00025124682],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997298,0.0000991334,0.00002369928,0.000050858147,0.00005460827,0.000041921037],"domain_scores_gemma":[0.9996772,0.00011217044,0.00008601816,0.000036102356,0.00004208169,0.000046472942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036246877,0.0002845497,0.0003267347,0.0003071097,0.00013582844,0.0002901105,0.00022743858,0.00023451027,0.00058868673],"category_scores_gemma":[0.0018528759,0.00019120857,0.00017075731,0.000103521794,0.00023850394,0.0002126277,0.0004921988,0.00014583657,0.0001280221],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0056275115,0.0015888354,0.13207743,0.0010392516,0.0003642863,0.0009868812,0.0063872538,0.010209897,0.6377199,0.0004137691,0.00084203866,0.20274292],"study_design_scores_gemma":[0.00015831993,0.008959353,0.9404207,0.00013222377,0.00015711845,0.0015411162,0.002348042,0.01693214,0.026409851,0.00045639058,0.0023866806,0.00009808284],"about_ca_topic_score_codex":0.0015641354,"about_ca_topic_score_gemma":0.0036878404,"teacher_disagreement_score":0.0015641354,"about_ca_system_score_codex":0.000082937535,"about_ca_system_score_gemma":0.00016615534,"threshold_uncertainty_score":0.0031101108},"labels":[],"label_agreement":null},{"id":"W4408288333","doi":"10.3390/s25061719","title":"Resonant Drive Techniques for Electrostatic Microelectromechanical Systems (MEMS): A Comparative Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Microelectromechanical systems; Voltage; Amplifier; Coupling (piping); Power (physics); Electronic engineering; Electronic circuit; High voltage; Modulation (music); SIGNAL (programming language); Electrical engineering; Engineering; Materials science; Computer science; Optoelectronics; Physics; Acoustics; CMOS; Mechanical engineering","score_opus":0.012886002273946219,"score_gpt":0.28562510026123766,"score_spread":0.27273909798729146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408288333","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17465252,0.71508896,0.055659413,0.00061421137,0.00033434547,0.00019682685,0.00008980864,0.00024893004,0.053114988],"genre_scores_gemma":[0.5113985,0.4233844,0.047862824,0.0004162073,0.0006333827,0.0001409847,0.00017009425,0.00013782326,0.015855711],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992186,0.0001150858,0.000038765244,0.00012143862,0.0004516495,0.00005443744],"domain_scores_gemma":[0.9993855,0.00030126717,0.000075902724,0.000044152068,0.00017202017,0.000021174052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007890144,0.0004924238,0.0006948162,0.0016746656,0.0003311861,0.0010160301,0.00076523796,0.0010711189,0.0021344116],"category_scores_gemma":[0.0010467327,0.00026952257,0.00066135544,0.0013464713,0.0003748562,0.0014935831,0.00042015087,0.00043745243,0.0008065539],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004744004,0.00049082347,0.0014236281,0.0057155495,0.00019082952,0.00035644547,0.00050443783,0.0032350882,0.33376932,0.009029154,0.0015894099,0.64322084],"study_design_scores_gemma":[0.00009211114,0.010921474,0.023396468,0.0015392532,0.000800577,0.007814375,0.0010509446,0.03309689,0.55300623,0.004002448,0.36397788,0.00030132703],"about_ca_topic_score_codex":0.00028701904,"about_ca_topic_score_gemma":0.00044241326,"teacher_disagreement_score":0.0021344116,"about_ca_system_score_codex":0.00049551966,"about_ca_system_score_gemma":0.00020206449,"threshold_uncertainty_score":0.0071403384},"labels":[],"label_agreement":null},{"id":"W4408455016","doi":"10.3390/s25061806","title":"Indoor Localization Methods for Smartphones with Multi-Source Sensors Fusion: Tasks, Challenges, Strategies, and Perspectives","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Hybrid positioning system; Multipath propagation; Bluetooth; GNSS applications; Sensor fusion; Wireless; Real-time computing; Indoor positioning system; Inertial measurement unit; Global Positioning System; Telecommunications; Positioning system; Accelerometer; Engineering; Artificial intelligence; Channel (broadcasting)","score_opus":0.05137404508897613,"score_gpt":0.329526750712493,"score_spread":0.27815270562351685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408455016","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011659432,0.9785599,0.014356159,0.0006565709,0.00041962828,0.000029125667,0.000048046353,0.00005859748,0.00470589],"genre_scores_gemma":[0.012004278,0.9742309,0.010111319,0.0003029545,0.00047248605,0.00003685999,0.000099745856,0.000014132298,0.002727398],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995158,0.00008309802,0.00005288766,0.00011075727,0.0002014311,0.00003596057],"domain_scores_gemma":[0.99912506,0.00040394347,0.00006753522,0.000038624643,0.00034185336,0.000023058834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091522606,0.000961867,0.00093279197,0.0022704492,0.00024565763,0.0011287217,0.0010623655,0.0012238619,0.0025561398],"category_scores_gemma":[0.001384016,0.000511896,0.00084703247,0.0020616495,0.00044008557,0.0022718345,0.000731814,0.0010098183,0.0015454913],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004640041,0.000054256678,0.0006541725,0.011879547,0.00009169748,0.00019057252,0.00013501184,0.0025564206,0.005190423,0.011114969,0.008710991,0.95937544],"study_design_scores_gemma":[0.000014633135,0.00044994205,0.0030820414,0.00580671,0.00034861313,0.0024185395,0.00046356072,0.011508426,0.011216144,0.009396088,0.9551473,0.00014812143],"about_ca_topic_score_codex":0.0014977611,"about_ca_topic_score_gemma":0.0016535635,"teacher_disagreement_score":0.0025561398,"about_ca_system_score_codex":0.00046178696,"about_ca_system_score_gemma":0.00078643067,"threshold_uncertainty_score":0.00855118},"labels":[],"label_agreement":null},{"id":"W4408549098","doi":"10.3390/s25061879","title":"Unobtrusive Bed Monitor State of the Art","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"State (computer science); Computer science; Section (typography); Quarter (Canadian coin); Engineering; Geography; Operating system","score_opus":0.014916852363040498,"score_gpt":0.26129332403702754,"score_spread":0.24637647167398705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408549098","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00075469434,0.9894883,0.0027589279,0.0003285579,0.00051668513,0.000026446345,0.000089000314,0.000062797626,0.0059745633],"genre_scores_gemma":[0.005604737,0.9877263,0.002220186,0.00037781603,0.00038959013,0.000036887548,0.00017002693,0.000014060961,0.0034605453],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942315,0.0000726679,0.000058209884,0.00015316486,0.00025080578,0.00004203899],"domain_scores_gemma":[0.9989496,0.00055394525,0.00009521094,0.000037775582,0.00032688197,0.000036527417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009479252,0.0010420429,0.0008973689,0.0015876784,0.0001843933,0.0009940062,0.0011338466,0.0010015635,0.006399451],"category_scores_gemma":[0.001858028,0.00040022115,0.00078741234,0.0012591879,0.00034481555,0.0014200778,0.00060657616,0.0009861548,0.0031031526],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008224729,0.00007396846,0.00028669258,0.012495877,0.00006808435,0.00006062228,0.000043239008,0.00043592043,0.0027701615,0.0019267905,0.013151781,0.9686046],"study_design_scores_gemma":[0.000013102518,0.00041680384,0.002726748,0.008401237,0.00029517285,0.0015290404,0.00012614139,0.0013323949,0.006064689,0.002252799,0.97677386,0.00006810101],"about_ca_topic_score_codex":0.0012179519,"about_ca_topic_score_gemma":0.0013260469,"teacher_disagreement_score":0.006399451,"about_ca_system_score_codex":0.00032378727,"about_ca_system_score_gemma":0.00073758105,"threshold_uncertainty_score":0.02140832},"labels":[],"label_agreement":null},{"id":"W4408651032","doi":"10.3390/s25061937","title":"Long-Term Wavelength Stability of Large Type II FBG Arrays in Different Silica-Based Fibers at High Temperature","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Materials science; Wavelength; Fiber Bragg grating; Isothermal process; Optics; Laser; Optical fiber; Fiber optic sensor; Optoelectronics; Fiber; Wavelength-division multiplexing; Fabrication; Physics; Composite material","score_opus":0.007348080400499139,"score_gpt":0.22937957665102326,"score_spread":0.22203149625052412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408651032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975432,0.0004342371,0.0015056139,0.000024985924,0.000018132769,0.000005499074,0.00010640118,0.000045794797,0.00031618786],"genre_scores_gemma":[0.99790144,0.0001764801,0.0011279829,0.000016539945,0.000006039266,0.000011872128,0.00010913742,0.000014986151,0.0006355108],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975437,0.000020186528,0.000016432454,0.000080219426,0.0000873515,0.00004147058],"domain_scores_gemma":[0.99949324,0.00011483114,0.00012612289,0.00006642837,0.00015389409,0.000045382458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003691301,0.0002691202,0.00019692667,0.00025707996,0.00032510713,0.00027803282,0.00024268801,0.00026402582,0.00051547936],"category_scores_gemma":[0.0007341236,0.00016979224,0.00016368556,0.0003089336,0.00037164576,0.00036064436,0.00019135867,0.00035716945,0.00015975999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015863206,0.000026710764,0.0014792813,0.00003068564,0.000011407781,0.00003913398,0.00014033838,0.00054102176,0.994214,0.000050826373,0.000046873844,0.003261119],"study_design_scores_gemma":[0.000003886081,0.00013747187,0.010283695,0.0000057344328,0.000014542032,0.000046559446,0.00008382971,0.0022069335,0.9867764,0.000026696636,0.00040343622,0.000010786159],"about_ca_topic_score_codex":0.0014460896,"about_ca_topic_score_gemma":0.0020552306,"teacher_disagreement_score":0.0014460896,"about_ca_system_score_codex":0.00044845432,"about_ca_system_score_gemma":0.00014804173,"threshold_uncertainty_score":0.003253758},"labels":[],"label_agreement":null},{"id":"W4408687490","doi":"10.3390/s25061945","title":"Combining 24-Hour Continuous Monitoring of Time-Locked Heart Rate, Physical Activity and Gait in Older Adults: Preliminary Findings","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute on Aging; National Institutes of Health","keywords":"Heart rate; Sitting; Hemodynamics; Blood pressure; Medicine; Orthostatic vital signs; Physical medicine and rehabilitation; Homeostasis; Cardiology; Internal medicine; Pathology","score_opus":0.007571456507948123,"score_gpt":0.2581501317251003,"score_spread":0.2505786752171522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408687490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9926595,0.001028517,0.004855073,0.00007543793,0.000023277802,0.00016595102,0.00057226163,0.000037429734,0.0005825857],"genre_scores_gemma":[0.98422676,0.0007242056,0.013239001,0.00014910597,0.00013313879,0.00032275548,0.0007886178,0.00001219881,0.00040420052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99945253,0.0001927568,0.000044103177,0.000130924,0.00013220718,0.000047502173],"domain_scores_gemma":[0.9992348,0.00021634182,0.00009818786,0.00006498858,0.0002514772,0.00013421522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015589799,0.0005496683,0.00039770527,0.00049780816,0.00021823477,0.00043465392,0.00033505485,0.00058761047,0.00068751234],"category_scores_gemma":[0.0017870723,0.000202373,0.0003753713,0.00045712478,0.00022409609,0.00044566233,0.0004545132,0.00034376935,0.00021287678],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047876327,0.002657921,0.71897906,0.00066928286,0.00079599273,0.000451041,0.0014533494,0.00062463497,0.13067876,0.00011118859,0.0008221069,0.137969],"study_design_scores_gemma":[0.00008289167,0.0049531446,0.9881734,0.000027396885,0.00027236578,0.00039755646,0.00032032144,0.0014032517,0.0034096397,0.00009872604,0.0008360759,0.000025370982],"about_ca_topic_score_codex":0.001987796,"about_ca_topic_score_gemma":0.0039845365,"teacher_disagreement_score":0.001987796,"about_ca_system_score_codex":0.00009416313,"about_ca_system_score_gemma":0.00028769768,"threshold_uncertainty_score":0.008244753},"labels":[],"label_agreement":null},{"id":"W4408692705","doi":"10.3390/s25071971","title":"Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Orchard; False positive paradox; Computer science; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Precision and recall; Recall; F1 score; Biology; Horticulture","score_opus":0.009525351284811622,"score_gpt":0.223129534819485,"score_spread":0.21360418353467336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408692705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15329517,0.0009166767,0.8327027,0.0002117337,0.00012592465,0.00014516414,0.0007681519,0.008373228,0.0034612708],"genre_scores_gemma":[0.81689507,0.00035626814,0.17194916,0.00025694774,0.000051169238,0.00018171371,0.0022330196,0.00026768865,0.007808996],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998223,0.00002109551,0.000011094438,0.00007911115,0.000040192645,0.000026294052],"domain_scores_gemma":[0.9997849,0.000058254333,0.00002306766,0.000052678417,0.0000698968,0.000011271598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003964716,0.00086602184,0.00064160046,0.0004734085,0.0001827053,0.00044861226,0.0011542443,0.00039758722,0.001862462],"category_scores_gemma":[0.0010105593,0.00028174435,0.00078081153,0.00025610067,0.00019479344,0.0009297862,0.000713577,0.00064047595,0.00087331946],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061183417,0.000301377,0.012113547,0.00016134487,0.00021694695,0.00021723712,0.000084641404,0.33306175,0.06402528,0.002170885,0.005997508,0.58103776],"study_design_scores_gemma":[0.00001263281,0.00008945638,0.0018326291,0.0000064628925,0.000028139624,0.00008504987,0.000012888175,0.9851039,0.010002505,0.00070499204,0.0021057294,0.000015570025],"about_ca_topic_score_codex":0.004267963,"about_ca_topic_score_gemma":0.0072371513,"teacher_disagreement_score":0.004267963,"about_ca_system_score_codex":0.00044033295,"about_ca_system_score_gemma":0.00044399887,"threshold_uncertainty_score":0.008486211},"labels":[],"label_agreement":null},{"id":"W4408770679","doi":"10.3390/s25071976","title":"Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Key Research and Development Program of Zhejiang Province; National Key Research and Development Program of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Inertial measurement unit; Accelerometer; Gyroscope; Sampling (signal processing); Orientation (vector space); Computer science; Artificial intelligence; Computer vision; Mathematics; Engineering","score_opus":0.012132724111625222,"score_gpt":0.30111134823210456,"score_spread":0.2889786241204793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408770679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83739793,0.0025045576,0.15660997,0.00030579398,0.00034281643,0.00013239295,0.00030083838,0.0006376564,0.0017679804],"genre_scores_gemma":[0.9630188,0.0006759357,0.035480917,0.00009682935,0.000057759986,0.00008460407,0.0002121784,0.00006785731,0.00030524627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981919,0.00063295686,0.00024129359,0.00027065538,0.0005323118,0.00013095167],"domain_scores_gemma":[0.9923382,0.004825199,0.0005382572,0.00079144875,0.0013884385,0.00011843861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002652498,0.0007874648,0.0005418243,0.000612837,0.00027253656,0.0005867766,0.00036563218,0.0006081136,0.0006563003],"category_scores_gemma":[0.022623137,0.00021795217,0.00035558757,0.0005532688,0.0003134909,0.0006926318,0.0004970218,0.00036926437,0.00027966866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00832292,0.00038676025,0.13793819,0.0016083812,0.00036761182,0.0008143599,0.0013151157,0.060639556,0.16718432,0.0013493582,0.0025297643,0.61754364],"study_design_scores_gemma":[0.00031105938,0.0048921322,0.33669227,0.00060895324,0.0007722666,0.003041388,0.0013978755,0.43580797,0.20608282,0.0032211258,0.0069151567,0.0002569259],"about_ca_topic_score_codex":0.0015992807,"about_ca_topic_score_gemma":0.0015447948,"teacher_disagreement_score":0.002652498,"about_ca_system_score_codex":0.00015502716,"about_ca_system_score_gemma":0.0002571346,"threshold_uncertainty_score":0.014027894},"labels":[],"label_agreement":null},{"id":"W4408776997","doi":"10.3390/s25072027","title":"Assessing Effectiveness of Passive Exoskeletons and Tool Selection on Ergonomic Safety in Manhole Cover Removal","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Exoskeleton; Lever; Electromyography; Task (project management); Extractor; Physical medicine and rehabilitation; Cover (algebra); Engineering; Simulation; Computer science; Medicine; Mechanical engineering","score_opus":0.006809382391578699,"score_gpt":0.29305974604385465,"score_spread":0.28625036365227596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408776997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99886465,0.00010116146,0.0008097028,0.000009957158,0.0000047224676,0.000033328633,0.000015126416,0.000004376692,0.000156923],"genre_scores_gemma":[0.99710876,0.00019010915,0.0020679927,0.000016511462,0.000009687473,0.00006923777,0.000044489807,0.000002443812,0.0004908282],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995103,0.00015576708,0.000052483054,0.000074821015,0.00014302963,0.00006367733],"domain_scores_gemma":[0.9991117,0.00039941902,0.00022552804,0.0000517614,0.00012366359,0.00008787814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081804156,0.0005717757,0.00034882536,0.00032423736,0.00020179476,0.00021818338,0.00023308696,0.0004280348,0.0011283847],"category_scores_gemma":[0.0018590452,0.00015217037,0.00033394,0.00010862374,0.00026260913,0.0002700504,0.0004433104,0.00013918038,0.00015493375],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.021393351,0.012622617,0.12300863,0.0021848388,0.00045017668,0.00033958638,0.0017750056,0.003101081,0.54038346,0.000068256544,0.00023123383,0.29444188],"study_design_scores_gemma":[0.00030259456,0.10679353,0.8324781,0.00013479835,0.00036782914,0.0002806563,0.0014096836,0.0023959284,0.054638315,0.00008831057,0.001062648,0.000047569425],"about_ca_topic_score_codex":0.00033837502,"about_ca_topic_score_gemma":0.0007995391,"teacher_disagreement_score":0.0011283847,"about_ca_system_score_codex":0.00007717782,"about_ca_system_score_gemma":0.00019301745,"threshold_uncertainty_score":0.004326284},"labels":[],"label_agreement":null},{"id":"W4408777410","doi":"10.3390/s25072026","title":"Clustering and Interpretability of Residential Electricity Demand Profiles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Interpretability; Cluster analysis; Electricity; Electricity demand; Demand response; Computer science; Data mining; Engineering; Artificial intelligence; Electricity generation; Electrical engineering; Power (physics)","score_opus":0.005032771326364237,"score_gpt":0.21252093812446013,"score_spread":0.2074881667980959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408777410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.768478,0.0002976267,0.22290783,0.00050447753,0.000047724658,0.00016938556,0.0016090239,0.0008384034,0.0051475405],"genre_scores_gemma":[0.9703535,0.00010440696,0.02773976,0.00002628805,0.000012646919,0.000034271012,0.0012953964,0.00004877875,0.0003850117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984174,0.0006704549,0.0001243367,0.0002905151,0.00038864152,0.00010863378],"domain_scores_gemma":[0.9952561,0.0026816423,0.0006412005,0.00044506628,0.00090400444,0.00007210218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029010838,0.0006786964,0.0005304362,0.0026662517,0.0004083075,0.0019534782,0.0005683578,0.0006202308,0.00079537206],"category_scores_gemma":[0.014541004,0.00023183844,0.00055502425,0.0015954518,0.0005004802,0.0014340298,0.0008828376,0.0007557437,0.00032486703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013199553,0.0002459825,0.12247338,0.00034887588,0.00023489662,0.00052218663,0.003801631,0.521454,0.011985247,0.012678117,0.0047501605,0.32018566],"study_design_scores_gemma":[0.000015722882,0.000060323324,0.0409465,0.000064709086,0.00003301734,0.00009890962,0.0013443995,0.94153064,0.0041416283,0.010245429,0.0014721231,0.00004648351],"about_ca_topic_score_codex":0.006846031,"about_ca_topic_score_gemma":0.004976399,"teacher_disagreement_score":0.006846031,"about_ca_system_score_codex":0.00078677887,"about_ca_system_score_gemma":0.0005913007,"threshold_uncertainty_score":0.015342534},"labels":[],"label_agreement":null},{"id":"W4408807968","doi":"10.3390/s25072035","title":"Respiratory Monitoring with Textile Inductive Electrodes in Driving Applications: Effect of Electrode’s Positioning and Form Factor on Signal Quality","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; CTT Group (Canada); Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Mitacs; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Electrode; SIGNAL (programming language); Textile; Quality (philosophy); Materials science; Acoustics; Optoelectronics; Biomedical engineering; Computer science; Electrical engineering; Engineering; Chemistry; Composite material; Physics","score_opus":0.007916860812898283,"score_gpt":0.26462929394127044,"score_spread":0.25671243312837216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408807968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92556936,0.0021473395,0.070782214,0.00026296495,0.00006277927,0.000075084085,0.000051814564,0.00018002753,0.0008684778],"genre_scores_gemma":[0.96930164,0.0007258483,0.029296495,0.00011826073,0.00004104425,0.000016832355,0.000037436283,0.000034837693,0.00042769584],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990633,0.00035057607,0.000091935544,0.00016610918,0.0002580236,0.00007011906],"domain_scores_gemma":[0.99745554,0.0015430964,0.0003117983,0.00017835741,0.00041240006,0.00009881859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012268388,0.0005193838,0.00032601468,0.0003587573,0.00016277302,0.000810862,0.00052420393,0.0008936529,0.000689041],"category_scores_gemma":[0.005750822,0.00028008278,0.00029815198,0.00028688717,0.00046315198,0.0008974873,0.00042517713,0.00023965808,0.00023166367],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012664259,0.00008875359,0.010133723,0.0003166664,0.000053674907,0.00039431712,0.00024286627,0.0009995599,0.92632693,0.00007667714,0.00007026279,0.060030285],"study_design_scores_gemma":[0.000118863245,0.007473926,0.13724421,0.00008307518,0.00042034997,0.0047532944,0.00060863263,0.016986879,0.8295557,0.00035938103,0.0023188686,0.0000768448],"about_ca_topic_score_codex":0.00027380468,"about_ca_topic_score_gemma":0.000602919,"teacher_disagreement_score":0.0012268388,"about_ca_system_score_codex":0.00012979843,"about_ca_system_score_gemma":0.000087955676,"threshold_uncertainty_score":0.0064882636},"labels":[],"label_agreement":null},{"id":"W4408946682","doi":"10.3390/s25072072","title":"EEG-Based Engagement Monitoring in Cognitive Games","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"AGE-WELL","keywords":"Electroencephalography; Cognition; Psychology; Dementia; Scale (ratio); Cognitive psychology; Cognitive impairment; User engagement; Cognitive decline; Video game; Applied psychology; Physical medicine and rehabilitation; Developmental psychology; Computer science; Clinical psychology; Medicine; Psychiatry; Multimedia","score_opus":0.048018243085811436,"score_gpt":0.3264814735518976,"score_spread":0.2784632304660862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408946682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9789863,0.00023363289,0.01820598,0.00003891524,0.000014265404,0.000121236866,0.0004948092,0.00012359291,0.0017812076],"genre_scores_gemma":[0.9922334,0.00019335194,0.00661211,0.000024797717,0.00001984667,0.00008316217,0.0002794094,0.000010586414,0.0005432441],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997775,0.00007544203,0.000015212823,0.00004678164,0.00006208901,0.000022993803],"domain_scores_gemma":[0.9996574,0.00015076059,0.0000661973,0.000014827719,0.000071358256,0.00003952927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026058863,0.00044455746,0.00025275795,0.0006025227,0.00008193823,0.0004714909,0.00019826226,0.00023895348,0.00081515696],"category_scores_gemma":[0.0020372742,0.0001015319,0.0001423288,0.0003745713,0.00012508154,0.00036280585,0.00033029818,0.00019896503,0.00021217062],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004796872,0.0010916161,0.24936043,0.0009844706,0.0003894516,0.0005965521,0.0015428487,0.00804152,0.21839713,0.0005751148,0.0016841511,0.51253986],"study_design_scores_gemma":[0.000105316525,0.0016737691,0.94313174,0.000071203525,0.00011729047,0.0007694067,0.0004395424,0.032107495,0.01958553,0.0007672294,0.0011868647,0.000044669076],"about_ca_topic_score_codex":0.0013064924,"about_ca_topic_score_gemma":0.0023938266,"teacher_disagreement_score":0.0013064924,"about_ca_system_score_codex":0.00012509132,"about_ca_system_score_gemma":0.00009511849,"threshold_uncertainty_score":0.002726972},"labels":[],"label_agreement":null},{"id":"W4408992882","doi":"10.3390/s25072175","title":"Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"Mitacs","keywords":"Autoencoder; Interpretability; Skewness; Computer science; Kurtosis; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Data mining; Entropy (arrow of time); Fault detection and isolation; Mathematics; Statistics; Deep learning","score_opus":0.003458863249450557,"score_gpt":0.21300831691600258,"score_spread":0.20954945366655203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408992882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01227111,0.00011535701,0.9870728,0.00005669449,0.000012523325,0.000010437716,0.00002726267,0.00015272503,0.00028101524],"genre_scores_gemma":[0.76874834,0.00028958128,0.22878262,0.000099819736,0.000050693634,0.00007477529,0.00020715741,0.00007710757,0.0016699227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997284,0.00007431375,0.000014337328,0.00007449441,0.00007485655,0.000033611897],"domain_scores_gemma":[0.99945265,0.0003180867,0.000080071186,0.000050874798,0.00008107533,0.000017278766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062805664,0.0007645659,0.0006569207,0.00047862701,0.00016922041,0.0005273917,0.00088101206,0.00069448183,0.00046370973],"category_scores_gemma":[0.0019771906,0.00043871882,0.0007168295,0.00043383264,0.0006161031,0.00077340775,0.00071216805,0.0009734835,0.00012144607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057010413,0.000029268771,0.0012025206,0.00005568281,0.00007585012,0.000071714625,0.00006624048,0.9148749,0.008729435,0.0069264583,0.0004584123,0.06745246],"study_design_scores_gemma":[7.038078e-7,0.000005140025,0.0001142016,0.0000016585235,0.0000018813779,0.0000069085772,0.0000021255607,0.99811065,0.00054251857,0.0011492394,0.00006287714,0.000002151006],"about_ca_topic_score_codex":0.00557855,"about_ca_topic_score_gemma":0.0042304825,"teacher_disagreement_score":0.00557855,"about_ca_system_score_codex":0.0004579692,"about_ca_system_score_gemma":0.0005046759,"threshold_uncertainty_score":0.011092126},"labels":[],"label_agreement":null},{"id":"W4409210680","doi":"10.3390/s25072310","title":"Smartphone-Based Analysis for Early Detection of Aging Impact on Gait and Stair Negotiation: A Cross-Sectional Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Negotiation; Gait; Gait analysis; Cross-sectional study; Engineering; Physical medicine and rehabilitation; Psychology; Computer science; Applied psychology; Medicine; Sociology","score_opus":0.025908451284929867,"score_gpt":0.39628591510965167,"score_spread":0.3703774638247218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409210680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99894005,0.0002143319,0.00025593475,0.000012226369,0.0000074747563,0.000034004788,0.00027630792,0.0000033361466,0.00025625565],"genre_scores_gemma":[0.99871695,0.0001489843,0.00042781178,0.000036440462,0.000012331347,0.000051197287,0.00032422735,0.000002148826,0.00027984177],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996345,0.00010917119,0.000045801262,0.000086142805,0.00007443132,0.000049940238],"domain_scores_gemma":[0.9990159,0.00015678309,0.00026858482,0.00009411899,0.00032675907,0.00013787192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009302013,0.00041655166,0.00037427756,0.0006617652,0.00036845845,0.00060524157,0.00021738207,0.00059266726,0.00090218615],"category_scores_gemma":[0.0020581863,0.00033191533,0.00054610206,0.00049025344,0.00015282896,0.00049022754,0.00043381462,0.00038846195,0.0004504231],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023591667,0.00021133381,0.99607843,0.00002906706,0.00013005291,0.000051315194,0.0001725029,0.000024190414,0.0006257683,0.000011154562,0.000085436506,0.0023447743],"study_design_scores_gemma":[0.000008263371,0.00055900373,0.998607,0.000008113612,0.00006215161,0.00013387951,0.00022345142,0.00016334746,0.00008299959,0.000010205224,0.00013788357,0.0000037884495],"about_ca_topic_score_codex":0.0032888737,"about_ca_topic_score_gemma":0.0063776486,"teacher_disagreement_score":0.0032888737,"about_ca_system_score_codex":0.00014573593,"about_ca_system_score_gemma":0.0002098945,"threshold_uncertainty_score":0.006539464},"labels":[],"label_agreement":null},{"id":"W4409235435","doi":"10.3390/s25072326","title":"Using an Electronic Goniometer to Assess the Influence of Single-Application Kinesiology Taping on Unstable Shoulder Proprioception and Function","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Uniwersytet Medyczny im. Karola Marcinkowskiego w Poznaniu","keywords":"Kinesiology; Goniometer; Proprioception; Physical medicine and rehabilitation; Physical therapy; Function (biology); Engineering; Medicine; Physics","score_opus":0.05896385893761337,"score_gpt":0.35263282809876983,"score_spread":0.2936689691611565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409235435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961863,0.00022245783,0.0030020485,0.000016598748,0.000014878312,0.00016380267,0.000055800458,0.000019681238,0.00031853368],"genre_scores_gemma":[0.9942972,0.000159457,0.005036602,0.000046712823,0.000013047488,0.00017555349,0.0000703522,0.0000044224007,0.00019663702],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913174,0.0003233024,0.00011286564,0.00010822904,0.00027334763,0.00005048159],"domain_scores_gemma":[0.9981542,0.0007473597,0.00043225472,0.00012443369,0.00043141624,0.00011027189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011897166,0.0007173848,0.00039810702,0.00065938744,0.00018638956,0.00024183822,0.00026450705,0.0004324894,0.00080229423],"category_scores_gemma":[0.0039708233,0.00021288653,0.00031257828,0.00030672434,0.00031560776,0.00021980789,0.00046537587,0.00027192896,0.00014866857],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004339241,0.0016395093,0.6876109,0.00059532584,0.00026392873,0.00056486094,0.0013176303,0.0007354147,0.18436134,0.00007128016,0.00023717855,0.118263364],"study_design_scores_gemma":[0.0001530636,0.016468462,0.96307594,0.000034296027,0.00013865446,0.0012660116,0.0004212614,0.001359197,0.016526753,0.000037626745,0.00048750316,0.000031236566],"about_ca_topic_score_codex":0.0005193346,"about_ca_topic_score_gemma":0.00066854525,"teacher_disagreement_score":0.0011897166,"about_ca_system_score_codex":0.00008759149,"about_ca_system_score_gemma":0.00022646633,"threshold_uncertainty_score":0.006291926},"labels":[],"label_agreement":null},{"id":"W4409236717","doi":"10.3390/s25072320","title":"Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm2 and an Ultra-High Input Impedance","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Die (integrated circuit); CMOS; Ultra low power; Electrical engineering; Amplifier; Electrical impedance; Power (physics); Engineering; Materials science; Electronic engineering; Physics; Power consumption; Mechanical engineering","score_opus":0.007043630090126249,"score_gpt":0.239450738038164,"score_spread":0.23240710794803776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409236717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1117036,0.0011089911,0.8694364,0.0006127942,0.00023289987,0.00050930714,0.00039332054,0.0028481288,0.013154628],"genre_scores_gemma":[0.48558724,0.0007077711,0.5023588,0.00030320676,0.00009039016,0.00043028904,0.0003399387,0.0001234133,0.010058962],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997577,0.000014159117,0.00001593593,0.00006875789,0.0001115757,0.000031895714],"domain_scores_gemma":[0.9998443,0.00002261686,0.000030019073,0.000013477791,0.00007374371,0.00001570933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018192998,0.0004751368,0.0003200904,0.00027574642,0.00021897994,0.00056882034,0.001376433,0.0005108865,0.001778974],"category_scores_gemma":[0.00027068245,0.00021493508,0.00023122563,0.0002243924,0.00023159255,0.00051005953,0.0002522264,0.00037708427,0.0013350416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006356865,0.000050840325,0.0005711908,0.00025771395,0.0000400911,0.00022432402,0.000093658535,0.0025045578,0.93368,0.0033875792,0.0014063733,0.057720132],"study_design_scores_gemma":[0.00005315926,0.0009346346,0.0017670302,0.00003389226,0.00008891775,0.001429729,0.00005911118,0.039071076,0.9152344,0.00069023203,0.04059497,0.000042958432],"about_ca_topic_score_codex":0.0006315028,"about_ca_topic_score_gemma":0.0015738134,"teacher_disagreement_score":0.001778974,"about_ca_system_score_codex":0.0005573034,"about_ca_system_score_gemma":0.0009224869,"threshold_uncertainty_score":0.0059512258},"labels":[],"label_agreement":null},{"id":"W4409264418","doi":"10.3390/s25082349","title":"A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Michael Smith Health Research BC","keywords":"Jaccard index; Computer science; Convolutional neural network; Artificial intelligence; Mammography; Pattern recognition (psychology); Workflow; Breast cancer; Cancer; Medicine","score_opus":0.0057095726549707525,"score_gpt":0.2842162219492688,"score_spread":0.2785066492942981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409264418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15894195,0.0007859443,0.8330559,0.00040628674,0.00016925337,0.00007574346,0.0005172223,0.0029347483,0.0031129376],"genre_scores_gemma":[0.846127,0.0003348934,0.1472857,0.0002661097,0.00005488237,0.00008893494,0.00079915056,0.00013169541,0.004911594],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985886,0.00002012288,0.000005436715,0.000042950676,0.00004551116,0.00002704526],"domain_scores_gemma":[0.99981934,0.000052020132,0.000024206216,0.000030825257,0.000059704715,0.000013928554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004335412,0.0006369465,0.00033324002,0.0002870754,0.00013260978,0.00031800286,0.00067179813,0.0004804181,0.0011179529],"category_scores_gemma":[0.00088943145,0.00021765644,0.0004158982,0.0002936871,0.00015548179,0.00049225014,0.000539053,0.00045786946,0.00041005365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004989015,0.0002463354,0.0058169877,0.00015738395,0.00019708335,0.00028508852,0.00008273416,0.36989638,0.1877873,0.0020637927,0.0052190875,0.42774895],"study_design_scores_gemma":[0.000004634982,0.00006449482,0.0012008983,0.0000042492647,0.000020045429,0.000056611003,0.000006539814,0.9809214,0.01630286,0.00044787562,0.00095966645,0.000010735809],"about_ca_topic_score_codex":0.0054273396,"about_ca_topic_score_gemma":0.00822967,"teacher_disagreement_score":0.0054273396,"about_ca_system_score_codex":0.00051946996,"about_ca_system_score_gemma":0.00053017033,"threshold_uncertainty_score":0.010791481},"labels":[],"label_agreement":null},{"id":"W4409282033","doi":"10.3390/s25082382","title":"Improved Fully 3D-Printed SIW-Based Sensor for Non-Invasive Glucose Measurement","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detection limit; Sensitivity (control systems); Materials science; Microfluidics; 3d printed; Microwave; Optoelectronics; Electronic engineering; Computer science; Biomedical engineering; Chemistry; Nanotechnology; Engineering; Telecommunications; Chromatography","score_opus":0.017122677925083686,"score_gpt":0.2284152241307429,"score_spread":0.2112925462056592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409282033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37029424,0.0038475797,0.6153942,0.0003743182,0.00047505667,0.00016002833,0.00092528434,0.004043616,0.0044856905],"genre_scores_gemma":[0.5303089,0.0018694313,0.46084195,0.00036734607,0.00009701308,0.00021138904,0.0008187654,0.0001523951,0.0053327745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999556,0.00004195906,0.000026643269,0.00011107017,0.00023491993,0.000029296922],"domain_scores_gemma":[0.99972504,0.00007100803,0.000077087454,0.00004905966,0.00006157939,0.000016136664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002623636,0.00059956673,0.00053787563,0.00033425915,0.00009053152,0.00049881084,0.0010007942,0.00078848336,0.0006225829],"category_scores_gemma":[0.00043363523,0.00042537213,0.000611776,0.00034663986,0.00021970145,0.00062260247,0.00039743865,0.00055122835,0.00075715804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025471829,0.00001897793,0.00012231819,0.000079990954,0.000010658292,0.00008227544,0.000013892551,0.00064027956,0.9901654,0.00018434881,0.00015917736,0.00849713],"study_design_scores_gemma":[0.0000074230597,0.0001803783,0.0008933108,0.0000064892756,0.000020812471,0.00043223117,0.00000956831,0.01588495,0.9793149,0.00009004145,0.0031306527,0.000029202147],"about_ca_topic_score_codex":0.00017952968,"about_ca_topic_score_gemma":0.0003527361,"teacher_disagreement_score":0.0010007942,"about_ca_system_score_codex":0.00022829915,"about_ca_system_score_gemma":0.00023389974,"threshold_uncertainty_score":0.0020827055},"labels":[],"label_agreement":null},{"id":"W4409282063","doi":"10.3390/s25082388","title":"Ensemble Machine Learning Models Utilizing a Hybrid Recursive Feature Elimination (RFE) Technique for Detecting GPS Spoofing Attacks Against Unmanned Aerial Vehicles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Spoofing attack; Computer science; Global Positioning System; Artificial intelligence; Machine learning; Random forest; Decision tree; Ensemble learning; Intrusion detection system; Data mining; Pattern recognition (psychology); Computer security","score_opus":0.020961872574740508,"score_gpt":0.26996949194549,"score_spread":0.24900761937074953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409282063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14251775,0.0012993594,0.85341895,0.00024838286,0.000099927696,0.00005094213,0.000113445625,0.0010070121,0.0012442769],"genre_scores_gemma":[0.9191162,0.000426189,0.078212574,0.00010605042,0.00007479311,0.000066537286,0.00040098082,0.000027845585,0.001568742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994935,0.00013336742,0.000040497183,0.0001038506,0.00015130852,0.000077439],"domain_scores_gemma":[0.9988575,0.000495368,0.00011881242,0.0001016782,0.0003961007,0.000030514719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012638952,0.0007572208,0.0011167092,0.00092121237,0.00033359663,0.0006451599,0.00089493446,0.00060883764,0.00038710027],"category_scores_gemma":[0.0025614458,0.00027056196,0.00088739564,0.0007232343,0.00018205683,0.0007392975,0.00044534553,0.0008499082,0.00021336082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013072311,0.00014997115,0.009348353,0.00004693191,0.00021158157,0.00011846238,0.000077764395,0.6802542,0.00394181,0.0018031835,0.0017227909,0.30219415],"study_design_scores_gemma":[0.0000013911373,0.000026045334,0.0005082375,0.0000021858627,0.000013420331,0.000010430634,0.0000038280054,0.99863225,0.00038592724,0.00026569507,0.00014736885,0.00000319166],"about_ca_topic_score_codex":0.006433266,"about_ca_topic_score_gemma":0.005771993,"teacher_disagreement_score":0.006433266,"about_ca_system_score_codex":0.00034371327,"about_ca_system_score_gemma":0.0006322815,"threshold_uncertainty_score":0.012791693},"labels":[],"label_agreement":null},{"id":"W4409282093","doi":"10.3390/s25082379","title":"Development and Validation of a Modular Sensor-Based System for Gait Analysis and Control in Lower-Limb Exoskeletons","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Carl-Zeiss-Stiftung","keywords":"Exoskeleton; Modular design; Wearable computer; Ground reaction force; Motion capture; Gait; Inertial measurement unit; Gait analysis; Crutch; Simulation; Engineering; Work (physics); Robot; Robotics; Accelerometer; Biomechanics; Computer science; Control engineering; Kinematics; Artificial intelligence; Motion (physics); Physical medicine and rehabilitation; Embedded system; Mechanical engineering","score_opus":0.0052000182146342755,"score_gpt":0.21047510159895988,"score_spread":0.2052750833843256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409282093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17468609,0.00017781284,0.81643385,0.00018572775,0.00018784143,0.0012760871,0.0005519127,0.004440272,0.0020603992],"genre_scores_gemma":[0.60584414,0.00017117136,0.38794106,0.00020434878,0.00003524248,0.0013993734,0.00046713534,0.000157613,0.0037798632],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992638,0.00008919085,0.00006938921,0.0001772872,0.0003547738,0.000045543107],"domain_scores_gemma":[0.99932647,0.00010258901,0.000079886726,0.00011287009,0.00032009804,0.000058117057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011829163,0.00058155786,0.000595897,0.000556434,0.00022977887,0.00048641395,0.0010876402,0.00069068925,0.0026916554],"category_scores_gemma":[0.0014334434,0.00021834404,0.0003635875,0.00022619721,0.00033171885,0.0004840869,0.0006189909,0.0003611756,0.0008276642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052399316,0.00053491176,0.0040552537,0.0006472063,0.00009860433,0.00038803704,0.0003545943,0.01122276,0.7992035,0.0016037885,0.0020633338,0.17930396],"study_design_scores_gemma":[0.00034811333,0.006134766,0.030396048,0.00019948937,0.00019331947,0.0015894178,0.00017968629,0.23244722,0.6993994,0.0015150473,0.027434794,0.00016273139],"about_ca_topic_score_codex":0.00050022907,"about_ca_topic_score_gemma":0.0006030265,"teacher_disagreement_score":0.0026916554,"about_ca_system_score_codex":0.00022585045,"about_ca_system_score_gemma":0.0007011053,"threshold_uncertainty_score":0.009004474},"labels":[],"label_agreement":null},{"id":"W4409304147","doi":"10.3390/s25082356","title":"Thermography in Bike Fitting: A Literature Review","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Agentschap Innoveren en Ondernemen","keywords":"Thermography; Computer science; Simulation; Infrared","score_opus":0.017777143025450675,"score_gpt":0.3523647242940219,"score_spread":0.3345875812685712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409304147","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009932721,0.9992199,0.0000903968,0.00012978588,0.00007690579,0.000006912035,0.000028634677,0.000003902013,0.00034424456],"genre_scores_gemma":[0.00083132484,0.99866414,0.00018599405,0.00008953707,0.00007070109,0.000009077364,0.00003215637,0.0000014460483,0.00011565453],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99928325,0.00014949971,0.000201983,0.00012631253,0.00020242133,0.00003652171],"domain_scores_gemma":[0.9965029,0.0024636243,0.0003897193,0.00006195385,0.0005157199,0.000066104425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014697787,0.0011196876,0.0019243088,0.0058150887,0.0004283448,0.0019400241,0.0011516146,0.0014376595,0.007719012],"category_scores_gemma":[0.0048063346,0.00050142565,0.0017194035,0.0054515675,0.00066651456,0.0020147525,0.00095114036,0.0011846179,0.0015730859],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010168826,0.00008503172,0.0005178996,0.18544443,0.0003915434,0.00018971106,0.00021879781,0.00031216707,0.00064500625,0.0016467434,0.013806778,0.7966402],"study_design_scores_gemma":[0.000035252302,0.00025096722,0.0048118634,0.1703004,0.0029872982,0.002875366,0.00048632175,0.00026057367,0.00090354885,0.0027459117,0.8142642,0.00007840011],"about_ca_topic_score_codex":0.0026573555,"about_ca_topic_score_gemma":0.0038900117,"teacher_disagreement_score":0.007719012,"about_ca_system_score_codex":0.0006458213,"about_ca_system_score_gemma":0.0029205158,"threshold_uncertainty_score":0.02582264},"labels":[],"label_agreement":null},{"id":"W4409361976","doi":"10.3390/s25082424","title":"High-Voltage Gain Single-Switch Quadratic Semi-SEPIC Converters for Powering High-Voltage Sensors Suitable for Renewable Energy Systems and Industrial Automation with Low Voltage Stresses","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Converters; Inductor; Voltage; Transformer; Duty cycle; High voltage; Voltage multiplier; Engineering; Electrical engineering; Capacitor; Voltage regulation; Electronic engineering; Voltage divider; Computer science; Dropout voltage","score_opus":0.012468136094522927,"score_gpt":0.2015116037779313,"score_spread":0.18904346768340835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409361976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14941184,0.0024885049,0.820444,0.00019836354,0.00017582854,0.00015244608,0.00013922679,0.00077239977,0.026217392],"genre_scores_gemma":[0.9065476,0.00071790616,0.08676583,0.00013707178,0.00005956997,0.00004457421,0.00009932889,0.000044853132,0.0055832476],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979335,0.000028271174,0.000010072517,0.00003606825,0.000116540716,0.000015685087],"domain_scores_gemma":[0.9998652,0.000037505597,0.000021594971,0.000019412013,0.000048411723,0.000007866324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022115961,0.0003332429,0.00023825803,0.00031489186,0.00016511943,0.0006166767,0.0008072424,0.00029711594,0.0027342064],"category_scores_gemma":[0.00027358677,0.00014540889,0.00018613026,0.0005108441,0.00025025452,0.00073529565,0.0002528917,0.0005793886,0.0007009352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031690713,0.00018266744,0.00088258827,0.0006842935,0.000043347118,0.00025359617,0.0001452705,0.004715964,0.7554843,0.017797925,0.0020215202,0.21747173],"study_design_scores_gemma":[0.00009905016,0.0010933016,0.0033104985,0.0001122272,0.00007406511,0.002121734,0.00012971464,0.1675816,0.77938247,0.008455937,0.03759732,0.000042140062],"about_ca_topic_score_codex":0.0000970049,"about_ca_topic_score_gemma":0.00043005586,"teacher_disagreement_score":0.0027342064,"about_ca_system_score_codex":0.00023373078,"about_ca_system_score_gemma":0.00015810631,"threshold_uncertainty_score":0.009146869},"labels":[],"label_agreement":null},{"id":"W4409502394","doi":"10.3390/s25082516","title":"Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Hong Kong Polytechnic University","keywords":"Waist; Prospective cohort study; Cohort; Poison control; Medicine; Test (biology); Cohort study; Wearable computer; Physical therapy; Gerontology; Engineering; Body mass index; Medical emergency; Surgery; Internal medicine","score_opus":0.014036828291187589,"score_gpt":0.3502919209356175,"score_spread":0.33625509264442993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409502394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999613,0.00004194077,0.00013241694,0.000004422215,0.0000041890935,0.00001618167,0.0000996814,0.000001625296,0.00008645838],"genre_scores_gemma":[0.99951065,0.000044217522,0.00016663029,0.000010941359,0.0000036307792,0.000012080019,0.00014625085,0.0000010050476,0.00010466931],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994062,0.00018622281,0.000076011005,0.00013785872,0.00012228664,0.00007150932],"domain_scores_gemma":[0.9984485,0.000314154,0.00032291346,0.00025657474,0.00046783613,0.0001899415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020365743,0.000481048,0.0003440493,0.0004476317,0.00036876064,0.00061336346,0.0003041748,0.00036835464,0.0005028239],"category_scores_gemma":[0.002872393,0.0002946468,0.0007124348,0.00037271992,0.00023789144,0.00035906167,0.0003657969,0.0003315099,0.00024138302],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019202108,0.000045343062,0.9984475,0.0000046343034,0.00005272749,0.000029408991,0.00007875863,0.00002975137,0.00024603214,0.0000027481253,0.000015012581,0.0008561754],"study_design_scores_gemma":[0.000009955817,0.0004054194,0.9987739,0.0000038371454,0.000059289825,0.00007395054,0.00015112318,0.00033153364,0.00011338731,0.000004501712,0.000068571295,0.000004605066],"about_ca_topic_score_codex":0.013197679,"about_ca_topic_score_gemma":0.011355829,"teacher_disagreement_score":0.013197679,"about_ca_system_score_codex":0.00022468077,"about_ca_system_score_gemma":0.0003482045,"threshold_uncertainty_score":0.02624172},"labels":[],"label_agreement":null},{"id":"W4409606814","doi":"10.3390/s25082583","title":"Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Environmental science; Convolutional neural network; Land cover; Random forest; Artificial neural network; Boreal; Computer science; Remote sensing; Soil science; Machine learning; Land use; Geography; Ecology","score_opus":0.010550253960775467,"score_gpt":0.2216192995157459,"score_spread":0.21106904555497044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409606814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972921,0.00004838251,0.0013263592,0.000057256384,0.0000053050367,0.000014733952,0.00055758556,0.000052388707,0.000645736],"genre_scores_gemma":[0.9971752,0.00003888783,0.0014479492,0.000006343579,0.0000020066943,0.000008426348,0.00064436084,0.0000056532344,0.0006710998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991417,0.000008478277,0.000004913359,0.00002994514,0.000016336244,0.000026235899],"domain_scores_gemma":[0.99979585,0.000048181122,0.000019533974,0.000009887279,0.00010485775,0.00002169377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024246253,0.00056472287,0.00022181087,0.00031485612,0.0006024184,0.00053822534,0.0005042105,0.00037137093,0.0005696703],"category_scores_gemma":[0.0006066464,0.00019099774,0.00029402107,0.0004647302,0.00025544176,0.00027653837,0.00027148728,0.0002834228,0.00011262412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043333677,0.00033058863,0.35120583,0.00012974514,0.000121732686,0.00070048246,0.00046542817,0.57786125,0.012341058,0.0004494672,0.0017653451,0.05419579],"study_design_scores_gemma":[0.000020744932,0.000030777082,0.110615194,0.000006930139,0.000022702297,0.000022746031,0.00028496754,0.8868236,0.0016182238,0.00010939973,0.0004263346,0.000018474166],"about_ca_topic_score_codex":0.91286224,"about_ca_topic_score_gemma":0.9532482,"teacher_disagreement_score":0.08713776,"about_ca_system_score_codex":0.0055695157,"about_ca_system_score_gemma":0.0035795844,"threshold_uncertainty_score":0.17530179},"labels":[],"label_agreement":null},{"id":"W4409682483","doi":"10.3390/s25092638","title":"Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Beacon; Bluetooth; Computer science; Real-time computing; Bluetooth Low Energy; Occupancy; Embedded system; Wearable computer; Building automation; Indoor positioning system; Wireless; Home automation; Engineering; Telecommunications; Accelerometer; Operating system","score_opus":0.01347021395893806,"score_gpt":0.2481824416248508,"score_spread":0.23471222766591276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409682483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15983519,0.00093432906,0.8226266,0.00024675558,0.00019968092,0.00012457625,0.00039500947,0.009515054,0.006122769],"genre_scores_gemma":[0.9473868,0.00018330186,0.049141254,0.00011544585,0.000044092372,0.00007038151,0.0002795673,0.00005342917,0.0027257672],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996076,0.000105691004,0.00002194975,0.00009736619,0.0001159618,0.000051520834],"domain_scores_gemma":[0.9997466,0.000056901663,0.00006344771,0.000038070168,0.00006623757,0.000028735638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030129208,0.0003678609,0.0004685089,0.00073524343,0.0001846134,0.00046042394,0.00089341664,0.0005254363,0.0013055663],"category_scores_gemma":[0.0006812737,0.00019685045,0.0002807581,0.00034672118,0.00013582339,0.00047997342,0.00057975424,0.00024723972,0.0007845583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016489614,0.00042589533,0.028369233,0.00049517973,0.00018547596,0.0005979386,0.0005248348,0.031665806,0.28326628,0.0031374113,0.009423299,0.6402597],"study_design_scores_gemma":[0.00033221327,0.0023558324,0.03413066,0.0001285629,0.00028610675,0.002874825,0.0002512036,0.73792136,0.17952701,0.00225888,0.03971487,0.00021848876],"about_ca_topic_score_codex":0.00042392436,"about_ca_topic_score_gemma":0.00050869805,"teacher_disagreement_score":0.0013055663,"about_ca_system_score_codex":0.00014524344,"about_ca_system_score_gemma":0.00012490811,"threshold_uncertainty_score":0.00436759},"labels":[],"label_agreement":null},{"id":"W4409765662","doi":"10.3390/s25092703","title":"An Evaluation of the Acoustic Activity Emitted in Fiber-Reinforced Concrete Under Flexure at Low Temperature","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Innovative concrete reinforcement materials","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Kinectrics (Canada)","funders":"","keywords":"Materials science; Composite material; Fiber; Acoustic emission; Cracking; Bending","score_opus":0.012553769244901614,"score_gpt":0.2631153521786072,"score_spread":0.2505615829337056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409765662","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99785787,0.0002638922,0.0015335997,0.0000075630496,0.0000037715074,0.000007579122,0.000049130635,0.000010806544,0.00026572714],"genre_scores_gemma":[0.9982816,0.00020973134,0.000955005,0.0000088351735,0.000003084377,0.000009040821,0.00006929918,0.000007175209,0.00045629436],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973756,0.000021969108,0.000011780745,0.000053386488,0.0001448398,0.000030424446],"domain_scores_gemma":[0.9997634,0.000031597017,0.00007432005,0.000012088065,0.00009150654,0.000026962412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023103677,0.00034067352,0.00024064447,0.00031055574,0.00013822838,0.00020642288,0.00016864616,0.00026782553,0.0005546195],"category_scores_gemma":[0.00030348366,0.00013849356,0.00021809124,0.00016565058,0.00024612324,0.00028289584,0.00018905757,0.00028358662,0.00013620338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006136448,0.000008329606,0.002886343,0.000032365962,0.000006885363,0.00005332445,0.000038436963,0.00025055322,0.99506986,0.0000094455945,0.0000061750493,0.0015770294],"study_design_scores_gemma":[0.0000034571594,0.00062337425,0.07782749,0.000012744496,0.000039105267,0.0002231097,0.0001936334,0.0021892753,0.91831565,0.000025004632,0.0005282107,0.00001885414],"about_ca_topic_score_codex":0.0008446062,"about_ca_topic_score_gemma":0.0014525037,"teacher_disagreement_score":0.0008446062,"about_ca_system_score_codex":0.00014423676,"about_ca_system_score_gemma":0.00011928559,"threshold_uncertainty_score":0.0018553734},"labels":[],"label_agreement":null},{"id":"W4409767084","doi":"10.3390/s25092698","title":"Smartphone-Based Deep Learning System for Detecting Ractopamine-Fed Pork Using Visual Classification Techniques","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Pharmacological Effects and Assays","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ractopamine; Artificial intelligence; Deep learning; Computer science; Pattern recognition (psychology); Machine learning; Chromatography; Chemistry","score_opus":0.029204695404722855,"score_gpt":0.3022100511384229,"score_spread":0.27300535573370005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409767084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5077351,0.0022180495,0.4595835,0.0004734967,0.00040790706,0.00037038687,0.0021444988,0.017369144,0.009698003],"genre_scores_gemma":[0.8791907,0.0007656134,0.105900094,0.000583484,0.00006549091,0.00029747482,0.0019268347,0.000097406286,0.011172906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998658,0.000010024142,0.000008448761,0.000054086224,0.000039188693,0.000022438555],"domain_scores_gemma":[0.9998652,0.000029731653,0.000019374877,0.000011638641,0.00006263335,0.000011423439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017970317,0.00055319734,0.00044268792,0.00046465264,0.00014110196,0.0002801108,0.0005612118,0.00045936045,0.0023393533],"category_scores_gemma":[0.00038110203,0.00014572384,0.00027777976,0.0001956551,0.0000890186,0.0003173799,0.00032787645,0.00029968462,0.0010642228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001311514,0.0007323675,0.012525108,0.0003778129,0.00013675285,0.0005585172,0.00010049997,0.009873648,0.2269615,0.00039351668,0.00930657,0.73772216],"study_design_scores_gemma":[0.000104735635,0.0010973129,0.024653664,0.000066348686,0.00017291897,0.00075918157,0.00011068755,0.8205279,0.14325488,0.0007676545,0.008400165,0.00008453946],"about_ca_topic_score_codex":0.0030637444,"about_ca_topic_score_gemma":0.004847263,"teacher_disagreement_score":0.0030637444,"about_ca_system_score_codex":0.00028324884,"about_ca_system_score_gemma":0.00027554695,"threshold_uncertainty_score":0.007825971},"labels":[],"label_agreement":null},{"id":"W4409801887","doi":"10.3390/s25092728","title":"Agility in Handball: Position- and Age-Specific Insights in Performance and Kinematics Using Proximity and Wearable Inertial Sensors","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Bundesinstitut für Sportwissenschaft","keywords":"Wearable computer; Kinematics; Inertial measurement unit; Position (finance); Inertial frame of reference; Wearable technology; Computer science; Physical medicine and rehabilitation; Engineering; Simulation; Human–computer interaction; Aeronautics; Artificial intelligence; Medicine; Physics; Embedded system","score_opus":0.02172002536445576,"score_gpt":0.26809522563366445,"score_spread":0.24637520026920867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409801887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951184,0.0004370931,0.0030766088,0.00002435776,0.000012004623,0.000025343406,0.0003161936,0.000031944688,0.0009580236],"genre_scores_gemma":[0.9955253,0.00039657849,0.0024924683,0.000033649143,0.000032936714,0.00004924298,0.0003974082,0.000011495676,0.0010608124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982053,0.00002348095,0.000014948362,0.00005726098,0.000044260723,0.000039605235],"domain_scores_gemma":[0.99974304,0.000031324424,0.00011051675,0.000013278291,0.00005403005,0.000047901078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023265534,0.00061825773,0.00030140267,0.0010000127,0.00013845884,0.00038238775,0.00023233413,0.00044255523,0.0009965735],"category_scores_gemma":[0.0005665875,0.00018059759,0.00022925434,0.0005592731,0.00015508008,0.00031120333,0.00040014752,0.00026056526,0.0003715673],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010485991,0.0005994348,0.7386494,0.00029405812,0.00018704614,0.0007155613,0.001189533,0.001339584,0.16280562,0.000117089,0.0005349816,0.092519045],"study_design_scores_gemma":[0.0000040751756,0.00041842877,0.9946701,0.000017127231,0.000029422694,0.00035892634,0.000270296,0.0012518152,0.0026282861,0.000043735665,0.00029539442,0.000012380655],"about_ca_topic_score_codex":0.0028737672,"about_ca_topic_score_gemma":0.00661512,"teacher_disagreement_score":0.0028737672,"about_ca_system_score_codex":0.00011892158,"about_ca_system_score_gemma":0.00013072953,"threshold_uncertainty_score":0.0057141185},"labels":[],"label_agreement":null},{"id":"W4409893893","doi":"10.3390/s25092762","title":"Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; Université de Montréal; Hotchkiss Brain Institute; University of British Columbia; Pan Am Clinic; University of Manitoba","funders":"Canadian Institutes of Health Research; Svenska Läkaresällskapet; Svenska Kulturfonden; Research Manitoba; Health Sciences Centre Foundation; University of Manitoba; Hjärnfonden; Finska Läkaresällskapet; Natural Sciences and Engineering Research Council of Canada; Karolinska Institutet; Familjen Erling-Perssons Stiftelse","keywords":"Autoregressive integrated moving average; Traumatic brain injury; Computer science; Autocorrelation; Population; Temporal resolution; Artificial intelligence; Autoregressive model; Data mining; Machine learning; Time series; Medicine; Statistics; Mathematics","score_opus":0.012960537281318706,"score_gpt":0.2781333747107556,"score_spread":0.2651728374294369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409893893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98841465,0.0008438279,0.0057155034,0.00044383883,0.00002735498,0.000022818786,0.0026407863,0.000037931033,0.0018533523],"genre_scores_gemma":[0.9953436,0.00037905126,0.0019375661,0.00004989931,0.00001657963,0.000017231278,0.0018761705,0.000024622854,0.0003550843],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999134,0.00022003459,0.000073085364,0.00018782436,0.000253075,0.00013195134],"domain_scores_gemma":[0.9965323,0.0013577059,0.0006275404,0.00059174845,0.0007063547,0.0001842472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029180204,0.00027163123,0.00028398677,0.0005867439,0.0003790292,0.0008979967,0.00052906957,0.00028501698,0.0012226525],"category_scores_gemma":[0.012868204,0.00012180537,0.0005657131,0.000998517,0.00042112483,0.0003899362,0.0007407057,0.00066298485,0.00019471899],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086498,0.000053336113,0.9345849,0.00009423496,0.0004361877,0.0004130721,0.0009965986,0.0046110754,0.0024942697,0.0011932023,0.0016317185,0.052626383],"study_design_scores_gemma":[0.00000593817,0.00006269222,0.9897132,0.00003536298,0.00012947065,0.00033050077,0.00066849095,0.0059572,0.00063250784,0.0005025473,0.00193723,0.000024734354],"about_ca_topic_score_codex":0.15071082,"about_ca_topic_score_gemma":0.2119811,"teacher_disagreement_score":0.15071082,"about_ca_system_score_codex":0.0009711494,"about_ca_system_score_gemma":0.0023375177,"threshold_uncertainty_score":0.29966718},"labels":[],"label_agreement":null},{"id":"W4409944986","doi":"10.3390/s25092804","title":"Centralized Measurement Level Fusion of GNSS and Inertial Sensors for Robust Positioning and Navigation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"GNSS positioning and interference","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"GNSS applications; Dilution of precision; Computer science; Inertial navigation system; Context (archaeology); Air navigation; Sensor fusion; Global Positioning System; Satellite system; Real-time computing; Artificial intelligence; Inertial frame of reference; Telecommunications","score_opus":0.031972302520832975,"score_gpt":0.23583466734798214,"score_spread":0.20386236482714917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409944986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03396826,0.00042233258,0.9620886,0.00010784172,0.0001313461,0.000040820414,0.000077842036,0.000955217,0.0022077719],"genre_scores_gemma":[0.67199683,0.00036942973,0.32493085,0.00013676229,0.00009748882,0.000078904966,0.0004726311,0.00010119538,0.0018158737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992316,0.000119092576,0.000040166902,0.0002113058,0.0003202655,0.00007755157],"domain_scores_gemma":[0.999524,0.00007364434,0.00006630662,0.00012444495,0.00018999854,0.000021650942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068422384,0.0010628449,0.00083256286,0.0007384796,0.000478256,0.000826885,0.0008704495,0.0006060988,0.00067321624],"category_scores_gemma":[0.0016637398,0.0003636322,0.0005462596,0.0011038871,0.00045749705,0.0010662535,0.0013815055,0.00082131743,0.00060007384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003496457,0.0001239049,0.0072869645,0.00027835753,0.00022261722,0.00024613555,0.0003785467,0.29722857,0.092693545,0.010060505,0.0039728736,0.5871583],"study_design_scores_gemma":[0.00003900808,0.00021838801,0.0057728556,0.0000389761,0.00008375426,0.00017846814,0.00015198876,0.9520082,0.02740041,0.0063362485,0.0077118827,0.000059734433],"about_ca_topic_score_codex":0.0073188283,"about_ca_topic_score_gemma":0.008844007,"teacher_disagreement_score":0.0073188283,"about_ca_system_score_codex":0.0004888258,"about_ca_system_score_gemma":0.0013556192,"threshold_uncertainty_score":0.014552474},"labels":[],"label_agreement":null},{"id":"W4410099644","doi":"10.3390/s25092912","title":"Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Mitacs; Egg Farmers of Canada","keywords":"Random forest; Mel-frequency cepstrum; Computer science; Artificial intelligence; Context (archaeology); Feature (linguistics); Machine learning; Generalizability theory; Speech recognition; Feature extraction; Statistics; Mathematics; Biology","score_opus":0.046150117262053425,"score_gpt":0.3505313966274097,"score_spread":0.30438127936535625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410099644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024159683,0.0003707243,0.97329694,0.00019385578,0.00004155692,0.000030778807,0.00015414711,0.0009032313,0.0008489962],"genre_scores_gemma":[0.726723,0.0005219825,0.26787326,0.00023206767,0.00012355644,0.00022866619,0.00071836694,0.00012590412,0.0034532174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998287,0.000041416115,0.000009661266,0.000060727507,0.00003451363,0.000024930743],"domain_scores_gemma":[0.9996394,0.00018196164,0.000042143794,0.000029040322,0.00008894706,0.000018534756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055113825,0.0007983065,0.00041522135,0.00048159735,0.00017753222,0.000657638,0.0008827717,0.0006704614,0.0009136438],"category_scores_gemma":[0.0015595685,0.00024810067,0.00055071845,0.00031944318,0.00037322164,0.0006117658,0.00062665984,0.000974899,0.00036913666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015397162,0.00015282235,0.0033316643,0.00012331679,0.000101988786,0.00018329764,0.0001192728,0.5918448,0.031898923,0.006473402,0.0019962694,0.36362028],"study_design_scores_gemma":[0.0000014457942,0.000018684907,0.00038271287,0.000004844312,0.000005421421,0.00001142119,0.000007002912,0.99608433,0.0011775313,0.0020657165,0.000237109,0.000003879898],"about_ca_topic_score_codex":0.00416279,"about_ca_topic_score_gemma":0.004942711,"teacher_disagreement_score":0.00416279,"about_ca_system_score_codex":0.00046716857,"about_ca_system_score_gemma":0.0005874162,"threshold_uncertainty_score":0.008277118},"labels":[],"label_agreement":null},{"id":"W4410122179","doi":"10.3390/s25092926","title":"Validation of an Open-Source Smartwatch for Continuous Monitoring of Physical Activity and Heart Rate in Adults","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Wearable computer; Heart rate; Computer science; Treadmill; Physical activity; Wearable technology; Heart rate monitor; Simulation; Medicine; Physical therapy; Embedded system; Internal medicine","score_opus":0.012499731537481082,"score_gpt":0.26875595386727186,"score_spread":0.2562562223297908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410122179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88237345,0.00072693784,0.07796575,0.0007444834,0.0006251542,0.0037592854,0.01411357,0.0033179186,0.016373418],"genre_scores_gemma":[0.8755765,0.00057706994,0.09134432,0.0011323135,0.00015936482,0.0045896,0.014109378,0.00041692806,0.012094442],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9960347,0.0013601907,0.00065524154,0.0007634357,0.0010443116,0.00014217157],"domain_scores_gemma":[0.99287224,0.0019588668,0.0005339489,0.0006706739,0.0036849536,0.00027927457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054537836,0.0009329804,0.000616205,0.0007493257,0.00036921896,0.0010330225,0.0010295204,0.0009772009,0.0042557465],"category_scores_gemma":[0.009768726,0.00036136052,0.0008446645,0.00051156577,0.0004309868,0.0006274227,0.0014086445,0.00035718578,0.0033395104],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011025089,0.0024575084,0.40120074,0.0040632887,0.0008660337,0.0010180896,0.0050005815,0.0017146586,0.10523619,0.0014102231,0.02249957,0.4435081],"study_design_scores_gemma":[0.0011400625,0.004783492,0.8779863,0.000952433,0.0006910626,0.002197594,0.0026639497,0.010283765,0.045941524,0.00096037186,0.052131895,0.00026744718],"about_ca_topic_score_codex":0.0013582174,"about_ca_topic_score_gemma":0.0035543155,"teacher_disagreement_score":0.0054537836,"about_ca_system_score_codex":0.000270284,"about_ca_system_score_gemma":0.00055811595,"threshold_uncertainty_score":0.028842688},"labels":[],"label_agreement":null},{"id":"W4410308503","doi":"10.3390/s25103024","title":"RFID Sensor with Integrated Energy Harvesting for Wireless Measurement of dc Magnetic Fields","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Manitoba Hydro","keywords":"Electrical engineering; Interfacing; Microcontroller; Ultra high frequency; Energy harvesting; Radio-frequency identification; Rectifier (neural networks); Wireless; Engineering; Wireless sensor network; Antenna (radio); Hall effect sensor; High-voltage direct current; Electronic engineering; Current sensor; Radio frequency; Computer science; Voltage; Power (physics); Direct current; Computer hardware; Telecommunications; Magnet","score_opus":0.009476127577004698,"score_gpt":0.20057238672324318,"score_spread":0.19109625914623848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410308503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24509995,0.008044238,0.7300235,0.00045783378,0.0007145216,0.0001965953,0.00037848143,0.0032749637,0.011809953],"genre_scores_gemma":[0.8377759,0.0020375927,0.14883009,0.00049237924,0.0001353968,0.00011775599,0.00028503055,0.00007462019,0.010251212],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965143,0.00005517956,0.000021474989,0.0000864472,0.00016062065,0.000024796915],"domain_scores_gemma":[0.9998091,0.00004033549,0.000043392727,0.000030400694,0.00006635463,0.0000104563005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025813832,0.00028393237,0.00031924376,0.00025392047,0.00012159847,0.0002648563,0.00064263126,0.0005370094,0.0007221382],"category_scores_gemma":[0.00035862532,0.00019257423,0.0002466492,0.00038364492,0.00016564282,0.00072195486,0.00032626837,0.0003043375,0.00059672305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013808855,0.000052357747,0.0008888136,0.0003125257,0.000022220443,0.00014931017,0.000060614646,0.0013862645,0.9357612,0.0012103817,0.0011074167,0.05891077],"study_design_scores_gemma":[0.000027537712,0.0007616226,0.0029612284,0.000031492484,0.00006678401,0.0011132961,0.00004418904,0.025916565,0.9455251,0.0004436849,0.023066172,0.000042228457],"about_ca_topic_score_codex":0.00007944677,"about_ca_topic_score_gemma":0.00013649474,"teacher_disagreement_score":0.0007221382,"about_ca_system_score_codex":0.00019736578,"about_ca_system_score_gemma":0.00013753917,"threshold_uncertainty_score":0.0024157763},"labels":[],"label_agreement":null},{"id":"W4410322002","doi":"10.3390/s25103069","title":"Real-Time Current Volume Estimation System from an Azure Kinect Camera in Pediatric Intensive Care: Technical Development","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; École de Technologie Supérieure","funders":"Fonds de Recherche du Québec - Santé","keywords":"Tidal volume; Volume (thermodynamics); Intensive care; Respiratory minute volume; Ventilation (architecture); Computer science; Simulation; Spirometry; Medicine; Respiratory rate; Real-time computing; Respiratory system; Engineering; Intensive care medicine; Internal medicine","score_opus":0.011266488120301142,"score_gpt":0.288068304131475,"score_spread":0.2768018160111739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410322002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06154982,0.0025520266,0.9273162,0.00035914,0.000277921,0.0006688497,0.0009005439,0.00319101,0.0031845893],"genre_scores_gemma":[0.24920772,0.002289395,0.73845315,0.00046428034,0.00013069453,0.0010573943,0.001231705,0.00026442195,0.0069012013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992543,0.00011047283,0.000063565414,0.00017639101,0.00035802656,0.00003712727],"domain_scores_gemma":[0.9995454,0.00009629424,0.000056160057,0.000054485477,0.00019517491,0.000052474687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010579848,0.0005300736,0.0005410162,0.00052911043,0.00015019358,0.0007383093,0.0013211916,0.00075166905,0.0040697725],"category_scores_gemma":[0.0012465252,0.00036571219,0.00037464077,0.00038667154,0.00019823523,0.00083156663,0.00073999097,0.0004800336,0.0013135436],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007485313,0.00031305576,0.008512748,0.0012330596,0.000102210906,0.00051280187,0.0003223588,0.0059795105,0.41215178,0.0013783473,0.007489026,0.5612567],"study_design_scores_gemma":[0.0003224432,0.002534191,0.11006643,0.0006987906,0.00029487186,0.0064910436,0.00033846972,0.36822823,0.42591807,0.0015913101,0.0830707,0.00044541658],"about_ca_topic_score_codex":0.0011421639,"about_ca_topic_score_gemma":0.0016095823,"teacher_disagreement_score":0.0040697725,"about_ca_system_score_codex":0.00024919547,"about_ca_system_score_gemma":0.00056401826,"threshold_uncertainty_score":0.013614774},"labels":[],"label_agreement":null},{"id":"W4410500889","doi":"10.3390/s25103191","title":"Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Cloud computing; Computer science; Machine learning; Artificial intelligence; Usability; Data science; Human–computer interaction","score_opus":0.053364464996565476,"score_gpt":0.33472015950191075,"score_spread":0.28135569450534526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410500889","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016754308,0.9962565,0.0008570923,0.00074345164,0.0002489476,0.000013162095,0.000027118487,0.000019666864,0.0016664212],"genre_scores_gemma":[0.0011941423,0.99688405,0.0007838048,0.00042565723,0.00019943657,0.00001990067,0.00003627233,0.0000060791576,0.000450717],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930143,0.00016192919,0.00010317985,0.00012495923,0.00025765056,0.00005081144],"domain_scores_gemma":[0.9968677,0.0022996922,0.00018373814,0.00007685188,0.0004910841,0.0000809919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001850425,0.0008779082,0.0014327768,0.0028295442,0.00045786946,0.0016501279,0.0011970646,0.0016710096,0.0044591855],"category_scores_gemma":[0.0036603922,0.0006056758,0.0009818181,0.0032353525,0.00085914007,0.003185754,0.0009861591,0.0020071627,0.0018849237],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061393286,0.00008893891,0.0004083122,0.04356946,0.00012625275,0.00016995663,0.00016857324,0.0011588936,0.0013837138,0.018904282,0.02392916,0.91003096],"study_design_scores_gemma":[0.000012944685,0.00017398401,0.00067410176,0.011863359,0.00018831325,0.0007054125,0.00018752544,0.0006749387,0.00096272584,0.008819418,0.9756865,0.000050681792],"about_ca_topic_score_codex":0.0018889469,"about_ca_topic_score_gemma":0.0026172814,"teacher_disagreement_score":0.0044591855,"about_ca_system_score_codex":0.0008616175,"about_ca_system_score_gemma":0.0025530888,"threshold_uncertainty_score":0.014917433},"labels":[],"label_agreement":null},{"id":"W4410539606","doi":"10.3390/s25103223","title":"Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Key Research and Development Program of China","keywords":"Computer science; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Convolution (computer science); Artificial intelligence; Artificial neural network","score_opus":0.009898469104142448,"score_gpt":0.24835196680154484,"score_spread":0.2384534976974024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410539606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14841999,0.00037015916,0.84714574,0.0001364338,0.00007288532,0.00006968257,0.00027194674,0.0018196153,0.0016935593],"genre_scores_gemma":[0.87353665,0.0003728811,0.1231509,0.000081652724,0.000036912803,0.00007928391,0.00038586574,0.00007388128,0.002282015],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998487,0.00000930831,0.000009102409,0.0000514699,0.000053334912,0.000028129927],"domain_scores_gemma":[0.99979156,0.000044840686,0.00004060913,0.000020502797,0.00008726225,0.000015267075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000238048,0.0005982022,0.00041737923,0.00093415315,0.000118406635,0.0004128676,0.0005384114,0.00050006504,0.00068550574],"category_scores_gemma":[0.000778393,0.00022753986,0.00054768985,0.0005764307,0.00022780513,0.00093266886,0.00053694233,0.00038167412,0.00022616918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027011687,0.00015250118,0.010015561,0.00027316425,0.000092163966,0.00040746544,0.00014826054,0.14729795,0.22418274,0.0016438939,0.0017484745,0.61376774],"study_design_scores_gemma":[0.000008696617,0.0000725085,0.005526386,0.000012847866,0.00003313333,0.00016554109,0.000037781825,0.95165676,0.040675607,0.00076931354,0.0010153651,0.000025998737],"about_ca_topic_score_codex":0.0028046693,"about_ca_topic_score_gemma":0.0037042229,"teacher_disagreement_score":0.0028046693,"about_ca_system_score_codex":0.0004114425,"about_ca_system_score_gemma":0.00038453506,"threshold_uncertainty_score":0.00557673},"labels":[],"label_agreement":null},{"id":"W4410564314","doi":"10.3390/s25103239","title":"Analytical Framework for Online Calibration of Sensor Systematic Errors Under the Generic Multisensor Integration Strategy","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Inertial measurement unit; Observability; Calibration; Computer science; Kalman filter; Trajectory; Sensor fusion; Kinematics; Units of measurement; Real-time computing; Artificial intelligence; Mathematics","score_opus":0.026211424227848647,"score_gpt":0.2909699978544776,"score_spread":0.2647585736266289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410564314","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016765164,0.00014556327,0.995849,0.000057102203,0.000013736928,0.000024248426,0.000019535397,0.00008208723,0.0021321457],"genre_scores_gemma":[0.73280585,0.001495767,0.25799537,0.0002474269,0.00011957962,0.00037722645,0.00016311211,0.0001699676,0.006625704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989059,0.0002694156,0.0000521707,0.00019607953,0.0004500813,0.00012628097],"domain_scores_gemma":[0.9984993,0.00067882845,0.00025923754,0.00016279427,0.0003656167,0.00003419344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026738618,0.0013315699,0.00084282464,0.0010404523,0.00044312343,0.0010869364,0.0017103936,0.001216676,0.0023494766],"category_scores_gemma":[0.006368466,0.0005326805,0.000948104,0.0007674076,0.0013673062,0.0023053982,0.0016923904,0.0011899088,0.0005361466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024212237,0.00002205769,0.00039510016,0.00012357223,0.000027649741,0.00011830554,0.00010750464,0.8782287,0.0026467047,0.093202524,0.00060452655,0.024499154],"study_design_scores_gemma":[0.0000025438794,0.000022892233,0.00015153462,0.000018449466,0.000008245157,0.000040201074,0.000013891635,0.98453355,0.00088287477,0.013651593,0.00066298095,0.000011228737],"about_ca_topic_score_codex":0.0041035637,"about_ca_topic_score_gemma":0.0021852436,"teacher_disagreement_score":0.0041035637,"about_ca_system_score_codex":0.0012299347,"about_ca_system_score_gemma":0.0013506166,"threshold_uncertainty_score":0.014140904},"labels":[],"label_agreement":null},{"id":"W4410567963","doi":"10.3390/s25103226","title":"Automated Implementation of the Edinburgh Visual Gait Score (EVGS)","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Computer science; Gait; Sagittal plane; Artificial intelligence; Computer vision; Gait analysis; Physical medicine and rehabilitation; Medicine","score_opus":0.012162011382627609,"score_gpt":0.34349408920281105,"score_spread":0.3313320778201834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410567963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0724014,0.00020988254,0.8858953,0.00012346174,0.00025706002,0.0007911092,0.00569483,0.02927925,0.005347714],"genre_scores_gemma":[0.4147049,0.00027405485,0.566285,0.00022595364,0.00014393161,0.0012231865,0.009553485,0.001434686,0.0061547873],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991328,0.00011661631,0.000071299655,0.0003053601,0.0003032752,0.00007062662],"domain_scores_gemma":[0.9991943,0.00013773225,0.000078722594,0.0001023506,0.00042905373,0.00005777523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063521723,0.0010521249,0.00077798887,0.0020246173,0.00021193488,0.0008550248,0.0009393682,0.0005153762,0.0069553694],"category_scores_gemma":[0.00240322,0.0003310393,0.00047970196,0.0007333366,0.00018795757,0.0005316579,0.0010587318,0.00036985098,0.0038923428],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011386084,0.00026046293,0.018314797,0.00039007657,0.00017942222,0.00034417066,0.00022516202,0.0076172086,0.07424066,0.0013991925,0.020593334,0.87529695],"study_design_scores_gemma":[0.00040722673,0.0012938381,0.14438261,0.0002075898,0.0001982561,0.0024792037,0.00044116742,0.6319936,0.16737147,0.005864286,0.04496253,0.00039823554],"about_ca_topic_score_codex":0.0025033487,"about_ca_topic_score_gemma":0.00432313,"teacher_disagreement_score":0.0069553694,"about_ca_system_score_codex":0.00023355368,"about_ca_system_score_gemma":0.00059845485,"threshold_uncertainty_score":0.023267984},"labels":[],"label_agreement":null},{"id":"W4410721750","doi":"10.3390/s25113297","title":"Measuring the Impact of Limb Asymmetry on Movement Irregularity and Complexity Changes During an Incremental Step Test in Para-Swimmers Using Inertial Measurement Units","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Inertial measurement unit; Units of measurement; Inertial frame of reference; Physical medicine and rehabilitation; Test (biology); Movement (music); Simulation; Computer science; Engineering; Medicine; Physics; Acoustics; Aerospace engineering; Geology","score_opus":0.1568763908192501,"score_gpt":0.3306595129848476,"score_spread":0.1737831221655975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410721750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999711,0.0000082147235,0.00020041932,0.0000026543623,5.552121e-7,0.0000032555506,0.000021340986,0.0000017344036,0.000050761635],"genre_scores_gemma":[0.9996469,0.0000087303015,0.00018709943,0.0000028441075,0.0000014423659,0.000007894214,0.00005117302,0.000001025991,0.00009298575],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998258,0.000046256973,0.000014103262,0.000033530865,0.000040970117,0.00003943008],"domain_scores_gemma":[0.9994369,0.00018438515,0.0001500338,0.00004480034,0.00007256406,0.000111348796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036793295,0.00024460428,0.00024950356,0.00040064813,0.00011591216,0.00023956828,0.0001296778,0.00021619024,0.0009004647],"category_scores_gemma":[0.0013355153,0.000119916505,0.000162864,0.00016895858,0.00019371067,0.0001452113,0.00033295454,0.00018290976,0.00015763033],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004438505,0.0004286832,0.8540416,0.00006699645,0.0001498622,0.00025903672,0.0005372305,0.0010920252,0.10984058,0.000048172322,0.000102589795,0.02899475],"study_design_scores_gemma":[0.0000042956735,0.00075646595,0.9965636,0.0000020208054,0.000012417172,0.00004275078,0.00007694018,0.0010196613,0.0014792504,0.000012427751,0.000026962889,0.0000031776635],"about_ca_topic_score_codex":0.0015017956,"about_ca_topic_score_gemma":0.002409838,"teacher_disagreement_score":0.0015017956,"about_ca_system_score_codex":0.00008333178,"about_ca_system_score_gemma":0.00010413009,"threshold_uncertainty_score":0.0030124187},"labels":[],"label_agreement":null},{"id":"W4410784478","doi":"10.3390/s25113360","title":"A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fonds De La Recherche Scientifique - FNRS; Fonds Erasme","keywords":"Sound (geography); Cardiology; Internal medicine; Aortic valve; Medicine; Segmentation; Computer science; Artificial intelligence; Acoustics; Physics","score_opus":0.01100634739680241,"score_gpt":0.3047124616190428,"score_spread":0.2937061142222404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410784478","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25284854,0.0035667075,0.73198754,0.0006976787,0.0003799712,0.00028290946,0.0023020895,0.004546934,0.0033876689],"genre_scores_gemma":[0.6710171,0.0013678377,0.3185268,0.00052975235,0.0004721246,0.00019833363,0.003420483,0.00032612763,0.004141426],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996195,0.00006281833,0.00004962283,0.0001483394,0.00007661314,0.000043101758],"domain_scores_gemma":[0.9993901,0.0002626666,0.00006319119,0.000059111462,0.00014757155,0.00007729031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054943096,0.00073383964,0.0007136165,0.0015599716,0.00025886734,0.00076253654,0.00063721795,0.0011966696,0.0012236468],"category_scores_gemma":[0.0017264903,0.00026464375,0.00072517915,0.0004980331,0.00015606478,0.000408605,0.0008097829,0.0006300311,0.0010234461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010838872,0.00029995103,0.055531975,0.0003471879,0.0002494962,0.0010546496,0.00021877892,0.008775899,0.09279206,0.00063631486,0.008125733,0.83088404],"study_design_scores_gemma":[0.00040599325,0.0008285379,0.14697036,0.00019829489,0.0006203477,0.0095137395,0.00032490614,0.73997265,0.08047759,0.0036931278,0.016825547,0.00016886414],"about_ca_topic_score_codex":0.0012756889,"about_ca_topic_score_gemma":0.004089992,"teacher_disagreement_score":0.0015599716,"about_ca_system_score_codex":0.00012262733,"about_ca_system_score_gemma":0.00049290137,"threshold_uncertainty_score":0.004093528},"labels":[],"label_agreement":null},{"id":"W4410814304","doi":"10.3390/s25113387","title":"Wideband Dual-Polarized PRGW Antenna Array with High Isolation for Millimeter-Wave IoT Applications","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Concordia University","funders":"","keywords":"Wideband; Extremely high frequency; Isolation (microbiology); Antenna (radio); Dual (grammatical number); Antenna array; Optoelectronics; Telecommunications; Physics; Electrical engineering; Computer science; Engineering; Art","score_opus":0.010021145120052338,"score_gpt":0.21015900825269826,"score_spread":0.20013786313264592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410814304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4730382,0.000511987,0.5053088,0.00042697866,0.00019922626,0.00003559915,0.00011630033,0.0011750455,0.01918787],"genre_scores_gemma":[0.8939045,0.00025235032,0.101811215,0.00013979862,0.000044474094,0.00005116477,0.00014062124,0.00009592133,0.003559991],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998406,0.000022870243,0.0000064835363,0.000034607052,0.000066005174,0.000029416595],"domain_scores_gemma":[0.9998369,0.00002206671,0.00004413908,0.00003245431,0.0000484225,0.000016007782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012379138,0.00036309025,0.0002717545,0.0001677834,0.000092921655,0.00048058183,0.00030549074,0.0003925575,0.0005138272],"category_scores_gemma":[0.00021474477,0.00016419862,0.00024339849,0.00029978348,0.00020041665,0.0003281341,0.0003751912,0.0003317524,0.00097651547],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007603817,0.000035386536,0.0008280732,0.00006645096,0.000015664838,0.00013339974,0.000048908878,0.003869313,0.9655916,0.0019768598,0.0007639575,0.02659424],"study_design_scores_gemma":[0.000033190234,0.0004979616,0.0022867196,0.0000159757,0.000039652277,0.0011665345,0.0000932094,0.09556071,0.8826674,0.0010656075,0.016536493,0.00003659578],"about_ca_topic_score_codex":0.00005219341,"about_ca_topic_score_gemma":0.00009159334,"teacher_disagreement_score":0.0005138272,"about_ca_system_score_codex":0.00013350642,"about_ca_system_score_gemma":0.00012737322,"threshold_uncertainty_score":0.0017188787},"labels":[],"label_agreement":null},{"id":"W4410956222","doi":"10.3390/s25113509","title":"Using Wearable Sensors for Sex Classification and Age Estimation from Walking Patterns","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Gait; Identification (biology); Wearable computer; Accelerometer; Artificial intelligence; Computer science; Machine learning; Feature (linguistics); Gyroscope; Field (mathematics); Gait analysis; Inertial measurement unit; Population; Pattern recognition (psychology); Engineering; Physical medicine and rehabilitation; Mathematics","score_opus":0.05359172742237141,"score_gpt":0.307762141852872,"score_spread":0.2541704144305006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410956222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60997444,0.00698869,0.3654199,0.0005435569,0.0009005517,0.0002356539,0.0036047103,0.0026233003,0.009709163],"genre_scores_gemma":[0.90341794,0.0024802892,0.08828526,0.00020267686,0.00018798432,0.00009300752,0.0014782557,0.000037812104,0.00381679],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997974,0.000040756728,0.000017780723,0.000053674245,0.00006950754,0.000020918133],"domain_scores_gemma":[0.9997254,0.000053153675,0.000057392088,0.000025897249,0.000120978526,0.000017103835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002515518,0.0007135818,0.00047312203,0.00093163375,0.0001241363,0.0003696925,0.00031414762,0.00041269563,0.0012706316],"category_scores_gemma":[0.00089724705,0.00013879738,0.00033760024,0.00068852503,0.00008946836,0.0004938024,0.00024837404,0.00020795387,0.0009294314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004740418,0.00030775645,0.12060253,0.00044257985,0.00023839818,0.0004939626,0.00014680257,0.006801749,0.084885225,0.0008091076,0.0054941536,0.77930367],"study_design_scores_gemma":[0.00007481092,0.0014754893,0.37326378,0.00032428402,0.00048862566,0.004303163,0.0005305583,0.45940307,0.1363473,0.0040217927,0.019602371,0.00016482829],"about_ca_topic_score_codex":0.00074600836,"about_ca_topic_score_gemma":0.0017833529,"teacher_disagreement_score":0.0012706316,"about_ca_system_score_codex":0.00008651911,"about_ca_system_score_gemma":0.00011536489,"threshold_uncertainty_score":0.0042506456},"labels":[],"label_agreement":null},{"id":"W4410956618","doi":"10.3390/s25113514","title":"Quantitative Analysis of Situation Awareness During Autonomous Vehicle Handover on the Da Vinci Research Kit","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Handover; Situation awareness; Task (project management); Autonomy; Process (computing); Control (management); Test (biology); Psychological intervention; Computer science; Simulation; Engineering; Psychology; Systems engineering; Artificial intelligence; Telecommunications","score_opus":0.11614352040207263,"score_gpt":0.4698122476115373,"score_spread":0.35366872720946463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410956618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951697,0.000045199547,0.0034253027,0.00001164163,0.000005127432,0.00007088132,0.00026000946,0.000055681667,0.00095638505],"genre_scores_gemma":[0.99648666,0.00004171568,0.002248301,0.000009474844,0.000005714462,0.00006591425,0.00028771663,0.000015264806,0.0008392674],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99931514,0.00022341993,0.000043629865,0.00010030347,0.0002549094,0.000062660896],"domain_scores_gemma":[0.9964813,0.0019205479,0.00038160462,0.0001321162,0.0008290261,0.0002553903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063319237,0.00029709103,0.00021355458,0.0010430977,0.00020341204,0.00047220968,0.00021162692,0.0002951701,0.0025050319],"category_scores_gemma":[0.0043438748,0.00012249364,0.00019535387,0.0005313131,0.00027729978,0.00025062822,0.00045034135,0.00023606986,0.00035056003],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0052701808,0.0016723531,0.26278552,0.0014827643,0.00025230873,0.0012810678,0.025219513,0.00808829,0.46001193,0.00072950876,0.0020070192,0.23119962],"study_design_scores_gemma":[0.00003152097,0.0031015405,0.95330733,0.00003948589,0.00009130991,0.00056390325,0.0056243706,0.008284609,0.027137725,0.00015981661,0.0015571159,0.00010110845],"about_ca_topic_score_codex":0.00097110134,"about_ca_topic_score_gemma":0.0013109802,"teacher_disagreement_score":0.0025050319,"about_ca_system_score_codex":0.0001901448,"about_ca_system_score_gemma":0.00016008523,"threshold_uncertainty_score":0.008380175},"labels":[],"label_agreement":null},{"id":"W4410956690","doi":"10.3390/s25113496","title":"Swarm Intelligence for Collaborative Play in Humanoid Soccer Teams","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Network packet; Swarm intelligence; Humanoid robot; Robustness (evolution); Distributed computing; Swarm behaviour; Artificial intelligence; Flocking (texture); Robot; Computer network; Simulation; Machine learning; Particle swarm optimization","score_opus":0.014529486042454742,"score_gpt":0.2857464296267059,"score_spread":0.27121694358425114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410956690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16132334,0.0010528108,0.8206024,0.0009253062,0.0001264356,0.000100428246,0.00003084342,0.00038133955,0.015457138],"genre_scores_gemma":[0.9421508,0.0002280658,0.055394817,0.00004870401,0.000026610132,0.00007635169,0.000018329962,0.000023900166,0.0020324471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997694,0.00009949013,0.000011113092,0.00003520058,0.00006387176,0.000020951464],"domain_scores_gemma":[0.9996145,0.00018218526,0.000064656255,0.000035021723,0.000045610996,0.000057903515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059431425,0.0003751971,0.00033996085,0.0003260669,0.0005892579,0.0010358271,0.00046145383,0.00046282346,0.001615592],"category_scores_gemma":[0.0016250985,0.00022807026,0.0003342429,0.00015026478,0.0013935886,0.0008003272,0.0011672709,0.0006462575,0.00015785565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025568044,0.0000964175,0.003259441,0.00016725658,0.000092469476,0.00030370595,0.00094052096,0.756587,0.01702647,0.14112717,0.0023042853,0.07783963],"study_design_scores_gemma":[0.00002030078,0.00007273421,0.0006583688,0.00001761271,0.000010624302,0.00004023167,0.00016425288,0.9681675,0.0010002791,0.027292402,0.0025433311,0.00001229902],"about_ca_topic_score_codex":0.002500595,"about_ca_topic_score_gemma":0.0022961448,"teacher_disagreement_score":0.002500595,"about_ca_system_score_codex":0.00055606634,"about_ca_system_score_gemma":0.0005999314,"threshold_uncertainty_score":0.005404651},"labels":[],"label_agreement":null},{"id":"W4411034475","doi":"10.3390/s25113535","title":"UAV-Based LiDAR and Multispectral Imaging for Estimating Dry Bean Plant Height, Lodging and Seed Yield","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Alberta Pulse Growers Commission","keywords":"Multispectral image; Lidar; Yield (engineering); Remote sensing; Environmental science; Agronomy; Geology; Biology; Materials science","score_opus":0.005804413812353508,"score_gpt":0.2126812095318064,"score_spread":0.2068767957194529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411034475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95953256,0.0007305933,0.037228107,0.000046461766,0.000012071297,0.000034830526,0.0006202215,0.00046129906,0.0013337577],"genre_scores_gemma":[0.97201806,0.00016707338,0.026985329,0.000020808697,0.00000421111,0.000014720999,0.00047000847,0.000010030131,0.00030987148],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983776,0.000032388416,0.000005998276,0.000048316902,0.000056934154,0.000018531788],"domain_scores_gemma":[0.9998041,0.0000553865,0.000050037106,0.000021177872,0.00005482953,0.000014509561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037227463,0.0003398792,0.0002719015,0.0005669006,0.000110559085,0.0003091278,0.0002643651,0.00020871309,0.00026649667],"category_scores_gemma":[0.0004287194,0.00015185367,0.00029101636,0.0005469838,0.000069292764,0.0003285965,0.00021664092,0.00016235666,0.00015757309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000604388,0.00035292984,0.28029758,0.0003461889,0.00024185817,0.00024919465,0.00022973797,0.043653782,0.32513508,0.0003365707,0.0011087217,0.34744397],"study_design_scores_gemma":[0.00003337445,0.0004935071,0.4280662,0.000037889473,0.00017059103,0.00032949532,0.00034714284,0.52220154,0.045615666,0.00028958934,0.0023551227,0.000059859383],"about_ca_topic_score_codex":0.011115726,"about_ca_topic_score_gemma":0.01822083,"teacher_disagreement_score":0.011115726,"about_ca_system_score_codex":0.00030079982,"about_ca_system_score_gemma":0.00022034906,"threshold_uncertainty_score":0.022102058},"labels":[],"label_agreement":null},{"id":"W4411133762","doi":"10.3390/s25123610","title":"A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"FedDev Ontario","keywords":"Robustness (evolution); Artificial intelligence; Image processing; Process (computing); Pipeline (software); Laser; Computer vision; Computer science; Engineering; Optics; Image (mathematics); Mechanical engineering","score_opus":0.006361053881207311,"score_gpt":0.22273777624185753,"score_spread":0.21637672236065023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411133762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038667675,0.00038169368,0.9585458,0.000059411628,0.000038856786,0.00006295474,0.000050193048,0.0016842785,0.00050918],"genre_scores_gemma":[0.33890438,0.00030993865,0.65837073,0.00009045873,0.000037177902,0.00015389266,0.00018875959,0.000095325755,0.0018492737],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948466,0.000032998872,0.000038024533,0.00014502692,0.00025112758,0.00004808021],"domain_scores_gemma":[0.99949646,0.00012921127,0.00009889659,0.000046478144,0.0002057745,0.000023211329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053161226,0.0006205299,0.0007341893,0.0010434556,0.00033122115,0.0007682329,0.0009986645,0.00069055427,0.00088929076],"category_scores_gemma":[0.0011413302,0.00035159627,0.00045788995,0.0005992905,0.00032842017,0.0006033677,0.00043325202,0.00065835885,0.0005220954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045305607,0.0001966649,0.0023518263,0.00015619794,0.000070202186,0.00017402589,0.00011339424,0.052287787,0.25154594,0.0010434072,0.0017020798,0.6899054],"study_design_scores_gemma":[0.000021409425,0.00019507628,0.0029526644,0.000011536891,0.000022968085,0.00018352814,0.000022293001,0.92800754,0.066385,0.00042651265,0.0017486751,0.00002270903],"about_ca_topic_score_codex":0.0018395424,"about_ca_topic_score_gemma":0.0026025006,"teacher_disagreement_score":0.0018395424,"about_ca_system_score_codex":0.0005465952,"about_ca_system_score_gemma":0.00072671846,"threshold_uncertainty_score":0.003965795},"labels":[],"label_agreement":null},{"id":"W4411184906","doi":"10.3390/s25123635","title":"Non-Destructive Testing and Evaluation of Hybrid and Advanced Structures: A Comprehensive Review of Methods, Applications, and Emerging Trends","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Engineering; Systems engineering; Computer science; Reliability engineering","score_opus":0.03731146527417932,"score_gpt":0.3704767817489785,"score_spread":0.3331653164747992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411184906","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061312836,0.9942708,0.0036826523,0.00021360409,0.00012760187,0.000016775373,0.00004365961,0.000027285734,0.0010045035],"genre_scores_gemma":[0.003946216,0.99001503,0.004841947,0.00022755154,0.0002055216,0.00003591599,0.00008685128,0.000012226887,0.00062872213],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99864286,0.0002236479,0.00016056975,0.00023684186,0.00067726156,0.000058847196],"domain_scores_gemma":[0.9978123,0.0014398418,0.00022992691,0.00006374303,0.00041365027,0.000040529587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002377064,0.0013351853,0.0017363748,0.0041841706,0.00037056458,0.0017215885,0.0012257682,0.001638566,0.0019305109],"category_scores_gemma":[0.0018782949,0.00069046224,0.0010509038,0.0031944409,0.00094979117,0.0022659656,0.00083738397,0.0012596882,0.0008899147],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006570653,0.00013104481,0.0009997559,0.052457657,0.00017102536,0.00031649155,0.00025479204,0.0022103547,0.024587037,0.008541686,0.011298682,0.8989657],"study_design_scores_gemma":[0.000017754219,0.00051191845,0.0038552263,0.009662036,0.0004417146,0.0033210209,0.0004161939,0.003769157,0.017642612,0.00760575,0.9525567,0.00019995682],"about_ca_topic_score_codex":0.0015407623,"about_ca_topic_score_gemma":0.0020481562,"teacher_disagreement_score":0.0041841706,"about_ca_system_score_codex":0.0006972539,"about_ca_system_score_gemma":0.0014415777,"threshold_uncertainty_score":0.012571275},"labels":[],"label_agreement":null},{"id":"W4411236977","doi":"10.3390/s25123694","title":"Evaluating the Usability of Inertial Measurement Units for Measuring and Monitoring Activity Post-Stroke: A Scoping Review","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; GF Strong Rehabilitation Centre; University of British Columbia","funders":"","keywords":"Usability; CINAHL; Stroke (engine); MEDLINE; Medicine; Psychological intervention; Physical medicine and rehabilitation; Computer science; Human–computer interaction; Nursing; Engineering","score_opus":0.27751730446334744,"score_gpt":0.4584204128211323,"score_spread":0.18090310835778484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411236977","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052863394,0.9979664,0.00023605836,0.00018605475,0.00012146122,0.00033030377,0.00012808348,0.000008006439,0.000495151],"genre_scores_gemma":[0.0032098545,0.9952585,0.0006567768,0.00016603916,0.000045730583,0.0004360685,0.00010623142,0.000005987522,0.000114810435],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9878615,0.004322781,0.0044595287,0.00058308005,0.0025206264,0.0002524595],"domain_scores_gemma":[0.93991077,0.049099203,0.005013452,0.00063092954,0.005054385,0.0002911978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015028923,0.0018670495,0.0062546437,0.013858721,0.0009686304,0.0046769944,0.0021501991,0.0025938177,0.0043532397],"category_scores_gemma":[0.06566037,0.001111953,0.00577909,0.012067103,0.001176064,0.003099797,0.001996343,0.0013883197,0.00065926806],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009315575,0.000033619937,0.000298971,0.8686435,0.0019195629,0.000074502925,0.00049581134,0.000100544,0.00021083801,0.00034352005,0.0016612257,0.1261247],"study_design_scores_gemma":[0.000042258147,0.00014809672,0.0012009084,0.9579918,0.01095406,0.00020591983,0.0004510641,0.000062814644,0.00024085399,0.00024462916,0.028428886,0.00002879717],"about_ca_topic_score_codex":0.0074926526,"about_ca_topic_score_gemma":0.0159725,"teacher_disagreement_score":0.015028923,"about_ca_system_score_codex":0.0028710752,"about_ca_system_score_gemma":0.013445676,"threshold_uncertainty_score":0.07948148},"labels":[],"label_agreement":null},{"id":"W4411339574","doi":"10.3390/s25123733","title":"Comparability of Methods for Remotely Assessing Gait Quality","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"McGill University Health Centre; Mitacs; McGill University","keywords":"Comparability; Gait; Quality (philosophy); Computer science; Environmental science; Gait analysis; Remote sensing; Physical medicine and rehabilitation; Reliability engineering; Engineering; Medicine; Geography; Mathematics","score_opus":0.07852863493726625,"score_gpt":0.4444137520238316,"score_spread":0.36588511708656535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411339574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89899683,0.0036575668,0.08547964,0.00019909214,0.00060628855,0.0028859274,0.0017069089,0.00021771193,0.0062500415],"genre_scores_gemma":[0.9743155,0.00052727776,0.020451715,0.000097748336,0.00012797682,0.0022997966,0.0013954187,0.000077320285,0.00070719275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9500345,0.028696127,0.006296422,0.0037744408,0.010593902,0.0006046326],"domain_scores_gemma":[0.92036897,0.042328585,0.01113632,0.011706585,0.01364919,0.00081035605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029483799,0.00081598776,0.000793026,0.0023143422,0.00039984955,0.0014322263,0.001036845,0.001011964,0.0017044313],"category_scores_gemma":[0.09476131,0.0003099127,0.0013819417,0.0011776922,0.0009405929,0.001295694,0.0019740965,0.00065559417,0.00049805496],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014480318,0.00086853193,0.6934914,0.0023545907,0.005368205,0.00024532157,0.00563998,0.0026453133,0.02214336,0.0013018949,0.0013970126,0.25006402],"study_design_scores_gemma":[0.00047243023,0.013076265,0.95512265,0.00052432035,0.0009804689,0.000987037,0.0033874759,0.008201282,0.009959617,0.0018655066,0.005271308,0.00015166671],"about_ca_topic_score_codex":0.0006961531,"about_ca_topic_score_gemma":0.0013024154,"teacher_disagreement_score":0.029483799,"about_ca_system_score_codex":0.00037641622,"about_ca_system_score_gemma":0.0002908566,"threshold_uncertainty_score":0.15592706},"labels":[],"label_agreement":null},{"id":"W4411351205","doi":"10.3390/s25123741","title":"Characterizing the Cracking Behavior of Large-Scale Multi-Layered Reinforced Concrete Beams by Acoustic Emission Analysis","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kinectrics (Canada); Memorial University of Newfoundland","funders":"","keywords":"Materials science; Cracking; Acoustic emission; Beam (structure); Crumb rubber; Composite material; Cementitious; Flexural strength; Natural rubber; Structural engineering; Tension (geology); Intensity (physics); Compression (physics); Cement; Engineering","score_opus":0.01011917123830207,"score_gpt":0.24735534835223502,"score_spread":0.23723617711393294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411351205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95652723,0.0002686133,0.04272183,0.00000831403,0.0000032537814,0.000027528806,0.00005758249,0.00008626902,0.00029936305],"genre_scores_gemma":[0.9746027,0.00016954904,0.0246753,0.00001200584,0.0000032289654,0.000023715906,0.000076224555,0.000014237641,0.00042297877],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997495,0.000029413857,0.000011059004,0.000048640413,0.00013962218,0.0000216713],"domain_scores_gemma":[0.99940157,0.0002128233,0.00015253229,0.000051578347,0.00015681503,0.000024721965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036436768,0.0003710839,0.00019012632,0.00083343993,0.00008311838,0.00012603107,0.0002441732,0.00034815373,0.00048488562],"category_scores_gemma":[0.00048202084,0.00015191159,0.00015477517,0.00023326949,0.00018504851,0.00036770574,0.00025494685,0.00018207316,0.00015010324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051028164,0.000029638839,0.010222954,0.00005248194,0.00001037703,0.00009166518,0.00008384958,0.0022235133,0.97383636,0.000032023792,0.000014469882,0.01335165],"study_design_scores_gemma":[0.000010443383,0.00069642346,0.2012642,0.00003124167,0.00005699618,0.00073940074,0.00027576633,0.07035926,0.72547203,0.00013824874,0.0008984216,0.000057554193],"about_ca_topic_score_codex":0.00051244034,"about_ca_topic_score_gemma":0.0013694305,"teacher_disagreement_score":0.00083343993,"about_ca_system_score_codex":0.0000740285,"about_ca_system_score_gemma":0.00006390734,"threshold_uncertainty_score":0.0019269586},"labels":[],"label_agreement":null},{"id":"W4411427907","doi":"10.3390/s25123799","title":"Design and Development of a Precision Spraying Control System for Orchards Based on Machine Vision Detection","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chongqing University of Science and Technology; Chongqing University","keywords":"Machine vision; Computer science; Artificial intelligence; Control (management); Engineering; Computer vision","score_opus":0.013163927176789762,"score_gpt":0.22374409897319344,"score_spread":0.21058017179640368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411427907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049798362,0.00034196043,0.94527334,0.0000813215,0.00007699439,0.000257993,0.00003432353,0.0015494683,0.0025862448],"genre_scores_gemma":[0.73121524,0.00035044743,0.26394287,0.00010934044,0.000040316423,0.0003653576,0.00009816349,0.000030282928,0.0038478714],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965763,0.000018894358,0.000022487606,0.00011048348,0.00016065137,0.000029864968],"domain_scores_gemma":[0.99979633,0.000025332163,0.000029753006,0.000016278755,0.00011575727,0.00001654937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003237083,0.00040136048,0.00052569533,0.00038505613,0.00033751945,0.00043631007,0.0008601189,0.0004940392,0.0008294872],"category_scores_gemma":[0.00034635066,0.0003098288,0.0003457173,0.00021027788,0.00020484863,0.00048771067,0.0003394341,0.00033873555,0.0002468532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021602582,0.00019008243,0.0047316835,0.00051132747,0.00007578759,0.0003392985,0.00031851066,0.058231782,0.46595395,0.0035450624,0.0018887975,0.46399772],"study_design_scores_gemma":[0.00012997116,0.0011522656,0.012422448,0.000044750293,0.000117990516,0.00053909066,0.000080753736,0.8555953,0.1179471,0.0007263305,0.01116214,0.00008181979],"about_ca_topic_score_codex":0.0038253118,"about_ca_topic_score_gemma":0.0028788545,"teacher_disagreement_score":0.0038253118,"about_ca_system_score_codex":0.00029764356,"about_ca_system_score_gemma":0.001051091,"threshold_uncertainty_score":0.007606089},"labels":[],"label_agreement":null},{"id":"W4411485707","doi":"10.3390/s25133858","title":"Design and Evaluation of a Soft Robotic Actuator with Non-Intrusive Vision-Based Bending Measurement","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Soft robotics; Actuator; Bending; Robot; Computer science; Engineering; Artificial intelligence; Computer vision; Structural engineering","score_opus":0.023624154880763068,"score_gpt":0.26222150810881795,"score_spread":0.23859735322805486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411485707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19400817,0.0013214255,0.794365,0.0004345674,0.00025542098,0.00061415345,0.00015578202,0.002120388,0.00672511],"genre_scores_gemma":[0.76847005,0.00037529622,0.22488904,0.0002414023,0.000042095184,0.0003255404,0.00011566592,0.00006296556,0.00547791],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989907,0.00009361326,0.00006161702,0.00015111304,0.0006483397,0.00005467703],"domain_scores_gemma":[0.9991578,0.00015797114,0.00020551671,0.00009555893,0.00027066443,0.00011263094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066053495,0.0005237704,0.0004201785,0.00043063326,0.00022394612,0.0005020909,0.001137131,0.0007806055,0.0014082822],"category_scores_gemma":[0.00097117026,0.00027369897,0.00030715772,0.00012545254,0.00037372968,0.0005176806,0.0005443733,0.00038275684,0.00050514925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017165314,0.00017101722,0.0014870502,0.00054614403,0.0000426485,0.0002731719,0.00010915414,0.0077850535,0.8853612,0.0008901076,0.000615125,0.10254775],"study_design_scores_gemma":[0.00012221564,0.004862295,0.013481501,0.00009706244,0.00012474743,0.0017669576,0.0000950833,0.22471894,0.73277354,0.00042572065,0.021401467,0.00013048144],"about_ca_topic_score_codex":0.00042178572,"about_ca_topic_score_gemma":0.0006039819,"teacher_disagreement_score":0.0014082822,"about_ca_system_score_codex":0.00035427994,"about_ca_system_score_gemma":0.0006981349,"threshold_uncertainty_score":0.004711151},"labels":[],"label_agreement":null},{"id":"W4411802263","doi":"10.3390/s25134058","title":"AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Egg Farmers of Canada","keywords":"Interpretability; Computer science; Artificial intelligence; Data science; Software deployment; Machine learning; Software engineering","score_opus":0.032239939969265684,"score_gpt":0.3850297375018067,"score_spread":0.352789797532541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411802263","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056936423,0.9975535,0.00047235005,0.00047838673,0.00012972574,0.00006030437,0.00036802818,0.000013020968,0.00035541123],"genre_scores_gemma":[0.007834676,0.9891755,0.0013806439,0.00072201237,0.00012818459,0.00021208468,0.00040376693,0.000012355137,0.00013071416],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9955183,0.0013159281,0.0016886395,0.00055786205,0.00080475444,0.000114521376],"domain_scores_gemma":[0.96084243,0.031502146,0.003308851,0.00079844706,0.0032637713,0.0002843414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0078043854,0.0010450443,0.0034987207,0.0083749555,0.0004887797,0.0024318104,0.0017571268,0.0016643212,0.00392605],"category_scores_gemma":[0.039601445,0.00066482165,0.0045556854,0.0068070707,0.0009858378,0.0020935375,0.0017006295,0.0010941144,0.0005914194],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017693116,0.0000392841,0.0019265603,0.6511972,0.0034051503,0.00011742414,0.00047970237,0.00030646438,0.0004390848,0.0010498419,0.006823524,0.3340388],"study_design_scores_gemma":[0.00009548929,0.00032269003,0.011672361,0.7717371,0.029049736,0.00062559685,0.0008597316,0.00032526933,0.00063315174,0.00220882,0.18237132,0.00009873698],"about_ca_topic_score_codex":0.0068501756,"about_ca_topic_score_gemma":0.017078552,"teacher_disagreement_score":0.0083749555,"about_ca_system_score_codex":0.0015888838,"about_ca_system_score_gemma":0.01069946,"threshold_uncertainty_score":0.04127401},"labels":[],"label_agreement":null},{"id":"W4411925889","doi":"10.3390/s25134130","title":"Polymer-Based Chemicapacitive Hybrid Sensor Array for Improved Selectivity in e-Nose Systems","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Windsor Cancer Centre Foundation; University of Windsor; CMC Microsystems","keywords":"Electronic nose; Sensitivity (control systems); Computer science; Microelectromechanical systems; Selectivity; Materials science; Embedded system; Process engineering; Nanotechnology; Automotive engineering; Engineering; Electronic engineering; Chemistry","score_opus":0.005872480693627346,"score_gpt":0.2212706038836046,"score_spread":0.21539812318997725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411925889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7280066,0.0054560695,0.2550886,0.0005368469,0.00044711528,0.00017069619,0.0006393338,0.0025470662,0.0071076867],"genre_scores_gemma":[0.8607064,0.0014326839,0.13181835,0.0004472012,0.00009326391,0.00011310414,0.00025115037,0.0000630474,0.005074725],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967587,0.00003752375,0.000017177952,0.00010461525,0.00013421914,0.00003062091],"domain_scores_gemma":[0.99983144,0.000053639025,0.00003360132,0.000013872739,0.000056723246,0.000010624625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026015667,0.00042976567,0.00029725168,0.00022846834,0.00011318343,0.000336829,0.0006506295,0.00062395236,0.0011268766],"category_scores_gemma":[0.00035614904,0.00023971633,0.00021076527,0.0002140228,0.0001661403,0.000601407,0.00035368514,0.0003497614,0.000690399],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033119097,0.000015491061,0.000104749975,0.00004899883,0.0000054591887,0.00002364661,0.0000060375296,0.00026632365,0.99266994,0.00012840859,0.00012713353,0.00657069],"study_design_scores_gemma":[0.000004366346,0.00014426543,0.0005093725,0.0000033843955,0.0000093282415,0.0000968871,0.000008404682,0.008953811,0.9882095,0.00004585496,0.0020047245,0.0000100614],"about_ca_topic_score_codex":0.00015267413,"about_ca_topic_score_gemma":0.00043346864,"teacher_disagreement_score":0.0011268766,"about_ca_system_score_codex":0.00020133638,"about_ca_system_score_gemma":0.00013534876,"threshold_uncertainty_score":0.003769815},"labels":[],"label_agreement":null},{"id":"W4412008032","doi":"10.3390/s25134145","title":"Comb-Tipped Coupled Cantilever Sensor for Enhanced Real-Time Detection of E. coli Bacteria","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Eli Lilly and Company","keywords":"Cantilever; Bacteria; Materials science; Computer science; Nanotechnology; Biology; Composite material","score_opus":0.006579023555299794,"score_gpt":0.24529232226627776,"score_spread":0.23871329871097796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412008032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83305645,0.006722956,0.15623118,0.00037004918,0.0004773156,0.00018432629,0.00050662085,0.0005820973,0.0018690138],"genre_scores_gemma":[0.731476,0.0018094906,0.2628434,0.00040833204,0.00008665805,0.00019178957,0.0003957958,0.00004231411,0.002746177],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995932,0.000057337566,0.000024842002,0.000107157044,0.00018970534,0.000027877766],"domain_scores_gemma":[0.99971384,0.000088953515,0.000054135795,0.000018656556,0.00009963993,0.000024781677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037200996,0.0004987852,0.00045611258,0.00034151148,0.00016620242,0.00019110048,0.0007818051,0.00097503356,0.00082757906],"category_scores_gemma":[0.0006126302,0.00028682893,0.00027575903,0.00026601512,0.00020694135,0.00043119004,0.00035409423,0.00043249785,0.0003765402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017550568,0.000016756294,0.00008320706,0.00005125901,0.000004439459,0.00003274331,0.0000075572075,0.0000717933,0.9977729,0.000033063472,0.000048321752,0.0018604893],"study_design_scores_gemma":[0.000016263974,0.00032312016,0.0017643108,0.000007809346,0.000021394377,0.00035114324,0.000015997917,0.010861117,0.98484564,0.00004119385,0.0017257357,0.00002627185],"about_ca_topic_score_codex":0.000228359,"about_ca_topic_score_gemma":0.000866293,"teacher_disagreement_score":0.00097503356,"about_ca_system_score_codex":0.00021683992,"about_ca_system_score_gemma":0.00016857173,"threshold_uncertainty_score":0.0027685165},"labels":[],"label_agreement":null},{"id":"W4412068976","doi":"10.3390/s25134230","title":"Data-Driven Image-Based Protocol for Brain PET Image Harmonization","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Javna Agencija za Raziskovalno Dejavnost RS; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Harmonization; Protocol (science); Image (mathematics); Computer science; Computer vision; Artificial intelligence; Medicine; Pathology; Physics; Acoustics","score_opus":0.0502735565417535,"score_gpt":0.4110327706786906,"score_spread":0.3607592141369371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412068976","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0113077145,0.00024469674,0.97588694,0.00019971639,0.00017629904,0.0037305586,0.0008447622,0.005989081,0.0016202745],"genre_scores_gemma":[0.09703819,0.00026315957,0.8720935,0.00047561148,0.000111429945,0.019854518,0.003941018,0.0041391733,0.002083432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99281996,0.0031238957,0.0009344196,0.0013182977,0.0015757695,0.00022763484],"domain_scores_gemma":[0.986099,0.004150615,0.0007781503,0.00460347,0.0041045295,0.00026413065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016804615,0.0013561519,0.0012769258,0.0017684258,0.00094721554,0.0023637512,0.0026341446,0.0015459447,0.0092698485],"category_scores_gemma":[0.034896445,0.0011593364,0.0012034556,0.0016061983,0.001282821,0.0014780626,0.0035043242,0.0023038962,0.005351765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035746603,0.0009949798,0.005982526,0.0021438934,0.00064644223,0.00096577517,0.003341751,0.03896608,0.23251869,0.02045411,0.03599739,0.65441364],"study_design_scores_gemma":[0.0014820055,0.0024527337,0.027996505,0.00049963756,0.0007941274,0.0031557854,0.0013620789,0.3085636,0.3698058,0.05239032,0.23062807,0.0008692815],"about_ca_topic_score_codex":0.0008301914,"about_ca_topic_score_gemma":0.0011377333,"teacher_disagreement_score":0.016804615,"about_ca_system_score_codex":0.00078795385,"about_ca_system_score_gemma":0.0024269118,"threshold_uncertainty_score":0.08887237},"labels":[],"label_agreement":null},{"id":"W4412094177","doi":"10.3390/s25134191","title":"Non-Linear Gait Dynamics Are Affected by Commonly Occurring Outdoor Surfaces and Sex in Healthy Adults","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; McGill University","funders":"","keywords":"Gait; Dynamics (music); Physical medicine and rehabilitation; Medicine; Psychology","score_opus":0.012834326374620378,"score_gpt":0.3360465639729399,"score_spread":0.3232122375983195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412094177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99947435,0.00013578776,0.00004213197,0.000007575107,0.0000028529853,0.0000037988234,0.0001503851,0.0000022591926,0.00018081302],"genre_scores_gemma":[0.9995828,0.000045692923,0.00005364072,0.000008752033,0.000003340816,0.0000040680784,0.00016012651,0.0000010688127,0.00014051692],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990535,0.000011727398,0.000014997754,0.000031754425,0.000017960236,0.000018113325],"domain_scores_gemma":[0.99974567,0.00003367848,0.00013065428,0.000014990869,0.000029913495,0.00004508217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121581994,0.00035125588,0.00029310363,0.000367362,0.0002052923,0.000300947,0.000101922546,0.00031659866,0.0016964751],"category_scores_gemma":[0.000620955,0.00014906118,0.0001847164,0.0003017828,0.00015583905,0.00021477364,0.00024040848,0.00016365045,0.0002604679],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025364853,0.000045872708,0.99351716,0.00001874477,0.000039746043,0.00019458446,0.00022210926,0.00003319228,0.0017123284,0.000009956668,0.00006858958,0.0038841658],"study_design_scores_gemma":[0.0000016624371,0.00009215693,0.9995427,0.0000016658238,0.0000060161697,0.00015940324,0.000087621665,0.000036602753,0.000033933793,0.0000078067515,0.000029454533,0.0000011386809],"about_ca_topic_score_codex":0.0014142549,"about_ca_topic_score_gemma":0.0031659189,"teacher_disagreement_score":0.0016964751,"about_ca_system_score_codex":0.00006471132,"about_ca_system_score_gemma":0.000065544904,"threshold_uncertainty_score":0.0056752563},"labels":[],"label_agreement":null},{"id":"W4412193083","doi":"10.3390/s25144262","title":"Investigating Performance of an Embedded Machine Learning Solution for Classifying Postural Behaviors","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robustness (evolution); Perceptron; Computer science; Artificial intelligence; Machine learning; Artificial neural network; Noisy data; Classifier (UML); Multilayer perceptron; Pattern recognition (psychology); Data mining","score_opus":0.03900622864782005,"score_gpt":0.37870617821123526,"score_spread":0.3396999495634152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412193083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6892039,0.0006277238,0.30547768,0.00017025942,0.00022189731,0.00012987904,0.00014676704,0.0015647621,0.0024571542],"genre_scores_gemma":[0.9583802,0.0000926414,0.040150963,0.000029326076,0.000017117107,0.000053905947,0.00012379965,0.000021865342,0.0011300944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994248,0.00010788634,0.00005688237,0.00017966377,0.0001570604,0.00007376214],"domain_scores_gemma":[0.9985328,0.0007054636,0.00012627378,0.00014160454,0.0004490019,0.0000448821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011816634,0.0010078466,0.0005550227,0.00041217174,0.0001983587,0.00066740956,0.00054257404,0.0012500356,0.0016916824],"category_scores_gemma":[0.0046598027,0.00018905452,0.00030249197,0.00024258022,0.00019206188,0.0005264457,0.0004498428,0.00046016288,0.00062933884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026039435,0.0010545725,0.012902263,0.0010413832,0.000495022,0.0003815844,0.0002981835,0.36030307,0.15710962,0.0011199337,0.0008056792,0.46188483],"study_design_scores_gemma":[0.000025938329,0.0014272853,0.005798701,0.000037697562,0.00006853812,0.000115306146,0.000044011846,0.95611775,0.03564758,0.00029857695,0.0004013059,0.000017225659],"about_ca_topic_score_codex":0.0012912854,"about_ca_topic_score_gemma":0.00082204706,"teacher_disagreement_score":0.0016916824,"about_ca_system_score_codex":0.00021312822,"about_ca_system_score_gemma":0.0003591589,"threshold_uncertainty_score":0.0062493086},"labels":[],"label_agreement":null},{"id":"W4412195264","doi":"10.3390/s25144311","title":"Porous-Cladding Polydimethylsiloxane Optical Waveguide for Biomedical Pressure Sensing Applications","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Materials science; Cladding (metalworking); Polydimethylsiloxane; Fabrication; Waveguide; Optical power; Pressure sensor; Optical fiber; Porosity; Optoelectronics; Molding (decorative); Composite material; Casting; Optics; Laser; Mechanical engineering","score_opus":0.009450076532795795,"score_gpt":0.2562592686795214,"score_spread":0.24680919214672564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412195264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6751808,0.0054210736,0.31480336,0.00027515035,0.00025360018,0.000088552646,0.00043869737,0.000742856,0.0027958986],"genre_scores_gemma":[0.81473607,0.0021656516,0.18039155,0.00013614052,0.00007628318,0.00006545426,0.00024732045,0.000041422427,0.0021401434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999025,0.000008349076,0.0000062504446,0.000026377087,0.000042532214,0.000014005051],"domain_scores_gemma":[0.99981767,0.000047970392,0.000064933345,0.000017674134,0.000029331264,0.000022382272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020571711,0.00043432793,0.00017119128,0.00015631737,0.00009568754,0.00019101196,0.00039919672,0.00026451435,0.00043776113],"category_scores_gemma":[0.00016008693,0.0001764306,0.00023786991,0.00014578567,0.00033832065,0.00031282243,0.00018074938,0.00026841884,0.00024201676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018994095,0.000006796403,0.00009005252,0.00006177343,0.0000028345323,0.000029014871,0.0000072721214,0.0002004095,0.99666625,0.00025208353,0.000040589,0.0026238866],"study_design_scores_gemma":[0.000007737166,0.00016562382,0.0006906803,0.0000061309543,0.000011909708,0.0002033768,0.0000074056893,0.005540037,0.9901035,0.00010764754,0.0031477034,0.000008267832],"about_ca_topic_score_codex":0.00024741373,"about_ca_topic_score_gemma":0.00054827513,"teacher_disagreement_score":0.00043776113,"about_ca_system_score_codex":0.00018630711,"about_ca_system_score_gemma":0.0002546927,"threshold_uncertainty_score":0.0014644861},"labels":[],"label_agreement":null},{"id":"W4412195273","doi":"10.3390/s25144312","title":"Plantar Pressure Distribution in Charcot–Marie–Tooth Disease: A Systematic Review","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Hereditary Neurological Disorders","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Forefoot; Plantar pressure; Medicine; Physical medicine and rehabilitation; Tooth disease; Center of pressure (fluid mechanics); Foot Orthoses; Gait; Disease; Physical therapy; Surgery; Internal medicine; Pressure sensor; Complication","score_opus":0.03893270298410692,"score_gpt":0.3136863946594384,"score_spread":0.2747536916753315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412195273","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009791582,0.9983367,0.00006537417,0.00009078383,0.00005241708,0.00008092898,0.00024952734,0.000004985897,0.00014010143],"genre_scores_gemma":[0.013218545,0.9858849,0.00022984981,0.00017679353,0.000046926107,0.00018205798,0.00019161368,0.0000032426785,0.00006597987],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.996772,0.00095620076,0.0012293338,0.00032237737,0.000591158,0.00012893454],"domain_scores_gemma":[0.98718137,0.009228868,0.0022749559,0.00015487942,0.0010126738,0.00014728523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035642546,0.0014142152,0.007111614,0.009375263,0.0004805924,0.0021999478,0.001604931,0.0015228427,0.003953298],"category_scores_gemma":[0.017761562,0.000780749,0.005180531,0.010037001,0.0005888226,0.0015986718,0.0012268119,0.0006870839,0.00026910022],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015432676,0.000011096752,0.0006433441,0.963663,0.0063406522,0.00009791491,0.00010591459,0.00006977765,0.00011405307,0.00010168188,0.0010687129,0.027629718],"study_design_scores_gemma":[0.00025773942,0.00029525236,0.00791452,0.8856762,0.086295046,0.0009314384,0.00036536335,0.00010739711,0.00020506904,0.00027992428,0.017623598,0.000048488666],"about_ca_topic_score_codex":0.006770867,"about_ca_topic_score_gemma":0.016190229,"teacher_disagreement_score":0.009375263,"about_ca_system_score_codex":0.0021491374,"about_ca_system_score_gemma":0.0062160054,"threshold_uncertainty_score":0.01884979},"labels":[],"label_agreement":null},{"id":"W4412195314","doi":"10.3390/s25144288","title":"Phoneme-Aware Hierarchical Augmentation and Semantic-Aware SpecAugment for Low-Resource Cantonese Speech Recognition","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Speech recognition; Pronunciation; Word error rate; Context (archaeology); Masking (illustration); Artificial intelligence","score_opus":0.016940222352218908,"score_gpt":0.2642564729911856,"score_spread":0.2473162506389667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412195314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100218594,0.00053140736,0.8894449,0.00021447241,0.0001266335,0.000051525833,0.00026197615,0.0056714294,0.0034790987],"genre_scores_gemma":[0.82823396,0.00020664131,0.16427699,0.00019497967,0.00005892266,0.00010757085,0.0009586762,0.00031418,0.0056479964],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997284,0.00006871804,0.000009458193,0.00008030904,0.00007006136,0.000043103697],"domain_scores_gemma":[0.999739,0.0000963489,0.000020665277,0.000068458176,0.000055123586,0.00002038414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003634873,0.00087257376,0.00045277335,0.00024358957,0.0002697807,0.00038229465,0.0007378985,0.00036208867,0.0016383225],"category_scores_gemma":[0.000936108,0.00021703643,0.0004368263,0.00019629979,0.0004293967,0.0005819338,0.0009673855,0.0008105267,0.0008598714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056802697,0.00014677823,0.0016440293,0.000107489635,0.00006292038,0.00021913754,0.00025375313,0.2581974,0.18546501,0.0049353205,0.0047645685,0.54363555],"study_design_scores_gemma":[0.000009465203,0.00011053841,0.0006230129,0.0000069751177,0.000015900105,0.00006095564,0.000032455213,0.96149075,0.033313315,0.001851924,0.0024670053,0.00001783571],"about_ca_topic_score_codex":0.005847094,"about_ca_topic_score_gemma":0.0123421615,"teacher_disagreement_score":0.005847094,"about_ca_system_score_codex":0.00028738953,"about_ca_system_score_gemma":0.00072746543,"threshold_uncertainty_score":0.011626124},"labels":[],"label_agreement":null},{"id":"W4412369164","doi":"10.3390/s25134240","title":"Screen Printing Conductive Inks on Textiles: Impact of Plasma Treatment","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Textile; Printed electronics; Electronics; Screen printing; Flexibility (engineering); Materials science; Inkwell; Flexible electronics; Wearable technology; Electrically conductive; Polyester; Substrate (aquarium); Wearable computer; Nanotechnology; Personalization; Electronic component; Computer science; Mechanical engineering; Embedded system; Electrical engineering; Composite material; Engineering; World Wide Web","score_opus":0.01705725508975974,"score_gpt":0.27636977388582157,"score_spread":0.2593125187960618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412369164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801758,0.0035105103,0.009094241,0.00020825432,0.00016032063,0.00004673715,0.00012586893,0.00018176588,0.0064964737],"genre_scores_gemma":[0.99164176,0.0014574471,0.0044875327,0.0000826628,0.000015841422,0.000019734192,0.00006108208,0.00005864142,0.002175389],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964285,0.000046745994,0.000024401745,0.000071782575,0.00015951121,0.000054669224],"domain_scores_gemma":[0.9994061,0.0003221301,0.00008619765,0.00007128101,0.00009708233,0.000017184164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025850208,0.00031665087,0.00025199997,0.00023240347,0.00024906624,0.00072243734,0.00019613944,0.00047044083,0.0012223445],"category_scores_gemma":[0.0008750862,0.00018470723,0.00027709734,0.00025706575,0.00033404285,0.0005299703,0.00024416414,0.000490249,0.00035469222],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009751787,0.000024226705,0.00023719012,0.0001833454,0.000009639714,0.0002292303,0.00009303428,0.00032260132,0.9924401,0.00012187933,0.000097247146,0.006144032],"study_design_scores_gemma":[0.0000032666421,0.000115267714,0.001123166,0.0000075025623,0.000009924704,0.000115292,0.00005361556,0.00085307023,0.9968278,0.000043383934,0.0008419513,0.000005733005],"about_ca_topic_score_codex":0.00035625958,"about_ca_topic_score_gemma":0.00051978795,"teacher_disagreement_score":0.0012223445,"about_ca_system_score_codex":0.00015497778,"about_ca_system_score_gemma":0.000093636714,"threshold_uncertainty_score":0.004089117},"labels":[],"label_agreement":null},{"id":"W4412422713","doi":"10.3390/s25144402","title":"Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Inhalation and Respiratory Drug Delivery","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inhalation; Airflow; Computer science; Biomedical engineering; Volumetric flow rate; Medicine; Simulation; Anesthesia; Engineering; Mechanical engineering","score_opus":0.021962139113006585,"score_gpt":0.2975881723756705,"score_spread":0.2756260332626639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412422713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14758895,0.0025571205,0.83419824,0.0009907051,0.0007354036,0.0006863998,0.0009624068,0.0053786784,0.0069021727],"genre_scores_gemma":[0.6286988,0.0012566732,0.36173192,0.0010779463,0.0002526274,0.0006809325,0.00048616878,0.000097716256,0.005717212],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994598,0.0000557208,0.00004922415,0.00014048663,0.00026308655,0.000031721695],"domain_scores_gemma":[0.99959785,0.000102550744,0.000083631094,0.00004458998,0.0001448418,0.00002644155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006665597,0.00045211025,0.0005616808,0.0006452359,0.00021476483,0.0006685303,0.001010892,0.0008494241,0.0017936006],"category_scores_gemma":[0.0011740727,0.0003055117,0.00035989014,0.00039077183,0.0002585489,0.001028938,0.0006898972,0.00058239605,0.0007096636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003698425,0.00026283797,0.004292327,0.00070834137,0.00005857894,0.0003458903,0.00018175202,0.0048994953,0.73193115,0.0020748372,0.0036062985,0.25126863],"study_design_scores_gemma":[0.00014481704,0.0018014996,0.009881543,0.00010133073,0.00019352583,0.0014378692,0.00009932572,0.3057867,0.64752984,0.0012210214,0.031593073,0.00020950673],"about_ca_topic_score_codex":0.0004497339,"about_ca_topic_score_gemma":0.000524164,"teacher_disagreement_score":0.0017936006,"about_ca_system_score_codex":0.0002741649,"about_ca_system_score_gemma":0.00048067348,"threshold_uncertainty_score":0.006000161},"labels":[],"label_agreement":null},{"id":"W4412513884","doi":"10.3390/s25144490","title":"SmartBoot: Real-Time Monitoring of Patient Activity via Remote Edge Computing Technologies","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Aging","funders":"National Institutes of Health; Pharmaceutical Research and Manufacturers of America Foundation","keywords":"Real-time computing; Edge computing; Computer science; Enhanced Data Rates for GSM Evolution; Embedded system; Remote patient monitoring; Internet of Things; Telecommunications; Medicine","score_opus":0.018531800778463076,"score_gpt":0.3405693179862752,"score_spread":0.32203751720781215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412513884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8977768,0.003849863,0.07912735,0.0005576692,0.00029338867,0.00067290175,0.0043602763,0.0032796005,0.010082276],"genre_scores_gemma":[0.9605577,0.0010882524,0.033181563,0.00044468368,0.000115267554,0.000378037,0.0012115072,0.00008947414,0.0029335076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995883,0.00010693428,0.00002836426,0.00008410771,0.00016078843,0.00003155011],"domain_scores_gemma":[0.9994093,0.0002106082,0.00014652165,0.000049501003,0.00012171714,0.00006223501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003881662,0.0005935557,0.00053564244,0.00073555077,0.00013494321,0.00063167705,0.00046008214,0.00039496983,0.0022683935],"category_scores_gemma":[0.0015306065,0.00013628094,0.0002282454,0.0005567653,0.00011233432,0.00040829138,0.0005490933,0.00025673778,0.00074796274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003953436,0.001086818,0.18213513,0.001306096,0.00029163208,0.00037740718,0.0009464419,0.0023509571,0.076574184,0.0007393982,0.010645807,0.71959263],"study_design_scores_gemma":[0.0005804343,0.009248373,0.7892206,0.0007554276,0.00064389664,0.0038264792,0.001477406,0.08651405,0.06872522,0.0027493208,0.035943292,0.00031566079],"about_ca_topic_score_codex":0.0007606913,"about_ca_topic_score_gemma":0.0016343537,"teacher_disagreement_score":0.0022683935,"about_ca_system_score_codex":0.00012029833,"about_ca_system_score_gemma":0.00015762869,"threshold_uncertainty_score":0.0075885653},"labels":[],"label_agreement":null},{"id":"W4412602691","doi":"10.3390/s25154566","title":"Raw-Data Driven Functional Data Analysis with Multi-Adaptive Functional Neural Networks for Ergonomic Risk Classification Using Facial and Bio-Signal Time-Series Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Computer science; Artificial intelligence; Discriminative model; Raw data; Machine learning; Pattern recognition (psychology); Robustness (evolution); Artificial neural network; Data mining","score_opus":0.27220192594132897,"score_gpt":0.4476799492444777,"score_spread":0.1754780233031487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412602691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23658693,0.0009292268,0.75867224,0.00041332023,0.00015251704,0.00010838433,0.00054214627,0.0011800398,0.0014151663],"genre_scores_gemma":[0.9091696,0.00028109734,0.08826587,0.000094443494,0.0000419312,0.0001230193,0.00073915056,0.000041454463,0.0012433756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981016,0.00003923498,0.0000123319005,0.000068196336,0.000043165724,0.000027069462],"domain_scores_gemma":[0.999689,0.00012395944,0.000040786443,0.000037826772,0.00009135935,0.000017040824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000707729,0.00088716444,0.00040178772,0.00055537064,0.00021886283,0.00040376015,0.0005813631,0.000629445,0.0006384669],"category_scores_gemma":[0.0018354704,0.00021873871,0.0006636119,0.00052236323,0.00027532663,0.00061830995,0.0005634814,0.0010372496,0.00023474808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040762336,0.00036848342,0.011610082,0.00013988334,0.0001966772,0.00027449607,0.00016992634,0.50160635,0.029813228,0.0014959887,0.002225631,0.4516917],"study_design_scores_gemma":[0.0000022263769,0.000036486887,0.002244619,0.000006598286,0.000011665181,0.0000249159,0.00002010224,0.9944469,0.0022894414,0.0006780056,0.00023183336,0.000007316192],"about_ca_topic_score_codex":0.0061140563,"about_ca_topic_score_gemma":0.0071254317,"teacher_disagreement_score":0.0061140563,"about_ca_system_score_codex":0.00044742823,"about_ca_system_score_gemma":0.00050097896,"threshold_uncertainty_score":0.012156963},"labels":[],"label_agreement":null},{"id":"W4412692749","doi":"10.3390/s25154636","title":"Discrete Unilateral Constrained Extended Kalman Filter in an Embedded System","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Outotec (Canada)","funders":"","keywords":"Kalman filter; Extended Kalman filter; Control theory (sociology); Invariant extended Kalman filter; Computer science; Fast Kalman filter; Ensemble Kalman filter; Artificial intelligence","score_opus":0.006302544461428506,"score_gpt":0.23633970631921108,"score_spread":0.23003716185778258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412692749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005420243,0.00013890014,0.9925943,0.000040613206,0.000040421975,0.000013686287,0.000015144948,0.00022326405,0.0015133441],"genre_scores_gemma":[0.71645033,0.0006497794,0.27418128,0.00010621134,0.00006793656,0.00013960313,0.00011680218,0.00003828669,0.008249831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951494,0.00010711219,0.000039152386,0.00013865934,0.00016598578,0.00003411218],"domain_scores_gemma":[0.99968684,0.00011284222,0.000047072215,0.000041436233,0.00009938111,0.000012412425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005584049,0.00035952494,0.00045948723,0.00021212794,0.00031442806,0.00064704206,0.00046592604,0.0005941483,0.0012807619],"category_scores_gemma":[0.0012163132,0.00020051909,0.00034924544,0.00028347765,0.0004136955,0.00079295307,0.00070435397,0.00055383105,0.00028562412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015776357,0.000042357144,0.0010778285,0.00019870586,0.000062956446,0.00024669373,0.00017434581,0.7730753,0.015751304,0.0488344,0.0011915527,0.15918684],"study_design_scores_gemma":[0.000009372191,0.000034996632,0.00018816885,0.000008571404,0.000011012074,0.000023284387,0.000008153068,0.992359,0.0023702816,0.0027368865,0.0022421407,0.000008163033],"about_ca_topic_score_codex":0.007211201,"about_ca_topic_score_gemma":0.004376426,"teacher_disagreement_score":0.007211201,"about_ca_system_score_codex":0.00036892624,"about_ca_system_score_gemma":0.00075106195,"threshold_uncertainty_score":0.014338493},"labels":[],"label_agreement":null},{"id":"W4412785707","doi":"10.3390/s25144430","title":"Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Canadian Armed Forces","keywords":"Payload (computing); Computer science; CAN bus; Network packet; Real-time computing; Protocol (science); Term (time); Computer network; Embedded system","score_opus":0.013367341517721645,"score_gpt":0.23105109895009443,"score_spread":0.21768375743237278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412785707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7764601,0.00073833053,0.21725442,0.0005340153,0.00015925727,0.000066764806,0.00030662367,0.0018851908,0.0025951983],"genre_scores_gemma":[0.9883996,0.00010363563,0.010016907,0.00007521498,0.000014976434,0.000015555705,0.00027054508,0.000015427893,0.001088173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997298,0.0000390633,0.000018735413,0.000073816176,0.00006298052,0.00007569593],"domain_scores_gemma":[0.9991609,0.00038604872,0.00013931966,0.000067729845,0.00019795404,0.000047977574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007296511,0.0009481284,0.0005225766,0.0006670449,0.00029172696,0.0005794573,0.00087148196,0.00064162735,0.00046911486],"category_scores_gemma":[0.001962217,0.00028477877,0.000351961,0.000354641,0.00038921577,0.00085540174,0.0005529638,0.00097181456,0.0002113531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035868477,0.0002452434,0.015596441,0.00004436693,0.0000857884,0.00015824231,0.000052684925,0.8784344,0.006463282,0.0010133932,0.0013154793,0.09623201],"study_design_scores_gemma":[0.0000019739061,0.000027639635,0.00051567977,0.0000022577765,0.000004381654,0.000008383467,0.000006714145,0.9977471,0.0012767446,0.00035066257,0.000056017496,0.0000023786656],"about_ca_topic_score_codex":0.012894604,"about_ca_topic_score_gemma":0.014797401,"teacher_disagreement_score":0.012894604,"about_ca_system_score_codex":0.00081570365,"about_ca_system_score_gemma":0.0006309718,"threshold_uncertainty_score":0.025639117},"labels":[],"label_agreement":null},{"id":"W4412931375","doi":"10.3390/s25154737","title":"A Preprocessing Pipeline for Pupillometry Signal from Multimodal iMotion Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Manitoba","funders":"University of Manitoba","keywords":"Computer science; Pupillometry; Missing data; Artificial intelligence; Pipeline (software); Preprocessor; Data pre-processing; Data quality; Pattern recognition (psychology); Data mining; Computer vision; Pupil; Machine learning; Engineering; Metric (unit)","score_opus":0.03392988752176354,"score_gpt":0.3132383436291774,"score_spread":0.27930845610741384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412931375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019136019,0.00028637878,0.9443034,0.00028824198,0.00020532256,0.0007250965,0.0048609003,0.0270773,0.0031173704],"genre_scores_gemma":[0.102172144,0.0005436692,0.8726668,0.00030846195,0.00014073485,0.0018935984,0.012985253,0.002771474,0.0065178825],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935323,0.00005178695,0.000060150265,0.00019304559,0.00026674152,0.00007501558],"domain_scores_gemma":[0.99889576,0.00022190058,0.000075940814,0.00016432637,0.0005826198,0.000059410384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010181153,0.0012530109,0.00068885717,0.0019498031,0.0007006857,0.0012904763,0.0009150805,0.0007457358,0.011602736],"category_scores_gemma":[0.003527588,0.00051342696,0.00093101437,0.0012151529,0.00031821337,0.00091069535,0.0013774578,0.0010715888,0.0062658987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084106764,0.0002583427,0.008643231,0.0010108841,0.00019235593,0.0006973337,0.0008572629,0.00720474,0.30125365,0.0038223057,0.04253214,0.6326866],"study_design_scores_gemma":[0.00018725215,0.0009001795,0.07125211,0.00032104598,0.00027710333,0.0017323106,0.00089462334,0.27414286,0.4433003,0.011400046,0.19520722,0.00038492837],"about_ca_topic_score_codex":0.002556958,"about_ca_topic_score_gemma":0.0041293083,"teacher_disagreement_score":0.011602736,"about_ca_system_score_codex":0.00043000255,"about_ca_system_score_gemma":0.0012518723,"threshold_uncertainty_score":0.03881502},"labels":[],"label_agreement":null},{"id":"W4413159518","doi":"10.3390/s25165028","title":"Development and Implementation of an IoT-Enabled Smart Poultry Slaughtering System Using Dynamic Object Tracking and Recognition","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Agriculture - Saskatchewan","keywords":"Process (computing); Poultry farming; Feature (linguistics); Stunning; Artificial intelligence; Computer science; Tracking system; Object (grammar); Computer vision; Veterinary medicine; Operating system","score_opus":0.01229253449340335,"score_gpt":0.26205626155863704,"score_spread":0.2497637270652337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413159518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15379536,0.000318175,0.8163553,0.00028622168,0.00023284397,0.0006109901,0.00038996967,0.011106962,0.016904233],"genre_scores_gemma":[0.77799183,0.00030578763,0.20573534,0.00031012416,0.000033977893,0.00045425034,0.00096895045,0.00012567859,0.014074067],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979466,0.000010712504,0.000016263055,0.000058232465,0.00008781601,0.000032361026],"domain_scores_gemma":[0.99987614,0.000009438639,0.00001369209,0.000022713662,0.00006127182,0.000016702306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020800879,0.00027493105,0.0003136296,0.00032761178,0.00021710845,0.0004204257,0.0007783712,0.00042401653,0.0022428788],"category_scores_gemma":[0.00021044919,0.00019099156,0.00027964968,0.00017825584,0.00014250331,0.0005221952,0.0004721808,0.0002972871,0.001065459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061995664,0.0005508488,0.014976739,0.0005244372,0.00008930692,0.001161735,0.00048549662,0.03783147,0.48914284,0.0049761715,0.009400281,0.44024074],"study_design_scores_gemma":[0.00014753547,0.001127635,0.019584527,0.000109333014,0.00012109612,0.0010083392,0.00020276303,0.6854916,0.23456763,0.0012705715,0.056255013,0.000113957336],"about_ca_topic_score_codex":0.002538562,"about_ca_topic_score_gemma":0.002159249,"teacher_disagreement_score":0.002538562,"about_ca_system_score_codex":0.00023540073,"about_ca_system_score_gemma":0.0006420436,"threshold_uncertainty_score":0.0075032115},"labels":[],"label_agreement":null},{"id":"W4413165789","doi":"10.3390/s25165012","title":"Deep Learning-Based Denoising of Noisy Vibration Signals from Wavefront Sensors Using BiL-DCAE","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Wavefront; Vibration; SIGNAL (programming language); Acoustics; Noise reduction; Noise (video); Offset (computer science); Wavefront sensor; Computer science; Sensitivity (control systems); Remote sensing; Artificial intelligence; Geology; Engineering; Optics; Physics; Electronic engineering","score_opus":0.011517984011402823,"score_gpt":0.26003381555467703,"score_spread":0.2485158315432742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413165789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097841844,0.0005929579,0.89862514,0.00019441069,0.00008706466,0.000031047297,0.000098487224,0.0009119083,0.0016172304],"genre_scores_gemma":[0.7648015,0.0004133564,0.22918506,0.0002254583,0.000040548897,0.00005784861,0.00064799393,0.00010581813,0.0045224223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997702,0.00002968217,0.000013960503,0.000059344275,0.00009187618,0.000034958804],"domain_scores_gemma":[0.99964774,0.0001113948,0.00003646705,0.00003621685,0.00014977534,0.00001839434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069074053,0.00065899314,0.00055545475,0.00042932224,0.00019789625,0.00041091847,0.000679634,0.0006419485,0.0010225382],"category_scores_gemma":[0.001417813,0.00026621378,0.0004368488,0.00042397183,0.00035859112,0.00079770386,0.0007111846,0.0010040714,0.00038961743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038532788,0.00033199193,0.0055530067,0.00027900565,0.0001737866,0.00015638921,0.00015304264,0.40333122,0.13468781,0.0047598504,0.002072025,0.44811654],"study_design_scores_gemma":[0.0000029458527,0.00003278099,0.0005686918,0.000006473917,0.000008256074,0.000018182556,0.0000070523856,0.9881291,0.0103410045,0.00049079664,0.00038917924,0.0000056146096],"about_ca_topic_score_codex":0.003390945,"about_ca_topic_score_gemma":0.005911167,"teacher_disagreement_score":0.003390945,"about_ca_system_score_codex":0.00032403524,"about_ca_system_score_gemma":0.0004957545,"threshold_uncertainty_score":0.006742418},"labels":[],"label_agreement":null},{"id":"W4413165816","doi":"10.3390/s25165017","title":"Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Robustness (evolution); Algorithm; Computer science; Reuse; Recursive least squares filter; Regularization (linguistics); Adaptive filter; Artificial intelligence; Engineering","score_opus":0.05215261459383843,"score_gpt":0.29310360168300703,"score_spread":0.2409509870891686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413165816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001465158,0.0001504938,0.9977951,0.00003890217,0.000014048736,0.000011273673,0.000015663727,0.00017732536,0.000332044],"genre_scores_gemma":[0.16505913,0.0007125801,0.83103824,0.00008412439,0.00007924249,0.00020866489,0.0002835153,0.00019184906,0.002342645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994382,0.0001891775,0.000040579762,0.00010074535,0.00019802693,0.000033216398],"domain_scores_gemma":[0.9990343,0.0004923082,0.00011005904,0.00016378275,0.00018181092,0.000017778984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008519101,0.0009152828,0.0008260447,0.00048352795,0.00025127397,0.0006717107,0.00081804197,0.00086050574,0.0016420573],"category_scores_gemma":[0.0033431994,0.0003706983,0.0009029928,0.0007664597,0.00056131417,0.00071775954,0.0007413056,0.0013065588,0.00093892444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013637177,0.00006760879,0.00047690803,0.00032519476,0.00014849647,0.00009803642,0.00015350146,0.6882967,0.027224105,0.03966477,0.002323709,0.24108458],"study_design_scores_gemma":[0.000008653864,0.00002297751,0.000100451936,0.000011160539,0.000009228451,0.000031698077,0.000009100934,0.98604727,0.004450131,0.0069365636,0.0023601537,0.00001263542],"about_ca_topic_score_codex":0.0016723888,"about_ca_topic_score_gemma":0.0018649076,"teacher_disagreement_score":0.0016723888,"about_ca_system_score_codex":0.00037614472,"about_ca_system_score_gemma":0.00093633716,"threshold_uncertainty_score":0.0054932237},"labels":[],"label_agreement":null},{"id":"W4413247450","doi":"10.3390/s25164899","title":"Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Traceability; Middleware (distributed applications); Distributed computing; Systems engineering; Software engineering; Engineering","score_opus":0.028447689189906606,"score_gpt":0.3454398924981452,"score_spread":0.31699220330823863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413247450","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035078542,0.7809642,0.19820106,0.00524439,0.0007451794,0.00029861962,0.000655474,0.0007563767,0.009626823],"genre_scores_gemma":[0.045549214,0.7569682,0.18987775,0.0018628641,0.00039926294,0.00081384287,0.0014470002,0.00028196146,0.0027998367],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99836,0.00051630026,0.00030044556,0.00022649468,0.0005147447,0.00008212581],"domain_scores_gemma":[0.9942101,0.003661579,0.00034798915,0.0003769317,0.001324577,0.00007881697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003981517,0.0009812126,0.0010019954,0.0021583645,0.00027465753,0.0018425796,0.002041415,0.0009980173,0.0023478288],"category_scores_gemma":[0.013604516,0.00066038454,0.0015029724,0.002038462,0.00066769967,0.0025283464,0.0013078152,0.0016231061,0.0011189608],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008068874,0.00003190171,0.0009684879,0.02146973,0.00035823978,0.000082001854,0.00018561445,0.016462835,0.0024525959,0.04775879,0.010846085,0.89930296],"study_design_scores_gemma":[0.00006361126,0.00052024337,0.002321551,0.030385956,0.0013901856,0.0005894382,0.00032268654,0.034369387,0.0096205715,0.054962423,0.86531377,0.00014015265],"about_ca_topic_score_codex":0.0044248686,"about_ca_topic_score_gemma":0.0043495167,"teacher_disagreement_score":0.0044248686,"about_ca_system_score_codex":0.0016302787,"about_ca_system_score_gemma":0.0051967786,"threshold_uncertainty_score":0.021056533},"labels":[],"label_agreement":null},{"id":"W4413351421","doi":"10.3390/s25165168","title":"A Compact and Wideband Active Asymmetric Transmit Array Unit Cell for Millimeter-Wave Applications","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Extremely high frequency; Wideband; Bandwidth (computing); Unit (ring theory); Diode; Insertion loss; Optoelectronics; Electronic engineering; Optics; Physics; Computer science; Electrical engineering; Engineering; Telecommunications; Mathematics","score_opus":0.012729790503992849,"score_gpt":0.22117338451712112,"score_spread":0.20844359401312829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413351421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58652455,0.0022788048,0.38752165,0.00040733605,0.0005243063,0.0001305881,0.00063602783,0.0013163661,0.020660404],"genre_scores_gemma":[0.8604398,0.0005057292,0.13229212,0.0001393817,0.000098357355,0.00011047937,0.0003311105,0.000074298,0.0060087247],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998549,0.00001900871,0.000008250092,0.000028077382,0.00007212976,0.000017687675],"domain_scores_gemma":[0.9997812,0.00003065324,0.00006587887,0.000041754887,0.000060744198,0.000019719004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008634092,0.0002946988,0.00027487287,0.00022130136,0.00014992029,0.0005303389,0.000865863,0.00034305282,0.0011682443],"category_scores_gemma":[0.00027575562,0.00013560287,0.00014966419,0.0002777533,0.00013928913,0.000542446,0.00027749577,0.00029904873,0.0010509284],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012180117,0.000039768558,0.0004768303,0.0001174583,0.000012820202,0.00016074993,0.00004549281,0.0024362032,0.9431222,0.004112597,0.0011400393,0.04821407],"study_design_scores_gemma":[0.000025925285,0.00044220572,0.00079192745,0.000011312252,0.000029135297,0.0008789238,0.00005220103,0.041322764,0.92704546,0.0005009318,0.028865412,0.000033725802],"about_ca_topic_score_codex":0.00014504399,"about_ca_topic_score_gemma":0.0002574074,"teacher_disagreement_score":0.0011682443,"about_ca_system_score_codex":0.00017842572,"about_ca_system_score_gemma":0.00015231002,"threshold_uncertainty_score":0.003908217},"labels":[],"label_agreement":null},{"id":"W4413426527","doi":"10.3390/s25175234","title":"Cooperative Schemes for Joint Latency and Energy Consumption Minimization in UAV-MEC Networks","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Energy consumption; Joint (building); Latency (audio); Energy minimization; Minification; Computer science; Energy (signal processing); Embedded system; Real-time computing; Engineering; Telecommunications; Electrical engineering; Structural engineering; Mathematics; Chemistry","score_opus":0.00874728278447617,"score_gpt":0.21567990384734712,"score_spread":0.20693262106287094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413426527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041022006,0.00043128565,0.9544609,0.00032842226,0.000061964005,0.000042051397,0.000034130924,0.00023918031,0.0033800513],"genre_scores_gemma":[0.96789694,0.00017113001,0.029709963,0.000115796014,0.00002111974,0.000054350035,0.000029414869,0.00002414037,0.001977169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938524,0.00016766967,0.000025098061,0.00012705725,0.0001236031,0.00017133477],"domain_scores_gemma":[0.9986957,0.0007011425,0.00020183844,0.00008160417,0.00020500031,0.000114743234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011221167,0.0008818834,0.0009327236,0.00036272022,0.000648334,0.00085612293,0.0015671663,0.0008459605,0.0016409657],"category_scores_gemma":[0.002828328,0.0003309882,0.0004256729,0.00039478406,0.00096846896,0.0011646964,0.0017055033,0.0011182575,0.0002412098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000907655,0.000037198184,0.00034920924,0.0000398888,0.000017666433,0.00008149966,0.00006855254,0.97101486,0.0019059643,0.011068247,0.0007322445,0.014593933],"study_design_scores_gemma":[0.0000043607324,0.000013777836,0.000027823526,0.0000018547291,0.0000027168614,0.000007304093,0.0000058956552,0.99785775,0.00019968906,0.0017708445,0.000105519975,0.0000024302235],"about_ca_topic_score_codex":0.004709318,"about_ca_topic_score_gemma":0.0045750323,"teacher_disagreement_score":0.004709318,"about_ca_system_score_codex":0.0011414998,"about_ca_system_score_gemma":0.0012791643,"threshold_uncertainty_score":0.0093637705},"labels":[],"label_agreement":null},{"id":"W4413431817","doi":"10.3390/s25175226","title":"Multi-Agent DDPG-Based Multi-Device Charging Scheduling for IIoT Smart Grids","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China; British Columbia Innovation Council; Natural Science Foundation of Guangxi Zhuang Autonomous Region","keywords":"Scheduling (production processes); Computer science; Smart grid; Distributed computing; Embedded system; Materials science; Engineering; Electrical engineering","score_opus":0.014329671222642518,"score_gpt":0.2494214812099803,"score_spread":0.2350918099873378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413431817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059891485,0.0004053236,0.9328902,0.00036799928,0.00013968404,0.000087069086,0.00008998636,0.0013112637,0.0048170043],"genre_scores_gemma":[0.9532392,0.00008152047,0.044815592,0.00009879747,0.000016317457,0.00004630509,0.00008272538,0.000039906485,0.0015796194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997743,0.000049698054,0.000012329365,0.000049449754,0.0000537421,0.00006051054],"domain_scores_gemma":[0.99970335,0.000105155974,0.000044978187,0.000030251955,0.00006533798,0.000050989474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051700306,0.0005696114,0.0009115044,0.0002681976,0.00040938676,0.0006640567,0.0010675788,0.00052354956,0.0014080207],"category_scores_gemma":[0.00092302845,0.00031066884,0.0003263832,0.0003443668,0.00039498866,0.00065369933,0.0007550087,0.00074838905,0.00023094362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008488119,0.000052902134,0.00065178773,0.00003235036,0.000017945602,0.000048063823,0.000028303511,0.9550589,0.0009517132,0.00251528,0.0012369631,0.03932087],"study_design_scores_gemma":[0.0000064923233,0.000011355699,0.00005402791,0.0000010034438,0.000002144689,0.0000053965264,0.0000048041848,0.9988563,0.00020250857,0.0006422092,0.00021218082,0.0000015671692],"about_ca_topic_score_codex":0.008046052,"about_ca_topic_score_gemma":0.009077022,"teacher_disagreement_score":0.008046052,"about_ca_system_score_codex":0.00086265715,"about_ca_system_score_gemma":0.0014084815,"threshold_uncertainty_score":0.015998483},"labels":[],"label_agreement":null},{"id":"W4413772255","doi":"10.3390/s25175331","title":"GICEDCam: A Geospatial Internet of Things Framework for Complex Event Detection in Camera Streams","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geospatial analysis; STREAMS; Event (particle physics); The Internet; Computer science; Remote sensing; World Wide Web; Geography; Computer network; Physics","score_opus":0.013307528580364051,"score_gpt":0.2885387308388215,"score_spread":0.27523120225845743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413772255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011354483,0.0005283634,0.9338319,0.00023920868,0.00012051901,0.00021229996,0.0034449466,0.047825683,0.0024426046],"genre_scores_gemma":[0.24672554,0.0007198586,0.73464745,0.00046812443,0.00009461149,0.00052335236,0.011045072,0.0015065117,0.004269447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981946,0.000019720474,0.000011461054,0.000061377184,0.00006844018,0.000019560384],"domain_scores_gemma":[0.99976784,0.00007408084,0.00002979864,0.000050962233,0.000054669545,0.00002260465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034637793,0.0012450733,0.0006984879,0.0010810688,0.00035490262,0.00096088,0.0016708721,0.0007914659,0.0026135063],"category_scores_gemma":[0.0016957975,0.0004423901,0.00089008,0.0009021832,0.00030822813,0.0014610926,0.0012330466,0.0012145032,0.0008156986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008785227,0.00035426603,0.010725714,0.001115149,0.00077879574,0.0011114145,0.00055319665,0.17392246,0.036279004,0.024214378,0.09804714,0.65202],"study_design_scores_gemma":[0.000054575437,0.00006191099,0.0026881706,0.000052321717,0.00006166904,0.00026930383,0.00007736166,0.9509499,0.009592892,0.014730824,0.021402389,0.000058664475],"about_ca_topic_score_codex":0.012752966,"about_ca_topic_score_gemma":0.032865133,"teacher_disagreement_score":0.012752966,"about_ca_system_score_codex":0.0006387204,"about_ca_system_score_gemma":0.0007391336,"threshold_uncertainty_score":0.025357485},"labels":[],"label_agreement":null},{"id":"W4413783551","doi":"10.3390/s25175320","title":"Design of Monitoring Systems for Contaminant Detection in Water Networks Under Pipe Break-Induced Events","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Environmental science; Computer science; Engineering; Forensic engineering","score_opus":0.01532489893703513,"score_gpt":0.21706043652172552,"score_spread":0.2017355375846904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413783551","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2351318,0.0002554863,0.7601536,0.00026289912,0.000033398574,0.00025432865,0.00012336508,0.0008080398,0.0029772043],"genre_scores_gemma":[0.9731276,0.000089641515,0.026045397,0.000022258491,0.0000065797167,0.00011057781,0.000038840382,0.00001388005,0.0005451948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999569,0.00011171997,0.000020802656,0.00013229474,0.000096266675,0.00006989157],"domain_scores_gemma":[0.9994079,0.00016817488,0.00017749397,0.000039442595,0.00016532933,0.000041598858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004979638,0.000623026,0.00045670563,0.00036438365,0.00042550964,0.0006552887,0.00096930814,0.00050763995,0.00072417606],"category_scores_gemma":[0.0014890239,0.00031512583,0.00025953556,0.00020308491,0.00044761828,0.000783351,0.00070473936,0.00032445966,0.00017043913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028823628,0.000108511646,0.0045923134,0.00014217019,0.000042807293,0.00010374457,0.0001106928,0.89535666,0.053698074,0.0029256698,0.00043566272,0.0421955],"study_design_scores_gemma":[0.000013671361,0.0002751476,0.0017875162,0.000010028859,0.000022958562,0.00002481208,0.00004921345,0.98353124,0.012569529,0.0010558296,0.0006497759,0.000010250338],"about_ca_topic_score_codex":0.0021548786,"about_ca_topic_score_gemma":0.002231461,"teacher_disagreement_score":0.0021548786,"about_ca_system_score_codex":0.0008589587,"about_ca_system_score_gemma":0.00074724,"threshold_uncertainty_score":0.0062322617},"labels":[],"label_agreement":null},{"id":"W4413930157","doi":"10.3390/s25175417","title":"Self-Sensing with Hollow Cylindrical Transducers for Histotripsy-Enhanced Aspiration Mechanical Thrombectomy Applications","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Transducer; Biomedical engineering; Materials science; Acoustics; Engineering; Physics","score_opus":0.011242819814746273,"score_gpt":0.26977150367722486,"score_spread":0.25852868386247857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413930157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6103952,0.0062236963,0.3784978,0.000538671,0.00019314433,0.00028245844,0.00020552364,0.0009353372,0.002728159],"genre_scores_gemma":[0.8905024,0.0013420598,0.10560074,0.00025084196,0.000059706974,0.0001369587,0.00011135917,0.000043804284,0.0019522451],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997062,0.00004721259,0.000022993698,0.000050466853,0.00014716029,0.000025878779],"domain_scores_gemma":[0.99952674,0.00015220899,0.00015519714,0.00003358054,0.000106954816,0.000025200894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003491567,0.00028984123,0.00023932377,0.00021246112,0.00011699083,0.0003264722,0.0004300546,0.00059524365,0.0005467463],"category_scores_gemma":[0.0008761499,0.00021851086,0.00016973511,0.00017982008,0.0003234143,0.00050294684,0.00031738137,0.0002962182,0.00026873176],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020868361,0.0000049223295,0.000064398606,0.000032184154,0.0000012817761,0.00002476065,0.000022655375,0.00009986682,0.99634534,0.000077423945,0.00004126364,0.003265026],"study_design_scores_gemma":[0.000010755734,0.0003375885,0.0019408879,0.000007154049,0.000010690989,0.00038182334,0.0000329328,0.0060942303,0.98819286,0.000091410395,0.0028773039,0.000022406744],"about_ca_topic_score_codex":0.00031294202,"about_ca_topic_score_gemma":0.0005779314,"teacher_disagreement_score":0.00059524365,"about_ca_system_score_codex":0.00024049677,"about_ca_system_score_gemma":0.00020074981,"threshold_uncertainty_score":0.0018465519},"labels":[],"label_agreement":null},{"id":"W4413930258","doi":"10.3390/s25175391","title":"Time-Domain Analysis of Low- and High-Frequency Near-Infrared Spectroscopy Sensor Technologies for Characterization of Cerebral Pressure–Flow and Oxygen Delivery Physiology: A Prospective Observational Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pan Am Clinic; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Research Manitoba; Health Sciences Centre Foundation","keywords":"Oxygen delivery; Observational study; Frequency domain; Characterization (materials science); Oxygen; Time domain; Flow (mathematics); Materials science; Biomedical engineering; Analytical Chemistry (journal); Medicine; Nanotechnology; Chemistry; Computer science; Physics; Internal medicine; Mechanics; Environmental chemistry","score_opus":0.010981970866520283,"score_gpt":0.2813663880455968,"score_spread":0.2703844171790765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413930258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985648,0.00014565502,0.00095684774,0.00001048654,0.000006687108,0.000030024177,0.00016274906,0.0000037229834,0.00011919996],"genre_scores_gemma":[0.9982241,0.000107543194,0.0009196968,0.000030130199,0.000021281381,0.000074118194,0.00049131346,0.0000061816995,0.00012562824],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99869674,0.00040674736,0.00014846811,0.00032318663,0.00028603166,0.0001387701],"domain_scores_gemma":[0.9967103,0.00067999447,0.0009324761,0.00063894497,0.0005707798,0.00046743295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002344255,0.00051172473,0.0005613137,0.00074668194,0.0006732839,0.0006894919,0.00040909613,0.00070408185,0.000756235],"category_scores_gemma":[0.0032196841,0.00041286286,0.00089449284,0.00083532947,0.0004412729,0.0006183332,0.0005840036,0.0010227652,0.00030695746],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007839744,0.00048110593,0.9939249,0.000030904732,0.00023861144,0.00021862643,0.00023646616,0.000083059145,0.0016426297,0.00006194268,0.0000797107,0.0022179903],"study_design_scores_gemma":[0.00003748851,0.0012447779,0.9965193,0.000007776927,0.0001319593,0.00049631397,0.00032320924,0.00055326096,0.00034556646,0.000048742088,0.00027692097,0.000014641744],"about_ca_topic_score_codex":0.0019707931,"about_ca_topic_score_gemma":0.0017329042,"teacher_disagreement_score":0.002344255,"about_ca_system_score_codex":0.00026019587,"about_ca_system_score_gemma":0.00057850266,"threshold_uncertainty_score":0.0123977065},"labels":[],"label_agreement":null},{"id":"W4413930278","doi":"10.3390/s25175388","title":"Neurophysiology of Downhill Mountain Bike Athletes—Benchmark Assessments of Event-Related Potentials","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of British Columbia; Provincial Health Services Authority; Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs; International Olympic Committee","keywords":"Athletes; Benchmark (surveying); Event (particle physics); Neurophysiology; Computer science; Physical medicine and rehabilitation; Psychology; Cartography; Neuroscience; Geography; Medicine; Physical therapy; Physics","score_opus":0.026526893558538895,"score_gpt":0.3662431096035063,"score_spread":0.33971621604496743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413930278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990409,0.0001593311,0.000105408086,0.000010070505,0.0000025746695,0.000021654041,0.00017417017,0.0000032197768,0.0004826247],"genre_scores_gemma":[0.999263,0.000072832954,0.000111552814,0.000016308424,0.0000056579975,0.000022199858,0.0003041266,0.0000012693367,0.00020311549],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983156,0.00003321293,0.000020761261,0.000047352216,0.000039334347,0.000027728382],"domain_scores_gemma":[0.99961877,0.000046860918,0.00014299087,0.00001586011,0.00009696326,0.000078482364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028526055,0.000261272,0.0002419757,0.00048118943,0.00022126654,0.00034775402,0.00015975328,0.00030183315,0.0011885767],"category_scores_gemma":[0.00091907586,0.00007258797,0.000121373145,0.00028780464,0.00011988334,0.00019422389,0.0003033014,0.00021508432,0.00025445974],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087582425,0.00028147837,0.976553,0.00010711913,0.00010193236,0.00059987983,0.00039234431,0.0001775846,0.007780084,0.000026452875,0.00024268539,0.012861531],"study_design_scores_gemma":[0.000002790636,0.00021542536,0.99916196,0.000004258216,0.0000056156905,0.0002889602,0.000080562546,0.000044354747,0.00012985499,0.000008199828,0.000056573095,0.0000014719031],"about_ca_topic_score_codex":0.003068426,"about_ca_topic_score_gemma":0.0043885056,"teacher_disagreement_score":0.003068426,"about_ca_system_score_codex":0.00016504971,"about_ca_system_score_gemma":0.00012663122,"threshold_uncertainty_score":0.0061011314},"labels":[],"label_agreement":null},{"id":"W4413994278","doi":"10.3390/s25175492","title":"Design of a Low-Cost Flat E-Band Down-Converter with Variable Conversion Gain","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Focus Microwaves (Canada); Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Veterans Affairs","keywords":"Variable (mathematics); Computer science; Electrical engineering; Engineering; Mathematics; Mathematical analysis","score_opus":0.005982173951364447,"score_gpt":0.18139691829816426,"score_spread":0.17541474434679982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413994278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2327625,0.001296362,0.7343048,0.00036176172,0.00030447997,0.00044915598,0.00032137928,0.0027847337,0.027414892],"genre_scores_gemma":[0.79663813,0.00060809887,0.1873042,0.0003842821,0.00010506159,0.000218743,0.00036366688,0.00016506329,0.014212806],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997285,0.00001991667,0.000014670276,0.00007483528,0.00012418514,0.000037911403],"domain_scores_gemma":[0.99985063,0.000018802226,0.000022506816,0.000024741332,0.000064521715,0.000018849014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019047996,0.00037017252,0.00045298465,0.00046337876,0.00024426985,0.0009530136,0.0013793001,0.00043054766,0.0026251539],"category_scores_gemma":[0.00020537883,0.0003269771,0.00030573754,0.00030640166,0.0001727385,0.0006238579,0.00040187832,0.0006143086,0.0019211117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016917849,0.000100479185,0.0010742461,0.00016430307,0.000043718173,0.00029158808,0.000064888525,0.0017639903,0.9248495,0.0037458546,0.0010498457,0.0666824],"study_design_scores_gemma":[0.00011101429,0.00080879737,0.003395923,0.000035697212,0.00010207102,0.0018349994,0.000060974973,0.05049002,0.9013272,0.0007808899,0.040982258,0.000070155664],"about_ca_topic_score_codex":0.0002621341,"about_ca_topic_score_gemma":0.00035504068,"teacher_disagreement_score":0.0026251539,"about_ca_system_score_codex":0.00036747445,"about_ca_system_score_gemma":0.0002896763,"threshold_uncertainty_score":0.008782089},"labels":[],"label_agreement":null},{"id":"W4414064446","doi":"10.3390/s25175593","title":"Influence of Sex and Body Size on the Validity of the Microsoft Kinect for Frontal Plane Knee Kinematics During Landings","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intraclass correlation; Knee flexion; Kinematics; Coronal plane; Anterior cruciate ligament; Motion (physics); Range of motion","score_opus":0.007804933962771979,"score_gpt":0.25466032369073893,"score_spread":0.24685538972796695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414064446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98854965,0.0011943195,0.0063231396,0.00019387114,0.00016285249,0.00007282019,0.00051568553,0.000041159306,0.0029464194],"genre_scores_gemma":[0.9971596,0.00010223797,0.0017467086,0.00006898477,0.000020291605,0.00007394178,0.00024618016,0.00005681372,0.0005252727],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98752743,0.0060607335,0.0013230097,0.001974227,0.0026276824,0.00048700033],"domain_scores_gemma":[0.9346559,0.048464872,0.0070415335,0.004055159,0.0050328798,0.00074974314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016896883,0.00055505015,0.0006097874,0.0006335564,0.00035475258,0.0010514255,0.0005927713,0.0005554344,0.0021720498],"category_scores_gemma":[0.06720326,0.00032832593,0.0008096506,0.00043622605,0.00094872835,0.00072635047,0.0010332282,0.00044955857,0.0004926682],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028026015,0.000098286095,0.9572471,0.00016029966,0.00057239435,0.00012796509,0.0018733954,0.0006181482,0.00409026,0.00014687369,0.00043548498,0.031827193],"study_design_scores_gemma":[0.000025156514,0.00049940037,0.99425584,0.000066362074,0.00013568043,0.00022898092,0.0005296321,0.002130197,0.001137376,0.00016746229,0.00080159254,0.000022253178],"about_ca_topic_score_codex":0.002181643,"about_ca_topic_score_gemma":0.0037487491,"teacher_disagreement_score":0.016896883,"about_ca_system_score_codex":0.00017882409,"about_ca_system_score_gemma":0.0003412947,"threshold_uncertainty_score":0.08936036},"labels":[],"label_agreement":null},{"id":"W4414091644","doi":"10.3390/s25185647","title":"A Framework to Evaluate Feasibility, Safety, and Accuracy of Wireless Sensors in the Neonatal Intensive Care Unit: Oxygen Saturation Monitoring","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Neonatal Respiratory Health Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé","keywords":"Bluetooth; Wireless; Neonatal intensive care unit; Intensive care; Continuous monitoring; Remote patient monitoring; Grid; Wireless network; Event monitoring","score_opus":0.07966147780567215,"score_gpt":0.43958211246567885,"score_spread":0.3599206346600067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414091644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058073744,0.0022812916,0.9223915,0.0014324114,0.000102009864,0.00344658,0.0011642334,0.00092856813,0.010179787],"genre_scores_gemma":[0.23523347,0.000588249,0.7604299,0.00020678785,0.00004630256,0.0020529237,0.00087791897,0.00007527282,0.0004891605],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.95942175,0.019345865,0.0050430065,0.0039568483,0.0111711705,0.0010614331],"domain_scores_gemma":[0.94648385,0.026049914,0.0075455834,0.0033764848,0.0155660845,0.0009779854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03946181,0.0026425286,0.0014323082,0.0100045465,0.0018122256,0.006217596,0.003318877,0.0019760628,0.0013183508],"category_scores_gemma":[0.0761799,0.00067006116,0.0032383818,0.0030460532,0.0041110534,0.0036343874,0.00683168,0.0014943314,0.00042020372],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074255804,0.0017716406,0.15117092,0.0038030357,0.0013303705,0.00075017207,0.0059520532,0.13447386,0.01581456,0.13716161,0.00833221,0.53869694],"study_design_scores_gemma":[0.00037095853,0.0085019935,0.1488017,0.0069593047,0.0016476067,0.0022451791,0.008292612,0.60135484,0.025950562,0.13736945,0.05751062,0.0009952325],"about_ca_topic_score_codex":0.013969169,"about_ca_topic_score_gemma":0.010702089,"teacher_disagreement_score":0.03946181,"about_ca_system_score_codex":0.0047975634,"about_ca_system_score_gemma":0.008673341,"threshold_uncertainty_score":0.20869648},"labels":[],"label_agreement":null},{"id":"W4414156949","doi":"10.3390/s25175603","title":"DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and Test-Time Adaptation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"European Research Area Network on Cardiovascular Diseases; Bundesministerium für Bildung und Forschung","keywords":"Segmentation; Domain adaptation; Image segmentation; Consistency (knowledge bases); Bridging (networking); Pattern recognition (psychology); Scale-space segmentation; Adaptation (eye)","score_opus":0.011041446613985425,"score_gpt":0.3001524031973003,"score_spread":0.28911095658331487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414156949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06546248,0.00062554446,0.92337114,0.00029990118,0.00014564978,0.0001590946,0.0003910399,0.0076558795,0.001889152],"genre_scores_gemma":[0.57154477,0.00037398527,0.4200711,0.0004760227,0.00008337104,0.00025292058,0.0025053376,0.00065711746,0.004035349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962497,0.000093976196,0.000020409454,0.00012865989,0.00008355786,0.000048406462],"domain_scores_gemma":[0.99913377,0.00029202594,0.00007692528,0.00028603894,0.00014493038,0.00006632967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011145677,0.0010484917,0.0006914321,0.0006313427,0.00019708766,0.00067748764,0.0015578853,0.0011700467,0.0017698945],"category_scores_gemma":[0.0027953389,0.00039167624,0.0008670126,0.0005783585,0.00059128157,0.0009294408,0.0015019816,0.0015442093,0.0010355879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040760342,0.00035223685,0.0035894716,0.00016253006,0.00017829373,0.0002490682,0.0001368442,0.35342327,0.0723012,0.0029200697,0.0091984505,0.5570809],"study_design_scores_gemma":[0.00001231731,0.00009389905,0.00074478384,0.000008725889,0.000015353471,0.000120553595,0.000014318148,0.981191,0.014611434,0.0015634305,0.0016069888,0.000017220722],"about_ca_topic_score_codex":0.0033304556,"about_ca_topic_score_gemma":0.004282184,"teacher_disagreement_score":0.0033304556,"about_ca_system_score_codex":0.00053570623,"about_ca_system_score_gemma":0.00097331894,"threshold_uncertainty_score":0.0066221952},"labels":[],"label_agreement":null},{"id":"W4414205910","doi":"10.3390/s25185741","title":"Markerless Motion Capture Parameters Associated with Fall Risk or Frailty: A Scoping Review","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fall prevention; Motion capture; STRIDE; Gait; CINAHL; Poison control; Risk assessment; Limiting; Motion (physics)","score_opus":0.0778083966379761,"score_gpt":0.419180330793031,"score_spread":0.3413719341550549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414205910","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016955764,0.9992118,0.00006993865,0.000104841776,0.000057148554,0.000052542888,0.00011055624,0.000004199646,0.00021946302],"genre_scores_gemma":[0.0017958756,0.99751234,0.00022053179,0.00012814785,0.000033047654,0.00009705634,0.00011840238,0.0000028699608,0.000091773996],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99676394,0.0008365339,0.0012815837,0.0003002652,0.00068448606,0.00013307807],"domain_scores_gemma":[0.9793513,0.016172823,0.0023454996,0.00024877157,0.0017554093,0.00012619015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056691472,0.0017147715,0.0059171384,0.015194328,0.00073095213,0.003201602,0.0019265127,0.0023284415,0.00691715],"category_scores_gemma":[0.028999131,0.0010057358,0.005539282,0.014489397,0.0008211445,0.002304454,0.0016639798,0.0011669568,0.0008147338],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009952333,0.00002571351,0.00048275202,0.81804174,0.0022281106,0.00009638665,0.0002082307,0.00012544889,0.00014046648,0.00046007798,0.003427597,0.17466407],"study_design_scores_gemma":[0.000036957816,0.000076796314,0.0023003307,0.93726534,0.013572573,0.0003573982,0.00024078363,0.00007477113,0.00014609225,0.00045103772,0.045447744,0.000030211795],"about_ca_topic_score_codex":0.010560544,"about_ca_topic_score_gemma":0.01940287,"teacher_disagreement_score":0.015194328,"about_ca_system_score_codex":0.0027211744,"about_ca_system_score_gemma":0.009240239,"threshold_uncertainty_score":0.029981673},"labels":[],"label_agreement":null},{"id":"W4414206350","doi":"10.3390/s25185728","title":"Bridging the Methodological Gap Between Inertial Sensors and Optical Motion Capture: Deep Learning as the Path to Accurate Joint Kinematic Modelling Using Inertial Sensors","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Inertial measurement unit; Mean squared error; Kinematics; Sagittal plane; Motion analysis; Gait; Units of measurement; Joint (building); Motion capture","score_opus":0.12084864585489725,"score_gpt":0.3288816744395837,"score_spread":0.20803302858468647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414206350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02811007,0.0020998525,0.9655957,0.0020990146,0.00016179444,0.000051446783,0.00019649102,0.0002731189,0.0014125501],"genre_scores_gemma":[0.5666237,0.003598115,0.42565626,0.0011962696,0.0003038257,0.00037784508,0.00051785563,0.00018390462,0.0015422247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761707,0.0010522217,0.00021369051,0.0004534996,0.0005649626,0.00009849736],"domain_scores_gemma":[0.9929438,0.004191875,0.0006009183,0.0012828358,0.0008405529,0.00013997861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006619167,0.0009556149,0.0007517443,0.0006028889,0.000287786,0.0020781702,0.0014544768,0.0011280059,0.001064554],"category_scores_gemma":[0.020231964,0.0007513533,0.0005605502,0.0009664471,0.0014036823,0.0028169279,0.0022145978,0.002525906,0.00038940483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005216478,0.00030289145,0.02623703,0.0016412108,0.0007186736,0.00025152814,0.001062388,0.26550394,0.026930926,0.04421836,0.004291469,0.6283199],"study_design_scores_gemma":[0.000046192625,0.0002811423,0.008459258,0.00051981356,0.00009564373,0.00013245009,0.00024640354,0.9241528,0.01097984,0.04544994,0.009553749,0.00008277811],"about_ca_topic_score_codex":0.004010846,"about_ca_topic_score_gemma":0.0049659596,"teacher_disagreement_score":0.006619167,"about_ca_system_score_codex":0.0006969745,"about_ca_system_score_gemma":0.0019248256,"threshold_uncertainty_score":0.035005927},"labels":[],"label_agreement":null},{"id":"W4414258578","doi":"10.3390/s25185702","title":"Monitoring Visual Fatigue with Eye Tracking in a Pharmaceutical Packing Area","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute of Steel Construction","keywords":"Eye tracking; Workload; Visual inspection; Fixation (population genetics); Eye movement; Visual search; Task (project management); Principal component analysis","score_opus":0.039678842883189284,"score_gpt":0.4082983237292288,"score_spread":0.3686194808460395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414258578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99224067,0.00015295991,0.006929724,0.000036783516,0.00000710005,0.00003620212,0.00013982535,0.000039604485,0.00041721613],"genre_scores_gemma":[0.99061954,0.0001480366,0.008619953,0.000055680055,0.000012631059,0.00004391954,0.0001364377,0.0000072217863,0.00035667312],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99950147,0.00016914272,0.000028298844,0.00012372325,0.00012911351,0.000048196474],"domain_scores_gemma":[0.99911267,0.0003277826,0.00025837639,0.000046726906,0.00019223495,0.000062272156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054946844,0.00028336095,0.0003044902,0.00059677684,0.0002474732,0.00046572287,0.00022155473,0.0004954273,0.00069646124],"category_scores_gemma":[0.0017119844,0.0001641759,0.00021376357,0.00033473587,0.00018603339,0.00030185445,0.00042071455,0.00020552007,0.0001834033],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028417292,0.0010549141,0.5066583,0.0008550343,0.00021482423,0.0006290742,0.0046152463,0.0051537952,0.3215693,0.00018849605,0.0011310368,0.15508826],"study_design_scores_gemma":[0.00004009545,0.0030745475,0.96386015,0.00006375417,0.0000896966,0.000821863,0.0013924356,0.01105744,0.018420264,0.00018482204,0.00094320066,0.00005160873],"about_ca_topic_score_codex":0.0019667149,"about_ca_topic_score_gemma":0.0037157957,"teacher_disagreement_score":0.0019667149,"about_ca_system_score_codex":0.00018869703,"about_ca_system_score_gemma":0.00027717662,"threshold_uncertainty_score":0.0039105415},"labels":[],"label_agreement":null},{"id":"W4414359894","doi":"10.3390/s25185835","title":"Fluorescent Probes for Monitoring Toxic Elements from the Nuclear Industry: A Review","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Molecular Sensors and Ion Detection","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Safety Commission","keywords":"Deoxyribozyme; Fluorescence; Carbon quantum dots; Nuclear power; Environmental monitoring; Key (lock)","score_opus":0.036958605734702436,"score_gpt":0.3227142103566362,"score_spread":0.2857556046219338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359894","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022564406,0.9973099,0.00052668276,0.00019324709,0.0002021243,0.000009016625,0.000023286948,0.000017633402,0.0014923539],"genre_scores_gemma":[0.0008606412,0.99736553,0.00056177075,0.00015387229,0.000101295846,0.00001357226,0.000033469867,0.0000026372072,0.0009072415],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998305,0.00002003205,0.000015437978,0.000041125015,0.00007291227,0.000019841065],"domain_scores_gemma":[0.9997452,0.00012696937,0.000036936453,0.00000834496,0.00005928063,0.000023274035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050640904,0.0012297344,0.0010375604,0.0027668262,0.00032735482,0.0008428716,0.0008465633,0.0011090846,0.0029465088],"category_scores_gemma":[0.0005082776,0.00042035655,0.00045563676,0.0028906201,0.0004593935,0.0018435173,0.00064300594,0.0016751967,0.0024162417],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038420527,0.00012453203,0.00013619571,0.021626126,0.000071235416,0.00020670626,0.00010593558,0.0006583027,0.0134233,0.0066907685,0.025254898,0.93166363],"study_design_scores_gemma":[0.00000616077,0.0000947086,0.00036475778,0.0014489592,0.00006218195,0.00071145315,0.00004149236,0.0001089499,0.0020661636,0.0013024264,0.9937691,0.00002367408],"about_ca_topic_score_codex":0.0009103696,"about_ca_topic_score_gemma":0.0017622882,"teacher_disagreement_score":0.0029465088,"about_ca_system_score_codex":0.00053309347,"about_ca_system_score_gemma":0.0007773749,"threshold_uncertainty_score":0.009856999},"labels":[],"label_agreement":null},{"id":"W4414411733","doi":"10.3390/s25185923","title":"A Multi-Channel Multi-Scale Spatiotemporal Convolutional Cross-Attention Fusion Network for Bearing Fault Diagnosis","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Shandong University of Technology; Korea Advanced Institute of Science and Technology; Shandong University; University of Ottawa","keywords":"Feature extraction; Fault (geology); Bearing (navigation); Pattern recognition (psychology); Wavelet; Fusion; Vibration; Block (permutation group theory)","score_opus":0.01900580003846762,"score_gpt":0.3111998249366529,"score_spread":0.29219402489818525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414411733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0784804,0.0015181941,0.91360325,0.0003900414,0.00020367664,0.00006413696,0.00018415344,0.0021775395,0.0033785882],"genre_scores_gemma":[0.8957282,0.00052699057,0.097533256,0.00032072267,0.00009482181,0.000056924637,0.00050381094,0.00005415711,0.005181101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997118,0.000028778404,0.000014517371,0.00008678036,0.00009123205,0.00006672846],"domain_scores_gemma":[0.99973553,0.000056012188,0.000029605771,0.00003004015,0.00012639345,0.000022364367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063398166,0.00084392604,0.00058003253,0.00077503786,0.0003373818,0.0004572096,0.0010778342,0.000912652,0.0013643788],"category_scores_gemma":[0.000827635,0.00030225416,0.0007530072,0.0004827901,0.00026644816,0.0009959205,0.00078934035,0.0007130261,0.00035693456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046703042,0.00030044903,0.004478473,0.00013299371,0.00025602375,0.0003695168,0.00009627912,0.2692393,0.060053818,0.0037504807,0.0061216448,0.65473396],"study_design_scores_gemma":[0.0000049125106,0.000055068664,0.0008440895,0.000005253614,0.00003658791,0.000060809252,0.000007083169,0.9898655,0.007849589,0.0006532093,0.0006097856,0.000008039274],"about_ca_topic_score_codex":0.011514487,"about_ca_topic_score_gemma":0.012093018,"teacher_disagreement_score":0.011514487,"about_ca_system_score_codex":0.0009041206,"about_ca_system_score_gemma":0.00077859557,"threshold_uncertainty_score":0.022894919},"labels":[],"label_agreement":null},{"id":"W4414433854","doi":"10.3390/s25195943","title":"Design of a Clip-On Modular Tactile Sensing Attachment Based on Fiber Bragg Gratings: Theoretical Modeling and Experimental Validation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; National Natural Science Foundation of China","keywords":"Modular design; Fiber Bragg grating; Tactile sensor; Sensitivity (control systems); Metric (unit); Linearity; Robot; Parameterized complexity","score_opus":0.03556602765623846,"score_gpt":0.3164330446672031,"score_spread":0.28086701701096467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414433854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6806177,0.00053398625,0.31094524,0.0002959808,0.00010637449,0.0004398886,0.00036431442,0.0011755998,0.005521],"genre_scores_gemma":[0.8464032,0.00030688252,0.15151055,0.00007482528,0.000013627916,0.00022377314,0.00011712643,0.00004151797,0.0013085221],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967,0.00002513154,0.000009925125,0.00006338202,0.00018436363,0.00004716399],"domain_scores_gemma":[0.99962974,0.0000633904,0.00009670353,0.000050555474,0.00011044605,0.000049103386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006035428,0.0005535434,0.00038688094,0.00028711595,0.00024403726,0.0004196307,0.0011686528,0.00081496744,0.0006029894],"category_scores_gemma":[0.0007140978,0.00037416,0.00036406858,0.00024007887,0.0005136379,0.0005299341,0.00047673192,0.000377327,0.00031477568],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013049405,0.00024276071,0.0018679858,0.00036643853,0.00003051231,0.0003728692,0.00012508841,0.08296225,0.8931913,0.0029835529,0.00062064617,0.017106025],"study_design_scores_gemma":[0.000037877227,0.0012550525,0.0046128905,0.000028927594,0.00003541492,0.00024307275,0.0000653604,0.49681777,0.49192652,0.00057469046,0.0043419567,0.000060467402],"about_ca_topic_score_codex":0.0012080208,"about_ca_topic_score_gemma":0.001542924,"teacher_disagreement_score":0.0012080208,"about_ca_system_score_codex":0.00084577233,"about_ca_system_score_gemma":0.0007539213,"threshold_uncertainty_score":0.0061365366},"labels":[],"label_agreement":null},{"id":"W4414592870","doi":"10.3390/s25195995","title":"Agentic Search Engine for Real-Time Internet of Things Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Metadata; Interoperability; Software deployment; Internet of Things; Web of Things; Ontology; Data discovery; Process (computing); The Internet","score_opus":0.028672709002503852,"score_gpt":0.28405784128133904,"score_spread":0.2553851322788352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414592870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20744723,0.0051882504,0.6495583,0.0021238672,0.0005559493,0.0021706345,0.019200794,0.09286241,0.020892603],"genre_scores_gemma":[0.4857275,0.00084280083,0.4817342,0.00067415385,0.0000659387,0.0004528438,0.025050864,0.0008712787,0.0045804265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859864,0.00032329085,0.0002348304,0.00022126074,0.0005283591,0.00009366214],"domain_scores_gemma":[0.99781835,0.001107611,0.00018208855,0.0003373847,0.0004224921,0.00013198234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021287035,0.0008119603,0.00093699625,0.0031889991,0.0007373102,0.0016783376,0.0015764146,0.0012157729,0.0019514433],"category_scores_gemma":[0.0064456463,0.00026710157,0.0008608317,0.0021726773,0.00038033695,0.0029708743,0.0013633552,0.0006959368,0.0010993053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032083613,0.0013977088,0.020330349,0.0037451747,0.0010115448,0.0027926797,0.0019824891,0.15045567,0.062765636,0.052092344,0.19207364,0.50814444],"study_design_scores_gemma":[0.00019822064,0.00033625343,0.0028536764,0.00007219343,0.00014968916,0.00067901163,0.0006047499,0.9095974,0.023864847,0.015536069,0.046022877,0.00008504162],"about_ca_topic_score_codex":0.012814897,"about_ca_topic_score_gemma":0.023561811,"teacher_disagreement_score":0.012814897,"about_ca_system_score_codex":0.0010101233,"about_ca_system_score_gemma":0.0015360679,"threshold_uncertainty_score":0.025480628},"labels":[],"label_agreement":null},{"id":"W4414704615","doi":"10.3390/s25196019","title":"Automated Shoulder Girdle Rigidity Assessment in Parkinson’s Disease via an Integrated Model- and Data-Driven Approach","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of British Columbia","funders":"Vancouver Coastal Health Research Institute","keywords":"Interpretability; Rigidity (electromagnetism); Orthotics; Shoulder girdle; Wearable computer; Biomechanics; Muscle Rigidity","score_opus":0.026399633545800265,"score_gpt":0.2923838932883358,"score_spread":0.26598425974253553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414704615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123739935,0.0004592015,0.8720827,0.00016409223,0.000039915656,0.000080304766,0.00036895534,0.002172786,0.0008920736],"genre_scores_gemma":[0.9051844,0.00014299146,0.09252783,0.000079011974,0.000031544103,0.00010486551,0.0007200653,0.00008134427,0.0011280277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997166,0.00006771132,0.000016994747,0.0000973275,0.000069417336,0.000031973],"domain_scores_gemma":[0.9995353,0.00017103268,0.00008253313,0.000049279446,0.00012845134,0.00003335581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006043262,0.0007437302,0.0007275753,0.0007680656,0.00016490514,0.0006515364,0.00054474466,0.0005977639,0.0004976144],"category_scores_gemma":[0.0012985893,0.00033106015,0.0007366138,0.00036362073,0.0002132632,0.0004053861,0.0005932573,0.0005007655,0.00038993472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000573202,0.0004791534,0.016580133,0.00032298392,0.00032505134,0.0002553246,0.00017171625,0.43228865,0.07985653,0.0009676922,0.0019413608,0.46623826],"study_design_scores_gemma":[0.0000063335215,0.0000752121,0.0031853917,0.000009276536,0.000017261607,0.000049186303,0.000015356596,0.9914669,0.004307725,0.0005754368,0.0002792391,0.000012698206],"about_ca_topic_score_codex":0.003989907,"about_ca_topic_score_gemma":0.008119097,"teacher_disagreement_score":0.003989907,"about_ca_system_score_codex":0.0003255885,"about_ca_system_score_gemma":0.0006075432,"threshold_uncertainty_score":0.007933378},"labels":[],"label_agreement":null},{"id":"W4414704986","doi":"10.3390/s25196026","title":"ConvNet-Generated Adversarial Perturbations for Evaluating 3D Object Detection Robustness","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Object detection; Convolutional neural network; Adversarial system; Inference; Novelty detection; Pattern recognition (psychology); Deep learning; Detector","score_opus":0.02296424581799517,"score_gpt":0.31094145924258837,"score_spread":0.2879772134245932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414704986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14772066,0.0009567507,0.83392215,0.00042225915,0.00025689986,0.00030571426,0.001453707,0.00902308,0.0059387656],"genre_scores_gemma":[0.8576132,0.00028952642,0.13577348,0.00031645724,0.00004133799,0.00023046808,0.0025397541,0.0006179415,0.0025779058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990752,0.00015365539,0.000036673733,0.0002501238,0.00038998184,0.000094403964],"domain_scores_gemma":[0.99877375,0.00059903646,0.00014660048,0.00020692879,0.0002155426,0.000058198915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015944458,0.0017502701,0.00063534745,0.0008132069,0.00029542317,0.0006721241,0.0014246174,0.0010418667,0.0020368388],"category_scores_gemma":[0.006565263,0.00048520786,0.0007514692,0.00040077514,0.0011043056,0.0009410451,0.0014799436,0.0014142306,0.00065142964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013673256,0.000031784886,0.0012881809,0.000094067975,0.000071420516,0.000072350245,0.00002217775,0.9583189,0.007221958,0.0016806552,0.0017335071,0.02932823],"study_design_scores_gemma":[0.000004250325,0.00004092926,0.0003399539,0.000009278794,0.0000065561176,0.000035402678,0.000004943916,0.9926998,0.0056409976,0.00079816603,0.0004139978,0.000005789402],"about_ca_topic_score_codex":0.0068074903,"about_ca_topic_score_gemma":0.006033817,"teacher_disagreement_score":0.0068074903,"about_ca_system_score_codex":0.0014291308,"about_ca_system_score_gemma":0.00094538747,"threshold_uncertainty_score":0.013535678},"labels":[],"label_agreement":null},{"id":"W4414705105","doi":"10.3390/s25196035","title":"Fall Detection by Deep Learning-Based Bimodal Movement and Pose Sensing with Late Fusion","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Robustness (evolution); RGB color model; False positive paradox; Autoencoder; Deep neural networks; Artificial neural network; Abnormality; Deep learning","score_opus":0.005714525031718935,"score_gpt":0.2069438110926951,"score_spread":0.20122928606097618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414705105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18256143,0.0010426396,0.8063756,0.00038206083,0.0002245679,0.00013174274,0.00078679976,0.0050317594,0.0034634017],"genre_scores_gemma":[0.8931524,0.00025460726,0.102121085,0.0003495242,0.00008099349,0.0001123374,0.00088176527,0.00007273501,0.0029744925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995927,0.00004942409,0.000024329627,0.00013948529,0.000117908225,0.00007625567],"domain_scores_gemma":[0.9996973,0.00006175046,0.000047561458,0.00004265207,0.00011367911,0.000037077476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046782632,0.0012768637,0.000892258,0.0007764391,0.00021992234,0.00044645875,0.0009298801,0.00067053805,0.001459934],"category_scores_gemma":[0.0015899038,0.0002632963,0.00047134756,0.00056615105,0.00029449293,0.00076941977,0.0014094944,0.00075981324,0.00078248896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011551048,0.000830786,0.016213814,0.00021906756,0.00016072832,0.00027086883,0.00013928289,0.056068048,0.07000081,0.0009272486,0.0058439667,0.84817034],"study_design_scores_gemma":[0.000036201443,0.00037265406,0.013215388,0.00004007165,0.00006535846,0.000292985,0.000058938644,0.9609098,0.020482494,0.002835999,0.0016499892,0.000040108884],"about_ca_topic_score_codex":0.0030199885,"about_ca_topic_score_gemma":0.006156521,"teacher_disagreement_score":0.0030199885,"about_ca_system_score_codex":0.0003426944,"about_ca_system_score_gemma":0.0005605443,"threshold_uncertainty_score":0.0060048103},"labels":[],"label_agreement":null},{"id":"W4414749338","doi":"10.3390/s25196082","title":"Personalized Smart Home Automation Using Machine Learning: Predicting User Activities","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Home automation; Adaptability; Software deployment; Activity recognition; Automation; Boosting (machine learning); Enhanced Data Rates for GSM Evolution; Software; Field (mathematics)","score_opus":0.009135004001628253,"score_gpt":0.215718626525094,"score_spread":0.20658362252346574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414749338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044831675,0.0004494936,0.9441778,0.0002678234,0.00006604007,0.00011198527,0.0004143453,0.007127836,0.0025530339],"genre_scores_gemma":[0.75992733,0.00042389272,0.23498377,0.00014278752,0.00006946674,0.00017058199,0.0008772626,0.00015537295,0.0032495654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997414,0.000058232246,0.000010311375,0.000075199896,0.00007390959,0.00004092109],"domain_scores_gemma":[0.9997955,0.0000796914,0.000022155984,0.00004390568,0.000037727397,0.000021017373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039330762,0.0006446,0.000665643,0.00042650822,0.0002678544,0.00062726845,0.000602198,0.0005287638,0.0009776494],"category_scores_gemma":[0.00094163127,0.0002058307,0.0004011178,0.0005043799,0.00020497563,0.00068087707,0.00051726925,0.00083524385,0.00054091925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025946627,0.0005156669,0.010749385,0.000120259436,0.00011130053,0.00017781976,0.00015656541,0.34039944,0.010777901,0.005931128,0.010254083,0.620547],"study_design_scores_gemma":[0.0000046724417,0.000038618404,0.0013833881,0.000007431065,0.000008410837,0.000036968457,0.000012664075,0.99191695,0.002569097,0.0023368162,0.0016757842,0.0000091206875],"about_ca_topic_score_codex":0.005923705,"about_ca_topic_score_gemma":0.00816672,"teacher_disagreement_score":0.005923705,"about_ca_system_score_codex":0.00041693263,"about_ca_system_score_gemma":0.00055221224,"threshold_uncertainty_score":0.011778474},"labels":[],"label_agreement":null},{"id":"W4414776472","doi":"10.3390/s25196104","title":"Enhanced Detection and Segmentation of Sit Phases in Patients with Parkinson’s Disease Using a Single SmartWatch and Random Forest Algorithms","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Sherbrooke; Université du Québec à Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Random forest; Smartwatch; Segmentation; Pattern recognition (psychology); Two step","score_opus":0.009758326428495697,"score_gpt":0.247410147270256,"score_spread":0.23765182084176031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414776472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4747988,0.001131645,0.51854366,0.00014417496,0.00010751647,0.00019501254,0.00064401975,0.0033047318,0.0011304165],"genre_scores_gemma":[0.74468696,0.0003052217,0.25285715,0.00009099423,0.000053889835,0.00017585675,0.0008635005,0.00012028744,0.0008462193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955326,0.000106389285,0.000035850222,0.00018098929,0.000065590306,0.000057880374],"domain_scores_gemma":[0.99912053,0.0004968347,0.000104790124,0.000039400205,0.0001994919,0.000038954404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012031741,0.0007489834,0.00065832,0.0012287648,0.0002103491,0.00036036133,0.0003450446,0.0007273933,0.0012615077],"category_scores_gemma":[0.0018317953,0.00022823775,0.0007339218,0.00054750015,0.00017061441,0.00039764747,0.00027420907,0.00032420893,0.00076400576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002554805,0.00044306286,0.08047312,0.0003700233,0.00033413328,0.00043009606,0.00030402036,0.035138253,0.0895705,0.0003161387,0.0024758063,0.7875901],"study_design_scores_gemma":[0.0001772061,0.0013773444,0.1584394,0.00009674793,0.00032582745,0.0022928533,0.00016147729,0.79691976,0.036347657,0.0012686391,0.002480311,0.00011276264],"about_ca_topic_score_codex":0.003388503,"about_ca_topic_score_gemma":0.00737566,"teacher_disagreement_score":0.003388503,"about_ca_system_score_codex":0.00019470375,"about_ca_system_score_gemma":0.00034823024,"threshold_uncertainty_score":0.00673753},"labels":[],"label_agreement":null},{"id":"W4414776553","doi":"10.3390/s25196097","title":"Cardiac Monitoring with Textile Capacitive Electrodes in Driving Applications: Characterization of Signal Quality and RR Duration Accuracy","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; CTT Group (Canada); Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Mitacs; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Capacitive sensing; SIGNAL (programming language); Electrode; Electronic circuit; Textile; Alertness; Quality (philosophy)","score_opus":0.009872787109667432,"score_gpt":0.25112894455308343,"score_spread":0.241256157443416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414776553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9145164,0.0016834144,0.08227425,0.000121231365,0.00007342572,0.000082089275,0.00019338976,0.00015823712,0.0008975016],"genre_scores_gemma":[0.97381365,0.0005948038,0.024789399,0.00006805501,0.00005247506,0.000028942773,0.000119794815,0.000024830691,0.00050813763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927455,0.00020149967,0.00006783252,0.00015834952,0.00025801122,0.000039833692],"domain_scores_gemma":[0.9988092,0.000533062,0.00020694817,0.00013817032,0.00025489164,0.000057658966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007612022,0.00044938378,0.00029241195,0.00043314032,0.000121876714,0.00051178323,0.00032899706,0.0008598742,0.0005955767],"category_scores_gemma":[0.0036188764,0.00013843297,0.00019938739,0.00036656455,0.0002797089,0.00039622962,0.00027148315,0.00018907164,0.00017678255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012053611,0.00010545564,0.019034106,0.00040554363,0.00009005487,0.0002313693,0.00025529016,0.0017476198,0.8904613,0.00012883585,0.00015391043,0.08618114],"study_design_scores_gemma":[0.0001267355,0.005979837,0.27426234,0.00006987961,0.00034586136,0.005658764,0.00035301718,0.03394977,0.67576224,0.00065094593,0.0027511232,0.00008965363],"about_ca_topic_score_codex":0.00031139713,"about_ca_topic_score_gemma":0.0005956877,"teacher_disagreement_score":0.0008598742,"about_ca_system_score_codex":0.00009881633,"about_ca_system_score_gemma":0.00007384687,"threshold_uncertainty_score":0.004025638},"labels":[],"label_agreement":null},{"id":"W4414776574","doi":"10.3390/s25196099","title":"Multi-Objective Feature Selection for Intrusion Detection Systems: A Comparative Analysis of Bio-Inspired Optimization Algorithms","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Firat Üniversitesi","keywords":"Intrusion detection system; Feature selection; Particle swarm optimization; Ant colony optimization algorithms; Curse of dimensionality; Genetic algorithm; Selection (genetic algorithm); Sophistication; Feature (linguistics)","score_opus":0.01622443352428205,"score_gpt":0.2728301498501409,"score_spread":0.2566057163258589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414776574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4954207,0.02535126,0.46781024,0.0018353252,0.0002177941,0.0003124014,0.00032734638,0.001153279,0.0075716483],"genre_scores_gemma":[0.8439886,0.0031719503,0.15093169,0.0002227459,0.00007645362,0.00014745511,0.0003848119,0.00009867469,0.000977627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986551,0.00049933913,0.00013573417,0.00016375932,0.0004645521,0.00008157238],"domain_scores_gemma":[0.99629873,0.0027718293,0.00026245014,0.00013486142,0.00048129237,0.000050884588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043436913,0.0012349956,0.001220182,0.0030714544,0.00034402308,0.0012078069,0.0006365818,0.0007673783,0.00056638103],"category_scores_gemma":[0.0062811156,0.0002378166,0.001121733,0.0017467943,0.00033605006,0.00089702226,0.0005179864,0.00055709976,0.00011668056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049444527,0.00035066652,0.018051194,0.0005069932,0.0006944555,0.00012282922,0.000120590375,0.63105875,0.0037945756,0.002812541,0.0013870016,0.340606],"study_design_scores_gemma":[0.000030380845,0.0003564616,0.005389523,0.000055638993,0.000094496754,0.000090389236,0.000060666123,0.9894237,0.0022009553,0.0010235958,0.0012561133,0.00001807814],"about_ca_topic_score_codex":0.002591359,"about_ca_topic_score_gemma":0.0016459556,"teacher_disagreement_score":0.0043436913,"about_ca_system_score_codex":0.0007508147,"about_ca_system_score_gemma":0.0007071654,"threshold_uncertainty_score":0.022971869},"labels":[],"label_agreement":null},{"id":"W4414777313","doi":"10.3390/s25196118","title":"A Systematic Literature Review on the Implementation and Challenges of Zero Trust Architecture Across Domains","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Università degli Studi di Genova","keywords":"Cloud computing; Big data; Orchestration; Scalability; Systematic review; Trusted Computing; Supply chain; Edge computing; The Internet","score_opus":0.037281968988600034,"score_gpt":0.351109114771635,"score_spread":0.31382714578303494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414777313","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009530591,0.99439657,0.0007568458,0.0015433484,0.00021835961,0.00010148507,0.00037349053,0.000016861932,0.0016399634],"genre_scores_gemma":[0.00907433,0.9875711,0.001367156,0.0011746439,0.00011435284,0.00016398355,0.0003175483,0.00001374886,0.00020310265],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99095577,0.0026819196,0.0030342708,0.000866883,0.002148359,0.0003127942],"domain_scores_gemma":[0.92614424,0.05789972,0.007823469,0.001254689,0.0062601026,0.0006177436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009768232,0.0010996374,0.0026807424,0.014910761,0.0009651019,0.003706684,0.0018197275,0.002121523,0.006303256],"category_scores_gemma":[0.052630294,0.00086212665,0.003664262,0.013046148,0.0015797301,0.004629973,0.0022131717,0.001839558,0.0009925053],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001417057,0.000044751952,0.0017676294,0.6309678,0.0013881553,0.0003430487,0.0014657398,0.0005019378,0.0005150852,0.0073389662,0.01331788,0.34220737],"study_design_scores_gemma":[0.000039697275,0.00012643277,0.0032567491,0.80284077,0.005449118,0.0006846321,0.0014880128,0.00012767353,0.0003988502,0.0034531478,0.18208402,0.0000509312],"about_ca_topic_score_codex":0.0068959873,"about_ca_topic_score_gemma":0.01800503,"teacher_disagreement_score":0.014910761,"about_ca_system_score_codex":0.0030637605,"about_ca_system_score_gemma":0.023018042,"threshold_uncertainty_score":0.05165994},"labels":[],"label_agreement":null},{"id":"W4414860764","doi":"10.3390/s25196148","title":"Denoising and Simplification of 3D Scan Data of Damaged Aero-Engine Blades for Accurate and Efficient Rigid and Non-Rigid Registration","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Outlier; Noise (video); Noise reduction; Hausdorff distance; Process (computing); Domain (mathematical analysis); Thresholding; Point (geometry)","score_opus":0.03535592502082904,"score_gpt":0.27581820356138964,"score_spread":0.2404622785405606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414860764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073334076,0.00013858685,0.9249211,0.00008383646,0.00003595364,0.00006987011,0.0002636179,0.0005430678,0.0006099514],"genre_scores_gemma":[0.34379753,0.00042033702,0.65172875,0.00006826879,0.000037800975,0.00015732722,0.0021354782,0.0002372568,0.0014172292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992285,0.00008150161,0.000057639063,0.00013283199,0.00044558628,0.00005386701],"domain_scores_gemma":[0.99876034,0.0002745002,0.00017579879,0.00039996722,0.00035769024,0.000031703006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061323424,0.00062544085,0.00084094785,0.0013552358,0.0003047693,0.00079207926,0.00086351327,0.00063648325,0.00087712554],"category_scores_gemma":[0.0028020712,0.00044467745,0.00089963886,0.0013545397,0.0005908889,0.000889239,0.0010678676,0.0009929012,0.0006622286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024894418,0.00013224996,0.0070711556,0.00044270672,0.00008111972,0.0006857868,0.00082792627,0.21336883,0.45118254,0.0040338496,0.00234827,0.31957662],"study_design_scores_gemma":[0.000013217394,0.00020209001,0.015873069,0.000033578206,0.00003624622,0.0007549856,0.00034926133,0.85654163,0.11441935,0.0029820206,0.0087226285,0.00007185025],"about_ca_topic_score_codex":0.0020407303,"about_ca_topic_score_gemma":0.0041912836,"teacher_disagreement_score":0.0020407303,"about_ca_system_score_codex":0.00027874493,"about_ca_system_score_gemma":0.0008398371,"threshold_uncertainty_score":0.0040577054},"labels":[],"label_agreement":null},{"id":"W4414860792","doi":"10.3390/s25196154","title":"Resonant Ultrasound Spectroscopy Detection Using a Non-Contact Ultrasound Microphone","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microphone; Resonant ultrasound spectroscopy; Broadband; Ultrasound; Sapphire; Range (aeronautics); Piezoelectricity; Spectroscopy","score_opus":0.006777237654205572,"score_gpt":0.25395487477741807,"score_spread":0.2471776371232125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414860792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82339585,0.0014845369,0.16822846,0.0003323919,0.00023917718,0.00017130643,0.0002948779,0.0008238807,0.00502956],"genre_scores_gemma":[0.8330528,0.000698424,0.1601199,0.00014987169,0.000062062565,0.00019549993,0.00015142244,0.00006051663,0.0055094906],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919635,0.000085109685,0.00003934019,0.00023886863,0.00038473192,0.000055650144],"domain_scores_gemma":[0.9993561,0.00028244784,0.00010859705,0.00007704078,0.00012909582,0.000046685902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045910565,0.0005150371,0.00051282486,0.00023081087,0.00022556295,0.00038709235,0.0008073051,0.00074348925,0.0020321056],"category_scores_gemma":[0.0009954415,0.0003814637,0.0002206495,0.00016575327,0.00037207274,0.0005548571,0.00078267243,0.00032453856,0.0007761976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014757033,0.0000048442053,0.00014130044,0.000031219373,0.0000020994894,0.000029741504,0.000029656732,0.000046248933,0.9981097,0.00009579621,0.00003417638,0.0014603996],"study_design_scores_gemma":[0.000017050867,0.00024639347,0.0027092574,0.0000057483403,0.000012565844,0.00033060982,0.000052630276,0.0042011025,0.990359,0.0000777756,0.0019629865,0.000024938334],"about_ca_topic_score_codex":0.0005298162,"about_ca_topic_score_gemma":0.0010297936,"teacher_disagreement_score":0.0020321056,"about_ca_system_score_codex":0.00029881843,"about_ca_system_score_gemma":0.00026336685,"threshold_uncertainty_score":0.006798029},"labels":[],"label_agreement":null},{"id":"W4414910590","doi":"10.3390/s25196217","title":"Validation of Novel Stride Length Model-Based Approaches to Estimate Distance Covered Based on Acceleration and Pressure Data During Walking","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"STRIDE; Acceleration; Dynamic time warping; Gait; Distance measurement; Mean difference; Accelerometer; Estimation","score_opus":0.12041833883133966,"score_gpt":0.3743993812239781,"score_spread":0.2539810423926384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414910590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55396247,0.00089314533,0.44102895,0.00014800773,0.00016511533,0.00026783693,0.0008011446,0.00093043025,0.0018029251],"genre_scores_gemma":[0.87154496,0.0003220768,0.12590645,0.000086474836,0.00004162722,0.0003081493,0.0009112021,0.00007576432,0.00080333784],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861956,0.00043872072,0.00011046732,0.00042691242,0.0003390111,0.000065346314],"domain_scores_gemma":[0.99730635,0.001009865,0.00035767365,0.0002608698,0.0009500434,0.000115316885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022838656,0.00093981036,0.0006518324,0.0011016541,0.00021103492,0.00085357093,0.0010330741,0.0009434696,0.0008178588],"category_scores_gemma":[0.007047246,0.00034634324,0.0006445297,0.000550719,0.00022127245,0.0009790853,0.0010633959,0.00059918995,0.0005175168],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036921739,0.0014911508,0.17871165,0.0016088962,0.0017516913,0.0006159926,0.0016452229,0.16047913,0.120387055,0.0017852221,0.0031082698,0.52472353],"study_design_scores_gemma":[0.00018187884,0.001550112,0.08848094,0.00014294416,0.00024229144,0.00078947324,0.00041351773,0.88772154,0.016976425,0.0009164219,0.0024622234,0.00012226756],"about_ca_topic_score_codex":0.0031583854,"about_ca_topic_score_gemma":0.0054582236,"teacher_disagreement_score":0.0031583854,"about_ca_system_score_codex":0.0003038047,"about_ca_system_score_gemma":0.00052432716,"threshold_uncertainty_score":0.012078404},"labels":[],"label_agreement":null},{"id":"W4414918015","doi":"10.3390/s25196203","title":"Sensor Input Type and Location Influence Outdoor Running Terrain Classification via Deep Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Inertial measurement unit; Deep learning; Convolutional neural network; SIGNAL (programming language); Acceleration; Preprocessor; Terrain; Pattern recognition (psychology)","score_opus":0.03889294272321766,"score_gpt":0.2613999537842792,"score_spread":0.22250701106106155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414918015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8680391,0.000779791,0.12702747,0.00026038822,0.00010994345,0.00005710742,0.00042859412,0.00062998413,0.0026677893],"genre_scores_gemma":[0.98592454,0.0001452961,0.012251723,0.00005545798,0.00001973734,0.000026555921,0.0004065383,0.000028176875,0.0011419851],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969816,0.00007344057,0.000016622018,0.000106621075,0.000038589602,0.00006667825],"domain_scores_gemma":[0.99939656,0.00032099974,0.000066069544,0.00003507087,0.00014373669,0.000037538033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070110156,0.000832085,0.000364987,0.00044896477,0.00017701213,0.00058928377,0.00047156235,0.00048436498,0.001324077],"category_scores_gemma":[0.002114719,0.00024774845,0.00049253175,0.0003721478,0.0002026758,0.00053003064,0.00056314527,0.00058155484,0.00042201017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012011527,0.00085612293,0.0805661,0.0002805726,0.0003866953,0.0002135352,0.00029518615,0.27191263,0.03974093,0.00042956293,0.0029852688,0.60113233],"study_design_scores_gemma":[0.000013645465,0.00020571455,0.032215767,0.000041124356,0.00009561398,0.00004976664,0.00009915134,0.9597998,0.0063311267,0.0005977997,0.0005319971,0.000018438153],"about_ca_topic_score_codex":0.004537447,"about_ca_topic_score_gemma":0.006825891,"teacher_disagreement_score":0.004537447,"about_ca_system_score_codex":0.00027685228,"about_ca_system_score_gemma":0.00031129533,"threshold_uncertainty_score":0.009022057},"labels":[],"label_agreement":null},{"id":"W4415047222","doi":"10.3390/s25206280","title":"Assessing Obstructive Sleep Apnea Severity During Wakefulness via Tracheal Breathing Sound Analysis","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supine position; Polysomnography; Obstructive sleep apnea; Wakefulness; Sleep apnea; Gold standard (test); Apnea; Robustness (evolution)","score_opus":0.012631322907765107,"score_gpt":0.2980788721284366,"score_spread":0.2854475492206715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415047222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97553796,0.00050084834,0.022240777,0.000047002828,0.000038527025,0.000086074,0.0005839327,0.00017064999,0.000794181],"genre_scores_gemma":[0.9908772,0.00021350347,0.008067894,0.000017780209,0.000025987778,0.000034782388,0.0005040004,0.000007446703,0.0002513393],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99961996,0.00010913785,0.000044588243,0.000091993075,0.000103696584,0.0000306797],"domain_scores_gemma":[0.9995161,0.0001970995,0.000105764506,0.00003531283,0.000104765146,0.000040913783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006802198,0.0006746436,0.00036894155,0.0013846027,0.00016887789,0.0005504453,0.00023441928,0.000378234,0.0005044121],"category_scores_gemma":[0.0016773767,0.0001606429,0.0005536713,0.0004107207,0.000151692,0.00038732766,0.00032483047,0.00023469504,0.00026458144],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010588043,0.00025286234,0.7527524,0.0002495681,0.00038606618,0.00022782433,0.0003364717,0.0072131506,0.060696606,0.00013186668,0.0005922312,0.17610209],"study_design_scores_gemma":[0.000023452949,0.000826454,0.9070909,0.000050151284,0.00020350049,0.00067537924,0.0003182934,0.080480434,0.009533157,0.00024764956,0.00049437204,0.000056144392],"about_ca_topic_score_codex":0.002753369,"about_ca_topic_score_gemma":0.005115532,"teacher_disagreement_score":0.002753369,"about_ca_system_score_codex":0.00010671086,"about_ca_system_score_gemma":0.0002136882,"threshold_uncertainty_score":0.0054746866},"labels":[],"label_agreement":null},{"id":"W4415209400","doi":"10.3390/s25206318","title":"Augmenting a ResNet + BiLSTM Deep Learning Model with Clinical Mobility Data Helps Outperform a Heuristic Frequency-Based Model for Walking Bout Segmentation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Frailty Network; Arthritis Society","keywords":"Generalizability theory; Deep learning; Gait; Heuristic; Segmentation; Recall; Wearable computer; Data modeling","score_opus":0.040036417845009394,"score_gpt":0.31422683025321335,"score_spread":0.27419041240820397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415209400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6120692,0.0024796093,0.36656162,0.0017031397,0.0010232126,0.00034030943,0.0032497444,0.0057709026,0.0068022306],"genre_scores_gemma":[0.95000577,0.00039956393,0.042429738,0.000409687,0.000095799725,0.00014679131,0.002973552,0.000095821655,0.0034432807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996828,0.00006196489,0.000024276584,0.0001266567,0.000043412616,0.00006093147],"domain_scores_gemma":[0.99955076,0.0001742574,0.000032485117,0.0000509572,0.00015356603,0.000037938804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014576252,0.0014975579,0.0008512658,0.00085817504,0.00023847395,0.00079919735,0.0010810656,0.0009493415,0.0015058697],"category_scores_gemma":[0.0029243606,0.00031197985,0.00079808396,0.00052024523,0.00025347856,0.00083279854,0.00063030294,0.0012672577,0.00123847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009359364,0.0012557546,0.03823413,0.0002765372,0.00041212907,0.00038988516,0.0001579331,0.40184128,0.014100635,0.0009476952,0.011643641,0.5298044],"study_design_scores_gemma":[0.00001410404,0.00016983323,0.0035081212,0.000038226124,0.00004357483,0.00005629905,0.000023672323,0.99295545,0.0017894403,0.0005585317,0.0008246157,0.000018083783],"about_ca_topic_score_codex":0.012515921,"about_ca_topic_score_gemma":0.020024475,"teacher_disagreement_score":0.012515921,"about_ca_system_score_codex":0.00055508484,"about_ca_system_score_gemma":0.0010205572,"threshold_uncertainty_score":0.02488619},"labels":[],"label_agreement":null},{"id":"W4415209724","doi":"10.3390/s25206306","title":"Optimizing Detection Reliability in Safety-Critical Computer Vision: Transfer Learning and Hyperparameter Tuning with Multi-Task Learning","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"Trent University","keywords":"Hyperparameter optimization; Hyperparameter; Reliability (semiconductor); Generalizability theory; Transfer of learning; Focus (optics); Grid; Task (project management); SPARK (programming language)","score_opus":0.009235358507921223,"score_gpt":0.2703607519428755,"score_spread":0.2611253934349543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415209724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08175064,0.0003102145,0.91491616,0.0005200484,0.00002842024,0.00011176799,0.00003544364,0.000954031,0.0013733271],"genre_scores_gemma":[0.8794778,0.0000883324,0.11887014,0.00025649345,0.00003040762,0.00017741622,0.000073911906,0.00016286156,0.0008626418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978242,0.0011405353,0.00011294519,0.00044603625,0.00028589187,0.00019040836],"domain_scores_gemma":[0.9925956,0.004898993,0.00068849133,0.00094495283,0.0006740466,0.00019790577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006424367,0.001796221,0.001112441,0.0011406825,0.0005828861,0.0015745844,0.0026679682,0.0022877303,0.0010816545],"category_scores_gemma":[0.027525555,0.00086702383,0.0009041297,0.0006651148,0.002079666,0.0029621613,0.0026114823,0.0034557858,0.0003595959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011399888,0.000120643715,0.0011325472,0.00005147455,0.000071017945,0.00003827227,0.000073077725,0.9590026,0.002620071,0.0025489759,0.00035183664,0.033875413],"study_design_scores_gemma":[0.0000080861755,0.00004556934,0.000110662084,0.000005624554,0.000005561858,0.0000081132885,0.000010624427,0.9947331,0.0011442974,0.0038493716,0.000073285824,0.000005625305],"about_ca_topic_score_codex":0.0045960797,"about_ca_topic_score_gemma":0.0034444095,"teacher_disagreement_score":0.006424367,"about_ca_system_score_codex":0.0020063252,"about_ca_system_score_gemma":0.0016053743,"threshold_uncertainty_score":0.03397572},"labels":[],"label_agreement":null},{"id":"W4415210561","doi":"10.3390/s25206312","title":"VR Human-Centric Winter Lane Detection: Performance and Driving Experience Evaluation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación; Canada Research Chairs","keywords":"Driving simulator; Intuition; Snow; Perception; Situation awareness; Poison control; Virtual reality; Detector","score_opus":0.008012825956749134,"score_gpt":0.23942946377183436,"score_spread":0.23141663781508523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415210561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949196,0.000049694594,0.0038254196,0.000015071416,0.00001011545,0.00014472623,0.00017185601,0.00012094682,0.0007425425],"genre_scores_gemma":[0.99211323,0.000093104536,0.0061113886,0.000019043464,0.000011769141,0.00019955449,0.00047057687,0.000030226069,0.0009511754],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99917716,0.00035107558,0.000064145774,0.00011297836,0.00018839273,0.00010613435],"domain_scores_gemma":[0.9978509,0.0009705573,0.00018844254,0.00017639561,0.00045718625,0.0003566448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014089717,0.0008381822,0.0004286093,0.00045835853,0.00016240994,0.0006838095,0.00063937483,0.00057084346,0.002655781],"category_scores_gemma":[0.0045378804,0.00019390174,0.0005019545,0.00018947896,0.00032107637,0.00051165523,0.00075110706,0.00027627376,0.00046117968],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.026880391,0.042674042,0.114066154,0.0042159935,0.0014035329,0.0013975672,0.020618713,0.08598141,0.26145267,0.0013842758,0.0059251203,0.43400013],"study_design_scores_gemma":[0.0031729368,0.17658347,0.45831585,0.00038855692,0.0014100273,0.0029554428,0.009739846,0.22427945,0.1053281,0.0013268252,0.015847793,0.0006516693],"about_ca_topic_score_codex":0.0012960822,"about_ca_topic_score_gemma":0.0013536382,"teacher_disagreement_score":0.002655781,"about_ca_system_score_codex":0.00024026299,"about_ca_system_score_gemma":0.0003065764,"threshold_uncertainty_score":0.00888443},"labels":[],"label_agreement":null},{"id":"W4415238977","doi":"10.3390/s25206377","title":"Early Motor Cortex Connectivity and Neuronal Reactivity in Intracerebral Hemorrhage: A Continuous-Wave Functional Near-Infrared Spectroscopy Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"University of Alberta","keywords":"Motor cortex; Resting state fMRI; Premotor cortex; Functional connectivity; Cortex (anatomy); Coherence (philosophical gambling strategy); Primary motor cortex; Reactivity (psychology)","score_opus":0.0116329122219186,"score_gpt":0.2756963847810067,"score_spread":0.26406347255908813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415238977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9996319,0.00007396975,0.00014024213,0.000009067637,0.0000011291685,0.000004496616,0.000025495601,0.0000013729948,0.00011242999],"genre_scores_gemma":[0.9997161,0.00004213618,0.00010591862,0.0000074879367,0.000007576267,0.0000041169105,0.000056974157,9.862537e-7,0.000058590012],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989593,0.000025373398,0.000009467577,0.000033419343,0.000018621662,0.00001720994],"domain_scores_gemma":[0.9996582,0.000086475826,0.00011253937,0.00005063238,0.000030092937,0.0000620291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003289635,0.00026174425,0.00025082924,0.0003811004,0.00017951685,0.00025652858,0.00018893972,0.00030486024,0.0007906751],"category_scores_gemma":[0.0008223781,0.00013381141,0.00021283538,0.00029465696,0.00033057632,0.0002363721,0.0002212697,0.00033014297,0.000119421034],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0068359356,0.00055086415,0.9297201,0.00009639717,0.0005069251,0.0027405822,0.0005603059,0.0004043236,0.045100346,0.00015713769,0.00014786544,0.013179199],"study_design_scores_gemma":[0.00001759965,0.00036080767,0.9980388,0.0000018815172,0.000038117112,0.0006031418,0.000059650647,0.00037125335,0.00041732914,0.000045910845,0.000042131727,0.0000033836477],"about_ca_topic_score_codex":0.0012723809,"about_ca_topic_score_gemma":0.0012948595,"teacher_disagreement_score":0.0012723809,"about_ca_system_score_codex":0.00015702008,"about_ca_system_score_gemma":0.00016610672,"threshold_uncertainty_score":0.002645135},"labels":[],"label_agreement":null},{"id":"W4415281664","doi":"10.3390/s25206425","title":"Enhanced Near-Surface Flaw Detection in Additively Manufactured Metal Ti-5Al-5V-5Mo-3Cr Using the Total Focusing Method","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Royal Military College of Canada; École de Technologie Supérieure; Queen's University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasonic sensor; Fabrication; Surface roughness; Porosity; Fusion; Surface finish; Selective laser melting; Rapid prototyping","score_opus":0.010469327310474372,"score_gpt":0.2687755523109178,"score_spread":0.2583062250004434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415281664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97325224,0.00074327324,0.025078954,0.000026375334,0.00001814189,0.000023407285,0.00005041697,0.00025934484,0.0005478627],"genre_scores_gemma":[0.97821265,0.00016149961,0.02117092,0.000015078885,0.0000037739335,0.00001085994,0.000034475644,0.00001260828,0.0003781449],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955004,0.0000384014,0.000015485664,0.00008834837,0.0002662072,0.000041549425],"domain_scores_gemma":[0.99954444,0.000113209426,0.0001756925,0.000037387657,0.000113638314,0.000015630985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004452339,0.00036255672,0.0002536826,0.00058357016,0.00009222683,0.00029428495,0.0005024722,0.0004841286,0.00042628954],"category_scores_gemma":[0.00067074964,0.0002516835,0.0002599199,0.0002424921,0.00034237275,0.00034280354,0.00031104404,0.0002008285,0.00012377663],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070689995,0.000008663451,0.00079448806,0.000065069034,0.000004446199,0.000026579906,0.00005053195,0.00024644376,0.9900738,0.000058333397,0.000027323866,0.008573652],"study_design_scores_gemma":[0.000008668248,0.00051075546,0.009883314,0.000007770411,0.000020714695,0.0002786748,0.000072440875,0.0076711765,0.9809378,0.000034112978,0.00055570767,0.000018890178],"about_ca_topic_score_codex":0.0009786861,"about_ca_topic_score_gemma":0.0023948571,"teacher_disagreement_score":0.0009786861,"about_ca_system_score_codex":0.00032315784,"about_ca_system_score_gemma":0.00017738483,"threshold_uncertainty_score":0.002354622},"labels":[],"label_agreement":null},{"id":"W4415350212","doi":"10.3390/s25206454","title":"Experimental Investigation of 3D-Printed TPU Triboelectric Composites for Biomechanical Energy Conversion in Knee Implants","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health","keywords":"Triboelectric effect; Piezoresistive effect; Power density; Composite number; Thermoplastic polyurethane; Durability; Mechanical energy; Energy harvesting","score_opus":0.010384977566864548,"score_gpt":0.23002092369955668,"score_spread":0.21963594613269213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415350212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916243,0.0005497398,0.005464356,0.000042732318,0.00007486613,0.000059128615,0.00021874906,0.0000913195,0.001874785],"genre_scores_gemma":[0.98819065,0.00043861812,0.009621651,0.00003148732,0.000014025816,0.00011308405,0.00010124615,0.000022120636,0.0014672726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962306,0.00003169531,0.000030848612,0.000074003816,0.0001932672,0.000047088517],"domain_scores_gemma":[0.9995863,0.00010525723,0.00011153549,0.000058823527,0.00010325494,0.000034824243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043257125,0.00035444513,0.00024277478,0.00043931787,0.00020114507,0.0003275237,0.00032008122,0.0005777329,0.0014691373],"category_scores_gemma":[0.00047524035,0.00031229886,0.00030595183,0.0004144923,0.0003129155,0.00029705846,0.00022619744,0.00036781968,0.00035117957],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004592237,0.00005321071,0.00016762147,0.000107660235,0.0000034913264,0.00012693014,0.000072947565,0.0004426602,0.9967824,0.00007445716,0.000050322902,0.0020724274],"study_design_scores_gemma":[0.000008825387,0.00040242364,0.0026424644,0.0000112113485,0.00001243184,0.0001360209,0.00006253803,0.0018069628,0.9937961,0.000042257234,0.001064245,0.000014566418],"about_ca_topic_score_codex":0.00016981993,"about_ca_topic_score_gemma":0.00052927434,"teacher_disagreement_score":0.0014691373,"about_ca_system_score_codex":0.0001441412,"about_ca_system_score_gemma":0.00011699418,"threshold_uncertainty_score":0.0049147606},"labels":[],"label_agreement":null},{"id":"W4415458164","doi":"10.3390/s25216506","title":"A2G-SRNet: An Adaptive Attention-Guided Transformer and Super-Resolution Network for Enhanced Aircraft Detection in Satellite Imagery","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Northwestern Polytechnical University; National Natural Science Foundation of China; Northwestern University","keywords":"Upsampling; Clutter; Satellite imagery; Pipeline (software); Cluster analysis; Deep learning; Key (lock); Scale (ratio); Feature extraction","score_opus":0.009704940242661407,"score_gpt":0.25182750702896356,"score_spread":0.24212256678630215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415458164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07127264,0.0014251225,0.9103869,0.00031597842,0.00019855094,0.00016743648,0.00073062914,0.009863277,0.005639517],"genre_scores_gemma":[0.5721338,0.0006200146,0.4162836,0.0005172493,0.000089888395,0.00015593908,0.002290789,0.0003581238,0.0075505227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999818,0.000023008994,0.0000054127995,0.00006385581,0.000056086996,0.000033648652],"domain_scores_gemma":[0.99976844,0.000071353184,0.000023873508,0.00004438129,0.00006965683,0.000022417718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042580097,0.0007929327,0.00058826193,0.00075820385,0.00025044498,0.00044489745,0.0015317563,0.00048919546,0.0025002265],"category_scores_gemma":[0.0010614399,0.00023630992,0.00036525071,0.00058549,0.00037330276,0.0010929441,0.0008924532,0.0006499283,0.0007618901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081854546,0.0003699658,0.0020313386,0.00025121818,0.00017556195,0.00028439506,0.00012918377,0.18073781,0.08398659,0.007927784,0.027957316,0.69533026],"study_design_scores_gemma":[0.000021663756,0.000098130105,0.0006202262,0.000007818896,0.000026261967,0.000076270095,0.000017015029,0.98207456,0.011130844,0.0028482582,0.0030657283,0.000013115983],"about_ca_topic_score_codex":0.009094293,"about_ca_topic_score_gemma":0.018602373,"teacher_disagreement_score":0.009094293,"about_ca_system_score_codex":0.0007650688,"about_ca_system_score_gemma":0.00068856444,"threshold_uncertainty_score":0.018082738},"labels":[],"label_agreement":null},{"id":"W4415465630","doi":"10.3390/s25216524","title":"Energy-Aware Sensor Fusion Architecture for Autonomous Channel Robot Navigation in Constrained Environments","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sensor fusion; Energy consumption; Robot; Inertial measurement unit; Kalman filter; Channel (broadcasting); Energy (signal processing); Mobile robot; Wireless sensor network","score_opus":0.006550428260224829,"score_gpt":0.20648650109166136,"score_spread":0.19993607283143652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415465630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047763135,0.00020783134,0.94736177,0.000081385435,0.000029096824,0.000040548555,0.00003711634,0.001755369,0.0027236894],"genre_scores_gemma":[0.87345093,0.0001590342,0.1233237,0.0000683416,0.000014715126,0.000086397726,0.00008157031,0.000044071454,0.0027711987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998878,0.0000105908985,0.0000054458446,0.000031897733,0.00004429491,0.000019830342],"domain_scores_gemma":[0.9999132,0.00001625258,0.000013836493,0.000011705993,0.000036938505,0.00000806074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015332815,0.0003273074,0.00031403053,0.00026209833,0.0003688649,0.00029585583,0.000785885,0.00035983697,0.001137033],"category_scores_gemma":[0.00025128716,0.00014831076,0.0002147241,0.00018696305,0.0002363721,0.00079841,0.0006511137,0.00032208257,0.00031258006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003473444,0.00023704661,0.0037379137,0.00015197686,0.00009602032,0.00038830232,0.00041397865,0.38390875,0.20974538,0.009821333,0.004465023,0.38668692],"study_design_scores_gemma":[0.000013839109,0.00019302014,0.001509744,0.000010545604,0.000031985463,0.00014616881,0.0000754787,0.95374566,0.0354956,0.0036032153,0.0051460033,0.000028849343],"about_ca_topic_score_codex":0.0027184272,"about_ca_topic_score_gemma":0.0050540436,"teacher_disagreement_score":0.0027184272,"about_ca_system_score_codex":0.00032011085,"about_ca_system_score_gemma":0.0005787704,"threshold_uncertainty_score":0.005405247},"labels":[],"label_agreement":null},{"id":"W4415650208","doi":"10.3390/s25216592","title":"A Wrist System for Daily Stress Monitoring Using Mid-Level Physiological Fusion and Late Fusion with Survey-Based Labels","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fusion; Sensor fusion; Workflow; Wrist; Stress (linguistics)","score_opus":0.04562791350306174,"score_gpt":0.26510350000278177,"score_spread":0.21947558649972004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415650208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21222241,0.0009643999,0.7482973,0.00034460588,0.00068907475,0.00065745076,0.0033571885,0.026716094,0.0067515033],"genre_scores_gemma":[0.8419225,0.00037396973,0.14592801,0.00066643034,0.00027078396,0.000746389,0.0024023477,0.00045576005,0.0072338465],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950314,0.00008362827,0.00003707756,0.00018238783,0.00015826554,0.000035531983],"domain_scores_gemma":[0.999416,0.00013257796,0.00008446383,0.00011525166,0.00018068937,0.00007102237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074222416,0.00093548605,0.0008504826,0.00089725724,0.00022520559,0.00078191276,0.00067091803,0.0007465627,0.004480074],"category_scores_gemma":[0.0012388817,0.00023905832,0.0004561174,0.00048906397,0.00015189884,0.0005999634,0.0009843427,0.00042509302,0.0025316593],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037650662,0.0011279911,0.04366131,0.0010155169,0.0003737027,0.000521742,0.0006427367,0.0056183003,0.18894972,0.0010052458,0.016379386,0.7369393],"study_design_scores_gemma":[0.000675145,0.0062263794,0.26120314,0.00045110693,0.0007903466,0.003393667,0.00068056287,0.4358077,0.23295829,0.0058361264,0.051437594,0.0005400237],"about_ca_topic_score_codex":0.00047437518,"about_ca_topic_score_gemma":0.00075114524,"teacher_disagreement_score":0.004480074,"about_ca_system_score_codex":0.00017869966,"about_ca_system_score_gemma":0.00022406274,"threshold_uncertainty_score":0.01498729},"labels":[],"label_agreement":null},{"id":"W4415650282","doi":"10.3390/s25216571","title":"An Enhanced TK Technology for Bearing Fault Detection Using Vibration Measurement","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; Lakehead University","funders":"","keywords":"Bearing (navigation); Hilbert–Huang transform; Fault detection and isolation; Vibration; Fault (geology); Rolling-element bearing; SIGNAL (programming language); Condition monitoring; Filter (signal processing)","score_opus":0.015166885854461085,"score_gpt":0.29503860707525403,"score_spread":0.27987172122079296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415650282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063366644,0.0015119511,0.9308029,0.00016955065,0.00018455167,0.00007645658,0.00016618025,0.001155724,0.0025660207],"genre_scores_gemma":[0.65105,0.0014246264,0.3406403,0.00017033811,0.00008895775,0.00009223135,0.000319506,0.00007721225,0.0061368416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931574,0.000085367144,0.000041847605,0.00014516209,0.00037175138,0.0000401385],"domain_scores_gemma":[0.9995345,0.00010462959,0.000103174985,0.00007025242,0.00016885143,0.000018661001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036416785,0.000475039,0.00046359148,0.00072559813,0.0001632001,0.0005453252,0.0007550958,0.0006647909,0.0018103144],"category_scores_gemma":[0.0008735924,0.00025488538,0.0003261698,0.00068631076,0.00035376355,0.0014775237,0.0005384088,0.00046780158,0.0009775083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033340068,0.00005688003,0.0012765724,0.00042617164,0.000017725122,0.00013207183,0.00010120678,0.0024727609,0.7890688,0.0019474002,0.00078112475,0.20338602],"study_design_scores_gemma":[0.000073060895,0.000985571,0.00867544,0.000067217625,0.00010417624,0.0025422669,0.00016600372,0.22535612,0.7399937,0.0017070553,0.02019271,0.00013669726],"about_ca_topic_score_codex":0.0003977235,"about_ca_topic_score_gemma":0.00058120105,"teacher_disagreement_score":0.0018103144,"about_ca_system_score_codex":0.00032264652,"about_ca_system_score_gemma":0.00030971807,"threshold_uncertainty_score":0.0060560703},"labels":[],"label_agreement":null},{"id":"W4415650332","doi":"10.3390/s25216569","title":"Traversal by Touch: Tactile-Based Robotic Traversal with Artificial Skin in Complex Environments","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tree traversal; Robustness (evolution); Arduino; Teleoperation; Graph traversal; Latency (audio); Robot","score_opus":0.011220468942871993,"score_gpt":0.21046146550624892,"score_spread":0.19924099656337693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415650332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.960873,0.00008551797,0.037032854,0.000032518477,0.000027747024,0.000067215806,0.00009100467,0.0004855316,0.0013046876],"genre_scores_gemma":[0.9764134,0.00005180126,0.022257132,0.000044289376,0.0000038110581,0.0000733558,0.000116450174,0.000047863028,0.0009918723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999723,0.000057360983,0.00001540197,0.00006309684,0.00008093609,0.000060202947],"domain_scores_gemma":[0.99953175,0.00016633404,0.000087198816,0.00009249553,0.000052118223,0.00007011421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003254613,0.00055270985,0.0002950156,0.00020668223,0.00016212632,0.00039241268,0.0006024882,0.00043503192,0.0018488343],"category_scores_gemma":[0.001429386,0.00015616094,0.00028441896,0.000109476845,0.00038004955,0.0006750173,0.00081629807,0.00025944924,0.0003090874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027470607,0.001859334,0.009231315,0.00073428446,0.00012588318,0.00037184995,0.00042026996,0.091452666,0.7280956,0.00095635175,0.0009868343,0.16301855],"study_design_scores_gemma":[0.00025953306,0.033411782,0.07139925,0.00013330216,0.00018820091,0.0013278754,0.0010329287,0.49770176,0.38463444,0.0021034852,0.007626344,0.000181033],"about_ca_topic_score_codex":0.0006214511,"about_ca_topic_score_gemma":0.0010404092,"teacher_disagreement_score":0.0018488343,"about_ca_system_score_codex":0.00015663815,"about_ca_system_score_gemma":0.00023838504,"threshold_uncertainty_score":0.006184995},"labels":[],"label_agreement":null},{"id":"W4415650355","doi":"10.3390/s25216565","title":"The Task Dependency of Spontaneous Rhythmic Performance in Movements Beyond Established Biomechanical Models: An Inertial Sensor-Based Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National and Kapodistrian University of Athens; McGill University","keywords":"Rhythm; Robustness (evolution); Task (project management); Motor control; Dependency (UML); Inertial frame of reference; Task analysis; Pace","score_opus":0.02066237296375591,"score_gpt":0.3280718287369518,"score_spread":0.3074094557731959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415650355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99490803,0.00015823194,0.003946141,0.000009889373,0.000007928016,0.000053929794,0.00021639914,0.000023211931,0.00067609386],"genre_scores_gemma":[0.9977087,0.00007052455,0.0012284395,0.000012736077,0.00001719978,0.00007319244,0.00040352743,0.000020416814,0.0004652388],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994167,0.00014044641,0.00006305958,0.00016163201,0.00016869568,0.000049549308],"domain_scores_gemma":[0.99730456,0.0013362631,0.00056982256,0.00028600034,0.00038477036,0.00011860914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094093214,0.0003189487,0.000342593,0.00042659134,0.0001358976,0.00038102176,0.00020409684,0.0002727877,0.001111188],"category_scores_gemma":[0.00617807,0.00015952768,0.00018529358,0.0002755162,0.00024904084,0.00021637813,0.00054259517,0.00019480812,0.00029652793],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051140897,0.00079514715,0.3031069,0.0009798487,0.00064062217,0.0004237413,0.0028499386,0.0039970903,0.56058425,0.0002348658,0.00053651247,0.12073702],"study_design_scores_gemma":[0.000014688172,0.0012838631,0.9889093,0.000014248855,0.00004892384,0.0002473486,0.00016339346,0.002100408,0.006717662,0.00008578429,0.00040115003,0.00001332829],"about_ca_topic_score_codex":0.0005348468,"about_ca_topic_score_gemma":0.0008372315,"teacher_disagreement_score":0.001111188,"about_ca_system_score_codex":0.00006598869,"about_ca_system_score_gemma":0.00011923624,"threshold_uncertainty_score":0.004976213},"labels":[],"label_agreement":null},{"id":"W4415820073","doi":"10.3390/s25216657","title":"Smart Total Knee Replacement: Recognition of Activities of Daily Living Using Embedded IMU Sensors and a Novel AI Model in a Cadaveric Proof-of-Concept Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Implantcast","keywords":"Cadaveric spasm; Inertial measurement unit; Activities of daily living; Total knee replacement; Knee replacement; Prosthesis; Rehabilitation; Knee Joint","score_opus":0.025537566687372254,"score_gpt":0.28912300334609176,"score_spread":0.2635854366587195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415820073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73457575,0.0014536388,0.25658485,0.00045171875,0.0003996853,0.0004565577,0.00083354895,0.000769898,0.0044743917],"genre_scores_gemma":[0.8876051,0.0011536615,0.10412573,0.00019496465,0.000036769416,0.0004499772,0.00062694156,0.000025744379,0.005781071],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971765,0.000033793553,0.000014651689,0.00005826753,0.00015343091,0.000022249686],"domain_scores_gemma":[0.9996929,0.000044447737,0.00005347057,0.00004431433,0.00012176665,0.000043020285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069383916,0.00038962634,0.0002893989,0.0002362072,0.000081444654,0.00038397935,0.00063217874,0.0006234281,0.0013211601],"category_scores_gemma":[0.0005900909,0.00016461435,0.00037553508,0.00013258892,0.00026005448,0.00033750286,0.00028415793,0.00031023583,0.0004302012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028523835,0.0006172151,0.003839907,0.00056019356,0.00007690107,0.0008285316,0.00021477496,0.038068265,0.8938574,0.0011019474,0.0015795147,0.058970086],"study_design_scores_gemma":[0.00014707712,0.011162867,0.027315432,0.00014276672,0.00020042644,0.001841066,0.00042178546,0.35828122,0.576832,0.00067009876,0.022843553,0.00014165542],"about_ca_topic_score_codex":0.00078961573,"about_ca_topic_score_gemma":0.0010548371,"teacher_disagreement_score":0.0013211601,"about_ca_system_score_codex":0.00018110285,"about_ca_system_score_gemma":0.0002873068,"threshold_uncertainty_score":0.004419744},"labels":[],"label_agreement":null},{"id":"W4415990854","doi":"10.3390/s25216795","title":"Enhancing Quality of Resident Care and Staff Efficiency Through Implementation of Sensors in the Long-Term Care Setting: A Multi-Site Mixed-Methods Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Workload; Quality (philosophy); Quality management; Health care; Nursing staff","score_opus":0.04434572441707387,"score_gpt":0.5088450155352463,"score_spread":0.46449929111817245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415990854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947719,0.0002956569,0.0013343813,0.00011220458,0.000014789882,0.002703344,0.00017918803,0.000011474505,0.0005770517],"genre_scores_gemma":[0.9747123,0.00069966295,0.009253091,0.00048479723,0.000043893724,0.013329192,0.00023381726,0.000012996842,0.0012301808],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9925092,0.004659393,0.0006701314,0.00061614095,0.00083942094,0.00070568925],"domain_scores_gemma":[0.9895862,0.0046603074,0.0016730556,0.0007119782,0.002471649,0.00089688104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019030014,0.00076340465,0.0010097384,0.0012931931,0.002331271,0.0020782442,0.0011705145,0.00096359046,0.002201092],"category_scores_gemma":[0.013421245,0.00074608054,0.0014951897,0.0011101903,0.0009896632,0.0012559574,0.0016490316,0.0008433789,0.0003662671],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007854011,0.06968702,0.4182984,0.004907872,0.0015057196,0.0007562556,0.21622722,0.00077093096,0.005245213,0.0007818468,0.0021675467,0.27179798],"study_design_scores_gemma":[0.003507816,0.12775433,0.60188484,0.0026258787,0.0014388259,0.0005392379,0.24392919,0.002499259,0.00504741,0.0009606146,0.009504704,0.00030796943],"about_ca_topic_score_codex":0.0075442237,"about_ca_topic_score_gemma":0.016690282,"teacher_disagreement_score":0.019030014,"about_ca_system_score_codex":0.0029475568,"about_ca_system_score_gemma":0.0060300226,"threshold_uncertainty_score":0.10064155},"labels":[],"label_agreement":null},{"id":"W4416088487","doi":"10.3390/s25226839","title":"Confounder-Invariant Representation Learning (CIRL) for Robust Olfaction with Scarce Aroma Sensor Data: Mitigating Humidity Effects in Breath Analysis","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Masking (illustration); Olfaction; Representation (politics); Odor; Pattern recognition (psychology); Overfitting; Confounding; Consistency (knowledge bases)","score_opus":0.025718295351454076,"score_gpt":0.2716102559183817,"score_spread":0.24589196056692764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416088487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042457446,0.0008464371,0.9530551,0.00039028004,0.00008160474,0.00007616466,0.00017884492,0.0019625826,0.00095157593],"genre_scores_gemma":[0.6908325,0.0005295899,0.30341056,0.0006899833,0.00015785947,0.00022832684,0.0011913847,0.00028365536,0.0026761838],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915385,0.00030655062,0.00004697834,0.00023780702,0.0001711749,0.00008372751],"domain_scores_gemma":[0.99810386,0.0010878678,0.00020112931,0.00027782988,0.0002683616,0.00006100872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027212997,0.0010148059,0.000710795,0.0004249148,0.00029225755,0.00077035744,0.0011050102,0.00082762906,0.0011749594],"category_scores_gemma":[0.0076523414,0.00032505754,0.000851005,0.00049868296,0.00067727297,0.0012123621,0.001782148,0.0020604117,0.0005677192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038262518,0.00035220824,0.0053299936,0.0003185434,0.00033892796,0.00022999311,0.00025276045,0.35814023,0.046176698,0.006925989,0.0052021467,0.5763499],"study_design_scores_gemma":[0.0000146429375,0.00016416736,0.00093268015,0.000015203638,0.000032741824,0.000049407103,0.000020316606,0.98530364,0.008756403,0.0034368061,0.001249442,0.000024598921],"about_ca_topic_score_codex":0.0025912505,"about_ca_topic_score_gemma":0.0029619471,"teacher_disagreement_score":0.0027212997,"about_ca_system_score_codex":0.000611827,"about_ca_system_score_gemma":0.0010529285,"threshold_uncertainty_score":0.01439178},"labels":[],"label_agreement":null},{"id":"W4416088547","doi":"10.3390/s25226851","title":"Stability and Repeatability Analysis of a Phase-Modulated Optical Fibre Sensor for Transformer Oil Ageing Detection","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Université du Québec à Chicoutimi","keywords":"Repeatability; Transformer oil; Transformer; Linearity; Optical fiber; Thermal; Ageing; Refractive index","score_opus":0.011051495002976081,"score_gpt":0.2550522848258976,"score_spread":0.2440007898229215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416088547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90288454,0.0011610724,0.093593225,0.00010431088,0.000106695676,0.00012360411,0.00028736982,0.00044590517,0.0012933536],"genre_scores_gemma":[0.95606905,0.0005217172,0.041715506,0.00006576983,0.000022633472,0.00011341067,0.00026138627,0.000061394974,0.0011691769],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984604,0.00020778939,0.0000768329,0.000263264,0.0009181288,0.00007344077],"domain_scores_gemma":[0.9987423,0.00031978465,0.00020339199,0.00014168424,0.0005638086,0.00002907154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014575738,0.00041761124,0.00031267124,0.00044503424,0.00023069428,0.00030410464,0.0005584799,0.00041162473,0.00041735178],"category_scores_gemma":[0.002979715,0.00020566043,0.00031774593,0.00033347963,0.0002922818,0.00038063616,0.00031095897,0.00040647777,0.00024025704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005935312,0.000022837909,0.0009859063,0.000042635264,0.000013482181,0.000018505252,0.000042343916,0.00037709353,0.9933269,0.000050892235,0.000037527658,0.0050225505],"study_design_scores_gemma":[0.000004233743,0.00032518522,0.0036547808,0.0000045920306,0.000027042683,0.00014796668,0.000022099672,0.006251034,0.9887862,0.000025775778,0.0007347799,0.000016358803],"about_ca_topic_score_codex":0.0017558935,"about_ca_topic_score_gemma":0.00271391,"teacher_disagreement_score":0.0017558935,"about_ca_system_score_codex":0.00038852476,"about_ca_system_score_gemma":0.00044173564,"threshold_uncertainty_score":0.00770849},"labels":[],"label_agreement":null},{"id":"W4416129862","doi":"10.3390/s25226837","title":"DRC2-Net: A Context-Aware and Geometry-Adaptive Network for Lightweight SAR Ship Detection","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Synthetic aperture radar; Robustness (evolution); False positive paradox; Adaptability; Convolutional neural network; Scalability; Object detection; Feature (linguistics); Feature extraction","score_opus":0.015966186471773353,"score_gpt":0.2560217302181622,"score_spread":0.24005554374638885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416129862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18904015,0.0032088123,0.7695443,0.0009812581,0.00068586174,0.0002588194,0.0034973426,0.021650719,0.0111327255],"genre_scores_gemma":[0.74948007,0.0007598337,0.22094317,0.0011431971,0.00016634648,0.00024196392,0.008760345,0.00057357934,0.017931515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983656,0.000015340607,0.0000045330453,0.000078238176,0.000031552678,0.000033713422],"domain_scores_gemma":[0.99984944,0.000037877682,0.000017687815,0.000029080571,0.00004734051,0.000018663286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032469144,0.0014153915,0.0005862617,0.0004928943,0.00026031322,0.0004598336,0.002002975,0.0007682581,0.0023854089],"category_scores_gemma":[0.0008364592,0.0004343472,0.0005411441,0.0003460901,0.00028862103,0.00089251145,0.0010814458,0.000979681,0.00081424403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005539228,0.0003286004,0.004080158,0.00023294947,0.00023946186,0.00029218243,0.000074646945,0.42266136,0.04264362,0.003797001,0.03924974,0.48584637],"study_design_scores_gemma":[0.000021928601,0.00008226375,0.0006889039,0.000011311962,0.000028577524,0.00006174818,0.000012347149,0.9894065,0.005654345,0.0013672519,0.0026511317,0.000013704376],"about_ca_topic_score_codex":0.013831566,"about_ca_topic_score_gemma":0.030541807,"teacher_disagreement_score":0.013831566,"about_ca_system_score_codex":0.0008378999,"about_ca_system_score_gemma":0.0008053485,"threshold_uncertainty_score":0.02750212},"labels":[],"label_agreement":null},{"id":"W4416208909","doi":"10.3390/s25226967","title":"A Survey on Privacy Preservation Techniques in IoT Systems","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Internet of Things; Differential privacy; Homomorphic encryption; Encryption; Cryptography; Data exchange; Information privacy; Blockchain","score_opus":0.03409045773064262,"score_gpt":0.2884056347665483,"score_spread":0.25431517703590567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416208909","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010347509,0.732733,0.17637068,0.0044219904,0.0011010705,0.0004062398,0.0007281934,0.0005909802,0.07330039],"genre_scores_gemma":[0.10008732,0.82536,0.061193675,0.00202738,0.0017163575,0.00040117442,0.0012648326,0.00016734822,0.007782011],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99678755,0.00076010387,0.00040102014,0.0003902772,0.0014173456,0.00024373071],"domain_scores_gemma":[0.99560213,0.0030342261,0.00023722758,0.00048510637,0.00058405165,0.00005719213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002560614,0.0010431355,0.0010485642,0.0027779539,0.0009263217,0.0030174272,0.0013778587,0.0013796246,0.0053482708],"category_scores_gemma":[0.006183248,0.0006370944,0.0011524189,0.0052905977,0.00088197,0.006965929,0.0014731741,0.0017385689,0.0016152344],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010647005,0.00010909611,0.0021967369,0.010571876,0.00011685531,0.00031142228,0.000579951,0.008078569,0.0023563285,0.10978643,0.022276612,0.8435096],"study_design_scores_gemma":[0.000024577876,0.00037252135,0.0030471303,0.0065598497,0.00020189318,0.002665218,0.0007465957,0.021367636,0.005260319,0.076436564,0.8832099,0.000107854474],"about_ca_topic_score_codex":0.0009443995,"about_ca_topic_score_gemma":0.00065074675,"teacher_disagreement_score":0.0053482708,"about_ca_system_score_codex":0.0009554469,"about_ca_system_score_gemma":0.0016133155,"threshold_uncertainty_score":0.017891705},"labels":[],"label_agreement":null},{"id":"W4416363117","doi":"10.3390/s25227062","title":"Improved Edge Pixel Resolution in Modular PET Detectors with Partly Segmented Light Guides","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; Ontario Institute for Cancer Research; Lakehead University","funders":"Canadian Institutes of Health Research; Mitacs","keywords":"Modular design; Pixel; Detector; Enhanced Data Rates for GSM Evolution; Coordinate system; Resolution (logic); Image resolution; Iterative reconstruction","score_opus":0.005239453539372432,"score_gpt":0.2216754413396312,"score_spread":0.21643598780025877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416363117","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8540708,0.0006014512,0.14263745,0.00005848027,0.000019240932,0.000040565043,0.00017656514,0.0014588083,0.0009365766],"genre_scores_gemma":[0.680196,0.00022465362,0.31747866,0.00007985399,0.000013911882,0.000061706975,0.00041541646,0.00018073182,0.001349078],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997341,0.00003774761,0.0000128581005,0.00007632728,0.000107865206,0.00003103216],"domain_scores_gemma":[0.99888116,0.00033489495,0.0002780441,0.00019573969,0.00024303557,0.00006710648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040125797,0.00043927453,0.00034752503,0.00040554616,0.00013172143,0.00044981236,0.0010329143,0.00054737856,0.0006232127],"category_scores_gemma":[0.00094313384,0.00042170697,0.0002946016,0.00044481223,0.00031555843,0.00060067204,0.0006628108,0.00034268,0.00028201498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038954426,0.000042247564,0.0024601566,0.00010532606,0.000028201193,0.00012358418,0.00008529857,0.0040329406,0.97353214,0.0005727175,0.00025687288,0.018370926],"study_design_scores_gemma":[0.000026520924,0.00036813194,0.0064277914,0.000010877088,0.00003443212,0.00047863138,0.000022900653,0.030740157,0.9589524,0.00017966985,0.0027308124,0.00002765203],"about_ca_topic_score_codex":0.00053618563,"about_ca_topic_score_gemma":0.0012874459,"teacher_disagreement_score":0.0010329143,"about_ca_system_score_codex":0.00049287884,"about_ca_system_score_gemma":0.00039293716,"threshold_uncertainty_score":0.0035761595},"labels":[],"label_agreement":null},{"id":"W4416422341","doi":"10.3390/s25227096","title":"Overview of Monitoring, Diagnostics, Aging Analysis, and Maintenance Strategies in High-Voltage AC/DC XLPE Cable Systems","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Software deployment; Reliability (semiconductor); Condition monitoring; Fault detection and isolation; Bridge (graph theory); Field (mathematics); Fault (geology); Predictive maintenance; Asset management","score_opus":0.01595119784244394,"score_gpt":0.2798118667627127,"score_spread":0.26386066892026877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416422341","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059182737,0.9302277,0.048863444,0.0007738077,0.0006136961,0.000104884144,0.00032730974,0.00027992376,0.012891052],"genre_scores_gemma":[0.036933035,0.92796624,0.02729063,0.0005257,0.0009004665,0.00010880178,0.0006257422,0.00004085717,0.0056084185],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960476,0.00006356552,0.000060052593,0.00009687308,0.00014734153,0.000027507564],"domain_scores_gemma":[0.9995018,0.00023111074,0.0000641963,0.000022237205,0.00015825132,0.000022322152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000744152,0.0010062328,0.0007472827,0.0024113688,0.00026869282,0.0011718855,0.0008193486,0.0011085216,0.0022966636],"category_scores_gemma":[0.0007501181,0.00043407464,0.00069895573,0.0020011656,0.0002444194,0.0017639499,0.0004917699,0.00064708706,0.0009799779],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009994449,0.00012258587,0.0015180613,0.024490707,0.00014766063,0.0003058648,0.00026712858,0.01064444,0.01666332,0.014859483,0.015517553,0.9153633],"study_design_scores_gemma":[0.000018424516,0.0009210954,0.005832591,0.0081843175,0.00053949544,0.0022640184,0.0003173091,0.018725693,0.015499869,0.010827368,0.936735,0.00013474002],"about_ca_topic_score_codex":0.0015864575,"about_ca_topic_score_gemma":0.0014839451,"teacher_disagreement_score":0.0024113688,"about_ca_system_score_codex":0.0005257299,"about_ca_system_score_gemma":0.00084768044,"threshold_uncertainty_score":0.0076830983},"labels":[],"label_agreement":null},{"id":"W4416502043","doi":"10.3390/s25237120","title":"Upper Limb Capacity, Performance, and Leisure Participation in Children with Unilateral Cerebral Palsy","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Canadian Institutes of Health Research; Université Laval","keywords":"Cerebral palsy; Association (psychology); Upper limb; Leisure activity; Motor function; Lower limb; Typically developing","score_opus":0.010210104167586201,"score_gpt":0.2503867572784851,"score_spread":0.2401766531108989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416502043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995652,0.00013231393,0.000016717555,0.000009710348,0.0000011737577,0.0000019582908,0.000069140515,0.0000015826284,0.00020203748],"genre_scores_gemma":[0.99960536,0.00009146834,0.00003731506,0.0000045492875,0.0000017027514,0.0000061283886,0.00014994264,0.0000010866413,0.00010257909],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994492,0.000090006055,0.00007024615,0.00011135208,0.00015398361,0.00012522266],"domain_scores_gemma":[0.9986979,0.00023286635,0.0006471675,0.000053257067,0.00012938277,0.00023941112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005181946,0.0005374518,0.00043138472,0.0012340766,0.00046644686,0.00063642557,0.00041703065,0.00043966097,0.0014064895],"category_scores_gemma":[0.0027095426,0.0002677487,0.0003139545,0.00094686,0.00056055235,0.00040429799,0.00096347625,0.00039351385,0.00029502067],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007432028,0.000059767935,0.99661094,0.00001975806,0.00002397687,0.00025940622,0.0003454298,0.000048215563,0.00025583673,0.0000105131985,0.000055201708,0.002236485],"study_design_scores_gemma":[0.0000012214738,0.00005012699,0.99931407,0.0000032003697,0.0000058599217,0.00031313716,0.00021031198,0.000032924912,0.00003244947,0.0000043943733,0.00003088885,0.0000015213177],"about_ca_topic_score_codex":0.020228157,"about_ca_topic_score_gemma":0.02187212,"teacher_disagreement_score":0.020228157,"about_ca_system_score_codex":0.00055597123,"about_ca_system_score_gemma":0.00046642034,"threshold_uncertainty_score":0.040220797},"labels":[],"label_agreement":null},{"id":"W4416593399","doi":"10.3390/s25237142","title":"XGBoost-Based Digital Twin Model for Predicting Trajectory Errors in a Hexapod Coordinated Machining System Using Positioning Accuracy and Vibration Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hexapod; Trajectory; Machining; Vibration; Acceleration; Feature (linguistics); Payload (computing); Pointwise; Control theory (sociology)","score_opus":0.0255918802177884,"score_gpt":0.25896456864523426,"score_spread":0.23337268842744585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416593399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1611126,0.00028495528,0.8341231,0.00011510281,0.000095331256,0.00006443574,0.00018910454,0.0025552863,0.0014601283],"genre_scores_gemma":[0.91910684,0.00006139608,0.07734932,0.00007432547,0.000016575685,0.00009906024,0.00038704582,0.000077512945,0.002828008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998356,0.000021811635,0.000007448617,0.000060386268,0.000040558512,0.00003419359],"domain_scores_gemma":[0.9997794,0.00007586367,0.000031596315,0.000022956181,0.000071605165,0.00001858155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005037522,0.000710751,0.0006086679,0.00029390643,0.0002640535,0.00044674912,0.0009145754,0.00052908144,0.0012768577],"category_scores_gemma":[0.00083801616,0.00028306284,0.00035589974,0.00032827552,0.000357949,0.0004308722,0.00049710425,0.00077853806,0.00038280757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018797418,0.00006507133,0.0013896447,0.000030835698,0.00003596522,0.000030914995,0.000024623807,0.92759913,0.003955959,0.00055494474,0.0006008093,0.0655242],"study_design_scores_gemma":[0.0000022256681,0.000017865992,0.00016569428,0.0000011878973,0.0000021281064,0.0000031865147,0.0000022581576,0.9991002,0.0005180547,0.00011047749,0.00007533717,0.0000014271009],"about_ca_topic_score_codex":0.011140284,"about_ca_topic_score_gemma":0.010782762,"teacher_disagreement_score":0.011140284,"about_ca_system_score_codex":0.00047155088,"about_ca_system_score_gemma":0.0010510299,"threshold_uncertainty_score":0.022150874},"labels":[],"label_agreement":null},{"id":"W4416782621","doi":"10.3390/s25237251","title":"Molecular Imprinting Polymer-Based Sensing of Neonicotinoids","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Insect and Pesticide Research","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Molecularly imprinted polymer; Neonicotinoid; Molecular imprinting; Quartz crystal microbalance; Analyte; Nanosensor","score_opus":0.03631199816556521,"score_gpt":0.3141514507555936,"score_spread":0.2778394525900284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416782621","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001312551,0.9943551,0.0011383881,0.00011751748,0.00016777811,0.0000141516,0.0000427334,0.00003873963,0.002813204],"genre_scores_gemma":[0.0048362035,0.9916293,0.0011231953,0.00012340142,0.00008812182,0.00002392761,0.00006914842,0.0000044537574,0.002102274],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997857,0.000023135339,0.0000163653,0.000046046593,0.00010374958,0.000024995998],"domain_scores_gemma":[0.99987733,0.00004852422,0.000028360313,0.000004471008,0.000031930085,0.000009371032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026770326,0.001065495,0.0010620083,0.0019076774,0.0001514297,0.0005032286,0.0006664231,0.00073061127,0.0016228443],"category_scores_gemma":[0.00039057818,0.00040456498,0.0004975643,0.0018154918,0.00023659217,0.00087899587,0.00048648415,0.00092062185,0.0014252973],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060366976,0.00013590085,0.00026669065,0.028495042,0.00014945972,0.00041184094,0.00007419078,0.001296898,0.06936677,0.0035598727,0.011838813,0.8843441],"study_design_scores_gemma":[0.000018806213,0.0002919996,0.0026404043,0.0028609838,0.00025645192,0.0025791842,0.000078871744,0.0011462974,0.044381294,0.0016527845,0.9440317,0.00006125872],"about_ca_topic_score_codex":0.00058397854,"about_ca_topic_score_gemma":0.0008039396,"teacher_disagreement_score":0.0019076774,"about_ca_system_score_codex":0.00033667762,"about_ca_system_score_gemma":0.0003803121,"threshold_uncertainty_score":0.00542897},"labels":[],"label_agreement":null},{"id":"W4417050487","doi":"10.3390/s25237296","title":"Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT Intrusion Detection","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"Concordia University; Natural Sciences and Engineering Research Council of Canada; Concordia University of Edmonton","keywords":"Federated learning; Intrusion detection system; Internet of Things; Differential privacy; Data aggregator; Data pre-processing; Fog computing","score_opus":0.019514861206564052,"score_gpt":0.2781773074807315,"score_spread":0.25866244627416746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417050487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03907303,0.00029334455,0.95788676,0.00024714295,0.000044578246,0.00008687612,0.00014749813,0.001313789,0.00090697757],"genre_scores_gemma":[0.88618064,0.00012243293,0.11212451,0.00035462907,0.00003540865,0.00008150201,0.00030930576,0.000031544143,0.00076004356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986488,0.00038647713,0.00007222181,0.00039452763,0.00029041484,0.00020754676],"domain_scores_gemma":[0.9981591,0.0006178798,0.00019094968,0.000694439,0.00023432578,0.000103260536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022359309,0.00059461617,0.0008121692,0.00050055,0.00075295917,0.0009886755,0.0016633722,0.0009734524,0.00053779484],"category_scores_gemma":[0.004609349,0.00020409666,0.0007266946,0.000658813,0.0009309846,0.0022488537,0.0019421007,0.0010543449,0.00014536675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005603534,0.00056400907,0.009008873,0.00016462566,0.00019578626,0.00033512316,0.0002614921,0.5500577,0.010710608,0.02252938,0.007227103,0.39838496],"study_design_scores_gemma":[0.000018093675,0.000098838194,0.00067352375,0.000008113612,0.000017883114,0.0001019076,0.00003071451,0.97751576,0.0034878517,0.017021662,0.0010130532,0.000012661426],"about_ca_topic_score_codex":0.002604565,"about_ca_topic_score_gemma":0.0034706064,"teacher_disagreement_score":0.002604565,"about_ca_system_score_codex":0.0009020675,"about_ca_system_score_gemma":0.0015734244,"threshold_uncertainty_score":0.011824906},"labels":[],"label_agreement":null},{"id":"W4417054948","doi":"10.3390/s25237381","title":"MA-EVIO: A Motion-Aware Approach to Event-Based Visual–Inertial Odometry","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Unavailability; Robustness (evolution); Visual odometry; Motion estimation; RGB color model; Feature (linguistics); Feature tracking; Inertial measurement unit","score_opus":0.007388282017675877,"score_gpt":0.23802513635914224,"score_spread":0.23063685434146636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417054948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021226656,0.00057132787,0.96839494,0.000067055844,0.00014502279,0.00006690012,0.0005328226,0.006565785,0.0024293975],"genre_scores_gemma":[0.48038748,0.0006948372,0.5075025,0.00022405706,0.00017709976,0.0001676248,0.00500688,0.0006946185,0.005144788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960846,0.00003141432,0.000020840112,0.00014756327,0.00013997909,0.00005175277],"domain_scores_gemma":[0.9997253,0.0000368237,0.000041763225,0.00008549732,0.000086524524,0.00002413012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002987048,0.0009851238,0.000867835,0.0011266405,0.000250906,0.00068385556,0.0024342535,0.00049058534,0.0013938507],"category_scores_gemma":[0.0012186029,0.00042291367,0.000574867,0.001280601,0.0002474463,0.0011317855,0.0018581395,0.00085203967,0.00072617986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047643305,0.00025448127,0.0059903227,0.00027105672,0.0002741407,0.00024489523,0.0002709766,0.13535778,0.057189133,0.00624997,0.012981261,0.7804395],"study_design_scores_gemma":[0.000042286345,0.00013935682,0.004636341,0.00002695713,0.000048768252,0.00017932465,0.00008964928,0.9610704,0.014877897,0.0041403016,0.014705161,0.00004348377],"about_ca_topic_score_codex":0.0054604732,"about_ca_topic_score_gemma":0.008507236,"teacher_disagreement_score":0.0054604732,"about_ca_system_score_codex":0.00025984162,"about_ca_system_score_gemma":0.0005580314,"threshold_uncertainty_score":0.010857403},"labels":[],"label_agreement":null},{"id":"W4417055081","doi":"10.3390/s25237325","title":"Enhanced Image Annotation in Wild Blueberry (Vaccinium angustifolium Ait.) Fields Using Sequential Zero-Shot Detection and Segmentation Models","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Annotation; Segmentation; Image processing; Pattern recognition (psychology); Object detection; Image segmentation; Intersection (aeronautics); Ripeness","score_opus":0.02081334188316298,"score_gpt":0.2521452513016354,"score_spread":0.23133190941847243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417055081","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56236356,0.0023630639,0.40497184,0.00037936537,0.0001981788,0.00022481049,0.0020532976,0.023530247,0.0039156303],"genre_scores_gemma":[0.7626864,0.00063025753,0.2229957,0.00033071366,0.00005092737,0.00011457348,0.007725933,0.00067801215,0.0047875545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995628,0.000042452128,0.000013927663,0.0002359168,0.00007093275,0.000073942705],"domain_scores_gemma":[0.99964046,0.00014199733,0.000029515373,0.000047211466,0.00010621941,0.000034641856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008733776,0.0009661295,0.00060525833,0.0011912652,0.00035370424,0.001074859,0.0010261597,0.0008910775,0.0007833432],"category_scores_gemma":[0.0008758966,0.00033235608,0.00077711424,0.00041553556,0.00031475318,0.00080519,0.00067895127,0.00044995346,0.0006517174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015630578,0.0005254761,0.02044832,0.0006454695,0.00030461428,0.0007633418,0.0006675285,0.10856365,0.27138123,0.0014993997,0.011961298,0.5816766],"study_design_scores_gemma":[0.00002388877,0.0001928867,0.011126132,0.0000324459,0.00006779431,0.00023776545,0.00020382553,0.9419655,0.04058364,0.0009141228,0.004619654,0.00003231722],"about_ca_topic_score_codex":0.018646173,"about_ca_topic_score_gemma":0.03206768,"teacher_disagreement_score":0.018646173,"about_ca_system_score_codex":0.0006916624,"about_ca_system_score_gemma":0.0006955655,"threshold_uncertainty_score":0.03707528},"labels":[],"label_agreement":null},{"id":"W4417156418","doi":"10.3390/s25247482","title":"Hybrid Time–Frequency Analysis for Micromobility-Based Indirect Bridge Health Monitoring","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Traverse; Bridge (graph theory); Robustness (evolution); Hilbert–Huang transform; Structural health monitoring; Acceleration; Intelligent transportation system; Wireless sensor network","score_opus":0.021748703922795494,"score_gpt":0.31007020734231167,"score_spread":0.2883215034195162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417156418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13813256,0.00039992918,0.8583333,0.00009488965,0.000042902964,0.00005338739,0.00012568342,0.0003725369,0.0024448335],"genre_scores_gemma":[0.8489742,0.00054513896,0.14767613,0.000059398535,0.00003820485,0.00009046663,0.00028426616,0.000044038312,0.0022882964],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987817,0.00001716754,0.0000063322423,0.000031824504,0.000056773682,0.000009827211],"domain_scores_gemma":[0.99988866,0.00003885893,0.00001948384,0.000012683601,0.00003488235,0.0000054790225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019490732,0.00046614878,0.0002729045,0.000730549,0.0001329983,0.00035331814,0.00025419032,0.00036589493,0.0011625467],"category_scores_gemma":[0.00045436877,0.00010884023,0.00034174355,0.0005720532,0.00012355705,0.00046948955,0.00030168184,0.0002857407,0.00027706195],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025589694,0.00020114142,0.011848561,0.00036735088,0.00012569127,0.0003794831,0.0003327632,0.12070668,0.22500685,0.0037354117,0.0015099944,0.6355302],"study_design_scores_gemma":[0.000008144185,0.00014920828,0.011906296,0.0000234051,0.000038807077,0.00022050484,0.00010437917,0.96311885,0.020297665,0.0013860731,0.0027221846,0.000024451238],"about_ca_topic_score_codex":0.00074596,"about_ca_topic_score_gemma":0.0011302108,"teacher_disagreement_score":0.0011625467,"about_ca_system_score_codex":0.00012133612,"about_ca_system_score_gemma":0.00016274123,"threshold_uncertainty_score":0.0038891435},"labels":[],"label_agreement":null},{"id":"W4417198424","doi":"10.3390/s25247504","title":"Design, Analysis, and Prototyping of a Multifunctional Digital Twin-Enabled Aerospace Drilling End-Effector Deployable by a Collaborative Robot","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Manitoba; National Research Council Canada","funders":"National Research Council Canada","keywords":"Aerospace; Clamping; Kinematics; Compensation (psychology); Robot; Rapid prototyping; Drilling; Integration platform","score_opus":0.00481633158171719,"score_gpt":0.2029634071255035,"score_spread":0.19814707554378633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417198424","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36798972,0.00030997992,0.6247622,0.000089713496,0.000059316342,0.00020668599,0.0000523416,0.0010680828,0.0054619825],"genre_scores_gemma":[0.85345316,0.000095086696,0.1435286,0.000016367461,0.000004232922,0.000059712474,0.000053448483,0.000040499075,0.0027488926],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959546,0.00003345569,0.000015737745,0.000052687228,0.0002591151,0.00004368083],"domain_scores_gemma":[0.99956375,0.00008151558,0.000076031036,0.00008823489,0.00014625069,0.000044268563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006048171,0.00028177176,0.00024064747,0.00026891503,0.00015926125,0.0004767101,0.00079917954,0.00040711084,0.0010488054],"category_scores_gemma":[0.00070911914,0.00017291482,0.00024296514,0.00010309584,0.00033839242,0.00037974137,0.00039985447,0.00030457845,0.00030254736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002308592,0.00014661238,0.003331391,0.0003732225,0.000027828497,0.0007209556,0.00036711778,0.11081757,0.7339459,0.00721882,0.00078455935,0.14203517],"study_design_scores_gemma":[0.00006405892,0.0029489635,0.0054111015,0.000046609322,0.000052223517,0.0010126574,0.00014088172,0.48475513,0.48061052,0.0007133518,0.024182295,0.00006219809],"about_ca_topic_score_codex":0.00040469403,"about_ca_topic_score_gemma":0.0004450905,"teacher_disagreement_score":0.0010488054,"about_ca_system_score_codex":0.00025670667,"about_ca_system_score_gemma":0.0005294666,"threshold_uncertainty_score":0.0035085678},"labels":[],"label_agreement":null},{"id":"W4417274056","doi":"10.3390/s25237376","title":"Association Between Stride Parameters and Racetrack Curvature for Thoroughbred Chuckwagon Horses","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Veterinary Equine Medical Research","field":"Veterinary","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"STRIDE; Curvature; Horse; Acceleration; Random effects model","score_opus":0.08150911845086958,"score_gpt":0.403063677908411,"score_spread":0.3215545594575414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417274056","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99967825,0.000076228156,0.000110470595,0.0000073207684,0.000001118469,0.0000032384378,0.0000693577,0.0000038134367,0.000050236154],"genre_scores_gemma":[0.9993647,0.00003504354,0.0001759889,0.0000036183658,0.0000024418637,0.000011350291,0.00021708757,0.000003749674,0.00018621827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999509,0.0001496028,0.000041359854,0.00015477225,0.00005866067,0.00008671022],"domain_scores_gemma":[0.9985084,0.00045579104,0.0005631492,0.00015015808,0.00015644183,0.00016603354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075447216,0.0002574204,0.00040416754,0.0006354053,0.00020741526,0.00046717725,0.00024822608,0.00061425264,0.0014152789],"category_scores_gemma":[0.002330058,0.00034585313,0.00056567806,0.00048632565,0.00026899195,0.00030693144,0.00040823748,0.00033518168,0.0001568791],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004253363,0.00005325455,0.99462354,0.000019462537,0.00017392093,0.00005396432,0.00012171779,0.00033702797,0.0023456383,0.00001406313,0.000038236372,0.0017938316],"study_design_scores_gemma":[0.0000011104835,0.00006975136,0.9993224,0.0000024237522,0.000018152192,0.000019699808,0.000026798256,0.00045510413,0.0000468949,0.000004653997,0.00003058452,0.0000024146943],"about_ca_topic_score_codex":0.0070244097,"about_ca_topic_score_gemma":0.016362699,"teacher_disagreement_score":0.0070244097,"about_ca_system_score_codex":0.00026471922,"about_ca_system_score_gemma":0.0002549804,"threshold_uncertainty_score":0.013967037},"labels":[],"label_agreement":null},{"id":"W4417360842","doi":"10.3390/s25247616","title":"Spectral Predictability of Soil Organic Matter Depends on Its Humin Fraction Rather than Spectral Fusion","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Predictability; Humin; Soil organic matter; Organic matter; Fusion; Partial least squares regression; Humic acid; Soil carbon","score_opus":0.008873693072609528,"score_gpt":0.23123929318695172,"score_spread":0.2223656001143422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417360842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93549883,0.00035162608,0.06150401,0.000098870376,0.000022260509,0.00001714806,0.00032578548,0.00032118274,0.0018602875],"genre_scores_gemma":[0.9943763,0.00010843902,0.0050218864,0.000019915364,0.0000074290992,0.000006559785,0.00025863756,0.000016996979,0.00018375427],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99974996,0.000047990296,0.000014621146,0.00009432577,0.000062412626,0.00003063618],"domain_scores_gemma":[0.99951744,0.0001929882,0.00010326566,0.000060873943,0.00010748451,0.000017935981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010369591,0.00061434373,0.0003180532,0.0005610487,0.0001952036,0.0006257369,0.00018183303,0.0002002984,0.00046379704],"category_scores_gemma":[0.0015713,0.00014793858,0.0005624351,0.00050816085,0.00034541686,0.000816626,0.00036660352,0.0004113388,0.00022788138],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006460657,0.0001466502,0.20204048,0.00033500654,0.0005125077,0.00026085932,0.00033697634,0.210448,0.3922758,0.0019733594,0.0009705909,0.1900537],"study_design_scores_gemma":[0.000011405076,0.00011587791,0.15667337,0.000023993283,0.00015753775,0.00014374212,0.0001795038,0.7679404,0.07071599,0.002721323,0.001257486,0.000059431193],"about_ca_topic_score_codex":0.0023806975,"about_ca_topic_score_gemma":0.0026992552,"teacher_disagreement_score":0.0023806975,"about_ca_system_score_codex":0.00018468662,"about_ca_system_score_gemma":0.00024149215,"threshold_uncertainty_score":0.0054840446},"labels":[],"label_agreement":null},{"id":"W4417361060","doi":"10.3390/s25247623","title":"Assessment of KN95 Mask Filtering Degradation and Breathing Detection: A Pilot Study","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Breathing; Airflow; Moisture; Humidity; Relative humidity; Saturation (graph theory); Respiration","score_opus":0.016710325037031445,"score_gpt":0.2621905514295606,"score_spread":0.24548022639252914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417361060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99456847,0.0005256897,0.0041403766,0.000039079412,0.00003126493,0.00003908959,0.00013848883,0.00008154542,0.00043599546],"genre_scores_gemma":[0.99118644,0.0003673692,0.0066593187,0.000074795265,0.000008973612,0.00003681578,0.00014709188,0.000037237358,0.0014819981],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969864,0.000025336161,0.000022697655,0.00007405106,0.00013878843,0.000040557832],"domain_scores_gemma":[0.99953115,0.00013419562,0.000099347155,0.00005143962,0.00014607375,0.000037813803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004200159,0.00032102157,0.00021482485,0.00016665627,0.00015014004,0.00027999197,0.00024671835,0.00053688604,0.0008646083],"category_scores_gemma":[0.00083925924,0.00014273231,0.00021024041,0.00011870781,0.00019839917,0.00028725172,0.00024334838,0.00019709667,0.00018478684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007071671,0.000038306844,0.00036955718,0.00004691278,0.000005289524,0.0000514091,0.000054269996,0.00016475529,0.99663913,0.000015418818,0.000047027675,0.0024971974],"study_design_scores_gemma":[0.0000044541666,0.00089727546,0.0048683006,0.0000068349377,0.000021363247,0.00012682275,0.000063699845,0.0029538875,0.99017525,0.000014837852,0.00085681194,0.000010482692],"about_ca_topic_score_codex":0.00088135083,"about_ca_topic_score_gemma":0.0014547145,"teacher_disagreement_score":0.00088135083,"about_ca_system_score_codex":0.0002087913,"about_ca_system_score_gemma":0.0001370953,"threshold_uncertainty_score":0.002892375},"labels":[],"label_agreement":null},{"id":"W4417455088","doi":"10.3390/s25247667","title":"Value of Robotics: Comparison of Three Different High-Intensity Training Programs for Rehabilitation After Stroke","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Stroke (engine); Rehabilitation; Prioritization; Gait; Gait training; Psychological intervention; Balance (ability)","score_opus":0.02789651478191351,"score_gpt":0.30839912296714733,"score_spread":0.2805026081852338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417455088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9485736,0.03997548,0.002966027,0.00072869373,0.0005607052,0.0016203241,0.000470493,0.00008482903,0.0050199274],"genre_scores_gemma":[0.9858385,0.0074509038,0.00445584,0.00023413869,0.00014358397,0.0009999801,0.00025774547,0.000013562938,0.0006058385],"study_design_codex":"design_other","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9960747,0.0019688327,0.00065320637,0.00027475393,0.00081283273,0.00021578642],"domain_scores_gemma":[0.9961228,0.0025425835,0.00060816447,0.000117727745,0.0002969755,0.00031174094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028180869,0.0007913984,0.0014265153,0.001092177,0.0003180329,0.00077960896,0.00063194113,0.00080630946,0.002988947],"category_scores_gemma":[0.0075364155,0.0002030398,0.0023983913,0.0007013314,0.00046204962,0.0010140829,0.0007198108,0.0005622534,0.00031760294],"study_design_candidate":"randomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.122289844,0.013428797,0.01665762,0.01862059,0.010893491,0.00009133162,0.0004951128,0.0034541644,0.0041018766,0.00060587877,0.00077324576,0.8085881],"study_design_scores_gemma":[0.036583092,0.39705426,0.50984734,0.008112037,0.023022026,0.00031810344,0.0014870365,0.0069683734,0.0051774015,0.00190945,0.009252339,0.00026863284],"about_ca_topic_score_codex":0.0010044713,"about_ca_topic_score_gemma":0.0025350088,"teacher_disagreement_score":0.002988947,"about_ca_system_score_codex":0.0006895826,"about_ca_system_score_gemma":0.0010295033,"threshold_uncertainty_score":0.014903665},"labels":[],"label_agreement":null},{"id":"W4417490231","doi":"10.3390/s26010002","title":"Integrating VNIR–SWIR Spectroscopy and Handheld XRF for Enhanced Mineralogical Characterization of Phosphate Mine Waste Rocks in Benguerir, Morocco: Implications for Sustainable Mine Reclamation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Mine drainage and remediation techniques","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"OCP Group","keywords":"Spectroradiometer; Reuse; Phosphate; Phosphorite; Residual; Land reclamation; Spectral signature; Dolomite","score_opus":0.006699533313046781,"score_gpt":0.2563441923417675,"score_spread":0.24964465902872068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417490231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.985644,0.0014736578,0.007888235,0.000167382,0.000047577876,0.00003795584,0.0020314031,0.0004429739,0.0022668033],"genre_scores_gemma":[0.97885704,0.0005077703,0.018143894,0.00008837131,0.000027563148,0.000037152506,0.0014206377,0.000121528275,0.0007960718],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941015,0.000055525634,0.00002848193,0.00019638162,0.00019997626,0.00010938793],"domain_scores_gemma":[0.99971145,0.000044785473,0.000048354774,0.000025131527,0.00015484334,0.000015454407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009082936,0.00095591036,0.0006714045,0.0019867376,0.0008072474,0.0012018661,0.00078197353,0.00069279416,0.0009184566],"category_scores_gemma":[0.00064234657,0.00028693888,0.00067600684,0.0015751543,0.00032910562,0.00066478807,0.00057781744,0.00036231466,0.0005994816],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000583874,0.0002716955,0.25223106,0.0008683488,0.0004097307,0.0017249113,0.0016821513,0.011395598,0.58925736,0.00030464574,0.0024930956,0.13877755],"study_design_scores_gemma":[0.000055660134,0.00022370984,0.803732,0.00014198222,0.00032498688,0.00087904674,0.0025533887,0.05813531,0.11950877,0.00042126514,0.013899273,0.00012458347],"about_ca_topic_score_codex":0.039205775,"about_ca_topic_score_gemma":0.086686134,"teacher_disagreement_score":0.039205775,"about_ca_system_score_codex":0.0007297578,"about_ca_system_score_gemma":0.0006941275,"threshold_uncertainty_score":0.07795519},"labels":[],"label_agreement":null},{"id":"W7116836736","doi":"10.3390/s26010057","title":"Spectral Unmixing to Reduce Refraction Effects in Feulgen-Stained Slides","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Terry Fox Research Institute","keywords":"Texture (cosmology); Refraction; Spectral imaging; Multispectral image; Pattern recognition (psychology); Hyperspectral imaging","score_opus":0.0046432088708350154,"score_gpt":0.26953046922845353,"score_spread":0.2648872603576185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116836736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15281622,0.00062023435,0.8405216,0.00026212382,0.00012527972,0.00020857556,0.00015348554,0.0025012733,0.0027911998],"genre_scores_gemma":[0.18306558,0.00069997954,0.81039274,0.00012973919,0.000039038707,0.00032901333,0.0003242371,0.0006793489,0.00434027],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939585,0.00010457119,0.000040730876,0.00014378209,0.0002736852,0.00004144705],"domain_scores_gemma":[0.9988464,0.00043917494,0.00020864152,0.0002147095,0.00025536757,0.000035699017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011015172,0.00086600083,0.00030917433,0.0010205276,0.00050322764,0.00086182565,0.00060366624,0.0005754012,0.0045134593],"category_scores_gemma":[0.0025064466,0.00047386944,0.00033832452,0.0007055367,0.0008116451,0.0008449781,0.0006268797,0.0009359992,0.0012842977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000925327,0.000056042303,0.0010234074,0.00019262459,0.000022932387,0.000064748354,0.00022973948,0.0028033422,0.9248067,0.0018684244,0.0005096521,0.06832983],"study_design_scores_gemma":[0.000008087478,0.0000899292,0.0021544397,0.000018676772,0.00001858619,0.00015508896,0.000056332796,0.022755928,0.9677863,0.0007949003,0.00613897,0.000022851202],"about_ca_topic_score_codex":0.0006035057,"about_ca_topic_score_gemma":0.0018289194,"teacher_disagreement_score":0.0045134593,"about_ca_system_score_codex":0.0005133484,"about_ca_system_score_gemma":0.00059634954,"threshold_uncertainty_score":0.015099049},"labels":[],"label_agreement":null},{"id":"W7116903477","doi":"10.3390/s26010037","title":"Novelty Detection in Underwater Acoustic Environments for Maritime Surveillance Using an Out-of-Distribution Detector for Neural Networks","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Defense Acquisition Program Administration; Korea Research Institute for Defense Technology Planning and Advancement","keywords":"Softmax function; Underwater; Detector; Probabilistic logic; Robustness (evolution); Inference; Monte Carlo method; Artificial neural network; Novelty detection; Dropout (neural networks)","score_opus":0.03192978848166214,"score_gpt":0.2753767810116158,"score_spread":0.24344699252995364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116903477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0745057,0.0005340633,0.9224564,0.00037892256,0.000076675424,0.00004198863,0.00010536789,0.00080992456,0.0010909479],"genre_scores_gemma":[0.89364755,0.00033263135,0.10304921,0.00031646492,0.0000961475,0.000088846886,0.00032589465,0.00007883508,0.0020643251],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908173,0.00020504612,0.00004754359,0.00025980567,0.00031308227,0.00009274248],"domain_scores_gemma":[0.99780566,0.0011116808,0.00040837986,0.00016704823,0.0003944971,0.000112759815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00186909,0.0010045862,0.00085133844,0.000817343,0.0004038831,0.0008712457,0.0015965117,0.001263717,0.00057525554],"category_scores_gemma":[0.006284665,0.00040735057,0.0006967796,0.00050848426,0.0008069486,0.0017464752,0.0021469533,0.0019554994,0.0002684181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006828386,0.00038178085,0.023364855,0.00025758072,0.00023867005,0.0005098002,0.00027929206,0.34869164,0.03642597,0.010809121,0.003849376,0.5745091],"study_design_scores_gemma":[0.000008137954,0.00006494175,0.0018230578,0.000012526275,0.000015659905,0.00008755268,0.000016479727,0.98839635,0.005723416,0.003418869,0.00041802364,0.0000150852675],"about_ca_topic_score_codex":0.0019834945,"about_ca_topic_score_gemma":0.0028992265,"teacher_disagreement_score":0.0019834945,"about_ca_system_score_codex":0.0010055791,"about_ca_system_score_gemma":0.0007363843,"threshold_uncertainty_score":0.009884834},"labels":[],"label_agreement":null},{"id":"W7117118301","doi":"10.3390/s26010113","title":"High-Accuracy Indoor Positioning and Smart Home Technologies for Assessing and Monitoring Frailty in Older Adults","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Frailty in Older Adults","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta; University of Waterloo","funders":"","keywords":"Activities of daily living; Independent living; Home automation; Bluetooth; Frailty syndrome; Older people; Assisted living","score_opus":0.014253304405962843,"score_gpt":0.3031731945570616,"score_spread":0.28891989015109876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117118301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85581815,0.0027009936,0.13528413,0.00020074444,0.00016080412,0.00031956608,0.0005643569,0.0005518411,0.004399473],"genre_scores_gemma":[0.93212306,0.00059728586,0.06565357,0.0001379267,0.00006466301,0.0001665544,0.00021737709,0.000011211099,0.001028425],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992793,0.0002929201,0.00005290304,0.00010386737,0.00023954411,0.000031469237],"domain_scores_gemma":[0.99922657,0.00027619692,0.00015080157,0.00008152058,0.00023670202,0.00002824798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009630421,0.0003809188,0.00028248242,0.00048081324,0.00012387322,0.0004127574,0.0003051909,0.0003862462,0.0007071229],"category_scores_gemma":[0.0024911144,0.0001277291,0.00025029914,0.00039811648,0.00014383407,0.00038635518,0.00051907747,0.00021742022,0.000236341],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094526727,0.00046913026,0.33983034,0.0007644834,0.00026193247,0.00021822263,0.00096646114,0.0034240193,0.086718135,0.0009205224,0.001546798,0.5639346],"study_design_scores_gemma":[0.00017861387,0.004939348,0.8760554,0.00028886163,0.0006585206,0.00289208,0.0011380442,0.03430099,0.06694073,0.0014120805,0.011069556,0.0001258087],"about_ca_topic_score_codex":0.00068464456,"about_ca_topic_score_gemma":0.0015950387,"teacher_disagreement_score":0.0009630421,"about_ca_system_score_codex":0.00012537026,"about_ca_system_score_gemma":0.00020396574,"threshold_uncertainty_score":0.0050931573},"labels":[],"label_agreement":null},{"id":"W7117453359","doi":"10.3390/s26010184","title":"Intelligent Identification of Micro-NPR Bolt Shear Deformation Based on Modular Convolutional Neural Network","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Anchoring; Modular design; Convolutional neural network; Rock bolt; Shear (geology); Artificial neural network; Deformation (meteorology)","score_opus":0.012604235525330345,"score_gpt":0.26496248077203266,"score_spread":0.2523582452467023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117453359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27160996,0.00092819147,0.7213159,0.00027759478,0.000109956054,0.000058920774,0.0002495273,0.0022409097,0.0032089525],"genre_scores_gemma":[0.93882585,0.00028098357,0.05682401,0.00007579155,0.000026408212,0.000047017515,0.00035467572,0.00003336516,0.0035318788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998462,0.000012772739,0.000006586026,0.00005484135,0.000041823845,0.000037756345],"domain_scores_gemma":[0.9998336,0.000042524574,0.00003038173,0.000015770825,0.000065727625,0.00001207412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029284327,0.00072569284,0.0003645011,0.00057561375,0.00017457046,0.00033280157,0.0006712644,0.00047563802,0.0007435693],"category_scores_gemma":[0.0005600661,0.0002533155,0.00037331702,0.00034859314,0.00023829234,0.00048156525,0.00035780045,0.000436573,0.00024039591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003520231,0.00021355171,0.00866484,0.00010377035,0.00015162969,0.00026758705,0.00008369324,0.37564224,0.09974786,0.0017975648,0.0029435703,0.51003164],"study_design_scores_gemma":[0.000002397985,0.00001934396,0.0014365797,0.0000029076896,0.0000118236885,0.000019749728,0.0000039554793,0.99239784,0.005676201,0.00024807328,0.00017683102,0.0000043607565],"about_ca_topic_score_codex":0.006822139,"about_ca_topic_score_gemma":0.008280564,"teacher_disagreement_score":0.006822139,"about_ca_system_score_codex":0.00058159337,"about_ca_system_score_gemma":0.00041680527,"threshold_uncertainty_score":0.013564885},"labels":[],"label_agreement":null},{"id":"W7117465268","doi":"10.3390/s26010172","title":"Real-Time Radar-Based Hand Motion Recognition on FPGA Using a Hybrid Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Robustness (evolution); Field-programmable gate array; Deep learning; Convolutional neural network; Normalization (sociology); Pipeline (software); Support vector machine; Discriminative model","score_opus":0.02639725690308513,"score_gpt":0.2569198000255178,"score_spread":0.23052254312243264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117465268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15528663,0.0010068438,0.8171941,0.00027723596,0.00018076289,0.00015043688,0.00071594014,0.01632436,0.008863703],"genre_scores_gemma":[0.8389202,0.00033840834,0.15221559,0.00021539483,0.00002393347,0.000121111785,0.0008991063,0.00011482523,0.007151363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988794,0.000009776315,0.0000063674274,0.000034099176,0.000041841668,0.000020034271],"domain_scores_gemma":[0.9999043,0.000023749304,0.000013912902,0.000018547804,0.000032229178,0.0000073157157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017744846,0.00052993186,0.0003113146,0.0002899037,0.00009761295,0.00033875377,0.000672127,0.00024191709,0.0032803877],"category_scores_gemma":[0.00036034535,0.00018059139,0.00021286786,0.0002114938,0.0001038978,0.00045045363,0.00026675698,0.0003958568,0.0010796387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056374975,0.00018600906,0.003306993,0.00024624012,0.000106963715,0.0002674189,0.000057247435,0.21421301,0.09574831,0.0024485,0.008372292,0.6744832],"study_design_scores_gemma":[0.000017770606,0.000103232545,0.0009616937,0.000013725749,0.00001392149,0.000065207496,0.00000892564,0.9758263,0.020591045,0.00030411608,0.0020838042,0.000010208538],"about_ca_topic_score_codex":0.005373812,"about_ca_topic_score_gemma":0.010086258,"teacher_disagreement_score":0.005373812,"about_ca_system_score_codex":0.0003825192,"about_ca_system_score_gemma":0.00050456845,"threshold_uncertainty_score":0.01097405},"labels":[],"label_agreement":null},{"id":"W7117478825","doi":"10.3390/s26010175","title":"Advancing Home Rehabilitation: The PlanAID Robot’s Approach to Upper-Body Exercise Through Impedance Control","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Limiting; Inertia; Robot; Stiffness; Rehabilitation; Impedance control; Haptic technology; Control (management)","score_opus":0.0033483563922577482,"score_gpt":0.2171111536633959,"score_spread":0.21376279727113814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117478825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1406051,0.00064191,0.83301586,0.0005872095,0.00019416187,0.00056093617,0.00015262904,0.003483975,0.0207582],"genre_scores_gemma":[0.74642617,0.0005367593,0.2366498,0.00031788505,0.000059076556,0.00041102088,0.00018303028,0.00010613427,0.015310144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985635,0.000021195407,0.0000074935383,0.00002583411,0.00007268478,0.000016346316],"domain_scores_gemma":[0.9999205,0.000017141763,0.0000092752325,0.000014774192,0.000022686245,0.000015603258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017503316,0.00047176515,0.00024113644,0.00020751951,0.00019024003,0.0004314966,0.00066172925,0.00040296416,0.0037511825],"category_scores_gemma":[0.00030366945,0.00017036569,0.00018325997,0.00009683447,0.00046224165,0.00044333976,0.000795488,0.0003909164,0.0006408515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008811624,0.00041752655,0.0028294118,0.00096490345,0.00007126588,0.0006987098,0.00072760135,0.018864749,0.3936044,0.007376997,0.0065171714,0.5670461],"study_design_scores_gemma":[0.0006871571,0.007986426,0.02738457,0.00032292088,0.000246235,0.0055015837,0.0008454007,0.34455815,0.37340036,0.007362945,0.2314717,0.00023255304],"about_ca_topic_score_codex":0.00081997266,"about_ca_topic_score_gemma":0.0011830741,"teacher_disagreement_score":0.0037511825,"about_ca_system_score_codex":0.0001251327,"about_ca_system_score_gemma":0.0003928598,"threshold_uncertainty_score":0.012548983},"labels":[],"label_agreement":null},{"id":"W7117497929","doi":"10.3390/s26010179","title":"RSONAR: Data-Driven Evaluation of Dual-Use Star Tracker for Stratospheric Space Situational Awareness (SSA)","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; York University","funders":"Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Astrometry; Situation awareness; Calibration; Monochromatic color; Image quality; Radiometric calibration; Object (grammar); Space (punctuation)","score_opus":0.052241535267696024,"score_gpt":0.30045713866272933,"score_spread":0.2482156033950333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117497929","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97736835,0.000079551515,0.011691332,0.00006493418,0.000042474763,0.00033477877,0.0034110036,0.0028954153,0.0041121985],"genre_scores_gemma":[0.9594588,0.000041269188,0.029108193,0.000044816636,0.000011861745,0.00012419159,0.009707902,0.0002558727,0.0012470889],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992663,0.00019779564,0.000033179153,0.00015289888,0.00026652814,0.00008323248],"domain_scores_gemma":[0.9992093,0.000112536036,0.00009249249,0.00012050831,0.0003356692,0.00012959947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020967308,0.00047460856,0.00025814938,0.0005450576,0.0002243556,0.0005783471,0.00066355284,0.00029207874,0.00087185693],"category_scores_gemma":[0.0015553901,0.00010895124,0.00022190757,0.00035862412,0.00021519058,0.0004973891,0.0004888731,0.00022859711,0.0005587648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060548927,0.0024742146,0.3715324,0.0004222959,0.0005203119,0.00077135593,0.0013377345,0.08579649,0.18028353,0.0027738442,0.021091372,0.32694164],"study_design_scores_gemma":[0.0005663539,0.004933001,0.46073663,0.000040667765,0.00014497602,0.00036740425,0.0007348453,0.44261873,0.06927132,0.0005712947,0.019902913,0.000111895846],"about_ca_topic_score_codex":0.008774587,"about_ca_topic_score_gemma":0.016330244,"teacher_disagreement_score":0.008774587,"about_ca_system_score_codex":0.00046837653,"about_ca_system_score_gemma":0.0005602807,"threshold_uncertainty_score":0.017446995},"labels":[],"label_agreement":null},{"id":"W7117695578","doi":"10.3390/s26010232","title":"Outdoor Walking Classification Based on Inertial Measurement Unit and Foot Pressure Sensor Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds National de la Recherche Luxembourg","keywords":"Inertial measurement unit; Gait; Pressure sensor; Units of measurement; Stairs; Gait analysis; Sliding window protocol; Segmentation","score_opus":0.11949944888630759,"score_gpt":0.38789462375888395,"score_spread":0.26839517487257636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117695578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96597487,0.00027654914,0.03083922,0.00006188136,0.0000493278,0.000045638895,0.0010524794,0.0003736318,0.0013264273],"genre_scores_gemma":[0.99346775,0.00006430797,0.004908109,0.000016759266,0.000015973712,0.000027919446,0.0010019104,0.0000101995,0.00048708523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986887,0.000021605083,0.000009401674,0.00005083961,0.000022157375,0.000027047672],"domain_scores_gemma":[0.99975985,0.00006780743,0.000043762073,0.000020123363,0.00007513462,0.00003338425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021390243,0.00060354744,0.00033645798,0.00078523444,0.00009599122,0.00031795196,0.00020462314,0.00035940754,0.0012607829],"category_scores_gemma":[0.0010342534,0.00013017225,0.00033209161,0.0005275016,0.00008667957,0.0003038864,0.0002731596,0.00018866699,0.0006338315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021907797,0.0006497513,0.40703547,0.00038781113,0.00038129758,0.0005188147,0.00029771254,0.04743733,0.0599465,0.0002750286,0.0040516327,0.47682783],"study_design_scores_gemma":[0.000027894399,0.00041845732,0.546297,0.00005409812,0.00010473162,0.00026720398,0.00016404848,0.44376138,0.007611163,0.00046376995,0.0008010268,0.000029262339],"about_ca_topic_score_codex":0.002548659,"about_ca_topic_score_gemma":0.0044507687,"teacher_disagreement_score":0.002548659,"about_ca_system_score_codex":0.00011066836,"about_ca_system_score_gemma":0.00011404537,"threshold_uncertainty_score":0.005067587},"labels":[],"label_agreement":null},{"id":"W7117744886","doi":"10.3390/s26010226","title":"Comparative Evaluation of Bandit-Style Heuristic Policies for Moving Target Detection in a Linear Grid Environment","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Defense Acquisition Program Administration","keywords":"Heuristics; Grid; Heuristic; Greedy algorithm; Monte Carlo method; Posterior probability; Probability distribution; Sampling (signal processing); Bayesian probability","score_opus":0.139089121442262,"score_gpt":0.4653359788925978,"score_spread":0.3262468574503358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117744886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84763044,0.00352864,0.13341494,0.001015976,0.00018997346,0.00020744724,0.00026738457,0.000996492,0.012748743],"genre_scores_gemma":[0.98305917,0.0003147562,0.015868878,0.000089901034,0.000012596817,0.000050519153,0.000113747374,0.00003882432,0.0004517597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788314,0.0010639223,0.0001192716,0.0002497996,0.00035754582,0.0003262093],"domain_scores_gemma":[0.9817122,0.015030489,0.0008294057,0.0009091565,0.00093811646,0.0005806124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041936347,0.00081730605,0.0010822378,0.00084589404,0.00050379446,0.001087301,0.0012212105,0.0012124216,0.001062318],"category_scores_gemma":[0.020123549,0.00028067327,0.00031441456,0.00082286296,0.0009204873,0.0013135443,0.0009790331,0.00088385906,0.00022455827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080698763,0.0002445761,0.0019462141,0.00012566954,0.000060541508,0.000034050507,0.00005556814,0.9677183,0.00039935947,0.0035579607,0.0007354576,0.024315238],"study_design_scores_gemma":[0.00006566554,0.00027313302,0.00039294147,0.000014193357,0.000014293535,0.00001531929,0.000046057972,0.99657404,0.00048702757,0.001933754,0.00017575656,0.000007895884],"about_ca_topic_score_codex":0.008892919,"about_ca_topic_score_gemma":0.006558863,"teacher_disagreement_score":0.008892919,"about_ca_system_score_codex":0.0018743122,"about_ca_system_score_gemma":0.0019517574,"threshold_uncertainty_score":0.022178292},"labels":[],"label_agreement":null}]}