{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":166,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":166,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"833d7226e829","filters":{"venue":"Measurement"}},"results":[{"id":"W2049550263","doi":"10.1016/j.measurement.2013.11.012","title":"Condition monitoring and fault diagnosis of planetary gearboxes: A review","year":2013,"lang":"en","type":"review","venue":"Measurement","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":712,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Fault (geology); Notice; Condition monitoring; Engineering; Computer science; Systems engineering; Geology; Seismology; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06588097731286838,"gpt":0.2750077527214904,"spread":0.209126775408622,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003355789,0.0002570818,0.001078523,0.0001192899,0.00002172638,0.00001971111,0.0001150233,0.0001045749,0.00006890458],"category_scores_gemma":[0.00002826554,0.0002142466,0.0002134902,0.0001446334,0.00001424403,0.00003374466,0.00002626235,0.0001830265,0.00004316858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078065,"about_ca_system_score_gemma":0.00001833481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005671207,"about_ca_topic_score_gemma":0.000004381368,"domain_scores_codex":[0.9987689,0.0000440729,0.0004695371,0.0001777746,0.0003854923,0.0001541682],"domain_scores_gemma":[0.9994769,0.0000268588,0.0001108966,0.0002488355,0.00007471101,0.00006186566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[9.805515e-8,0.0000142701,0.0001141504,0.1434197,0.0005565731,0.000002226707,0.00000893309,0.00006806697,0.000002511746,0.000002379703,0.0009017776,0.8549093],"study_design_scores_gemma":[0.00006753439,0.00002209558,0.0001019331,0.1305762,0.003040064,0.000006521185,0.000004125467,0.000215186,0.00001062099,0.000007341454,0.865582,0.000366376],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002245529,0.9990193,0.0000291187,0.000004361897,0.0001517652,0.0004881581,0.00002460054,0.00004624576,0.0002139668],"genre_scores_gemma":[0.0003492956,0.999102,0.0001336582,0.000002654942,0.00006398616,0.0002437683,0.00005749117,0.0000346828,0.00001246872],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8646802,"threshold_uncertainty_score":0.8736721,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994198993","doi":"10.1016/j.measurement.2012.05.032","title":"State of the art review of inspection technologies for condition assessment of water pipes","year":2012,"lang":"en","type":"article","venue":"Measurement","topic":"Water Systems and Optimization","field":"Engineering","cited_by":462,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"Battelle; U.S. Environmental Protection Agency","keywords":"Mains electricity; Structural health monitoring; Engineering; Emerging technologies; State (computer science); Construction engineering; Forensic engineering; Reliability engineering; Computer science; Civil engineering; Systems engineering; Risk analysis (engineering); Electrical engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02751132919675369,"gpt":0.2410179579384182,"spread":0.2135066287416645,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000487706,0.00004725678,0.0001239563,0.00002826522,0.00001212817,0.000001417155,0.00004475508,0.0000170804,0.000004317947],"category_scores_gemma":[0.00001240171,0.00002713775,0.00004016052,0.00004442436,0.00001386449,0.00005535064,0.000009567843,0.00002171365,4.997533e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005797965,"about_ca_system_score_gemma":0.000004784473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003372838,"about_ca_topic_score_gemma":0.000009045055,"domain_scores_codex":[0.9994475,0.00001492107,0.0002415237,0.00003341005,0.0001896252,0.00007302538],"domain_scores_gemma":[0.9996567,0.000002860561,0.00006695565,0.0001198467,0.0001482991,0.000005273507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001716465,0.0004056353,0.018712,0.0703807,0.0005210036,6.444702e-8,0.001341382,0.07731684,0.7805948,0.001393662,0.03856056,0.01075617],"study_design_scores_gemma":[0.00014681,0.00003632723,0.005232395,0.001832597,0.00003744695,3.36359e-7,0.00003151008,0.001214222,0.9858004,0.00007097986,0.005546344,0.00005061156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1817247,0.03312831,0.7619284,0.001371237,0.004310594,0.007045984,0.0001352099,0.0007475481,0.009608039],"genre_scores_gemma":[0.9988827,0.000432695,0.0005862335,0.000003394715,0.000009060745,0.00005455709,0.000005500069,0.000005790053,0.00002012161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8171579,"threshold_uncertainty_score":0.1106645,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3173408620","doi":"10.1016/j.measurement.2021.109790","title":"Predicting the compressive strength of concrete containing metakaolin with different properties using ANN","year":2021,"lang":"en","type":"article","venue":"Measurement","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":187,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Metakaolin; Compressive strength; Materials science; Pozzolan; Cementitious; Properties of concrete; Composite material; Structural engineering; Geotechnical engineering; Cement; Engineering; Portland cement","retraction":null,"screen_n_in":null,"score":{"opus":0.0868389172463353,"gpt":0.2434418649039695,"spread":0.1566029476576342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003607394,0.0001489749,0.0002552686,0.00002960344,0.000097065,0.00006362633,0.0001293372,0.00002696183,0.00008952113],"category_scores_gemma":[0.0000474739,0.00008844961,0.00004298896,0.0000893475,0.00005342737,0.00005850703,0.00006680874,0.000121503,9.655232e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009432808,"about_ca_system_score_gemma":0.0000712861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006247014,"about_ca_topic_score_gemma":0.00004355953,"domain_scores_codex":[0.9985086,0.0001241044,0.0002649428,0.0001432634,0.0007002994,0.0002587907],"domain_scores_gemma":[0.9993239,0.00003034715,0.00005444653,0.0002403001,0.0003042108,0.00004678579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007025866,0.000006127453,0.003108896,0.0002248757,0.0003907937,0.00001071323,0.001052819,0.005080842,0.9894838,0.0000275445,0.00001700912,0.0005263163],"study_design_scores_gemma":[0.0005850458,0.00009107833,0.001066195,0.0003873032,0.000089484,0.000005938231,0.001629845,0.0439034,0.9518846,0.000002768618,0.0002255746,0.0001288268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963037,0.001904222,0.0007199813,0.0000364996,0.0001218358,0.0002521341,0.000009678633,0.00005315727,0.0005987642],"genre_scores_gemma":[0.9997168,0.00004886208,0.00008416003,0.00001296735,0.00006548794,0.0000324685,0.000004208444,0.0000225709,0.00001252804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03882256,"threshold_uncertainty_score":0.360687,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2606830439","doi":"10.1016/j.measurement.2017.04.028","title":"Review of efficiency ranking methods in data envelopment analysis","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":154,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Data envelopment analysis; Ranking (information retrieval); Computer science; Data mining; Process (computing); Machine learning; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.5498016735291982,"gpt":0.5494259526118173,"spread":0.0003757209173809173,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.09335574,0.0001829726,0.000990956,0.0008877868,0.0003229736,0.0002188454,0.005588767,0.00004754576,0.000389428],"category_scores_gemma":[0.03170832,0.0001327952,0.0002919837,0.002399245,0.0001653231,0.0003198624,0.001014087,0.0001443415,0.00004962656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001592391,"about_ca_system_score_gemma":0.0002350633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000246743,"about_ca_topic_score_gemma":0.0006417555,"domain_scores_codex":[0.9911195,0.001197605,0.001839677,0.001010979,0.004493493,0.0003387392],"domain_scores_gemma":[0.9908828,0.0004496576,0.00137073,0.006332786,0.0008808677,0.00008318224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000317188,0.0009926674,0.1271327,0.000723966,0.001147046,0.00001936343,0.0009451205,0.003062663,0.007713718,0.0003600511,0.007190304,0.8506807],"study_design_scores_gemma":[0.002100179,0.0001159929,0.5847705,0.01213114,0.005263257,0.000004771529,0.0004523373,0.1420717,0.01537714,0.003616858,0.2324144,0.001681788],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006981378,0.09252167,0.8747582,0.005841612,0.0008036926,0.0009185259,0.00002635081,0.00003983515,0.01810876],"genre_scores_gemma":[0.9297581,0.003356852,0.06608199,0.000576603,0.00003303855,0.00001577541,0.0000101,0.00001230786,0.0001552456],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9227767,"threshold_uncertainty_score":0.9997915,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084321111","doi":"10.1016/j.measurement.2010.09.010","title":"Design and fabrication of a six-dimensional wrist force/torque sensor based on E-type membranes compared to cross beams","year":2010,"lang":"en","type":"article","venue":"Measurement","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":121,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Decoupling (probability); Torque; Linearity; Calibration; Sensitivity (control systems); Nonlinear system; Fabrication; Engineering; Interference (communication); Materials science; Electronic engineering; Control theory (sociology); Computer science; Acoustics; Physics; Electrical engineering; Control engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03045942653513514,"gpt":0.2353360924995861,"spread":0.204876665964451,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004311019,0.0001181589,0.0001507136,0.00005743074,0.00004706447,0.00002221817,0.00006649143,0.00005391217,0.00003051462],"category_scores_gemma":[0.00006604435,0.0001091133,0.00002268546,0.00008362423,0.00001700931,0.00002407452,0.00000985729,0.00008460339,0.00001013061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004698615,"about_ca_system_score_gemma":0.00002436245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001752603,"about_ca_topic_score_gemma":0.00001193726,"domain_scores_codex":[0.999125,0.00001997255,0.0001884486,0.0001473898,0.000379966,0.0001392092],"domain_scores_gemma":[0.9994107,0.00003769425,0.000030188,0.0002116839,0.0002180896,0.00009159111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000456225,0.00002752015,0.00008560232,0.00004662088,0.000009515743,6.138468e-7,0.00003835374,0.7125461,0.2867614,0.0001481711,0.00009049086,0.0002000299],"study_design_scores_gemma":[0.0004367063,0.0001046422,0.00214637,0.00003341119,0.00001332829,0.000001538733,0.000007162736,0.9318333,0.06505806,0.00008346481,0.0001409633,0.0001410593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1516698,0.00002784742,0.8467144,0.0000799519,0.0006506827,0.0005087081,0.000004843051,0.00008852682,0.0002552916],"genre_scores_gemma":[0.8763026,0.000001018985,0.123515,0.00008472738,0.00002977418,0.0000152602,0.000005683842,0.00001873555,0.00002717746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7246328,"threshold_uncertainty_score":0.4449513,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2469821678","doi":"10.1016/j.measurement.2016.06.042","title":"Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany","year":2016,"lang":"en","type":"article","venue":"Measurement","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extreme learning machine; Flood myth; Mean squared error; Flood forecasting; Support vector machine; Computer science; Artificial neural network; Flooding (psychology); Ensemble forecasting; Coefficient of determination; Machine learning; Data mining; Artificial intelligence; Statistics; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.3011702310744153,"gpt":0.3056766215370506,"spread":0.00450639046263529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002197849,0.0003307226,0.0003147793,0.0000969918,0.000364184,0.00004632392,0.0002828315,0.00008606388,0.000430069],"category_scores_gemma":[0.0002509524,0.0002345809,0.00007650613,0.0002718189,0.0001493197,0.0004040004,0.0003889347,0.0003246758,0.00004468241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054651,"about_ca_system_score_gemma":0.00003005196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004331368,"about_ca_topic_score_gemma":0.00573119,"domain_scores_codex":[0.9964968,0.0004372374,0.0005482762,0.0007683114,0.001032827,0.0007165676],"domain_scores_gemma":[0.9991366,0.00003259264,0.0001920346,0.0003747043,0.00003148194,0.0002325683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007893462,0.002031376,0.244039,0.00001053117,0.00002808313,0.001979877,0.002686935,0.6237965,0.1019711,0.000002718421,0.00001216467,0.02336272],"study_design_scores_gemma":[0.0015995,0.000414522,0.004188097,0.00009078542,0.00004683613,0.0004079154,0.0001351854,0.9922386,0.0002620278,0.000102226,0.0001023648,0.0004118866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850143,0.00002629344,0.01392686,0.00008488292,0.0001079027,0.0005408814,0.0000103303,0.0001266765,0.0001619301],"genre_scores_gemma":[0.9952076,0.000001523363,0.004510791,0.00006014902,0.0000728797,0.00001825683,0.00000311074,0.00004823427,0.00007745001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3684421,"threshold_uncertainty_score":0.956593,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2357131595","doi":"10.1016/j.measurement.2016.05.029","title":"Strength measurement and textural characteristics of tropical residual soil stabilised with liquid polymer","year":2016,"lang":"en","type":"article","venue":"Measurement","topic":"Geotechnical Engineering and Soil Stabilization","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Universiti Teknologi Malaysia; Ministry of Higher Education, Malaysia","keywords":"Laterite; Compressive strength; Curing (chemistry); Materials science; Porosity; Soil water; Atterberg limits; Particle size; Composite material; Scanning electron microscope; Specific surface area; Geotechnical engineering; Soil science; Environmental science; Metallurgy; Geology; Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.01523267350289088,"gpt":0.1744827636688457,"spread":0.1592500901659548,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002758436,0.0001794166,0.0002316247,0.00005327187,0.00002800439,0.00001170387,0.00007907492,0.00006946604,0.00002462803],"category_scores_gemma":[0.0001156768,0.0001149736,0.00002941806,0.00008274055,0.00006121084,0.00005815757,0.00001858007,0.00008381515,0.000002356338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716604,"about_ca_system_score_gemma":0.00003465619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009208477,"about_ca_topic_score_gemma":0.00004591424,"domain_scores_codex":[0.9984844,0.00002851763,0.0003018916,0.0001761628,0.0007716133,0.0002373666],"domain_scores_gemma":[0.9994035,0.00002065206,0.00003789466,0.0002262019,0.0002051641,0.0001065451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005148983,0.0002477768,0.004856816,0.0005848237,0.0002785533,0.000005911519,0.0003807155,0.00425575,0.9429979,0.0008595692,0.0002033312,0.04481398],"study_design_scores_gemma":[0.005160816,0.002158727,0.3147888,0.001318464,0.0002329108,0.0000132238,0.000112925,0.005980645,0.6661018,0.00004247792,0.002807961,0.001281247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876249,0.0004579126,0.01110755,0.0001864866,0.0001048826,0.0001644847,0.00001381357,0.0001996318,0.0001402859],"genre_scores_gemma":[0.9997442,0.00004835417,0.00008632705,0.000006077789,0.00005223731,0.00002261451,0.000001504526,0.00002873774,0.000009975462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3099319,"threshold_uncertainty_score":0.4688489,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995286370","doi":"10.1016/j.measurement.2014.11.002","title":"Developing a practical evaluation framework for identifying critical factors to achieve supply chain agility","year":2014,"lang":"en","type":"article","venue":"Measurement","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":87,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Supply chain; Fuzzy logic; Construct (python library); Computer science; Key (lock); Process management; Agile software development; Process (computing); Vagueness; Automotive industry; Analytic network process; Risk analysis (engineering); Critical success factor; Supply chain network; Agile manufacturing; Systems engineering; Supply chain management; Knowledge management; Engineering; Analytic hierarchy process; Operations research; Business; Artificial intelligence; Software engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2836306405717745,"gpt":0.3998136848294496,"spread":0.1161830442576751,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01042349,0.0002667537,0.0003007621,0.0002276095,0.0004695308,0.0006009164,0.0002598815,0.0001084685,0.0002354098],"category_scores_gemma":[0.01458319,0.0002551249,0.000134612,0.0003571942,0.00004222645,0.0007447904,0.0002202802,0.0001978837,0.0002102271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004115871,"about_ca_system_score_gemma":0.0000763191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001452533,"about_ca_topic_score_gemma":0.000240195,"domain_scores_codex":[0.9964151,0.000138115,0.0005261471,0.0005867361,0.001760741,0.0005731591],"domain_scores_gemma":[0.9980916,0.0003552189,0.0001320477,0.000411181,0.0009595693,0.00005033752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001763028,0.0002389356,0.003771807,0.0008326102,0.00007800262,0.000001113541,0.0003608531,0.0001651256,0.0002799577,0.9744969,0.004117012,0.0154814],"study_design_scores_gemma":[0.001355722,0.0001322902,0.06608243,0.0006693165,0.0005079104,7.336033e-7,0.002050166,0.02477155,0.0007444398,0.632682,0.2698281,0.001175323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03275427,0.00002218329,0.9270722,0.03568316,0.001149573,0.001640111,0.000003010864,0.0001363305,0.001539133],"genre_scores_gemma":[0.9556046,7.742021e-7,0.0373431,0.005603859,0.0009700965,0.0003950967,0.00003417064,0.00003228056,0.00001602076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9228503,"threshold_uncertainty_score":0.9999901,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993608687","doi":"10.1016/j.measurement.2013.04.035","title":"MEMS-based rotary strapdown inertial navigation system","year":2013,"lang":"en","type":"article","venue":"Measurement","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Calgary","keywords":"Inertial measurement unit; Inertial navigation system; Inertial reference unit; Rotation (mathematics); Accelerometer; Gyroscope; Inertial frame of reference; Acceleration; Computer science; Engineering; Control theory (sociology); Computer vision; Artificial intelligence; Physics; Aerospace engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01540670765069955,"gpt":0.1818958874738899,"spread":0.1664891798231904,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001973991,0.0001322748,0.0001131337,0.00004657088,0.00005834635,0.00003990957,0.00007631496,0.00006416927,0.0001024059],"category_scores_gemma":[0.000007395439,0.0001231548,0.00004978678,0.0001108361,0.00001053597,0.0001221085,0.000004640091,0.00009994482,0.000313108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003227903,"about_ca_system_score_gemma":0.00001329085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002657405,"about_ca_topic_score_gemma":0.000009486437,"domain_scores_codex":[0.9989427,0.00002538584,0.0002366699,0.0001176176,0.0004779728,0.0001996826],"domain_scores_gemma":[0.9995774,0.000005822953,0.00002478069,0.0001662993,0.000156601,0.00006908631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001173309,0.00003481374,0.0003795481,0.0003014418,0.0000461251,0.000004829514,0.0001069559,0.07271506,0.9091945,0.0001066997,0.002798192,0.01430012],"study_design_scores_gemma":[0.0009423075,0.00007879543,0.01122876,0.0003122385,0.0000449498,0.000005294622,0.0000941051,0.2649091,0.7199715,0.00002988854,0.001960195,0.0004229201],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811346,0.0002251716,0.01096355,0.000110754,0.0009248761,0.0006671918,0.000004683205,0.0007439451,0.005225249],"genre_scores_gemma":[0.9993473,7.061389e-7,0.0002534039,0.0000232415,0.0002148599,0.00009218193,0.00003258139,0.00002433744,0.00001133265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.192194,"threshold_uncertainty_score":0.5022106,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2792557492","doi":"10.1016/j.measurement.2018.02.070","title":"Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":69,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph; University of Ottawa","funders":"","keywords":"Adaptive neuro fuzzy inference system; Particle swarm optimization; Fitness function; Genetic algorithm; Algorithm; Channel (broadcasting); Computer science; Fuzzy logic; Artificial intelligence; Machine learning; Fuzzy control system","retraction":null,"screen_n_in":null,"score":{"opus":0.02526651951010572,"gpt":0.206480159710049,"spread":0.1812136401999433,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003112049,0.0001207371,0.0001938095,0.00007287804,0.00007739156,0.0000118476,0.00007511352,0.00001635387,0.00005804441],"category_scores_gemma":[0.00001447817,0.00009121899,0.00003596826,0.0003002818,0.0001790109,0.00005570922,0.00008758094,0.00003181687,0.000003753492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007641036,"about_ca_system_score_gemma":0.000006369535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003805231,"about_ca_topic_score_gemma":0.00001870742,"domain_scores_codex":[0.9988149,0.0000213724,0.0002015748,0.0003012531,0.000482329,0.0001785996],"domain_scores_gemma":[0.999608,0.000008606628,0.00007198098,0.0002046372,0.00004685749,0.00005985068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001007496,0.000161647,0.02136594,0.00001026214,0.0002027818,7.937649e-7,0.0002742447,0.9738019,0.0001736104,0.000001006179,0.00006872215,0.003838323],"study_design_scores_gemma":[0.0002262746,0.000388277,0.004091805,0.0000134842,0.0002823239,1.500214e-7,0.00005278699,0.9804302,0.01439937,0.00001186057,0.000007704942,0.00009578817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4974155,0.000004433896,0.5014032,0.0000665023,0.000009832454,0.0002848695,0.00001708446,0.00001125124,0.0007873456],"genre_scores_gemma":[0.9748924,0.000002488279,0.02491698,0.0001307885,0.000006863105,0.00001393073,0.00001097863,0.000007171471,0.0000183406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.477477,"threshold_uncertainty_score":0.3719802,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404367609","doi":"10.1016/j.measurement.2024.116216","title":"A roadmap to fault diagnosis of industrial machines via machine learning: A brief review","year":2024,"lang":"en","type":"review","venue":"Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Fault (geology); Engineering; Machine learning; Artificial intelligence; Computer science; Manufacturing engineering; Systems engineering; Industrial engineering; Reliability engineering; Geology; Seismology","retraction":null,"screen_n_in":null,"score":{"opus":0.08398462316522147,"gpt":0.2963229695184545,"spread":0.2123383463532331,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001306046,0.0006241225,0.002356045,0.000308608,0.00003928904,0.0000497967,0.0003268554,0.0002821092,0.000176859],"category_scores_gemma":[0.0003306498,0.0004786404,0.0008031284,0.0007624447,0.00001110708,0.0000316115,0.00006501885,0.0008307587,0.0004348357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004619339,"about_ca_system_score_gemma":0.00009034075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002120519,"about_ca_topic_score_gemma":0.0001302099,"domain_scores_codex":[0.9967505,0.0002999356,0.001272578,0.0004185874,0.0009375597,0.0003207906],"domain_scores_gemma":[0.9990261,0.00003907833,0.0001872884,0.000437934,0.0001190617,0.0001905413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002641172,0.00002268284,0.000004282987,0.06834983,0.0005576909,0.000007470524,0.00002067618,0.00008511534,0.000003708453,0.000002609145,0.01802465,0.9129186],"study_design_scores_gemma":[0.0001846628,0.00008473203,1.7918e-7,0.1068564,0.001589392,0.00001982352,0.000001239639,0.0005284926,0.000006486484,0.000001172359,0.8903516,0.0003758719],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.568162e-7,0.9942495,0.0002117677,0.00009356433,0.001934945,0.002219724,0.00005450342,0.0003759918,0.0008593093],"genre_scores_gemma":[0.0002034368,0.9969718,0.00001163501,0.00005267546,0.000534195,0.001782349,0.00002744063,0.0001371978,0.0002792574],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9125428,"threshold_uncertainty_score":0.9997665,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2804872033","doi":"10.1016/j.measurement.2018.05.038","title":"Condition monitoring and state classification of gearboxes using stochastic resonance and hidden Markov models","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Hidden Markov model; Computer science; Stochastic resonance; Pattern recognition (psychology); Viterbi algorithm; Artificial intelligence; Feature extraction; Probabilistic logic; Fault detection and isolation; Algorithm; Noise (video)","retraction":null,"screen_n_in":null,"score":{"opus":0.05728096283180144,"gpt":0.2753328246908843,"spread":0.2180518618590828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002186538,0.00007241873,0.0000859815,0.00003750288,0.00007957541,0.00002305169,0.00003002289,0.00001378345,0.000003893816],"category_scores_gemma":[0.0000051549,0.00007065611,0.00001137753,0.00004862315,0.00006399348,0.00008891064,0.00001933636,0.00003352718,5.351798e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003547914,"about_ca_system_score_gemma":0.00002643441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001166756,"about_ca_topic_score_gemma":0.000003168273,"domain_scores_codex":[0.9994175,0.00001664783,0.0001457027,0.0001455746,0.0001790158,0.00009554022],"domain_scores_gemma":[0.9995706,0.00001107429,0.0001005873,0.00008957838,0.000196131,0.00003204695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000180567,0.000250656,0.06183146,0.0001274316,0.0001865085,2.063092e-7,0.003188846,0.001444564,0.4894156,0.02563581,0.00004907428,0.4176892],"study_design_scores_gemma":[0.0009713818,0.0001602457,0.248301,0.0004064691,0.00009297024,0.000001119142,0.0005990278,0.7035832,0.007067917,0.03849588,0.00002682146,0.0002939652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.722693,0.0001240833,0.2767181,0.00001674619,0.00007905851,0.0001359235,0.00001174796,0.000005350269,0.0002159759],"genre_scores_gemma":[0.9980251,0.000002283099,0.001781298,0.000001584959,0.0001567901,0.0000109345,0.000003505619,0.000007224907,0.0000112331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7021387,"threshold_uncertainty_score":0.2881273,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1985986137","doi":"10.1016/j.measurement.2010.02.014","title":"Support vector machine based data processing algorithm for wear degree classification of slurry pump systems","year":2010,"lang":"en","type":"article","venue":"Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Feature selection; Support vector machine; Outlier; Data mining; Fault (geology); Computer science; Algorithm; Feature (linguistics); Statistical classification; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.086666520177986,"gpt":0.2647865828598872,"spread":0.1781200626819012,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068891,0.0001368349,0.0002091873,0.00007607455,0.00006723582,0.00004887126,0.0002648828,0.00008455564,0.00002095635],"category_scores_gemma":[0.00006094247,0.0001267712,0.0000428296,0.0001024634,0.00001590864,0.0001135954,0.00001236437,0.000131294,0.00001116206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007775483,"about_ca_system_score_gemma":0.00007611922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005672226,"about_ca_topic_score_gemma":0.0001650122,"domain_scores_codex":[0.9987214,0.00002566113,0.0003772878,0.000216691,0.0004743077,0.00018472],"domain_scores_gemma":[0.9990658,0.00001566489,0.00009540637,0.0005494972,0.0002029883,0.00007061059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002839485,0.0001275062,0.000248865,0.001011451,0.00009846757,9.295934e-7,0.00008487221,0.000961833,0.7698805,0.00003429415,0.001687336,0.2258356],"study_design_scores_gemma":[0.0007275483,0.00005051414,0.0009583645,0.00005481327,0.00003606888,0.000003041859,0.00005057251,0.9357777,0.008932773,0.000001228958,0.05327456,0.0001328064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007958712,0.0009310213,0.9825292,0.0001542607,0.004295765,0.001848006,0.0003677509,0.0005391458,0.001376182],"genre_scores_gemma":[0.997604,0.000001361843,0.001794254,0.000009080513,0.0002454869,0.0001776716,0.00007595262,0.00003564099,0.00005658863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9896452,"threshold_uncertainty_score":0.5169579,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392652657","doi":"10.1016/j.measurement.2024.114451","title":"Extracting random forest features with improved adaptive particle swarm optimization for industrial robot fault diagnosis","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Random forest; Fault (geology); Robot; Computer science; Feature (linguistics); Artificial intelligence; Wavelet; Feature extraction; Set (abstract data type); Pattern recognition (psychology); Data mining; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.04222036006455371,"gpt":0.2295403305296606,"spread":0.1873199704651068,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004469325,0.0001580859,0.0001733219,0.00004830782,0.00008860629,0.0001586468,0.00005694958,0.00008418942,0.00001460856],"category_scores_gemma":[0.00009330187,0.0001258737,0.00007476134,0.0001526854,0.000009528545,0.0001463469,0.000005151422,0.0001506311,0.000004910883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002180276,"about_ca_system_score_gemma":0.00003178962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007328062,"about_ca_topic_score_gemma":0.0004146706,"domain_scores_codex":[0.9990313,0.00003220324,0.000218415,0.0001959813,0.0002876991,0.0002343668],"domain_scores_gemma":[0.9996071,0.00008395933,0.00002957778,0.0001094277,0.0001014548,0.00006851226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00036257,0.00002429661,0.0001157246,0.00006108266,0.0002258098,0.000002623084,0.0002297762,0.9680331,0.005520866,0.00002784743,0.0009583118,0.02443803],"study_design_scores_gemma":[0.003296515,0.0001963619,0.00006971332,0.0001858888,0.00009569235,0.000004431537,0.0002666324,0.9640533,0.02672555,0.000005508297,0.00491027,0.0001900851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04008386,0.003267967,0.949223,0.0004438879,0.002613703,0.002683265,0.00002030977,0.001103741,0.0005602706],"genre_scores_gemma":[0.9973261,0.00001356709,0.0007936886,0.0000155184,0.000456161,0.001288697,0.000004271463,0.00004335546,0.00005863331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9572423,"threshold_uncertainty_score":0.513298,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2074512445","doi":"10.1016/j.measurement.2012.05.031","title":"Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking","year":2012,"lang":"en","type":"article","venue":"Measurement","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Ordinal optimization; Ordinal data; Feature selection; Ordinal regression; Feature (linguistics); Selection (genetic algorithm); Machine learning; Artificial intelligence; Computer science; Data mining; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.07615207938449267,"gpt":0.2425724288296769,"spread":0.1664203494451842,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000808786,0.0001212333,0.0002603622,0.00008326564,0.00003328717,0.00001129299,0.0001000008,0.00005208707,0.0001788994],"category_scores_gemma":[0.0000294395,0.0001192301,0.00006357473,0.0001897484,0.00002145222,0.00005966974,0.00002322277,0.00008367568,0.000007580057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008927235,"about_ca_system_score_gemma":0.00001860779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004681138,"about_ca_topic_score_gemma":0.00007128216,"domain_scores_codex":[0.9988573,0.00003807354,0.0003066326,0.00009083788,0.0004611683,0.000246],"domain_scores_gemma":[0.9995559,0.0000222597,0.00006919148,0.0001920298,0.000103387,0.00005716602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001091077,0.0001117141,0.1776814,0.0001799046,0.0003309342,0.000001418073,0.0003724605,0.3927143,0.4275082,0.00003905838,0.00007314487,0.0009764819],"study_design_scores_gemma":[0.0007561248,0.00007077083,0.2798457,0.0003800828,0.0007808746,0.00000350457,0.0001874025,0.3101072,0.4064569,0.00003019543,0.0007733238,0.0006077902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958681,0.0003530281,0.002933585,0.000004706363,0.0001281194,0.00008181435,0.00003419128,0.0000370747,0.0005593305],"genre_scores_gemma":[0.9981689,0.00001253863,0.00174406,0.000002337122,0.00003325169,0.000001532483,0.000007372981,0.00002127653,0.000008717873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1021643,"threshold_uncertainty_score":0.4862062,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2969247748","doi":"10.1016/j.measurement.2019.106965","title":"In-line inspection solution for codes on complex backgrounds for the plastic container industry","year":2019,"lang":"en","type":"article","venue":"Measurement","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; Hunan Key Laboratory of Land and Resources Evaluation and Utilization","keywords":"Container (type theory); Line (geometry); Engineering drawing; Assembly line; Engineering; Computer science; Manufacturing engineering; Mechanical engineering; Mathematics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.123754333061636,"gpt":0.2958285551378412,"spread":0.1720742220762052,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004824153,0.00008085401,0.00009126782,0.00006791959,0.0001319388,0.00005495706,0.0003434729,0.0000799387,0.000004276348],"category_scores_gemma":[0.00007023537,0.00005670258,0.00003527869,0.0001681648,0.00002432458,0.0000979115,0.0000477619,0.0001156799,0.00001535077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002105828,"about_ca_system_score_gemma":0.00003428502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003132969,"about_ca_topic_score_gemma":0.000126505,"domain_scores_codex":[0.9992554,0.00001103873,0.0001449468,0.0002180496,0.0002074149,0.0001631485],"domain_scores_gemma":[0.9992692,0.0001591624,0.00005571398,0.0003557816,0.0001470291,0.00001309156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001476878,0.0005202522,0.002694448,0.00007442931,0.00007615358,2.668239e-7,0.0001879078,0.006764383,0.04364771,0.8773694,0.01780419,0.05071319],"study_design_scores_gemma":[0.002884907,0.001120366,0.05901439,0.00006608546,0.00002103019,0.000002998536,0.0002133155,0.8147299,0.01112341,0.01574326,0.0947489,0.0003314397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02627367,0.00005319031,0.9650474,0.006845418,0.0003369912,0.001082849,0.000004513103,0.0001407137,0.0002152438],"genre_scores_gemma":[0.9938219,0.000003288451,0.005198787,0.0002672159,0.00004875171,0.0005910489,0.000002571774,0.000005176045,0.00006132521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9675482,"threshold_uncertainty_score":0.2312264,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3043449013","doi":"10.1016/j.measurement.2020.108243","title":"Multi-frequency and multi-attribute GPR data fusion based on 2-D wavelet transform","year":2020,"lang":"en","type":"article","venue":"Measurement","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China; Zhejiang University; Ministry of Natural Resources","keywords":"Ground-penetrating radar; Wavelet transform; Wavelet; Computer science; Sensor fusion; Fusion; Image fusion; Remote sensing; Data mining; Artificial intelligence; Entropy (arrow of time); Pattern recognition (psychology); Computer vision; Geology; Radar; Image (mathematics); Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.1959774732062984,"gpt":0.2902823238791923,"spread":0.09430485067289396,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499488,0.0001337659,0.00013747,0.00001722842,0.00006243221,0.00002075051,0.00019527,0.00003768628,0.00002961187],"category_scores_gemma":[0.00005868367,0.0001187606,0.00002711521,0.0001157463,0.00001430626,0.00005072233,0.00002509792,0.0001317966,0.00003213101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003691762,"about_ca_system_score_gemma":0.00001202684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001501553,"about_ca_topic_score_gemma":0.00001538011,"domain_scores_codex":[0.9991291,0.00002372301,0.0001539506,0.0002571465,0.0002682875,0.0001677987],"domain_scores_gemma":[0.9994267,0.00001976188,0.00001382111,0.0003584256,0.00003947408,0.000141883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001715211,0.0004721996,0.0004524091,0.0003820166,0.0000695787,0.000006199835,0.0004030943,0.002501653,0.8115098,0.0001554865,0.002877587,0.1811528],"study_design_scores_gemma":[0.001980947,0.0001232454,0.02978685,0.00007160185,0.00005529803,3.642789e-7,0.00003642494,0.9299947,0.01770134,0.00004859613,0.01981731,0.0003833198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01112313,0.000250467,0.9816237,0.005130528,0.0001112151,0.0007269498,0.0003072465,0.0003226522,0.0004041115],"genre_scores_gemma":[0.8577827,0.0000265152,0.1416392,0.0003653278,0.00006376134,0.00004652698,0.00004735534,0.00002277479,0.000005854953],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.927493,"threshold_uncertainty_score":0.4842919,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2393219631","doi":"10.1016/j.measurement.2016.05.001","title":"Prioritizing deterioration factors of water pipelines using Delphi method","year":2016,"lang":"en","type":"article","venue":"Measurement","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Delphi method; Pipeline (software); Construct (python library); Set (abstract data type); Rank (graph theory); Delphi; Risk analysis (engineering); Computer science; Engineering; Operations research; Forensic engineering; Mathematics; Business; Artificial intelligence; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.09046211404576696,"gpt":0.3168071235415886,"spread":0.2263450094958216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005644496,0.0001186355,0.0001371848,0.00001970313,0.00009173306,0.0000113771,0.00009943038,0.00004261723,0.0004835251],"category_scores_gemma":[0.00002506416,0.00006483662,0.00005893149,0.0000451562,0.00006601565,0.0002509147,0.00009970342,0.00002919398,0.00005047965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005249733,"about_ca_system_score_gemma":0.000006035543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002997244,"about_ca_topic_score_gemma":0.00006558651,"domain_scores_codex":[0.9986401,0.00009070331,0.0002412018,0.000175421,0.0006276703,0.0002249286],"domain_scores_gemma":[0.9997013,0.00001230654,0.00007324148,0.0001330981,0.00001112628,0.00006898138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005195091,0.00004171998,0.1018582,0.000003562388,0.000008709051,4.89803e-7,0.0004410671,0.00001807857,0.8910828,0.000001774847,0.00001361978,0.006524822],"study_design_scores_gemma":[0.0003584253,0.00007069707,0.1951865,0.00004291099,0.0000313736,0.000001176196,0.0002414054,0.0000815461,0.8028536,0.0002896566,0.0006602964,0.0001823959],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970675,0.00001027671,0.02854488,0.00008547027,0.0001246781,0.000146946,0.000003334037,0.00001437122,0.0003950386],"genre_scores_gemma":[0.9934953,0.000005518922,0.00635929,0.00003338844,0.0000302257,0.000003710105,0.000001054245,0.00001192231,0.00005959228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09332839,"threshold_uncertainty_score":0.5294261,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139196926","doi":"10.1016/j.measurement.2008.03.018","title":"Power system frequency estimation using supervised Gauss–Newton algorithm","year":2008,"lang":"en","type":"article","venue":"Measurement","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Amplitude; Gauss; Algorithm; Convergence (economics); Mathematics; Newton's method; Power (physics); Control theory (sociology); SIGNAL (programming language); Tracking (education); Computer science; Nonlinear system; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.04213918438552415,"gpt":0.2129210679800045,"spread":0.1707818835944803,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004643481,0.0001689888,0.000203201,0.00007752976,0.0001289572,0.00002456674,0.00009947291,0.00007021497,0.0001104654],"category_scores_gemma":[0.00002626568,0.0001681718,0.00006235532,0.0001790683,0.00001832821,0.0001535716,0.00001025253,0.00007801586,0.0000578677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008489921,"about_ca_system_score_gemma":0.00005213031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000412993,"about_ca_topic_score_gemma":0.000004859037,"domain_scores_codex":[0.9985568,0.00006600893,0.0003637632,0.0001833223,0.0005974673,0.0002326616],"domain_scores_gemma":[0.9993559,0.000006487548,0.00003458337,0.0003039817,0.0001999495,0.0000990964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002004141,0.0003123376,0.008510171,0.001373158,0.0003652264,0.0001044966,0.005504992,0.9343176,0.03994081,0.000783911,0.004268687,0.004498535],"study_design_scores_gemma":[0.0004234575,0.00001991801,0.002108596,0.0001023507,0.00001402411,0.00004210978,0.0001148678,0.9938957,0.002541337,0.000006824196,0.0004920624,0.0002387674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03552214,0.0005115089,0.9540203,0.00002345813,0.001059519,0.0003838894,0.00001073243,0.0006177037,0.007850754],"genre_scores_gemma":[0.9737297,0.000004980922,0.02615852,0.00001047248,0.0000226213,0.00002034779,0.000006795649,0.0000284684,0.00001805755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9382076,"threshold_uncertainty_score":0.6857846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2900741959","doi":"10.1016/j.measurement.2018.11.052","title":"Research on the improved method for dual foot-mounted Inertial/Magnetometer pedestrian positioning based on adaptive inequality constraints Kalman Filter algorithm","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Military College of Canada","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Kalman filter; Inertial measurement unit; Computer science; Azimuth; Position (finance); Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1240958854402709,"gpt":0.3508628516076268,"spread":0.2267669661673559,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003548881,0.0002177956,0.0001971902,0.0002807436,0.0003633072,0.00008492353,0.0002265399,0.0001494109,0.0001479571],"category_scores_gemma":[0.0005552614,0.0001597013,0.00008776758,0.0004004628,0.000215981,0.00005232801,0.00003536326,0.000352016,0.00003116879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003941789,"about_ca_system_score_gemma":0.00006636883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000550732,"about_ca_topic_score_gemma":0.00005167917,"domain_scores_codex":[0.9978091,0.0003202168,0.0003224048,0.0003132696,0.0007582656,0.0004767141],"domain_scores_gemma":[0.9983166,0.0003906006,0.00004710221,0.0004280905,0.000758666,0.00005888951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002288561,0.00100133,0.0001924906,0.0004264321,0.001062385,0.000026571,0.003017208,0.02228107,0.2837256,0.01937677,0.03669237,0.6299092],"study_design_scores_gemma":[0.001281412,0.001564757,0.0004808303,0.0001154351,0.00002533007,0.000001209702,0.0005409517,0.649417,0.3439215,0.0005523669,0.001828795,0.0002704388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005491474,0.00002363966,0.9882545,0.0009237937,0.0004184669,0.001264955,0.00009011444,0.0004377102,0.00309528],"genre_scores_gemma":[0.9808047,0.000001068351,0.01816635,0.0003614496,0.0002361846,0.0003479826,0.00002020095,0.00003996338,0.00002210207],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9753132,"threshold_uncertainty_score":0.6512429,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3126104479","doi":"10.1016/j.measurement.2016.06.036","title":"Theory-based metrological traceability in education: A reading measurement network","year":2016,"lang":"en","type":"article","venue":"Measurement","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":44,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Bill and Melinda Gates Foundation","keywords":"Traceability; Implementation; Reading (process); Computer science; Variety (cybernetics); Metrology; Value (mathematics); Software engineering; Mathematics; Statistics; Artificial intelligence; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.3763633302387225,"gpt":0.4077568386872086,"spread":0.0313935084484861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01669016,0.0001936384,0.0003131996,0.0001006802,0.00008265332,0.00002444007,0.0002098995,0.00008914086,0.0005131362],"category_scores_gemma":[0.01839809,0.0001232031,0.00008453195,0.0002300095,0.00008439316,0.00004073935,0.00002560851,0.0001208867,0.00003585922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141579,"about_ca_system_score_gemma":0.0006377395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009047537,"about_ca_topic_score_gemma":0.00008191061,"domain_scores_codex":[0.9963339,0.001417475,0.0005188893,0.0003790362,0.0009612693,0.000389451],"domain_scores_gemma":[0.9972671,0.001446378,0.0001620659,0.000489207,0.0005252669,0.0001099578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005620809,0.003092529,0.03473262,0.0002576317,0.000128852,0.000004392394,0.0007140948,0.00004965471,0.004810487,0.628426,0.07445222,0.2527695],"study_design_scores_gemma":[0.001177047,0.0001477945,0.03822563,0.0003840299,0.00007652522,0.00000233309,0.0003584871,0.00003111712,0.004307458,0.9352357,0.01970232,0.0003515682],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03205594,0.001128806,0.9416804,0.008798051,0.004036102,0.001838747,0.00001531799,0.0005005333,0.009946072],"genre_scores_gemma":[0.833872,0.000009561321,0.1648565,0.0004419485,0.0002048763,0.0003500453,0.000001060682,0.00002010906,0.0002439034],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.801816,"threshold_uncertainty_score":0.9898704,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966040000","doi":"10.1016/j.measurement.2019.106858","title":"Acoustic guided wave techniques for detecting corrosion damage of electrical grounding rods","year":2019,"lang":"en","type":"article","venue":"Measurement","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; Manitoba Hydro; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Manitoba Hydro","keywords":"Rod; Acoustics; Guided wave testing; Materials science; Attenuation; Corrosion; Pulse (music); Echo (communications protocol); Structural engineering; Engineering; Composite material; Optics; Physics; Computer science; Electrical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03510054132548611,"gpt":0.2374318463649414,"spread":0.2023313050394552,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000755396,0.0001126993,0.0001624867,0.0000788173,0.000042271,0.00002023401,0.00007079281,0.00006163322,0.00001403311],"category_scores_gemma":[0.0001877637,0.0001080805,0.00005581938,0.0001195754,0.00000632545,0.00005653788,0.00001275668,0.0000974285,0.000003227163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002573892,"about_ca_system_score_gemma":0.00001905726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000500853,"about_ca_topic_score_gemma":0.000003164258,"domain_scores_codex":[0.9990464,0.0000141709,0.0002652911,0.0001372124,0.0003239542,0.0002128978],"domain_scores_gemma":[0.9995314,0.00005178725,0.00005734327,0.0001505184,0.0001763436,0.00003256595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007153405,0.00001558942,0.0001493678,0.0001640189,0.000009778717,2.345492e-7,0.0000406913,0.003382331,0.9846511,0.00005555994,0.0001284171,0.01139573],"study_design_scores_gemma":[0.0002378555,0.0001344883,0.0004463017,0.00012342,0.0000339952,0.000002187679,0.00002479043,0.3134469,0.684994,0.0001957298,0.0002281065,0.0001322097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2659506,0.00009800884,0.7321363,0.000008537859,0.0002851035,0.0005939894,0.000002934517,0.0001652672,0.0007592089],"genre_scores_gemma":[0.9892569,0.00001047572,0.0105623,0.000009229688,0.00006322737,0.00004053802,0.000003955092,0.00002981438,0.00002356874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7233062,"threshold_uncertainty_score":0.4407395,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3120371870","doi":"10.1016/j.measurement.2021.109012","title":"Development of a CNN edge detection model of noised X-ray images for enhanced performance of non-destructive testing","year":2021,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Beijing Institute of Technology Research Fund Program for Young Scholars; Beijing Institute of Technology; Google","keywords":"Canny edge detector; Edge detection; Artificial intelligence; Convolutional neural network; Noise (video); Computer science; Nondestructive testing; Enhanced Data Rates for GSM Evolution; Deriche edge detector; Pattern recognition (psychology); Computer vision; Filter (signal processing); Image processing; Image (mathematics); Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03854272541550052,"gpt":0.226636116575722,"spread":0.1880933911602215,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690095,0.0001195528,0.000225031,0.00006382522,0.00004499233,0.000003208452,0.00007139007,0.00002909454,0.000002061839],"category_scores_gemma":[0.00009770114,0.0001284217,0.00004512806,0.0001838282,0.0000229573,0.000110327,0.00001989049,0.00006363611,3.979195e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001296234,"about_ca_system_score_gemma":0.0001126508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001287347,"about_ca_topic_score_gemma":0.000004315935,"domain_scores_codex":[0.999006,0.000006982257,0.0003917389,0.000132201,0.0002976446,0.000165475],"domain_scores_gemma":[0.9990032,0.00002298644,0.0001177749,0.0001369733,0.000692171,0.00002686652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002293136,0.00002618396,0.00007514806,0.0005496176,0.00003800539,6.875085e-8,0.0007027303,0.1930931,0.7661233,0.000001715269,9.420105e-7,0.03936623],"study_design_scores_gemma":[0.0004318054,0.00003262272,0.001508175,0.0002567516,0.00002056805,3.270374e-7,0.0002083655,0.1513479,0.8460476,0.00003518604,0.000008796922,0.0001019724],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5409257,0.00008014216,0.4584729,0.000001205726,0.00006059645,0.0001277919,0.000003090154,0.00001811588,0.0003104105],"genre_scores_gemma":[0.7955639,0.000005537154,0.2043429,0.000001748459,0.00001232072,0.00004982322,0.000001320372,0.00001570791,0.000006717125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2546382,"threshold_uncertainty_score":0.5236884,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017673278","doi":"10.1016/j.measurement.2014.08.010","title":"Modeling outlier formation in scanning reflective surfaces using a laser stripe scanner","year":2014,"lang":"en","type":"article","venue":"Measurement","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laser scanning; Specular reflection; Outlier; Reflection (computer programming); Point cloud; Artificial intelligence; Computer vision; Optics; Computer science; Anomaly detection; Scanner; Materials science; Laser; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.1009248631749795,"gpt":0.2558684055027839,"spread":0.1549435423278045,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001459474,0.0001108528,0.000130253,0.00005209561,0.0001876052,0.00008372922,0.00008592393,0.00004295798,0.00009889724],"category_scores_gemma":[0.00008945735,0.00008680412,0.00002729299,0.0002163736,0.00001239318,0.00033669,0.000005330514,0.0001092458,0.00003194388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004442207,"about_ca_system_score_gemma":0.00002321686,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005221064,"about_ca_topic_score_gemma":0.01948015,"domain_scores_codex":[0.9986954,0.0001948772,0.0002101094,0.0001791554,0.0004564004,0.0002640222],"domain_scores_gemma":[0.9996536,0.00001393166,0.00004233371,0.00009510685,0.0001336507,0.00006137657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002821902,0.00001006556,0.06197993,0.00001524975,0.000006671838,0.000001436386,0.0008568951,0.9254566,0.0007492811,0.000001459572,0.00004126774,0.01085294],"study_design_scores_gemma":[0.0002586215,0.0000412398,0.02493726,0.00009119491,0.000006242991,0.000002285619,0.0005974584,0.9728994,0.0007903594,0.0001061471,0.0001192031,0.0001505464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896676,0.0002118314,0.004979096,0.00004307234,0.000170028,0.0001212648,0.000004781389,0.00003331843,0.004769025],"genre_scores_gemma":[0.9984469,0.000004064365,0.001372015,0.00007026909,0.00004701378,0.000001014299,0.0000144904,0.000003155952,0.00004101549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04744286,"threshold_uncertainty_score":0.9984118,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3011475457","doi":"10.1016/j.measurement.2020.107719","title":"A deep bi-directional long short-term memory model for automatic rotating speed extraction from raw vibration signals","year":2020,"lang":"en","type":"article","venue":"Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; FP7 Energy; State Key Laboratory of Mechanical System and Vibration; Canada First Research Excellence Fund; China Scholarship Council","keywords":"Computer science; Vibration; Rotor (electric); Term (time); Long short term memory; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Feature extraction; Artificial neural network; Acoustics; Recurrent neural network; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06452954097405934,"gpt":0.2975834145922925,"spread":0.2330538736182332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003695518,0.0002090892,0.0002204528,0.00007168984,0.00008861763,0.00007618849,0.0001127763,0.00007909786,0.0001093172],"category_scores_gemma":[0.0001618548,0.0002293132,0.0001017059,0.0001050674,0.000008144484,0.0002661532,0.00001343147,0.0001424935,0.00001189511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002374752,"about_ca_system_score_gemma":0.00002691269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001992605,"about_ca_topic_score_gemma":0.0001014829,"domain_scores_codex":[0.9985286,0.00004022476,0.0004050808,0.0002681715,0.0005522512,0.0002056777],"domain_scores_gemma":[0.9994991,0.0000744964,0.00006056389,0.0001451974,0.0001196778,0.0001009003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001296751,0.00005370268,0.0008184327,0.0001514225,0.00008575025,0.000001605213,0.0006131116,0.5714914,0.3992684,0.000004468091,0.001137994,0.02636069],"study_design_scores_gemma":[0.0002132333,0.00003239522,0.002888753,0.00007388982,0.000047763,7.178856e-7,0.00001725376,0.7970727,0.1993378,0.0001181667,0.00001078482,0.0001864942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1135316,0.0001915772,0.88353,0.0002017956,0.0001534328,0.0008774013,0.000008518383,0.001226562,0.0002791921],"genre_scores_gemma":[0.9542766,0.000006891687,0.0448194,0.0001537643,0.0002638513,0.0003598272,0.00005987045,0.00005645966,0.00000335424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.840745,"threshold_uncertainty_score":0.9351119,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2767593850","doi":"10.1016/j.measurement.2017.11.006","title":"An efficient data compression and encryption technique for PPG signal","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"University of Calcutta","keywords":"Encryption; Compression (physics); SIGNAL (programming language); Computer science; Data compression; Signal compression; Electronic engineering; Materials science; Computer hardware; Algorithm; Signal processing; Engineering; Computer security; Digital signal processing; Composite material","retraction":null,"screen_n_in":null,"score":{"opus":0.109737612794975,"gpt":0.3020706708143361,"spread":0.1923330580193611,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006346135,0.0001078066,0.00009676033,0.00003408433,0.0002431108,0.0001081525,0.0003665673,0.00004549339,0.000004689481],"category_scores_gemma":[0.00004186268,0.000102424,0.00001414856,0.00001327463,0.00002410396,0.0001774543,0.00009760182,0.00006745922,0.000002651668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009304517,"about_ca_system_score_gemma":0.000009608621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001698443,"about_ca_topic_score_gemma":0.000008943576,"domain_scores_codex":[0.9992166,0.00001470096,0.0001159086,0.0002118076,0.0002767941,0.0001642057],"domain_scores_gemma":[0.9990911,0.00001151267,0.00003667026,0.0007197288,0.00006752869,0.00007348415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008269149,0.0000247885,0.00117489,0.00006624066,0.0000101723,9.255342e-7,0.00002828879,0.00159173,0.9901121,0.00002151349,0.0001784253,0.006782663],"study_design_scores_gemma":[0.0004175405,0.00008546897,0.006259932,0.0002088509,0.0000202621,0.000002043577,0.00001924045,0.02210033,0.969269,0.0001499541,0.00126343,0.000203951],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08679966,0.0003446724,0.9106529,0.00002659534,0.0005019408,0.0009439018,0.00003569165,0.0001762243,0.0005183541],"genre_scores_gemma":[0.9907324,0.000008874249,0.008922617,0.000003121053,0.0001987599,0.0000978629,0.00001298046,0.00002174048,0.00000160141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9039328,"threshold_uncertainty_score":0.4176728,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1161031029","doi":"10.1016/j.measurement.2015.08.009","title":"Enhancement of oil particle sensor capability via resonance-based signal decomposition and fractional calculus","year":2015,"lang":"en","type":"article","venue":"Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"SIGNAL (programming language); Particle (ecology); Resonance (particle physics); Acoustics; Materials science; Biological system; Computer science; Physics; Atomic physics","retraction":null,"screen_n_in":null,"score":{"opus":0.0298836164189638,"gpt":0.2326705670475901,"spread":0.2027869506286263,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003999095,0.00009343622,0.0001197946,0.00002860055,0.0000288319,0.000007684122,0.00003561049,0.00003386624,0.00003033602],"category_scores_gemma":[0.00001293501,0.00008266939,0.00003954156,0.0001430545,0.00003752572,0.000044038,0.000006118524,0.00006894771,0.000006025812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001549896,"about_ca_system_score_gemma":0.00002799141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004273628,"about_ca_topic_score_gemma":0.00002509103,"domain_scores_codex":[0.9989837,0.00003777337,0.0001973392,0.0001295712,0.0004822284,0.0001693377],"domain_scores_gemma":[0.9995902,0.00001637354,0.00002668913,0.00009360084,0.0001643847,0.0001087694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001982655,0.0003983042,0.01140779,0.0001487656,0.0000555183,0.000002911597,0.0001179927,0.004450968,0.9004067,0.00003981713,0.0005752553,0.08219771],"study_design_scores_gemma":[0.0008474187,0.0002404095,0.009563932,0.00003695475,0.00002477585,0.000001675831,0.00001240842,0.0722864,0.9149723,0.0001275904,0.001730696,0.0001554419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525127,0.0020893,0.04459644,0.0001758383,0.00009692636,0.00009017812,0.000005209145,0.00006897172,0.0003644397],"genre_scores_gemma":[0.9989681,0.00001467846,0.0008999491,0.00003695577,0.00003838504,0.00002629632,0.000002592842,0.00000652028,0.000006477008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08204226,"threshold_uncertainty_score":0.337116,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2989826852","doi":"10.1016/j.measurement.2019.107291","title":"Application of optimized Artificial and Radial Basis neural networks by using modified Genetic Algorithm on discharge coefficient prediction of modified labyrinth side weir with two and four cycles","year":2019,"lang":"en","type":"article","venue":"Measurement","topic":"Hydraulic flow and structures","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Artificial neural network; Genetic algorithm; Weir; Basis (linear algebra); Algorithm; Neutral network; Computer science; Engineering; Artificial intelligence; Mathematics; Machine learning; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.01904077124018678,"gpt":0.2058672026016678,"spread":0.186826431361481,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001779476,0.0001564546,0.0002442313,0.00006209977,0.00003864388,0.00001661419,0.00005173303,0.00005345798,0.000002873056],"category_scores_gemma":[0.000005025974,0.0001319067,0.00002651084,0.0000793756,0.00004113876,0.00003452741,0.00001360729,0.00008868691,1.57493e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004493955,"about_ca_system_score_gemma":0.000009208762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000959391,"about_ca_topic_score_gemma":0.00001046705,"domain_scores_codex":[0.9989777,0.00003911036,0.0002692791,0.0002053395,0.0003418002,0.0001668167],"domain_scores_gemma":[0.999631,0.00001709227,0.00007206618,0.0001648857,0.00005360435,0.00006128356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008367589,0.00002563663,0.0003791122,0.00004355074,0.00004798213,2.300149e-7,0.0001146927,0.9206436,0.04188756,0.00001654145,0.000008423262,0.03674895],"study_design_scores_gemma":[0.001152413,0.0001061088,0.005400077,0.00003294196,0.00005079847,0.000002789669,0.00002494,0.9792534,0.01384822,0.00001256959,0.000005932309,0.000109855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6139117,0.0001671718,0.3853909,0.000007643075,0.00008391126,0.000348044,0.00002445965,0.00002461342,0.00004159117],"genre_scores_gemma":[0.9974231,0.00001654972,0.002442365,0.000006725189,0.00005971874,0.00001871013,0.00001037099,0.00002130026,0.000001149865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3835114,"threshold_uncertainty_score":0.5378999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2890293164","doi":"10.1016/j.measurement.2018.09.008","title":"Novel two-measurements-only Cole-Cole bio-impedance parameters extraction technique","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"University of Patras","keywords":"Electrical impedance; Focused Impedance Measurement; Nonlinear system; Oscillation (cell signaling); Network analyzer (electrical); Amplifier; Dispersion (optics); Output impedance; Mathematics; Electronic engineering; Acoustics; Control theory (sociology); Physics; Computer science; Engineering; Electrical engineering; Optics; Chemistry; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.07450535742539695,"gpt":0.2647561104407496,"spread":0.1902507530153527,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001422314,0.0003134687,0.0002607345,0.000151738,0.0001725151,0.00006447936,0.0002449263,0.0001237591,0.0000959129],"category_scores_gemma":[0.00007232991,0.0003264569,0.000112105,0.0003027314,0.00008793137,0.0001816348,0.00001629884,0.0002382877,0.0002682242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007041131,"about_ca_system_score_gemma":0.00009899669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001042568,"about_ca_topic_score_gemma":0.0002185065,"domain_scores_codex":[0.9977205,0.00006157502,0.0003945663,0.0003674581,0.0009376927,0.0005182309],"domain_scores_gemma":[0.9989564,0.00002073072,0.0000819679,0.0004111814,0.0003679905,0.0001616711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002500831,0.0001176906,0.0005392455,0.00005293214,0.0001286218,0.00000380274,0.00009593173,0.001412308,0.9769669,0.0004013183,0.004163184,0.01609312],"study_design_scores_gemma":[0.001553851,0.0003875639,0.002656783,0.0002907306,0.000145593,0.00007746822,0.00009069691,0.001936386,0.9623649,0.0006463806,0.02892948,0.0009201882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01103965,0.0003755708,0.9725466,0.0000178713,0.0008631809,0.0008340994,0.000009134611,0.0005943227,0.01371954],"genre_scores_gemma":[0.9955528,0.00001355287,0.003584674,0.0001451298,0.0002678079,0.0002364132,0.0000051033,0.00006593391,0.0001286157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9845131,"threshold_uncertainty_score":0.9999188,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3011340685","doi":"10.1016/j.measurement.2020.107739","title":"A novel knowledge transfer network with fluctuating operational condition adaptation for bearing fault pattern recognition","year":2020,"lang":"en","type":"article","venue":"Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Convolutional neural network; Fault (geology); Computer science; Bearing (navigation); Transfer of learning; Artificial intelligence; Deep learning; Domain knowledge; Artificial neural network; Vibration; Pattern recognition (psychology); Knowledge transfer; Encoder; Domain (mathematical analysis); Machine learning; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.08389522785124395,"gpt":0.267504579645332,"spread":0.1836093517940881,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002891657,0.0001547518,0.0001360375,0.00003172267,0.00009330335,0.00004975122,0.00006212541,0.00004972758,0.00004080349],"category_scores_gemma":[0.00004731431,0.000152947,0.00004113936,0.0001103862,0.000008281938,0.000156907,0.000005841344,0.0001103201,0.00001112139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298174,"about_ca_system_score_gemma":0.0000254365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001689337,"about_ca_topic_score_gemma":0.0002257138,"domain_scores_codex":[0.9990883,0.0000190822,0.0002331672,0.0001936492,0.0002813793,0.0001843783],"domain_scores_gemma":[0.9995819,0.00003388825,0.00001830877,0.00006598573,0.0002367873,0.00006313097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000105934,0.0002050607,0.0009496842,0.0008682257,0.0003040786,0.000001707229,0.005556296,0.4898786,0.154656,0.0001666825,0.007357071,0.3399507],"study_design_scores_gemma":[0.001411837,0.0002831639,0.001882775,0.0003439395,0.00007501218,0.000002590489,0.00008426256,0.9467084,0.04601926,0.0000732111,0.002743643,0.0003719424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02456078,0.00008969731,0.9732459,0.0002524429,0.00008145011,0.0008205909,0.00004205314,0.0004327364,0.0004743105],"genre_scores_gemma":[0.972778,0.000006444066,0.02558151,0.0002581361,0.000368843,0.0007236254,0.0002374079,0.00004481412,0.00000119657],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9482172,"threshold_uncertainty_score":0.6236998,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1975279313","doi":"10.1016/j.measurement.2014.12.033","title":"Effect of secondary hardening on cutting forces, cutting temperature, and tool wear in hard turning of high alloy tool steels","year":2014,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Machinability; Tempering; Materials science; Metallurgy; Hardening (computing); Machining; Alloy; Work hardening; Hardened steel; Tool wear; Composite material; Microstructure","retraction":null,"screen_n_in":null,"score":{"opus":0.006411615373033485,"gpt":0.2037842463389178,"spread":0.1973726309658844,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001281051,0.0001807211,0.0003292289,0.0001096989,0.0000504734,0.00002498031,0.00007924574,0.00006971773,0.000007758729],"category_scores_gemma":[0.000368602,0.0001679423,0.00003669912,0.0001186628,0.00001497807,0.0001407065,0.00002852948,0.0002406296,7.659629e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006540052,"about_ca_system_score_gemma":0.0000131126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001110508,"about_ca_topic_score_gemma":0.000004458136,"domain_scores_codex":[0.9988146,0.00007190434,0.0003801018,0.0001972507,0.0003302577,0.0002059],"domain_scores_gemma":[0.9995221,0.0001265084,0.0001156904,0.0001408331,0.00006774255,0.00002708118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007056565,0.00001303316,0.00342323,0.001529926,0.00004152086,0.000001162343,0.0005420211,0.8397233,0.1100775,0.000143346,0.00001730398,0.04441712],"study_design_scores_gemma":[0.003235668,0.0009130713,0.0076366,0.003224641,0.0000716217,0.000004974224,0.0001059879,0.1349354,0.8484929,0.0001004076,0.0007365577,0.0005421269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885257,0.000487272,0.009953983,0.00000943884,0.000159297,0.0002425477,0.000003142455,0.00007444918,0.0005441515],"genre_scores_gemma":[0.9939668,0.00003508453,0.005860223,0.0000164007,0.000047257,0.00001708857,0.000006839874,0.00003576093,0.00001459157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7384154,"threshold_uncertainty_score":0.684849,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377115757","doi":"10.1016/j.measurement.2023.113053","title":"A multi-sensor mapping Bi-LSTM model of bridge monitoring data based on spatial-temporal attention mechanism","year":2023,"lang":"en","type":"article","venue":"Measurement","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Science Fund for Distinguished Young Scholars of Gansu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Process (computing); Embedding; Data mining; Bridge (graph theory); Visualization; Temporal database; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.2976258864474085,"gpt":0.3329661819038222,"spread":0.03534029545641371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009131846,0.0002192982,0.0002429562,0.0002874098,0.00007749903,0.00002010023,0.0003762434,0.00009431616,0.000003031073],"category_scores_gemma":[0.00007815405,0.0002308175,0.00005583036,0.000275807,0.00001224599,0.00009942096,0.0000994841,0.0001878325,0.00001775713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003594743,"about_ca_system_score_gemma":0.00004047777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004080883,"about_ca_topic_score_gemma":0.00002141893,"domain_scores_codex":[0.9979787,0.00004413252,0.0004248383,0.0003378207,0.000845055,0.0003694395],"domain_scores_gemma":[0.9988366,0.00002253925,0.0000836726,0.0008388535,0.0001281948,0.00009017088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006684109,0.00009900219,0.01476883,0.001864277,0.00011161,0.00001825025,0.0003672935,0.1923572,0.7423015,0.00003018453,0.001200099,0.04681491],"study_design_scores_gemma":[0.0003344029,0.0000346905,0.08012662,0.0004397549,0.00001247704,3.401548e-7,0.00001937525,0.8536044,0.06513625,0.00006863271,0.00003997459,0.0001831236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6286774,0.00005226635,0.3660299,0.000100429,0.00207008,0.0007659595,0.00009907873,0.002158026,0.00004692357],"genre_scores_gemma":[0.9606208,0.00002290423,0.03898077,0.000007418853,0.0001934929,0.00005875907,0.00004338529,0.00006146666,0.0000110202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6771652,"threshold_uncertainty_score":0.9412466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4381952223","doi":"10.1016/j.measurement.2023.113228","title":"Combined Kalman and sliding innovation filtering: An adaptive estimation strategy","year":2023,"lang":"en","type":"article","venue":"Measurement","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extended Kalman filter; Control theory (sociology); Robustness (evolution); Kalman filter; Unscented transform; Estimator; Nonlinear system; Taylor series; Invariant extended Kalman filter; Engineering; Computer science; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.08902436955168998,"gpt":0.250131600939786,"spread":0.161107231388096,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000366809,0.00008271118,0.00008412752,0.0001006143,0.00006966621,0.00004022297,0.00003823345,0.00003978708,0.00002403191],"category_scores_gemma":[0.00003748264,0.00007406183,0.000009337471,0.000297488,0.00000805984,0.0001665021,0.00001130544,0.00005616145,0.00002212156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004398635,"about_ca_system_score_gemma":0.000007262633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003330505,"about_ca_topic_score_gemma":0.000005219639,"domain_scores_codex":[0.9993893,0.00002172271,0.0001636946,0.0001059217,0.0001958208,0.0001235309],"domain_scores_gemma":[0.9997875,0.000009433837,0.00001434557,0.00007340326,0.00008219303,0.00003310665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005327399,0.0000394548,0.0004845114,0.00007092652,0.00007076137,0.000006491528,0.0003793721,0.3309076,0.5358688,0.05538873,0.00130739,0.07542266],"study_design_scores_gemma":[0.0003987197,0.0002177359,0.02440476,0.00001892327,0.000006449516,5.741406e-7,0.00006612291,0.9652434,0.007476624,0.001927556,0.0001215094,0.0001176292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7442835,0.00007906387,0.2512735,0.0002277192,0.0003017125,0.0003047135,0.000003973847,0.001020937,0.002504935],"genre_scores_gemma":[0.9993199,0.000008367884,0.0005160859,0.00002709267,0.00003689795,0.00002850576,0.00002467006,0.00000968321,0.00002878432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6343358,"threshold_uncertainty_score":0.3020154,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4221079999","doi":"10.1016/j.measurement.2022.111046","title":"Gated recurrent unit least-squares generative adversarial network for battery cycle life prediction","year":2022,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Prognostics; Discriminator; Computer science; Perceptron; Battery (electricity); Set (abstract data type); Generator (circuit theory); Artificial intelligence; Data mining; Machine learning; Artificial neural network; Reliability engineering; Engineering; Detector","retraction":null,"screen_n_in":null,"score":{"opus":0.0635894485596158,"gpt":0.2640243288650511,"spread":0.2004348803054353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005591777,0.0001756091,0.0001806651,0.0001110154,0.0003492296,0.00002590435,0.0002641397,0.00005241077,0.0002239662],"category_scores_gemma":[0.0001355564,0.0001913955,0.00006463933,0.0003077287,0.00003245523,0.00009292761,0.000150587,0.0003677917,0.00001215043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000737023,"about_ca_system_score_gemma":0.00005797776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005648013,"about_ca_topic_score_gemma":0.00001862531,"domain_scores_codex":[0.9981546,0.00009152035,0.0002669398,0.0002660244,0.0007429956,0.0004778514],"domain_scores_gemma":[0.999416,0.00004030845,0.00004002901,0.0002878128,0.000138841,0.00007699952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001365831,0.00005141883,0.0004692379,0.00004203676,0.0001183317,0.000002287475,0.00009249328,0.9372796,0.005339842,0.00005916982,0.04062062,0.01578839],"study_design_scores_gemma":[0.003342063,0.001321461,0.00319981,0.00007631695,0.00007243161,0.000005933435,0.001008427,0.5894178,0.01540309,0.001566088,0.3838304,0.0007562107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1464144,0.003601292,0.8290392,0.00217642,0.008624442,0.004433712,0.0007404819,0.003651944,0.00131814],"genre_scores_gemma":[0.9958352,0.00003904893,0.0023019,0.00009402775,0.0004695536,0.001050804,0.0001090228,0.00005543939,0.00004502786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8494208,"threshold_uncertainty_score":0.780488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2036704883","doi":"10.1016/j.measurement.2012.12.001","title":"Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking","year":2012,"lang":"en","type":"article","venue":"Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Debris; Interference (communication); Signature (topology); Wavelet; Noise (video); SIGNAL (programming language); Debris flow; Computer science; Environmental science; Remote sensing; Geology; Artificial intelligence; Physics; Mathematics; Meteorology; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01619786387055704,"gpt":0.25047883928076,"spread":0.2342809754102029,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001291959,0.0002705817,0.0003072961,0.00007371286,0.00005867625,0.00002374012,0.0001241899,0.000143795,0.0002168792],"category_scores_gemma":[0.00005948769,0.0002629838,0.00006192799,0.0001143159,0.00003982627,0.0002605999,0.00003449805,0.0003184689,0.000004598464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003973632,"about_ca_system_score_gemma":0.00001238006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002051605,"about_ca_topic_score_gemma":0.00003402489,"domain_scores_codex":[0.9982439,0.00008640865,0.0004482921,0.0002566622,0.0005517476,0.0004130371],"domain_scores_gemma":[0.9993306,0.00004922423,0.0001019399,0.0002729081,0.000109209,0.0001361634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000285329,0.0002699188,0.006029206,0.0003618765,0.00006565435,8.055447e-7,0.0005401003,0.00008885666,0.9644107,0.000009533419,0.007231683,0.02096312],"study_design_scores_gemma":[0.0002406663,0.00008713267,0.002234665,0.0001664096,0.00003170616,0.000004707411,0.00005515676,0.001946742,0.9870328,0.00009908248,0.007813506,0.0002874054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7556169,0.005647134,0.2357116,0.0001431156,0.0003333791,0.000436434,0.00003394193,0.000359725,0.001717753],"genre_scores_gemma":[0.9837655,0.000156191,0.0157984,0.00003822547,0.00006658074,0.0001081268,0.00001366731,0.00003527461,0.0000180109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2281486,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159162003","doi":"10.1016/j.measurement.2013.06.016","title":"Item response drift in the Family Affluence Scale: A study on three consecutive surveys of the Health Behaviour in School-aged Children (HBSC) survey","year":2013,"lang":"en","type":"article","venue":"Measurement","topic":"Child and Adolescent Psychosocial and Emotional Development","field":"Psychology","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Scale (ratio); Psychology; Gerontology; Geography; Environmental health; Medicine; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.0708043981428221,"gpt":0.3111465072990944,"spread":0.2403421091562723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01443686,0.0002307727,0.0003275406,0.0001328918,0.000174435,0.00003590245,0.0005553575,0.00007167046,0.00007150797],"category_scores_gemma":[0.0002861308,0.0001409357,0.00008488085,0.000605182,0.00009798841,0.00004332182,0.00005893887,0.0004978436,0.00008122422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454477,"about_ca_system_score_gemma":0.0003030242,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01872492,"about_ca_topic_score_gemma":0.02751953,"domain_scores_codex":[0.9893381,0.008059714,0.0006568132,0.0004467989,0.001079681,0.0004188934],"domain_scores_gemma":[0.9987606,0.0002762594,0.0002080349,0.0005089929,0.0001597282,0.00008641004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003808461,0.002742924,0.9857556,0.000004693271,0.00003929744,0.000001998729,0.006164968,0.000003148324,0.0001175779,0.00001514297,0.004129385,0.0006443838],"study_design_scores_gemma":[0.001894447,0.0004044264,0.9941664,0.0002401233,0.000005284027,0.000001454566,0.003047946,3.571822e-7,0.000007643305,0.00006848953,0.00001282638,0.0001506335],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934001,0.0002445299,0.00001284402,0.002827454,0.0004856445,0.002652238,0.00004563342,0.0000128597,0.0003187169],"genre_scores_gemma":[0.998217,0.00000783011,0.000004448896,0.001517405,0.00003224738,0.0001333498,0.000008835588,0.00001382591,0.00006499246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01415073,"threshold_uncertainty_score":0.9902257,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2995479783","doi":"10.1016/j.measurement.2019.107370","title":"Kriging versus Bezier and regression methods for modeling and prediction of cutting force and surface roughness during high speed edge trimming of carbon fiber reinforced polymers","year":2019,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Corporación Tecnológica de Andalucía; Bombardier","keywords":"Trimming; Kriging; Surface roughness; Materials science; Enhanced Data Rates for GSM Evolution; Surface finish; Surface (topology); Polymer; Composite material; Regression analysis; Bézier curve; Geometry; Mathematics; Mechanical engineering; Engineering; Computer science; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02734901822632121,"gpt":0.2753920600387506,"spread":0.2480430418124294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000435317,0.0001306035,0.0002260814,0.00006140312,0.0000568411,0.00001181992,0.00002981634,0.0000577145,0.000001613987],"category_scores_gemma":[0.00006279668,0.00012637,0.00001936677,0.00007605368,0.00001351225,0.0001615961,0.00002851366,0.00006675001,1.592906e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004524261,"about_ca_system_score_gemma":0.00001033643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003063926,"about_ca_topic_score_gemma":7.667894e-7,"domain_scores_codex":[0.9992377,0.00001751714,0.0002730087,0.0001744355,0.0001489776,0.0001483153],"domain_scores_gemma":[0.9996177,0.00005552929,0.0001018414,0.00009316207,0.00009599679,0.00003576888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001814549,0.00000199159,0.0002133305,0.000902683,0.00003533317,4.596476e-8,0.0004927835,0.8105687,0.1792799,0.00002024308,1.183038e-7,0.008303383],"study_design_scores_gemma":[0.00169374,0.00005587755,0.00008381208,0.0004166047,0.00005462598,7.535745e-7,0.0002595344,0.8533595,0.1439483,0.00002095205,0.000005332939,0.0001009158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8165183,0.001951791,0.1808457,0.000005258993,0.0001756518,0.0002721805,0.000002598032,0.0000398456,0.0001886403],"genre_scores_gemma":[0.9532909,0.0001641926,0.04644119,8.220129e-7,0.00001876031,0.000003766287,0.000004182446,0.0000267204,0.00004943156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1367726,"threshold_uncertainty_score":0.515322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2800822684","doi":"10.1016/j.measurement.2018.04.090","title":"Surface texture evaluation using 3D reconstruction from images by parametric anisotropic BRDF","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Bidirectional reflectance distribution function; Stylus; Artificial intelligence; Surface roughness; Profilometer; Computer vision; Photometric stereo; Surface finish; Optics; Computer science; Bidirectional texture function; Wavelet; Materials science; Image texture; Image processing; Physics; Reflectivity; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.06012242700712891,"gpt":0.2615585815806702,"spread":0.2014361545735413,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861407,0.0002354799,0.0002235748,0.00006553525,0.0001412544,0.00008883004,0.0001311356,0.0001260992,0.0005711968],"category_scores_gemma":[0.0001669652,0.0002298262,0.00005371973,0.0003826517,0.00005613526,0.000215263,0.00001912288,0.000153645,0.0001305381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007734697,"about_ca_system_score_gemma":0.00004519541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001726266,"about_ca_topic_score_gemma":0.00003867875,"domain_scores_codex":[0.997655,0.0001125443,0.0003164087,0.0003250033,0.001249015,0.0003420485],"domain_scores_gemma":[0.9989665,0.00002351933,0.00006034287,0.0003091513,0.0005341714,0.000106288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003700738,0.0001209731,0.02043694,0.00005353363,0.0003187864,0.000001456241,0.0001484914,0.03108676,0.7573106,0.000006073019,0.004194797,0.1862846],"study_design_scores_gemma":[0.0024944,0.0001878326,0.03243059,0.0003217865,0.0004706081,0.000007078041,0.0001998328,0.5662759,0.3900899,0.0005486626,0.005924617,0.001048874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95724,0.003743857,0.03409407,0.00003486955,0.001948805,0.000447689,0.00002770475,0.0002168451,0.002246175],"genre_scores_gemma":[0.9897966,0.00004662042,0.009835886,0.00002386442,0.000223695,0.00001211978,0.0000115239,0.00003947702,0.00001020257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5351891,"threshold_uncertainty_score":0.937204,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1849416658","doi":"10.1016/j.measurement.2015.08.040","title":"Experimental evaluation of a distributed Brillouin sensing system for measuring extensional and shear deformation in rock","year":2015,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Shearing (physics); Geology; Displacement (psychology); Structural health monitoring; Structural engineering; Materials science; Geotechnical engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1061201042555241,"gpt":0.2701072373295016,"spread":0.1639871330739775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353482,0.00009750522,0.000149579,0.00007167736,0.00002139684,0.000008810835,0.00002421189,0.00003741726,6.299307e-7],"category_scores_gemma":[0.0001439451,0.0001019507,0.00002104882,0.00007918281,0.000006724193,0.0001127205,0.00001097615,0.00003907476,0.000001337375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128439,"about_ca_system_score_gemma":0.00003169864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001103537,"about_ca_topic_score_gemma":0.00000852835,"domain_scores_codex":[0.9986925,0.00004218077,0.0002785122,0.0001095253,0.0007365539,0.0001407401],"domain_scores_gemma":[0.9993935,0.00001402331,0.00004323542,0.00009586661,0.0003982247,0.00005510784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005161827,0.0000189045,0.0001335258,0.0001338625,0.00002397608,6.290851e-7,0.001070448,0.8595769,0.1362163,0.00002946457,0.00003562287,0.002708713],"study_design_scores_gemma":[0.001575509,0.00003095415,0.001387822,0.0002089025,0.00002334633,0.00001347114,0.001794577,0.7978402,0.1969517,0.00002934804,0.00003621443,0.0001079806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9044523,0.0009320377,0.09365948,0.000008677806,0.0002076822,0.0005731047,0.000007209461,0.00006402669,0.00009547686],"genre_scores_gemma":[0.9966204,5.775728e-7,0.003302693,0.000001781641,0.00002450972,0.00002226698,0.00001161557,0.0000158023,3.922391e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09216806,"threshold_uncertainty_score":0.4157428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2617297034","doi":"10.1016/j.measurement.2017.05.056","title":"Developing skin model in coordinate metrology using a finite element method","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metrology; Coordinate-measuring machine; Surface (topology); Process (computing); Point (geometry); Coordinate system; Computer science; Ideal (ethics); Finite element method; Standard deviation; Surface metrology; Algorithm; Mathematics; Mechanical engineering; Geometry; Engineering; Profilometer; Computer vision; Structural engineering; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1338170869300515,"gpt":0.3432906397768253,"spread":0.2094735528467738,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002289992,0.0002695561,0.0003888134,0.0002869387,0.0002073338,0.00004188389,0.0003970331,0.0001312134,0.00001222521],"category_scores_gemma":[0.0001969793,0.0002796008,0.00007331239,0.00009215751,0.00004123744,0.000212916,0.00009762301,0.0002613007,0.000005403524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009199976,"about_ca_system_score_gemma":0.00007311715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004904444,"about_ca_topic_score_gemma":0.0003202301,"domain_scores_codex":[0.9982172,0.00007307613,0.0004141086,0.0003110792,0.000420026,0.0005645693],"domain_scores_gemma":[0.9991093,0.00002088395,0.0001189182,0.0005422906,0.000139987,0.00006860943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007922261,0.00008994916,0.01011519,0.0001603762,0.0002542748,0.0000328418,0.0003217951,0.7735904,0.1639646,0.00391698,0.0002423295,0.04723197],"study_design_scores_gemma":[0.001494383,0.0000574363,0.003399368,0.0001371894,0.00006375825,0.000004224329,0.00003242306,0.8177243,0.1643572,0.007912935,0.004248195,0.0005685379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714185,0.0004285851,0.9789348,0.0001874612,0.0002625524,0.0003570728,0.00000239135,0.0002236667,0.002461598],"genre_scores_gemma":[0.7183127,0.00005670479,0.2813597,0.00009343439,0.00003546992,0.00008791954,0.000001347693,0.0000351375,0.00001756342],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7011709,"threshold_uncertainty_score":0.9999656,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4296285938","doi":"10.1016/j.measurement.2022.111924","title":"GNSS precise positioning for smartphones based on the integration of factor graph optimization and solution separation","year":2022,"lang":"en","type":"article","venue":"Measurement","topic":"GNSS positioning and interference","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"GNSS applications; Separation (statistics); Computer science; Graph; Factor graph; Factor (programming language); Global Positioning System; Real-time computing; Algorithm; Telecommunications; Theoretical computer science; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.05266531345719292,"gpt":0.2406694466472082,"spread":0.1880041331900153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000295998,0.00006981832,0.00006184162,0.0000669543,0.0002173595,0.00002554896,0.00004374216,0.00001671486,0.00003325151],"category_scores_gemma":[0.0000310039,0.00005924215,0.00002976391,0.00008493766,0.0000115244,0.00006016345,0.000006440605,0.00006256731,3.93076e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001246889,"about_ca_system_score_gemma":0.00001169039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009640467,"about_ca_topic_score_gemma":0.000007298065,"domain_scores_codex":[0.9994038,0.00005286888,0.0001407705,0.00008795939,0.0002416987,0.0000728881],"domain_scores_gemma":[0.9997153,0.00003476139,0.00004216455,0.00008615808,0.000108045,0.00001362196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003698366,0.0000334884,0.00003824961,0.00002153486,0.00001193413,1.964232e-8,0.0004103472,0.9284514,0.06894407,0.0004842448,0.0004772708,0.001090416],"study_design_scores_gemma":[0.0002062373,0.0002071308,0.0008623918,0.00005536643,0.0000139726,4.375257e-7,0.00007898238,0.9272512,0.07109641,0.0001021899,0.0000595169,0.00006619069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08041704,0.0000693394,0.9180782,0.000215607,0.0002519828,0.0004049375,0.00003091881,0.00006106611,0.0004708807],"genre_scores_gemma":[0.9979626,0.000003682741,0.001666697,0.00003108748,0.00001518928,0.0002530396,0.00004876261,0.000008950864,0.0000099909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9175456,"threshold_uncertainty_score":0.2415825,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2891983683","doi":"10.1016/j.measurement.2018.09.025","title":"Quantitative study of magnetic memory signal characteristic affected by external magnetic field","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":27,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; University of Toronto","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Magnetic field; Earth's magnetic field; Magnetic memory; Magnetic reactance; SIGNAL (programming language); Materials science; Magnetic domain; Magnetic pressure; Ferromagnetism; Condensed matter physics; Stress (linguistics); Magnetic energy; Nuclear magnetic resonance; Physics; Magnetization; Composite material; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.03971958340915578,"gpt":0.2603116201565356,"spread":0.2205920367473798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005426722,0.000155903,0.0002201578,0.00004304456,0.0001305458,0.00004616391,0.0003134315,0.00003531127,0.00742342],"category_scores_gemma":[0.00008904663,0.0001282261,0.00003251407,0.000121802,0.0001213641,0.00004632748,0.0000793319,0.00006834984,0.0002425927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003756174,"about_ca_system_score_gemma":0.00004082697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004842942,"about_ca_topic_score_gemma":0.0001108522,"domain_scores_codex":[0.9982072,0.0001578284,0.0003756516,0.0003235133,0.0006832746,0.0002525656],"domain_scores_gemma":[0.9990529,0.00003889998,0.0001351706,0.0003576397,0.0003334798,0.00008193805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009807626,0.0007773303,0.0001271609,0.00002706639,0.000004947594,0.000001320197,0.0008513072,9.178523e-7,0.9900191,0.00002830057,0.001633052,0.006431352],"study_design_scores_gemma":[0.001784433,0.02700702,0.02783988,0.0001293469,0.0001391719,0.000005137189,0.001559868,0.0007610261,0.9391053,0.0001394276,0.001104936,0.0004244384],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99461,0.0007242394,0.002266109,0.0001278814,0.0002007739,0.0007694148,0.00001506797,0.00004077228,0.001245712],"genre_scores_gemma":[0.9985404,0.000005491138,0.0007038811,0.000108744,0.00007630768,0.0001602693,0.000001295624,0.00001294028,0.0003906568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05091386,"threshold_uncertainty_score":0.9934839,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4327694205","doi":"10.1016/j.measurement.2023.112722","title":"Modeling, refinement and evaluation of multipath mitigation based on the hemispherical map in BDS2/BDS3 relative precise positioning","year":2023,"lang":"en","type":"article","venue":"Measurement","topic":"GNSS positioning and interference","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Multipath propagation; Ambiguity resolution; Float (project management); Precise Point Positioning; Computer science; Remote sensing; Environmental science; Global Positioning System; Telecommunications; Geography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.07745694242534677,"gpt":0.2637877714522598,"spread":0.186330829026913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001336718,0.00008318153,0.00008039599,0.00005660798,0.0000435781,0.00001507722,0.00004531781,0.00003297126,0.0000263586],"category_scores_gemma":[0.0001164706,0.00006870655,0.00002142315,0.0001411527,0.00001381467,0.00004738778,0.00000902382,0.0001035338,0.00001120153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002351535,"about_ca_system_score_gemma":0.00002110035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004374594,"about_ca_topic_score_gemma":0.00002587513,"domain_scores_codex":[0.9988503,0.0001023788,0.0002030859,0.0001226516,0.000613817,0.0001077898],"domain_scores_gemma":[0.9996193,0.00003656301,0.00002498434,0.0001225287,0.0001746625,0.00002192676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001299106,0.00002861893,0.0004226335,0.00002817461,0.00001155612,1.89054e-7,0.0006146474,0.9852416,0.01204464,0.0001259508,0.0002129413,0.001256047],"study_design_scores_gemma":[0.000410956,0.00005358503,0.006231225,0.0004779922,0.00001934558,1.006398e-7,0.0001052254,0.981532,0.01070819,0.0003864502,0.000007417254,0.00006751993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848733,0.000139315,0.01115954,0.0004705001,0.0001001665,0.0003652461,0.00000383292,0.0000787454,0.002809391],"genre_scores_gemma":[0.9994807,0.000005081051,0.0003427205,0.00002209835,0.00001181213,0.0001071395,0.00001330986,0.000009706021,0.00000743483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01460743,"threshold_uncertainty_score":0.2801772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2568866527","doi":"10.1016/j.measurement.2017.01.012","title":"High resolution mass identification using nonlinear vibrations of nanoplates","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonlinear system; Vibration; Parametric statistics; Galerkin method; Added mass; Identification (biology); Control theory (sociology); Sensitivity (control systems); Computer science; Mechanics; Statistical physics; Physics; Biological system; Engineering; Mathematics; Acoustics; Electronic engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.06258840722281976,"gpt":0.2811804728191221,"spread":0.2185920655963023,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003144153,0.00006198995,0.00009710952,0.00001870627,0.000252234,0.00005356411,0.0001407688,0.00002226847,0.0001454767],"category_scores_gemma":[0.00005457754,0.00005396312,0.00005153491,0.00002653214,0.00003489945,0.000102073,0.00002891198,0.00004884146,0.00002003032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000283092,"about_ca_system_score_gemma":0.0000351553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001788856,"about_ca_topic_score_gemma":0.000005819737,"domain_scores_codex":[0.999222,0.00002047376,0.0002128841,0.0001233138,0.0003145137,0.0001068026],"domain_scores_gemma":[0.9993066,0.000008798784,0.0001697227,0.00029712,0.0001701982,0.00004760046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002088764,0.0002009119,0.004621061,0.00002037183,0.00006903589,2.792184e-7,0.00003021764,0.0004401371,0.830282,0.155861,0.0001028487,0.008351153],"study_design_scores_gemma":[0.0005788762,0.00004630433,0.01286587,0.00009171896,0.00008302923,8.669092e-8,0.00004791704,0.04369062,0.9068888,0.03360176,0.001906592,0.0001983948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5868488,0.0000552226,0.4086537,0.0006635605,0.0006906887,0.0003276088,0.00004808953,0.00002126807,0.002691088],"genre_scores_gemma":[0.992381,7.769717e-7,0.007376392,0.000004341672,0.0001411382,0.000006667932,0.0000079396,0.000005362775,0.00007633482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4055323,"threshold_uncertainty_score":0.2200552,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2754455648","doi":"10.1016/j.measurement.2017.09.008","title":"Naturalness and convention in the International System of Units☆","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Scientific Measurement and Uncertainty Evaluation","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec-Société et Culture","keywords":"Naturalness; Convention; Set (abstract data type); Metric (unit); Units of measurement; Mathematics; Law; Computer science; Law and economics; Political science; Sociology; Engineering; Operations management; Programming language; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.5576272019770802,"gpt":0.4293873840767105,"spread":0.1282398179003696,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02645583,0.00006140518,0.0001151667,0.0001402899,0.0002317581,0.0004382964,0.001046135,0.00002688304,0.00005671597],"category_scores_gemma":[0.004552151,0.00003513586,0.00003018588,0.0001608074,0.00008815802,0.000234732,0.00006882666,0.00006040313,0.00001598579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008447973,"about_ca_system_score_gemma":0.00006298789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001024012,"about_ca_topic_score_gemma":0.0004814222,"domain_scores_codex":[0.9949613,0.0002484518,0.0004168716,0.0002238443,0.004055044,0.00009452955],"domain_scores_gemma":[0.9976584,0.00009964472,0.0003814421,0.000562536,0.00127659,0.00002144161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003472862,0.0003143659,0.5442483,0.0001016559,0.0001176475,0.00001214009,0.009779801,0.0002331858,0.05900586,0.09426851,0.0289144,0.2626568],"study_design_scores_gemma":[0.00183295,0.00005342325,0.9441855,0.000249625,0.00002936893,0.000006449389,0.01074209,0.01191727,0.007880718,0.006241065,0.01669813,0.000163401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640315,0.0002059804,0.0009465208,0.003726165,0.003039028,0.0004631135,0.000004870502,0.000009224715,0.02757364],"genre_scores_gemma":[0.9996547,0.000001752166,0.00002626856,0.00003122444,0.00004258939,0.00001230312,0.000001610565,0.000001716705,0.0002278126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3999372,"threshold_uncertainty_score":0.9169115,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2604546713","doi":"10.1016/j.measurement.2017.04.004","title":"Dielectric properties of the normal and malignant breast tissues in xenograft mice at low frequencies (100 Hz–1 MHz)","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Robarts Clinical Trials; University of Toronto; Sunnybrook Health Science Centre; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dielectric; Materials science; Conductivity; Permittivity; Human breast; Biomedical engineering; Low frequency; Breast cancer; Nuclear magnetic resonance; Pathology; Cancer; Medicine; Optoelectronics; Chemistry; Physics; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.02388739104393521,"gpt":0.1917343399772582,"spread":0.167846948933323,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002449716,0.0001359883,0.0001647717,0.00006752208,0.0001529853,0.0000384715,0.0002556044,0.0000451703,0.000006791411],"category_scores_gemma":[0.0000233841,0.00008201313,0.00005247943,0.000141844,0.0001026456,0.00008960727,0.00006122477,0.0001045303,0.000003171802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008294091,"about_ca_system_score_gemma":0.0000154293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003517006,"about_ca_topic_score_gemma":0.0008675916,"domain_scores_codex":[0.9990311,0.00002426065,0.0001872728,0.0001325731,0.0003504609,0.000274325],"domain_scores_gemma":[0.9995821,0.000004375969,0.00005250917,0.0002584483,0.00006337213,0.00003920687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002807096,0.00005913766,0.08688124,0.0002607943,0.00004732923,0.000002698541,0.0004149619,0.00008878535,0.904261,0.00001887495,0.0001302087,0.007806888],"study_design_scores_gemma":[0.0003076213,0.00006417748,0.3343095,0.0002998693,0.00002237264,0.00001475033,0.00002036924,0.0007036402,0.6634761,0.00003437961,0.0005538026,0.0001933968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910102,0.007716004,0.00001011134,0.0002004996,0.00009224581,0.0002015606,0.000003926377,0.00003551812,0.0007298919],"genre_scores_gemma":[0.999505,0.0003055415,0.000031361,0.00001490282,0.00003279646,0.00002211282,2.365008e-7,0.00001112219,0.00007688635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2474283,"threshold_uncertainty_score":0.3344398,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2734987164","doi":"10.1016/j.measurement.2017.07.027","title":"Relative range error evaluation of terrestrial laser scanners using a plate, a sphere, and a novel dual-sphere-plate target","year":2017,"lang":"en","type":"article","venue":"Measurement","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Point cloud; Range (aeronautics); Fiducial marker; Point (geometry); Integrating sphere; Optics; SPHERES; Computer science; Laser scanning; Geometry; Laser; Artificial intelligence; Physics; Mathematics; Engineering; Aerospace engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1225977288281021,"gpt":0.308953786691101,"spread":0.186356057862999,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001495043,0.0001261695,0.0001574275,0.00001702832,0.0003352175,0.00005029448,0.0001093892,0.00006481718,0.0002151691],"category_scores_gemma":[0.0001955079,0.0001156085,0.0000446324,0.00006102264,0.0002008754,0.0002009077,0.00007660398,0.00009129799,0.00002736822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002919542,"about_ca_system_score_gemma":0.00005807906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002613451,"about_ca_topic_score_gemma":0.0009994454,"domain_scores_codex":[0.9983026,0.00008010381,0.0002268669,0.0002860778,0.0009277364,0.0001765992],"domain_scores_gemma":[0.9991972,0.00001351139,0.000271037,0.0003919535,0.00004916161,0.00007712251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007057614,0.0009482186,0.06217859,0.00005453738,0.0004828848,0.000009506284,0.006277768,0.07794408,0.7734747,0.0001027477,0.00407626,0.07374495],"study_design_scores_gemma":[0.01152634,0.0002388224,0.5328152,0.000386406,0.0007165433,0.00002945851,0.0006444448,0.4068967,0.03460956,0.003037696,0.008185741,0.0009130537],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793896,0.0000433068,0.004160435,0.000200889,0.0001597597,0.0006404045,0.00001033553,0.0000152403,0.01538002],"genre_scores_gemma":[0.990436,0.000001693702,0.009364874,0.00001696658,0.00004553702,0.00000618427,0.00000268568,0.00001305175,0.0001130327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7388651,"threshold_uncertainty_score":0.4714379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1450319353","doi":"10.1016/j.measurement.2015.06.017","title":"Electromagnetically controlled microfluidic chip for DNA extraction","year":2015,"lang":"en","type":"article","venue":"Measurement","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Microfluidics; Materials science; Extraction (chemistry); Buffer (optical fiber); Chip; Chromatography; DNA extraction; Microfluidic chip; Elution; Solid phase extraction; Cartridge; Layer (electronics); DNA; Nanotechnology; Chemistry; Engineering; Polymerase chain reaction","retraction":null,"screen_n_in":null,"score":{"opus":0.0298116610113822,"gpt":0.2316986900791136,"spread":0.2018870290677314,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005863836,0.0001522323,0.0002169935,0.000054911,0.00006112109,0.00003085747,0.0001129218,0.00006612146,0.0000374415],"category_scores_gemma":[0.00006010755,0.0001440938,0.00009235494,0.0001009584,0.00001601358,0.00003764665,0.000005549276,0.00008258972,0.00006613958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002834513,"about_ca_system_score_gemma":0.00007925738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002949909,"about_ca_topic_score_gemma":0.000002239553,"domain_scores_codex":[0.9989031,0.00002532402,0.0002636723,0.0001696259,0.0003226973,0.0003155629],"domain_scores_gemma":[0.9993796,0.00001937447,0.00002631945,0.0002087445,0.000225046,0.000140936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008558228,0.00003352047,0.000005809906,0.00001142903,0.00004596711,2.620352e-7,0.00002331978,0.000004242788,0.720165,0.0004868171,0.2781543,0.0009837645],"study_design_scores_gemma":[0.003202755,0.0002146282,0.0001406164,0.00000762409,0.00007568839,0.000007552195,0.0000211501,0.0003749864,0.5589632,0.0007560517,0.4360464,0.000189262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2463809,0.4519756,0.2791942,0.001029509,0.0007768231,0.004007263,0.00001698064,0.0009668474,0.0156519],"genre_scores_gemma":[0.9872299,0.01089979,0.0003247341,0.0001240497,0.0002387153,0.0009171437,0.00001948346,0.00005134895,0.0001948076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.740849,"threshold_uncertainty_score":0.5875973,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362722442","doi":"10.1016/j.measurement.2023.112843","title":"Experimental investigation of interfacial behavior of fiber optic cables embedded in frozen soil for in-situ deformation monitoring","year":2023,"lang":"en","type":"article","venue":"Measurement","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"China National Funds for Distinguished Young Scientists; State Key Laboratory of Frozen Soil Engineering; National Natural Science Foundation of China","keywords":"Softening; Materials science; Optical fiber; Geotechnical engineering; Ground freezing; Deformation (meteorology); Shear (geology); Settlement (finance); Composite material; Optics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.03830885320129183,"gpt":0.2490449456088707,"spread":0.2107360924075789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002273055,0.00008388235,0.0001408565,0.0001707433,0.000009555562,0.000006014155,0.00006113666,0.00005740506,0.000002755588],"category_scores_gemma":[0.00002791434,0.00008767277,0.00003227211,0.000164595,0.0000115525,0.00007281558,0.00001272842,0.00006344343,0.000001181425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844088,"about_ca_system_score_gemma":0.00001150896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004449396,"about_ca_topic_score_gemma":0.00003780565,"domain_scores_codex":[0.9992881,0.000008944221,0.0002966027,0.0000717011,0.0002029448,0.0001316451],"domain_scores_gemma":[0.9998245,0.0000126383,0.00002848929,0.00007907451,0.00003355561,0.00002173006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007431654,0.000010052,0.0007002641,0.0001182959,0.000005501394,3.335309e-7,0.0006728476,0.2876293,0.7102941,0.00002148015,0.00001048925,0.000529898],"study_design_scores_gemma":[0.0004775486,0.0000418383,0.06771921,0.0001550307,0.000007471579,4.955888e-7,0.0003120507,0.01138839,0.9197192,0.00008307669,0.000005903202,0.00008979194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985979,0.000107037,0.0006841774,0.00000443594,0.0002291817,0.0002124072,0.000002469345,0.00006957094,0.00009281484],"genre_scores_gemma":[0.9989581,0.000002400799,0.0008803369,4.045905e-7,0.00001975472,0.0001156742,0.000006843869,0.0000123388,0.000004216914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2762409,"threshold_uncertainty_score":0.3575192,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399358716","doi":"10.1016/j.measurement.2024.115066","title":"Precision drilling optimization in jute/palm fiber reinforced hybrid composites","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Natural Fiber Reinforced Composites","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Composite material; Drilling; Palm; Materials science; Fiber","retraction":null,"screen_n_in":null,"score":{"opus":0.02482916700482315,"gpt":0.2485022910402604,"spread":0.2236731240354373,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009785533,0.0002164564,0.0002202427,0.0002349337,0.00009971231,0.0003547935,0.0002792069,0.00006030192,0.0007446365],"category_scores_gemma":[0.00008314112,0.0001846159,0.00007871002,0.0003260638,0.00002964248,0.0004413791,0.0001053083,0.000178049,0.0006855684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254826,"about_ca_system_score_gemma":0.0000662194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004617975,"about_ca_topic_score_gemma":0.000005104625,"domain_scores_codex":[0.9977005,0.00007910536,0.0004888569,0.000421004,0.0009547933,0.0003557344],"domain_scores_gemma":[0.9993026,0.00008485476,0.00006402684,0.0003037149,0.0001629792,0.0000817885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000332342,0.000006070238,0.0000155124,0.00005364691,0.000007072359,0.00001686241,0.00009664257,0.4230354,0.5745957,0.000159768,0.0005158268,0.001464247],"study_design_scores_gemma":[0.0002650061,0.0000603485,0.00004438909,0.0005573104,0.00001716526,0.00001536409,0.000006095886,0.2394957,0.7557598,0.0001011565,0.003436679,0.0002410042],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8651224,0.006374874,0.1125163,0.0009195314,0.002910498,0.001719299,0.00002072262,0.001085774,0.009330636],"genre_scores_gemma":[0.9817685,0.00003066593,0.01711041,0.00008118906,0.0001260588,0.00003814295,0.00002801839,0.00003268352,0.0007842652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1835397,"threshold_uncertainty_score":0.8811823,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}