{"meta":{"query_hash":"5d6a4145de5f","filters":{"topic":"Advanced Data Processing Techniques"},"cohort_total":199,"direct_labels_cover":0,"predictions_cover":199,"exported":199,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/5d6a4145de5f","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Data+Processing+Techniques"},"results":[{"id":"W118116490","doi":"10.5220/0002257003250330","title":"REACTIVE AUTONOMIC SYSTEM PERFORMANCE MODELING AND SELF-MONITORING WITH CATEGORY THEORY","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Autonomic computing; Operating system","score_opus":0.007056388525925287,"score_gpt":0.20997665416266967,"score_spread":0.20292026563674437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118116490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034093093,0.00011275122,0.96173704,0.00009500239,0.000034131193,0.000020986789,0.000022137123,0.00037356684,0.0035113026],"genre_scores_gemma":[0.9298675,0.00009257246,0.06806056,0.000035394696,0.00003678266,0.000067819106,0.00003856,0.00008544913,0.0017153973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992495,0.00027626308,0.000030698706,0.00011146167,0.00023962311,0.00009236664],"domain_scores_gemma":[0.9983897,0.0007685102,0.00015605328,0.0002898034,0.00033903169,0.000056822104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013731479,0.00039016528,0.0005428443,0.0008835222,0.00048446585,0.0013132803,0.0014692408,0.0004831462,0.0010379099],"category_scores_gemma":[0.0039025133,0.0003000068,0.0007090958,0.0007332933,0.00080329436,0.0021074312,0.0009257063,0.00076019776,0.0001664257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008302661,0.00006438753,0.002077938,0.000046458543,0.00006365968,0.00006221182,0.00021301075,0.76465976,0.002266795,0.19408683,0.00094142236,0.035434503],"study_design_scores_gemma":[0.000001578757,0.000007562735,0.00009679829,0.0000016345488,0.000005049315,0.000007564668,0.000007827247,0.97726804,0.0002604636,0.022082373,0.00025773374,0.00000342515],"about_ca_topic_score_codex":0.004269716,"about_ca_topic_score_gemma":0.002996918,"teacher_disagreement_score":0.004269716,"about_ca_system_score_codex":0.0010809087,"about_ca_system_score_gemma":0.0007128182,"threshold_uncertainty_score":0.008489728},"labels":[],"label_agreement":null},{"id":"W129335492","doi":"10.5220/0002701801840189","title":"POSSIBILISTIC ACTIVITY RECOGNITION","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université du Québec; Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.013084236736340798,"score_gpt":0.24712493384005105,"score_spread":0.23404069710371025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W129335492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026587998,0.00042601753,0.9627278,0.00017101044,0.000060187027,0.00007883667,0.0003316292,0.0009637753,0.008652774],"genre_scores_gemma":[0.78345394,0.00032923635,0.2075952,0.00008390624,0.000059903872,0.00010649058,0.0008450749,0.00012396683,0.0074024233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886656,0.00020769676,0.00008957034,0.00039473525,0.00035597492,0.000085496],"domain_scores_gemma":[0.9985176,0.0006011424,0.00012430092,0.00037946587,0.0002831158,0.00009445103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000916815,0.00073390047,0.00066835445,0.0015920647,0.0005527697,0.0020618034,0.0015007342,0.0010660194,0.007225091],"category_scores_gemma":[0.004499315,0.0004871078,0.0010741425,0.0011481396,0.0008956075,0.0023013677,0.0016053978,0.0011845733,0.0020746063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095275114,0.00022872361,0.00540766,0.00035968245,0.00019906981,0.0004877469,0.00028675047,0.08444334,0.0477607,0.10337139,0.0042377743,0.7522643],"study_design_scores_gemma":[0.000014436276,0.0000972532,0.0020908478,0.000036300928,0.000043692173,0.00060565287,0.000059061636,0.9085293,0.012036388,0.07217648,0.0042741676,0.000036520418],"about_ca_topic_score_codex":0.0017544007,"about_ca_topic_score_gemma":0.0024304665,"teacher_disagreement_score":0.007225091,"about_ca_system_score_codex":0.00056496647,"about_ca_system_score_gemma":0.0006156631,"threshold_uncertainty_score":0.02417034},"labels":[],"label_agreement":null},{"id":"W130229173","doi":"10.11575/prism/30308","title":"MULTIDATABASE QUERYING BY CONTEXT","year":2000,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Database; Geography","score_opus":0.004937057556923909,"score_gpt":0.2103645057198682,"score_spread":0.20542744816294428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W130229173","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030608768,0.004092581,0.9202213,0.0022771186,0.00023193429,0.00027331247,0.0012146971,0.01601781,0.025062421],"genre_scores_gemma":[0.3326487,0.0026267206,0.64643985,0.0015698228,0.00022339849,0.0002650496,0.0025157474,0.0022429167,0.0114677595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939857,0.0013788145,0.00078596955,0.0012914083,0.0022019234,0.00035615804],"domain_scores_gemma":[0.99576205,0.0012665838,0.00027305752,0.0017359863,0.00071374077,0.00024860108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005874424,0.00080604176,0.0010876366,0.0035303414,0.0013216566,0.009694778,0.0029929697,0.0013390611,0.0041820654],"category_scores_gemma":[0.008628101,0.0008296963,0.0017148833,0.0035951529,0.0015869191,0.013329262,0.009118138,0.0022263613,0.0013887441],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032515635,0.00011698464,0.00690839,0.00071310747,0.00012679417,0.001129224,0.00422638,0.009348535,0.015913127,0.69141316,0.018680135,0.25109908],"study_design_scores_gemma":[0.00007628057,0.00013014516,0.0018685089,0.0005088978,0.00014966122,0.001794321,0.0014066459,0.1062929,0.03299003,0.41195622,0.4426447,0.00018172918],"about_ca_topic_score_codex":0.0031899528,"about_ca_topic_score_gemma":0.0050333645,"teacher_disagreement_score":0.009694778,"about_ca_system_score_codex":0.0017389398,"about_ca_system_score_gemma":0.0013673577,"threshold_uncertainty_score":0.031067312},"labels":[],"label_agreement":null},{"id":"W1486729477","doi":"10.5555/1357910.1358066","title":"The importance of a comprehensive and integrative view of modeling and simulation","year":2007,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Knowledge management; Management science; Process management; Engineering","score_opus":0.0473208360176164,"score_gpt":0.3231350859919287,"score_spread":0.2758142499743123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486729477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006352861,0.017680831,0.81044036,0.08881866,0.001653097,0.00017029916,0.00014018743,0.00089724193,0.073846444],"genre_scores_gemma":[0.42379382,0.02118314,0.5285897,0.007420993,0.0039864285,0.00073610124,0.00030504336,0.0005506412,0.013434068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9833815,0.010029375,0.00077304535,0.00097592256,0.004126246,0.00071403704],"domain_scores_gemma":[0.9809554,0.0108441,0.0010278275,0.0033986368,0.002081805,0.001692273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016673535,0.0017172195,0.0022706448,0.004492452,0.0036501985,0.020199677,0.0045342846,0.004941041,0.004261896],"category_scores_gemma":[0.01827474,0.0015862241,0.0023388115,0.0023946008,0.015828876,0.029458085,0.01056831,0.014484685,0.0012074215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012017527,0.0000365026,0.000494941,0.00012606343,0.000054145316,0.000055100725,0.0009052458,0.007418623,0.00016946306,0.9773226,0.0021774364,0.011227927],"study_design_scores_gemma":[0.0000122111705,0.000043543536,0.00036611178,0.00030465252,0.000043594722,0.00014037563,0.0009664016,0.022226255,0.00014340624,0.93219733,0.04351602,0.00004014556],"about_ca_topic_score_codex":0.0065713925,"about_ca_topic_score_gemma":0.0047964645,"teacher_disagreement_score":0.020199677,"about_ca_system_score_codex":0.0057940716,"about_ca_system_score_gemma":0.008478959,"threshold_uncertainty_score":0.08817911},"labels":[],"label_agreement":null},{"id":"W1521067239","doi":"10.5772/14540","title":"New Applications of Fuzzy Logic Methodologies in Aerospace Field","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Bombardier (Canada); Polytechnique Montréal; National Research Council Canada; Consortium For Research and Innovation In Aerospace In Quebec; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Aerospace; Fuzzy logic; Field (mathematics); Computer science; Systems engineering; Engineering; Aerospace engineering; Artificial intelligence; Mathematics","score_opus":0.077951599751267,"score_gpt":0.31934932186456033,"score_spread":0.24139772211329333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521067239","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050496575,0.10618893,0.80008334,0.0011418035,0.0012336,0.000051802188,0.00011700532,0.000594002,0.08553991],"genre_scores_gemma":[0.15405625,0.12419698,0.631899,0.000894576,0.002224336,0.00015294965,0.0003538915,0.00020944815,0.08601261],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974555,0.000037817947,0.000017703256,0.00004242629,0.00014275596,0.000013708723],"domain_scores_gemma":[0.9997876,0.00012173627,0.000009243855,0.000022234148,0.0000487707,0.000010338123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041772594,0.00074580085,0.00071028684,0.001391189,0.00036105665,0.0014176673,0.00085906754,0.00085294695,0.0088092685],"category_scores_gemma":[0.0007464614,0.00036889987,0.0006892518,0.0017109539,0.000689812,0.001570929,0.00061298854,0.0017226598,0.0019624198],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055856897,0.00007554807,0.00021766384,0.00096898165,0.00006237018,0.00022262103,0.00024761597,0.026446614,0.014515883,0.19159079,0.014732987,0.7508631],"study_design_scores_gemma":[0.000039690898,0.00014368711,0.00104591,0.00081892975,0.00008361475,0.00090282754,0.00017682851,0.21371152,0.008438427,0.47882488,0.29572165,0.000091951675],"about_ca_topic_score_codex":0.0009062332,"about_ca_topic_score_gemma":0.001274668,"teacher_disagreement_score":0.0088092685,"about_ca_system_score_codex":0.0006145885,"about_ca_system_score_gemma":0.0003565234,"threshold_uncertainty_score":0.029469967},"labels":[],"label_agreement":null},{"id":"W1583968016","doi":"10.1002/0471722960.ch3","title":"Models of Linear Systems","year":2003,"lang":"en","type":"other","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Mathematics","score_opus":0.01797246412158023,"score_gpt":0.24406050606795723,"score_spread":0.226088041946377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583968016","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004564766,0.013986364,0.91784376,0.002829292,0.00074227765,0.000074754775,0.0011251442,0.00067667087,0.058157012],"genre_scores_gemma":[0.58002996,0.04056542,0.24013174,0.0015610949,0.0035230801,0.00093628094,0.0035806787,0.000379077,0.1292927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987852,0.0005599987,0.000065154156,0.00017864151,0.00035829423,0.0000528363],"domain_scores_gemma":[0.99816173,0.0012262957,0.0001539581,0.00022187842,0.0002035704,0.00003248217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010400069,0.0011882478,0.0011142185,0.00085344363,0.00036168727,0.0027985666,0.0008995703,0.0011774511,0.013614029],"category_scores_gemma":[0.0050707376,0.00046144178,0.00083205494,0.0016556437,0.00132792,0.0021894847,0.001007546,0.0021671918,0.00396883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008638148,0.000019904712,0.00028919743,0.00016740593,0.000033393713,0.000041309064,0.00013688784,0.03375658,0.00031146634,0.93296736,0.0084720375,0.023795895],"study_design_scores_gemma":[0.000012028378,0.000029883873,0.0002822136,0.00007790089,0.000020882213,0.00008284927,0.000051555046,0.12738042,0.00020512998,0.83434683,0.037494928,0.000015436908],"about_ca_topic_score_codex":0.0020687073,"about_ca_topic_score_gemma":0.0013628232,"teacher_disagreement_score":0.013614029,"about_ca_system_score_codex":0.00088339596,"about_ca_system_score_gemma":0.00096165994,"threshold_uncertainty_score":0.04554349},"labels":[],"label_agreement":null},{"id":"W1901194031","doi":"10.5539/mas.v9n8p429","title":"Development of Models and Methods of Data Analysis for Enhancing Efficiency of the Processes of Quality Management Systems","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Nuclear University MEPhI","keywords":"Computer science; Quality (philosophy); Process (computing); Product (mathematics); Management system; Quality management system; Risk analysis (engineering); Order (exchange); Process management; Quality management; Industrial engineering; Operations management; Business; Mathematics; Engineering","score_opus":0.14821590060218834,"score_gpt":0.4005287640881068,"score_spread":0.2523128634859184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901194031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010821284,0.0003016746,0.99752694,0.00017590182,0.000029319932,0.000040726187,0.000048989812,0.00019815101,0.00059613655],"genre_scores_gemma":[0.1044477,0.0018868727,0.8905485,0.00011310768,0.00014904863,0.0007042493,0.00039992228,0.00014684536,0.0016037769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958549,0.0018231325,0.0003293014,0.0004922564,0.0013848296,0.00011555554],"domain_scores_gemma":[0.9920008,0.0053665494,0.0004657417,0.0006677903,0.0014088029,0.00009031708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005800472,0.0012818591,0.0011867422,0.0024032842,0.00060786185,0.0031913512,0.0019719612,0.001483656,0.0014852704],"category_scores_gemma":[0.017273162,0.0008181429,0.0019066327,0.0018756309,0.0011167502,0.0035466636,0.0015272588,0.0023213574,0.00078072783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012816869,0.00015568575,0.003969924,0.00074747694,0.00032326774,0.00020129622,0.0004832701,0.57044923,0.0046793846,0.20553105,0.0038842624,0.20944706],"study_design_scores_gemma":[0.000012711278,0.00003506478,0.00031445644,0.00008984634,0.000036153226,0.000057784488,0.00004172449,0.947255,0.0019453058,0.04434578,0.0058371928,0.000029055418],"about_ca_topic_score_codex":0.0047400277,"about_ca_topic_score_gemma":0.0027178675,"teacher_disagreement_score":0.005800472,"about_ca_system_score_codex":0.0015691492,"about_ca_system_score_gemma":0.003061901,"threshold_uncertainty_score":0.030676186},"labels":[],"label_agreement":null},{"id":"W1910715712","doi":"10.1109/ccece.1993.332439","title":"Approximation of spectrogrammes by cubic splines using the Kalman filter","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Kalman filter; Spline (mechanical); Convolution (computer science); Applied mathematics; Algorithm; Mathematics; Set (abstract data type); Computer science; Artificial intelligence; Artificial neural network","score_opus":0.027110660904625505,"score_gpt":0.2523410436186221,"score_spread":0.2252303827139966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910715712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004292083,0.0000380258,0.9950252,0.000023047633,0.000008897609,0.0000053040953,0.000028950639,0.0003001356,0.00027828035],"genre_scores_gemma":[0.27642637,0.00040253336,0.71899533,0.000019720299,0.00002944257,0.00008519815,0.00042156377,0.00021202207,0.003407806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960786,0.00011662871,0.000023181925,0.00007422059,0.00014131078,0.00003677025],"domain_scores_gemma":[0.9993686,0.00031191227,0.00007522873,0.00006687913,0.00016083775,0.00001659937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009336616,0.00046536073,0.00053176505,0.000856748,0.00034584338,0.0008515323,0.00057247456,0.0005489309,0.0018613259],"category_scores_gemma":[0.0032608667,0.0004810303,0.00071599626,0.0013802982,0.00043715604,0.0008295529,0.0005145089,0.00093346584,0.0007910704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044059372,0.00001460487,0.0008677212,0.000051290674,0.000025982368,0.000027844246,0.000087571905,0.9003522,0.002458036,0.011253499,0.0007422725,0.084075026],"study_design_scores_gemma":[0.000002540472,0.000004580841,0.00018556151,0.0000039659453,0.0000024622157,0.00000638479,0.0000058207015,0.99658114,0.00046392836,0.0018345625,0.000903546,0.000005428172],"about_ca_topic_score_codex":0.024168955,"about_ca_topic_score_gemma":0.019172914,"teacher_disagreement_score":0.024168955,"about_ca_system_score_codex":0.0006706547,"about_ca_system_score_gemma":0.0012749223,"threshold_uncertainty_score":0.048056602},"labels":[],"label_agreement":null},{"id":"W1967682537","doi":"10.1145/1066677.1066702","title":"Reliability analysis of mobile agent-based systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Robustness (evolution); Reliability (semiconductor); Reliability theory; Monte Carlo method; Mobile agent; Graph theory; Distributed computing; Random walk; Multi-agent system; Theoretical computer science; Reliability engineering; Artificial intelligence; Mathematics; Engineering; Failure rate","score_opus":0.009200903200740415,"score_gpt":0.2587912705576151,"score_spread":0.24959036735687468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967682537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04329422,0.00078004313,0.9538219,0.00020716725,0.000029002606,0.000047032394,0.0000500928,0.00024124382,0.0015291793],"genre_scores_gemma":[0.94495994,0.00077264983,0.052946724,0.000036529203,0.0000690122,0.00009872672,0.00011723776,0.000060138205,0.00093891023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983966,0.0006182573,0.0000642744,0.00018255097,0.0006273888,0.00011103617],"domain_scores_gemma":[0.99424416,0.0036720154,0.0005937944,0.00036924018,0.00102033,0.00010042317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016877274,0.00057519367,0.00069715624,0.0013158295,0.00036673236,0.0008396635,0.0007965678,0.00055487244,0.0010659453],"category_scores_gemma":[0.012358122,0.0003344737,0.00046652087,0.00059549615,0.00082650955,0.001212377,0.0007642679,0.00069651776,0.00028794107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054088618,0.000010828231,0.0016698039,0.0000889082,0.000049041744,0.000113614304,0.00008435685,0.95462316,0.002143015,0.025816975,0.0003595527,0.01498668],"study_design_scores_gemma":[0.0000023498874,0.000011640604,0.00021282918,0.00000610249,0.000005456226,0.00002229932,0.000008964384,0.99058455,0.0003719848,0.008529532,0.00024006036,0.0000042770985],"about_ca_topic_score_codex":0.002461058,"about_ca_topic_score_gemma":0.00070838287,"teacher_disagreement_score":0.002461058,"about_ca_system_score_codex":0.0007539949,"about_ca_system_score_gemma":0.00056223787,"threshold_uncertainty_score":0.008925617},"labels":[],"label_agreement":null},{"id":"W1976745190","doi":"10.5539/mas.v9n5p125","title":"Probabilistic Approach to the Synthesis of Algorithm for Solving Problems","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; Probabilistic logic; Flexibility (engineering); Randomized algorithm; Forward algorithm; Sequence (biology); Markov chain; Stability (learning theory); Probabilistic analysis of algorithms; Axiom; Hidden Markov model; Markov model; Machine learning; Mathematics; Artificial intelligence; Variable-order Markov model; Statistics","score_opus":0.036472063794620826,"score_gpt":0.25320518276235415,"score_spread":0.21673311896773334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976745190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078954216,0.00011125062,0.997917,0.00007270288,0.000017765064,0.000021336216,0.0000122575,0.00006504477,0.0009931867],"genre_scores_gemma":[0.07875527,0.0005992609,0.9177222,0.00012691587,0.0001134189,0.00043891935,0.00013344477,0.00012977117,0.0019807299],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99554896,0.0016120049,0.00038235285,0.0009262387,0.0013318295,0.00019859213],"domain_scores_gemma":[0.99535793,0.003280952,0.00023505771,0.00065333425,0.00041381366,0.000058859943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037348843,0.0010074044,0.001112156,0.0013563785,0.0007768443,0.0018661239,0.002040407,0.0012918118,0.0041816086],"category_scores_gemma":[0.012324532,0.0006305841,0.0018658539,0.0011015602,0.0027710565,0.0027120253,0.0017743796,0.00276813,0.00093716057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054832057,0.000041304826,0.00047156686,0.00038848392,0.0000745285,0.00006745481,0.00016824386,0.1540651,0.0028119094,0.7684666,0.00087089156,0.07251915],"study_design_scores_gemma":[0.000043937016,0.00016425402,0.00014510138,0.000104051935,0.000051469033,0.00014983823,0.00004203461,0.51884645,0.0043895305,0.463462,0.012566674,0.00003468803],"about_ca_topic_score_codex":0.0009776386,"about_ca_topic_score_gemma":0.00077045726,"teacher_disagreement_score":0.0041816086,"about_ca_system_score_codex":0.0011804726,"about_ca_system_score_gemma":0.0025173943,"threshold_uncertainty_score":0.019752204},"labels":[],"label_agreement":null},{"id":"W199386351","doi":"","title":"A Computer Program for Obtaining Subsystems.","year":2001,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Computer program; Computer science; Arithmetic; Programming language","score_opus":0.01499881400213835,"score_gpt":0.27298488629964685,"score_spread":0.2579860722975085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W199386351","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002857779,0.00021429038,0.9148893,0.000090021385,0.00005748207,0.00026132178,0.004026334,0.060572974,0.01703052],"genre_scores_gemma":[0.03158686,0.00040568996,0.92073554,0.00015578844,0.00005741135,0.0010206741,0.012795482,0.008488483,0.024754077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997328,0.00004114359,0.000026368536,0.000080857106,0.00008229711,0.000036556718],"domain_scores_gemma":[0.99939144,0.00026763364,0.000027225027,0.00015404858,0.00011116579,0.00004847243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068555406,0.0011998431,0.0006975769,0.0015906533,0.0007742089,0.00092729524,0.0011746426,0.00045258284,0.07838685],"category_scores_gemma":[0.0017783093,0.00061486126,0.0007617289,0.0016285992,0.00036554036,0.0013607155,0.0016031372,0.001393726,0.040009044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005768934,0.00024741885,0.001813036,0.00082783157,0.000108784436,0.00030327347,0.00039887894,0.004554383,0.02446267,0.0480684,0.16600233,0.75263596],"study_design_scores_gemma":[0.0009287039,0.00039900775,0.0044386038,0.0002639683,0.00021176111,0.0011037359,0.00027333034,0.114496455,0.0677686,0.1429791,0.667017,0.00011993291],"about_ca_topic_score_codex":0.0013410577,"about_ca_topic_score_gemma":0.0025755223,"teacher_disagreement_score":0.07838685,"about_ca_system_score_codex":0.00039053758,"about_ca_system_score_gemma":0.000943017,"threshold_uncertainty_score":0.2622301},"labels":[],"label_agreement":null},{"id":"W1996347944","doi":"10.1061/(asce)0733-9399(2009)135:4(358)","title":"Discussion of “Model Selection in Applied Science and Engineering: A Decision-Theoretic Approach” by R. V. Field Jr. and M. Grigoriu","year":2009,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Selection (genetic algorithm); Field (mathematics); Management science; Computer science; Engineering; Mathematics; Artificial intelligence; Pure mathematics","score_opus":0.005141481205008322,"score_gpt":0.21613728308843502,"score_spread":0.2109958018834267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996347944","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033051006,0.023072446,0.65636134,0.29268336,0.006763464,0.00011784659,0.00023301203,0.00009248588,0.017370936],"genre_scores_gemma":[0.41306892,0.04292699,0.3424216,0.14487952,0.036489457,0.0013103436,0.00045792153,0.0002371944,0.01820802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9858608,0.01034955,0.0004898699,0.0009028231,0.001997181,0.00039970025],"domain_scores_gemma":[0.976172,0.02020863,0.0008785986,0.0008628162,0.0014927225,0.0003852364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022775156,0.0014857068,0.0021887992,0.002207647,0.0024596073,0.0043072067,0.00484851,0.0072172047,0.004105983],"category_scores_gemma":[0.035548672,0.0007944999,0.0031413424,0.003622627,0.008037011,0.0066929334,0.0029228993,0.009644504,0.00073244754],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000144621445,0.000025180994,0.0001569045,0.00013796183,0.00007125791,0.00008879701,0.00014716372,0.010095059,0.00008738299,0.95904124,0.021531207,0.008603448],"study_design_scores_gemma":[0.000013399005,0.000021869844,0.00007650532,0.00009128361,0.00001633025,0.00004631445,0.000044709464,0.01406664,0.000117349504,0.96844625,0.017039215,0.000020175266],"about_ca_topic_score_codex":0.003071942,"about_ca_topic_score_gemma":0.0034489117,"teacher_disagreement_score":0.022775156,"about_ca_system_score_codex":0.0037790218,"about_ca_system_score_gemma":0.0035603733,"threshold_uncertainty_score":0.12044799},"labels":[],"label_agreement":null},{"id":"W1996637843","doi":"10.1007/s10479-006-0067-y","title":"Multiobjective design of survivable IP networks","year":2006,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Engineering Link (Canada)","funders":"Vetenskapsrådet","keywords":"Computer network; Private Network-to-Network Interface; Computer science; Open Shortest Path First; Constrained Shortest Path First; Shortest path problem; Routing protocol; Survivability; Distributed computing; IP forwarding; Routing (electronic design automation); Internet Protocol; The Internet; Link-state routing protocol; K shortest path routing; Graph","score_opus":0.20657839439451176,"score_gpt":0.435748254671344,"score_spread":0.22916986027683223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996637843","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086134814,0.0005780885,0.90156764,0.00042672327,0.00007643568,0.00016690376,0.00015349172,0.0001360563,0.0107599115],"genre_scores_gemma":[0.87006253,0.00070795126,0.12099061,0.00012543472,0.00006353267,0.0004296389,0.00017570665,0.000092239505,0.0073522953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994399,0.00026780186,0.000018324354,0.000069037764,0.00010811117,0.00009679071],"domain_scores_gemma":[0.9990288,0.000588826,0.00013799709,0.000028830693,0.00014759025,0.00006794706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001573011,0.0014389649,0.001094715,0.001067826,0.00042248596,0.0011916562,0.0011801987,0.0013020062,0.0025671877],"category_scores_gemma":[0.0028721772,0.0008283115,0.0006780477,0.0006840814,0.000690177,0.0009319866,0.0010542705,0.00076467113,0.00024445972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015176306,0.000014859256,0.00007863201,0.000023664274,0.000014279626,0.000019286075,0.000009448784,0.9933717,0.0003103009,0.001717232,0.00011232396,0.004313127],"study_design_scores_gemma":[0.0000068898316,0.000030717274,0.000038437884,0.0000052158866,0.0000066892694,0.000005972868,0.000008591371,0.99743396,0.00012413891,0.0021488674,0.00018851548,0.000002056334],"about_ca_topic_score_codex":0.0024913468,"about_ca_topic_score_gemma":0.0019262641,"teacher_disagreement_score":0.0025671877,"about_ca_system_score_codex":0.001123167,"about_ca_system_score_gemma":0.00094268145,"threshold_uncertainty_score":0.008588135},"labels":[],"label_agreement":null},{"id":"W2004536376","doi":"10.1109/tpami.2013.118","title":"Guest Editors' Introduction: Special Section on Learning Deep Architectures","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université de Sherbrooke","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Unsupervised learning; Machine learning; Feature learning; Feature extraction; Representation (politics)","score_opus":0.007718599298558523,"score_gpt":0.23771784374897623,"score_spread":0.2299992444504177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004536376","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013590661,0.011799026,0.0011317957,0.030780157,0.9493479,0.000022192473,0.00016870002,0.00013256073,0.006481767],"genre_scores_gemma":[0.0014639487,0.012134906,0.00078629993,0.013128616,0.94035846,0.00003933178,0.00015868084,0.00014707904,0.031782586],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828315,0.00017220479,0.00021866434,0.00042885178,0.0007111497,0.00018604654],"domain_scores_gemma":[0.991413,0.0019285328,0.0005261205,0.0003214485,0.0041865897,0.0016242306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032246306,0.0020284718,0.0014551093,0.0024492014,0.0010306234,0.0042730765,0.0021225198,0.004475693,0.037034564],"category_scores_gemma":[0.009087249,0.0005968204,0.0012768504,0.0012609402,0.0008722362,0.003486703,0.0014876414,0.0076957494,0.025706451],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025501391,0.000015922895,0.00007244054,0.00014876909,0.000008768127,0.00005772881,0.0000067279448,0.00011730428,0.00010600924,0.001014107,0.978309,0.020117735],"study_design_scores_gemma":[0.000018825494,0.00004039718,0.00028874291,0.00022303897,0.000015709784,0.00020024722,0.000013232612,0.00037448693,0.00020255824,0.00184204,0.9967648,0.000015974887],"about_ca_topic_score_codex":0.0006131399,"about_ca_topic_score_gemma":0.0016864793,"teacher_disagreement_score":0.037034564,"about_ca_system_score_codex":0.0013475869,"about_ca_system_score_gemma":0.0015437742,"threshold_uncertainty_score":0.12389296},"labels":[],"label_agreement":null},{"id":"W2009589674","doi":"10.1016/s0735-1097(12)60739-6","title":"IMPROVING SURVIVAL BY TARGETING ERRORS","year":2012,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation","funders":"","keywords":"Medicine; Intensive care medicine","score_opus":0.008440918387074437,"score_gpt":0.24693539230951572,"score_spread":0.23849447392244127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009589674","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41915393,0.007025162,0.52587545,0.018890347,0.0028231663,0.00019747361,0.0013758868,0.0052308985,0.019427752],"genre_scores_gemma":[0.9691222,0.0010233932,0.023928493,0.0011000378,0.0007630746,0.000048378224,0.00047733507,0.0002295819,0.0033075796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978927,0.00058080105,0.00022098543,0.00042917943,0.0005902563,0.00028608306],"domain_scores_gemma":[0.9882394,0.005145168,0.0019763475,0.0017913466,0.0022786062,0.00056907436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028531312,0.0007905128,0.0008662546,0.0012095522,0.0005434611,0.0016567191,0.0009059768,0.00094147294,0.0041148565],"category_scores_gemma":[0.022792395,0.000250763,0.00055270887,0.00070241606,0.00042180906,0.0013601636,0.0015318671,0.0014519334,0.0017332269],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012045833,0.00069691543,0.13995838,0.00022630731,0.00028740853,0.0005248532,0.00027915044,0.048939057,0.0055887518,0.0077764443,0.023327632,0.77119046],"study_design_scores_gemma":[0.00036063718,0.003021364,0.09172617,0.0004987921,0.0010931235,0.0037992145,0.0007024335,0.7196859,0.03187129,0.11456564,0.032515693,0.00015976888],"about_ca_topic_score_codex":0.001182393,"about_ca_topic_score_gemma":0.0014618678,"teacher_disagreement_score":0.0041148565,"about_ca_system_score_codex":0.00050532504,"about_ca_system_score_gemma":0.0012802754,"threshold_uncertainty_score":0.015088975},"labels":[],"label_agreement":null},{"id":"W2033089695","doi":"10.1080/0953531042000219277","title":"An Algorithm for the Consistent Inclusion of Partial Information in the Revision of Input-Output Tables","year":2004,"lang":"en","type":"article","venue":"Economic Systems Research","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Inclusion (mineral); Algorithm; Computer science; Econometrics; Mathematical economics; Mathematics; Sociology; Social science","score_opus":0.05093656265659348,"score_gpt":0.351724612054167,"score_spread":0.30078804939757353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033089695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003795137,0.000060227867,0.9896828,0.0001360384,0.000074624986,0.00018773801,0.0003246174,0.0051035355,0.0006352352],"genre_scores_gemma":[0.043766525,0.000045960744,0.9536804,0.000055481974,0.0000658243,0.00019433601,0.0006002151,0.00036762914,0.0012236099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9915173,0.0028379199,0.0010098983,0.0014876424,0.0027416039,0.00040563592],"domain_scores_gemma":[0.95694524,0.017841434,0.0028511984,0.010962843,0.01085456,0.0005446919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010868043,0.0013820357,0.0015565435,0.0032102542,0.0016937492,0.0038083417,0.0042753005,0.0011953794,0.00855986],"category_scores_gemma":[0.060553227,0.0014103686,0.0016864898,0.0031105294,0.0015558063,0.004550207,0.0034919078,0.0028717974,0.004075151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061957305,0.00013226719,0.0029790972,0.00023742342,0.0002016355,0.00016102589,0.0004714421,0.06765347,0.0059904684,0.037627477,0.016426185,0.86749995],"study_design_scores_gemma":[0.0002611421,0.00020251946,0.0010538689,0.000075651675,0.00013361959,0.00040438084,0.00023297577,0.8904899,0.020436477,0.064423084,0.022167286,0.000119003395],"about_ca_topic_score_codex":0.005907548,"about_ca_topic_score_gemma":0.010576894,"teacher_disagreement_score":0.010868043,"about_ca_system_score_codex":0.0013649917,"about_ca_system_score_gemma":0.006042474,"threshold_uncertainty_score":0.0574764},"labels":[],"label_agreement":null},{"id":"W2036994031","doi":"10.7232/ieif.2012.25.4.382","title":"A Case Study of the Commom Cause Failure Analysis of Digital Reactor Protection System","year":2012,"lang":"en","type":"article","venue":"IE interfaces","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Common cause failure; Engineering; Common cause and special cause; Computer science; Operations management","score_opus":0.03522672974512592,"score_gpt":0.27305751701688,"score_spread":0.2378307872717541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036994031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9308527,0.00039578552,0.056686323,0.0006194824,0.000060805207,0.0002342733,0.0009553375,0.00027774277,0.009917623],"genre_scores_gemma":[0.98290735,0.00012357516,0.013191216,0.000035375477,0.00001642413,0.000053596013,0.00033157427,0.00003840459,0.0033024587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985072,0.00046918134,0.00008769006,0.00019292705,0.0005285664,0.00021426355],"domain_scores_gemma":[0.99689806,0.001927275,0.00020158757,0.00026601998,0.0005381565,0.00016897847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016457519,0.0007632781,0.0006094057,0.0016222972,0.0015908404,0.0010601343,0.0011433788,0.0021814073,0.00340118],"category_scores_gemma":[0.0034644294,0.0002866206,0.0010796845,0.0012576807,0.0009389294,0.0007632174,0.0007886825,0.00082560565,0.0003756516],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001311475,0.0011092433,0.08929692,0.001058198,0.0003566458,0.044437297,0.003280595,0.7436367,0.020000927,0.016349323,0.008231554,0.07093111],"study_design_scores_gemma":[0.00019624799,0.0011885227,0.047665335,0.00009268171,0.00021271578,0.006940659,0.0032686482,0.9006113,0.021070436,0.006937073,0.011685854,0.00013050162],"about_ca_topic_score_codex":0.019599466,"about_ca_topic_score_gemma":0.019491196,"teacher_disagreement_score":0.019599466,"about_ca_system_score_codex":0.0012949401,"about_ca_system_score_gemma":0.0010529512,"threshold_uncertainty_score":0.03897077},"labels":[],"label_agreement":null},{"id":"W2038801804","doi":"10.1080/14399776.2002.10781137","title":"Establishing Operating Points for a Linearized Model of a Load Sensing System","year":2002,"lang":"en","type":"article","venue":"International Journal of Fluid Power","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Engineering; Control theory (sociology); Control (management)","score_opus":0.02244439695266396,"score_gpt":0.2631974897295356,"score_spread":0.24075309277687165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038801804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2260016,0.000115362476,0.759311,0.00031208317,0.00001988941,0.00023285554,0.00025364078,0.002514514,0.01123903],"genre_scores_gemma":[0.9755485,0.000073462754,0.022062276,0.000022232554,0.0000057269044,0.00017117748,0.00008822797,0.00009430546,0.0019340374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995401,0.00009544282,0.00003099751,0.000091602386,0.00018247668,0.000059430677],"domain_scores_gemma":[0.9985442,0.0006897872,0.00033081003,0.00014291483,0.00026180234,0.000030590327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072480645,0.0006059296,0.00046819908,0.00046162432,0.000597392,0.0010443536,0.0005766414,0.0007171545,0.0041687456],"category_scores_gemma":[0.0027431063,0.00034050658,0.000551499,0.00016442421,0.000816521,0.00087956677,0.0008029519,0.00085905526,0.0012912903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048578763,0.00013592797,0.004120807,0.00049514713,0.000057252462,0.0004958709,0.0019939232,0.7454038,0.187144,0.021065028,0.0016662243,0.036936212],"study_design_scores_gemma":[0.000024137607,0.00013379808,0.0006693631,0.000023134831,0.000019877069,0.00006571226,0.00009596557,0.9641933,0.031513322,0.0020872303,0.0011429386,0.000031274358],"about_ca_topic_score_codex":0.004656069,"about_ca_topic_score_gemma":0.0025316568,"teacher_disagreement_score":0.004656069,"about_ca_system_score_codex":0.0006160775,"about_ca_system_score_gemma":0.0007455156,"threshold_uncertainty_score":0.013945878},"labels":[],"label_agreement":null},{"id":"W2056356212","doi":"10.5339/qfarf.2013.sshp-012","title":"Training model to develop the Qatar workforce using emerging learning technologies","year":2013,"lang":"en","type":"article","venue":"Qatar Foundation Annual Research Forum Volume 2013 Issue 1","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Workforce; Flexibility (engineering); Presentation (obstetrics); Engineering; Engineering management; Knowledge management; Business; Computer science; Management; Economic growth; Medicine","score_opus":0.10471084170863197,"score_gpt":0.3895643443422932,"score_spread":0.2848535026336612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056356212","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1462934,0.0012326263,0.21931562,0.045992933,0.0016397374,0.0039408985,0.0005113545,0.0011612198,0.5799122],"genre_scores_gemma":[0.5571887,0.0021633587,0.21650869,0.005653637,0.00016130292,0.00333369,0.00055584183,0.00007445867,0.21436034],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994691,0.00017651024,0.000022247528,0.000058400507,0.00011550801,0.00015826651],"domain_scores_gemma":[0.999514,0.00006297269,0.000035707482,0.00001844575,0.00014084036,0.0002279803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011362366,0.00041890488,0.000114554685,0.0004456067,0.0016457065,0.00122857,0.0012645393,0.0012987687,0.015178881],"category_scores_gemma":[0.0008229994,0.0001244993,0.00035713642,0.0002770889,0.00054685056,0.0011657465,0.0019060884,0.0010486281,0.004655315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025013386,0.003793675,0.010559394,0.0010971942,0.00002365845,0.0011088271,0.023292804,0.022083817,0.012656095,0.21649486,0.10160459,0.607035],"study_design_scores_gemma":[0.00039095784,0.0034689028,0.014299949,0.0017095011,0.000059886814,0.0011986835,0.023612706,0.043407824,0.006367141,0.04783182,0.8575455,0.00010712019],"about_ca_topic_score_codex":0.0047782827,"about_ca_topic_score_gemma":0.010504098,"teacher_disagreement_score":0.015178881,"about_ca_system_score_codex":0.0021951036,"about_ca_system_score_gemma":0.00606485,"threshold_uncertainty_score":0.05077845},"labels":[],"label_agreement":null},{"id":"W2059723446","doi":"10.2523/iptc-12516-ms","title":"Integrated Technologies of Testing and Controlling for High Efficiency Separate Layer Water Injection","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Petro-Canada","funders":"","keywords":"Layer (electronics); Computer science; Reliability engineering; Materials science; Engineering; Nanotechnology","score_opus":0.026576064340609162,"score_gpt":0.24848795835656567,"score_spread":0.2219118940159565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059723446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106981315,0.010385927,0.7362662,0.0015210708,0.0012692326,0.0011200723,0.0012780337,0.007903594,0.13327454],"genre_scores_gemma":[0.7015967,0.006125042,0.19204901,0.00048228714,0.0002003164,0.000512402,0.0012133517,0.00031999603,0.09750085],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99833906,0.00006682183,0.00003843547,0.00013370237,0.001326836,0.00009508371],"domain_scores_gemma":[0.9993611,0.000081070924,0.00007021953,0.000082375554,0.00036650503,0.000038783826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007573074,0.00054167357,0.00050940993,0.0016539028,0.0003809771,0.0015367336,0.0013644029,0.0008535742,0.0066960156],"category_scores_gemma":[0.0007684075,0.0004983057,0.0004895203,0.0014545871,0.00056206965,0.0022441042,0.00086459506,0.00086985424,0.0029689595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029542766,0.00021013326,0.0019145557,0.0006229075,0.00005249385,0.0002025391,0.00017271875,0.0028600793,0.6656109,0.012553271,0.010202369,0.3053025],"study_design_scores_gemma":[0.00008584249,0.00081688643,0.004367646,0.00007719877,0.00010438082,0.0005678073,0.0001400371,0.031437702,0.85190403,0.0020953098,0.10832739,0.00007574345],"about_ca_topic_score_codex":0.0017938559,"about_ca_topic_score_gemma":0.0020084411,"teacher_disagreement_score":0.0066960156,"about_ca_system_score_codex":0.00143659,"about_ca_system_score_gemma":0.0013159235,"threshold_uncertainty_score":0.02240038},"labels":[],"label_agreement":null},{"id":"W2066253228","doi":"10.1115/detc2010-28103","title":"Neural Network Bushing Model Development Using Simulation","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Bushing; Artificial neural network; Computer science; Data modeling; Training set; Artificial intelligence; Data acquisition; Network model; Set (abstract data type); Simulation; Engineering; Mechanical engineering; Database","score_opus":0.03309774802368856,"score_gpt":0.2900960139403141,"score_spread":0.25699826591662556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066253228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018427465,0.0001519589,0.9741086,0.00016383031,0.000033717075,0.000100441284,0.00018394919,0.0008470068,0.0059830784],"genre_scores_gemma":[0.61163557,0.00065970124,0.37474173,0.000074228854,0.000027071208,0.00083644077,0.0005085661,0.0002857023,0.011231001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982065,0.00004384268,0.000013109112,0.000039513034,0.000068574394,0.000014371849],"domain_scores_gemma":[0.9994105,0.00031022786,0.0000367339,0.000047198628,0.00017941858,0.000015917638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000513764,0.0004349569,0.00053611846,0.00045450736,0.00030269593,0.0006686,0.0012037916,0.0009431334,0.0036262737],"category_scores_gemma":[0.0018372079,0.00046262937,0.00059529784,0.0004600179,0.0004268356,0.00089990115,0.0005829536,0.0009635817,0.0005485498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068264876,0.0000040292757,0.000077363715,0.0000128838865,0.000003974616,0.000009136454,0.000008946298,0.99456066,0.00039661935,0.0014610626,0.00009456743,0.0033639674],"study_design_scores_gemma":[8.577979e-7,0.0000019400604,0.00000974519,0.0000012149401,6.086017e-7,0.0000015478622,0.0000010540977,0.99927706,0.00016460997,0.00037457698,0.00016589517,8.8611597e-7],"about_ca_topic_score_codex":0.012180711,"about_ca_topic_score_gemma":0.008062347,"teacher_disagreement_score":0.012180711,"about_ca_system_score_codex":0.0007620103,"about_ca_system_score_gemma":0.0009833022,"threshold_uncertainty_score":0.024219632},"labels":[],"label_agreement":null},{"id":"W2082225313","doi":"10.3183/npprj-2006-21-04-p534-541","title":"Optimal operation of TMP-newsprint refiners","year":2006,"lang":"en","type":"article","venue":"Nordic Pulp & Paper Research Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Newsprint; Pulp and paper industry; Process engineering; Industrial chemistry; Environmental science; Waste management; Materials science; Biochemical engineering; Engineering; Kraft paper","score_opus":0.03329971685167244,"score_gpt":0.34892602575443915,"score_spread":0.3156263089027667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082225313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93258107,0.00028675314,0.05337483,0.0001479437,0.000024082125,0.0000910876,0.00019708801,0.00037185135,0.012925218],"genre_scores_gemma":[0.9935469,0.000042399006,0.004298793,0.0000062944155,0.000003039409,0.000013668311,0.000036507692,0.000017027205,0.0020353745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966383,0.000057953006,0.000011160453,0.00006641449,0.0000835808,0.000117055],"domain_scores_gemma":[0.999819,0.000057276997,0.000032254004,0.000016891647,0.000053200132,0.000021453285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039196425,0.00039766284,0.0007222356,0.00037961552,0.0006401971,0.0010990865,0.00065200665,0.0006554224,0.00321041],"category_scores_gemma":[0.0006506984,0.00040930617,0.0002823343,0.00034200394,0.0003281191,0.00059925165,0.0004794254,0.00038367914,0.0005382698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044148467,0.0003257257,0.0075044446,0.00029886077,0.00005520203,0.00041175252,0.00025122947,0.6293566,0.25870353,0.0032709802,0.0014700851,0.09393678],"study_design_scores_gemma":[0.00020566442,0.0012784281,0.0128933145,0.000020739877,0.000055589717,0.00009223373,0.00046669488,0.78521883,0.19388357,0.0019257555,0.003910994,0.000048287653],"about_ca_topic_score_codex":0.011015601,"about_ca_topic_score_gemma":0.014008289,"teacher_disagreement_score":0.011015601,"about_ca_system_score_codex":0.0009588978,"about_ca_system_score_gemma":0.0011233423,"threshold_uncertainty_score":0.021902978},"labels":[],"label_agreement":null},{"id":"W2082367394","doi":"10.1037/h0087420","title":"Introduction to the special issue on alternative methods of data interpretation.","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Psychology; Interpretation (philosophy); Epistemology; Cognitive psychology; Cognitive science; Philosophy; Linguistics","score_opus":0.0551786862552924,"score_gpt":0.38640560618273345,"score_spread":0.331226919927441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082367394","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005445161,0.031432156,0.7250307,0.018409006,0.19709599,0.00061885436,0.0019508939,0.0046990113,0.020218834],"genre_scores_gemma":[0.0074988706,0.043476295,0.56234,0.021458477,0.26025987,0.0022034196,0.0054140184,0.0053784316,0.09197064],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932389,0.0028584197,0.0007881605,0.0008501663,0.0021313825,0.00013291686],"domain_scores_gemma":[0.9596858,0.026660847,0.00087789487,0.0048190947,0.00679941,0.0011568342],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00949475,0.0020125005,0.0021886555,0.0041085226,0.0010729914,0.0044772127,0.0029045194,0.003357515,0.07491057],"category_scores_gemma":[0.041999444,0.0012892374,0.002622885,0.0024593603,0.002297081,0.0043487446,0.0030557546,0.0075813155,0.049132325],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100148,0.00007984601,0.00025649506,0.0010119665,0.00012140153,0.0002482718,0.0001634239,0.00030664788,0.0016325621,0.014422574,0.73761046,0.24404617],"study_design_scores_gemma":[0.000032529668,0.00007341616,0.0006504122,0.0003904992,0.000047286805,0.00081445917,0.000067018394,0.0014826951,0.001003003,0.047333766,0.94803405,0.00007087821],"about_ca_topic_score_codex":0.00058146426,"about_ca_topic_score_gemma":0.0009295873,"teacher_disagreement_score":0.9905053,"about_ca_system_score_codex":0.00064811006,"about_ca_system_score_gemma":0.0014659243,"threshold_uncertainty_score":0.25060087},"labels":[],"label_agreement":null},{"id":"W2103159331","doi":"10.5539/mas.v9n4p135","title":"Methods for Controlling Autonomous DC Systemson the Basis of Switching by Capacitors","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pulse-width modulation; Capacitor; Basis (linear algebra); Power (physics); Pulse (music); Computer science; Control theory (sociology); Amplitude; Transistor; Electrical engineering; Electronic engineering; Topology (electrical circuits); Mathematics; Physics; Voltage; Engineering; Control (management)","score_opus":0.0313928057523691,"score_gpt":0.3200928861823358,"score_spread":0.2887000804299667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103159331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018804987,0.00055433175,0.9919708,0.00007579237,0.00008008981,0.000067170164,0.00002510636,0.00023133462,0.005114954],"genre_scores_gemma":[0.117089644,0.0019246632,0.87130505,0.00009051552,0.00011245314,0.0004079586,0.00009543893,0.00011368076,0.00886047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971586,0.000042028052,0.000014608049,0.000039165858,0.00017607157,0.000012290856],"domain_scores_gemma":[0.99982494,0.00007128314,0.000018577699,0.00003791398,0.000039626866,0.000007689247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039580735,0.0005279203,0.00025529566,0.0006526924,0.00043991924,0.00079091894,0.0008908479,0.00043202584,0.0037820677],"category_scores_gemma":[0.0008466786,0.00024782523,0.00029318858,0.00054539205,0.0009439601,0.0009601749,0.00047064378,0.0010559985,0.00086532696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056040746,0.000050464358,0.00024099203,0.00048179083,0.000034704855,0.00006087181,0.00024970522,0.036119636,0.05619172,0.54011387,0.0026579094,0.36374238],"study_design_scores_gemma":[0.0001229794,0.00030817153,0.0005870758,0.00021393609,0.000062138,0.00046412737,0.000111961854,0.45066857,0.079889335,0.26744226,0.20000225,0.00012720341],"about_ca_topic_score_codex":0.000497686,"about_ca_topic_score_gemma":0.0008542389,"teacher_disagreement_score":0.0037820677,"about_ca_system_score_codex":0.00049396954,"about_ca_system_score_gemma":0.00048185015,"threshold_uncertainty_score":0.012652278},"labels":[],"label_agreement":null},{"id":"W2111345321","doi":"10.1109/aina.2005.295","title":"Reliability Estimation of Mobile Agent Systems Using the Monte Carlo Approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Monte Carlo method; Robustness (evolution); Reliability (semiconductor); Reliability theory; Mobile agent; Cellular network; Distributed computing; Mathematical optimization; Reliability engineering; Mathematics; Engineering; Computer network; Failure rate","score_opus":0.019706519933422238,"score_gpt":0.2696999875027362,"score_spread":0.24999346756931398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111345321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008093906,0.0001820047,0.9910981,0.00005717084,0.0000104827,0.000014835147,0.000009042747,0.00018565673,0.00034881057],"genre_scores_gemma":[0.7121614,0.0005558377,0.28586766,0.000062758416,0.000070737435,0.00013441156,0.0001125245,0.00009269635,0.00094191503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853146,0.0006902434,0.00006288823,0.00017806722,0.00043282963,0.0001045209],"domain_scores_gemma":[0.9933115,0.004933168,0.00056469045,0.00046662265,0.00061298034,0.000110933164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020651182,0.0008000432,0.0011888999,0.0015477343,0.0005284361,0.00095675554,0.0009846358,0.00090613135,0.0008967773],"category_scores_gemma":[0.011591067,0.0005731513,0.0007347395,0.0005991203,0.0009841176,0.0014230385,0.0008087199,0.0010783031,0.00026469879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006932417,0.00001690503,0.0009322877,0.000032281867,0.000045082146,0.000049916474,0.000041639294,0.960201,0.0012630001,0.014238893,0.00022098976,0.022888595],"study_design_scores_gemma":[0.0000038325215,0.000008918548,0.00011406209,0.0000041415733,0.000004095646,0.000018954788,0.0000031871382,0.99352103,0.00055839896,0.0055678026,0.00018809857,0.0000074921077],"about_ca_topic_score_codex":0.0034342925,"about_ca_topic_score_gemma":0.0017840643,"teacher_disagreement_score":0.0034342925,"about_ca_system_score_codex":0.00092903076,"about_ca_system_score_gemma":0.0010272616,"threshold_uncertainty_score":0.010921538},"labels":[],"label_agreement":null},{"id":"W2118442472","doi":"10.1109/wcica.2002.1020017","title":"An intelligent system for real-time monitoring and fault predicting","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fault (geology); Real-time computing; Embedded system; Geology","score_opus":0.014811980317394271,"score_gpt":0.27650404273573276,"score_spread":0.2616920624183385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118442472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04221338,0.0009249511,0.90385056,0.00027468769,0.00043720045,0.00017906156,0.00075746624,0.04677801,0.004584667],"genre_scores_gemma":[0.50097513,0.0005391601,0.483983,0.0007324574,0.00036938227,0.00031758097,0.0016654581,0.0005000002,0.010917774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952114,0.000044715325,0.000044775832,0.00017313675,0.00017462553,0.0000414935],"domain_scores_gemma":[0.99912876,0.00021607475,0.00011572654,0.00019499795,0.00026928945,0.00007511749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007139144,0.0008295252,0.0010195839,0.0016098223,0.00060770276,0.0010191842,0.0014147924,0.00095325965,0.0042063324],"category_scores_gemma":[0.0013982021,0.0003359461,0.0003192742,0.00074979966,0.0003242772,0.0013899158,0.0007434538,0.000636512,0.002112633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015404123,0.0003856972,0.004246205,0.0002415285,0.00012231315,0.000288794,0.0001319024,0.0122983735,0.09837559,0.0026674385,0.022045128,0.8576566],"study_design_scores_gemma":[0.0002757276,0.0009707945,0.010675425,0.000053320673,0.0005436733,0.0011501181,0.000054193835,0.8196437,0.120034076,0.006452442,0.03998478,0.00016174138],"about_ca_topic_score_codex":0.0017762302,"about_ca_topic_score_gemma":0.0030275257,"teacher_disagreement_score":0.0042063324,"about_ca_system_score_codex":0.00047292587,"about_ca_system_score_gemma":0.0006824125,"threshold_uncertainty_score":0.014071584},"labels":[],"label_agreement":null},{"id":"W2118921095","doi":"10.1109/acc.1995.529311","title":"Advanced state space analysis using computer algebra","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Symbolic computation; Algebra over a field; Numerical linear algebra; Computation; Matrix algebra; State space; Computer science; State (computer science); Simple (philosophy); Linear algebra; Applied mathematics; Matrix (chemical analysis); Theoretical computer science; Numerical analysis; Algorithm; Mathematics; Pure mathematics; Eigenvalues and eigenvectors; Mathematical analysis","score_opus":0.009120455710681888,"score_gpt":0.2569732219102578,"score_spread":0.2478527661995759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118921095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052854763,0.0018353339,0.9694169,0.00067548687,0.00019038633,0.00005390736,0.00009675713,0.00039790643,0.022047922],"genre_scores_gemma":[0.32332838,0.006805594,0.65380955,0.00052314857,0.0009576903,0.00042613034,0.0005412215,0.0002905558,0.013317751],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991025,0.00032623557,0.00006124695,0.00009679199,0.00035801344,0.000055338816],"domain_scores_gemma":[0.9992561,0.00031555226,0.000046609788,0.00021595396,0.00014141564,0.000024457058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012676253,0.00072004093,0.0010960386,0.0011979241,0.0007307293,0.0021839954,0.0008844226,0.0005341496,0.006687965],"category_scores_gemma":[0.0023138798,0.00027369062,0.0010674379,0.0013365211,0.0018443019,0.002938515,0.0018108512,0.0024307834,0.0018440841],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018379333,0.000028391913,0.00013993152,0.00013727383,0.00003321567,0.000052735297,0.00009305568,0.031601388,0.0016248053,0.9244731,0.0020421532,0.039755464],"study_design_scores_gemma":[0.000012453248,0.000021124459,0.00008089141,0.000029552542,0.0000069726207,0.0000383277,0.000017185646,0.12873721,0.001097583,0.8571995,0.012744017,0.000015186512],"about_ca_topic_score_codex":0.001352199,"about_ca_topic_score_gemma":0.0010453406,"teacher_disagreement_score":0.006687965,"about_ca_system_score_codex":0.0008633471,"about_ca_system_score_gemma":0.00102471,"threshold_uncertainty_score":0.022373497},"labels":[],"label_agreement":null},{"id":"W2137023923","doi":"","title":"Comparative Analysis of Pyrolysis Products from a Variety of Herbaceous Canadian Crop Residues","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lignin; Guaiacol; Chemistry; Biomass (ecology); Vanillin; Levoglucosan; Pyrolysis; Straw; Cellulose; Lignocellulosic biomass; Bran; Organic chemistry; Pulp and paper industry; Food science; Agronomy; Raw material","score_opus":0.03367708615875733,"score_gpt":0.25064006077611195,"score_spread":0.21696297461735461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137023923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968665,0.0006077625,0.0006464171,0.000008393507,0.0000020760197,0.000018027373,0.0008525702,0.000012110465,0.000986075],"genre_scores_gemma":[0.9924488,0.0011171533,0.002144869,0.000022326463,0.0000022873394,0.000019394163,0.0022313404,0.000016798052,0.0019970178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999169,0.000002916793,0.0000032943562,0.000016180293,0.000040228122,0.000020478594],"domain_scores_gemma":[0.99989116,0.000012381075,0.000018112187,0.0000040928994,0.000056217,0.000017934204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009891276,0.00031560686,0.00012393371,0.0010303986,0.00041189147,0.00025900535,0.00014394945,0.0001299235,0.00089803216],"category_scores_gemma":[0.00012804041,0.00010371428,0.00021489264,0.00092843967,0.00013784062,0.00014268265,0.00012641404,0.00021579751,0.00016492071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010744665,0.000014884343,0.0074323774,0.000089903464,0.00002437702,0.00012369677,0.000063368265,0.00011262611,0.9875652,0.000024540648,0.00002691228,0.004414676],"study_design_scores_gemma":[0.0000061705687,0.00013917405,0.37751967,0.000025420328,0.000064883745,0.00047951192,0.00023152912,0.0006093913,0.61882645,0.000037673428,0.00204831,0.000011788092],"about_ca_topic_score_codex":0.041266214,"about_ca_topic_score_gemma":0.082474746,"teacher_disagreement_score":0.9587338,"about_ca_system_score_codex":0.00047434648,"about_ca_system_score_gemma":0.00058222783,"threshold_uncertainty_score":0.08205199},"labels":[],"label_agreement":null},{"id":"W2148758416","doi":"10.1109/iscc.2005.66","title":"Estimating the Task Route Reliability of Mobile Agent-Based Systems Using Monte Carlo Simulation","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Monte Carlo method; Robustness (evolution); Reliability (semiconductor); Task (project management); Distributed computing; Mobile agent; Mobile computing; Computer network; Engineering; Mathematics","score_opus":0.019116854867166258,"score_gpt":0.2906178788985039,"score_spread":0.27150102403133763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148758416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0808595,0.00017247161,0.9177564,0.00009493718,0.0000131627785,0.000037994414,0.00003057211,0.00036309805,0.0006719803],"genre_scores_gemma":[0.87414414,0.00014415798,0.12516735,0.000024560939,0.000017049117,0.00008071914,0.000083824154,0.000056216693,0.00028200305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849737,0.0006930977,0.000077830104,0.00016188518,0.00043722714,0.0001326112],"domain_scores_gemma":[0.9868221,0.01007279,0.0009662411,0.0008943802,0.001053537,0.00019080738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024219318,0.0007402439,0.0008457325,0.0013411656,0.00042794787,0.0008692742,0.0008683913,0.0008737593,0.00067605934],"category_scores_gemma":[0.018068722,0.0005524676,0.00052798976,0.00055304135,0.0007978954,0.001362291,0.0007616288,0.00090097194,0.00018448722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005883363,0.000010399698,0.0010625713,0.000014423028,0.000016618416,0.000022085262,0.000019728874,0.9896854,0.0008024769,0.0022162506,0.00007973318,0.0060115214],"study_design_scores_gemma":[0.000002922059,0.0000068105974,0.00015225368,0.0000018624288,0.0000024363405,0.00000872072,0.0000039686406,0.9979929,0.0005339463,0.0012348617,0.00005543912,0.00000397701],"about_ca_topic_score_codex":0.005759204,"about_ca_topic_score_gemma":0.0028912371,"teacher_disagreement_score":0.005759204,"about_ca_system_score_codex":0.0010467173,"about_ca_system_score_gemma":0.0010594008,"threshold_uncertainty_score":0.012808561},"labels":[],"label_agreement":null},{"id":"W2149190554","doi":"10.1109/nssmic.1990.693349","title":"DSP Coprocessor For Event Filtering In A Data Acquisition System","year":2005,"lang":"en","type":"article","venue":"1990 IEEE Nuclear Science Symposium Conference Record","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF","funders":"","keywords":"Coprocessor; Computer science; Digital signal processing; Embedded system; Event (particle physics); Data acquisition; Computer architecture; Computer hardware; Operating system","score_opus":0.031137119680037153,"score_gpt":0.28859383363515917,"score_spread":0.25745671395512204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149190554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105143495,0.0010043832,0.8707518,0.00034594513,0.00040995603,0.0003097088,0.00036685527,0.008146219,0.01352163],"genre_scores_gemma":[0.6576212,0.000816721,0.2995842,0.00076762884,0.0004752968,0.00027643086,0.00090982695,0.00072316284,0.03882553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997538,0.000035385554,0.000021682348,0.000057611815,0.00010075543,0.000030817126],"domain_scores_gemma":[0.9993316,0.0002231151,0.00004160354,0.00009029774,0.0002623781,0.000051179915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032639407,0.00062596536,0.00044071046,0.0010604459,0.00062027894,0.00083054905,0.00075683085,0.00043211607,0.013688493],"category_scores_gemma":[0.0013089658,0.00029655872,0.00019409506,0.00077480933,0.00016999409,0.0004945844,0.00027496592,0.00068066886,0.0035030374],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025523773,0.00039936486,0.0021619468,0.00047308434,0.00009282681,0.0007164668,0.00025008043,0.0036926318,0.5079409,0.007769245,0.014979154,0.458972],"study_design_scores_gemma":[0.0003823166,0.0022400704,0.009631115,0.00018534342,0.00030604162,0.0026744704,0.00010902679,0.21790059,0.6875186,0.0029615946,0.07600615,0.00008466838],"about_ca_topic_score_codex":0.0009569205,"about_ca_topic_score_gemma":0.0020598343,"teacher_disagreement_score":0.013688493,"about_ca_system_score_codex":0.0003257062,"about_ca_system_score_gemma":0.000653595,"threshold_uncertainty_score":0.04579264},"labels":[],"label_agreement":null},{"id":"W2257365734","doi":"10.5539/mas.v9n11p176","title":"Research of the Robust Stability of Control Systems Using a New Approach to the Lyapunov Functions Construction","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lyapunov function; Control theory (sociology); Mathematics; Stability (learning theory); Lyapunov equation; State vector; Lyapunov redesign; State (computer science); Applied mathematics; Matrix (chemical analysis); Computer science; Control (management); Nonlinear system; Algorithm; Physics","score_opus":0.16631640949294113,"score_gpt":0.322315379889196,"score_spread":0.15599897039625488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257365734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027724137,0.0010613622,0.99219453,0.00024527017,0.00014731045,0.000016727352,0.000016471473,0.000046204907,0.003499751],"genre_scores_gemma":[0.38565516,0.010670308,0.5823904,0.00057331583,0.0026791985,0.0004308694,0.00020092723,0.00034342555,0.01705635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992299,0.00029519782,0.000045582972,0.00012688518,0.00026007305,0.00004232518],"domain_scores_gemma":[0.9990464,0.00050121045,0.00009256035,0.00009049625,0.0002442462,0.000025070487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017690775,0.0011231096,0.00064590096,0.0016659573,0.00028318513,0.0015516629,0.00083449367,0.0007269443,0.0014389998],"category_scores_gemma":[0.0025829806,0.00035746256,0.00128117,0.0006361891,0.0017646007,0.0021226278,0.00089742447,0.0015950456,0.00044049666],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015557805,0.000031214244,0.00021443142,0.0002451985,0.000071270995,0.00018618278,0.00022774703,0.100889474,0.011354705,0.8182163,0.0016039454,0.06694393],"study_design_scores_gemma":[0.000012599134,0.00013718405,0.00031068712,0.000079791505,0.000032388543,0.00019582274,0.000042041553,0.71756405,0.0065618465,0.2553811,0.01962469,0.000057856392],"about_ca_topic_score_codex":0.00072008074,"about_ca_topic_score_gemma":0.00033797298,"teacher_disagreement_score":0.0017690775,"about_ca_system_score_codex":0.0007823536,"about_ca_system_score_gemma":0.00065943395,"threshold_uncertainty_score":0.009355903},"labels":[],"label_agreement":null},{"id":"W2257972972","doi":"10.5539/jmr.v4n3p89","title":"Application of Methods of Computer Algebra for Doing Physical Sums","year":2012,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Symbolic computation; Algebra over a field; Mathematics; Pure mathematics; Mathematical analysis","score_opus":0.11817383033302765,"score_gpt":0.5002147378814095,"score_spread":0.3820409075483818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257972972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045836405,0.004958307,0.95026803,0.0012897004,0.00082224474,0.000096988755,0.00009972207,0.0003297402,0.037551627],"genre_scores_gemma":[0.18494068,0.012047788,0.7778074,0.000786409,0.0018393473,0.0005458703,0.00026362098,0.0005174398,0.021251418],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850816,0.00050814025,0.000089656205,0.00016933987,0.00065795274,0.00006675747],"domain_scores_gemma":[0.9991554,0.00032567777,0.00004838303,0.00023936552,0.0001942542,0.00003690164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012780427,0.0005686417,0.0007454828,0.0017891848,0.0011662318,0.002264823,0.0011634324,0.00075268076,0.0049949987],"category_scores_gemma":[0.0036788986,0.0003131659,0.0013450679,0.0014464345,0.0020770598,0.0025994764,0.0022250072,0.002681117,0.001750491],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012214317,0.000019530919,0.0001963087,0.0001934225,0.000029546853,0.000062202045,0.00017996013,0.004323296,0.0016066394,0.9537744,0.0031433278,0.03645923],"study_design_scores_gemma":[0.000011577838,0.000021960213,0.00017328186,0.00006731621,0.000013073934,0.00020265544,0.00004031828,0.03679894,0.0017757418,0.9202564,0.04061289,0.000025872989],"about_ca_topic_score_codex":0.00077392126,"about_ca_topic_score_gemma":0.0005862427,"teacher_disagreement_score":0.0049949987,"about_ca_system_score_codex":0.0007319601,"about_ca_system_score_gemma":0.0011729292,"threshold_uncertainty_score":0.016709924},"labels":[],"label_agreement":null},{"id":"W2287804374","doi":"","title":"Generic Simulator Model for Training and Learning in Virtual Laboratory Environments","year":2002,"lang":"en","type":"article","venue":"E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; Université TÉLUQ","funders":"","keywords":"Training (meteorology); Computer science; Simulation; Human–computer interaction; Virtual reality","score_opus":0.08569578028967219,"score_gpt":0.28248951340703005,"score_spread":0.19679373311735787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287804374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029038552,0.00011479905,0.9598163,0.00015617421,0.00009151487,0.00021277154,0.000631294,0.002288568,0.0076501393],"genre_scores_gemma":[0.7379914,0.00033356043,0.24111943,0.00012675446,0.000040926694,0.0008519283,0.0016719603,0.0004037897,0.017460188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999658,0.00008801452,0.000026411964,0.000070637805,0.000110355475,0.000046580357],"domain_scores_gemma":[0.9996087,0.000093473594,0.00003668044,0.00008613713,0.0001254034,0.000049673952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042802136,0.0005935299,0.00051686756,0.00059235474,0.00031865234,0.0008783905,0.0021267233,0.0014729532,0.0077350517],"category_scores_gemma":[0.0014589584,0.00032181555,0.00081145175,0.0004208267,0.00037798495,0.00095135975,0.0009328951,0.0007270246,0.002058428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118173215,0.00012891255,0.0010402125,0.000067774505,0.000029374487,0.00007846819,0.00006693407,0.9528779,0.0038331435,0.01374306,0.0015207662,0.0264953],"study_design_scores_gemma":[0.000010918696,0.000030041125,0.00019632925,0.0000059461663,0.0000075894814,0.000026456291,0.0000071254844,0.99505854,0.00069892587,0.002406074,0.0015456489,0.0000063530465],"about_ca_topic_score_codex":0.005363192,"about_ca_topic_score_gemma":0.0043649552,"teacher_disagreement_score":0.0077350517,"about_ca_system_score_codex":0.000705729,"about_ca_system_score_gemma":0.0010917671,"threshold_uncertainty_score":0.025876284},"labels":[],"label_agreement":null},{"id":"W2388375060","doi":"10.11575/prism/27187","title":"On Steam Based Recovery Process Design","year":2015,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Carbon Management Canada","keywords":"Process (computing); Computer science; Process engineering; Engineering","score_opus":0.014581266196835408,"score_gpt":0.23143067056790126,"score_spread":0.21684940437106584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2388375060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016625196,0.00036826858,0.9648335,0.00013361992,0.000047365083,0.00014520541,0.000096298834,0.00091401004,0.016836528],"genre_scores_gemma":[0.75869864,0.0007942228,0.21576014,0.0001257931,0.0000398549,0.00037035937,0.00035623176,0.0001991833,0.023655476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967515,0.000039331124,0.00001748757,0.00007950881,0.00015518349,0.000033284083],"domain_scores_gemma":[0.99981993,0.000050281717,0.000027937936,0.000023600955,0.00006802279,0.000010309294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035494828,0.0005071783,0.00048734862,0.00036605733,0.00033448898,0.0010432677,0.00083063176,0.00059184793,0.006747076],"category_scores_gemma":[0.0006690456,0.00029177064,0.00056080084,0.00037294882,0.00041495662,0.0008180489,0.00077691016,0.0005778226,0.0012956613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032248045,0.00016606363,0.00090275996,0.00051483937,0.00004104284,0.00023735102,0.00015224214,0.62255704,0.11328497,0.054287028,0.0022381933,0.205296],"study_design_scores_gemma":[0.000024517416,0.00020142268,0.00019005444,0.000014894395,0.000015244928,0.00005790631,0.000015542822,0.9615303,0.022345081,0.00542549,0.010168887,0.000010520752],"about_ca_topic_score_codex":0.0014324957,"about_ca_topic_score_gemma":0.0013622248,"teacher_disagreement_score":0.006747076,"about_ca_system_score_codex":0.0006426604,"about_ca_system_score_gemma":0.0008778617,"threshold_uncertainty_score":0.022571206},"labels":[],"label_agreement":null},{"id":"W2404652880","doi":"10.2316/journal.206.2015.5.206-4325","title":"INTELLIGENT FAULT-TOLERANT CONTROL OF LINEAR DRIVES USING SOFT COMPUTING","year":2015,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Soft computing; Fault tolerance; Control (management); Fault (geology); Embedded system; Distributed computing; Artificial intelligence; Artificial neural network; Geology; Seismology","score_opus":0.026239028858265272,"score_gpt":0.3042923579365525,"score_spread":0.2780533290782872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404652880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13436551,0.00047430702,0.85864174,0.00028453142,0.000117826225,0.000046925998,0.000018474113,0.00044561693,0.005605078],"genre_scores_gemma":[0.98594946,0.000068788286,0.0130514335,0.000029034358,0.000010256528,0.000021569158,0.000007857273,0.000007412157,0.0008541884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998074,0.00003544913,0.000015859776,0.000038530565,0.00007051273,0.000032266587],"domain_scores_gemma":[0.99953616,0.0002030122,0.00008677529,0.000036853584,0.00011478275,0.000022463055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002665668,0.00034829334,0.0004389539,0.00026271204,0.0004175832,0.0008955606,0.00041345935,0.00029781475,0.00078984],"category_scores_gemma":[0.0008954104,0.00017415649,0.00024518216,0.00023891247,0.00046713252,0.0004829213,0.0006075424,0.00041348574,0.00011581239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029159206,0.00009637127,0.00076813984,0.00017517428,0.00005376703,0.00013484585,0.00019536048,0.7947421,0.028961333,0.010642621,0.00069974025,0.16323891],"study_design_scores_gemma":[0.0000066828,0.00007065815,0.00015771131,0.0000058790806,0.000005941062,0.000014084137,0.000011156077,0.9943486,0.0030082369,0.0021239272,0.00024329138,0.0000038092444],"about_ca_topic_score_codex":0.001569537,"about_ca_topic_score_gemma":0.0017487853,"teacher_disagreement_score":0.001569537,"about_ca_system_score_codex":0.00034890647,"about_ca_system_score_gemma":0.00045192929,"threshold_uncertainty_score":0.0031207204},"labels":[],"label_agreement":null},{"id":"W2408790143","doi":"10.2139/ssrn.2777765","title":"Improving the Accuracy of Automated Occupation Coding at Any Production Rate","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Coding (social sciences); Production (economics); Computer science; Statistics; Econometrics; Mathematics; Economics; Microeconomics","score_opus":0.007929461539594045,"score_gpt":0.25182190203410476,"score_spread":0.24389244049451073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408790143","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13787654,0.0008907124,0.81408906,0.00083438575,0.0005439701,0.00016835674,0.0015466169,0.03389463,0.010155733],"genre_scores_gemma":[0.5592792,0.00039062276,0.4249772,0.00034442928,0.0001629102,0.00012487984,0.0021182997,0.001977974,0.010624459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99539465,0.0009237796,0.00029475157,0.00074909296,0.002253058,0.0003847021],"domain_scores_gemma":[0.9691264,0.012786399,0.0013647164,0.009269263,0.0071627004,0.00029049383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021368046,0.0012527043,0.0009472706,0.0019752665,0.0008951895,0.0028168424,0.002458566,0.0015045746,0.01049831],"category_scores_gemma":[0.032296192,0.00054874463,0.00049798563,0.002030616,0.0006512052,0.0032147157,0.0021195738,0.0013308289,0.009506771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014689727,0.0002772392,0.009716157,0.00023091941,0.00007093752,0.00026317258,0.00028838599,0.034124013,0.075719066,0.0041899467,0.013489291,0.86016184],"study_design_scores_gemma":[0.00008223985,0.0003168345,0.0082535045,0.00009445333,0.000080548794,0.0006031009,0.000343297,0.709891,0.25384486,0.008619431,0.01776488,0.0001058262],"about_ca_topic_score_codex":0.0056749694,"about_ca_topic_score_gemma":0.0064578,"teacher_disagreement_score":0.01049831,"about_ca_system_score_codex":0.000688362,"about_ca_system_score_gemma":0.0021329233,"threshold_uncertainty_score":0.035120368},"labels":[],"label_agreement":null},{"id":"W2480344113","doi":"10.1007/978-1-4614-8839-2","title":"Primates in Fragments","year":2013,"lang":"en","type":"book","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":187,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Psychology","score_opus":0.005170267188954427,"score_gpt":0.1981594128176077,"score_spread":0.1929891456286533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480344113","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014557187,0.017810818,0.024538381,0.0016868637,0.0018769754,0.000050696362,0.0014253098,0.0013205592,0.94983476],"genre_scores_gemma":[0.010561746,0.00947025,0.017644811,0.0006807655,0.00067708205,0.00006082338,0.001821584,0.00109642,0.95798653],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99986935,0.000014749702,0.0000045293814,0.00003986535,0.000060718045,0.000010775583],"domain_scores_gemma":[0.9998994,0.000027987438,0.0000045166207,0.000024298246,0.00002345053,0.000020321664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018647754,0.0007557912,0.0005067226,0.0013019013,0.0013079839,0.0023689927,0.0008107342,0.0006301041,0.15900888],"category_scores_gemma":[0.00057438586,0.00035945533,0.00034730576,0.001665139,0.0012631442,0.0024044102,0.0013070779,0.0016524068,0.053328488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039253286,0.000012002131,0.00018006093,0.00017087247,0.000007960951,0.00011230002,0.00042347825,0.0004441306,0.0010630456,0.10150659,0.4059737,0.49006665],"study_design_scores_gemma":[0.0000015524591,0.0000049323594,0.00017096997,0.00005090461,0.0000028652921,0.00017572519,0.000057917296,0.00012548204,0.00020790372,0.02042894,0.9787692,0.0000036186577],"about_ca_topic_score_codex":0.0034407056,"about_ca_topic_score_gemma":0.010530586,"teacher_disagreement_score":0.15900888,"about_ca_system_score_codex":0.0008315718,"about_ca_system_score_gemma":0.00056987803,"threshold_uncertainty_score":0.5319377},"labels":[],"label_agreement":null},{"id":"W2494229806","doi":"10.1007/978-3-642-10439-8","title":"AI 2009: Advances in Artificial Intelligence","year":2009,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.013433996450964056,"score_gpt":0.28305192907811305,"score_spread":0.269617932627149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2494229806","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019049518,0.2094593,0.19246964,0.0074757626,0.02601219,0.0001925811,0.0019524787,0.0045480146,0.55598515],"genre_scores_gemma":[0.007315277,0.083411776,0.06995438,0.0020326874,0.003963168,0.00015033863,0.0027894569,0.0012717388,0.8291111],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951756,0.000038563136,0.000025078125,0.00008153716,0.0003147631,0.000022468806],"domain_scores_gemma":[0.99911934,0.00035052872,0.00003754537,0.00011432606,0.00027738174,0.00010093359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005767227,0.0012778491,0.0014158282,0.0014671785,0.00047612996,0.0033463133,0.0013984945,0.000961408,0.071799435],"category_scores_gemma":[0.0017622043,0.0005113688,0.0006244908,0.0028631794,0.0005867383,0.0040672547,0.0011232905,0.0030855616,0.056626275],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031038588,0.000042945692,0.000059840146,0.00072517525,0.000023343337,0.00003607551,0.00005326121,0.000983227,0.0016800296,0.03125792,0.56871355,0.39639363],"study_design_scores_gemma":[0.000007594098,0.000019034802,0.0002409987,0.00022935067,0.000015198838,0.00019723842,0.000022621292,0.0021013177,0.0005958044,0.020154057,0.9764049,0.0000119116285],"about_ca_topic_score_codex":0.0008282492,"about_ca_topic_score_gemma":0.0018360391,"teacher_disagreement_score":0.071799435,"about_ca_system_score_codex":0.0008300803,"about_ca_system_score_gemma":0.0013100667,"threshold_uncertainty_score":0.24019301},"labels":[],"label_agreement":null},{"id":"W2513784016","doi":"10.1007/978-3-319-44230-3_2","title":"System Model for the Internet of Things","year":2016,"lang":"en","type":"book-chapter","venue":"Springer briefs in electrical and computer engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ericsson (Canada)","funders":"","keywords":"Computer science; Internet of Things; The Internet; Internet privacy; World Wide Web","score_opus":0.008778950006428912,"score_gpt":0.1944108802946543,"score_spread":0.1856319302882254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513784016","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004394096,0.0011472718,0.9516069,0.0016664553,0.00055757863,0.00015332522,0.0011753386,0.0011157088,0.03818338],"genre_scores_gemma":[0.5815526,0.005244451,0.30792645,0.0014644448,0.0011438035,0.0017983351,0.003720151,0.0007499322,0.09639982],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994272,0.00017595293,0.0000358459,0.00011543688,0.00018002558,0.000065521774],"domain_scores_gemma":[0.99965286,0.00011910916,0.000025834075,0.000048829235,0.00013445845,0.000018932906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067086145,0.0010634095,0.0011658315,0.0007153061,0.0007344489,0.0019731394,0.0021268143,0.0020828103,0.013255202],"category_scores_gemma":[0.0012306724,0.0003882239,0.0013012697,0.00090783293,0.0006579423,0.002676825,0.0012965513,0.0020719427,0.004172867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069307345,0.00006799527,0.00034075737,0.00034205386,0.00008451785,0.00043834263,0.00022121293,0.43219918,0.002562299,0.5153807,0.016292755,0.032000877],"study_design_scores_gemma":[0.000027781021,0.00004808143,0.00011687427,0.000038840284,0.000037486876,0.00013792526,0.000047392103,0.839734,0.00029193933,0.13678786,0.02270872,0.000023112489],"about_ca_topic_score_codex":0.0062477714,"about_ca_topic_score_gemma":0.0073985835,"teacher_disagreement_score":0.013255202,"about_ca_system_score_codex":0.0010258802,"about_ca_system_score_gemma":0.0011998444,"threshold_uncertainty_score":0.044343114},"labels":[],"label_agreement":null},{"id":"W2564313974","doi":"10.5376/ijms.2016.06.0048","title":"Polycyclic Aromatic Hydrocarbons (PAHs) in the Soil of West Qurna-2 Oil Field Southern Iraq","year":2016,"lang":"en","type":"article","venue":"International Journal of Marine Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Acenaphthene; Chrysene; Acenaphthylene; Phenanthrene; Anthracene; Pyrene; Fluorene; Fluoranthene; Chemistry; Environmental chemistry; Naphthalene; Polycyclic aromatic hydrocarbon; Organic chemistry","score_opus":0.009351432222335197,"score_gpt":0.26219378077026173,"score_spread":0.25284234854792653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564313974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.999114,0.00014042076,0.000168998,0.00002344646,0.0000028329814,0.000007094957,0.0001817689,0.0000036588224,0.0003578061],"genre_scores_gemma":[0.9984554,0.00024654847,0.0003520557,0.000035771693,0.000008066339,0.000011370024,0.00029547242,0.0000016403899,0.00059374026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998324,0.000011979275,0.000008551442,0.0000491821,0.00005582509,0.000042057207],"domain_scores_gemma":[0.99988985,0.000013714834,0.000034183016,0.0000039124193,0.000047426885,0.000010899916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013261472,0.00035139572,0.00018130604,0.0008710867,0.0005434129,0.0003961614,0.00022707562,0.00032525096,0.00042859724],"category_scores_gemma":[0.00013621846,0.00017611832,0.0001659451,0.00075324264,0.00032213016,0.0002599531,0.00021055958,0.00015102854,0.00021269867],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013729319,0.0001257333,0.830359,0.00018524587,0.000068883426,0.0011089791,0.0016068106,0.0009907343,0.14296113,0.00012880487,0.00022070923,0.022106532],"study_design_scores_gemma":[0.0000063477323,0.000105250205,0.99111533,0.00001631764,0.000020438993,0.0002743748,0.001206848,0.0007715637,0.005222648,0.000037331578,0.001215562,0.000007970059],"about_ca_topic_score_codex":0.039063793,"about_ca_topic_score_gemma":0.046499956,"teacher_disagreement_score":0.039063793,"about_ca_system_score_codex":0.00049935654,"about_ca_system_score_gemma":0.0005408556,"threshold_uncertainty_score":0.07767284},"labels":[],"label_agreement":null},{"id":"W2596865948","doi":"","title":"A BOREHOLE SEISMIC SYSTEM FOR ACTIVE AND PASSIVE SEIMSIC STUDIES TO 3 KM AT PTRC’S AQUISTORE PROJECT","year":2015,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Geology; Borehole; Remote sensing; Seismology; Geotechnical engineering","score_opus":0.045385466617952154,"score_gpt":0.30386891573832603,"score_spread":0.25848344912037385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596865948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8803576,0.00007099302,0.0587857,0.00039822582,0.0001509065,0.0012398247,0.020813651,0.006220363,0.031962845],"genre_scores_gemma":[0.89295924,0.00004651443,0.082649216,0.0001077034,0.000047567377,0.0007426697,0.015363633,0.00047784235,0.007605629],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99931204,0.00012140863,0.000040004106,0.00014263652,0.0002771248,0.00010682299],"domain_scores_gemma":[0.9988274,0.000068838315,0.00008372123,0.00022080104,0.00053587026,0.00026344392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013373061,0.00043328706,0.00030674014,0.0013272503,0.00092192274,0.0004768851,0.000990104,0.00063175353,0.0059456835],"category_scores_gemma":[0.0012166464,0.00034764627,0.00023723232,0.0012688332,0.00027055733,0.0009334169,0.0010644044,0.0006285111,0.0016432686],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004279369,0.0023246573,0.18137737,0.00023069781,0.00008455214,0.0011403235,0.0031351168,0.029148126,0.46724695,0.0033088513,0.041099556,0.26662448],"study_design_scores_gemma":[0.0016216333,0.005160337,0.6910519,0.000095372234,0.00014652073,0.00093394093,0.0020696172,0.105085544,0.08309561,0.0014970107,0.10904786,0.00019466717],"about_ca_topic_score_codex":0.023147324,"about_ca_topic_score_gemma":0.05605046,"teacher_disagreement_score":0.023147324,"about_ca_system_score_codex":0.0005976238,"about_ca_system_score_gemma":0.0026634093,"threshold_uncertainty_score":0.046025157},"labels":[],"label_agreement":null},{"id":"W2626170788","doi":"","title":"Fuzzy Power Management For Automatic Monitoring Stations In the Arctic","year":2011,"lang":"en","type":"article","venue":"The Twenty-first International Offshore and Polar Engineering Conference","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy logic; Environmental science; Arctic; Power (physics); Computer science; Artificial intelligence; Geology; Oceanography","score_opus":0.025252899392147834,"score_gpt":0.25596526202552716,"score_spread":0.23071236263337933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626170788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5683238,0.00033560226,0.42133674,0.00023249349,0.00007662129,0.000088314286,0.00016935972,0.00035574677,0.009081353],"genre_scores_gemma":[0.988913,0.000037401114,0.010123763,0.000010971469,0.0000104273195,0.000012891796,0.000030318517,0.000006064964,0.0008551641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998043,0.000033430464,0.000014554105,0.000044323744,0.00006802088,0.00003531223],"domain_scores_gemma":[0.99977213,0.00007055747,0.000036163576,0.000011206395,0.000094038725,0.000016004422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044916343,0.0002954688,0.00029347718,0.0006138699,0.00081644434,0.00090549915,0.0006000815,0.00034355506,0.00081830035],"category_scores_gemma":[0.0009141288,0.00017353703,0.00024133976,0.0004509088,0.00022222707,0.0004916546,0.00024244009,0.0002819787,0.0001166732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005880345,0.000118677366,0.0050999876,0.00010130568,0.00008435453,0.00017801503,0.0002737661,0.77071303,0.025839709,0.0058743143,0.0014107521,0.18971804],"study_design_scores_gemma":[0.000013764664,0.000059681934,0.0021082212,0.0000058612063,0.000018599805,0.000018834602,0.00006454313,0.9913037,0.0037159456,0.002153319,0.0005294106,0.000008231682],"about_ca_topic_score_codex":0.013787484,"about_ca_topic_score_gemma":0.02024337,"teacher_disagreement_score":0.013787484,"about_ca_system_score_codex":0.00095819647,"about_ca_system_score_gemma":0.00046630105,"threshold_uncertainty_score":0.027414441},"labels":[],"label_agreement":null},{"id":"W2746213838","doi":"10.1071/aj12137","title":"Project Updates Panel Session","year":2013,"lang":"en","type":"article","venue":"The APPEA Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Session (web analytics); Computer science; Panel discussion; World Wide Web; Business; Advertising","score_opus":0.025073871077518847,"score_gpt":0.25516348403587996,"score_spread":0.2300896129583611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746213838","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022653649,0.0017364683,0.007686042,0.0349749,0.12812941,0.0027845176,0.019644884,0.006480866,0.7962975],"genre_scores_gemma":[0.0046987217,0.00060398836,0.0018151468,0.003054404,0.006934833,0.0006021006,0.0062105814,0.0017448455,0.97433543],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99709535,0.00045318052,0.00009423918,0.00038420717,0.0015638479,0.00040908082],"domain_scores_gemma":[0.9856845,0.0009649971,0.00035755662,0.0013289023,0.0075865085,0.0040774792],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00845207,0.0010298912,0.0007601648,0.0017837714,0.002113725,0.0069378065,0.0018249039,0.0033666221,0.65447164],"category_scores_gemma":[0.012063868,0.0004948753,0.0009901159,0.0011641181,0.00041888873,0.0031152023,0.0037914845,0.0034172565,0.50668067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004999628,0.000026406558,0.000052028976,0.000039088794,0.000001784539,0.000012835552,0.00001594561,0.000014250324,0.00015916053,0.0005951426,0.9860569,0.012976313],"study_design_scores_gemma":[0.00001606915,0.000037394013,0.00024531627,0.000047284346,0.0000029559312,0.000019708943,0.00004869746,0.000041010226,0.00021670341,0.0004109503,0.99890804,0.0000057829307],"about_ca_topic_score_codex":0.0018707489,"about_ca_topic_score_gemma":0.0041664797,"teacher_disagreement_score":0.65447164,"about_ca_system_score_codex":0.0012903147,"about_ca_system_score_gemma":0.003979173,"threshold_uncertainty_score":0.49285424},"labels":[],"label_agreement":null},{"id":"W2757777593","doi":"10.7298/x4b56gqk","title":"Tools For Modeling Sparse Vector Autoregressions","year":2016,"lang":"en","type":"article","venue":"eCommons (Cornell University)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Innovative Research Group Project of the National Natural Science Foundation of China; Amazon Web Services; National Science Foundation","keywords":"Computer science; Artificial intelligence","score_opus":0.0659255494573267,"score_gpt":0.22659353346064984,"score_spread":0.16066798400332313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757777593","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008574183,0.00013406145,0.997424,0.00009954111,0.000028981489,0.000020727333,0.00016945162,0.0005208161,0.0007450761],"genre_scores_gemma":[0.1416801,0.0014828283,0.8443034,0.00028033103,0.0002726024,0.00085018267,0.002563613,0.00071086956,0.00785597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904567,0.00048467654,0.00006734386,0.000112896385,0.00022224651,0.00006719874],"domain_scores_gemma":[0.99626166,0.002648587,0.00032133376,0.00027771964,0.00040128976,0.0000893284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020017752,0.0011595738,0.00081853976,0.001212119,0.00036068168,0.0015552369,0.0014203564,0.0012391171,0.008723095],"category_scores_gemma":[0.013094956,0.0007816357,0.0013971027,0.0013301199,0.0005464434,0.0012921755,0.0020904168,0.0026622177,0.002665477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053392047,0.000063174775,0.0010586714,0.0001865372,0.000105478284,0.00015590836,0.00012062817,0.7104857,0.00110922,0.1755406,0.008838785,0.10228196],"study_design_scores_gemma":[0.00000934127,0.000011874762,0.00007533852,0.000021170203,0.000005882348,0.000021638538,0.000010371783,0.95501494,0.00019605798,0.0414351,0.0031905416,0.000007689535],"about_ca_topic_score_codex":0.0046346253,"about_ca_topic_score_gemma":0.005872516,"teacher_disagreement_score":0.008723095,"about_ca_system_score_codex":0.0005298402,"about_ca_system_score_gemma":0.001611717,"threshold_uncertainty_score":0.029181719},"labels":[],"label_agreement":null},{"id":"W2779888817","doi":"10.5539/elt.v11n1p164","title":"The Effect of Buzz Group Technique and Clustering Technique in Teaching Writing at the First Class of SMA HKBP I Tarutung","year":2017,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Marketing buzz; SMA*; Cluster analysis; Psychology; Class (philosophy); Mathematics education; Test (biology); Group (periodic table); Computer science; Artificial intelligence; Botany","score_opus":0.003943434125568536,"score_gpt":0.2500705941748017,"score_spread":0.24612716004923318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779888817","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971631,0.00021077269,0.00054170337,0.00014050589,0.000026695428,0.00009788464,0.00002061311,0.00002809094,0.0017705342],"genre_scores_gemma":[0.9854197,0.00043412828,0.0075740577,0.00006868965,0.00003412368,0.00016864209,0.000088019115,0.000019225003,0.006193456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987041,0.00046172817,0.00010151628,0.00023169174,0.0003564306,0.0001445374],"domain_scores_gemma":[0.99593747,0.0017059828,0.0006320063,0.00025492682,0.00064287183,0.0008267436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013094392,0.0005693728,0.00048198152,0.0008045626,0.0008236058,0.0008470418,0.00056409667,0.0004131613,0.0027415976],"category_scores_gemma":[0.004825566,0.00018204469,0.00037503563,0.0004969226,0.00038783846,0.00037160062,0.00059506984,0.00069191895,0.0006591844],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005750295,0.0184493,0.086918704,0.0016918594,0.00018224563,0.0010660497,0.027245792,0.0011863445,0.13269468,0.0004410014,0.0032704724,0.72110325],"study_design_scores_gemma":[0.00072981906,0.06079647,0.82187784,0.0004011489,0.00056899054,0.0013255,0.029662766,0.002155958,0.0638861,0.00082906184,0.017612882,0.00015348941],"about_ca_topic_score_codex":0.0011616948,"about_ca_topic_score_gemma":0.0037511007,"teacher_disagreement_score":0.0027415976,"about_ca_system_score_codex":0.00046786517,"about_ca_system_score_gemma":0.0007315787,"threshold_uncertainty_score":0.009171605},"labels":[],"label_agreement":null},{"id":"W2782802057","doi":"10.1016/s0252-9602(17)30273-4","title":"ROBUST GLOBAL EXPONENTIAL STABILITY OF UNCERTAIN IMPULSIVE SYSTEMS","year":2005,"lang":"en","type":"article","venue":"Acta Mathematica Scientia","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Exponential stability; Mathematics; Lyapunov function; Exponential function; Applied mathematics; Nonlinear system; Stability (learning theory); Control theory (sociology); Riccati equation; Mathematical analysis; Computer science; Differential equation; Control (management); Physics","score_opus":0.03127335176976842,"score_gpt":0.2647061377732608,"score_spread":0.23343278600349238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782802057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11581663,0.00079959165,0.8707414,0.00048859784,0.00009975579,0.000027617905,0.0000782122,0.00026232647,0.011685871],"genre_scores_gemma":[0.989317,0.00028081494,0.0065554376,0.000041590483,0.000037399375,0.000033020355,0.000051924933,0.000030151345,0.00365271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955326,0.00012516776,0.000024631203,0.00009103968,0.00015337803,0.000052458938],"domain_scores_gemma":[0.99835724,0.0008894942,0.00033357943,0.00010303745,0.0002639608,0.00005268242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010777217,0.0007872611,0.0008785848,0.00065200974,0.0003562319,0.0013490706,0.0007844469,0.00086217304,0.0017079366],"category_scores_gemma":[0.0042903097,0.00033200553,0.0004983919,0.0004645552,0.0011907972,0.0008014494,0.0014841111,0.00096108194,0.00024491933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019415973,0.000024287692,0.0002533033,0.00009760051,0.000070155016,0.00016059348,0.00011847532,0.9075931,0.0060780593,0.0712976,0.00045980295,0.013652873],"study_design_scores_gemma":[0.000010530166,0.000037283186,0.00013764968,0.0000067036844,0.0000072502985,0.000019541896,0.000014080118,0.9753141,0.00066640123,0.023481712,0.0002955474,0.000009258507],"about_ca_topic_score_codex":0.0017505133,"about_ca_topic_score_gemma":0.00075234415,"teacher_disagreement_score":0.0017505133,"about_ca_system_score_codex":0.00056545564,"about_ca_system_score_gemma":0.0004721678,"threshold_uncertainty_score":0.0057136416},"labels":[],"label_agreement":null},{"id":"W2803941974","doi":"10.31482/mmsl.2011.003","title":"ONTOLOGICAL MODELS AND EXPERT SYSTEMS IN DECISION SUPPORT OF EMERGENCY SITUATIONS","year":2011,"lang":"en","type":"article","venue":"Military Medical Science Letters","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Decision support system; Computer science; Management science; Expert system; Knowledge management; Artificial intelligence; Engineering","score_opus":0.05906204890036086,"score_gpt":0.30689792723345416,"score_spread":0.24783587833309328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803941974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010599735,0.003221975,0.9362807,0.011442294,0.000275955,0.00017545419,0.00041432667,0.00026101168,0.037328515],"genre_scores_gemma":[0.30922332,0.00476569,0.6726414,0.0019717466,0.0005709479,0.00048132217,0.0012192284,0.000073502095,0.009052785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965062,0.0017513953,0.00041596073,0.00034816223,0.0008110206,0.00016725363],"domain_scores_gemma":[0.9956344,0.002843902,0.0003489428,0.00048012225,0.0005377474,0.00015484773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038386453,0.0005937956,0.00067778985,0.0021615587,0.0011941463,0.004220996,0.0017665714,0.0026551636,0.0031706323],"category_scores_gemma":[0.009405861,0.0004957696,0.0012527,0.00199744,0.0035623617,0.0067455806,0.002311822,0.0021493635,0.0008345808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013944416,0.00002612786,0.00023271989,0.00011653424,0.000027903983,0.000206155,0.00061781454,0.01887228,0.00039222286,0.959719,0.0022237971,0.017551584],"study_design_scores_gemma":[0.000015579359,0.000012676587,0.0001723987,0.00011522988,0.000019783873,0.00009769441,0.00025889365,0.06286339,0.00030494598,0.9142878,0.021829031,0.000022633012],"about_ca_topic_score_codex":0.004656144,"about_ca_topic_score_gemma":0.0043733725,"teacher_disagreement_score":0.004656144,"about_ca_system_score_codex":0.0019210011,"about_ca_system_score_gemma":0.0018374397,"threshold_uncertainty_score":0.020300925},"labels":[],"label_agreement":null},{"id":"W2896348576","doi":"10.2118/191621-18rptc-ms","title":"Adaptive Option in Geological Modeling of Petroleum Reservoirs","year":2018,"lang":"en","type":"article","venue":"SPE Russian Petroleum Technology Conference","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Fuzzy logic; Grid; Interpolation (computer graphics); Basis (linear algebra); Computer science; Object (grammar); Function (biology); Data mining; Geology; Artificial intelligence; Mathematics","score_opus":0.02481255546742575,"score_gpt":0.2593827671481439,"score_spread":0.23457021168071815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896348576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13096653,0.0005032297,0.8554709,0.00056752854,0.000054370645,0.000053948974,0.0002735296,0.00023334459,0.011876617],"genre_scores_gemma":[0.96470755,0.0002661781,0.02969981,0.000043868044,0.000021130056,0.00009885317,0.00010123515,0.000033100772,0.0050282255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976104,0.00009949234,0.000013009472,0.00004538189,0.000054169755,0.000026814749],"domain_scores_gemma":[0.9996815,0.00015191437,0.00005636334,0.000015427437,0.00006622422,0.000028616065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005127127,0.0004065595,0.0005815526,0.0008061393,0.00047942472,0.0010319375,0.0010380137,0.0010083008,0.0016683064],"category_scores_gemma":[0.0012976712,0.00038936993,0.0005851525,0.0008044419,0.0009662686,0.0010500441,0.0009617089,0.00078593625,0.0001554262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000082256975,0.0000057595353,0.00034406869,0.000009345566,0.0000055964433,0.00004437137,0.000015544192,0.98785,0.00016930081,0.009882545,0.000069666494,0.0015956017],"study_design_scores_gemma":[0.0000011518697,0.0000029933412,0.000027590091,0.000001185613,0.0000010809674,0.000003490844,0.000002743373,0.9978544,0.000025488474,0.0019506216,0.00012765902,0.0000015407562],"about_ca_topic_score_codex":0.012389671,"about_ca_topic_score_gemma":0.005198408,"teacher_disagreement_score":0.012389671,"about_ca_system_score_codex":0.0008347171,"about_ca_system_score_gemma":0.00079623057,"threshold_uncertainty_score":0.024635077},"labels":[],"label_agreement":null},{"id":"W2901985144","doi":"10.1155/2018/5308012","title":"Diagnostic Evaluation and Uncertainty Quantification of Earth and Environmental Systems Models","year":2018,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Prince Edward Island","funders":"","keywords":"Earth system science; Earth (classical element); Environmental science; Earth science; Geology; Mathematics; Oceanography","score_opus":0.023511189695942507,"score_gpt":0.24685388142331774,"score_spread":0.22334269172737523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901985144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058689304,0.0006735306,0.9337339,0.00069775904,0.00006284534,0.000086903245,0.00042463432,0.00048448375,0.005146641],"genre_scores_gemma":[0.90030915,0.00028852007,0.09690269,0.00007832582,0.00005005118,0.00008024722,0.00044862364,0.000090938345,0.0017514956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848264,0.0008365503,0.00006911473,0.00012199713,0.00040201828,0.00008770981],"domain_scores_gemma":[0.9948074,0.003999427,0.00028902004,0.00027715534,0.00055755675,0.000069440655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00408819,0.00063181913,0.0008631949,0.0022690692,0.00044718862,0.0019370497,0.00079649425,0.00094460195,0.0020107508],"category_scores_gemma":[0.019577533,0.0004293998,0.00084300217,0.00086048903,0.00097158266,0.0014583952,0.0013684958,0.00084897666,0.00013749904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006261453,0.00002322053,0.0009871714,0.000064723856,0.000048430367,0.00004772411,0.00003015047,0.94086856,0.0006665132,0.027890272,0.00067940046,0.028631203],"study_design_scores_gemma":[0.0000020508403,0.0000065114687,0.00009771643,0.0000061387627,0.000004913863,0.000006357707,0.0000032301684,0.9900955,0.00033026477,0.009248739,0.00019606449,0.0000024733092],"about_ca_topic_score_codex":0.008246465,"about_ca_topic_score_gemma":0.0046359957,"teacher_disagreement_score":0.008246465,"about_ca_system_score_codex":0.0019745887,"about_ca_system_score_gemma":0.0019040537,"threshold_uncertainty_score":0.02162069},"labels":[],"label_agreement":null},{"id":"W2910920659","doi":"","title":"College Management System and Forum using Web-Application","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Login; World Wide Web; Computer science; Attendance; Workload; Web application; Management system; Face (sociological concept); Computer security; Engineering; Operations management; Operating system; Political science; Sociology","score_opus":0.008811063250927826,"score_gpt":0.2459260645445067,"score_spread":0.23711500129357888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910920659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19365142,0.0016003784,0.5288132,0.0015588365,0.0009632675,0.0021695816,0.0028536415,0.20200793,0.06638173],"genre_scores_gemma":[0.81191427,0.0007536139,0.12594658,0.00057030114,0.0007468858,0.0007758425,0.0059992364,0.002133786,0.05115941],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914384,0.00015146779,0.0001037349,0.00019899795,0.00026991987,0.00013206518],"domain_scores_gemma":[0.99888533,0.0002008603,0.00009461945,0.00028474754,0.00020838338,0.00032595944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246169,0.0004925933,0.00051748427,0.0015770707,0.00083347375,0.0021091208,0.0010738063,0.0007476449,0.008685382],"category_scores_gemma":[0.0022188514,0.00027122366,0.00042772663,0.0009931674,0.00020774856,0.0040753367,0.0020293505,0.0007078113,0.0030562761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011540491,0.0020780978,0.022587644,0.00062447484,0.00013773696,0.0027104786,0.001864446,0.0028316898,0.05378867,0.017380694,0.10801384,0.78682816],"study_design_scores_gemma":[0.00048881746,0.0012377352,0.034924082,0.00029510207,0.00024451734,0.0049875807,0.0010913827,0.19123375,0.088660285,0.021947041,0.6544636,0.0004260255],"about_ca_topic_score_codex":0.0009460885,"about_ca_topic_score_gemma":0.0005227811,"teacher_disagreement_score":0.008685382,"about_ca_system_score_codex":0.00032074132,"about_ca_system_score_gemma":0.00054429704,"threshold_uncertainty_score":0.029055476},"labels":[],"label_agreement":null},{"id":"W2923769456","doi":"10.1108/dl-01-2019-0006","title":"A Comprehensive Model for Evaluating E-Learning Systems Success","year":2019,"lang":"en","type":"article","venue":"Distance Learning","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.02798548411706243,"score_gpt":0.3113135638536678,"score_spread":0.2833280797366054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923769456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44677967,0.0019290564,0.39556333,0.0051394748,0.00025354815,0.008407784,0.004026944,0.0016539228,0.1362463],"genre_scores_gemma":[0.8732661,0.0009336701,0.11500202,0.00020503275,0.00003310784,0.00471146,0.0015816935,0.00006403572,0.0042028422],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98317116,0.0067309747,0.0016840135,0.0016930111,0.0055982866,0.0011225075],"domain_scores_gemma":[0.97949255,0.011417731,0.00286399,0.00080635224,0.004456138,0.00096332695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011738237,0.0030437054,0.0011434675,0.009715273,0.0013494863,0.0067675295,0.002632525,0.0028163749,0.006933948],"category_scores_gemma":[0.02317392,0.0005702282,0.0025867757,0.006536828,0.002200867,0.00803152,0.004328737,0.0020645652,0.0017340158],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005885557,0.0030227436,0.3488706,0.0028790317,0.0012214537,0.0011252172,0.01650078,0.120648004,0.005146084,0.17930676,0.013194085,0.30749664],"study_design_scores_gemma":[0.00021529861,0.0048789103,0.20825586,0.0025199768,0.0008855594,0.00093543035,0.021647278,0.6046708,0.0036520986,0.109514624,0.042308368,0.0005157844],"about_ca_topic_score_codex":0.007258986,"about_ca_topic_score_gemma":0.006757246,"teacher_disagreement_score":0.011738237,"about_ca_system_score_codex":0.007245337,"about_ca_system_score_gemma":0.006681322,"threshold_uncertainty_score":0.062078476},"labels":[],"label_agreement":null},{"id":"W2927087467","doi":"10.1515/ijcre-2019-0050","title":"To the Distinguished Contribution of Professor Gulsen Dogu and Professor Timur Dogu to Chemical Reaction Engineering","year":2019,"lang":"en","type":"article","venue":"International Journal of Chemical Reactor Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Industrial chemistry; Philosophy; Engineering; Biochemical engineering","score_opus":0.005861991906293526,"score_gpt":0.25726046298560185,"score_spread":0.2513984710793083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927087467","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016439242,0.06260886,0.02919562,0.4781295,0.37191483,0.0004838792,0.0018569853,0.0012715105,0.038099688],"genre_scores_gemma":[0.17999974,0.048064303,0.023155332,0.12489334,0.16219264,0.00047215883,0.0019907828,0.0012741556,0.4579576],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997486,0.00055162597,0.00012561119,0.00048166793,0.0010028469,0.00035225568],"domain_scores_gemma":[0.9743148,0.0031121864,0.0014193056,0.0010111135,0.012319628,0.007822898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033183962,0.0007433128,0.0010057344,0.0017625482,0.0011863823,0.0017945707,0.0017845443,0.0020472656,0.03612837],"category_scores_gemma":[0.021950714,0.00036648524,0.0005488257,0.00071001914,0.0013379373,0.0013629828,0.0035952504,0.0061638984,0.02332104],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059534446,0.00012045525,0.0022842537,0.0005361146,0.00009354568,0.0012099986,0.00067268376,0.0006594694,0.0050193765,0.011609751,0.82879263,0.14840633],"study_design_scores_gemma":[0.00003823973,0.00013228491,0.0016629483,0.000093120325,0.000041176187,0.0036505226,0.0003976115,0.0007091816,0.0037460735,0.005460588,0.9840216,0.00004657995],"about_ca_topic_score_codex":0.0018551913,"about_ca_topic_score_gemma":0.002233681,"teacher_disagreement_score":0.03612837,"about_ca_system_score_codex":0.0017399332,"about_ca_system_score_gemma":0.0051343106,"threshold_uncertainty_score":0.12086147},"labels":[],"label_agreement":null},{"id":"W2931994188","doi":"10.29173/jchla29398","title":"Book Review: Using Digital Analytics for Smart Assessment.","year":2019,"lang":"fr","type":"article","venue":"Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre","funders":"","keywords":"Analytics; Computer science; Data science","score_opus":0.006068705759772361,"score_gpt":0.2706750324854765,"score_spread":0.2646063267257041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931994188","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002655797,0.79158384,0.001431143,0.03420196,0.034379058,0.0001333048,0.0010217502,0.0004026324,0.13658072],"genre_scores_gemma":[0.0024137965,0.6596082,0.0021231002,0.02653867,0.018811185,0.0001685755,0.0015877874,0.00027311483,0.2884755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828583,0.00018636954,0.00010616575,0.00018179517,0.0011612582,0.00007852308],"domain_scores_gemma":[0.9963043,0.0013005009,0.00022754834,0.000120398836,0.0017095366,0.00033769733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009906678,0.0011026259,0.0013641216,0.0042625414,0.00089482026,0.0055244947,0.0015124485,0.002312503,0.067425504],"category_scores_gemma":[0.006451354,0.0005806897,0.0008531351,0.0074676485,0.0009417784,0.0043750484,0.0016119543,0.0035183309,0.056271],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060479165,0.000013024973,0.00004836453,0.0008776895,0.000007697974,0.000023563887,0.00003293614,0.000035753917,0.00004757926,0.0012209063,0.9148118,0.0828748],"study_design_scores_gemma":[0.0000035909825,0.000008159281,0.00022405543,0.0011319929,0.0000068671084,0.0001870497,0.000036443038,0.000022980765,0.00002717396,0.0007938532,0.997551,0.0000068879067],"about_ca_topic_score_codex":0.0038462433,"about_ca_topic_score_gemma":0.010451771,"teacher_disagreement_score":0.067425504,"about_ca_system_score_codex":0.0016049974,"about_ca_system_score_gemma":0.0034333365,"threshold_uncertainty_score":0.22556078},"labels":[],"label_agreement":null},{"id":"W2950212403","doi":"","title":"Stabilization, Safety, and Security of Distributed Systems: 17th International Symposium, SSS 2015 Edmonton, AB, Canada, August 18-21, 2015 Proceedings","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; SSS*; Computer security","score_opus":0.010050577309840342,"score_gpt":0.24303023448383163,"score_spread":0.23297965717399127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950212403","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03941262,0.10004661,0.7305405,0.036968764,0.013266981,0.00035867575,0.0010833585,0.0063633546,0.07195906],"genre_scores_gemma":[0.41211158,0.08001668,0.24609728,0.0016619392,0.009519103,0.00026961378,0.0041652797,0.0015882641,0.24457033],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977852,0.00040395535,0.00013793497,0.000258404,0.0011997451,0.00021465123],"domain_scores_gemma":[0.99324167,0.0014361581,0.00016806624,0.0008178355,0.003483724,0.00085263996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075104744,0.0012194036,0.0013938978,0.0017291616,0.0017519005,0.0057446035,0.0013450448,0.0011786221,0.011324187],"category_scores_gemma":[0.004871544,0.0007465229,0.000628858,0.0014886347,0.0034918594,0.0025416054,0.0021911594,0.0032355804,0.003048219],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007790605,0.00026970715,0.004179739,0.0005937979,0.00014015046,0.00019127289,0.0008233075,0.014410037,0.010720514,0.061597586,0.36159688,0.54469806],"study_design_scores_gemma":[0.00018990297,0.00059340603,0.009759219,0.0009961834,0.00023032757,0.00054818555,0.0011946054,0.18158719,0.026248235,0.101963475,0.6765171,0.00017217062],"about_ca_topic_score_codex":0.05319336,"about_ca_topic_score_gemma":0.083634526,"teacher_disagreement_score":0.05319336,"about_ca_system_score_codex":0.0053658835,"about_ca_system_score_gemma":0.010459601,"threshold_uncertainty_score":0.10576749},"labels":[],"label_agreement":null},{"id":"W2957383338","doi":"10.3390/designs3030037","title":"An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel","year":2019,"lang":"en","type":"article","venue":"Designs","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Shutdown; Grid; Nuclear reactor core; Computer science; Core (optical fiber); Engineering; Nuclear engineering","score_opus":0.21168732233773502,"score_gpt":0.40681886734243156,"score_spread":0.19513154500469654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957383338","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001368864,0.2095842,0.74634135,0.0015780233,0.00079228944,0.00037319624,0.0005975261,0.002411458,0.036953118],"genre_scores_gemma":[0.025424547,0.22461829,0.72091055,0.0011202685,0.0017984816,0.00087149465,0.002110006,0.0007729385,0.022373412],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99875176,0.00023325787,0.0001346926,0.0002898866,0.0005279859,0.00006236523],"domain_scores_gemma":[0.99896824,0.0005731778,0.00007279648,0.00011045848,0.00022730845,0.000047983514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010611746,0.0027956937,0.0012851548,0.0027970194,0.0007946062,0.0034421105,0.003977225,0.0031791239,0.012176134],"category_scores_gemma":[0.0021173349,0.0011627355,0.0015054173,0.0041980515,0.0013691436,0.0028121301,0.0013704565,0.0029774946,0.008476081],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008836013,0.00022958993,0.0007165158,0.0069199274,0.00017021014,0.00029625586,0.00037985627,0.036056317,0.006356359,0.06259585,0.027772242,0.85841846],"study_design_scores_gemma":[0.00004259823,0.00016498881,0.0010969593,0.0023765573,0.00010970832,0.0008769416,0.00014866231,0.099052384,0.0052006054,0.09987982,0.7909128,0.0001379259],"about_ca_topic_score_codex":0.0026435282,"about_ca_topic_score_gemma":0.0017976466,"teacher_disagreement_score":0.012176134,"about_ca_system_score_codex":0.0011760264,"about_ca_system_score_gemma":0.0013280716,"threshold_uncertainty_score":0.04073322},"labels":[],"label_agreement":null},{"id":"W296384149","doi":"","title":"Virtual Reality (VR) as a Disruptive Technology","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Virtual reality; Disruptive technology; Maturity (psychological); Emerging technologies; Position (finance); Key (lock); Identification (biology); Immersive technology; Training (meteorology); Computer science; Engineering; Business; Psychology; Computer security; Human–computer interaction; Artificial intelligence","score_opus":0.026706391506317328,"score_gpt":0.2766853086552132,"score_spread":0.24997891714889586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W296384149","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08372442,0.08421507,0.07795047,0.12655286,0.0033889618,0.0002820099,0.00062326394,0.0007076752,0.6225553],"genre_scores_gemma":[0.84880877,0.050369542,0.028359756,0.006513136,0.0006352959,0.000076863216,0.00021584283,0.000103638115,0.06491714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99502087,0.0014502719,0.00021795018,0.00031844,0.0024631815,0.00052939146],"domain_scores_gemma":[0.99293387,0.0017049271,0.00069882633,0.00029853775,0.0037245795,0.0006393342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032446384,0.00047466284,0.00021808002,0.0014611126,0.0030354315,0.01146282,0.000985434,0.0017889225,0.005938487],"category_scores_gemma":[0.0037019113,0.0002619552,0.00026737855,0.0015874299,0.007254809,0.0032610125,0.0029510201,0.0025722343,0.0007059261],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009679826,0.000053694715,0.007068701,0.0009853152,0.00002929698,0.00049039844,0.01207108,0.0025427055,0.0064588026,0.65927964,0.03943263,0.27149093],"study_design_scores_gemma":[0.000012745222,0.00013823052,0.011388042,0.0017395436,0.000046521043,0.0009584535,0.010068368,0.0025083707,0.005843341,0.024855612,0.9423178,0.00012283237],"about_ca_topic_score_codex":0.21373901,"about_ca_topic_score_gemma":0.25103986,"teacher_disagreement_score":0.21373901,"about_ca_system_score_codex":0.01284126,"about_ca_system_score_gemma":0.018724022,"threshold_uncertainty_score":0.42498982},"labels":[],"label_agreement":null},{"id":"W2971315193","doi":"","title":"RAMFIS System Report TAC 2018.","year":2018,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.00673596730808937,"score_gpt":0.2489826832664038,"score_spread":0.24224671595831443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971315193","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017605282,0.0039088423,0.09589955,0.01532353,0.009714886,0.0017547143,0.32887608,0.1861948,0.3407223],"genre_scores_gemma":[0.05108968,0.0013937176,0.07569751,0.0015424796,0.0025836206,0.0011537478,0.702147,0.009314016,0.15507826],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.995419,0.0008600673,0.00023001179,0.00064277864,0.0023601076,0.0004880647],"domain_scores_gemma":[0.9894637,0.0014918989,0.00033330792,0.0024273172,0.0052623083,0.0010214114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007409734,0.0014784378,0.0020484836,0.0035859768,0.0016716262,0.0045155357,0.0034297246,0.0023463203,0.11574669],"category_scores_gemma":[0.019623496,0.00049723504,0.00069764734,0.0023744805,0.0006361418,0.0050384174,0.0026935055,0.0019130508,0.15556982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005020352,0.00011405637,0.00089456706,0.00013880612,0.00003160469,0.000073644886,0.000055090022,0.0007723942,0.0016559998,0.0066272537,0.943094,0.046040643],"study_design_scores_gemma":[0.0003283082,0.0002698432,0.00173841,0.00011744343,0.000043822,0.00023581662,0.000102747,0.011582709,0.007798323,0.011943144,0.9657788,0.000060714636],"about_ca_topic_score_codex":0.01573445,"about_ca_topic_score_gemma":0.012881707,"teacher_disagreement_score":0.11574669,"about_ca_system_score_codex":0.0016854928,"about_ca_system_score_gemma":0.00424548,"threshold_uncertainty_score":0.38721126},"labels":[],"label_agreement":null},{"id":"W2971668727","doi":"","title":"Deep Probabilistic Regression of Elements of SO(3) using Quaternion Averaging and Uncertainty Injection.","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Quaternion; Probabilistic logic; Statistics; Regression; Regression analysis; Artificial intelligence; Measurement uncertainty; Mathematics; Computer science","score_opus":0.01887778893913278,"score_gpt":0.2692569197375203,"score_spread":0.25037913079838753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971668727","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01117625,0.00014909256,0.98721594,0.000098564844,0.000049061437,0.000009764985,0.00007183717,0.00049356406,0.00073599356],"genre_scores_gemma":[0.6556736,0.0004292958,0.3371229,0.0001460494,0.000104241815,0.00007429966,0.0006677698,0.00038717585,0.005394547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978083,0.00006291457,0.000009636602,0.00004860864,0.000068435256,0.000029568655],"domain_scores_gemma":[0.99964607,0.0001392353,0.000054845175,0.000056980163,0.00007337263,0.00002952711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000488547,0.0006005278,0.0004967251,0.0003734222,0.00018419245,0.0004875245,0.00071440404,0.0005253587,0.002314874],"category_scores_gemma":[0.0018464827,0.00041206152,0.00062156457,0.00065843476,0.00048997457,0.0009686318,0.00086264766,0.0011022335,0.00063489104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013155113,0.00005453883,0.0013095961,0.000090395406,0.00012005508,0.00007853831,0.00008109384,0.7678135,0.0129798865,0.05764639,0.00491274,0.15478174],"study_design_scores_gemma":[0.0000019553806,0.000008018964,0.00011781096,0.0000029812613,0.0000030894525,0.000006956925,0.0000034333943,0.9918996,0.0008730309,0.006632691,0.00044659254,0.0000039155666],"about_ca_topic_score_codex":0.0047521023,"about_ca_topic_score_gemma":0.00798773,"teacher_disagreement_score":0.0047521023,"about_ca_system_score_codex":0.000314518,"about_ca_system_score_gemma":0.0006311884,"threshold_uncertainty_score":0.009448886},"labels":[],"label_agreement":null},{"id":"W2973167495","doi":"10.5267/j.ijdns.2019.9.003","title":"Comparison of machine learning algorithms for the automatic programming of computer numerical control machine","year":2019,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Numerical control; Control (management); Artificial intelligence; Algorithm; Machine learning; Engineering; Mechanical engineering","score_opus":0.026632460272231716,"score_gpt":0.3498622582516063,"score_spread":0.32322979797937457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973167495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025179403,0.0016046887,0.9664817,0.00025095078,0.00016070531,0.00011513912,0.000076826866,0.0023591141,0.0037714413],"genre_scores_gemma":[0.4083566,0.0010901878,0.5859516,0.00019019224,0.00008682808,0.00054593384,0.0005094271,0.00042471816,0.0028444063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997997,0.00072476553,0.0001678326,0.00032454656,0.00064514345,0.00014066068],"domain_scores_gemma":[0.99448156,0.003330908,0.00024386356,0.0003598218,0.0014906946,0.00009320805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030633088,0.0010170358,0.000985943,0.0013669638,0.0005093291,0.0011498476,0.0015285835,0.0011238069,0.002494278],"category_scores_gemma":[0.010403262,0.00033082828,0.0007495411,0.0011331971,0.0004584748,0.0013503823,0.0008990664,0.0018651088,0.00068753655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003336046,0.00018335563,0.0015827204,0.00022990932,0.00009111749,0.000042709733,0.000081060665,0.5240818,0.0016876193,0.009966056,0.0028016116,0.45891842],"study_design_scores_gemma":[0.000006923138,0.000026033855,0.00019483278,0.0000083272225,0.0000040486957,0.000008509085,0.000007769549,0.9973947,0.0005791202,0.0012928187,0.00047259495,0.0000043346863],"about_ca_topic_score_codex":0.0067919646,"about_ca_topic_score_gemma":0.003108051,"teacher_disagreement_score":0.0067919646,"about_ca_system_score_codex":0.0011173513,"about_ca_system_score_gemma":0.0017831238,"threshold_uncertainty_score":0.016200483},"labels":[],"label_agreement":null},{"id":"W2977749933","doi":"10.6000/1929-7092.2019.08.69","title":"Instrumental Variables Estimation of Systems of Simultaneous Equations: Interrelation of Methods","year":2019,"lang":"en","type":"article","venue":"Journal of Reviews on Global Economics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimation; Instrumental variable; Simultaneous equations; Econometrics; Simultaneous equations model; Applied mathematics; Mathematics; Economics; Statistics; Computer science; Mathematical analysis; Differential equation","score_opus":0.025682644491297858,"score_gpt":0.3349378771049648,"score_spread":0.30925523261366694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977749933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010029549,0.00065074774,0.9968375,0.00019957681,0.000061470724,0.00010076897,0.00007579063,0.00008589145,0.00098526],"genre_scores_gemma":[0.082961544,0.0031502182,0.90927553,0.0002805941,0.0003513067,0.0014415805,0.00050741894,0.00018118003,0.0018506243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94802886,0.041522484,0.0017689934,0.0032335068,0.00487975,0.0005663597],"domain_scores_gemma":[0.92591935,0.0632849,0.003061656,0.0044600056,0.003073526,0.00020056868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025043538,0.0019651693,0.002605349,0.003695703,0.0010478911,0.003980706,0.003182632,0.0016959487,0.0059598116],"category_scores_gemma":[0.104089536,0.0014167582,0.0023587942,0.0058331597,0.0024648365,0.0031449448,0.0055813203,0.004467413,0.0012844005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069697155,0.00013767397,0.008224548,0.0012179251,0.0011840964,0.00020351863,0.00084049534,0.05987861,0.0006720312,0.6289905,0.0039412333,0.2946397],"study_design_scores_gemma":[0.00010054429,0.000121737634,0.0031943996,0.00072003144,0.0003242803,0.00017867517,0.00034793353,0.379552,0.0018122694,0.5884457,0.025051257,0.00015113103],"about_ca_topic_score_codex":0.0024459276,"about_ca_topic_score_gemma":0.001802733,"teacher_disagreement_score":0.025043538,"about_ca_system_score_codex":0.0012794038,"about_ca_system_score_gemma":0.0035472214,"threshold_uncertainty_score":0.13244444},"labels":[],"label_agreement":null},{"id":"W2996462410","doi":"10.32370/ia_2019_12_8","title":"Technological Vessels Online Monitoring Systems","year":2019,"lang":"en","type":"article","venue":"Intellectual Archive","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Volume (thermodynamics); Mechanical engineering; Computer science; Process control; Dead time; Process engineering; Work in process; Engineering","score_opus":0.017950697066126828,"score_gpt":0.25076365251200183,"score_spread":0.232812955445875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996462410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06013171,0.0036049027,0.7823014,0.0012156909,0.0014041883,0.00047000518,0.0029426357,0.04687067,0.10105868],"genre_scores_gemma":[0.7203316,0.002661455,0.16339725,0.0008816436,0.0011579006,0.0004984394,0.004876801,0.0011307019,0.10506424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874485,0.00014408967,0.00007197016,0.0003588068,0.0005840787,0.00009620491],"domain_scores_gemma":[0.9986786,0.00025960183,0.00019013319,0.00034977382,0.00044683026,0.00007509961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072832074,0.00083337445,0.00060122623,0.0018889218,0.0006587266,0.0024608043,0.0015116495,0.0012439176,0.019859832],"category_scores_gemma":[0.0018162946,0.00033584255,0.00032575647,0.0014292111,0.00035024603,0.0023987168,0.0014513707,0.0008360059,0.009959543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079193996,0.00023731016,0.004662093,0.0005006587,0.000059048354,0.00036034847,0.00035752237,0.0073466618,0.06556308,0.017146962,0.04762569,0.85534877],"study_design_scores_gemma":[0.00015747016,0.00089114846,0.012528399,0.0002751763,0.00018321868,0.0015293888,0.00025625707,0.21947278,0.18753938,0.013411324,0.5635625,0.00019293446],"about_ca_topic_score_codex":0.00086069974,"about_ca_topic_score_gemma":0.00048467808,"teacher_disagreement_score":0.019859832,"about_ca_system_score_codex":0.000591181,"about_ca_system_score_gemma":0.0005770117,"threshold_uncertainty_score":0.06643772},"labels":[],"label_agreement":null},{"id":"W3009681768","doi":"10.33832/ijca.2019.12.1.03","title":"Cyber Physical Systems: A New Frontier of Artificial Intelligence: Summary Paper","year":2019,"lang":"en","type":"article","venue":"International Journal of Control and Automation","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Frontier; Cyber-physical system; Computer science; Artificial intelligence; Engineering; Geography","score_opus":0.006363028764746059,"score_gpt":0.25132904948335605,"score_spread":0.24496602071860998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009681768","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025120517,0.8163933,0.028780231,0.04578836,0.017413115,0.00004232743,0.00024640645,0.00017170917,0.0886525],"genre_scores_gemma":[0.066689074,0.8109072,0.0137658315,0.00722513,0.025707187,0.00008576908,0.00050735124,0.00014742196,0.074965075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994561,0.00015675243,0.000029639077,0.00009672048,0.00022171298,0.00003906341],"domain_scores_gemma":[0.9983919,0.00081455865,0.00007652351,0.00013024575,0.00046599226,0.00012077096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012514396,0.00065945496,0.00057563075,0.0014082299,0.00063352165,0.004840696,0.00057349086,0.00181871,0.010387328],"category_scores_gemma":[0.0019499813,0.00025761314,0.00036926704,0.0028898076,0.0022589948,0.005293311,0.0011395914,0.0019967507,0.0029598875],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068670815,0.000080463295,0.0009687107,0.0025905753,0.00006420001,0.00015951101,0.00045795692,0.0026426425,0.00056061597,0.3384501,0.33600518,0.31795138],"study_design_scores_gemma":[0.0000074363834,0.000048378835,0.00090327946,0.0012402533,0.000023839677,0.00018464957,0.0003293143,0.0017336409,0.00028763668,0.2262562,0.7689689,0.000016417616],"about_ca_topic_score_codex":0.0020236473,"about_ca_topic_score_gemma":0.0018385394,"teacher_disagreement_score":0.010387328,"about_ca_system_score_codex":0.0016156343,"about_ca_system_score_gemma":0.0020362835,"threshold_uncertainty_score":0.03474909},"labels":[],"label_agreement":null},{"id":"W3011181961","doi":"10.5539/mas.v14n4p34","title":"Mathematical Methods of Calculating the Reliability of Standby Systems with Renewal","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Computer science; Markov chain; Markov process; Redundancy (engineering); Mathematical optimization; Mathematics; Statistics; Engineering; Power (physics)","score_opus":0.025943250089586815,"score_gpt":0.29737190982310435,"score_spread":0.27142865973351754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011181961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014467906,0.0015041769,0.99246347,0.00010824043,0.000073756,0.000027008105,0.000041978998,0.0001515414,0.00418295],"genre_scores_gemma":[0.2040887,0.00975674,0.7683111,0.00024982746,0.00067000685,0.00059986126,0.00036055266,0.00047792122,0.015485258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99865866,0.0003350388,0.00008473565,0.000120581084,0.0007441786,0.00005678388],"domain_scores_gemma":[0.997537,0.0013019325,0.0002731053,0.00026982056,0.00058381434,0.000034272347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019253708,0.0008920247,0.0007581197,0.0024401902,0.00049759157,0.001302795,0.0017183332,0.00078407733,0.0029024193],"category_scores_gemma":[0.0075032837,0.0005435774,0.0010764607,0.0014193268,0.001245444,0.0019568987,0.0010093354,0.001999651,0.0017462025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020073634,0.00002848178,0.00030255716,0.0003354115,0.000048088885,0.00010396713,0.0002098388,0.2521017,0.0039778547,0.6594962,0.0027689915,0.08060691],"study_design_scores_gemma":[0.0000097079965,0.000025160416,0.00024123647,0.00007070923,0.000023451837,0.00016010064,0.000021580592,0.70635515,0.0026006459,0.27630046,0.014158365,0.000033441793],"about_ca_topic_score_codex":0.0013220236,"about_ca_topic_score_gemma":0.00080444425,"teacher_disagreement_score":0.0029024193,"about_ca_system_score_codex":0.0010118233,"about_ca_system_score_gemma":0.0010123376,"threshold_uncertainty_score":0.01018244},"labels":[],"label_agreement":null},{"id":"W3018590582","doi":"10.1080/03155986.2020.1733249","title":"Preface to the special issue of INFOR on “continuous optimization and applications in machine learning and data analytics”","year":2020,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Analytics; Computer science; Data science; Data analysis; Artificial intelligence; Machine learning; Data mining","score_opus":0.056887749831139665,"score_gpt":0.3581630548255409,"score_spread":0.30127530499440125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018590582","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046858133,0.03749255,0.003098555,0.04892675,0.7637227,0.00026972764,0.005480116,0.0009645596,0.13957644],"genre_scores_gemma":[0.003497962,0.018877214,0.0013321587,0.01676474,0.48743317,0.00021600306,0.007159611,0.0010702757,0.46364883],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830365,0.00020015762,0.00013589038,0.00021498572,0.0010013274,0.00014391688],"domain_scores_gemma":[0.98505545,0.0028923475,0.0009716664,0.00049826974,0.00830372,0.0022785193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002247766,0.0021537982,0.0022436087,0.0037095184,0.0013689727,0.005668204,0.0014649566,0.0033613434,0.3419802],"category_scores_gemma":[0.010718319,0.0005982829,0.0012619201,0.0024197751,0.00068139576,0.0038546969,0.0019011707,0.0041857385,0.30178723],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146911625,0.0000071624,0.00001869988,0.00007213294,0.000002158235,0.000009912111,0.0000033178271,0.000029172985,0.00006764615,0.0002033863,0.9912977,0.008274051],"study_design_scores_gemma":[0.000014175876,0.000044147295,0.0004635552,0.00024833888,0.000008359238,0.00009044453,0.00001960045,0.00018477872,0.00014385465,0.0015917283,0.9971775,0.000013392247],"about_ca_topic_score_codex":0.002076641,"about_ca_topic_score_gemma":0.0024656293,"teacher_disagreement_score":0.3419802,"about_ca_system_score_codex":0.0019272757,"about_ca_system_score_gemma":0.0022791917,"threshold_uncertainty_score":0.9385854},"labels":[],"label_agreement":null},{"id":"W3032194532","doi":"10.1007/978-3-030-41560-0_6","title":"CPS-Based Approach to Improve Management of Heavy Construction Projects","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Control reconfiguration; Cyber-physical system; Software deployment; Systems engineering; Computer science; Monitoring and control; Control (management); Computation; Engineering; Control engineering; Distributed computing; Embedded system; Software engineering; Operating system","score_opus":0.02004520027370206,"score_gpt":0.22925128890222296,"score_spread":0.20920608862852091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032194532","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053905523,0.00074160326,0.92487663,0.00046548684,0.0003891117,0.00009341914,0.0005095133,0.0019624229,0.06557121],"genre_scores_gemma":[0.24485452,0.002938772,0.6723768,0.00034385553,0.0002121964,0.0002415417,0.0019957377,0.00036315457,0.07667346],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997174,0.000045764977,0.00001699609,0.000055758763,0.00013785443,0.000026121847],"domain_scores_gemma":[0.99978703,0.00005622986,0.00002416422,0.000030827734,0.000085514665,0.000016199065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003768882,0.0005754514,0.00031099838,0.0009287551,0.0003454333,0.0015022526,0.00079606177,0.0004888626,0.009551078],"category_scores_gemma":[0.00073500414,0.00019686828,0.0005257582,0.0015431121,0.00024953688,0.001149605,0.0010219008,0.00066781294,0.0020030674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008006293,0.00015752362,0.0015070403,0.00040072735,0.000060891758,0.00026927138,0.00014733073,0.12407732,0.011020624,0.15635209,0.03793128,0.6679959],"study_design_scores_gemma":[0.000016567876,0.00010393221,0.003034704,0.00015848978,0.00008115589,0.00032784144,0.00035384312,0.6028971,0.011622602,0.10230668,0.27905738,0.000039667513],"about_ca_topic_score_codex":0.0052611367,"about_ca_topic_score_gemma":0.0065868916,"teacher_disagreement_score":0.009551078,"about_ca_system_score_codex":0.00077212637,"about_ca_system_score_gemma":0.0010536667,"threshold_uncertainty_score":0.031951547},"labels":[],"label_agreement":null},{"id":"W3036377927","doi":"10.1016/b978-0-12-820543-3.00015-8","title":"On neural-network training algorithms","year":2020,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial neural network; Computer science; Training (meteorology); Algorithm; Process (computing); Artificial intelligence; Machine learning; Geography","score_opus":0.02784957118866888,"score_gpt":0.24554963977670674,"score_spread":0.21770006858803786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036377927","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017449718,0.018265156,0.88933164,0.0009641149,0.002027051,0.000035722453,0.0003373026,0.0016336862,0.085660286],"genre_scores_gemma":[0.042978797,0.030249268,0.57259536,0.0009373932,0.0028053261,0.00021649741,0.002006615,0.0017238662,0.34648687],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997521,0.000060691356,0.000015747988,0.000044601453,0.00011154006,0.000015368832],"domain_scores_gemma":[0.9993247,0.0004367662,0.000014345608,0.00009754232,0.000115934716,0.000010869865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052368385,0.001151563,0.00075657445,0.0008290886,0.00027889168,0.0010802359,0.0010261148,0.0010520197,0.027821176],"category_scores_gemma":[0.002420353,0.0004819503,0.0004367913,0.0020263032,0.0006131752,0.0017740071,0.0010003172,0.0019341672,0.015135412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025821624,0.00003419661,0.00018134157,0.00025960055,0.000041622017,0.00005964354,0.000035716956,0.058351494,0.0012371577,0.062414367,0.07971343,0.79764557],"study_design_scores_gemma":[0.000013431195,0.000037829388,0.0007249741,0.00037996302,0.000043413318,0.0002598254,0.00003287699,0.48584977,0.0034763182,0.2463101,0.26283893,0.00003249945],"about_ca_topic_score_codex":0.0024753297,"about_ca_topic_score_gemma":0.0033461223,"teacher_disagreement_score":0.027821176,"about_ca_system_score_codex":0.0004884751,"about_ca_system_score_gemma":0.0004057012,"threshold_uncertainty_score":0.0930711},"labels":[],"label_agreement":null},{"id":"W3036513400","doi":"","title":"Machine Learning 2020 and Big data 2020:Machine learning for data acquisition in dynamic real-time: Erwin E Sniedzins - Mount Knowledge Inc.- Canada","year":2018,"lang":"en","type":"article","venue":"International journal of advanced research in electrical, electronics and instrumentation engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mount; Big data; Data acquisition; Computer science; Artificial intelligence; Machine learning; Data science; Data mining; Operating system","score_opus":0.023949603937894675,"score_gpt":0.3437576393659208,"score_spread":0.3198080354280261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036513400","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002204846,0.34130776,0.06834976,0.3923676,0.033805147,0.00020011686,0.0022613166,0.0037721999,0.1557313],"genre_scores_gemma":[0.02682729,0.34138188,0.05156606,0.043960746,0.01432255,0.00026469532,0.0029288777,0.0022135228,0.51653445],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702054,0.000476824,0.00011289858,0.00045063125,0.0017498703,0.00018917468],"domain_scores_gemma":[0.99069095,0.0036911678,0.00017137267,0.0004381358,0.0040674373,0.000940908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005097213,0.0012604407,0.00097427727,0.0017950191,0.0013792489,0.0050528953,0.0009949954,0.0034190367,0.020660475],"category_scores_gemma":[0.008624505,0.00065088563,0.00040744356,0.00251347,0.00249824,0.0068910867,0.0024237875,0.0077817696,0.01845379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004828793,0.000023690789,0.00030286136,0.00013100283,0.000011591513,0.000035117388,0.00007281678,0.0006084833,0.00026869963,0.019399252,0.8573371,0.12176118],"study_design_scores_gemma":[0.000015660382,0.000030983527,0.00067420467,0.0005759345,0.000007819665,0.00009553688,0.000114159084,0.0049127224,0.00060106284,0.0157184,0.9772017,0.00005186187],"about_ca_topic_score_codex":0.048900574,"about_ca_topic_score_gemma":0.046970464,"teacher_disagreement_score":0.048900574,"about_ca_system_score_codex":0.0037155503,"about_ca_system_score_gemma":0.0047899247,"threshold_uncertainty_score":0.097231865},"labels":[],"label_agreement":null},{"id":"W3037541938","doi":"10.11575/prism/37939","title":"Road network vulnerability analysis with consideration of probability and consequences of disruptive events","year":2020,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Hong Kong Polytechnic University","keywords":"Vulnerability (computing); Vulnerability assessment; Geography; Computer science; Psychology; Computer security; Social psychology; Psychological resilience","score_opus":0.010968777795217545,"score_gpt":0.2249060912706137,"score_spread":0.21393731347539616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037541938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21871237,0.00074805185,0.767733,0.00065505574,0.000060185837,0.00019368547,0.001168222,0.00033587954,0.010393569],"genre_scores_gemma":[0.97105837,0.00057988765,0.025944367,0.00004258347,0.000059778275,0.00015311356,0.0004491995,0.000033322493,0.0016794734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904233,0.00025015615,0.000050778795,0.0001909891,0.0003037392,0.00016212916],"domain_scores_gemma":[0.9971102,0.0016364583,0.0005581837,0.00016974812,0.00041801282,0.00010737228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001150134,0.00070103776,0.00052917376,0.00340719,0.00045722548,0.0012451842,0.0011031296,0.0009630494,0.0016299706],"category_scores_gemma":[0.0049399934,0.00030390493,0.0011609697,0.0017007557,0.00068211596,0.0021394712,0.0013705121,0.00088313216,0.00014923184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023987344,0.000023691588,0.0142006725,0.00006509657,0.00009116016,0.00029588895,0.00009829007,0.94918054,0.00089165155,0.019361507,0.0007524574,0.015015172],"study_design_scores_gemma":[0.0000020011544,0.000032729316,0.0062637376,0.000018759143,0.000032010747,0.00017369294,0.00015860869,0.9700256,0.00044160953,0.021807786,0.001024425,0.000019003128],"about_ca_topic_score_codex":0.00550342,"about_ca_topic_score_gemma":0.0034455815,"teacher_disagreement_score":0.00550342,"about_ca_system_score_codex":0.0012649418,"about_ca_system_score_gemma":0.0009476263,"threshold_uncertainty_score":0.010942757},"labels":[],"label_agreement":null},{"id":"W3044002203","doi":"10.5539/cis.v13n3p89","title":"Main Scientific and Technological Problems in the Field of Architectural Solutions for Supercomputers","year":2020,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Graphics; Field (mathematics); Graph; General-purpose computing on graphics processing units; Data science; Computer architecture; Artificial intelligence; Human–computer interaction; Multimedia; Theoretical computer science; Computer graphics (images)","score_opus":0.023669620648601584,"score_gpt":0.257901621251049,"score_spread":0.2342320006024474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044002203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028962027,0.10684067,0.5186694,0.09497745,0.009977425,0.00030100363,0.00040957093,0.0017649966,0.23809738],"genre_scores_gemma":[0.20971736,0.08196011,0.5390214,0.008973632,0.006370371,0.00057265,0.000685949,0.00093827443,0.15176034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998798,0.00033178995,0.00008480188,0.00024678273,0.00043350022,0.00010513391],"domain_scores_gemma":[0.99840456,0.0005185011,0.0000839222,0.0003097554,0.0005438277,0.00013943222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019123218,0.0005895262,0.0004236363,0.0010169923,0.0016308313,0.004338918,0.0013038408,0.0023714076,0.009849354],"category_scores_gemma":[0.0029604184,0.00038396032,0.00057174003,0.0014833694,0.001762784,0.0048321057,0.0016907132,0.0031022956,0.0039166687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047883554,0.00008003308,0.0009742506,0.0008650856,0.00003241969,0.00024662132,0.00076938013,0.004807491,0.0034486584,0.65159225,0.03566954,0.30146635],"study_design_scores_gemma":[0.000016667633,0.00011432212,0.0006774411,0.00039943936,0.00002126586,0.0007308934,0.00087603246,0.0073295566,0.0031335915,0.3227809,0.6638827,0.000037313206],"about_ca_topic_score_codex":0.0007358971,"about_ca_topic_score_gemma":0.001079303,"teacher_disagreement_score":0.009849354,"about_ca_system_score_codex":0.0013329663,"about_ca_system_score_gemma":0.0019438834,"threshold_uncertainty_score":0.03294933},"labels":[],"label_agreement":null},{"id":"W3084313711","doi":"10.32393/csme.2020.31","title":"Modelling of Hydrocarbon and Non-hydrocarbon Gases Viscosity by Using an Artificial Neural Networks Model","year":2020,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering. Volume 3","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Hydrocarbon; Artificial neural network; Petroleum engineering; Viscosity; Computer science; Geology; Artificial intelligence; Chemistry; Thermodynamics; Organic chemistry; Physics","score_opus":0.024004547869952684,"score_gpt":0.2358919258906871,"score_spread":0.21188737802073443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084313711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2044311,0.001411096,0.7763494,0.00037647152,0.00015817842,0.00017473024,0.00058868184,0.0009528318,0.015557357],"genre_scores_gemma":[0.9220043,0.001027055,0.06471035,0.000060584025,0.00003854497,0.0003755548,0.00053947355,0.0000588003,0.011185388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978334,0.000043185562,0.000017422837,0.00005639963,0.000073435134,0.000026323136],"domain_scores_gemma":[0.99971753,0.00013557609,0.00004078267,0.000013952101,0.000083850435,0.000008222772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035424854,0.0008104688,0.0005295577,0.00067424675,0.00035068206,0.00086681684,0.00094370963,0.0011990262,0.0013379215],"category_scores_gemma":[0.0009745312,0.00048626584,0.0009536117,0.000736164,0.00030456568,0.00095061853,0.00036112,0.00079409784,0.00040705435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001945105,0.00002778147,0.00061186595,0.000034099587,0.000014956595,0.000038871338,0.00001652298,0.9901432,0.0016358407,0.00049142423,0.00012800834,0.0068379543],"study_design_scores_gemma":[8.6095247e-7,0.0000059713866,0.000098027165,0.0000024228682,0.0000022900347,0.0000034146124,0.0000017313789,0.99925524,0.00040314416,0.00010490765,0.00011972503,0.0000023012483],"about_ca_topic_score_codex":0.010621859,"about_ca_topic_score_gemma":0.0061403,"teacher_disagreement_score":0.010621859,"about_ca_system_score_codex":0.0007420972,"about_ca_system_score_gemma":0.0007046964,"threshold_uncertainty_score":0.021120012},"labels":[],"label_agreement":null},{"id":"W3121841706","doi":"10.3390/modelling2010003","title":"Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines","year":2021,"lang":"en","type":"article","venue":"Modelling—International Open Access Journal of Modelling in Engineering Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nuclear power plant; Cluster analysis; Representation (politics); Computer science; Linear discriminant analysis; Feature (linguistics); Multivariable calculus; Finite-state machine; Principal component analysis; Data mining; Artificial intelligence; Machine learning; Control engineering; Algorithm; Engineering","score_opus":0.12741992646403963,"score_gpt":0.3558295882909587,"score_spread":0.22840966182691905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121841706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10225769,0.00016010542,0.89000934,0.00033176673,0.000062573046,0.00013317316,0.0021779248,0.0022963032,0.0025710019],"genre_scores_gemma":[0.88712513,0.00024129207,0.10819729,0.000048438105,0.000022350641,0.00027287143,0.002317352,0.000092611415,0.0016826079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955827,0.00011543782,0.000039733175,0.00011704086,0.00014443179,0.000025117326],"domain_scores_gemma":[0.99855167,0.0008815174,0.0001365858,0.00018904448,0.000216329,0.000024889854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070468365,0.0005935192,0.00046998364,0.0007339998,0.00024383357,0.0012226112,0.0008594262,0.00062329863,0.0015270225],"category_scores_gemma":[0.0031117243,0.00028099483,0.0006845326,0.0008046052,0.0004448114,0.0010355277,0.00041302622,0.0011987768,0.0004407186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005259652,0.00006490153,0.0027536377,0.0000808242,0.000029456121,0.00010633222,0.00012418923,0.9641782,0.0036905513,0.0071627204,0.0005726811,0.021183938],"study_design_scores_gemma":[0.0000016017057,0.000011951281,0.00051122386,0.0000040909176,0.000002458762,0.000016813714,0.000012250157,0.9954756,0.0010143202,0.00249442,0.00045003148,0.000005288959],"about_ca_topic_score_codex":0.0066097984,"about_ca_topic_score_gemma":0.0064739524,"teacher_disagreement_score":0.0066097984,"about_ca_system_score_codex":0.0006443187,"about_ca_system_score_gemma":0.0006880984,"threshold_uncertainty_score":0.013142645},"labels":[],"label_agreement":null},{"id":"W3138621382","doi":"10.33425/2639-9474.1152","title":"A Scoping Review and Analysis of Simulation Facilitator Essential Elements","year":2020,"lang":"en","type":"review","venue":"Nursing & Primary Care","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; University of Connecticut","keywords":"Facilitator; Computer science; Psychology; Social psychology","score_opus":0.027440670277104905,"score_gpt":0.37564982555940624,"score_spread":0.34820915528230134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138621382","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060530775,0.9932156,0.0011635332,0.0010282359,0.00047966026,0.0010434187,0.00072172284,0.000025848958,0.0017165685],"genre_scores_gemma":[0.004756059,0.9897489,0.0022457324,0.00069243094,0.00009988384,0.0014754352,0.00059015636,0.000014000694,0.00037747406],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9840149,0.0053215353,0.005689118,0.00090277125,0.0036219892,0.00044973328],"domain_scores_gemma":[0.9230761,0.05077096,0.009626467,0.0018196511,0.013982052,0.0007248523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029162843,0.0021568667,0.0054437006,0.027731434,0.0015101237,0.0046252967,0.0025027965,0.0026843217,0.007836808],"category_scores_gemma":[0.09952349,0.0013458572,0.0067671547,0.021794662,0.0015667157,0.004614458,0.0037463992,0.0021719022,0.0014258982],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012406432,0.00003129233,0.0004618371,0.80200416,0.0017778666,0.000121128665,0.00052858895,0.0001967602,0.00031032518,0.0020547158,0.006494371,0.1858949],"study_design_scores_gemma":[0.00002729777,0.00005198404,0.00073650473,0.9433855,0.00661425,0.00014662345,0.0002695887,0.00005590876,0.00018801703,0.00064047344,0.047865354,0.000018537141],"about_ca_topic_score_codex":0.008538625,"about_ca_topic_score_gemma":0.026033716,"teacher_disagreement_score":0.029162843,"about_ca_system_score_codex":0.0067173718,"about_ca_system_score_gemma":0.035222232,"threshold_uncertainty_score":0.1542297},"labels":[],"label_agreement":null},{"id":"W3158212986","doi":"10.1017/9781009504942.004","title":"Basics of machine learning","year":2025,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo; McMaster University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.012517400931364693,"score_gpt":0.19307797689385908,"score_spread":0.1805605759624944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158212986","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018259643,0.13660248,0.45398134,0.01928633,0.003904359,0.00018443422,0.0013051626,0.0015236981,0.38138622],"genre_scores_gemma":[0.09339306,0.21744476,0.444807,0.012441777,0.011847474,0.001065426,0.003298697,0.0011452187,0.21455653],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981591,0.00038306645,0.00013750809,0.00034863185,0.0008677106,0.00010391977],"domain_scores_gemma":[0.9985274,0.0009499719,0.000055052005,0.00020378115,0.0002186974,0.000045212826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001742345,0.0015011629,0.0011535953,0.0020309628,0.0010372649,0.004515553,0.0018597551,0.0025673185,0.021350887],"category_scores_gemma":[0.004763485,0.0006400369,0.0010227257,0.0028718738,0.0036834914,0.0058281873,0.0017006617,0.0047205174,0.018233843],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011348893,0.00002201772,0.0001659757,0.00057122286,0.0000255215,0.00010108408,0.0002560369,0.0028931939,0.0004604162,0.83096087,0.04563231,0.11890009],"study_design_scores_gemma":[0.0000044567723,0.0000148276995,0.00016822388,0.0003171976,0.0000067344245,0.00020624256,0.00004752309,0.0030159215,0.0002647337,0.6449115,0.3510271,0.00001541318],"about_ca_topic_score_codex":0.0010976514,"about_ca_topic_score_gemma":0.0007853564,"teacher_disagreement_score":0.021350887,"about_ca_system_score_codex":0.0014546599,"about_ca_system_score_gemma":0.0012627424,"threshold_uncertainty_score":0.071425855},"labels":[],"label_agreement":null},{"id":"W3166654637","doi":"10.2139/ssrn.3635351","title":"Digitalization in the Sulfuric Acid Plant of the Future","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Outotec (Canada)","funders":"","keywords":"Sulfuric acid; Chemistry; Environmental science; Inorganic chemistry","score_opus":0.005364153868454599,"score_gpt":0.19852089890970712,"score_spread":0.19315674504125252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166654637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77937675,0.011344703,0.0932623,0.010866892,0.00034094762,0.000035764566,0.0008096775,0.0006772665,0.10328568],"genre_scores_gemma":[0.97160476,0.002431677,0.012668767,0.00015731102,0.00006994889,0.000004063697,0.00017292747,0.000019020326,0.012871529],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9999211,0.000010121336,0.000002941704,0.000021999433,0.00002929597,0.000014508698],"domain_scores_gemma":[0.99982953,0.00003598425,0.00002666205,0.00004285274,0.00004487251,0.000020146023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027941703,0.00010176104,0.00012939236,0.00034743306,0.0003440562,0.0008873419,0.00020116521,0.0003470469,0.0048845294],"category_scores_gemma":[0.0004433752,0.00006624883,0.0001246769,0.0006320206,0.000544475,0.0017643321,0.00037601526,0.00043247602,0.00044543235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010280947,0.00011762566,0.011048955,0.00028295736,0.000032657455,0.0006952948,0.000977193,0.018902697,0.067710236,0.47914538,0.008829477,0.4112295],"study_design_scores_gemma":[0.00010347125,0.0006098581,0.032195546,0.00018395904,0.00007584962,0.0012457565,0.0024714968,0.14763531,0.10123128,0.40555382,0.30860057,0.000093060335],"about_ca_topic_score_codex":0.0030300035,"about_ca_topic_score_gemma":0.0036963476,"teacher_disagreement_score":0.0048845294,"about_ca_system_score_codex":0.0009598808,"about_ca_system_score_gemma":0.0005266118,"threshold_uncertainty_score":0.016340435},"labels":[],"label_agreement":null},{"id":"W3183358015","doi":"10.34874/imist.prsm/fsejournal-v6i1.27190","title":"Efficient Algorithms for Reliability Evaluation of General Networks","year":2021,"lang":"en","type":"article","venue":"PRSM","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Reliability (semiconductor); Algorithm","score_opus":0.0329603288455947,"score_gpt":0.32284163167281105,"score_spread":0.28988130282721636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183358015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049572685,0.000234894,0.99244565,0.00007363457,0.000023256192,0.00005634457,0.00007327024,0.0007861615,0.0013495656],"genre_scores_gemma":[0.17879272,0.00042188112,0.8169545,0.00006205182,0.000103346996,0.00030642655,0.00046625495,0.0003469924,0.0025458187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984546,0.0006596904,0.00008678545,0.00022415596,0.00043436652,0.0001404787],"domain_scores_gemma":[0.99372166,0.0039466186,0.00035323203,0.0008320637,0.0010550258,0.00009136519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002672885,0.001462988,0.0010632246,0.00201782,0.0006515619,0.0014917473,0.0018395932,0.0009590718,0.0053068763],"category_scores_gemma":[0.014455568,0.00063376414,0.0010265422,0.0017137912,0.0007862614,0.0024667708,0.0017049562,0.00145707,0.0013049068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003043353,0.00009973607,0.001061313,0.00027617835,0.00008147429,0.000064694585,0.00010825143,0.55105984,0.0028972183,0.06820666,0.006964268,0.36887595],"study_design_scores_gemma":[0.000024079305,0.000021934999,0.00013392692,0.000016816943,0.000014071774,0.000028573511,0.000015006779,0.95040995,0.0008642734,0.04761302,0.00085299095,0.000005433219],"about_ca_topic_score_codex":0.0027392206,"about_ca_topic_score_gemma":0.0040112515,"teacher_disagreement_score":0.0053068763,"about_ca_system_score_codex":0.0015515389,"about_ca_system_score_gemma":0.0017746177,"threshold_uncertainty_score":0.017753243},"labels":[],"label_agreement":null},{"id":"W3195182661","doi":"","title":"THE REAL-ESSI SIMULATOR SYSTEM, CURRENT STATUS","year":2019,"lang":"en","type":"article","venue":"NCSU Libraries Repository (North Carolina State University Libraries)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Federal Emergency Management Agency; International Atomic Energy Agency; U.S. Nuclear Regulatory Commission; Canadian Nuclear Safety Commission; Bureau of Reclamation; National Science Foundation; U.S. Department of Transportation; California Department of Transportation; U.S. Department of Energy","keywords":"Current (fluid); Simulation; Environmental science; Computer science; Engineering; Electrical engineering","score_opus":0.0041197002386888825,"score_gpt":0.17269035208144645,"score_spread":0.16857065184275757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195182661","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05469059,0.003353892,0.25861883,0.0022293846,0.0016887361,0.0011799295,0.117914714,0.44700998,0.11331394],"genre_scores_gemma":[0.27875274,0.003897276,0.23813775,0.0015769833,0.0005203524,0.002847247,0.3333821,0.07306244,0.06782314],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989949,0.0002656785,0.00007575651,0.000109426976,0.00044646693,0.000107768385],"domain_scores_gemma":[0.9969452,0.00057836785,0.00009457221,0.00084728125,0.0012033925,0.00033112458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035247265,0.0016638087,0.0014813873,0.0010727348,0.0005796457,0.0017417604,0.00794464,0.0011901486,0.05341659],"category_scores_gemma":[0.0049689175,0.0011839669,0.0010015001,0.0016990831,0.00068893825,0.0027500559,0.001111242,0.0023609812,0.027473226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035268844,0.0008326015,0.0052714683,0.0015820379,0.00053071586,0.00025034614,0.00036170718,0.23130977,0.014872696,0.013591105,0.56874806,0.1591226],"study_design_scores_gemma":[0.002033332,0.0004200133,0.0030641,0.00019106622,0.0001997099,0.00014167238,0.000105686384,0.6244335,0.026307587,0.006754569,0.3361623,0.0001864612],"about_ca_topic_score_codex":0.015285885,"about_ca_topic_score_gemma":0.012348487,"teacher_disagreement_score":0.05341659,"about_ca_system_score_codex":0.0014896593,"about_ca_system_score_gemma":0.0027365945,"threshold_uncertainty_score":0.17869633},"labels":[],"label_agreement":null},{"id":"W3202178370","doi":"","title":"General Planning / Plenaries / virtual rooms","year":2021,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Architectural engineering; Engineering","score_opus":0.011943731931045696,"score_gpt":0.23185276703215718,"score_spread":0.21990903510111148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202178370","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050202245,0.012192035,0.010107677,0.02536066,0.057249527,0.00090860797,0.00793021,0.002914162,0.8783169],"genre_scores_gemma":[0.009036382,0.0029167905,0.0014183723,0.00072722667,0.0034164977,0.00008735783,0.0020653938,0.00037925076,0.97995275],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992685,0.00006607147,0.000026558495,0.000116831754,0.00034480903,0.00017719794],"domain_scores_gemma":[0.9956131,0.0001305002,0.00009241625,0.00022074571,0.0013043208,0.0026388124],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0018120653,0.0011979156,0.0004600105,0.0014713745,0.0027234373,0.0034557153,0.0012660525,0.0019138275,0.48409045],"category_scores_gemma":[0.0017203195,0.0004727715,0.00062352384,0.0013527632,0.00067602034,0.0018230008,0.0027694206,0.0019159355,0.21521865],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009842647,0.000076228214,0.00028467717,0.00013899777,0.0000034142695,0.0000947304,0.00007453934,0.000455712,0.0016417629,0.0034170842,0.9099664,0.08374806],"study_design_scores_gemma":[0.000006561877,0.000031845168,0.0008407601,0.000044389824,0.0000013543756,0.000031696338,0.00013809513,0.00010551621,0.0002715985,0.00045404077,0.99806863,0.0000055745822],"about_ca_topic_score_codex":0.039177783,"about_ca_topic_score_gemma":0.09951817,"teacher_disagreement_score":0.51590955,"about_ca_system_score_codex":0.0036259845,"about_ca_system_score_gemma":0.008170221,"threshold_uncertainty_score":0.73588234},"labels":[],"label_agreement":null},{"id":"W3211007339","doi":"10.5281/zenodo.3874137","title":"Supplementary Material of \"NoRBERT: Transfer Learning for Requirements Classification\"","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence","score_opus":0.06029600918913115,"score_gpt":0.27034775553799073,"score_spread":0.2100517463488596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211007339","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018362158,0.0004433995,0.07037145,0.0012651976,0.0014508261,0.0003857496,0.7941624,0.09876426,0.031320482],"genre_scores_gemma":[0.007229092,0.00027164066,0.0480329,0.0007953117,0.0002181926,0.000860572,0.91270256,0.013936999,0.01595282],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800116,0.00041942942,0.00017309452,0.0005019061,0.00074534875,0.00015898263],"domain_scores_gemma":[0.99027866,0.004661452,0.00027622323,0.002396417,0.0020274657,0.00035971613],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018928554,0.0025084324,0.001293033,0.0024153844,0.00065488194,0.0023382006,0.0025818606,0.0016444956,0.47398373],"category_scores_gemma":[0.02042925,0.0010362546,0.0015880829,0.003030221,0.00030589467,0.0030261483,0.0031402882,0.0025164257,0.2849768],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005448705,0.000079120786,0.00031045478,0.00036265526,0.000020155618,0.000036983572,0.000017982324,0.001148635,0.00033962107,0.0013869819,0.97007793,0.026165105],"study_design_scores_gemma":[0.00029962618,0.00010534723,0.0034753366,0.00035291226,0.000025411935,0.0002571731,0.00007406747,0.030947657,0.0036615513,0.026779762,0.93394107,0.000080227604],"about_ca_topic_score_codex":0.0056250007,"about_ca_topic_score_gemma":0.01088872,"teacher_disagreement_score":0.47398373,"about_ca_system_score_codex":0.0013101508,"about_ca_system_score_gemma":0.0017702618,"threshold_uncertainty_score":0.7502984},"labels":[],"label_agreement":null},{"id":"W329024442","doi":"10.2316/journal.206.2009.3.206-3261","title":"SERVICE ROBOT SYSTEM BASED ON NETWORKED ROBOTS FOR USING PERSONAL ATTRIBUTE AND TO GET PREFERENCE ATTRIBUTE","year":2009,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preference; Computer science; Robot; Service robot; Service (business); Human–computer interaction; Artificial intelligence; Business; Mathematics; Statistics; Marketing","score_opus":0.036398715261622416,"score_gpt":0.2860545637432399,"score_spread":0.24965584848161748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W329024442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4984462,0.00044257395,0.4674863,0.00046173812,0.00044187513,0.00041105316,0.00036463252,0.0070690345,0.024876507],"genre_scores_gemma":[0.9286649,0.00009718633,0.06225263,0.00012207766,0.000055882327,0.00015618767,0.00026713207,0.000042088042,0.008341885],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957913,0.000068606234,0.000025739755,0.000113178154,0.00016803759,0.00004539043],"domain_scores_gemma":[0.99967206,0.000043970314,0.000027080052,0.00005901772,0.00014130771,0.000056571214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028468238,0.00044981213,0.0006880101,0.0003691296,0.0006760323,0.0004814214,0.0007380026,0.00042449837,0.0041237646],"category_scores_gemma":[0.00057291944,0.00017292208,0.00031536265,0.00045138554,0.0002616704,0.00065437786,0.00059356587,0.00035365956,0.0010571984],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028388896,0.001148885,0.019559016,0.000606804,0.00023804903,0.001301473,0.0013929756,0.026859362,0.29422808,0.012987338,0.023887023,0.61495215],"study_design_scores_gemma":[0.0005143372,0.0021996766,0.028310299,0.000079262514,0.00043268973,0.0031141287,0.0009887954,0.7849584,0.13405955,0.008056656,0.037004955,0.00028127493],"about_ca_topic_score_codex":0.002715152,"about_ca_topic_score_gemma":0.0032541442,"teacher_disagreement_score":0.0041237646,"about_ca_system_score_codex":0.00032402042,"about_ca_system_score_gemma":0.000687879,"threshold_uncertainty_score":0.013795435},"labels":[],"label_agreement":null},{"id":"W4200422747","doi":"10.1002/cjce.24332","title":"Cover Image","year":2021,"lang":"en","type":"paratext","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cover (algebra); Environmental science; Geology; Remote sensing; Engineering","score_opus":0.007307285178204279,"score_gpt":0.21455157253598303,"score_spread":0.20724428735777875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200422747","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025758348,0.00070414605,0.0007539479,0.0028089688,0.010588985,0.00014073389,0.0052761096,0.0012760157,0.9781935],"genre_scores_gemma":[0.0008821949,0.00042271355,0.00024365826,0.00055765576,0.0011750915,0.000024749093,0.0016804693,0.00034626178,0.9946673],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999566,0.000021752312,0.000010460041,0.000042962594,0.0003148159,0.00004398532],"domain_scores_gemma":[0.99813443,0.0002373196,0.0000495024,0.00012305769,0.0010314279,0.00042436668],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00032153339,0.00078596093,0.00062496297,0.0015348529,0.0015437668,0.0038476249,0.0008852645,0.001529634,0.89050853],"category_scores_gemma":[0.0028140885,0.00028796494,0.00039091063,0.0017951158,0.00041994304,0.0023382518,0.0012717872,0.001499113,0.71609837],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076284628,0.0000043801397,0.000011520723,0.000028418908,2.8990036e-7,0.000014878939,0.0000040086315,0.000017685092,0.0000760787,0.00048854627,0.98608124,0.013265248],"study_design_scores_gemma":[0.0000036596502,0.000005091947,0.0001126788,0.000031218595,5.504424e-7,0.000033117365,0.000013666177,0.00005791457,0.00006332666,0.00033978137,0.9993363,0.0000026297755],"about_ca_topic_score_codex":0.01071451,"about_ca_topic_score_gemma":0.02211928,"teacher_disagreement_score":0.10949147,"about_ca_system_score_codex":0.0015178198,"about_ca_system_score_gemma":0.0014445892,"threshold_uncertainty_score":0.15617621},"labels":[],"label_agreement":null},{"id":"W4205761284","doi":"10.32920/ryerson.14657349","title":"A Simulation Algorithm Capable Of Modelling Spatial Impact Points From The Neutralization Of An Improvised Explosive Device","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Explosive material; Computer science; Container (type theory); Neutralization; Dispersion (optics); Simulation; Engineering; Mechanical engineering","score_opus":0.03297168225424865,"score_gpt":0.30010826122915485,"score_spread":0.26713657897490617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205761284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018463507,0.00004990037,0.9766377,0.00010753346,0.00004014386,0.000065563836,0.00014355751,0.0011033951,0.0033887106],"genre_scores_gemma":[0.37674233,0.00019098437,0.61686486,0.00008211175,0.000025979261,0.00033524635,0.0006670486,0.00026304906,0.004828457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978954,0.00005344999,0.00001623988,0.000051774983,0.000065256005,0.000023764318],"domain_scores_gemma":[0.9992042,0.00043541437,0.00007550016,0.000079632504,0.00016714809,0.000038175676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047229335,0.00074097473,0.0005446009,0.00048525483,0.0005501206,0.0010895971,0.0013278567,0.001411775,0.0039504133],"category_scores_gemma":[0.0023500815,0.00046345234,0.00070523686,0.00046612747,0.00056194374,0.00086824194,0.00065501116,0.0008636868,0.00071463187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025530137,0.000015278638,0.00046657893,0.000016750035,0.000010020644,0.000022703249,0.000025828664,0.9896798,0.00096814084,0.0027949985,0.00027072892,0.0057035238],"study_design_scores_gemma":[0.000003846473,0.000003904018,0.000020766962,0.000001983419,0.0000016824072,0.000004118434,0.0000028315897,0.9987128,0.00030964662,0.00050918286,0.0004278651,0.0000014426998],"about_ca_topic_score_codex":0.011415902,"about_ca_topic_score_gemma":0.0068865004,"teacher_disagreement_score":0.011415902,"about_ca_system_score_codex":0.00074717554,"about_ca_system_score_gemma":0.0015461324,"threshold_uncertainty_score":0.02269888},"labels":[],"label_agreement":null},{"id":"W4206793367","doi":"10.1088/1742-6596/2014/1/011001","title":"Preface","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Presentation (obstetrics); Session (web analytics); Passion; Test (biology); Face (sociological concept); Zoom; Library science; Mathematics education; Engineering; Mathematics; Computer science; Psychology; Sociology; Medicine; World Wide Web; Social science","score_opus":0.020195801674506377,"score_gpt":0.24901301463606076,"score_spread":0.22881721296155438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206793367","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019663062,0.008453006,0.0072193225,0.02274058,0.10617654,0.00080130657,0.02210981,0.0029425677,0.8275906],"genre_scores_gemma":[0.0091589745,0.0052616233,0.0035220017,0.0066409013,0.0150551135,0.000513164,0.021036591,0.0017214908,0.93709004],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842864,0.00022500062,0.00013838084,0.00034120306,0.00070580386,0.00016087554],"domain_scores_gemma":[0.9952944,0.00059451343,0.00019125099,0.00048726093,0.0027055326,0.00072706357],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015402018,0.0012871056,0.0009974597,0.0029810767,0.0031285894,0.0052489154,0.0018390049,0.0017475904,0.5880448],"category_scores_gemma":[0.00952757,0.00040363122,0.0008451025,0.0025439372,0.0008407027,0.004086849,0.0030279472,0.0031971692,0.4186926],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038507256,0.000021699097,0.0001277502,0.00015207622,0.0000027735111,0.000070079754,0.00013366558,0.00005865029,0.000120375866,0.004394057,0.9569419,0.037938464],"study_design_scores_gemma":[0.0000039134165,0.00001306843,0.0002208503,0.000101099526,0.0000016850604,0.00006406384,0.00011811225,0.00002139131,0.00006931942,0.0013085216,0.9980732,0.000004711603],"about_ca_topic_score_codex":0.003414234,"about_ca_topic_score_gemma":0.0033276663,"teacher_disagreement_score":0.41195518,"about_ca_system_score_codex":0.0022811226,"about_ca_system_score_gemma":0.0033329686,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4210917212","doi":"10.1017/s0269964821000103","title":"SIGNATURES OF MULTI-STATE SYSTEMS BASED ON A SERIES/PARALLEL/RECURRENT STRUCTURE OF MODULES","year":2021,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"State (computer science); Signature (topology); Series (stratigraphy); Computer science; Series and parallel circuits; Connection (principal bundle); Binary number; Theoretical computer science; Algorithm; Mathematics; Arithmetic; Physics","score_opus":0.01544712008188571,"score_gpt":0.23918166455672604,"score_spread":0.22373454447484034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210917212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3064873,0.00026940147,0.68397886,0.0002453064,0.000080377475,0.000057221692,0.00014847217,0.00044597292,0.008287039],"genre_scores_gemma":[0.9591119,0.00007316996,0.03845616,0.0000578117,0.000038496062,0.00003918912,0.00008841947,0.000039271352,0.0020953913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999405,0.00012403562,0.00005192108,0.00015188656,0.00019040598,0.00007667147],"domain_scores_gemma":[0.99814117,0.00041545683,0.00061034865,0.00030482732,0.00035789557,0.00017027499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005921967,0.0002907468,0.0004825433,0.0008679612,0.0003722945,0.0010299481,0.0006362482,0.00054221856,0.0025265408],"category_scores_gemma":[0.0024400093,0.00018894691,0.00047469453,0.0005285935,0.0010704864,0.0020010618,0.0009889347,0.00072718505,0.00033469475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018475961,0.00006273816,0.0029643576,0.000110563684,0.00006565441,0.00073480274,0.00045041216,0.051708512,0.03760094,0.87074584,0.0006743495,0.03469702],"study_design_scores_gemma":[0.00002740722,0.0001397897,0.0019395518,0.000028936423,0.00004275951,0.0007238071,0.00012664309,0.58264303,0.017025387,0.39386997,0.0033637935,0.0000688982],"about_ca_topic_score_codex":0.0002693158,"about_ca_topic_score_gemma":0.00025767012,"teacher_disagreement_score":0.0025265408,"about_ca_system_score_codex":0.00043793558,"about_ca_system_score_gemma":0.0003541768,"threshold_uncertainty_score":0.008452177},"labels":[],"label_agreement":null},{"id":"W4220961998","doi":"10.1016/s0735-1097(22)01268-2","title":"USING ARTIFICIAL INTELLIGENCE TO ACCURATELY PREDICT ALL-CAUSE DEATH OR READMISSION FOR DECOMPENSATED HEART FAILURE WITH PRESERVED EJECTION FRACTION","year":2022,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Medicine; Ejection fraction; Acute decompensated heart failure; Heart failure; Cardiology; Internal medicine; Fraction (chemistry); Heart failure with preserved ejection fraction; Intensive care medicine","score_opus":0.10118800933791429,"score_gpt":0.36149178700507123,"score_spread":0.26030377766715695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220961998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9671834,0.0010220387,0.02271956,0.0010053475,0.000302221,0.0000767458,0.0007140882,0.00027552748,0.00670109],"genre_scores_gemma":[0.9953585,0.00015744973,0.0034909155,0.00011944527,0.00005407078,0.000011344827,0.0004488822,0.0000037983712,0.00035552436],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996438,0.00012328586,0.000053521253,0.000060377446,0.00007896972,0.00003996891],"domain_scores_gemma":[0.9983005,0.0011666588,0.00016081378,0.00007879856,0.00021528374,0.000078056546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009575297,0.00048127986,0.0004162254,0.0009883794,0.00020430773,0.0012797682,0.00028370856,0.00048528047,0.00067842094],"category_scores_gemma":[0.005448882,0.000110386005,0.00040147224,0.00037393393,0.00016612207,0.00044957208,0.0003134936,0.0005703005,0.00021205655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015327963,0.0015013535,0.69496447,0.00012350551,0.00086111366,0.00038859525,0.00012061724,0.07249597,0.0035192522,0.00073449046,0.0046081985,0.21914963],"study_design_scores_gemma":[0.000074829775,0.00066628435,0.121193655,0.00004800391,0.00028427775,0.00020116771,0.00014597308,0.8714748,0.0019869383,0.0028903345,0.0010035984,0.000030161102],"about_ca_topic_score_codex":0.003136942,"about_ca_topic_score_gemma":0.0032730415,"teacher_disagreement_score":0.003136942,"about_ca_system_score_codex":0.00025463154,"about_ca_system_score_gemma":0.00044448988,"threshold_uncertainty_score":0.006237328},"labels":[],"label_agreement":null},{"id":"W4231355435","doi":"10.32920/ryerson.14657349.v1","title":"A Simulation Algorithm Capable Of Modelling Spatial Impact Points From The Neutralization Of An Improvised Explosive Device","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Explosive material; Computer science; Neutralization; Container (type theory); Spatial dispersion; Simulation; Engineering; Mechanical engineering","score_opus":0.03297168225424865,"score_gpt":0.30010826122915485,"score_spread":0.26713657897490617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231355435","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018463507,0.00004990037,0.9766377,0.00010753346,0.00004014386,0.000065563836,0.00014355751,0.0011033951,0.0033887106],"genre_scores_gemma":[0.37674233,0.00019098437,0.61686486,0.00008211175,0.000025979261,0.00033524635,0.0006670486,0.00026304906,0.004828457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978954,0.00005344999,0.00001623988,0.000051774983,0.000065256005,0.000023764318],"domain_scores_gemma":[0.9992042,0.00043541437,0.00007550016,0.000079632504,0.00016714809,0.000038175676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047229335,0.00074097473,0.0005446009,0.00048525483,0.0005501206,0.0010895971,0.0013278567,0.001411775,0.0039504133],"category_scores_gemma":[0.0023500815,0.00046345234,0.00070523686,0.00046612747,0.00056194374,0.00086824194,0.00065501116,0.0008636868,0.00071463187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025530137,0.000015278638,0.00046657893,0.000016750035,0.000010020644,0.000022703249,0.000025828664,0.9896798,0.00096814084,0.0027949985,0.00027072892,0.0057035238],"study_design_scores_gemma":[0.000003846473,0.000003904018,0.000020766962,0.000001983419,0.0000016824072,0.000004118434,0.0000028315897,0.9987128,0.00030964662,0.00050918286,0.0004278651,0.0000014426998],"about_ca_topic_score_codex":0.011415902,"about_ca_topic_score_gemma":0.0068865004,"teacher_disagreement_score":0.011415902,"about_ca_system_score_codex":0.00074717554,"about_ca_system_score_gemma":0.0015461324,"threshold_uncertainty_score":0.02269888},"labels":[],"label_agreement":null},{"id":"W4231569191","doi":"10.1145/1016998.1017015","title":"Calendar","year":2004,"lang":"en","type":"article","venue":"Queue","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Summit; Computer science; Operating system; Kernel (algebra); Linux kernel; Programming language; Geography; Mathematics; Discrete mathematics; Cartography","score_opus":0.005209048288521319,"score_gpt":0.21428641659865483,"score_spread":0.20907736831013352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231569191","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018780181,0.0030626203,0.0033734124,0.005925386,0.013900551,0.00062487816,0.06458949,0.0145705715,0.89207494],"genre_scores_gemma":[0.0021355385,0.00090508163,0.0005672078,0.0003475669,0.00049454207,0.000067508685,0.016085362,0.0011188798,0.9782783],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993451,0.000028010927,0.000029112425,0.000084561805,0.00034694746,0.00016629504],"domain_scores_gemma":[0.9949249,0.00015683472,0.00015713355,0.00041140476,0.0029409386,0.0014088196],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011037393,0.0010393715,0.0009038708,0.0027462414,0.0033838477,0.0055851163,0.0015483223,0.0011095606,0.64865154],"category_scores_gemma":[0.0039399387,0.0006927757,0.0004986317,0.0031576483,0.00046362905,0.0022624377,0.0019686953,0.0015519365,0.5301881],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027307977,0.000009596603,0.00010233936,0.00002370907,6.8452385e-7,0.00000809973,0.000014833659,0.000014616224,0.0000628719,0.00044310812,0.98670596,0.012586925],"study_design_scores_gemma":[0.000008803989,0.000005148949,0.00047253742,0.000014702246,0.0000011497298,0.000009787039,0.000028252,0.000035254157,0.00005007961,0.00009916085,0.99926966,0.000005456151],"about_ca_topic_score_codex":0.18000808,"about_ca_topic_score_gemma":0.30646756,"teacher_disagreement_score":0.35134846,"about_ca_system_score_codex":0.003108333,"about_ca_system_score_gemma":0.0059695262,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4232214503","doi":"10.1007/978-981-10-8569-7","title":"Advances in Machine Learning and Data Science","year":2018,"lang":"en","type":"book","venue":"Advances in intelligent systems and computing","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Computer science; Artificial intelligence; Data science; Machine learning","score_opus":0.019750011686960642,"score_gpt":0.3184508885770586,"score_spread":0.29870087689009794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232214503","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013821253,0.25054193,0.16738959,0.009836332,0.029849824,0.0001709917,0.0023172416,0.0034280266,0.535084],"genre_scores_gemma":[0.005696363,0.115087174,0.04238466,0.002704548,0.009930863,0.00018609609,0.002121822,0.0012639095,0.82062453],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998955,0.00007858688,0.000051386538,0.00012114867,0.0007549058,0.000038892646],"domain_scores_gemma":[0.9981148,0.00092337327,0.00007778679,0.0002417688,0.0005049785,0.0001372375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082412985,0.0016388819,0.0016332748,0.0030570796,0.00055078906,0.0034981954,0.0012435628,0.00087337056,0.06920876],"category_scores_gemma":[0.0035168168,0.00054625905,0.00055985874,0.0054625426,0.0008992579,0.005305276,0.0020557079,0.0033592018,0.058334444],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020686057,0.000034537137,0.000075382166,0.00065909117,0.000018975552,0.00003570377,0.000043789754,0.0009788112,0.0007634495,0.03596277,0.4852404,0.4761664],"study_design_scores_gemma":[0.0000038163885,0.000011511291,0.00016751602,0.00020735734,0.0000082933675,0.000105156665,0.000016758833,0.0013484575,0.00034893083,0.03606845,0.9617042,0.000009671465],"about_ca_topic_score_codex":0.00061963015,"about_ca_topic_score_gemma":0.0012872552,"teacher_disagreement_score":0.06920876,"about_ca_system_score_codex":0.0008864694,"about_ca_system_score_gemma":0.0015147539,"threshold_uncertainty_score":0.23152637},"labels":[],"label_agreement":null},{"id":"W4233036817","doi":"10.1088/1742-6596/1237/1/011001","title":"2019 4th International Conference on Intelligent Computing and Signal Processing (ICSP 2019)","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Signal processing; Field (mathematics); Computer science; SIGNAL (programming language); Service (business); Library science; Telecommunications; Artificial intelligence","score_opus":0.026377857545604597,"score_gpt":0.28365736314120826,"score_spread":0.25727950559560364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233036817","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007949818,0.07233857,0.12933254,0.032755982,0.2860046,0.0011483788,0.0067765573,0.010316235,0.4533774],"genre_scores_gemma":[0.025601622,0.040498417,0.03487734,0.005913872,0.04104096,0.000619175,0.017975297,0.00280389,0.8306694],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978181,0.00027957952,0.00017610962,0.00039896867,0.0010186792,0.00030843506],"domain_scores_gemma":[0.9952099,0.00052957056,0.00014191221,0.00040740255,0.0026330515,0.001078269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024050348,0.0013262565,0.0012602564,0.0029241517,0.0013625122,0.0072935675,0.0021529347,0.0026442257,0.21900065],"category_scores_gemma":[0.0044402312,0.00039554315,0.0009129016,0.0021965946,0.0007920789,0.004650671,0.0025086268,0.0035634434,0.17810081],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010795906,0.000058828187,0.00028785158,0.00038776282,0.000024763094,0.00014003423,0.000062022875,0.00036456957,0.0027829031,0.0061611743,0.82338065,0.1662415],"study_design_scores_gemma":[0.000009218681,0.000076308854,0.00055165804,0.00018939018,0.000016893111,0.0002928958,0.0000815066,0.0016682986,0.001104897,0.0042114872,0.9917685,0.000029069446],"about_ca_topic_score_codex":0.0013869539,"about_ca_topic_score_gemma":0.0014999585,"teacher_disagreement_score":0.21900065,"about_ca_system_score_codex":0.0012161331,"about_ca_system_score_gemma":0.0023965565,"threshold_uncertainty_score":0.73263013},"labels":[],"label_agreement":null},{"id":"W4236844453","doi":"10.4018/9781599041681.ch020","title":"An Approach for Intentional Modeling of Web Services Security Risk Assessment","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer security; Web application security; Web service; World Wide Web; Web development","score_opus":0.022435423763421572,"score_gpt":0.2759302774863841,"score_spread":0.25349485372296254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236844453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011588996,0.00028286097,0.9775454,0.0010422356,0.00006202844,0.00011717438,0.00010492831,0.00020230497,0.019484203],"genre_scores_gemma":[0.060981568,0.0011951912,0.9250863,0.00039294906,0.00009412883,0.000711642,0.0003775957,0.00016106915,0.010999514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978229,0.0009032722,0.00018588186,0.00023147164,0.0007091319,0.0001472942],"domain_scores_gemma":[0.9984768,0.0007866812,0.00011268328,0.00027439455,0.0002645065,0.00008488852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00340097,0.0014343433,0.0006034703,0.0037091025,0.0018760203,0.0051316833,0.0024894397,0.0023012387,0.0065654065],"category_scores_gemma":[0.0039279843,0.00097176037,0.0033975828,0.002635666,0.002981611,0.0058685816,0.004449003,0.004184558,0.00164544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004080405,0.000027070106,0.00018636542,0.000055316308,0.000016748718,0.00013382622,0.000710666,0.009076448,0.00044182435,0.9767287,0.0013807386,0.011238293],"study_design_scores_gemma":[0.000009239848,0.000016507083,0.00014095652,0.00020691226,0.0000361705,0.00021199448,0.00055710215,0.12761101,0.00077787,0.79122335,0.07917654,0.000032228316],"about_ca_topic_score_codex":0.0068728365,"about_ca_topic_score_gemma":0.007393531,"teacher_disagreement_score":0.0068728365,"about_ca_system_score_codex":0.003125618,"about_ca_system_score_gemma":0.0028774408,"threshold_uncertainty_score":0.022678077},"labels":[],"label_agreement":null},{"id":"W4244002528","doi":"10.2523/96031-ms","title":"Automated Process Control System for Steam-Injection Processes","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPE Annual Technical Conference and Exhibition","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Process (computing); Process control; Computer science; Control system; Process engineering; Steam injection; Petroleum engineering; Engineering; Operating system; Electrical engineering","score_opus":0.012749193989010778,"score_gpt":0.2657056654802014,"score_spread":0.25295647149119066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244002528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061871033,0.0004018922,0.888879,0.00018945882,0.0003810005,0.0009647626,0.0007094763,0.036905967,0.009697332],"genre_scores_gemma":[0.8788777,0.00016447646,0.10721075,0.00013622035,0.00011949303,0.0008137813,0.00083918165,0.00020183179,0.01163658],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897265,0.00012485954,0.00006034278,0.00025654328,0.0005252826,0.000060322356],"domain_scores_gemma":[0.9991167,0.00018359923,0.000098363875,0.00013455191,0.00043088812,0.0000358561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006918774,0.00062016037,0.00064582773,0.0006171124,0.00057103124,0.0010854339,0.0011464101,0.0005928893,0.007202126],"category_scores_gemma":[0.0015582547,0.00020059176,0.00030216167,0.0004828502,0.00035104548,0.0005093162,0.0003926067,0.0006166268,0.0016800449],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019113189,0.001002895,0.0053112064,0.00080239406,0.00016504781,0.0004201356,0.00056552736,0.122450665,0.17506705,0.010769974,0.023788592,0.6577451],"study_design_scores_gemma":[0.00027155087,0.0006344464,0.0035399438,0.00004828701,0.00008228598,0.00019542444,0.000028895398,0.8906763,0.06822104,0.00185321,0.03437642,0.000072161216],"about_ca_topic_score_codex":0.0060706967,"about_ca_topic_score_gemma":0.0034085813,"teacher_disagreement_score":0.007202126,"about_ca_system_score_codex":0.0007877617,"about_ca_system_score_gemma":0.0012394594,"threshold_uncertainty_score":0.024093509},"labels":[],"label_agreement":null},{"id":"W4246945255","doi":"10.1002/9781118164587.ch2","title":"Real Numbers","year":2009,"lang":"en","type":"other","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of British Columbia; University of Toronto","funders":"","keywords":"Real number; Completeness (order theory); Mathematics; Real analysis; Series (stratigraphy); Combinatorics; Discrete mathematics; Biology","score_opus":0.0062641149742986915,"score_gpt":0.24715753317040273,"score_spread":0.24089341819610405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246945255","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012906403,0.51197714,0.04082126,0.005625958,0.009909506,0.000083590116,0.0010006387,0.00039293952,0.4288983],"genre_scores_gemma":[0.029370392,0.60905886,0.0568207,0.0046207923,0.015472451,0.00024549785,0.0021503235,0.00054763065,0.28171337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985637,0.00021577407,0.00015505323,0.00028243393,0.0007310863,0.000051808103],"domain_scores_gemma":[0.99838567,0.000789826,0.000109262975,0.00020550798,0.00046226685,0.000047454996],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001061634,0.0008891718,0.00085746596,0.0031715655,0.00071612897,0.0033629604,0.0008615695,0.0007190792,0.039293747],"category_scores_gemma":[0.0057509392,0.0003706729,0.00047983803,0.005081187,0.0021832183,0.006319375,0.0012568089,0.0025864434,0.01938509],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001906639,0.000013135882,0.00010910357,0.0011650701,0.00001138186,0.000040169547,0.00026571512,0.00031070964,0.0005998805,0.65286744,0.11240073,0.2321976],"study_design_scores_gemma":[0.0000017234529,0.000007466189,0.00011885108,0.00026645596,0.0000026595633,0.00010789528,0.000040156425,0.00006607549,0.00012992109,0.067221634,0.93203175,0.000005401853],"about_ca_topic_score_codex":0.0010878941,"about_ca_topic_score_gemma":0.0012392984,"teacher_disagreement_score":0.96070623,"about_ca_system_score_codex":0.0015909685,"about_ca_system_score_gemma":0.0012083523,"threshold_uncertainty_score":0.13145065},"labels":[],"label_agreement":null},{"id":"W4247385531","doi":"10.1039/d0ta90062a","title":"Correction: Machine-learning-assisted screening of pure-silica zeolites for effective removal of linear siloxanes and derivatives","year":2020,"lang":"en","type":"article","venue":"Journal of Materials Chemistry A","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Materials science; Chemistry; Polymer science","score_opus":0.014107292973882066,"score_gpt":0.2561151621741628,"score_spread":0.24200786920028072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247385531","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003737429,0.0010854576,0.0021956142,0.02308212,0.96667683,0.000044235272,0.003022425,0.0017408421,0.0017788105],"genre_scores_gemma":[0.054508097,0.014021795,0.0364086,0.11627297,0.31400368,0.0009451444,0.030129941,0.010059125,0.42365065],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99137235,0.0008270406,0.0014347421,0.001094504,0.004354675,0.0009167378],"domain_scores_gemma":[0.9539406,0.008187231,0.0023079591,0.003189178,0.03022858,0.002146599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004393865,0.003202404,0.0040474525,0.005270563,0.003917852,0.0039625787,0.0053194086,0.007764778,0.096014455],"category_scores_gemma":[0.063172996,0.0023341195,0.003212231,0.003919291,0.0027852617,0.0029635613,0.0032204124,0.009167426,0.05694661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007228367,0.000012227373,0.00007113202,0.0003177117,0.000027919501,0.0002024041,0.000027246386,0.00009880243,0.00069321686,0.00071732485,0.9921375,0.0056223418],"study_design_scores_gemma":[0.00011045165,0.000054054904,0.0014186312,0.00022304145,0.00006702248,0.0006787855,0.00006147344,0.00064733485,0.0036315294,0.0018085347,0.9912067,0.00009246853],"about_ca_topic_score_codex":0.0085468115,"about_ca_topic_score_gemma":0.011842537,"teacher_disagreement_score":0.096014455,"about_ca_system_score_codex":0.0042723077,"about_ca_system_score_gemma":0.006070685,"threshold_uncertainty_score":0.32120037},"labels":[],"label_agreement":null},{"id":"W4247908489","doi":"10.32920/ryerson.14652726.v1","title":"Game-based threat assessment tool for improvised explosive device neutralization training","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Acronym; Explosive material; Computer science; Key (lock); Computer security; Human–computer interaction; Risk analysis (engineering); Simulation; Medicine","score_opus":0.052786275648430726,"score_gpt":0.33202451036052905,"score_spread":0.2792382347120983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247908489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06789002,0.000149183,0.886023,0.00029688707,0.00019186136,0.0016683128,0.0014429784,0.032861736,0.0094759995],"genre_scores_gemma":[0.4307017,0.00020388242,0.5489105,0.0003374202,0.000041622607,0.0016159744,0.002284269,0.0012968112,0.014607827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999501,0.00012963272,0.000044921293,0.000072664,0.00020374343,0.000048049962],"domain_scores_gemma":[0.9988444,0.0006531483,0.00006730571,0.00010235633,0.00020179016,0.00013101229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007375168,0.0013680767,0.0005110372,0.00097191124,0.000258206,0.0012191272,0.0020394886,0.0010128963,0.01186069],"category_scores_gemma":[0.003527898,0.00035636354,0.00049087027,0.000204327,0.00034784485,0.0011299987,0.0015288178,0.0008772818,0.0021197733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035075815,0.00332382,0.011359778,0.0015918809,0.0003477281,0.0027345875,0.002613219,0.1069151,0.1766355,0.019790506,0.05670539,0.61447483],"study_design_scores_gemma":[0.00024273377,0.0008779493,0.005951489,0.00017787494,0.00009561224,0.00082559633,0.00034790457,0.90410674,0.037677422,0.0089524565,0.040583752,0.00016046035],"about_ca_topic_score_codex":0.0015282895,"about_ca_topic_score_gemma":0.0022280344,"teacher_disagreement_score":0.01186069,"about_ca_system_score_codex":0.00040796943,"about_ca_system_score_gemma":0.00044599763,"threshold_uncertainty_score":0.039677978},"labels":[],"label_agreement":null},{"id":"W4249644145","doi":"10.32920/ryerson.14652726","title":"Game-based threat assessment tool for improvised explosive device neutralization training","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Acronym; Explosive material; Computer science; Key (lock); Computer security; Human–computer interaction; Risk analysis (engineering); Simulation; Medicine","score_opus":0.052786275648430726,"score_gpt":0.33202451036052905,"score_spread":0.2792382347120983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249644145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06789002,0.000149183,0.886023,0.00029688707,0.00019186136,0.0016683128,0.0014429784,0.032861736,0.0094759995],"genre_scores_gemma":[0.4307017,0.00020388242,0.5489105,0.0003374202,0.000041622607,0.0016159744,0.002284269,0.0012968112,0.014607827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999501,0.00012963272,0.000044921293,0.000072664,0.00020374343,0.000048049962],"domain_scores_gemma":[0.9988444,0.0006531483,0.00006730571,0.00010235633,0.00020179016,0.00013101229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007375168,0.0013680767,0.0005110372,0.00097191124,0.000258206,0.0012191272,0.0020394886,0.0010128963,0.01186069],"category_scores_gemma":[0.003527898,0.00035636354,0.00049087027,0.000204327,0.00034784485,0.0011299987,0.0015288178,0.0008772818,0.0021197733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035075815,0.00332382,0.011359778,0.0015918809,0.0003477281,0.0027345875,0.002613219,0.1069151,0.1766355,0.019790506,0.05670539,0.61447483],"study_design_scores_gemma":[0.00024273377,0.0008779493,0.005951489,0.00017787494,0.00009561224,0.00082559633,0.00034790457,0.90410674,0.037677422,0.0089524565,0.040583752,0.00016046035],"about_ca_topic_score_codex":0.0015282895,"about_ca_topic_score_gemma":0.0022280344,"teacher_disagreement_score":0.01186069,"about_ca_system_score_codex":0.00040796943,"about_ca_system_score_gemma":0.00044599763,"threshold_uncertainty_score":0.039677978},"labels":[],"label_agreement":null},{"id":"W4252646128","doi":"10.1088/1742-6596/1267/1/011001","title":"2019 3rd International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2019)","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prosperity; Automation; Control (management); Engineering ethics; Engineering management; Artificial intelligence; Computer science; Library science; Engineering; Operations research; Political science","score_opus":0.022381006601862152,"score_gpt":0.27385809546936385,"score_spread":0.2514770888675017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252646128","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008201027,0.08406538,0.08630666,0.033080287,0.23822005,0.0013330033,0.005497069,0.0071749426,0.53612155],"genre_scores_gemma":[0.020545907,0.03992646,0.026435904,0.0053494074,0.016050044,0.000493031,0.014158117,0.001393314,0.87564784],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99750805,0.00034702974,0.00019435014,0.00041946376,0.0011917647,0.00033936376],"domain_scores_gemma":[0.99631506,0.00031882318,0.0001238127,0.00023982752,0.0021449702,0.00085758226],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002716237,0.0012558576,0.0012749424,0.0025433335,0.0013965108,0.008343551,0.0019673991,0.0029906498,0.15037002],"category_scores_gemma":[0.004165415,0.0004074286,0.0011380152,0.0019603027,0.0008129052,0.0046801562,0.0023375421,0.0038633563,0.13706526],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011555599,0.000083949795,0.00048424306,0.0005202537,0.000029893197,0.0001949934,0.000116173236,0.00047042358,0.0029491263,0.008037406,0.81723595,0.16976203],"study_design_scores_gemma":[0.0000070792407,0.0000544471,0.00060187234,0.00021781249,0.000018357932,0.00029443172,0.000121977515,0.001073327,0.0008125134,0.0022879927,0.9944833,0.000026929965],"about_ca_topic_score_codex":0.00265745,"about_ca_topic_score_gemma":0.0026470062,"teacher_disagreement_score":0.84963,"about_ca_system_score_codex":0.0015201276,"about_ca_system_score_gemma":0.003226443,"threshold_uncertainty_score":0.5030378},"labels":[],"label_agreement":null},{"id":"W4255935670","doi":"10.4018/9781615209675.ch116","title":"An Approach for Intentional Modeling of Web Services Security Risk Assessment","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Identification (biology); Stakeholder; Process (computing); Web application security; Domain (mathematical analysis); Computer security; Web service; Knowledge management; Risk analysis (engineering); World Wide Web; Business; Web development; Political science; Public relations","score_opus":0.022435423763421572,"score_gpt":0.2759302774863841,"score_spread":0.25349485372296254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255935670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011588996,0.00028286097,0.9775454,0.0010422356,0.00006202844,0.00011717438,0.00010492831,0.00020230497,0.019484203],"genre_scores_gemma":[0.060981568,0.0011951912,0.9250863,0.00039294906,0.00009412883,0.000711642,0.0003775957,0.00016106915,0.010999514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978229,0.0009032722,0.00018588186,0.00023147164,0.0007091319,0.0001472942],"domain_scores_gemma":[0.9984768,0.0007866812,0.00011268328,0.00027439455,0.0002645065,0.00008488852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00340097,0.0014343433,0.0006034703,0.0037091025,0.0018760203,0.0051316833,0.0024894397,0.0023012387,0.0065654065],"category_scores_gemma":[0.0039279843,0.00097176037,0.0033975828,0.002635666,0.002981611,0.0058685816,0.004449003,0.004184558,0.00164544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004080405,0.000027070106,0.00018636542,0.000055316308,0.000016748718,0.00013382622,0.000710666,0.009076448,0.00044182435,0.9767287,0.0013807386,0.011238293],"study_design_scores_gemma":[0.000009239848,0.000016507083,0.00014095652,0.00020691226,0.0000361705,0.00021199448,0.00055710215,0.12761101,0.00077787,0.79122335,0.07917654,0.000032228316],"about_ca_topic_score_codex":0.0068728365,"about_ca_topic_score_gemma":0.007393531,"teacher_disagreement_score":0.0068728365,"about_ca_system_score_codex":0.003125618,"about_ca_system_score_gemma":0.0028774408,"threshold_uncertainty_score":0.022678077},"labels":[],"label_agreement":null},{"id":"W4281294895","doi":"10.5281/zenodo.6571103","title":"The role of big brain science in the development of artificial intelligence technologies","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Big data; Cognitive science; Psychology; Data science; Computer science","score_opus":0.03770175602919563,"score_gpt":0.26271339607230465,"score_spread":0.22501164004310903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281294895","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03115674,0.1721973,0.29433304,0.1988609,0.0073413835,0.00023514839,0.00054055976,0.00081052614,0.2945244],"genre_scores_gemma":[0.6554207,0.11436061,0.18223222,0.014945116,0.008567382,0.0006246411,0.00035901775,0.00042501462,0.023065338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980848,0.0008431099,0.00008901852,0.00024431336,0.0005696259,0.00016913137],"domain_scores_gemma":[0.99218166,0.0054759434,0.0003022941,0.0008279502,0.0006940304,0.0005180521],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006735307,0.0006819691,0.0007623142,0.002027421,0.0014714341,0.0070994697,0.001079865,0.002489255,0.004538221],"category_scores_gemma":[0.01001827,0.00040039403,0.00053453364,0.001344138,0.00879219,0.01033489,0.0035959661,0.00532379,0.0010953852],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021239945,0.0000169806,0.00043002603,0.00020836748,0.000017124505,0.000057263962,0.0001606084,0.0009932889,0.0003391,0.9651429,0.005816674,0.026796376],"study_design_scores_gemma":[0.000007907133,0.00002366111,0.0004939827,0.00014619865,0.000005787311,0.00006871366,0.00007868698,0.0026635106,0.0003300671,0.9641134,0.03205343,0.000014658922],"about_ca_topic_score_codex":0.000929276,"about_ca_topic_score_gemma":0.00096745184,"teacher_disagreement_score":0.99852854,"about_ca_system_score_codex":0.0029058356,"about_ca_system_score_gemma":0.003411678,"threshold_uncertainty_score":0.035620153},"labels":[],"label_agreement":null},{"id":"W4302604365","doi":"10.5376/ijms.2016.06.0048.","title":"Polycyclic Aromatic Hydrocarbons (PAHs) in the Soil of West Qurna-2 Oil Field Southern Iraq","year":2016,"lang":"en","type":"article","venue":"International Journal of Marine Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Environmental science; Oil spill; Environmental chemistry; Oil field; Field (mathematics); Polycyclic aromatic hydrocarbon; Geology; Chemistry; Environmental protection; Paleontology","score_opus":0.009351432222335197,"score_gpt":0.26219378077026173,"score_spread":0.25284234854792653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302604365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99927765,0.000076050965,0.00009239047,0.000024822346,0.000003521165,0.000006139955,0.0002305366,0.0000029153869,0.00028593885],"genre_scores_gemma":[0.99882776,0.00016144868,0.00017154551,0.000027996772,0.0000074811114,0.000008297492,0.00024055048,0.0000016477102,0.00055320625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984205,0.00001281764,0.00001144982,0.000044717566,0.000044705117,0.000044246444],"domain_scores_gemma":[0.99981016,0.00002415609,0.00004693233,0.0000050833205,0.000079677215,0.000033913006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015736256,0.00034427227,0.00029494165,0.001143936,0.0010531663,0.0006155939,0.0004165312,0.00051249063,0.0006063886],"category_scores_gemma":[0.0001623169,0.00026500507,0.0002459989,0.0011823103,0.00057307375,0.00039453385,0.00039845592,0.00023816516,0.00030282786],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039932242,0.0002189957,0.8795587,0.00021371855,0.00009900277,0.0018135784,0.0027405638,0.0010135674,0.101702005,0.00013910818,0.00028021037,0.011821297],"study_design_scores_gemma":[0.000012659861,0.00012455204,0.988586,0.000020300013,0.00004002026,0.00042338448,0.0047359797,0.0010061922,0.0038915612,0.000051737243,0.0010949575,0.000012588695],"about_ca_topic_score_codex":0.090878054,"about_ca_topic_score_gemma":0.08843974,"teacher_disagreement_score":0.090878054,"about_ca_system_score_codex":0.00070276787,"about_ca_system_score_gemma":0.0010214404,"threshold_uncertainty_score":0.18069822},"labels":[],"label_agreement":null},{"id":"W4304890014","doi":"","title":"Computing the Longest Previous Factor","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Factor (programming language); Computer science; Programming language","score_opus":0.01572839630574599,"score_gpt":0.236697987371912,"score_spread":0.220969591066166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304890014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14435357,0.0036360885,0.79825234,0.0041978676,0.0022292314,0.00017802625,0.0038923954,0.009785886,0.033474553],"genre_scores_gemma":[0.48096222,0.001624456,0.47340465,0.0005873999,0.0014475428,0.00018228621,0.0070573916,0.002397635,0.032336533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751484,0.00026521663,0.00019097919,0.0009191806,0.0006476752,0.00046202555],"domain_scores_gemma":[0.9916432,0.0030346299,0.00044021333,0.0028063464,0.0013597258,0.0007159487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020040818,0.0018645932,0.0021379925,0.0038010983,0.0017756697,0.0041520796,0.0020589132,0.001457068,0.039082706],"category_scores_gemma":[0.018610703,0.0005465476,0.0017612627,0.0042233216,0.0018385996,0.0076853493,0.0023514861,0.0023215825,0.0131173385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036005531,0.0003634377,0.007254045,0.0008653181,0.00019015544,0.0005170247,0.00032094744,0.041158225,0.025535975,0.09240911,0.0713976,0.75638765],"study_design_scores_gemma":[0.00030826425,0.0006246576,0.0037403263,0.00023667525,0.00021214866,0.0009497716,0.00038533122,0.43231675,0.044299036,0.46265846,0.054113466,0.00015512721],"about_ca_topic_score_codex":0.0031594846,"about_ca_topic_score_gemma":0.0052862936,"teacher_disagreement_score":0.039082706,"about_ca_system_score_codex":0.0019055197,"about_ca_system_score_gemma":0.002317675,"threshold_uncertainty_score":0.1307447},"labels":[],"label_agreement":null},{"id":"W4308098987","doi":"10.32920/21493941.v1","title":"Multi-level Radiation Protection Mechanism Based on Self-Restoration of Partially Reconfigurable FPGA Devices","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Integrity Testing Laboratory (Canada); Toronto Metropolitan University","funders":"","keywords":"Field-programmable gate array; Computer science; Embedded system; Reconfigurable computing; Virtex; Fault tolerance; Distributed computing","score_opus":0.05071694587263912,"score_gpt":0.27916374019065354,"score_spread":0.22844679431801443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308098987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6543178,0.0014157348,0.33047834,0.00015008078,0.00017040946,0.00010695647,0.00009309428,0.0051669874,0.008100607],"genre_scores_gemma":[0.97442496,0.00010554447,0.023377566,0.000030063653,0.000011379534,0.00001685476,0.000034267105,0.000042505773,0.0019569146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986386,0.000018292483,0.000009779484,0.000033450764,0.00004357471,0.000030928517],"domain_scores_gemma":[0.9996989,0.00003442605,0.00008182595,0.00011871014,0.00004811253,0.000017947246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012144007,0.00023570505,0.00021936529,0.00029704458,0.00017498742,0.0003725766,0.00072433095,0.0002390214,0.0012967607],"category_scores_gemma":[0.00027074182,0.00011831634,0.00021821563,0.000110968256,0.0002351977,0.00039678943,0.00030557928,0.00019919965,0.00021872869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050615723,0.00010695547,0.0022085924,0.00027875134,0.000062276245,0.0005002818,0.00020035951,0.031473946,0.8110729,0.0059822095,0.0014227225,0.14618488],"study_design_scores_gemma":[0.000050950905,0.0010711317,0.004753721,0.000037363032,0.00007305302,0.001075765,0.00005836878,0.21233915,0.76476026,0.00121094,0.014531121,0.00003821644],"about_ca_topic_score_codex":0.00026143997,"about_ca_topic_score_gemma":0.00026120103,"teacher_disagreement_score":0.0012967607,"about_ca_system_score_codex":0.00020871747,"about_ca_system_score_gemma":0.00016523773,"threshold_uncertainty_score":0.0043380857},"labels":[],"label_agreement":null},{"id":"W4308098988","doi":"10.32920/21493941","title":"Multi-level Radiation Protection Mechanism Based on Self-Restoration of Partially Reconfigurable FPGA Devices","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Integrity Testing Laboratory (Canada); Toronto Metropolitan University","funders":"","keywords":"Field-programmable gate array; Computer science; Embedded system; Reconfigurable computing; Virtex; Fault tolerance; Computer hardware; Distributed computing","score_opus":0.05071694587263912,"score_gpt":0.27916374019065354,"score_spread":0.22844679431801443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308098988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6543178,0.0014157348,0.33047834,0.00015008078,0.00017040946,0.00010695647,0.00009309428,0.0051669874,0.008100607],"genre_scores_gemma":[0.97442496,0.00010554447,0.023377566,0.000030063653,0.000011379534,0.00001685476,0.000034267105,0.000042505773,0.0019569146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986386,0.000018292483,0.000009779484,0.000033450764,0.00004357471,0.000030928517],"domain_scores_gemma":[0.9996989,0.00003442605,0.00008182595,0.00011871014,0.00004811253,0.000017947246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012144007,0.00023570505,0.00021936529,0.00029704458,0.00017498742,0.0003725766,0.00072433095,0.0002390214,0.0012967607],"category_scores_gemma":[0.00027074182,0.00011831634,0.00021821563,0.000110968256,0.0002351977,0.00039678943,0.00030557928,0.00019919965,0.00021872869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050615723,0.00010695547,0.0022085924,0.00027875134,0.000062276245,0.0005002818,0.00020035951,0.031473946,0.8110729,0.0059822095,0.0014227225,0.14618488],"study_design_scores_gemma":[0.000050950905,0.0010711317,0.004753721,0.000037363032,0.00007305302,0.001075765,0.00005836878,0.21233915,0.76476026,0.00121094,0.014531121,0.00003821644],"about_ca_topic_score_codex":0.00026143997,"about_ca_topic_score_gemma":0.00026120103,"teacher_disagreement_score":0.0012967607,"about_ca_system_score_codex":0.00020871747,"about_ca_system_score_gemma":0.00016523773,"threshold_uncertainty_score":0.0043380857},"labels":[],"label_agreement":null},{"id":"W4311538227","doi":"","title":"Machine Learning for Computer Communications (Survey)","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.026273404997132804,"score_gpt":0.26442525581859344,"score_spread":0.23815185082146065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311538227","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016375862,0.93683,0.036241703,0.007004283,0.0035672446,0.000029837009,0.00036277613,0.00025684256,0.014069786],"genre_scores_gemma":[0.024428468,0.92363995,0.02034871,0.0021773786,0.011077079,0.0000743135,0.00089379394,0.00015348238,0.017206851],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999111,0.0002483562,0.00006976641,0.00021376245,0.00029653055,0.000060549737],"domain_scores_gemma":[0.9959943,0.0024956744,0.00014194065,0.0004026274,0.00083790976,0.00012757415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017040515,0.00084013777,0.0012937697,0.0022463356,0.0004326754,0.0023154335,0.0011152767,0.0013841605,0.013160538],"category_scores_gemma":[0.005009253,0.00048107156,0.00047203325,0.006122938,0.00095894944,0.00382635,0.0011992451,0.0022553299,0.0070870975],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003455229,0.00008540592,0.0008028865,0.0020667375,0.00006416,0.000026315567,0.000033950913,0.0027658285,0.0002987713,0.023961307,0.14746739,0.8223927],"study_design_scores_gemma":[0.000018841278,0.000102098704,0.002666782,0.001544059,0.00007855281,0.00048713072,0.00008953986,0.012501715,0.0006845531,0.06439616,0.9173899,0.000040658328],"about_ca_topic_score_codex":0.0022726732,"about_ca_topic_score_gemma":0.002024336,"teacher_disagreement_score":0.013160538,"about_ca_system_score_codex":0.0011595666,"about_ca_system_score_gemma":0.0014548843,"threshold_uncertainty_score":0.044026375},"labels":[],"label_agreement":null},{"id":"W4312351785","doi":"10.1130/abs/2022am-383088","title":"BIG DATA ANALYSIS OF WASTEWATER PRODUCTION FROM CONVENTIONAL AND UNCONVENTIONAL HYDROCARBON RECOVERY PROCESSES","year":2022,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Production (economics); Petroleum engineering; Environmental science; Unconventional oil; Big data; Wastewater; Computer science; Waste management; Geology; Engineering; Environmental engineering; Data mining; Fossil fuel; Economics","score_opus":0.033483181409088174,"score_gpt":0.2518464463232239,"score_spread":0.2183632649141357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312351785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98615825,0.00017838734,0.0018509564,0.00039391022,0.000050496295,0.000041625866,0.010022162,0.00029036147,0.0010137995],"genre_scores_gemma":[0.9818044,0.00015514033,0.0030043821,0.00006536204,0.000029347018,0.0000465615,0.014208314,0.00003111133,0.0006553506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999463,0.00008510861,0.00005246666,0.00010213735,0.0002037487,0.00009360457],"domain_scores_gemma":[0.9982778,0.0006999811,0.0001724142,0.0001937374,0.00051495666,0.00014115493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006914157,0.000638658,0.0005511259,0.0018152731,0.000611421,0.0009332026,0.0005373258,0.0007386446,0.00077022926],"category_scores_gemma":[0.0012915228,0.00022536048,0.0010594932,0.0018772894,0.00046937587,0.0010201465,0.0007087688,0.0006052921,0.00024913394],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062445435,0.0027853025,0.5417979,0.001388885,0.0011381354,0.0016744799,0.00083902077,0.16892424,0.08964723,0.0033745805,0.023584891,0.15860085],"study_design_scores_gemma":[0.00016041518,0.00081197487,0.60029054,0.00007692622,0.0002834099,0.00027712382,0.0025533885,0.32512507,0.058010403,0.004256796,0.008027184,0.00012673037],"about_ca_topic_score_codex":0.009163589,"about_ca_topic_score_gemma":0.012626471,"teacher_disagreement_score":0.009163589,"about_ca_system_score_codex":0.00080615503,"about_ca_system_score_gemma":0.0009204654,"threshold_uncertainty_score":0.018220484},"labels":[],"label_agreement":null},{"id":"W4313342934","doi":"10.1007/978-3-031-23236-7","title":"Optimization, Learning Algorithms and Applications","year":2022,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Uniwersytet Opolski; Universidade Tecnológica Federal do Paraná; Slovenská technická univerzita v Bratislave; Kauno Technologijos Universitetas; Instituto Politécnico de Bragança; Technische Universität Wien; Universidade de Trás-os-Montes e Alto Douro; Hanzehogeschool Groningen; Université de Sherbrooke; Universidade do Minho; Politechnika Poznańska; Università degli Studi di Genova; Universidad de León; Politechnika Opolska; Universitatea Tehnică „Gheorghe Asachi” din Iaşi; Universidade do Porto; Université de Lorraine","keywords":"Computer science; Artificial intelligence; Information retrieval; Machine learning; Algorithm","score_opus":0.017893685971174567,"score_gpt":0.2806644264973409,"score_spread":0.26277074052616634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313342934","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014958025,0.23933287,0.38123804,0.004551118,0.008772518,0.00011979488,0.0011027032,0.0019538687,0.3614333],"genre_scores_gemma":[0.01805945,0.12335906,0.15621912,0.0021061357,0.008436506,0.00047682697,0.0012725235,0.0014488309,0.6886215],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941266,0.00009683396,0.000029732684,0.00010600044,0.0003304684,0.000024238036],"domain_scores_gemma":[0.99931574,0.0003944524,0.000029764296,0.00010083853,0.00012919,0.000030015482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006471614,0.0020512366,0.0025588218,0.0015802342,0.0005046831,0.0033017837,0.0012306648,0.0014997785,0.042205065],"category_scores_gemma":[0.0023472335,0.00071202044,0.0007500019,0.0049031693,0.0011642688,0.0026198693,0.0014738209,0.0029581953,0.026021805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034598546,0.00006735872,0.00013329691,0.001076895,0.00005601585,0.00005153561,0.000054149965,0.016089177,0.0012494519,0.08409234,0.37859166,0.5185034],"study_design_scores_gemma":[0.000021774922,0.000059060185,0.0007614993,0.00040136167,0.00004139487,0.00025136664,0.000051597082,0.0393431,0.0006114163,0.23117414,0.72724926,0.000034048604],"about_ca_topic_score_codex":0.0015938809,"about_ca_topic_score_gemma":0.0025921369,"teacher_disagreement_score":0.042205065,"about_ca_system_score_codex":0.0008758494,"about_ca_system_score_gemma":0.00095751695,"threshold_uncertainty_score":0.14118999},"labels":[],"label_agreement":null},{"id":"W4317677109","doi":"","title":"special issue : Discrete Models of Complex Systems: recent trends and analytical challenges","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"","keywords":"Computer science; Data science; Statistical physics; Management science; Engineering; Physics","score_opus":0.042046910395122504,"score_gpt":0.27488594699443825,"score_spread":0.23283903659931574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317677109","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071579213,0.20429139,0.056914277,0.07689962,0.5815189,0.00016599502,0.001988706,0.001068681,0.06999454],"genre_scores_gemma":[0.039222386,0.13670187,0.008132884,0.007349705,0.71400255,0.00023835378,0.0035608741,0.0007915314,0.089999795],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979963,0.00050356303,0.00017290526,0.00058337394,0.0006186634,0.00012524625],"domain_scores_gemma":[0.9881931,0.006776062,0.00062031206,0.0010541936,0.0022867562,0.0010695388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029038345,0.0020412675,0.005122699,0.0022581406,0.0013513165,0.007416334,0.0023490186,0.0041903346,0.052801594],"category_scores_gemma":[0.008257052,0.00067535933,0.001427364,0.0034518437,0.002002302,0.004232146,0.0015724525,0.0046982854,0.015709141],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017093998,0.00017592187,0.000445257,0.0019301099,0.00013985483,0.00026031595,0.000087885346,0.0033167077,0.0006804222,0.077536754,0.8499024,0.065353505],"study_design_scores_gemma":[0.00010914681,0.00027040797,0.0018363198,0.0007017563,0.00017241304,0.0011165015,0.0001859978,0.027793027,0.0007338995,0.15441468,0.8125791,0.000086857],"about_ca_topic_score_codex":0.00054810947,"about_ca_topic_score_gemma":0.00068107113,"teacher_disagreement_score":0.052801594,"about_ca_system_score_codex":0.0018587797,"about_ca_system_score_gemma":0.0017519306,"threshold_uncertainty_score":0.1766389},"labels":[],"label_agreement":null},{"id":"W4319003275","doi":"10.3390/pr11020449","title":"Editorial for Special Issue on “Intelligent Technologies and Processes for Advanced Nuclear Power and Energy Engineering”","year":2023,"lang":"en","type":"article","venue":"Processes","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Nuclear power; Energy (signal processing); Systems engineering; Computer science; Power (physics); Engineering management; Engineering; Physics","score_opus":0.008116090285973916,"score_gpt":0.2451569493452665,"score_spread":0.2370408590592926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319003275","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004581396,0.0013827184,0.000117139745,0.013386458,0.9833191,0.000019587427,0.000053721807,0.000056239114,0.001619153],"genre_scores_gemma":[0.0003607177,0.001597757,0.00006986206,0.008080079,0.98027223,0.00001726016,0.000048494137,0.000044895532,0.009508695],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966456,0.000356373,0.00037175225,0.00065908744,0.0016815474,0.00028567947],"domain_scores_gemma":[0.987531,0.0031177546,0.0007748798,0.00034115967,0.00608059,0.0021546062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038799862,0.0032534036,0.003476086,0.002558408,0.002160216,0.0066200416,0.0026440886,0.008088373,0.030591514],"category_scores_gemma":[0.011597525,0.0009731455,0.0028021405,0.0009252751,0.0015538299,0.0034727643,0.0011395182,0.012706761,0.022076389],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074153315,0.000018043811,0.00003338965,0.0001806076,0.000015411377,0.00007177411,0.0000068450277,0.000022682463,0.00015419161,0.0002810311,0.9946384,0.0045033856],"study_design_scores_gemma":[0.00008323031,0.00007853477,0.00050717953,0.00023449796,0.00005253612,0.00022754063,0.000033320186,0.00022415485,0.00036718717,0.00084362,0.9973296,0.000018646066],"about_ca_topic_score_codex":0.00078721496,"about_ca_topic_score_gemma":0.0019978867,"teacher_disagreement_score":0.030591514,"about_ca_system_score_codex":0.0018359369,"about_ca_system_score_gemma":0.002076225,"threshold_uncertainty_score":0.10233885},"labels":[],"label_agreement":null},{"id":"W4319299149","doi":"10.1016/j.isatra.2023.01.031","title":"Special Issue Editorial: Advances in Computational Intelligence for Perception and Decision-Making for Autonomous Systems","year":2023,"lang":"en","type":"editorial","venue":"ISA Transactions","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Perception; Computer science; Cognitive science; Artificial intelligence; Management science; Data science; Psychology; Engineering; Neuroscience","score_opus":0.011307991799618245,"score_gpt":0.32114463908752605,"score_spread":0.30983664728790783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299149","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001976369,0.0027029621,0.00014529153,0.010785191,0.9849688,0.000016749726,0.000050686853,0.00004167383,0.0012688407],"genre_scores_gemma":[0.00021848011,0.001993582,0.00010415298,0.006387465,0.98312795,0.00002143423,0.000038521284,0.000040037386,0.008068454],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931878,0.0009960992,0.00066669064,0.000754061,0.0039673196,0.0004279445],"domain_scores_gemma":[0.9706614,0.010837694,0.0021284574,0.00077024684,0.01160616,0.003996171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073415684,0.005106401,0.006152869,0.0055633048,0.0037616447,0.012844138,0.0037052492,0.016444057,0.03552073],"category_scores_gemma":[0.025446858,0.0016964831,0.0037410425,0.0025189766,0.0028939657,0.0042119864,0.0020837272,0.017427094,0.024129562],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040525407,0.00001529068,0.000017853989,0.00014986604,0.00002151295,0.000058015816,0.0000040872064,0.00003377755,0.000060291935,0.00018311189,0.9959449,0.0034708737],"study_design_scores_gemma":[0.0001338326,0.000048241625,0.0003447843,0.0003784186,0.00008750855,0.00019320201,0.000028301942,0.00045597632,0.0001729376,0.0018677867,0.996258,0.00003101961],"about_ca_topic_score_codex":0.0014343973,"about_ca_topic_score_gemma":0.005162353,"teacher_disagreement_score":0.03552073,"about_ca_system_score_codex":0.0029901369,"about_ca_system_score_gemma":0.0034388262,"threshold_uncertainty_score":0.118828714},"labels":[],"label_agreement":null},{"id":"W4320806923","doi":"10.18287/2412-6179-co-933","title":"High-performance digital image filtering architectures in the residue number system based on the Winograd method","year":2022,"lang":"en","type":"article","venue":"Computer Optics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Higher Education of the Russian Federation; Centre de Recherches Mathématiques","keywords":"Field-programmable gate array; Computer science; Image processing; Digital image processing; Computer hardware; Residue number system; Digital image; Gate array; Filter (signal processing); Median filter; Embedded system; Image (mathematics); Computer engineering; Computer vision; Algorithm","score_opus":0.010067682054575356,"score_gpt":0.2341822714195747,"score_spread":0.22411458936499934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320806923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13426378,0.0010403477,0.85334516,0.0001495723,0.00007383141,0.00007551304,0.000059875067,0.00088972837,0.010102151],"genre_scores_gemma":[0.33339152,0.00055829214,0.66015655,0.000048178277,0.000016798649,0.000063983214,0.00008928747,0.00006864436,0.0056066466],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998318,0.00003230136,0.000010035567,0.00002958514,0.000077149,0.000019161884],"domain_scores_gemma":[0.99987257,0.000028594688,0.000018614055,0.000025324265,0.000049054128,0.000005822775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025951702,0.00036620133,0.0003059107,0.0004868553,0.00027927774,0.0007545574,0.0004439644,0.00027709865,0.0025748168],"category_scores_gemma":[0.0003642076,0.00013927916,0.00024484788,0.00042230214,0.000291826,0.001000963,0.00018803788,0.0003550016,0.00073461386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007079288,0.00013859376,0.0019666632,0.0004600116,0.000119539225,0.0002677655,0.00027695805,0.06257685,0.38557193,0.15912223,0.003440426,0.38535106],"study_design_scores_gemma":[0.00013307581,0.00066123356,0.0019527859,0.000073425035,0.000085305655,0.0007672121,0.00009412778,0.6391031,0.30201373,0.014922516,0.040091213,0.00010227495],"about_ca_topic_score_codex":0.0008776229,"about_ca_topic_score_gemma":0.0014252505,"teacher_disagreement_score":0.0025748168,"about_ca_system_score_codex":0.0004845996,"about_ca_system_score_gemma":0.00053814123,"threshold_uncertainty_score":0.008613646},"labels":[],"label_agreement":null},{"id":"W4322096360","doi":"10.1007/978-981-99-0651-2","title":"Proceedings of the International Conference on Aerospace System Science and Engineering 2022","year":2023,"lang":"en","type":"book","venue":"Lecture notes in electrical engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Shanghai Jiao Tong University; University of Toronto","keywords":"Aerospace; Aeronautics; Engineering; Systems engineering; Engineering ethics; Library science; Engineering management; Aerospace engineering; Computer science","score_opus":0.008216435999841848,"score_gpt":0.21676003121721085,"score_spread":0.208543595217369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322096360","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010514054,0.038365964,0.07392061,0.0095735835,0.07090893,0.00027773745,0.0019498174,0.0029344838,0.7915548],"genre_scores_gemma":[0.022169923,0.013533704,0.014347678,0.00075607165,0.004290813,0.000090351656,0.0033843473,0.00043068695,0.94099635],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993735,0.000090517504,0.000022158081,0.00007913629,0.00035196464,0.000082844796],"domain_scores_gemma":[0.9990583,0.00009299413,0.000021658465,0.00008827763,0.00051959004,0.00021917604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010740642,0.0011712633,0.0010527441,0.0009706753,0.0006309902,0.0037237015,0.0007699277,0.0010694207,0.12513682],"category_scores_gemma":[0.0008358282,0.0002398094,0.00045663357,0.001175607,0.00049612956,0.0011672142,0.0010416241,0.0014290975,0.07183381],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019643725,0.00012297712,0.00052845944,0.0001656654,0.000034425393,0.00012359922,0.000051900595,0.0011848089,0.004691047,0.007724834,0.7479534,0.23722243],"study_design_scores_gemma":[0.0000129181035,0.00009003119,0.0010162712,0.00008526356,0.000019792096,0.00017212646,0.00006988571,0.0038583812,0.0018521915,0.0020695615,0.99074006,0.000013553894],"about_ca_topic_score_codex":0.003666383,"about_ca_topic_score_gemma":0.0074288645,"teacher_disagreement_score":0.12513682,"about_ca_system_score_codex":0.000737722,"about_ca_system_score_gemma":0.0015978942,"threshold_uncertainty_score":0.4186244},"labels":[],"label_agreement":null},{"id":"W4322709458","doi":"10.1007/978-3-031-25252-5","title":"15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022","year":2023,"lang":"en","type":"book","venue":"Lecture notes in networks and systems","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Soft computing; Fuzzy logic; Computer science; Artificial intelligence","score_opus":0.0395232240037311,"score_gpt":0.29152082376245014,"score_spread":0.25199759975871905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322709458","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016055321,0.052265488,0.25142038,0.0077620177,0.093530014,0.00046377245,0.0025937206,0.006013736,0.56989557],"genre_scores_gemma":[0.047678504,0.02129303,0.11017683,0.0013965371,0.0060798707,0.0002652885,0.005415094,0.00095426425,0.8067406],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993349,0.000112661546,0.000045754794,0.000097366916,0.00031055868,0.00009877194],"domain_scores_gemma":[0.9988563,0.000116222815,0.000031943873,0.00013975737,0.0006591199,0.00019672948],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013442251,0.0009845599,0.0010558154,0.0015235078,0.00068497413,0.0030977407,0.0012038208,0.0014007571,0.08716887],"category_scores_gemma":[0.0012858197,0.00023726761,0.0009530281,0.0012560597,0.0006884322,0.0016896116,0.0016469675,0.0014707953,0.037544314],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024747188,0.00021561992,0.0006926615,0.00034457725,0.000055241035,0.00019591849,0.00010168427,0.0021487335,0.0059766104,0.018254854,0.41137445,0.560392],"study_design_scores_gemma":[0.000015127123,0.00014955927,0.0015885931,0.00023321575,0.000047003236,0.0004065057,0.00010565184,0.010856819,0.003896863,0.008421203,0.9742522,0.000027221655],"about_ca_topic_score_codex":0.0041698916,"about_ca_topic_score_gemma":0.004009316,"teacher_disagreement_score":0.9128311,"about_ca_system_score_codex":0.00069816347,"about_ca_system_score_gemma":0.0023341463,"threshold_uncertainty_score":0.29160893},"labels":[],"label_agreement":null},{"id":"W4322760271","doi":"10.5281/zenodo.7693420","title":"Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Emulation; Computer science; Power (physics); Embedded system; Psychology","score_opus":0.019452821901834668,"score_gpt":0.238700705647964,"score_spread":0.21924788374612933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322760271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0152574275,0.00013894617,0.97848344,0.00007651808,0.000027024711,0.00003456355,0.00006781467,0.0017583404,0.0041559907],"genre_scores_gemma":[0.69578093,0.0003088258,0.29879656,0.000051257637,0.000019297768,0.000186759,0.00032461158,0.00026514335,0.004266544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986315,0.000040133076,0.000007831322,0.00001619727,0.000060828075,0.000011894967],"domain_scores_gemma":[0.9998215,0.00007304643,0.00002257833,0.00003462729,0.00004050437,0.0000078228295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029650924,0.0005238558,0.00026286437,0.00028549042,0.00020705623,0.0004134036,0.00062962953,0.00039118482,0.0032285214],"category_scores_gemma":[0.0007186014,0.00023239969,0.0003486257,0.00025044475,0.00019963222,0.0005583998,0.00028685434,0.0008187832,0.0008489516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031058247,0.000028308777,0.00032635685,0.000054613247,0.000017634828,0.00004009882,0.0000343673,0.94204456,0.007405889,0.0054792915,0.0005246195,0.04401317],"study_design_scores_gemma":[0.000003511425,0.00001955384,0.00010137712,0.0000062275194,0.000003923933,0.000009388408,0.0000031851678,0.99316823,0.0034808263,0.0012407221,0.0019606033,0.0000023995194],"about_ca_topic_score_codex":0.0028265612,"about_ca_topic_score_gemma":0.00273352,"teacher_disagreement_score":0.0032285214,"about_ca_system_score_codex":0.00044889527,"about_ca_system_score_gemma":0.00046618126,"threshold_uncertainty_score":0.010800481},"labels":[],"label_agreement":null},{"id":"W4323782439","doi":"10.1007/978-3-031-25891-6","title":"Machine Learning, Optimization, and Data Science","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences; Leibniz-Gemeinschaft; University of Ioannina; Lobachevsky State University of Nizhny Novgorod; National Technical University of Athens; Institut Teknologi Bandung; Università di Pisa; RWTH Aachen University; Università degli Studi di Verona; Ural Federal University; Università di Bologna; University College Dublin; North-West University; Kungliga Tekniska Högskolan; Ulster University; Universidad de Granada; National and Kapodistrian University of Athens; Universite Angers; National Research University Higher School of Economics; Technische Universität Wien; Università di Catania; Universidade de Coimbra; Universität Passau; Arizona State University; Vysoká Škola Ekonomická v Praze; University of Crete; Università degli Studi di Udine; Queen Mary University of London; Università della Calabria; Università degli Studi di Padova; Leibniz-Rechenzentrum; University of Reading; York University; Universiteit Leiden; Jyväskylän Yliopisto; Polytechnique Montréal; Akademia Górniczo-Hutnicza im. Stanislawa Staszica; McMaster University; University of Oxford; University of Alberta; Università degli Studi di Urbino Carlo Bo; University of Western Macedonia; Université de Lille; Università degli Studi di Parma; Queensland University of Technology; Chinese University of Hong Kong; Università Ca' Foscari Venezia; University of Minnesota; Centre National de la Recherche Scientifique; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional; Università degli Studi di Cagliari; Universidade de Aveiro; Università degli Studi di Torino; Università degli Studi di Milano","keywords":"Computer science; Artificial intelligence","score_opus":0.01948159415808589,"score_gpt":0.27984060711202574,"score_spread":0.2603590129539399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323782439","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020102502,0.20828798,0.49933338,0.009061565,0.00781973,0.00014416562,0.0026033532,0.0028381415,0.26790145],"genre_scores_gemma":[0.027090508,0.12753136,0.27788836,0.0036999176,0.0076982155,0.0003561403,0.0040957113,0.0025624218,0.5490773],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993575,0.00008679554,0.000034631623,0.00011772365,0.0003821612,0.00002110884],"domain_scores_gemma":[0.9983815,0.0011120931,0.00006162451,0.00019170319,0.00019420745,0.000058843743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007649171,0.0015236246,0.002290308,0.0018572094,0.0004381648,0.0033654447,0.001142652,0.0007995615,0.04283847],"category_scores_gemma":[0.0033427577,0.0007259228,0.00074691215,0.0051811407,0.0011092336,0.0034276359,0.0011361532,0.0031278266,0.026314767],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051894658,0.000047919246,0.00022655915,0.0008160692,0.00006755957,0.00004630544,0.000052562034,0.006426725,0.0010617191,0.119017154,0.37797782,0.49420768],"study_design_scores_gemma":[0.00002024378,0.00004033385,0.0007147972,0.00031070376,0.000036108166,0.0002798934,0.000038200942,0.019763255,0.00073594693,0.3176436,0.660387,0.000029909383],"about_ca_topic_score_codex":0.0011017402,"about_ca_topic_score_gemma":0.0018428752,"teacher_disagreement_score":0.04283847,"about_ca_system_score_codex":0.00070802413,"about_ca_system_score_gemma":0.0009222061,"threshold_uncertainty_score":0.143309},"labels":[],"label_agreement":null},{"id":"W4327903397","doi":"","title":"Methods to Identify the Family of Advanced Persistent Threats Based on Deep Neural Network and n-gram of API calls","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gram; Artificial neural network; n-gram; Computer science; Artificial intelligence; Psychology; Biology; Genetics","score_opus":0.03468993595297194,"score_gpt":0.3188474661157704,"score_spread":0.2841575301627985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327903397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08809219,0.0027528284,0.8942264,0.0008178345,0.0003924724,0.00030110305,0.0015880164,0.0061719767,0.0056572002],"genre_scores_gemma":[0.5992905,0.0016690465,0.37463954,0.0005243962,0.000406763,0.00047932693,0.005240696,0.00037748483,0.017372219],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999463,0.00007003428,0.000039408635,0.00017428564,0.00015849089,0.00009476053],"domain_scores_gemma":[0.9990656,0.00026837998,0.00019509667,0.0001276852,0.0002807613,0.00006258041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088257354,0.0017604488,0.0006948112,0.0026598966,0.00039266062,0.000933048,0.0011699053,0.0011080723,0.0021393907],"category_scores_gemma":[0.0021189929,0.00034821514,0.0010276102,0.00122433,0.00041974772,0.0013253246,0.0007318175,0.0017071514,0.0014455444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038257675,0.00045522535,0.012937358,0.00025177698,0.0002829447,0.00022228353,0.00010474319,0.08173414,0.018710434,0.0042872764,0.011498305,0.86913294],"study_design_scores_gemma":[0.0000145737895,0.000071073155,0.003124423,0.000026818061,0.00003837951,0.00015007897,0.00003134249,0.9841144,0.0065638125,0.0034364264,0.0024101236,0.000018631754],"about_ca_topic_score_codex":0.005020554,"about_ca_topic_score_gemma":0.00884567,"teacher_disagreement_score":0.005020554,"about_ca_system_score_codex":0.0008321647,"about_ca_system_score_gemma":0.0009415173,"threshold_uncertainty_score":0.0099826455},"labels":[],"label_agreement":null},{"id":"W4360909430","doi":"10.2139/ssrn.4399001","title":"Multivariate Optimization of Characteristic Parameters of Continuous-Flow System with a Front Buffer Tank for Industrial Reverse Osmosis Concentrate Treatment","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reverse osmosis; Multivariate statistics; Buffer (optical fiber); Flow (mathematics); Petroleum engineering; Process engineering; Environmental science; Computer science; Engineering; Mechanics; Chemistry; Mathematics; Membrane; Statistics; Physics","score_opus":0.017694661142896334,"score_gpt":0.23543780873270304,"score_spread":0.2177431475898067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360909430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97461605,0.00046839015,0.023947705,0.000053239477,0.00001791406,0.00003888336,0.00013937367,0.00013683549,0.00058166037],"genre_scores_gemma":[0.99100566,0.00018544497,0.0081893895,0.000012359867,0.0000053589033,0.00003247044,0.0001314485,0.000017631026,0.00042028254],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967206,0.00004695477,0.000016828615,0.00007845003,0.00015033624,0.000035393798],"domain_scores_gemma":[0.99978596,0.000092309645,0.000033673412,0.000011033061,0.00006323612,0.000013735974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065473316,0.00061967765,0.00085536623,0.00047198505,0.00048244392,0.00085067505,0.00046398124,0.00052828394,0.00048270545],"category_scores_gemma":[0.00057776924,0.0002871852,0.00087107765,0.000694889,0.0003051135,0.0005113096,0.0003079353,0.00051406573,0.000112155045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015964303,0.0008676476,0.0080357855,0.00046313094,0.00018450509,0.00011728819,0.00012721753,0.052395456,0.8794154,0.00031547254,0.00041429998,0.056067295],"study_design_scores_gemma":[0.00008903162,0.001767517,0.040622275,0.000016303453,0.00028067792,0.00008260143,0.00014015859,0.3258837,0.62975097,0.00016274783,0.0011092435,0.00009477141],"about_ca_topic_score_codex":0.0041537834,"about_ca_topic_score_gemma":0.0042104525,"teacher_disagreement_score":0.0041537834,"about_ca_system_score_codex":0.00052832277,"about_ca_system_score_gemma":0.00093559484,"threshold_uncertainty_score":0.008259237},"labels":[],"label_agreement":null},{"id":"W4362675401","doi":"10.1145/3585341.3587954","title":"Parallel Algorithm for a Hidden Markov Model with an Indefinite Number of States and Heterogeneous Observation Data","year":2023,"lang":"en","type":"article","venue":"International Workshop on OpenCL","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Hidden Markov model; Computer science; Code (set theory); Set (abstract data type); Algorithm; State (computer science); Markov model; Parallel computing; Markov chain; Theoretical computer science; Artificial intelligence; Machine learning; Programming language","score_opus":0.06385535604716223,"score_gpt":0.3421568787825102,"score_spread":0.278301522735348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362675401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037812414,0.000050274743,0.99260664,0.00012835559,0.000035624165,0.00003711325,0.00008919569,0.0022533576,0.0010180923],"genre_scores_gemma":[0.12895054,0.00009620252,0.8631529,0.00011715769,0.000058040758,0.00032502835,0.00072158326,0.0005807221,0.005997719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993861,0.00008766551,0.000048621805,0.0002173412,0.00017407954,0.000086269276],"domain_scores_gemma":[0.9991648,0.00040872343,0.00005218248,0.00015295653,0.00017881647,0.00004253274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008826291,0.0008666451,0.0007874791,0.0007755436,0.0007925474,0.001191701,0.0016779758,0.000849433,0.01075865],"category_scores_gemma":[0.0027742803,0.00064825674,0.0011068512,0.00072420365,0.0005176841,0.0013199791,0.0016424358,0.001700279,0.0026443314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004347607,0.00015025766,0.0018734309,0.00018459426,0.0001458884,0.000327289,0.00026196317,0.472271,0.0070134043,0.0376051,0.009674573,0.47005767],"study_design_scores_gemma":[0.000036034264,0.000014733181,0.000090979396,0.000005555593,0.000010905097,0.000040288396,0.000019030784,0.9804813,0.0015866361,0.015808998,0.0018980242,0.000007568298],"about_ca_topic_score_codex":0.010757066,"about_ca_topic_score_gemma":0.013817212,"teacher_disagreement_score":0.01075865,"about_ca_system_score_codex":0.0011761183,"about_ca_system_score_gemma":0.0026030256,"threshold_uncertainty_score":0.03599125},"labels":[],"label_agreement":null},{"id":"W4365459499","doi":"10.5539/apr.v15n1p101","title":"Uncovering the Hidden Information: A Novel Approach to Modeling Physical Phenomena Through Information Theory","year":2023,"lang":"en","type":"article","venue":"Applied Physics Research","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Novelty; Physical law; Key (lock); Observer (physics); Information theory; Selection (genetic algorithm); Physical system; Industrial engineering; Artificial intelligence; Mathematics; Epistemology","score_opus":0.07518480105275793,"score_gpt":0.33744379508766353,"score_spread":0.2622589940349056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365459499","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002943856,0.0010360191,0.9908506,0.0010918013,0.00005958041,0.000026953852,0.00008091298,0.000056986533,0.0038532165],"genre_scores_gemma":[0.44176397,0.0069839917,0.5449999,0.0008068301,0.00092513947,0.0003801308,0.00026999525,0.00010807845,0.0037619837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969118,0.0014426915,0.00019984024,0.00045995484,0.00083407597,0.00015168315],"domain_scores_gemma":[0.9902946,0.007155163,0.00075835694,0.0012702002,0.00037267257,0.00014896733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047614775,0.0012176243,0.0017635461,0.004100404,0.0012072255,0.0050520203,0.0023485515,0.0020657552,0.0018791273],"category_scores_gemma":[0.009765899,0.0007186846,0.0023064131,0.0023531388,0.0085744,0.009624976,0.003688971,0.0035004949,0.0003818681],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023720435,0.000022129558,0.0003187713,0.00018860186,0.00007652116,0.000108290995,0.00030665204,0.032496635,0.0007768428,0.95178115,0.0005386419,0.013362032],"study_design_scores_gemma":[0.0000072116472,0.000031561576,0.000099471064,0.000067337525,0.000026210771,0.0000716386,0.0000491904,0.109703116,0.00046616947,0.8864725,0.0029797114,0.00002582041],"about_ca_topic_score_codex":0.0016920046,"about_ca_topic_score_gemma":0.0010601863,"teacher_disagreement_score":0.0050520203,"about_ca_system_score_codex":0.0018974681,"about_ca_system_score_gemma":0.0021345285,"threshold_uncertainty_score":0.025181353},"labels":[],"label_agreement":null},{"id":"W4377024179","doi":"10.1007/s10270-023-01108-2","title":"Guest editorial for the special section on MODELS 2021","year":2023,"lang":"en","type":"editorial","venue":"Software & Systems Modeling","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Ottawa","funders":"","keywords":"Computer science; Special section; Section (typography); Software engineering; Data science; Engineering physics; Operating system","score_opus":0.024449714587548466,"score_gpt":0.2705132573822686,"score_spread":0.24606354279472015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377024179","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000018457287,0.0024321068,0.00019561815,0.020586263,0.9747727,0.000010829669,0.00005959449,0.00006159639,0.0018628155],"genre_scores_gemma":[0.00023205606,0.0019230768,0.00011631758,0.010634636,0.97559935,0.000017348888,0.00006232162,0.00009430435,0.011320663],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99406344,0.0010497804,0.00063289114,0.0008207963,0.0030223338,0.00041066651],"domain_scores_gemma":[0.9759897,0.008037042,0.0020533174,0.0010058555,0.009382564,0.003531572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00736143,0.004663259,0.0043051913,0.0052075223,0.0030742558,0.009215359,0.0031689089,0.014270306,0.04046131],"category_scores_gemma":[0.023692695,0.001716171,0.0040153456,0.0016405697,0.0020606853,0.0051401206,0.0024294597,0.016303603,0.029114146],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016423905,0.000006059201,0.000011017994,0.00005408032,0.000009508283,0.00006087144,0.0000029430857,0.000019813562,0.000032409545,0.00019784323,0.99763167,0.0019572421],"study_design_scores_gemma":[0.000049026243,0.000025631229,0.00015528426,0.00024895874,0.000049767863,0.00022398625,0.000015747255,0.00026441738,0.00008115371,0.0014924872,0.99737525,0.00001845247],"about_ca_topic_score_codex":0.0017439696,"about_ca_topic_score_gemma":0.0053311787,"teacher_disagreement_score":0.04046131,"about_ca_system_score_codex":0.0030397205,"about_ca_system_score_gemma":0.003090275,"threshold_uncertainty_score":0.13535655},"labels":[],"label_agreement":null},{"id":"W4379017240","doi":"10.18280/ijsse.130220","title":"Emergency Response Plan Modeling Using IDEF0 and BPMN Approaches","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"IDEF0; Business Process Model and Notation; Plan (archaeology); Emergency plan; Computer science; Emergency response; Process management; Systems engineering; Engineering; Business process; Medical emergency; Medicine; Operations management; Business process modeling; Geology; Work in process","score_opus":0.04269289356416899,"score_gpt":0.2680133873033718,"score_spread":0.22532049373920282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379017240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022193217,0.00016340056,0.993845,0.00011000334,0.000034207296,0.00014385462,0.00024623337,0.00078261306,0.0024554043],"genre_scores_gemma":[0.046766244,0.0005933438,0.94926786,0.00007763347,0.000028314273,0.00043545442,0.0010313235,0.0001472831,0.0016524739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977083,0.0008523029,0.00035238222,0.00033065828,0.0006270045,0.0001292107],"domain_scores_gemma":[0.9987268,0.00064736744,0.00016638309,0.00014306317,0.00026104733,0.00005524747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041779783,0.0012151981,0.00057466206,0.00201546,0.0005246586,0.0030086667,0.0017391035,0.0011041431,0.0022201966],"category_scores_gemma":[0.0034173473,0.00060924486,0.0015533505,0.0011881816,0.00072432,0.0025878611,0.0014851736,0.0014663956,0.0006310068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003257876,0.00043631325,0.0025607017,0.001459636,0.00022531179,0.0012898877,0.0016330049,0.35874534,0.014728344,0.35849765,0.0047753053,0.25532275],"study_design_scores_gemma":[0.00007645627,0.00008992193,0.00046242154,0.00032380276,0.000101859725,0.00031899373,0.00019892449,0.8618465,0.008518519,0.06877219,0.059230547,0.000059926228],"about_ca_topic_score_codex":0.004683972,"about_ca_topic_score_gemma":0.003788136,"teacher_disagreement_score":0.004683972,"about_ca_system_score_codex":0.0012263048,"about_ca_system_score_gemma":0.0020718756,"threshold_uncertainty_score":0.022095501},"labels":[],"label_agreement":null},{"id":"W4381956096","doi":"10.1007/978-3-031-36272-9","title":"Artificial Intelligence in Education","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Information and Communications Technology; University of California, Irvine; California State University, Fullerton; Universidade Federal de Alagoas; University of Colorado Boulder; U.S. Army; Singapore Management University; Stockholms Universitet; University of Massachusetts Amherst; Universidade Federal do Rio de Janeiro; Universidade Federal de Pernambuco; University of Tsukuba; Technion-Israel Institute of Technology; Ateneo de Manila University; Universidad de Chile; University of Pittsburgh; Hacettepe Üniversitesi; Iran Telecommunication Research Center; Universidad Autónoma de Madrid; University of Pennsylvania; Georgia Institute of Technology; Instituto Tecnológico y de Estudios Superiores de Monterrey; Simon Fraser University; Leibniz-Gemeinschaft; Sapienza Università di Roma; Gottfried Wilhelm Leibniz Universität Hannover; Sorbonne Université; Universidad Politécnica de Madrid; Universidade Federal de Uberlândia; Turun Yliopisto; U.S. Army Combat Capabilities Development Command; Athabasca University; University of Minnesota; Universiteit Utrecht; Trinity College Dublin; Universidad del Cauca; University of Sussex; Universidade Federal do Rio Grande do Sul; University of Alberta; Universidad Nacional de Educación a Distancia; Arizona State University; Universitat Pompeu Fabra; Université de Lorraine; North Carolina State University; Carnegie Mellon University; Université de Lyon; University of Technology Sydney; University of Central Florida; University of Southern California; University of Memphis; Università degli Studi di Cagliari; University of South Australia; McGill University; Beijing Normal University; Vanderbilt University; Euskal Herriko Unibertsitatea; Kanazawa University; Universiteit van Amsterdam; Georgia State University; University of Illinois at Urbana-Champaign; Colorado State University; Kindai University; École Polytechnique Fédérale de Lausanne; Valparaiso University; Northern Illinois University; Educational Testing Service; Northern Kentucky University; University College London","keywords":"Computer science; Artificial intelligence","score_opus":0.021356230240940632,"score_gpt":0.2953854935008512,"score_spread":0.27402926325991056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381956096","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000673885,0.07003842,0.008891965,0.0109029,0.0034602028,0.000037585494,0.000111510075,0.00021582679,0.90566766],"genre_scores_gemma":[0.010770624,0.02587299,0.0035640714,0.0022335115,0.0012926697,0.000051958585,0.000118704804,0.00010597247,0.95598936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968255,0.00008176415,0.000012256934,0.000044743105,0.00015137294,0.000027294007],"domain_scores_gemma":[0.99957806,0.00017692437,0.00002290459,0.00006624194,0.00008381883,0.0000721276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006858991,0.00080286624,0.0006238251,0.001159797,0.0009925606,0.0037680315,0.00046646837,0.0013362386,0.067173555],"category_scores_gemma":[0.0012232758,0.00034915042,0.00022755221,0.0015438469,0.0018096269,0.0038909803,0.0015068658,0.0028351787,0.0276514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013798575,0.000053521308,0.00016419447,0.0002049368,0.000007714609,0.000039004106,0.00039902417,0.0003177843,0.00025804705,0.21748129,0.45619047,0.32487032],"study_design_scores_gemma":[0.0000039316456,0.000013402219,0.00035472712,0.00022451355,0.0000033823192,0.00007271739,0.00014863857,0.00022348134,0.00010204504,0.06407959,0.93477017,0.0000034007357],"about_ca_topic_score_codex":0.0016328782,"about_ca_topic_score_gemma":0.004744828,"teacher_disagreement_score":0.067173555,"about_ca_system_score_codex":0.0014097462,"about_ca_system_score_gemma":0.001684306,"threshold_uncertainty_score":0.22471792},"labels":[],"label_agreement":null},{"id":"W4385797174","doi":"10.5281/zenodo.8247131","title":"Code of the Bayesian Age model of core MSM45_19-2.","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Code (set theory); Bayesian probability; Core (optical fiber); Computer science; Programming language; Artificial intelligence; Set (abstract data type)","score_opus":0.06227193101912677,"score_gpt":0.2667154190777774,"score_spread":0.20444348805865065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385797174","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038210726,0.00048487404,0.32940754,0.0010497458,0.0010507194,0.00055813056,0.5563328,0.072500505,0.034794547],"genre_scores_gemma":[0.053688608,0.00058146915,0.26507196,0.001667986,0.00057463755,0.0027811914,0.5722505,0.07302328,0.030360451],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994772,0.00009482308,0.00006460725,0.0001634131,0.00013324333,0.0000668053],"domain_scores_gemma":[0.99740595,0.00074979704,0.00020256157,0.00033361517,0.0011600897,0.0001478818],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014710873,0.0016713216,0.0012905415,0.0015757311,0.00083921116,0.0017895297,0.003819709,0.0026895695,0.30837432],"category_scores_gemma":[0.009091303,0.0012844305,0.0013398355,0.0013433716,0.00045940967,0.002501078,0.0015483402,0.002313457,0.17217873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005284626,0.00007444394,0.0059405626,0.0007485561,0.00010118999,0.00020810454,0.00019078616,0.06516383,0.0031793008,0.025672931,0.86428016,0.0339118],"study_design_scores_gemma":[0.00043206304,0.000060772367,0.0038745399,0.00037823524,0.00007923483,0.00039972982,0.00008207161,0.2164656,0.006245377,0.06888552,0.70284796,0.00024883176],"about_ca_topic_score_codex":0.021954356,"about_ca_topic_score_gemma":0.01345449,"teacher_disagreement_score":0.30837432,"about_ca_system_score_codex":0.0016705872,"about_ca_system_score_gemma":0.002500499,"threshold_uncertainty_score":0.9865201},"labels":[],"label_agreement":null},{"id":"W4386325377","doi":"10.18280/ts.400430","title":"Optimization of Deep Learning Algorithms for Image Segmentation in High-Dimensional Data Environments","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Image (mathematics); Image segmentation; Deep learning; Pattern recognition (psychology); Computer vision; Algorithm","score_opus":0.025145607807438557,"score_gpt":0.27646988520234556,"score_spread":0.251324277394907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00789566,0.00029739636,0.9901862,0.0002566939,0.00002324023,0.000020146705,0.000032947344,0.00031006546,0.0009775965],"genre_scores_gemma":[0.46515375,0.0009808284,0.52666634,0.00040111828,0.00010548066,0.00023766843,0.00035115785,0.0003233687,0.005780266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953043,0.0001435781,0.000025230805,0.00011421918,0.0001222116,0.00006425166],"domain_scores_gemma":[0.9988318,0.0007533334,0.00010666428,0.00008645437,0.00016764556,0.000054074215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014431339,0.0010453734,0.0008596846,0.00072647474,0.00043693627,0.001384059,0.0012381055,0.0017529798,0.0016607153],"category_scores_gemma":[0.005253172,0.00070944155,0.00054768205,0.00087635324,0.0014010165,0.0018092161,0.0018445073,0.002041319,0.00045817287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058634614,0.000029768664,0.00031070883,0.000073082134,0.00003477052,0.000035967158,0.000049498678,0.92391455,0.0022639986,0.016168792,0.0011875435,0.055872656],"study_design_scores_gemma":[0.0000018140107,0.000006613624,0.00002546563,0.0000040946866,0.000001472991,0.0000039707356,0.0000035085457,0.9950271,0.0003069418,0.004444823,0.00017263435,0.0000016005536],"about_ca_topic_score_codex":0.005076553,"about_ca_topic_score_gemma":0.0071016755,"teacher_disagreement_score":0.005076553,"about_ca_system_score_codex":0.0019478616,"about_ca_system_score_gemma":0.0018576317,"threshold_uncertainty_score":0.014132798},"labels":[],"label_agreement":null},{"id":"W4386366486","doi":"10.3102/ip.23.2008230","title":"Student Self-Assessment Profiles: Leveraging Trace Data to Unpack the Black Box of Self-Assessment","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Self-assessment; TRACE (psycholinguistics); Black box; Computer science; Data science; Artificial intelligence; Psychology","score_opus":0.03803778660203025,"score_gpt":0.3542534167229908,"score_spread":0.31621563012096054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386366486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46237314,0.0007783858,0.49296668,0.0015368558,0.00045732615,0.000540405,0.01712198,0.013731454,0.010493718],"genre_scores_gemma":[0.89737713,0.0002844226,0.09049864,0.00015169782,0.0001000537,0.00018449998,0.007164239,0.00043558734,0.003803772],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9972126,0.00075488276,0.0002931473,0.00051902543,0.0010377511,0.00018260909],"domain_scores_gemma":[0.97602147,0.009357566,0.0027066555,0.0061050775,0.0045478726,0.0012612297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003527422,0.0006091221,0.00068220636,0.0039267773,0.00040596796,0.002809684,0.0008580057,0.00073763315,0.0016401635],"category_scores_gemma":[0.029323258,0.00032071292,0.00033004134,0.0030921737,0.00026180813,0.0033491626,0.0019637407,0.0014688686,0.001792399],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007290025,0.0010187931,0.37333903,0.00022363637,0.00023264061,0.00021890881,0.002566843,0.007781917,0.007917811,0.005212461,0.011646293,0.5891127],"study_design_scores_gemma":[0.00007942423,0.0013246149,0.3222558,0.00052625884,0.00022541796,0.0009164071,0.004283794,0.5097409,0.044996046,0.059385873,0.055902224,0.0003632263],"about_ca_topic_score_codex":0.0043651373,"about_ca_topic_score_gemma":0.00803372,"teacher_disagreement_score":0.0043651373,"about_ca_system_score_codex":0.0003625472,"about_ca_system_score_gemma":0.0012008968,"threshold_uncertainty_score":0.018655002},"labels":[],"label_agreement":null},{"id":"W4386847617","doi":"10.1007/978-981-99-3963-3","title":"Proceedings of Third Emerging Trends and Technologies on Intelligent Systems","year":2023,"lang":"en","type":"book","venue":"Lecture notes in networks and systems","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kwantlen Polytechnic University","funders":"","keywords":"Emerging technologies; Data science; Computer science; Engineering management; Systems engineering; Engineering; Artificial intelligence","score_opus":0.014036539382897117,"score_gpt":0.24355148899779566,"score_spread":0.22951494961489854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386847617","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01336938,0.11768021,0.10436993,0.0150515055,0.07928939,0.00035179584,0.0018739477,0.0026106325,0.66540325],"genre_scores_gemma":[0.020841105,0.04113336,0.024641098,0.0017448993,0.0067885853,0.00015378627,0.002490181,0.0008395473,0.9013676],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99907494,0.00016659386,0.00003732173,0.00009259125,0.00053320173,0.000095346644],"domain_scores_gemma":[0.99846834,0.0003872737,0.000053440683,0.00021856229,0.00058128184,0.00029102908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016056013,0.0009425073,0.0012811286,0.0012809206,0.00063140166,0.003875275,0.0011973555,0.0010137246,0.07625977],"category_scores_gemma":[0.0016527215,0.00029185772,0.00069367816,0.0017396094,0.00070842745,0.0019850535,0.0015546113,0.0018322582,0.028459836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013706277,0.00013035076,0.00032254646,0.00032866592,0.000043977256,0.00011334632,0.00011984906,0.00055843237,0.0020870478,0.01494512,0.70869887,0.27251464],"study_design_scores_gemma":[0.00001248633,0.000044617424,0.0007686948,0.0001218092,0.000028652097,0.0001669891,0.000079728714,0.0014800238,0.0013086393,0.005852106,0.9901246,0.000011647767],"about_ca_topic_score_codex":0.0025375169,"about_ca_topic_score_gemma":0.0050930628,"teacher_disagreement_score":0.07625977,"about_ca_system_score_codex":0.0011773233,"about_ca_system_score_gemma":0.0018874425,"threshold_uncertainty_score":0.25511432},"labels":[],"label_agreement":null},{"id":"W4388580387","doi":"10.1007/978-3-031-47705-8","title":"Integrated Formal Methods","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Torino; Universitetet i Oslo; University of Surrey; Carl von Ossietzky Universität Oldenburg; University of Twente; McMaster University","keywords":"Computer science","score_opus":0.02320127823493744,"score_gpt":0.31977253110185283,"score_spread":0.2965712528669154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388580387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013787166,0.0023616173,0.8731993,0.00073752523,0.00048178848,0.00009985741,0.000351258,0.0035128666,0.11787712],"genre_scores_gemma":[0.063337184,0.0035617212,0.64131045,0.00053429353,0.00044509096,0.00038394053,0.0023470342,0.0032816275,0.28479865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826753,0.000339697,0.00009953232,0.00029558313,0.00089631305,0.00010130183],"domain_scores_gemma":[0.99867,0.0005243968,0.000035536475,0.0004847064,0.00023787099,0.000047553694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018378246,0.0010733794,0.00077164173,0.001763662,0.0008347466,0.0029548176,0.001716595,0.0008846363,0.053137332],"category_scores_gemma":[0.0032145358,0.0010314343,0.0013675289,0.0013063113,0.0015056136,0.0041387565,0.0027349098,0.0027581102,0.018186385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042415675,0.00005959874,0.0001254698,0.000222664,0.000025748745,0.000038079008,0.00030697434,0.002741869,0.002112557,0.5935542,0.035509065,0.36526126],"study_design_scores_gemma":[0.000022020884,0.000026973605,0.00013802473,0.00017019935,0.000024809633,0.00014011582,0.00006975727,0.011567789,0.0029669665,0.5863809,0.39847475,0.000017654143],"about_ca_topic_score_codex":0.00083777343,"about_ca_topic_score_gemma":0.0010484367,"teacher_disagreement_score":0.053137332,"about_ca_system_score_codex":0.0011632396,"about_ca_system_score_gemma":0.0013985131,"threshold_uncertainty_score":0.17776209},"labels":[],"label_agreement":null},{"id":"W4389584968","doi":"10.17118/11143/20927","title":"Efficient algorithm for thermomechanical simulation of industrialprocesses","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Algorithm","score_opus":0.03988409763189495,"score_gpt":0.3163513148531354,"score_spread":0.27646721722124046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389584968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012755145,0.000111788744,0.9968286,0.000029600087,0.000023627937,0.000026565782,0.00006453611,0.00055946206,0.0010803286],"genre_scores_gemma":[0.07713604,0.00033038444,0.9166794,0.000057967547,0.000039746395,0.00056751235,0.00047508732,0.0005020811,0.004211759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995127,0.000092855145,0.00003095371,0.00006495577,0.0002476116,0.000050978924],"domain_scores_gemma":[0.9995504,0.00015907915,0.00003677785,0.00008558914,0.00014534024,0.000022676291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062668795,0.00089426496,0.0010382838,0.0006519669,0.0005556449,0.00092908135,0.0020765027,0.0013386103,0.008454201],"category_scores_gemma":[0.0015286825,0.00052274973,0.0008622396,0.0010660634,0.0004957233,0.001007441,0.0012355449,0.0013434957,0.0029932032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005983348,0.00004231789,0.0002931142,0.0001442238,0.00004341246,0.0000823102,0.00004604713,0.8649613,0.005708725,0.045436233,0.0028177095,0.080364816],"study_design_scores_gemma":[0.000014639558,0.000009091859,0.000046281908,0.000006403446,0.000004153523,0.000017058033,0.0000028781042,0.98845553,0.0010005743,0.0063895965,0.0040489603,0.000004843547],"about_ca_topic_score_codex":0.0022949553,"about_ca_topic_score_gemma":0.002152618,"teacher_disagreement_score":0.008454201,"about_ca_system_score_codex":0.00064863067,"about_ca_system_score_gemma":0.0014554018,"threshold_uncertainty_score":0.028282106},"labels":[],"label_agreement":null},{"id":"W4390193942","doi":"10.18280/ijsse.130606","title":"Development of a Method for Determining the List of Key Threats to Information Security of Computer Networks","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Key (lock); Computer security; Computer science; Information security; Risk analysis (engineering); Business","score_opus":0.011665979155652912,"score_gpt":0.28242902052710017,"score_spread":0.27076304137144724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390193942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025028463,0.00010445053,0.9949315,0.00008929213,0.000023018047,0.00025021684,0.00023233928,0.00068958924,0.0011766974],"genre_scores_gemma":[0.026978288,0.00012609923,0.9712706,0.000025424095,0.000020748443,0.0003055849,0.00039805417,0.00007631445,0.0007988398],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962716,0.0008784216,0.0003877051,0.00064091774,0.0016620557,0.00015922498],"domain_scores_gemma":[0.99150664,0.00426129,0.0010032883,0.0008746989,0.002135748,0.00021845836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038135175,0.0019943358,0.0012086976,0.013716191,0.0016214965,0.004394854,0.0021062715,0.0012989715,0.0052030776],"category_scores_gemma":[0.015959146,0.0008222559,0.0016434361,0.0046309554,0.0014737487,0.0038326783,0.002444153,0.0020469334,0.0019714],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011825993,0.0003222447,0.016305605,0.0011097041,0.00023661497,0.00048291546,0.0016147639,0.042610005,0.02061631,0.11558214,0.0068228864,0.79417866],"study_design_scores_gemma":[0.00007031687,0.0004155886,0.0062820995,0.0005594242,0.00020492249,0.0019908182,0.002083576,0.7667897,0.018837543,0.1627224,0.03981839,0.00022523724],"about_ca_topic_score_codex":0.0039765984,"about_ca_topic_score_gemma":0.006740525,"teacher_disagreement_score":0.013716191,"about_ca_system_score_codex":0.0016907573,"about_ca_system_score_gemma":0.00482675,"threshold_uncertainty_score":0.020168066},"labels":[],"label_agreement":null},{"id":"W4390341667","doi":"10.18280/jesa.560614","title":"Software Quality Assessment Technique for the Autonomous Power Plants Automated Control Systems","year":2023,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Control (management); Software; Computer science; Reliability engineering; Power quality; Software engineering; Systems engineering; Engineering; Operating system; Artificial intelligence; Electrical engineering","score_opus":0.03065991732011844,"score_gpt":0.31805007610898317,"score_spread":0.28739015878886476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390341667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059943423,0.00009634469,0.9929946,0.000030986892,0.000010163812,0.000035663998,0.000020101328,0.00027519173,0.0005425085],"genre_scores_gemma":[0.2677009,0.0002671665,0.7303771,0.0000360827,0.000031415035,0.0002225826,0.00014724894,0.00006022786,0.0011573503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970439,0.00080147845,0.00020109821,0.0002761785,0.001608314,0.00006894685],"domain_scores_gemma":[0.99689674,0.0012979999,0.00044227295,0.00025133975,0.0010700113,0.000041616717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018350589,0.00046004684,0.00039860597,0.0020619868,0.00034424078,0.00093472423,0.0004575839,0.0003644926,0.00088346243],"category_scores_gemma":[0.0069214427,0.00018720192,0.0005714254,0.0010951724,0.0004724089,0.0007423027,0.0005105807,0.0008489908,0.0002635163],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113164264,0.00008965768,0.004529561,0.00047895432,0.00009006156,0.00015758014,0.0006421916,0.056558628,0.0736237,0.043544184,0.0016664134,0.81850594],"study_design_scores_gemma":[0.000039704824,0.0006474814,0.009930217,0.00015110568,0.00009426285,0.00078553095,0.00022855696,0.8943732,0.043828838,0.032369286,0.017479612,0.0000722442],"about_ca_topic_score_codex":0.0013167997,"about_ca_topic_score_gemma":0.0010411066,"teacher_disagreement_score":0.0020619868,"about_ca_system_score_codex":0.0005325774,"about_ca_system_score_gemma":0.00083023886,"threshold_uncertainty_score":0.009704828},"labels":[],"label_agreement":null},{"id":"W4390807528","doi":"","title":"On the monitoring of noisy data as a multidimensional shell","year":2019,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Computer science; Shell (structure); Data mining; Engineering","score_opus":0.018136572947197362,"score_gpt":0.24504984931622673,"score_spread":0.22691327636902936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390807528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048327576,0.00019701877,0.9494446,0.0003384859,0.00004439918,0.00002021661,0.000051080635,0.00027839586,0.0012982335],"genre_scores_gemma":[0.77839154,0.0009353599,0.21220444,0.00020607201,0.0001762945,0.000053705648,0.00020113372,0.0003773171,0.007454087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989222,0.00043869278,0.00007007867,0.00022357269,0.00028229994,0.000063136074],"domain_scores_gemma":[0.99503577,0.0026539154,0.0006822608,0.0008158995,0.00055628235,0.0002558907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002158311,0.0006627748,0.0010283093,0.00071237783,0.00049548375,0.0019364938,0.0012218788,0.0011910944,0.0011414204],"category_scores_gemma":[0.01117114,0.00045091138,0.00069495453,0.0010548297,0.0024921917,0.003443326,0.0024845882,0.0010647163,0.00021798376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064379186,0.00009052961,0.0051867487,0.00032577637,0.00013594615,0.0008458881,0.0011430953,0.6359564,0.064879745,0.17273311,0.0029192802,0.115139656],"study_design_scores_gemma":[0.000002477894,0.00002017402,0.00047396,0.000008577475,0.0000058417927,0.000060827093,0.000022380922,0.9844706,0.0023789154,0.01208993,0.0004541162,0.000012249146],"about_ca_topic_score_codex":0.0018770404,"about_ca_topic_score_gemma":0.0010943589,"teacher_disagreement_score":0.002158311,"about_ca_system_score_codex":0.0005333631,"about_ca_system_score_gemma":0.00038159444,"threshold_uncertainty_score":0.011414409},"labels":[],"label_agreement":null},{"id":"W4391320139","doi":"10.1007/978-3-031-51521-7","title":"12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022)","year":2024,"lang":"en","type":"book","venue":"Lecture notes in networks and systems","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Russian Academy of Sciences; Doğu Akdeniz Üniversitesi; South China University of Technology; Georgia State University; Dongguk University; Moscow Institute of Physics and Technology; University of Tabriz; Sharif University of Technology; University of Alberta; Российский экономический университет имени Г.В. Плеханова; Meiji University; University of Ulsan","keywords":"Automation; Computer science; Process automation system; Engineering; Systems engineering; Manufacturing engineering; Mechanical engineering","score_opus":0.03996890474815324,"score_gpt":0.25733610078849617,"score_spread":0.21736719604034294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391320139","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017935544,0.038980335,0.14443253,0.012555603,0.10607765,0.0006720043,0.0088929795,0.010267893,0.66018546],"genre_scores_gemma":[0.014302345,0.005566931,0.015546799,0.0010465411,0.0032020782,0.00015999896,0.008633463,0.00085316703,0.9506887],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990908,0.00017025699,0.000036979225,0.00013833217,0.00036189685,0.00020181491],"domain_scores_gemma":[0.9990434,0.00007103937,0.000026871963,0.000101751735,0.00048005502,0.00027676852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014493887,0.0014822269,0.001143064,0.0017745695,0.0006894942,0.003239575,0.0013558032,0.0021733276,0.13671957],"category_scores_gemma":[0.00088037224,0.00029619253,0.00092948816,0.0011704102,0.0004608604,0.0016953441,0.0015539038,0.001762956,0.11582121],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004104277,0.00017710145,0.00044509096,0.00027024015,0.000044138724,0.00010317515,0.000036286063,0.000900229,0.0061867144,0.003788848,0.70252746,0.2851103],"study_design_scores_gemma":[0.000027669827,0.00020135133,0.0015874337,0.00011424526,0.00004717905,0.00019104966,0.00004361669,0.0050153383,0.0062824567,0.0021209219,0.984347,0.000021767399],"about_ca_topic_score_codex":0.0039891037,"about_ca_topic_score_gemma":0.0059467326,"teacher_disagreement_score":0.13671957,"about_ca_system_score_codex":0.0008965122,"about_ca_system_score_gemma":0.0019409392,"threshold_uncertainty_score":0.45737255},"labels":[],"label_agreement":null},{"id":"W4391898321","doi":"10.1007/978-3-031-53488-1","title":"12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022)","year":2024,"lang":"en","type":"book","venue":"Lecture notes in networks and systems","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Russian Academy of Sciences; Doğu Akdeniz Üniversitesi; South China University of Technology; Georgia State University; Dongguk University; Moscow Institute of Physics and Technology; University of Tabriz; Sharif University of Technology; University of Alberta; Российский экономический университет имени Г.В. Плеханова; Meiji University; University of Ulsan","keywords":"Automation; Manufacturing engineering; Engineering; Computer science; Systems engineering; Engineering management; Mechanical engineering","score_opus":0.03996890474815324,"score_gpt":0.25733610078849617,"score_spread":0.21736719604034294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391898321","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017935544,0.038980335,0.14443253,0.012555603,0.10607765,0.0006720043,0.0088929795,0.010267893,0.66018546],"genre_scores_gemma":[0.014302345,0.005566931,0.015546799,0.0010465411,0.0032020782,0.00015999896,0.008633463,0.00085316703,0.9506887],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990908,0.00017025699,0.000036979225,0.00013833217,0.00036189685,0.00020181491],"domain_scores_gemma":[0.9990434,0.00007103937,0.000026871963,0.000101751735,0.00048005502,0.00027676852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014493887,0.0014822269,0.001143064,0.0017745695,0.0006894942,0.003239575,0.0013558032,0.0021733276,0.13671957],"category_scores_gemma":[0.00088037224,0.00029619253,0.00092948816,0.0011704102,0.0004608604,0.0016953441,0.0015539038,0.001762956,0.11582121],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004104277,0.00017710145,0.00044509096,0.00027024015,0.000044138724,0.00010317515,0.000036286063,0.000900229,0.0061867144,0.003788848,0.70252746,0.2851103],"study_design_scores_gemma":[0.000027669827,0.00020135133,0.0015874337,0.00011424526,0.00004717905,0.00019104966,0.00004361669,0.0050153383,0.0062824567,0.0021209219,0.984347,0.000021767399],"about_ca_topic_score_codex":0.0039891037,"about_ca_topic_score_gemma":0.0059467326,"teacher_disagreement_score":0.13671957,"about_ca_system_score_codex":0.0008965122,"about_ca_system_score_gemma":0.0019409392,"threshold_uncertainty_score":0.45737255},"labels":[],"label_agreement":null},{"id":"W4392387514","doi":"10.18280/ria.380115","title":"Machine Learning Prediction Model: A Case Study of Urban Transport of Medical and Pharmaceutical Products","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.04957898056973763,"score_gpt":0.3283038334122186,"score_spread":0.27872485284248094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392387514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8923428,0.0013442264,0.08534337,0.0062183733,0.00019297426,0.000246615,0.0024477276,0.00039209978,0.011471717],"genre_scores_gemma":[0.97800213,0.0003839302,0.015765512,0.00013624442,0.00005168385,0.00011481358,0.0008826281,0.000024629431,0.0046383706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995221,0.00021772308,0.000021921263,0.00009202741,0.000057078054,0.00008912121],"domain_scores_gemma":[0.99820614,0.001269465,0.000116781586,0.000071577626,0.00026057882,0.000075462944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014053208,0.0007961619,0.0006419264,0.0008754414,0.0008907706,0.0012364314,0.0013763044,0.0018502437,0.003823688],"category_scores_gemma":[0.002914569,0.0002052459,0.00083938596,0.0012565958,0.00063325244,0.00078781944,0.0009431567,0.0015508949,0.00046262058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020268459,0.00044508887,0.02906465,0.00015328733,0.000073165385,0.001662714,0.00021754608,0.9236774,0.00038679322,0.014699585,0.0043365783,0.025080575],"study_design_scores_gemma":[0.000011312816,0.000040248888,0.0021131025,0.000010973965,0.000011953025,0.00005005087,0.00013748817,0.9938373,0.0001956221,0.0025175451,0.0010653188,0.000009106051],"about_ca_topic_score_codex":0.070292726,"about_ca_topic_score_gemma":0.04962813,"teacher_disagreement_score":0.070292726,"about_ca_system_score_codex":0.0023012266,"about_ca_system_score_gemma":0.0011648573,"threshold_uncertainty_score":0.13976711},"labels":[],"label_agreement":null},{"id":"W4392913847","doi":"10.28924/2291-8639-22-2024-52","title":"Improving the Performance of a Series-Parallel System Based on Gamma Distribution","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Mathematics; Distribution (mathematics); Mathematical analysis","score_opus":0.004594316481499208,"score_gpt":0.2405033328815828,"score_spread":0.23590901640008358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392913847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3978264,0.0006348683,0.5904825,0.00015301771,0.00007450287,0.00004528171,0.000053819433,0.0027221066,0.008007502],"genre_scores_gemma":[0.9717773,0.0001893087,0.02625695,0.000013783847,0.000020051477,0.00001681868,0.00003419186,0.000063790656,0.0016278279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998222,0.000039700517,0.000007688008,0.000035958215,0.00006428587,0.00003016546],"domain_scores_gemma":[0.99950397,0.00018411233,0.000058652095,0.00007218411,0.00015987706,0.000021192118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004560222,0.00059518404,0.0003785704,0.00047418664,0.00030688636,0.00032963883,0.0005229705,0.00024985374,0.0016615093],"category_scores_gemma":[0.0013622054,0.00013478441,0.00033047897,0.0005450306,0.00027923062,0.00055118214,0.0002718315,0.00031830042,0.00036445368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047672243,0.00010831397,0.0025307543,0.00015919576,0.00008577318,0.00018687421,0.00016639652,0.7506535,0.10386405,0.006414797,0.0018410777,0.13351254],"study_design_scores_gemma":[0.000018789098,0.00022492443,0.00076464313,0.00000687161,0.000053857984,0.00009377864,0.000014962727,0.9697556,0.026344908,0.0014946085,0.0012144478,0.000012552361],"about_ca_topic_score_codex":0.0024877884,"about_ca_topic_score_gemma":0.0013661556,"teacher_disagreement_score":0.0024877884,"about_ca_system_score_codex":0.0005208421,"about_ca_system_score_gemma":0.0004441087,"threshold_uncertainty_score":0.005558312},"labels":[],"label_agreement":null},{"id":"W4392943775","doi":"10.1109/mosicom59118.2023.10458782","title":"Enhanced Average-Value Modeling of Voltage-Source Inverters in Variable Frequency Drives for Efficient Simulation of Marine Propulsion Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Propulsion; Variable (mathematics); Marine propulsion; Voltage; Value (mathematics); Switching frequency; Computer science; Control theory (sociology); Electrical engineering; Engineering; Aerospace engineering; Mathematics; Control (management)","score_opus":0.014649631861037586,"score_gpt":0.25693972148711725,"score_spread":0.24229008962607965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943775","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061727695,0.0002506962,0.9247472,0.00009896631,0.00006197538,0.00004122454,0.00017761062,0.0006037336,0.012290931],"genre_scores_gemma":[0.9237124,0.0002670088,0.07176008,0.00003295545,0.000024170029,0.00010526553,0.00015537189,0.000100997386,0.0038417785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998779,0.00003825865,0.000006267164,0.000013905956,0.000052149004,0.000011395066],"domain_scores_gemma":[0.99980456,0.0001000181,0.00002142707,0.000019000863,0.000045609402,0.000009328293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029930315,0.00036453412,0.0005082476,0.00027276843,0.00027086702,0.0006178613,0.0010059951,0.00057944376,0.0018516697],"category_scores_gemma":[0.00074072374,0.00021887264,0.0004346659,0.0004984615,0.00023184993,0.00064701826,0.00029150455,0.0005836538,0.00029098973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001916204,0.000013736035,0.0003169621,0.000030438969,0.000008384259,0.0000357316,0.000031099502,0.9819967,0.0027025116,0.0056025307,0.0002705565,0.008972046],"study_design_scores_gemma":[0.0000015083269,0.0000047784506,0.000026587271,0.0000011449737,9.176821e-7,0.0000029523387,0.0000015566399,0.9989907,0.00025148582,0.0003807142,0.0003368143,7.477171e-7],"about_ca_topic_score_codex":0.003770286,"about_ca_topic_score_gemma":0.0028011282,"teacher_disagreement_score":0.003770286,"about_ca_system_score_codex":0.00038354527,"about_ca_system_score_gemma":0.0004333751,"threshold_uncertainty_score":0.0074967146},"labels":[],"label_agreement":null},{"id":"W4393029470","doi":"10.36001/phmconf.2014.v6i1.2353","title":"Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton","year":2014,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Fuzzy logic; Fuzzy set operations; Ambiguity; Fuzzy number; Defuzzification; Neuro-fuzzy; Fuzzy set; Event (particle physics); USable; Artificial intelligence; Fuzzy control system; Data mining","score_opus":0.012393940722428378,"score_gpt":0.2362482723268687,"score_spread":0.22385433160444032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393029470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0769999,0.00022451664,0.91749686,0.00023257593,0.00005135482,0.000055216766,0.00007456692,0.0009289424,0.0039360845],"genre_scores_gemma":[0.9307494,0.00012529104,0.06684922,0.00004791374,0.000012208842,0.000061393635,0.00008673708,0.000017398826,0.002050435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994197,0.00010273145,0.00004300754,0.00018831504,0.00018504715,0.00006126061],"domain_scores_gemma":[0.99923515,0.00039522807,0.00007791852,0.0000741555,0.00017702093,0.00004051622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044914073,0.00036881093,0.0006690157,0.0005700499,0.0005054804,0.0009549259,0.00092227774,0.00074050494,0.0012992987],"category_scores_gemma":[0.0022955649,0.00019677538,0.00074019766,0.00035921144,0.0007973155,0.0007764276,0.0006973635,0.000707351,0.00016717752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018636014,0.00009597042,0.0038854165,0.0001135941,0.00007486526,0.00050177844,0.00051856437,0.8458352,0.013706548,0.04091906,0.0006640824,0.09349846],"study_design_scores_gemma":[0.0000051097454,0.000022220205,0.0001510425,0.0000035496194,0.000007477699,0.000031748274,0.00000960738,0.99453676,0.0011738419,0.0037797613,0.00027375502,0.000005280279],"about_ca_topic_score_codex":0.009882142,"about_ca_topic_score_gemma":0.0057915254,"teacher_disagreement_score":0.009882142,"about_ca_system_score_codex":0.0011153627,"about_ca_system_score_gemma":0.0008725234,"threshold_uncertainty_score":0.019649267},"labels":[],"label_agreement":null},{"id":"W4393299679","doi":"10.1016/j.dche.2024.100148","title":"Editorial: Special issue on emerging stars in digital chemical engineering","year":2024,"lang":"en","type":"editorial","venue":"Digital Chemical Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Stars; Computer science; Astrobiology; Astronomy; Physics","score_opus":0.0033246200548264033,"score_gpt":0.21941024694964134,"score_spread":0.21608562689481495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393299679","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020682,0.0021741556,0.00008284177,0.018181548,0.97779185,0.000017217293,0.000039345763,0.000038947313,0.0016535411],"genre_scores_gemma":[0.00022379565,0.0017455247,0.00007631973,0.010608301,0.9771597,0.000016376978,0.00003485718,0.000036193782,0.010098995],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99338865,0.00093855726,0.00068146666,0.0007416956,0.003758112,0.00049147394],"domain_scores_gemma":[0.97620267,0.0069334684,0.0017892934,0.0007498468,0.009509769,0.004815005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008182711,0.0049585006,0.004774343,0.006248755,0.0046465895,0.012075069,0.003711662,0.021830533,0.03597149],"category_scores_gemma":[0.024061758,0.0016679276,0.003601025,0.0024231765,0.0024426954,0.0053258487,0.002930101,0.017510738,0.024379577],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027521966,0.00000918068,0.000015556363,0.00009945819,0.000011542066,0.00007243726,0.0000046078817,0.000016263672,0.000042482094,0.00014151358,0.9965294,0.0030300338],"study_design_scores_gemma":[0.000085949294,0.000028876217,0.00024099376,0.00026140423,0.00004727565,0.00017297399,0.000026743855,0.00018211246,0.00010038424,0.0009960099,0.9978409,0.000016282358],"about_ca_topic_score_codex":0.0015015311,"about_ca_topic_score_gemma":0.0059528886,"teacher_disagreement_score":0.03597149,"about_ca_system_score_codex":0.0038382658,"about_ca_system_score_gemma":0.0029886062,"threshold_uncertainty_score":0.12033665},"labels":[],"label_agreement":null},{"id":"W4393565902","doi":"10.5281/zenodo.4898411","title":"Ethereum Cryptoasset Networks Extract for the KDD 2021 paper \"AlphaCore: Data Depth based Core Decomposition\"","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Core (optical fiber); Decomposition; Computer science; Data mining; Artificial intelligence; Biology; Telecommunications","score_opus":0.06518166862219957,"score_gpt":0.311886386863199,"score_spread":0.24670471824099946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393565902","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063024447,0.0004387296,0.008909151,0.00030852822,0.0002956402,0.00029694394,0.95289284,0.024927033,0.005628695],"genre_scores_gemma":[0.0064622876,0.00013499902,0.00954557,0.000057690395,0.000020228592,0.00021199776,0.9810783,0.00051139505,0.0019776127],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908316,0.00008144407,0.00008487853,0.00022410933,0.00037245575,0.00015393074],"domain_scores_gemma":[0.99917907,0.00013052495,0.000061495186,0.00030830284,0.00024715037,0.00007345712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008663966,0.0024274762,0.00086578913,0.004118563,0.00073633244,0.0017542361,0.001949468,0.001312492,0.020480886],"category_scores_gemma":[0.003928305,0.0004846816,0.0010299275,0.004077445,0.00040712536,0.0016050166,0.0015099655,0.0014559431,0.03167681],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019874828,0.00012828349,0.0014269075,0.00062648195,0.00005449033,0.00010414763,0.00005312471,0.0034424255,0.001278968,0.0020634737,0.9498926,0.040730394],"study_design_scores_gemma":[0.0003287081,0.00010741573,0.007612574,0.00024232679,0.00005277422,0.00050139026,0.00024610225,0.03515654,0.011424537,0.008150666,0.9360913,0.000085729254],"about_ca_topic_score_codex":0.011742774,"about_ca_topic_score_gemma":0.01929559,"teacher_disagreement_score":0.020480886,"about_ca_system_score_codex":0.0014407166,"about_ca_system_score_gemma":0.0018568364,"threshold_uncertainty_score":0.06851542},"labels":[],"label_agreement":null},{"id":"W4393631897","doi":"10.5281/zenodo.7705363","title":"ASE2021 vulnerability fix dataset","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Vulnerability (computing); Computer science; Computer security; Geography","score_opus":0.03570254469444355,"score_gpt":0.2787153586426227,"score_spread":0.24301281394817914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393631897","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006564762,0.0008720479,0.0010863243,0.000527637,0.00016804691,0.0000987617,0.9810106,0.0059572607,0.0037145172],"genre_scores_gemma":[0.0047352184,0.00016970985,0.0012243242,0.00014318505,0.000020266167,0.000107748405,0.99266946,0.00018564207,0.00074446294],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99727964,0.00043034015,0.00029261233,0.00070363714,0.0010269958,0.0002667586],"domain_scores_gemma":[0.9953896,0.0010966825,0.00038086518,0.0013860986,0.0014517393,0.00029504538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017570732,0.0025937892,0.0010563266,0.006858628,0.0012391349,0.0016963813,0.002892054,0.0027358544,0.014270633],"category_scores_gemma":[0.01035169,0.0005052403,0.0016932429,0.005392874,0.00059947814,0.0018796417,0.002339164,0.0020513488,0.02198782],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013426624,0.000096419266,0.0055102073,0.0008634404,0.000103213766,0.00032146898,0.000052714528,0.0022815815,0.00053454324,0.0008164001,0.97710973,0.012175927],"study_design_scores_gemma":[0.00028134248,0.00018392107,0.030964764,0.00056252617,0.00014548356,0.0013320835,0.00033584042,0.011446001,0.0029863548,0.0049836272,0.9466581,0.00011995991],"about_ca_topic_score_codex":0.014987262,"about_ca_topic_score_gemma":0.027227508,"teacher_disagreement_score":0.014987262,"about_ca_system_score_codex":0.001550864,"about_ca_system_score_gemma":0.0022458727,"threshold_uncertainty_score":0.047740042},"labels":[],"label_agreement":null},{"id":"W4393749247","doi":"10.5281/zenodo.6979991","title":"GENEA Challenge 2022 objective evaluation data","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Electronic Arts (Canada)","funders":"","keywords":"Computer science; Data science","score_opus":0.08138887783894873,"score_gpt":0.3070438007439275,"score_spread":0.2256549229049788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393749247","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044391032,0.0048604845,0.029221922,0.001379252,0.0022628026,0.0063830237,0.7882074,0.042187322,0.08110681],"genre_scores_gemma":[0.050065104,0.00046036975,0.02295443,0.0006029648,0.00021319168,0.0073788497,0.88872975,0.0039582164,0.025637088],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9865919,0.004272124,0.0009964085,0.0016902257,0.0056796013,0.000769847],"domain_scores_gemma":[0.9816426,0.0034296082,0.00079325313,0.0033643933,0.009364969,0.0014051385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009346053,0.0042995573,0.0024919275,0.0037507943,0.00122624,0.0031518512,0.0040496658,0.0029588551,0.050672162],"category_scores_gemma":[0.024283495,0.00079861045,0.0019523974,0.0022255343,0.000824179,0.0025160906,0.004233161,0.0023819006,0.056884833],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015832622,0.0010729585,0.0033152723,0.0022148604,0.00027837965,0.00016710084,0.00019594282,0.008804571,0.0032904705,0.001130601,0.8853442,0.09260236],"study_design_scores_gemma":[0.0023038925,0.002878232,0.0486907,0.0011494755,0.0003196148,0.0006612235,0.0008572588,0.07681769,0.0134386895,0.00559177,0.8466956,0.0005959221],"about_ca_topic_score_codex":0.014604274,"about_ca_topic_score_gemma":0.023593126,"teacher_disagreement_score":0.050672162,"about_ca_system_score_codex":0.0018521547,"about_ca_system_score_gemma":0.0022423293,"threshold_uncertainty_score":0.16951525},"labels":[],"label_agreement":null},{"id":"W4393833302","doi":"10.5281/zenodo.5513050","title":"ASE2021 vulnerability fix dataset","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Vulnerability (computing); Computer science; Environmental science; Computer security","score_opus":0.03570254469444355,"score_gpt":0.2787153586426227,"score_spread":0.24301281394817914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393833302","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006564762,0.0008720479,0.0010863243,0.000527637,0.00016804691,0.0000987617,0.9810106,0.0059572607,0.0037145172],"genre_scores_gemma":[0.0047352184,0.00016970985,0.0012243242,0.00014318505,0.000020266167,0.000107748405,0.99266946,0.00018564207,0.00074446294],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99727964,0.00043034015,0.00029261233,0.00070363714,0.0010269958,0.0002667586],"domain_scores_gemma":[0.9953896,0.0010966825,0.00038086518,0.0013860986,0.0014517393,0.00029504538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017570732,0.0025937892,0.0010563266,0.006858628,0.0012391349,0.0016963813,0.002892054,0.0027358544,0.014270633],"category_scores_gemma":[0.01035169,0.0005052403,0.0016932429,0.005392874,0.00059947814,0.0018796417,0.002339164,0.0020513488,0.02198782],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013426624,0.000096419266,0.0055102073,0.0008634404,0.000103213766,0.00032146898,0.000052714528,0.0022815815,0.00053454324,0.0008164001,0.97710973,0.012175927],"study_design_scores_gemma":[0.00028134248,0.00018392107,0.030964764,0.00056252617,0.00014548356,0.0013320835,0.00033584042,0.011446001,0.0029863548,0.0049836272,0.9466581,0.00011995991],"about_ca_topic_score_codex":0.014987262,"about_ca_topic_score_gemma":0.027227508,"teacher_disagreement_score":0.014987262,"about_ca_system_score_codex":0.001550864,"about_ca_system_score_gemma":0.0022458727,"threshold_uncertainty_score":0.047740042},"labels":[],"label_agreement":null},{"id":"W4393881276","doi":"10.5281/zenodo.7044699","title":"Appendices of the work \"On the perceived relevance of critical internal quality attributes when evolving software features\"","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Relevance (law); Quality (philosophy); Work (physics); Computer science; Software; Data science; Software engineering; Engineering; Epistemology; Political science; Programming language; Philosophy","score_opus":0.03917652827781351,"score_gpt":0.2845245504465445,"score_spread":0.245348022168731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393881276","genre_codex":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014097525,0.022454087,0.037659686,0.05906424,0.107711606,0.0036787044,0.13007608,0.005761073,0.61949694],"genre_scores_gemma":[0.07375477,0.04503364,0.04119917,0.027490482,0.029349739,0.0073105763,0.1611824,0.0048939735,0.6097853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871504,0.00034206815,0.00013376177,0.00016039737,0.00056796626,0.00008068748],"domain_scores_gemma":[0.97545344,0.011911144,0.000983273,0.0015640658,0.009201967,0.000886085],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016544493,0.0010362689,0.0007917612,0.0061895517,0.0016601289,0.0022375537,0.0012488426,0.0015578809,0.3690627],"category_scores_gemma":[0.030868128,0.00041047588,0.0010219904,0.0055540106,0.00063694443,0.0028113667,0.0023857823,0.0017785794,0.11848828],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005041179,0.000074458054,0.00057617604,0.0010055914,0.0000058843925,0.000068443085,0.00045337045,0.00015832328,0.00018106244,0.0017516001,0.9404369,0.05523777],"study_design_scores_gemma":[0.00002733493,0.000071449525,0.006845935,0.0027791965,0.00001766777,0.00032473714,0.00066760124,0.00025942575,0.00034468903,0.00471833,0.98390806,0.00003561725],"about_ca_topic_score_codex":0.007925792,"about_ca_topic_score_gemma":0.0067791096,"teacher_disagreement_score":0.3690627,"about_ca_system_score_codex":0.0018850043,"about_ca_system_score_gemma":0.002189796,"threshold_uncertainty_score":0.8999555},"labels":[],"label_agreement":null},{"id":"W4394250289","doi":"10.6084/m9.figshare.21456375","title":"Physicochemical parameters of groundwater in coastal sandy aquifer","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Aquifer; Groundwater; Geology; Hydrology (agriculture); Environmental science; Geotechnical engineering","score_opus":0.0338984663638418,"score_gpt":0.2770416815707585,"score_spread":0.24314321520691667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394250289","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987255,0.00017039091,0.00024205171,0.000032104752,0.0000035921428,0.000008631841,0.00040934025,0.000009708762,0.0003987273],"genre_scores_gemma":[0.9984428,0.00016924627,0.00033210684,0.00003392465,0.0000061746273,0.000020563197,0.0006567784,0.0000028546053,0.00033549167],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974924,0.00003204936,0.000035009347,0.000076665994,0.00006962992,0.000037385475],"domain_scores_gemma":[0.9998934,0.000013756067,0.00002689026,0.0000037106665,0.000043648994,0.000018489234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023124792,0.00036209007,0.00038932575,0.0007809757,0.00069399155,0.00063990004,0.00025418692,0.00038837828,0.000719783],"category_scores_gemma":[0.00031344814,0.00023175824,0.00026051782,0.0015682896,0.00034435268,0.00039171154,0.0004611142,0.00017864464,0.0001295147],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005531812,0.00011825629,0.7202714,0.00042376033,0.00011341966,0.001331195,0.0017698726,0.0015288973,0.2507261,0.0003700623,0.00063884986,0.022154944],"study_design_scores_gemma":[0.000072117335,0.0005816331,0.95789486,0.00004620314,0.00012980103,0.0015177878,0.00545964,0.004108264,0.024156658,0.00084443664,0.005077501,0.0001111387],"about_ca_topic_score_codex":0.0100601725,"about_ca_topic_score_gemma":0.012909876,"teacher_disagreement_score":0.0100601725,"about_ca_system_score_codex":0.0005809236,"about_ca_system_score_gemma":0.0005870804,"threshold_uncertainty_score":0.0200032},"labels":[],"label_agreement":null},{"id":"W4394368423","doi":"10.6084/m9.figshare.21456375.v2","title":"Physicochemical parameters of groundwater in coastal sandy aquifer","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Aquifer; Groundwater; Hydrology (agriculture); Environmental science; Geology; Geotechnical engineering","score_opus":0.026397740473991196,"score_gpt":0.2639356209833579,"score_spread":0.2375378805093667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394368423","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987255,0.00017039091,0.00024205171,0.000032104752,0.0000035921428,0.000008631841,0.00040934025,0.000009708762,0.0003987273],"genre_scores_gemma":[0.9984428,0.00016924627,0.00033210684,0.00003392465,0.0000061746273,0.000020563197,0.0006567784,0.0000028546053,0.00033549167],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974924,0.00003204936,0.000035009347,0.000076665994,0.00006962992,0.000037385475],"domain_scores_gemma":[0.9998934,0.000013756067,0.00002689026,0.0000037106665,0.000043648994,0.000018489234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023124792,0.00036209007,0.00038932575,0.0007809757,0.00069399155,0.00063990004,0.00025418692,0.00038837828,0.000719783],"category_scores_gemma":[0.00031344814,0.00023175824,0.00026051782,0.0015682896,0.00034435268,0.00039171154,0.0004611142,0.00017864464,0.0001295147],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005531812,0.00011825629,0.7202714,0.00042376033,0.00011341966,0.001331195,0.0017698726,0.0015288973,0.2507261,0.0003700623,0.00063884986,0.022154944],"study_design_scores_gemma":[0.000072117335,0.0005816331,0.95789486,0.00004620314,0.00012980103,0.0015177878,0.00545964,0.004108264,0.024156658,0.00084443664,0.005077501,0.0001111387],"about_ca_topic_score_codex":0.0100601725,"about_ca_topic_score_gemma":0.012909876,"teacher_disagreement_score":0.0100601725,"about_ca_system_score_codex":0.0005809236,"about_ca_system_score_gemma":0.0005870804,"threshold_uncertainty_score":0.0200032},"labels":[],"label_agreement":null},{"id":"W4394828285","doi":"10.1007/978-3-031-58547-0","title":"Advances in Intelligent Data Analysis XXII","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Namur; Högskolan i Halmstad; Région Normandie; Université François-Rabelais; Eötvös Loránd Tudományegyetem; Instituto Politécnico do Porto; Universität Mannheim; Masarykova Univerzita; Stockholms Universitet; Universidade do Porto; Université de Caen Normandie; Universidade de Coimbra; Universität Bielefeld; Centre National de la Recherche Scientifique; Université de Strasbourg; Universiteit Gent; Universidade do Minho; Université d'Orléans; Universidad Pública de Navarra; Kungliga Tekniska Högskolan; Télécom Paris; Universiteit Utrecht; KU Leuven; Philipps-Universität Marburg; Forschungszentrum Jülich; Università degli Studi di Torino; Università degli Studi di Trento; Dalhousie University; Deutsches Elektronen-Synchrotron; Technische Universiteit Delft; Institut National des Sciences Appliquées de Lyon; Politechnika Poznańska; University of Bristol; Universidad de Granada; Institut \"Jožef Stefan\"; Universiteit Leiden; Canterbury Christ Church University; Indian National Science Academy; Itä-Suomen Yliopisto; Università degli Studi di Napoli Federico II","keywords":"Computer science","score_opus":0.020390650210017208,"score_gpt":0.30237315192621195,"score_spread":0.28198250171619477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394828285","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024081988,0.1758725,0.72153264,0.0052332436,0.009298403,0.00011906411,0.001709306,0.004878001,0.07894859],"genre_scores_gemma":[0.026094984,0.15834112,0.50811404,0.0023193727,0.009000075,0.00022619343,0.0050259386,0.0021633613,0.2887148],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990256,0.00011965368,0.000078304845,0.00018230782,0.00055721856,0.000036816073],"domain_scores_gemma":[0.9977969,0.0011259173,0.00007412478,0.0004070795,0.00052766374,0.000068395915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001267309,0.0011308118,0.0016204594,0.0023261607,0.00036869227,0.0035811004,0.0010700689,0.000686159,0.025896762],"category_scores_gemma":[0.003530593,0.0006308026,0.00087008305,0.0038704185,0.00093694223,0.0035219307,0.0014418152,0.002335536,0.02332903],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046523935,0.000047153757,0.00032784272,0.00057295454,0.00005747997,0.000054274416,0.00008080597,0.0021210948,0.0024461146,0.032435145,0.18373255,0.778078],"study_design_scores_gemma":[0.000010444154,0.000050368435,0.001392763,0.00041144286,0.000060191207,0.00036792096,0.00008065919,0.027166203,0.0038790384,0.09276582,0.87377656,0.00003862821],"about_ca_topic_score_codex":0.00073844154,"about_ca_topic_score_gemma":0.0007457635,"teacher_disagreement_score":0.025896762,"about_ca_system_score_codex":0.0006162405,"about_ca_system_score_gemma":0.00076830137,"threshold_uncertainty_score":0.086633265},"labels":[],"label_agreement":null},{"id":"W4394828659","doi":"10.1007/978-3-031-58553-1","title":"Advances in Intelligent Data Analysis XXII","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Namur; Högskolan i Halmstad; Région Normandie; Université François-Rabelais; Eötvös Loránd Tudományegyetem; Instituto Politécnico do Porto; Universität Mannheim; Masarykova Univerzita; Stockholms Universitet; Universidade do Porto; Université de Caen Normandie; Universidade de Coimbra; Universität Bielefeld; Centre National de la Recherche Scientifique; Université de Strasbourg; Universiteit Gent; Universidade do Minho; Université d'Orléans; Universidad Pública de Navarra; Kungliga Tekniska Högskolan; Télécom Paris; Universiteit Utrecht; KU Leuven; Philipps-Universität Marburg; Forschungszentrum Jülich; Università degli Studi di Torino; Università degli Studi di Trento; Dalhousie University; Deutsches Elektronen-Synchrotron; Technische Universiteit Delft; Institut National des Sciences Appliquées de Lyon; Politechnika Poznańska; University of Bristol; Universidad de Granada; Institut \"Jožef Stefan\"; Universiteit Leiden; Canterbury Christ Church University; Indian National Science Academy; Itä-Suomen Yliopisto; Università degli Studi di Napoli Federico II","keywords":"Computer science","score_opus":0.020390650210017208,"score_gpt":0.30237315192621195,"score_spread":0.28198250171619477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394828659","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024081988,0.1758725,0.72153264,0.0052332436,0.009298403,0.00011906411,0.001709306,0.004878001,0.07894859],"genre_scores_gemma":[0.026094984,0.15834112,0.50811404,0.0023193727,0.009000075,0.00022619343,0.0050259386,0.0021633613,0.2887148],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990256,0.00011965368,0.000078304845,0.00018230782,0.00055721856,0.000036816073],"domain_scores_gemma":[0.9977969,0.0011259173,0.00007412478,0.0004070795,0.00052766374,0.000068395915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001267309,0.0011308118,0.0016204594,0.0023261607,0.00036869227,0.0035811004,0.0010700689,0.000686159,0.025896762],"category_scores_gemma":[0.003530593,0.0006308026,0.00087008305,0.0038704185,0.00093694223,0.0035219307,0.0014418152,0.002335536,0.02332903],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046523935,0.000047153757,0.00032784272,0.00057295454,0.00005747997,0.000054274416,0.00008080597,0.0021210948,0.0024461146,0.032435145,0.18373255,0.778078],"study_design_scores_gemma":[0.000010444154,0.000050368435,0.001392763,0.00041144286,0.000060191207,0.00036792096,0.00008065919,0.027166203,0.0038790384,0.09276582,0.87377656,0.00003862821],"about_ca_topic_score_codex":0.00073844154,"about_ca_topic_score_gemma":0.0007457635,"teacher_disagreement_score":0.025896762,"about_ca_system_score_codex":0.0006162405,"about_ca_system_score_gemma":0.00076830137,"threshold_uncertainty_score":0.086633265},"labels":[],"label_agreement":null},{"id":"W4395013599","doi":"10.33042/2522-1809-2024-1-182-14-19","title":"LIQUID NEURAL NETWORKS: PRINCIPLE OF WORK AND AREAS OF APPLICATION","year":2024,"lang":"en","type":"article","venue":"Municipal economy of cities","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universität Wien; Universität Wien; Institute for Catastrophic Loss Reduction","keywords":"Work (physics); Artificial neural network; Computer science; Artificial intelligence; Engineering; Mechanical engineering","score_opus":0.01203822475960331,"score_gpt":0.2477267024797826,"score_spread":0.23568847772017928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395013599","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039171977,0.041523725,0.87943345,0.0053981105,0.0007761545,0.00021016184,0.00013423448,0.00062374154,0.0679833],"genre_scores_gemma":[0.26207435,0.0805777,0.60759634,0.003182373,0.002615216,0.0012761307,0.00041933174,0.00037928726,0.041879274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99861884,0.00040400456,0.00008699165,0.00027063536,0.0005509668,0.00006854568],"domain_scores_gemma":[0.9991128,0.00043364512,0.000070751,0.000089960056,0.00024530012,0.000047469726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016084694,0.0009721789,0.0007299912,0.0015861781,0.0006300329,0.0032737292,0.001755266,0.0028618183,0.0037075172],"category_scores_gemma":[0.0032496017,0.00044923107,0.0006971154,0.00182134,0.002932596,0.0040417844,0.001871948,0.0021949094,0.0015543388],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009369801,0.00008847593,0.001112318,0.0008933146,0.000067865134,0.00022821421,0.00030057927,0.03674075,0.0035023964,0.5163317,0.009954536,0.4306863],"study_design_scores_gemma":[0.000030378998,0.00018789059,0.0006871906,0.0010384679,0.00006068899,0.0008484434,0.00023533306,0.21890415,0.004369512,0.53726476,0.23628734,0.00008588216],"about_ca_topic_score_codex":0.0014382668,"about_ca_topic_score_gemma":0.000640762,"teacher_disagreement_score":0.0037075172,"about_ca_system_score_codex":0.0013159534,"about_ca_system_score_gemma":0.001153252,"threshold_uncertainty_score":0.012402892},"labels":[],"label_agreement":null},{"id":"W4396212849","doi":"10.61091/jcmcc119-09","title":"Predictive Modelling of Students’ University English Language Performance by Classification with Gaussian Process Models","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Process (computing); Gaussian process; Natural language processing; Mathematics education; Artificial intelligence; Gaussian; Linguistics; Psychology; Programming language; Physics; Philosophy","score_opus":0.012412024039880606,"score_gpt":0.2382875136785237,"score_spread":0.22587548963864307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396212849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5811842,0.00025320906,0.41474465,0.0005089972,0.00007977354,0.00013214619,0.00045044793,0.00047242385,0.0021741872],"genre_scores_gemma":[0.9805879,0.00012028857,0.017340273,0.00003812345,0.000026001007,0.00007740017,0.00048807228,0.000014553718,0.0013073934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866664,0.00057517644,0.00006393525,0.00024476676,0.00026088406,0.00018864589],"domain_scores_gemma":[0.99472165,0.0036632079,0.00043908675,0.0003644753,0.0006909847,0.0001206149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044156653,0.00088823476,0.0007939193,0.0014811031,0.00039237004,0.0016327337,0.001190715,0.0009481077,0.0010625023],"category_scores_gemma":[0.009919334,0.0003258034,0.0012679218,0.0014097508,0.0006230444,0.0010949883,0.0007803526,0.0019242457,0.00056619826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023220273,0.00027965035,0.047645725,0.0000426781,0.0001246032,0.000122027785,0.00028191492,0.8939741,0.0010532006,0.0057450533,0.0010737232,0.049425125],"study_design_scores_gemma":[0.000002582512,0.000022787037,0.002319559,0.0000040303375,0.000005788049,0.0000060576544,0.000020171548,0.9961945,0.00018840731,0.001159555,0.00006983128,0.000006743066],"about_ca_topic_score_codex":0.020060185,"about_ca_topic_score_gemma":0.010286422,"teacher_disagreement_score":0.020060185,"about_ca_system_score_codex":0.0008543403,"about_ca_system_score_gemma":0.0008668318,"threshold_uncertainty_score":0.039886832},"labels":[],"label_agreement":null},{"id":"W4398920337","doi":"10.7910/dvn/tpzpio/9wc15m","title":"ANES_Sigma_Models.R","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sigma; Physics","score_opus":0.017153908431109702,"score_gpt":0.23532841428710935,"score_spread":0.21817450585599965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398920337","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016315456,0.00010961433,0.00030850916,0.00011633928,0.000056298897,0.000017070746,0.9954626,0.0027706204,0.0009957633],"genre_scores_gemma":[0.0006934088,0.000097046795,0.0010937306,0.00011006087,0.000016464892,0.00013809821,0.99630016,0.0006165885,0.0009344932],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844676,0.00037096223,0.0001364431,0.00051142747,0.0003261869,0.00020823428],"domain_scores_gemma":[0.9967006,0.0010189192,0.00025693147,0.0011164693,0.00063130766,0.00027582576],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021670114,0.004150376,0.001967754,0.0034788258,0.000985919,0.0034149399,0.0053239237,0.0027770835,0.10500173],"category_scores_gemma":[0.010373951,0.0010557108,0.002511701,0.004363194,0.00072897517,0.001887413,0.0025325385,0.002565806,0.17398228],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049712788,0.000015853591,0.00028131704,0.00042197233,0.000053252566,0.000009065822,0.00000882116,0.00038092097,0.00006260973,0.0004132731,0.9966383,0.001664819],"study_design_scores_gemma":[0.00054683245,0.000040435225,0.0019750958,0.00038186403,0.00008370786,0.000058929993,0.000047447233,0.0018974035,0.00066432514,0.0044254037,0.9898177,0.000060794202],"about_ca_topic_score_codex":0.01882258,"about_ca_topic_score_gemma":0.03152768,"teacher_disagreement_score":0.89499825,"about_ca_system_score_codex":0.001516914,"about_ca_system_score_gemma":0.0022559187,"threshold_uncertainty_score":0.3512658},"labels":[],"label_agreement":null},{"id":"W4399978728","doi":"10.18280/ijsse.140324","title":"Development of a Methodology for Pooling Resources and Optimising Investments in the Field of CBRN Training and Capacity Building","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pooling; Training (meteorology); Capacity building; Field (mathematics); Computer science; Risk analysis (engineering); Engineering; Business; Artificial intelligence; Economics; Geography; Economic growth","score_opus":0.04686230054175297,"score_gpt":0.3276500370252507,"score_spread":0.28078773648349775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399978728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008021462,0.0004349851,0.966008,0.001754886,0.00009111728,0.0045469054,0.0005605346,0.00039102763,0.018191058],"genre_scores_gemma":[0.018041596,0.0001679291,0.9784185,0.00007500148,0.000004985597,0.0022506765,0.00016213149,0.00003313166,0.00084611156],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9645505,0.022116778,0.003293641,0.0031999543,0.005610425,0.0012287782],"domain_scores_gemma":[0.9456605,0.03442197,0.0057561044,0.0038421466,0.009606224,0.0007131499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045364197,0.0025707055,0.0012957631,0.013352566,0.002519971,0.009899877,0.0042089955,0.0033167056,0.008226312],"category_scores_gemma":[0.06586321,0.0015868495,0.0029860013,0.01210444,0.0051863687,0.00851181,0.006007566,0.0027789078,0.0017582388],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001606428,0.00050690403,0.007064314,0.0057101697,0.0002967171,0.0008167304,0.020172993,0.03947699,0.0045012655,0.43215188,0.00801054,0.4811308],"study_design_scores_gemma":[0.0004227215,0.0010078175,0.006137896,0.007596462,0.0006017201,0.0011028827,0.04923034,0.16986196,0.015589324,0.4429334,0.3051258,0.00038958603],"about_ca_topic_score_codex":0.0072128573,"about_ca_topic_score_gemma":0.012598353,"teacher_disagreement_score":0.045364197,"about_ca_system_score_codex":0.00964644,"about_ca_system_score_gemma":0.025073277,"threshold_uncertainty_score":0.23991168},"labels":[],"label_agreement":null},{"id":"W4400090968","doi":"10.62492/sefijeea.v1i1.15","title":"A Causation-driven Approach to Engineering Education Using Data Analytics and Machine Learning Tools","year":2024,"lang":"en","type":"article","venue":"SEFI Journal of Engineering Education Advancement","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Causation; Analytics; Data science; Data analysis; Computer science; Learning analytics; Machine learning; Artificial intelligence; Data mining; Epistemology","score_opus":0.051369426600262405,"score_gpt":0.315144186839928,"score_spread":0.2637747602396656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400090968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004408905,0.00018092184,0.9892027,0.002249735,0.00005128969,0.00019672114,0.00006977627,0.00022739888,0.003412576],"genre_scores_gemma":[0.1652756,0.0006397429,0.8300135,0.00064013404,0.000112859845,0.0006548883,0.00018918068,0.00008688682,0.0023871944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99149597,0.0052640527,0.00041382908,0.0009677472,0.0016196278,0.00023886492],"domain_scores_gemma":[0.97353584,0.019773552,0.0017868115,0.002196217,0.002157233,0.00055035466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011742226,0.0012385849,0.000839098,0.0040616416,0.0011439386,0.0038622022,0.0029341872,0.0017674117,0.0046940725],"category_scores_gemma":[0.021915471,0.000801624,0.0019242229,0.0018697939,0.0030660813,0.004158878,0.004247615,0.0038117408,0.00079470273],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008237821,0.0010825816,0.01204858,0.0011174269,0.00019426562,0.0005211515,0.0022454062,0.096140444,0.0032187342,0.6790325,0.0032594504,0.2010571],"study_design_scores_gemma":[0.00005458281,0.00021394422,0.0020313433,0.00046242992,0.000057107452,0.0002638135,0.00075301476,0.26349667,0.004683635,0.7020012,0.025899578,0.000082644656],"about_ca_topic_score_codex":0.001471472,"about_ca_topic_score_gemma":0.001967945,"teacher_disagreement_score":0.011742226,"about_ca_system_score_codex":0.0022819594,"about_ca_system_score_gemma":0.0052215746,"threshold_uncertainty_score":0.062099516},"labels":[],"label_agreement":null},{"id":"W4400136137","doi":"10.1007/978-3-031-62843-6","title":"Artificial intelligence and Machine Learning","year":2024,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Univerzita Pardubice; Slezská Univerzita v Opavě; Uniwersytet Mikolaja Kopernika w Toruniu; Wojskowa Akademia Techniczna; Politechnika Wrocławska; Politechnika Warszawska; York University; Uniwersytet Szczeciński; Politechnika Poznańska; Západočeská Univerzita v Plzni","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.056795207967137475,"score_gpt":0.3151915606809349,"score_spread":0.2583963527137974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400136137","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007487786,0.10421651,0.047550578,0.004177272,0.005119133,0.000090179085,0.00059603545,0.000783738,0.8367179],"genre_scores_gemma":[0.006220566,0.035685185,0.014101275,0.0014800469,0.0024123078,0.00013900126,0.0005048926,0.00037967652,0.939077],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950266,0.000085645064,0.000019593912,0.00006478863,0.00030535695,0.00002202677],"domain_scores_gemma":[0.99918705,0.0005116468,0.00003028643,0.00009838197,0.00013069154,0.000042024174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005277912,0.0016236354,0.0016370844,0.0021944458,0.00075253594,0.0034545166,0.0009823018,0.0011567082,0.083341986],"category_scores_gemma":[0.0019626224,0.0005242754,0.0003923145,0.0040833433,0.0014045554,0.00398037,0.0012553385,0.0028563682,0.058378153],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002196742,0.00004531723,0.00011799174,0.0004989683,0.000024765479,0.000043902695,0.00013432444,0.0014140663,0.00042419037,0.14642368,0.49092138,0.3599295],"study_design_scores_gemma":[0.0000063952307,0.000018292412,0.00026955773,0.00023545843,0.000009879976,0.00008912481,0.00004276826,0.001638138,0.00018204085,0.110347256,0.88715243,0.000008760472],"about_ca_topic_score_codex":0.0013352837,"about_ca_topic_score_gemma":0.00324404,"teacher_disagreement_score":0.083341986,"about_ca_system_score_codex":0.0009985123,"about_ca_system_score_gemma":0.0009246564,"threshold_uncertainty_score":0.27880675},"labels":[],"label_agreement":null},{"id":"W4400682287","doi":"10.1145/3672198.3673793","title":"Proof-of-Concept of a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Emulation; Computer science; Fidelity; Proof of concept; Training (meteorology); High fidelity; Computer architecture; Distributed computing; Operating system; Engineering; Psychology; Telecommunications","score_opus":0.03408917210478044,"score_gpt":0.28233302196355003,"score_spread":0.2482438498587696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400682287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030367145,0.00010105487,0.9602806,0.0004283189,0.0002078239,0.00021160599,0.00015203329,0.0032904414,0.0049609966],"genre_scores_gemma":[0.568447,0.00012260469,0.42594832,0.00036855508,0.000044664484,0.0005057981,0.0002423735,0.00053554535,0.0037852663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932957,0.00011948025,0.000025838284,0.00009904487,0.00035407348,0.000072023555],"domain_scores_gemma":[0.9990471,0.00036858485,0.00007100571,0.00020007604,0.00023307532,0.000080189515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012133989,0.0007209593,0.00037747558,0.0001591166,0.00032746783,0.00085343275,0.0020559838,0.0008112562,0.004989381],"category_scores_gemma":[0.0033823296,0.00034182647,0.00028123742,0.00007871041,0.00080626644,0.0010508667,0.0016068936,0.0020080062,0.00097053894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005923708,0.00053676945,0.0026963076,0.00050269015,0.000108742155,0.0007978879,0.00036047114,0.5348097,0.29106694,0.053531338,0.0093389535,0.10565788],"study_design_scores_gemma":[0.000111276,0.00027040523,0.00038519443,0.000035318226,0.000010871017,0.00021826736,0.00003787438,0.86931795,0.11372085,0.0043624653,0.011504,0.000025523308],"about_ca_topic_score_codex":0.0016557532,"about_ca_topic_score_gemma":0.0017420714,"teacher_disagreement_score":0.004989381,"about_ca_system_score_codex":0.0005304152,"about_ca_system_score_gemma":0.000840631,"threshold_uncertainty_score":0.016691148},"labels":[],"label_agreement":null},{"id":"W4401457706","doi":"10.35490/ec3.2024.196","title":"Machine Learning Model Prediction of Project Success","year":2024,"lang":"en","type":"article","venue":"Computing in construction","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence","score_opus":0.017813429540123094,"score_gpt":0.27078598173768154,"score_spread":0.25297255219755843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401457706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8002437,0.00076988875,0.18839388,0.001438937,0.00015626552,0.00017748236,0.0020896473,0.00073092704,0.0059992056],"genre_scores_gemma":[0.9742434,0.00026564175,0.020345446,0.000059750044,0.00004533461,0.00017007817,0.0014658943,0.000020427478,0.0033840768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994904,0.00018507204,0.000036999514,0.000118600066,0.00009019382,0.0000786734],"domain_scores_gemma":[0.9950906,0.0037622806,0.00029323858,0.00015212684,0.00060850417,0.0000932148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022998499,0.0007145583,0.000586884,0.0012460806,0.0003098814,0.0009903286,0.00093774695,0.0008905949,0.0020218212],"category_scores_gemma":[0.0071264002,0.00026011947,0.0005981683,0.0011824534,0.000291,0.00080958154,0.00038442508,0.001291051,0.0007022339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008160688,0.00019322861,0.01620442,0.000034173394,0.00006093222,0.000039297116,0.00003897394,0.9493426,0.00025868954,0.0012316433,0.0013044226,0.031209916],"study_design_scores_gemma":[0.0000027018232,0.000012554853,0.0010013555,0.000003403261,0.0000028176325,0.000002774526,0.000005466329,0.99820685,0.00005328223,0.0006206956,0.000086000255,0.0000020605494],"about_ca_topic_score_codex":0.018947171,"about_ca_topic_score_gemma":0.014754106,"teacher_disagreement_score":0.018947171,"about_ca_system_score_codex":0.0010347669,"about_ca_system_score_gemma":0.001148529,"threshold_uncertainty_score":0.03767377},"labels":[],"label_agreement":null},{"id":"W4401769124","doi":"10.18280/isi.290417","title":"Evaluation of Financial Credit Risk Management Models Based on Gradient Descent and Meta-Heuristic Algorithms","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gradient descent; Heuristic; Computer science; Finance; Meta heuristic; Algorithm; Artificial intelligence; Economics","score_opus":0.030817322253222208,"score_gpt":0.2597990582735982,"score_spread":0.22898173602037603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401769124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63436985,0.0041095205,0.34081343,0.0015341297,0.00026666388,0.0003459824,0.00040324897,0.0008581954,0.017299112],"genre_scores_gemma":[0.94512117,0.0004764029,0.052687593,0.000081727594,0.00002570643,0.00013170976,0.00016384154,0.000030102483,0.0012817802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993394,0.0003261104,0.000048591304,0.00007204536,0.00013889332,0.0000749141],"domain_scores_gemma":[0.9975757,0.0015805372,0.00019439297,0.00010229283,0.00046566853,0.000081341044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002386997,0.0010813649,0.0012591225,0.0014204761,0.0004585738,0.0015106371,0.0009654859,0.0015085657,0.0008602111],"category_scores_gemma":[0.0045882696,0.0004125204,0.0008055204,0.0008841601,0.0004299847,0.00089959166,0.00054885657,0.0009594273,0.00015041087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005484261,0.00006493496,0.00113759,0.00003469452,0.000037850732,0.000017232966,0.000013456137,0.9853726,0.00012689117,0.00078151055,0.0002864248,0.012071931],"study_design_scores_gemma":[0.0000047983444,0.000026988822,0.0001385173,0.0000061026885,0.0000047203866,0.0000034274794,0.0000061190412,0.9994523,0.0001067395,0.00018484928,0.000063483196,0.0000018757875],"about_ca_topic_score_codex":0.011287015,"about_ca_topic_score_gemma":0.006220431,"teacher_disagreement_score":0.011287015,"about_ca_system_score_codex":0.0013689655,"about_ca_system_score_gemma":0.001461158,"threshold_uncertainty_score":0.022442639},"labels":[],"label_agreement":null},{"id":"W4402068290","doi":"10.18280/ijsse.140418","title":"Classification Models for Assessing the Severity of Marine Accidents Based on Machine Learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Poison control; Occupational safety and health; Injury prevention; Computer science; Human factors and ergonomics; Engineering; Medical emergency; Forensic engineering; Medicine","score_opus":0.014199874941345052,"score_gpt":0.28445301772659126,"score_spread":0.2702531427852462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402068290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42371297,0.0014265279,0.5663851,0.0007130239,0.00041075028,0.00034328212,0.001863601,0.0024211109,0.0027236983],"genre_scores_gemma":[0.96179765,0.00025207832,0.035043832,0.00007444836,0.00009931462,0.00018166895,0.0014153188,0.000026723232,0.0011089997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896264,0.0002711016,0.00014652756,0.00020999815,0.00026782232,0.00014198024],"domain_scores_gemma":[0.9948872,0.0032813323,0.00052760885,0.00022715708,0.00094284734,0.00013384578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025632994,0.0011443327,0.0011791504,0.002699472,0.00043949715,0.0013272485,0.0013636085,0.0010950264,0.0016996071],"category_scores_gemma":[0.006745708,0.0002490864,0.0011722747,0.0012824306,0.00032316218,0.0010693729,0.0005958507,0.0015025473,0.0008314787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008477477,0.0011833222,0.07976743,0.00015697784,0.0004586311,0.00015642996,0.00013921264,0.53827816,0.0025393693,0.001723543,0.00434838,0.3704008],"study_design_scores_gemma":[0.000007485271,0.00006296066,0.004238196,0.000012048117,0.00003299848,0.00002322426,0.000019834664,0.99418133,0.00036957423,0.0009109978,0.00013147293,0.000009863074],"about_ca_topic_score_codex":0.0077024223,"about_ca_topic_score_gemma":0.006449768,"teacher_disagreement_score":0.0077024223,"about_ca_system_score_codex":0.0007807178,"about_ca_system_score_gemma":0.00088774407,"threshold_uncertainty_score":0.015315175},"labels":[],"label_agreement":null},{"id":"W4402501940","doi":"10.2139/ssrn.4954413","title":"Implementation of Next-Generation Sequences of Operation in an Office Building Using Supervisory Control","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Supervisory control; Control (management); Business; Computer science; Process management; Artificial intelligence","score_opus":0.05470793941142055,"score_gpt":0.3374822208333359,"score_spread":0.28277428142191535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402501940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18465704,0.00010320865,0.80401534,0.00011413767,0.000120927165,0.00013131378,0.000072410134,0.004334312,0.0064513837],"genre_scores_gemma":[0.91171277,0.000023098664,0.08662703,0.000025994908,0.000010049044,0.00005074953,0.000076137294,0.00003597709,0.001438229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995338,0.000098566015,0.000026857195,0.000107039865,0.00015121479,0.00008264035],"domain_scores_gemma":[0.99909556,0.00025234927,0.00009243559,0.00019637166,0.00028232817,0.00008102232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063154765,0.00039389858,0.0003689537,0.00034432363,0.00051015284,0.000648237,0.00067121803,0.00043690088,0.002225477],"category_scores_gemma":[0.0015142031,0.00018248332,0.00024040141,0.00017151429,0.00037416053,0.00036118474,0.00051294756,0.00050117355,0.00039106063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013688463,0.00076869904,0.006919018,0.0001956202,0.00006953637,0.0008734879,0.0010041625,0.42890945,0.07110626,0.017207926,0.0036446599,0.46793234],"study_design_scores_gemma":[0.000055279226,0.0003383976,0.0012706642,0.000017185732,0.00002326161,0.00011645288,0.000073989984,0.9569577,0.034679595,0.0029459968,0.0035040309,0.000017419714],"about_ca_topic_score_codex":0.004022738,"about_ca_topic_score_gemma":0.004013237,"teacher_disagreement_score":0.004022738,"about_ca_system_score_codex":0.00033741278,"about_ca_system_score_gemma":0.0011491905,"threshold_uncertainty_score":0.007998645},"labels":[],"label_agreement":null},{"id":"W4403227220","doi":"10.53555/sfs.v9i2.2911","title":"Artificial Intelligence In The Military: An Overview Of The Capabilities, Applications, And Challenges","year":2022,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Engineering ethics; Data science; Computer science; Engineering","score_opus":0.316182800647292,"score_gpt":0.33125157367161184,"score_spread":0.01506877302431986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403227220","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006403672,0.95813894,0.002976914,0.008173864,0.0013554391,0.000021924725,0.000030484496,0.000038995913,0.028623119],"genre_scores_gemma":[0.006850795,0.97998774,0.0031974681,0.002258587,0.0021862413,0.000036523685,0.000048660386,0.000017829378,0.005416076],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991223,0.00029721422,0.0000722613,0.00008211111,0.00035702394,0.000069029586],"domain_scores_gemma":[0.9991217,0.00051688787,0.00005223729,0.000032997275,0.00019765351,0.0000785089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010877257,0.0007581009,0.0005233911,0.003709857,0.0008987041,0.0040406254,0.00068443187,0.0021803323,0.0033543904],"category_scores_gemma":[0.0011971022,0.00034325925,0.00044135976,0.004712235,0.002071138,0.0057877298,0.0012936316,0.00314244,0.0016069395],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004136887,0.00011028597,0.001291842,0.006339414,0.00006452222,0.00021846099,0.0012572655,0.0015790303,0.0011347629,0.22194794,0.12339897,0.64261615],"study_design_scores_gemma":[0.0000027657718,0.000039253035,0.00096396013,0.0026889832,0.000012624855,0.00033121376,0.00047224725,0.0005651642,0.00011324734,0.046627775,0.94816285,0.000020008032],"about_ca_topic_score_codex":0.002138123,"about_ca_topic_score_gemma":0.002828324,"teacher_disagreement_score":0.0040406254,"about_ca_system_score_codex":0.0017249214,"about_ca_system_score_gemma":0.0022878055,"threshold_uncertainty_score":0.0125153065},"labels":[],"label_agreement":null},{"id":"W4403447380","doi":"10.1109/acit62333.2024.10712496","title":"Mathematical Methods for Reducing the Search Space for Solutions in “Big Data” Analysis and Management","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Space (punctuation); Big data; Data science; Data mining","score_opus":0.15601185213339078,"score_gpt":0.4483368284342033,"score_spread":0.29232497630081256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403447380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010780997,0.0017135978,0.9940299,0.0008490139,0.00010214936,0.00004133975,0.000053990672,0.000059355654,0.0020724533],"genre_scores_gemma":[0.08932722,0.0089680515,0.89524233,0.0007706743,0.0008317157,0.0008265605,0.0003447147,0.00023176898,0.0034568803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972817,0.0011353779,0.00021390998,0.0003349432,0.00091475423,0.00011941258],"domain_scores_gemma":[0.9902631,0.007559713,0.0007186276,0.0007505724,0.0005810919,0.00012690273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055305837,0.0020150286,0.001558352,0.0031084882,0.0011475808,0.0030930147,0.0021353543,0.0016488339,0.0034222],"category_scores_gemma":[0.01791528,0.0010825333,0.0027390579,0.0027870345,0.0047977916,0.0062705083,0.003946042,0.0050017787,0.00091446075],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036376463,0.00007797806,0.00044926544,0.0008224669,0.00013324626,0.00005995332,0.00015244346,0.22489259,0.0011818047,0.7171862,0.0033725963,0.05163507],"study_design_scores_gemma":[0.0000221846,0.00006420478,0.0001512937,0.00016317506,0.000033786146,0.000048414466,0.000061134015,0.4722972,0.0009711929,0.5165164,0.009632597,0.000038469287],"about_ca_topic_score_codex":0.0012815277,"about_ca_topic_score_gemma":0.001359452,"teacher_disagreement_score":0.0055305837,"about_ca_system_score_codex":0.0017052444,"about_ca_system_score_gemma":0.002453288,"threshold_uncertainty_score":0.029248834},"labels":[],"label_agreement":null},{"id":"W4403860620","doi":"10.1108/ijicc-07-2024-0317","title":"A method for recognizing abnormal behaviors of personnel at petroleum stations based on GTB-ResNet","year":2024,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Residual neural network; Petroleum; Petroleum exploration; Artificial intelligence; Computer security; Geology; Deep learning; Paleontology","score_opus":0.018683572305287815,"score_gpt":0.32858829214131446,"score_spread":0.30990471983602663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403860620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1055352,0.0006917101,0.87588793,0.00041556012,0.00027432604,0.0002729945,0.0014747111,0.009788844,0.005658747],"genre_scores_gemma":[0.7501635,0.0004892538,0.23723352,0.0003612091,0.0000923368,0.00024328809,0.0032166904,0.00026228445,0.0079378635],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997639,0.000022966226,0.000013966541,0.00008492284,0.00006722974,0.000047065296],"domain_scores_gemma":[0.9997923,0.000033625394,0.00003992456,0.000027236569,0.00008849721,0.000018417097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033331342,0.0011233183,0.0005340078,0.001356929,0.00027991857,0.00039895097,0.0011459024,0.00062156375,0.0017420416],"category_scores_gemma":[0.00086039875,0.00032103504,0.0007101456,0.0006029472,0.00024166166,0.0008182275,0.0005504539,0.0006533655,0.0010262883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037628104,0.00027069647,0.010560186,0.00016287056,0.00016036278,0.00058017083,0.00015826614,0.1540437,0.03452879,0.0026708033,0.013232498,0.7832554],"study_design_scores_gemma":[0.00000870846,0.00007460233,0.0027530568,0.000018322686,0.000032280805,0.00012569332,0.000047160578,0.9828773,0.010806525,0.0013327915,0.0019073586,0.000016136824],"about_ca_topic_score_codex":0.016798938,"about_ca_topic_score_gemma":0.019317484,"teacher_disagreement_score":0.016798938,"about_ca_system_score_codex":0.00067537284,"about_ca_system_score_gemma":0.00064722926,"threshold_uncertainty_score":0.033402324},"labels":[],"label_agreement":null},{"id":"W4404125287","doi":"10.1109/access.2024.3493753","title":"Cycle Maximum Queue Length Estimation: An Integrated Deep Learning and Adaptive Neuro-Fuzzy Inference System Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial intelligence; Inference; Deep learning; Queue; Neuro-fuzzy; Estimation; Machine learning; Fuzzy control system; Fuzzy logic; Engineering; Computer network","score_opus":0.019027145244935817,"score_gpt":0.31366097603949905,"score_spread":0.29463383079456323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404125287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040824164,0.0007468045,0.954305,0.00041502534,0.00009094133,0.000052361054,0.00025434198,0.0008986602,0.0024126775],"genre_scores_gemma":[0.8731098,0.00038866332,0.121791355,0.0002224008,0.000108037784,0.0001158073,0.000532318,0.00004819691,0.003683386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997886,0.000027968357,0.00001381681,0.00006402875,0.000054359312,0.000051218998],"domain_scores_gemma":[0.99968076,0.000107441905,0.00003608146,0.000016342043,0.00013477918,0.00002461748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057104893,0.0006488208,0.0005614041,0.00054123567,0.00023594992,0.00062861835,0.0013876964,0.0008720505,0.0011691716],"category_scores_gemma":[0.0012607318,0.00034262188,0.00055821065,0.00041084737,0.00022449082,0.0007186411,0.0007107313,0.0011646543,0.00026095906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013350327,0.000146833,0.0037920808,0.00007177791,0.00008374459,0.00008786282,0.000058923477,0.817454,0.0029278293,0.004221274,0.0019703868,0.16905174],"study_design_scores_gemma":[0.0000019037196,0.000008422249,0.00014707664,0.0000021987244,0.000003825381,0.0000030525014,0.0000023047512,0.99896264,0.00017149391,0.0005987647,0.00009639324,0.000001914434],"about_ca_topic_score_codex":0.021217521,"about_ca_topic_score_gemma":0.019993166,"teacher_disagreement_score":0.021217521,"about_ca_system_score_codex":0.000890186,"about_ca_system_score_gemma":0.0013236571,"threshold_uncertainty_score":0.04218805},"labels":[],"label_agreement":null},{"id":"W4404801591","doi":"10.1016/j.procs.2024.09.632","title":"A heuristic method to solve an assignment problem using a random walk approximation","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Computer science; Heuristic; Random walk; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.020983853330012896,"score_gpt":0.3175615227836643,"score_spread":0.2965776694536514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009626696,0.00022377752,0.9872931,0.00014865557,0.000057588066,0.00012243124,0.000070313465,0.00035297498,0.0021044498],"genre_scores_gemma":[0.1704779,0.00026669662,0.8253782,0.0001843044,0.00006983887,0.00043488806,0.00039723812,0.00013292745,0.0026580752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990858,0.00039955782,0.00004184284,0.00017582053,0.00016280076,0.00013427227],"domain_scores_gemma":[0.99815387,0.0013938679,0.00011583921,0.00009306247,0.0001664052,0.00007698377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012022159,0.0013148217,0.0014118421,0.0014111928,0.00072591554,0.00096341054,0.0015059988,0.0013134718,0.0046221167],"category_scores_gemma":[0.0034781564,0.0006404973,0.0012124497,0.0017052385,0.00081882416,0.001115308,0.00085188745,0.001541766,0.0008291336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000957083,0.00012968377,0.00032940748,0.00010706225,0.00004113596,0.00008071431,0.000051649076,0.9342814,0.0012470669,0.0097899325,0.0022418804,0.051604368],"study_design_scores_gemma":[0.000020725207,0.000038363876,0.000046771787,0.000008491546,0.000006677255,0.000020968868,0.000013469015,0.99564725,0.00021585022,0.0034541897,0.0005222066,0.0000050569056],"about_ca_topic_score_codex":0.007736332,"about_ca_topic_score_gemma":0.0071966867,"teacher_disagreement_score":0.007736332,"about_ca_system_score_codex":0.0008959937,"about_ca_system_score_gemma":0.00202324,"threshold_uncertainty_score":0.015462577},"labels":[],"label_agreement":null},{"id":"W4405089054","doi":"10.48550/arxiv.2412.03018","title":"Hamiltonian-based neural networks for systems under nonholonomic constraints","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonholonomic system; Artificial neural network; Hamiltonian system; Computer science; Neural system; Artificial intelligence; Classical mechanics; Physics; Psychology; Neuroscience; Robot; Mobile robot","score_opus":0.057377085090955814,"score_gpt":0.19993825776190688,"score_spread":0.14256117267095106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405089054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022484774,0.00042081304,0.974683,0.00021544292,0.000031483658,0.000023626339,0.000063669984,0.0002754645,0.001801728],"genre_scores_gemma":[0.67839533,0.00081807456,0.31367832,0.00015989102,0.00006682083,0.00016196733,0.00030990894,0.00013605162,0.0062736785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985397,0.00004538161,0.000008949225,0.00003824854,0.00003880558,0.000014539553],"domain_scores_gemma":[0.99958307,0.00021142309,0.000073608106,0.000060390706,0.000055754354,0.000015755826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047563392,0.00049896276,0.00047394127,0.00035740074,0.00027822054,0.0004882253,0.00077165314,0.00060076226,0.002686683],"category_scores_gemma":[0.001675171,0.00040216593,0.0003851918,0.00044413752,0.0006320306,0.0012490412,0.0007993585,0.0011726114,0.00028910325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002445771,0.000018760642,0.000301601,0.000074908494,0.000033755856,0.0000368788,0.0000413147,0.93058866,0.002777477,0.026664466,0.0005111786,0.038926605],"study_design_scores_gemma":[0.0000010760928,0.0000042922334,0.000052485455,0.0000026040914,0.000001530494,0.000003450775,0.0000019122256,0.99301344,0.0002237632,0.0064995894,0.00019423796,0.0000015748391],"about_ca_topic_score_codex":0.0037744078,"about_ca_topic_score_gemma":0.006402441,"teacher_disagreement_score":0.0037744078,"about_ca_system_score_codex":0.0006189803,"about_ca_system_score_gemma":0.0005434533,"threshold_uncertainty_score":0.008987844},"labels":[],"label_agreement":null},{"id":"W4405558522","doi":"10.1002/cjce.25573","title":"Reliable modelling of the sulphur properties to calculate the process parameters of the Claus sulphur recovery plant","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; Toronto Metropolitan University; Memorial University of Newfoundland","funders":"","keywords":"Claus process; Sulfur; Process (computing); Process engineering; Environmental science; Mineralogy; Chemistry; Computer science; Materials science; Metallurgy; Engineering; Hydrogen sulfide","score_opus":0.018743287553576134,"score_gpt":0.19454096910246396,"score_spread":0.17579768154888784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405558522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36248508,0.00027047427,0.63017714,0.00016004972,0.000037781872,0.00009138973,0.00026892847,0.001705694,0.004803445],"genre_scores_gemma":[0.98956245,0.000040227937,0.009428234,0.0000070642054,0.0000023820637,0.000051283056,0.000098689976,0.000024605715,0.0007850638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980193,0.000039167437,0.000012474474,0.000045022407,0.00008096618,0.00002047831],"domain_scores_gemma":[0.9997726,0.000093545554,0.000034649343,0.000028005035,0.000063553096,0.000007588084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039073624,0.0006575772,0.00045638965,0.0003370254,0.00032462273,0.0007215559,0.0005109926,0.00076688157,0.0009913222],"category_scores_gemma":[0.0008009948,0.00027546473,0.00057987863,0.00024147188,0.00021499369,0.0005691919,0.00031590156,0.0005589691,0.00039723242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065546265,0.000038024333,0.0023014129,0.000073277675,0.000015629745,0.00005241381,0.000044796307,0.9619319,0.016267585,0.00040832357,0.00022536161,0.018575821],"study_design_scores_gemma":[0.0000019318645,0.000019900062,0.0004434976,0.000001915007,0.0000027792967,0.0000045356473,0.0000047245157,0.99563736,0.0036392526,0.00009557779,0.00014554053,0.0000031106406],"about_ca_topic_score_codex":0.0066913995,"about_ca_topic_score_gemma":0.0043144072,"teacher_disagreement_score":0.0066913995,"about_ca_system_score_codex":0.00061524136,"about_ca_system_score_gemma":0.0008014231,"threshold_uncertainty_score":0.013304889},"labels":[],"label_agreement":null},{"id":"W4406099622","doi":"10.2139/ssrn.5068980","title":"Towards A New Method Of Generating Predictive And Interpretable Machine Learning Models And Its Application To The Carbon Capture Process System","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Process (computing); Computer science; Machine learning; Artificial intelligence; Carbon fibers; Algorithm","score_opus":0.006122592090950675,"score_gpt":0.26748567144496027,"score_spread":0.2613630793540096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406099622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021782576,0.000040362116,0.99677104,0.0000663793,0.000019725798,0.00002346002,0.0000421838,0.0005810555,0.00027742845],"genre_scores_gemma":[0.10759887,0.00014155588,0.88944554,0.00010074066,0.000065701155,0.00021262077,0.000360066,0.00027365275,0.0018012826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925977,0.00022327508,0.000050160896,0.00017857153,0.00025701156,0.000031161446],"domain_scores_gemma":[0.9974827,0.0015517094,0.000120797995,0.00034238386,0.000463244,0.000039076094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017717041,0.00086954556,0.00084442477,0.0008783876,0.0004519648,0.0014957921,0.0013900448,0.0014338319,0.002258289],"category_scores_gemma":[0.0073836874,0.0005964985,0.0012027875,0.00077768863,0.0006454841,0.0012027377,0.0012397971,0.0023611563,0.0008766301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013374984,0.00017222943,0.0014847083,0.00018790776,0.00020064868,0.00022688734,0.00019269437,0.598873,0.014203356,0.02833334,0.003083132,0.3529084],"study_design_scores_gemma":[0.0000067614756,0.000011496404,0.00006116911,0.000004478085,0.000006885391,0.00001826685,0.000004786732,0.9933473,0.0016683184,0.0042630043,0.0006028369,0.0000046165883],"about_ca_topic_score_codex":0.002835021,"about_ca_topic_score_gemma":0.0030797222,"teacher_disagreement_score":0.002835021,"about_ca_system_score_codex":0.00047188098,"about_ca_system_score_gemma":0.0009529668,"threshold_uncertainty_score":0.009369791},"labels":[],"label_agreement":null},{"id":"W4406484597","doi":"10.1016/0967-0653(93)91646-t","title":"10.1016/0967-0653(93)91646-t","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cruise; Geology; Oceanography","score_opus":0.005007067173108697,"score_gpt":0.17652350881168377,"score_spread":0.17151644163857507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406484597","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005812454,0.00035002106,0.0011678407,0.0004455985,0.0003525371,0.00015377282,0.0010418874,0.0013860166,0.9945209],"genre_scores_gemma":[0.0008439755,0.0002346282,0.0006048176,0.00028240363,0.00007865396,0.00006674448,0.0006272671,0.00025217608,0.99700934],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999271,0.000049959413,0.00006353847,0.00026370047,0.00018765547,0.00016421339],"domain_scores_gemma":[0.997343,0.00067730725,0.00014752428,0.00035097226,0.0006202055,0.00086097023],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012263072,0.0029541673,0.0017484564,0.0029316384,0.0027768828,0.004294049,0.0033321355,0.0053905034,0.98874015],"category_scores_gemma":[0.0018756355,0.001150991,0.0014152013,0.0026739887,0.0024748105,0.005868078,0.003274968,0.0026642263,0.99314946],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004179626,0.00026916325,0.0012172724,0.0005831462,0.000046375848,0.00035359757,0.00013370601,0.0005814858,0.0034990106,0.005164441,0.4289996,0.55873424],"study_design_scores_gemma":[0.000068100766,0.0001594519,0.0010195008,0.00033885447,0.000021449527,0.0004737386,0.00019829485,0.00036193593,0.00065388985,0.0008565696,0.99581593,0.000032357315],"about_ca_topic_score_codex":0.005383482,"about_ca_topic_score_gemma":0.004275747,"teacher_disagreement_score":0.011259854,"about_ca_system_score_codex":0.0010562399,"about_ca_system_score_gemma":0.00096127106,"threshold_uncertainty_score":0.01606083},"labels":[],"label_agreement":null},{"id":"W4406516808","doi":"10.1016/s0967-0653(97)81333-6","title":"10.1016/s0967-0653(97)81333-6","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Seawater; Evaporation; Environmental science; Oceanography; Geology; Meteorology; Geography","score_opus":0.0066607264923160886,"score_gpt":0.1861927684237941,"score_spread":0.179532041931478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406516808","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005374153,0.00047481363,0.001332128,0.00042831904,0.00038619345,0.00014754504,0.0010401445,0.0015091315,0.99414426],"genre_scores_gemma":[0.00060299673,0.00022236494,0.0005317405,0.00020598134,0.000064127205,0.00006113141,0.0005275783,0.00022117315,0.9975629],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991628,0.00005491944,0.000072285795,0.00030411902,0.00022755664,0.00017839165],"domain_scores_gemma":[0.9971488,0.00068901945,0.00019776728,0.00038724407,0.0007168544,0.0008601797],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013614265,0.003228824,0.0020510978,0.0031996733,0.0025785312,0.0048597553,0.0037963793,0.0054661552,0.9884127],"category_scores_gemma":[0.0018984429,0.0011381237,0.0015580455,0.0032127546,0.0023070264,0.0066547412,0.0034667598,0.0030179652,0.9934902],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003726459,0.0002035373,0.0008635186,0.0006021772,0.00004871511,0.000265038,0.0001184862,0.00049554824,0.0027958504,0.005954504,0.3778728,0.6104071],"study_design_scores_gemma":[0.000053641503,0.0001296654,0.0006399548,0.0003500984,0.000019001256,0.0003825454,0.00013351733,0.00032347403,0.000514829,0.0007079939,0.996714,0.000031277148],"about_ca_topic_score_codex":0.00405731,"about_ca_topic_score_gemma":0.003409388,"teacher_disagreement_score":0.011587322,"about_ca_system_score_codex":0.0012643015,"about_ca_system_score_gemma":0.0011454675,"threshold_uncertainty_score":0.016527891},"labels":[],"label_agreement":null},{"id":"W4406533296","doi":"10.1016/0967-0653(95)91623-c","title":"10.1016/0967-0653(95)91623-c","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fish <Actinopterygii>; Fishery; Biology","score_opus":0.005593861531500046,"score_gpt":0.18138460987369573,"score_spread":0.17579074834219569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406533296","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004928654,0.0003450638,0.0009163511,0.0003943741,0.00033289412,0.00014593707,0.0009068286,0.0011499232,0.9953157],"genre_scores_gemma":[0.00077243376,0.0002275623,0.0005337061,0.00028363534,0.00007291384,0.00006845384,0.00057333795,0.00022798305,0.99723995],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916375,0.000056611407,0.00007278913,0.00030244177,0.00021388386,0.00019050766],"domain_scores_gemma":[0.99700963,0.000739118,0.00015391877,0.00038451518,0.0007947526,0.00091802183],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012688732,0.0030774784,0.0018780617,0.0031065708,0.003249364,0.0045497706,0.0035893845,0.0058008134,0.9888337],"category_scores_gemma":[0.0019999156,0.0011410351,0.0014645542,0.0028232208,0.002753593,0.005952372,0.003300199,0.002832185,0.99315816],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042400486,0.00025245384,0.0011324954,0.00061568036,0.000048374484,0.00032702155,0.00013238861,0.00056460814,0.0031369934,0.005406207,0.46159372,0.5263661],"study_design_scores_gemma":[0.000057831632,0.0001378545,0.000930533,0.0003481948,0.000019181256,0.00040585,0.00017747858,0.00030502537,0.00053977,0.00077301427,0.9962734,0.000031989268],"about_ca_topic_score_codex":0.0069545885,"about_ca_topic_score_gemma":0.0056826808,"teacher_disagreement_score":0.011166275,"about_ca_system_score_codex":0.0013489679,"about_ca_system_score_gemma":0.0010894804,"threshold_uncertainty_score":0.015927315},"labels":[],"label_agreement":null},{"id":"W4406669102","doi":"10.1016/b978-0-12-088407-0.50015-9","title":"10.1016/b978-0-12-088407-0.50015-9","year":2000,"lang":"en","type":"book-chapter","venue":"Time to knit","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Implementation; Computer science; Artificial intelligence; Programming language","score_opus":0.008655055011159778,"score_gpt":0.18122724303964957,"score_spread":0.17257218802848978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406669102","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021364797,0.00041154242,0.0016890288,0.00031485938,0.00016924285,0.000048067563,0.000869773,0.0014378785,0.994846],"genre_scores_gemma":[0.00059134845,0.0002555294,0.000604888,0.00011604514,0.000036175166,0.000033969653,0.0005711638,0.00033590433,0.99745494],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994677,0.00003146314,0.000034385015,0.00018006275,0.00019538116,0.00009102477],"domain_scores_gemma":[0.9981554,0.00056499534,0.00012593326,0.00031403181,0.00037154037,0.00046814705],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010173934,0.0020584771,0.0013570434,0.001946821,0.0012820343,0.005250972,0.002702897,0.0038378504,0.97737676],"category_scores_gemma":[0.0018008198,0.00083285925,0.0008640922,0.0022897457,0.0011748882,0.004935737,0.0029426531,0.0021002537,0.9899092],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001296716,0.00010837629,0.00041073558,0.00036764206,0.000019417128,0.00011547274,0.00007902934,0.0003786225,0.002718308,0.007483697,0.34651524,0.6416738],"study_design_scores_gemma":[0.00001593535,0.00004100266,0.00048164485,0.00020324976,0.000008247605,0.00019305281,0.0000742039,0.00016993865,0.0004220308,0.001282206,0.99709535,0.000013218595],"about_ca_topic_score_codex":0.0026431156,"about_ca_topic_score_gemma":0.002591009,"teacher_disagreement_score":0.022623241,"about_ca_system_score_codex":0.00091951515,"about_ca_system_score_gemma":0.0008055783,"threshold_uncertainty_score":0.0322693},"labels":[],"label_agreement":null},{"id":"W4406745471","doi":"10.18280/isi.300118","title":"An advanced AI framework for mental health diagnostics using Bidirectional Encoder Representations from Transformers with gated recurrent units and convolutional neural networks","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Transformer; Encoder; Computer science; Artificial intelligence; Engineering; Electrical engineering; Voltage","score_opus":0.015948002623119427,"score_gpt":0.29690582471196203,"score_spread":0.2809578220888426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406745471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067562866,0.0005385595,0.9878613,0.00036125895,0.000066212146,0.000040248993,0.00016724497,0.00081124937,0.0033976645],"genre_scores_gemma":[0.6790841,0.0013724089,0.30583045,0.00041111122,0.000113799375,0.00023721423,0.000678791,0.0001280036,0.012144048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998629,0.000034171695,0.000008792371,0.000034940113,0.000036403002,0.000022823015],"domain_scores_gemma":[0.9998505,0.000061465784,0.000015999465,0.00001499024,0.000042205025,0.000014815147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033166763,0.00053467555,0.0003513376,0.000426857,0.00020769746,0.00068834727,0.00095969246,0.00057209574,0.0022892938],"category_scores_gemma":[0.00086943415,0.00022567612,0.0006760245,0.00032868734,0.00036248015,0.0007533984,0.00070383557,0.0009541375,0.00049806066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012006472,0.00009464717,0.002123604,0.0001464186,0.0001146599,0.00039350515,0.00016684603,0.6530921,0.0073926793,0.09480401,0.005308071,0.23624338],"study_design_scores_gemma":[0.000003345628,0.000019417756,0.000107018095,0.000008059231,0.000010993079,0.00003765365,0.000007169123,0.985288,0.000674684,0.012501736,0.0013370266,0.0000048672287],"about_ca_topic_score_codex":0.012573003,"about_ca_topic_score_gemma":0.014744746,"teacher_disagreement_score":0.012573003,"about_ca_system_score_codex":0.0007321848,"about_ca_system_score_gemma":0.0011372913,"threshold_uncertainty_score":0.024999619},"labels":[],"label_agreement":null},{"id":"W4406859434","doi":"10.24018/compute.2025.5.1.144","title":"Computer Power Consumption while using Ad-Blocker on a System with AI Accelerators","year":2025,"lang":"en","type":"article","venue":"European Journal of Information Technologies and Computer Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Power consumption; Consumption (sociology); Power (physics); Computer science; Embedded system; Art; Physics","score_opus":0.011177374350119179,"score_gpt":0.22463980410440568,"score_spread":0.2134624297542865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406859434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767313,0.0011911305,0.009985053,0.0001401485,0.00010339286,0.000054069176,0.00020602874,0.0017394066,0.0098493835],"genre_scores_gemma":[0.9956072,0.00020942275,0.002058255,0.000057996753,0.000011050879,0.000017802648,0.0001308451,0.00006049723,0.0018468095],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996674,0.000059408216,0.00001702207,0.000063284235,0.00009024895,0.00010263644],"domain_scores_gemma":[0.99941874,0.00021202452,0.000043058633,0.00007634768,0.0001656395,0.00008425078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002371663,0.00045255132,0.0003525843,0.0003814444,0.0003460985,0.0007147602,0.00050908956,0.00017579318,0.0040158834],"category_scores_gemma":[0.0011146246,0.00013311807,0.00016450766,0.0004996617,0.00020914874,0.00066005293,0.00035236758,0.0002964267,0.000916739],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011688084,0.0009991376,0.07668199,0.00159373,0.00047980447,0.0017691477,0.0018219394,0.06806524,0.31321308,0.0044500316,0.02255951,0.49667826],"study_design_scores_gemma":[0.00057815795,0.008672655,0.12778462,0.00028378263,0.0010374143,0.00234288,0.0026335928,0.45138067,0.33059826,0.0028809987,0.07158952,0.00021747332],"about_ca_topic_score_codex":0.0013050758,"about_ca_topic_score_gemma":0.0017807591,"teacher_disagreement_score":0.0040158834,"about_ca_system_score_codex":0.00033975835,"about_ca_system_score_gemma":0.00032374097,"threshold_uncertainty_score":0.013434529},"labels":[],"label_agreement":null},{"id":"W4407658426","doi":"10.28924/2291-8639-23-2025-18","title":"A Comparative Study of Traditional Statistical Methods and Machine Learning Techniques for Improved Predictive Models","year":2025,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Northern Border University","keywords":"Mathematics; Machine learning; Statistical learning; Artificial intelligence; Computer science","score_opus":0.03842596999284398,"score_gpt":0.3996379217179233,"score_spread":0.3612119517250793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407658426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080334015,0.029952934,0.87115586,0.005397864,0.0004252297,0.00010830154,0.0004374232,0.0011993805,0.01098904],"genre_scores_gemma":[0.50943977,0.018446505,0.46694937,0.000666263,0.0008898264,0.0001321149,0.0007290931,0.0003830802,0.0023639756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98774976,0.006292218,0.0005292675,0.00062799465,0.0046172496,0.00018343682],"domain_scores_gemma":[0.8838298,0.10217074,0.002351243,0.0057437564,0.0055974056,0.0003070955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021342937,0.001260924,0.0009793306,0.0043076444,0.00041329424,0.0024380938,0.0015772065,0.0012143072,0.0016372442],"category_scores_gemma":[0.07361853,0.0005155543,0.0013822208,0.0055606994,0.0010428529,0.0056336834,0.0014905739,0.0026055016,0.000525971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047981818,0.00029601928,0.0132017685,0.0007839482,0.00081096287,0.00018966601,0.00046154705,0.327347,0.0017127176,0.090003505,0.005320076,0.5593929],"study_design_scores_gemma":[0.000026546493,0.00021573906,0.0029065036,0.0001648483,0.00009432035,0.00009101635,0.00007527861,0.96555924,0.0013152037,0.024404004,0.0051120617,0.00003518553],"about_ca_topic_score_codex":0.003779621,"about_ca_topic_score_gemma":0.0045529315,"teacher_disagreement_score":0.021342937,"about_ca_system_score_codex":0.0015112493,"about_ca_system_score_gemma":0.0015449778,"threshold_uncertainty_score":0.112873554},"labels":[],"label_agreement":null},{"id":"W4409345883","doi":"10.18280/ijdne.200314","title":"Temporal Variation in Water Quality Assessment Using WQI Methods: A Case Study of Alhussein Water Treatment Plant in Karbala","year":2025,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Variation (astronomy); Water quality; Quality (philosophy); Environmental science; Water resource management; Ecology; Biology; Epistemology; Philosophy","score_opus":0.03596893513097338,"score_gpt":0.39231645652523056,"score_spread":0.3563475213942572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409345883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99801207,0.00003300281,0.0008509605,0.000048750666,0.0000033817148,0.000030350595,0.00016138515,0.000010741402,0.0008493807],"genre_scores_gemma":[0.99740285,0.00005266846,0.0016403524,0.000012381905,0.0000022602367,0.000027912023,0.00017807793,0.000004435833,0.0006789984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993443,0.00017152802,0.00004619446,0.00013992784,0.00019536952,0.000102634775],"domain_scores_gemma":[0.9990914,0.00028427766,0.000178899,0.00006455664,0.0003112954,0.000069549074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007951766,0.00027479688,0.00031960465,0.0010607374,0.00090445543,0.0008796502,0.0007488796,0.0007068147,0.0005818914],"category_scores_gemma":[0.0010669103,0.00018337532,0.0003985564,0.0022410857,0.00055747107,0.00055392634,0.0008118903,0.00039278885,0.00008950002],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005192882,0.0017797769,0.8417377,0.0003466138,0.00030634625,0.017941138,0.009532516,0.046438903,0.021642057,0.001965567,0.002052312,0.055737868],"study_design_scores_gemma":[0.00004465831,0.0005851096,0.84794223,0.000036415873,0.0001178817,0.0012323087,0.031640634,0.10604749,0.0076399874,0.00078632525,0.0037725796,0.00015420915],"about_ca_topic_score_codex":0.11041887,"about_ca_topic_score_gemma":0.21170245,"teacher_disagreement_score":0.11041887,"about_ca_system_score_codex":0.0025248397,"about_ca_system_score_gemma":0.001246407,"threshold_uncertainty_score":0.21955228},"labels":[],"label_agreement":null},{"id":"W4409576530","doi":"10.61091/jcmcc127a-123","title":"Applied Research on Generative Artificial Intelligence for Modern Control Systems","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Computer science; Artificial intelligence; Control (management); Machine learning","score_opus":0.04871145855283705,"score_gpt":0.3430313100590382,"score_spread":0.29431985150620116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409576530","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008909866,0.0060775895,0.95375985,0.0010894163,0.00030780517,0.000046059577,0.00003376272,0.00029591794,0.02947968],"genre_scores_gemma":[0.7642103,0.012077588,0.20695837,0.0009385439,0.00087774056,0.00032124564,0.0001545578,0.0001311251,0.014330517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987815,0.0002815359,0.00008836045,0.00030304195,0.00047705616,0.000068467074],"domain_scores_gemma":[0.9987908,0.00058574235,0.00009367024,0.00022200045,0.00027765174,0.000030078096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013327295,0.00078755186,0.00051995297,0.0009795999,0.00067288236,0.002301069,0.0007905425,0.00089240377,0.004589204],"category_scores_gemma":[0.0031664816,0.00031461398,0.0010170349,0.0010359634,0.0021697187,0.0021239624,0.0010462601,0.0019253275,0.000761504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038619914,0.00004753489,0.0011910092,0.00051503925,0.0001087903,0.0001485037,0.00039412788,0.09403397,0.006623293,0.7366402,0.002100792,0.15815817],"study_design_scores_gemma":[0.000029365263,0.0002121298,0.0015739031,0.00017732487,0.000085488966,0.0002636482,0.00012684529,0.5862961,0.0053565684,0.35768357,0.048118193,0.000076845514],"about_ca_topic_score_codex":0.0028871696,"about_ca_topic_score_gemma":0.00095230556,"teacher_disagreement_score":0.004589204,"about_ca_system_score_codex":0.001673499,"about_ca_system_score_gemma":0.0010449925,"threshold_uncertainty_score":0.015352428},"labels":[],"label_agreement":null},{"id":"W4409800005","doi":"10.11159/iceptp25.160","title":"Assessment of Anaerobic Membrane Bioreactors in High-Strength Synthetic Wastewater Treatment","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Development Fund","keywords":"Bioreactor; Wastewater; Sewage treatment; Anaerobic exercise; Membrane bioreactor; Pulp and paper industry; Waste management; Biochemical engineering; Chemistry; Engineering; Biology","score_opus":0.0039948935824201375,"score_gpt":0.20715381210560468,"score_spread":0.20315891852318455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409800005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915115,0.002015099,0.0054508275,0.00015070834,0.000041126543,0.000051721774,0.0001103382,0.00004933684,0.00061923737],"genre_scores_gemma":[0.9903854,0.0019348941,0.0066335574,0.00006773888,0.000022471819,0.000064557426,0.00022487673,0.000014061527,0.0006525108],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873215,0.00044189318,0.00010223499,0.00012493976,0.00046831975,0.00013040389],"domain_scores_gemma":[0.99957806,0.00011972226,0.00009486874,0.000022146118,0.00012587786,0.00005935907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012497903,0.0007044748,0.00067003915,0.00024486447,0.0004262991,0.0012077907,0.00047849063,0.00096898584,0.00027665042],"category_scores_gemma":[0.0010189998,0.00020530305,0.00049168366,0.0002524357,0.00025131376,0.0007006991,0.00068502047,0.000517076,0.00025088713],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013689998,0.000064814536,0.0006307323,0.00013160473,0.000013764297,0.000051712963,0.000023735287,0.00037068673,0.9959014,0.000015910397,0.000017729992,0.0026409943],"study_design_scores_gemma":[0.000014022955,0.0011030209,0.00677622,0.000021169892,0.0000454093,0.00015579176,0.00011503303,0.0031921642,0.98751056,0.000029479912,0.001021623,0.000015455113],"about_ca_topic_score_codex":0.0015985789,"about_ca_topic_score_gemma":0.0016923228,"teacher_disagreement_score":0.0015985789,"about_ca_system_score_codex":0.00067085476,"about_ca_system_score_gemma":0.0004840862,"threshold_uncertainty_score":0.0066096187},"labels":[],"label_agreement":null},{"id":"W4409966425","doi":"10.15588/1607-6761-2025-1-4","title":"Features of the creation of electric power quality control systems in power supply systems of space rocket complexes","year":2025,"lang":"en","type":"article","venue":"Electrical Engineering and Power Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rocket (weapon); Power (physics); Space (punctuation); Electric power system; Electric power; Quality (philosophy); Control (management); Power control; Electrical engineering; Power quality; Aerospace engineering; Computer science; Engineering; Physics; Voltage; Artificial intelligence","score_opus":0.003759425417340826,"score_gpt":0.22939990732560103,"score_spread":0.2256404819082602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409966425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11324772,0.047764093,0.44440904,0.004622989,0.0020081731,0.0015827562,0.0007492332,0.0019335208,0.38368264],"genre_scores_gemma":[0.72388285,0.024514405,0.17224893,0.00070832105,0.0009637421,0.00076128275,0.0011722937,0.00022865244,0.07551953],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869424,0.00034687924,0.00013169467,0.00020730219,0.00056428666,0.00005555617],"domain_scores_gemma":[0.9987495,0.00029573962,0.00014472606,0.00011990822,0.0006425289,0.000047501686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010952224,0.00027911563,0.00024628593,0.0012673042,0.00050142227,0.0023069298,0.00052511663,0.0004646916,0.0032671061],"category_scores_gemma":[0.0024206294,0.00014229924,0.0003158546,0.0012954274,0.0007626808,0.0012641149,0.000634698,0.00054609374,0.0007780886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018544319,0.00009567406,0.0072810613,0.0045742854,0.00007278394,0.0018274472,0.0043382007,0.0143261235,0.018516496,0.20288846,0.01617994,0.7297141],"study_design_scores_gemma":[0.0000357627,0.00045372834,0.024911461,0.0018061469,0.00015149459,0.0028601761,0.002034061,0.012376923,0.015478104,0.03967553,0.9001288,0.000087910856],"about_ca_topic_score_codex":0.0020899542,"about_ca_topic_score_gemma":0.0019856913,"teacher_disagreement_score":0.0032671061,"about_ca_system_score_codex":0.0014280955,"about_ca_system_score_gemma":0.0019995025,"threshold_uncertainty_score":0.0109295845},"labels":[],"label_agreement":null},{"id":"W4410418351","doi":"10.5194/essd-2025-255","title":"Synthesis of data products for ocean carbonate chemistry","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada; Memorial University of Newfoundland; Bedford Institute of Oceanography","funders":"NOAA Pacific Marine Environmental Laboratory; Global Ocean Monitoring and Observing Program; Institut national des sciences de l'Univers; HORIZON EUROPE Framework Programme; National Oceanic and Atmospheric Administration; Institut Polaire Français Paul Emile Victor; Centre National de la Recherche Scientifique; Agencia Estatal de Investigación; University of Tasmania; Ocean Acidification Program; Fisheries and Oceans Canada; European Commission; Bundesministerium für Bildung und Forschung; University of Exeter; Horizon 2020 Framework Programme; Commonwealth Scientific and Industrial Research Organisation; Norges Forskningsråd; National Science Foundation; European Space Agency; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Carbonate; Chemistry; Environmental science; Oceanography; Environmental chemistry; Geology; Organic chemistry","score_opus":0.0370707765224139,"score_gpt":0.29914394361328983,"score_spread":0.2620731670908759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410418351","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025533553,0.003620354,0.41883695,0.0010164754,0.0012333045,0.0006787902,0.5113939,0.018213442,0.019473206],"genre_scores_gemma":[0.07196161,0.0039388207,0.38228825,0.00024957926,0.00018356188,0.0011182465,0.5333847,0.0026455785,0.004229598],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980268,0.00031567932,0.00033686517,0.00035131254,0.00087762915,0.000091653965],"domain_scores_gemma":[0.991831,0.0023576466,0.00044929388,0.0016670844,0.0035424482,0.0001524918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003833911,0.001412635,0.00066691363,0.003993818,0.0004553324,0.0026511096,0.0009224789,0.0006323966,0.009180013],"category_scores_gemma":[0.01594895,0.0005245196,0.0013378648,0.005202384,0.00035665158,0.0019938946,0.0016577969,0.0011079299,0.006186197],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001040062,0.00026605953,0.018420894,0.007251399,0.0008670901,0.000634138,0.000481378,0.085973926,0.024899265,0.053180203,0.20190722,0.60507834],"study_design_scores_gemma":[0.0001956818,0.00017786828,0.015244377,0.0013912509,0.0002458136,0.0002391483,0.00043393334,0.08054407,0.05686626,0.0448212,0.79964906,0.00019136461],"about_ca_topic_score_codex":0.007877482,"about_ca_topic_score_gemma":0.004400683,"teacher_disagreement_score":0.009180013,"about_ca_system_score_codex":0.00063131086,"about_ca_system_score_gemma":0.002847206,"threshold_uncertainty_score":0.03071022},"labels":[],"label_agreement":null},{"id":"W4410422936","doi":"10.1117/12.3061492","title":"Comparative analysis of stochastic optimization methods for image classification using convolutional neural networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Contextual image classification; Image (mathematics); Stochastic optimization; Artificial neural network; Machine learning; Mathematical optimization; Mathematics","score_opus":0.05857826264706141,"score_gpt":0.4187339811841995,"score_spread":0.3601557185371381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410422936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06841285,0.00695173,0.91848874,0.000901864,0.00012431204,0.0001331433,0.00021101507,0.0013726313,0.0034036934],"genre_scores_gemma":[0.49786553,0.004664623,0.49183074,0.00037791682,0.00021330065,0.00029897617,0.0015408319,0.00055525417,0.002652817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611306,0.0015826563,0.00038241324,0.00041988643,0.0013235632,0.00017845709],"domain_scores_gemma":[0.9885777,0.007967395,0.00066594116,0.00072050607,0.0019218662,0.00014653304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009549494,0.0016780004,0.0015236005,0.0023698572,0.00056623714,0.0013318977,0.0012816626,0.0015304324,0.0009084594],"category_scores_gemma":[0.018528445,0.0005973906,0.0015698657,0.0019234391,0.0007469022,0.0020268962,0.001267839,0.0014948822,0.0003240411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028256717,0.0001536765,0.0021963236,0.0003169977,0.00038087316,0.00003805157,0.000064429325,0.8396105,0.0022072445,0.007312506,0.001364719,0.14607216],"study_design_scores_gemma":[0.000007511803,0.00005515566,0.00049519335,0.00002064969,0.00002067493,0.000015959948,0.000011168647,0.99639696,0.0011485587,0.0014256121,0.00039174772,0.000010831562],"about_ca_topic_score_codex":0.008500276,"about_ca_topic_score_gemma":0.008975575,"teacher_disagreement_score":0.009549494,"about_ca_system_score_codex":0.0018425294,"about_ca_system_score_gemma":0.0019950876,"threshold_uncertainty_score":0.050503194},"labels":[],"label_agreement":null},{"id":"W4410877981","doi":"10.1016/j.dche.2025.100247","title":"Editorial: Special issue on Emerging Stars in Digital Chemical Engineering","year":2025,"lang":"en","type":"editorial","venue":"Digital Chemical Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Stars; Engineering ethics; Computer science; Engineering; Astrophysics; Physics","score_opus":0.0028933983644184396,"score_gpt":0.21770259113946125,"score_spread":0.21480919277504282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410877981","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001982978,0.0022461067,0.00008361622,0.017221047,0.9786773,0.000016884715,0.000039629373,0.000038761747,0.0016569636],"genre_scores_gemma":[0.00022637639,0.0018019673,0.000077520606,0.010269205,0.97734106,0.000015810965,0.000037225986,0.000035663335,0.0101951985],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.993112,0.0009877642,0.00069953455,0.0007573111,0.003946077,0.000497226],"domain_scores_gemma":[0.97517073,0.007174812,0.0018527823,0.00075501436,0.009932142,0.0051144985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008279773,0.00488101,0.004712886,0.006333212,0.004463076,0.0126143955,0.0035794247,0.021159261,0.038096037],"category_scores_gemma":[0.02434048,0.0015944305,0.0035532233,0.0024175073,0.0023976353,0.0053373924,0.0029233804,0.0168231,0.025884151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027184533,0.000009064056,0.000015206473,0.000103467544,0.000011384692,0.00006888902,0.000004475148,0.000016668248,0.000040415318,0.00014673607,0.9963283,0.003228197],"study_design_scores_gemma":[0.00008597991,0.000029320923,0.0002360218,0.00028349692,0.00004578813,0.00016925017,0.000027416045,0.00018926492,0.00009611614,0.0010795128,0.99774146,0.000016332382],"about_ca_topic_score_codex":0.0014108584,"about_ca_topic_score_gemma":0.0057514594,"teacher_disagreement_score":0.038096037,"about_ca_system_score_codex":0.0037585546,"about_ca_system_score_gemma":0.0030101717,"threshold_uncertainty_score":0.12744397},"labels":[],"label_agreement":null},{"id":"W4411788602","doi":"10.1115/1.4069056","title":"Machine Learning Prediction of the Morison Equation Coefficients","year":2025,"lang":"en","type":"article","venue":"Journal of Offshore Mechanics and Arctic Engineering","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"Interreg; Sustainable Energy Authority of Ireland; Science Foundation Ireland","keywords":"Morison equation; Mathematics; Computer science; Applied mathematics; Physics; Thermodynamics","score_opus":0.009467074327887822,"score_gpt":0.2165553961918642,"score_spread":0.20708832186397638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411788602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7521544,0.0006796123,0.24183849,0.00056175515,0.00012057473,0.00008112377,0.0005893966,0.00058187224,0.0033927686],"genre_scores_gemma":[0.97429425,0.0001025338,0.023887845,0.0000411891,0.000030852214,0.000038711005,0.0004151047,0.000020515974,0.0011690448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967897,0.00009996063,0.000019164256,0.00009657217,0.00005809859,0.000047147234],"domain_scores_gemma":[0.9943693,0.0041397354,0.00036879626,0.00019494905,0.0008360912,0.00009121381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016835821,0.0007508157,0.00051907945,0.0008661237,0.00026813443,0.00062162115,0.0005776467,0.00089059025,0.0011240494],"category_scores_gemma":[0.0081480285,0.00025839635,0.00044723638,0.0004573026,0.00029756868,0.00059852644,0.0003494999,0.0010860794,0.00041383575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001256121,0.0001353027,0.02095002,0.000041790252,0.00004628694,0.00004371886,0.000043805838,0.88887596,0.0016104097,0.0010524194,0.0015263297,0.08554827],"study_design_scores_gemma":[0.0000015169281,0.000007440481,0.00090752996,0.000002243329,0.0000011863368,0.0000020766483,0.0000022393724,0.99866927,0.00020258267,0.00015816776,0.000043669537,0.0000020789805],"about_ca_topic_score_codex":0.01350553,"about_ca_topic_score_gemma":0.008388616,"teacher_disagreement_score":0.01350553,"about_ca_system_score_codex":0.00066541566,"about_ca_system_score_gemma":0.0005160275,"threshold_uncertainty_score":0.0268538},"labels":[],"label_agreement":null},{"id":"W4412536966","doi":"10.1109/icssas66150.2025.11080760","title":"Intelligent Adaptive Systems for Visual Training and Assistance","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Training (meteorology); Artificial intelligence; Human–computer interaction","score_opus":0.03306050592782018,"score_gpt":0.3114258542981814,"score_spread":0.27836534837036125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412536966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061603957,0.024659127,0.8399207,0.0040643006,0.0016662529,0.0004083482,0.00058210455,0.008118221,0.11442054],"genre_scores_gemma":[0.27850965,0.026863458,0.49825624,0.0040875627,0.0016796152,0.0013719952,0.001963096,0.0007276716,0.18654074],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995534,0.00009307375,0.000034829474,0.00007340514,0.00021123119,0.000034090564],"domain_scores_gemma":[0.9994752,0.00021702981,0.000050608774,0.000069464935,0.00015485944,0.00003277704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041644648,0.00062596356,0.00037710747,0.00062788365,0.00031159283,0.0015353003,0.0010609911,0.0018068884,0.020215835],"category_scores_gemma":[0.001490383,0.00019225095,0.00039191195,0.0008591257,0.00060529663,0.0013173015,0.0011666401,0.0015235774,0.00574392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001892534,0.00014890669,0.0007431594,0.00097419356,0.00005904583,0.00042034843,0.00041667387,0.01908794,0.019980982,0.10895005,0.06381535,0.785214],"study_design_scores_gemma":[0.000075323434,0.00018693024,0.0018026322,0.00054347166,0.0000536497,0.0007380349,0.00024325347,0.13627721,0.006353191,0.103525706,0.75011265,0.00008795299],"about_ca_topic_score_codex":0.0015599456,"about_ca_topic_score_gemma":0.0014364115,"teacher_disagreement_score":0.020215835,"about_ca_system_score_codex":0.00060097786,"about_ca_system_score_gemma":0.00055161543,"threshold_uncertainty_score":0.06762874},"labels":[],"label_agreement":null},{"id":"W4412651391","doi":"10.1007/s10270-025-01303-3","title":"Guest editorial for the special section on MODELS 2022","year":2025,"lang":"en","type":"editorial","venue":"Software & Systems Modeling","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Section (typography); Special section; Computer science; Engineering; Engineering physics; Operating system","score_opus":0.014928817770414493,"score_gpt":0.2628935767484772,"score_spread":0.2479647589780627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412651391","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017830745,0.002422367,0.00018930105,0.020304507,0.9751106,0.000010553894,0.000058478647,0.000060128114,0.0018261853],"genre_scores_gemma":[0.0002293604,0.0017886455,0.00011238634,0.010290436,0.9764129,0.000016578946,0.00005897719,0.00008892662,0.011001837],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99405164,0.0010493045,0.0006328686,0.00081979536,0.0030471382,0.00039923203],"domain_scores_gemma":[0.9758626,0.008080853,0.0020419832,0.0010314394,0.009429248,0.0035538296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073739844,0.004761396,0.004293721,0.005434446,0.0030694925,0.009321728,0.003190649,0.014485012,0.041287962],"category_scores_gemma":[0.02391023,0.0017262972,0.0040370286,0.0016380254,0.0020934567,0.00522933,0.002457177,0.016277162,0.02907734],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016275078,0.000006006345,0.000010805772,0.0000529838,0.000009674276,0.000060050475,0.000002954593,0.000019930907,0.000031457403,0.0002009908,0.99766386,0.0019249727],"study_design_scores_gemma":[0.000049656694,0.0000256249,0.0001581722,0.0002508245,0.000050636423,0.00022065507,0.000016257047,0.00027311782,0.00007915095,0.0015304779,0.99732685,0.000018641515],"about_ca_topic_score_codex":0.0017632801,"about_ca_topic_score_gemma":0.005396366,"teacher_disagreement_score":0.041287962,"about_ca_system_score_codex":0.0030394616,"about_ca_system_score_gemma":0.0030033663,"threshold_uncertainty_score":0.13812196},"labels":[],"label_agreement":null},{"id":"W4413145680","doi":"10.1109/cvpr52734.2025.02835","title":"Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Computer science; Distillation; Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology)","score_opus":0.02596796499657976,"score_gpt":0.33808695986245435,"score_spread":0.3121189948658746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2576506,0.004914867,0.6618435,0.002390045,0.00082421204,0.00069283036,0.011218433,0.04735086,0.013114629],"genre_scores_gemma":[0.5165784,0.0007106136,0.44922537,0.0010799534,0.00014063227,0.0004009457,0.02450842,0.0020476438,0.0053081126],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894804,0.00024268751,0.00006351515,0.00035580032,0.00025074056,0.00013927442],"domain_scores_gemma":[0.9983536,0.0005265669,0.00010171465,0.00076453574,0.00015242377,0.00010110681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017553864,0.0020199383,0.0010268482,0.0013300154,0.00071492034,0.0012563063,0.0023606746,0.0013539947,0.004359906],"category_scores_gemma":[0.0069737053,0.00050942664,0.0009402507,0.0016669988,0.0009942931,0.0039439118,0.0027122716,0.0028525086,0.0017489318],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011097733,0.00062467385,0.0064039174,0.0009941503,0.00029673846,0.00039676024,0.00023001681,0.21444863,0.052410245,0.014227538,0.06825685,0.64060074],"study_design_scores_gemma":[0.00016744949,0.00041653038,0.0024479616,0.000066863926,0.000053501288,0.0004983619,0.00013733358,0.89804155,0.048320472,0.0182333,0.031546347,0.00007035457],"about_ca_topic_score_codex":0.0052307444,"about_ca_topic_score_gemma":0.0127099985,"teacher_disagreement_score":0.0052307444,"about_ca_system_score_codex":0.00095137424,"about_ca_system_score_gemma":0.0010415899,"threshold_uncertainty_score":0.014585316},"labels":[],"label_agreement":null},{"id":"W4413199870","doi":"10.3102/ip.25.2197254","title":"Evaluating the Efficacy of Generative AI in Automating Assessment in Introductory Computer Science Courses (Poster 47)","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Generative grammar; Software engineering; Artificial intelligence; Human–computer interaction","score_opus":0.027158583265007072,"score_gpt":0.4017002551157903,"score_spread":0.3745416718507832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413199870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869152,0.00009029499,0.00812739,0.00022409477,0.00008916101,0.0005330243,0.00006174966,0.00048192314,0.003477237],"genre_scores_gemma":[0.97834945,0.000111578825,0.018668978,0.00017321762,0.000039357088,0.0004803607,0.00015284448,0.000050907434,0.0019733112],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9946912,0.0029573825,0.00042450285,0.0007607743,0.00090191246,0.00026428435],"domain_scores_gemma":[0.8637915,0.11853325,0.0039125523,0.0051269936,0.0050176037,0.0036180639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0123438,0.0007654364,0.00055788463,0.0008253684,0.0004925545,0.0018980155,0.001779577,0.0015898531,0.0038422446],"category_scores_gemma":[0.085993506,0.0005043035,0.00053788256,0.00047057765,0.0006612152,0.0014959481,0.0014904999,0.0016956629,0.0012486855],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.036010947,0.052027423,0.10611762,0.0012055221,0.0005374998,0.00016889027,0.007918484,0.03755612,0.027576156,0.002006652,0.0035297072,0.72534496],"study_design_scores_gemma":[0.011809226,0.20203876,0.4051654,0.0010380578,0.0021799472,0.00054838555,0.005132879,0.2748956,0.07555481,0.00931961,0.011730934,0.00058631203],"about_ca_topic_score_codex":0.001953795,"about_ca_topic_score_gemma":0.0020206545,"teacher_disagreement_score":0.0123438,"about_ca_system_score_codex":0.00067022844,"about_ca_system_score_gemma":0.0014808705,"threshold_uncertainty_score":0.065280974},"labels":[],"label_agreement":null},{"id":"W4413958709","doi":"10.15587/1729-4061.2025.335712","title":"Construction of an information model of the digital twin of the technological process in a power unit at a nuclear power plant","year":2025,"lang":"en","type":"article","venue":"Eastern-European Journal of Enterprise Technologies","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Suncor Energy (Canada)","funders":"","keywords":"Unit (ring theory); Process (computing); Power (physics); Nuclear power; Nuclear power plant; Computer science; Mathematics; Operating system; Physics; Nuclear physics","score_opus":0.007462253809614867,"score_gpt":0.21250099039500653,"score_spread":0.20503873658539165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413958709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039423548,0.00021800265,0.9498662,0.0003761959,0.000052983185,0.00007592951,0.00032955018,0.0004588911,0.009198724],"genre_scores_gemma":[0.8126525,0.00059061364,0.17662649,0.00009770772,0.000059215552,0.00046448293,0.00085849775,0.00009618731,0.008554326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977154,0.00005308763,0.00001549208,0.000060510025,0.00007574726,0.000023591892],"domain_scores_gemma":[0.9996923,0.00012667627,0.000051717492,0.00003885493,0.00006978103,0.000020562127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035223772,0.00045457864,0.0006752734,0.0007343391,0.0006421182,0.0014311641,0.0010227496,0.0010627087,0.0021182357],"category_scores_gemma":[0.00082693814,0.00035221712,0.0011546875,0.00056860544,0.0007881082,0.0014388086,0.00087608857,0.00088000944,0.00035669171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042245258,0.00003231731,0.0006564828,0.00007638416,0.000029175275,0.00026636064,0.0001887262,0.8948425,0.003193205,0.09174474,0.00047622056,0.008451732],"study_design_scores_gemma":[0.000005442459,0.000015247966,0.00009549321,0.000006696842,0.000010215292,0.000019248104,0.000018759649,0.9903312,0.00034079046,0.008011972,0.0011384566,0.0000064711035],"about_ca_topic_score_codex":0.0122477235,"about_ca_topic_score_gemma":0.005628754,"teacher_disagreement_score":0.0122477235,"about_ca_system_score_codex":0.0011520919,"about_ca_system_score_gemma":0.0016174666,"threshold_uncertainty_score":0.024352849},"labels":[],"label_agreement":null},{"id":"W4413967161","doi":"10.1109/tmech.2025.3599061","title":"Closing the Simulation-to-Reality Gap for Fault Diagnosis in Unknown Environment: A Sim2Real Knowledge Transfer Approach With Contrastive Learning","year":2025,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Closing (real estate); Fault (geology); Computer science; Knowledge transfer; Artificial intelligence; Human–computer interaction; Knowledge management; Geology; Political science; Seismology","score_opus":0.022789159768164056,"score_gpt":0.28623403402298014,"score_spread":0.2634448742548161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413967161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028483748,0.00027391227,0.9678965,0.00036896253,0.00004132082,0.00006855183,0.00007145019,0.0013321661,0.0014634854],"genre_scores_gemma":[0.8036684,0.00020720303,0.19231293,0.0005278238,0.00009053395,0.0001648715,0.00045614006,0.00022271188,0.00234932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993635,0.0001600068,0.000029995625,0.0002595961,0.00012367363,0.000063251726],"domain_scores_gemma":[0.99829453,0.00084954407,0.00017251694,0.0003292705,0.000268783,0.00008521731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017017584,0.0011025281,0.0009266099,0.00093166437,0.00043616534,0.0009836864,0.0025411278,0.0015888226,0.0017315157],"category_scores_gemma":[0.0043804147,0.00055417296,0.0009759617,0.00047751254,0.0015030124,0.0018868111,0.002736073,0.0019541876,0.0005039818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021852097,0.00022784887,0.002040031,0.00011090243,0.00010200017,0.00023055052,0.00019616827,0.8387131,0.008746081,0.005952285,0.0017348953,0.1417276],"study_design_scores_gemma":[0.0000042132674,0.000026136619,0.00012380784,0.0000031993814,0.0000046931127,0.000022895398,0.0000079665,0.99636984,0.0011597043,0.002028952,0.00024369596,0.000004905795],"about_ca_topic_score_codex":0.004038319,"about_ca_topic_score_gemma":0.0040367143,"teacher_disagreement_score":0.004038319,"about_ca_system_score_codex":0.00084397825,"about_ca_system_score_gemma":0.00087901426,"threshold_uncertainty_score":0.008999884},"labels":[],"label_agreement":null},{"id":"W4415103367","doi":"","title":"Comparative analysis of multidimensional sequential trajectories clustering methods","year":2025,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cluster analysis; Categorical variable; Similarity (geometry); Sequence (biology); Multivariate statistics; Trajectory; Fuzzy clustering; Multidimensional scaling","score_opus":0.032396726047606184,"score_gpt":0.3303281272691734,"score_spread":0.2979314012215672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415103367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077137254,0.005450976,0.9106244,0.0004906444,0.0002358234,0.00036955733,0.0012781124,0.0005613329,0.0038518675],"genre_scores_gemma":[0.43373334,0.003152605,0.55504674,0.00009632069,0.00015561948,0.0009062175,0.004638623,0.00028714625,0.0019833243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9910343,0.004729985,0.0005770631,0.0011374975,0.0022358445,0.00028528163],"domain_scores_gemma":[0.9737448,0.017189687,0.0011879319,0.0020737243,0.0053135105,0.0004902636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013428425,0.001104556,0.0014166341,0.008567091,0.0012404474,0.0022725058,0.0017341807,0.0011622842,0.0040174588],"category_scores_gemma":[0.043074682,0.0002907005,0.0018801697,0.0068012252,0.000825885,0.002255896,0.0018817318,0.0010864928,0.0008945193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014338256,0.0002518508,0.022711245,0.002136557,0.0015526641,0.00021476015,0.0014121926,0.27187502,0.0035575868,0.07800436,0.008683009,0.60816693],"study_design_scores_gemma":[0.0000865283,0.00033783994,0.016834369,0.00035118609,0.0003122933,0.00028292858,0.0009729005,0.9192955,0.0034333805,0.04653386,0.01143751,0.00012168816],"about_ca_topic_score_codex":0.0059139803,"about_ca_topic_score_gemma":0.004764638,"teacher_disagreement_score":0.013428425,"about_ca_system_score_codex":0.0018898337,"about_ca_system_score_gemma":0.002389218,"threshold_uncertainty_score":0.071017146},"labels":[],"label_agreement":null},{"id":"W54342940","doi":"10.12681/eadd/16165","title":"Σχεδιασμός και αξιολόγηση ενός συστήματος διαχείρισης στόλου οχημάτων σε πραγματικό χρόνο για την αντιμετώπιση δυναμικών γεγονότων κατά την εκτέλεση αστικών διανομών προϊόντων","year":2008,"lang":"el","type":"dissertation","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petroleum Technology Research Centre; University of Washington; Washington State University","keywords":"Physics","score_opus":0.012865118612861366,"score_gpt":0.2737869675478727,"score_spread":0.2609218489350113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W54342940","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09518938,0.016996805,0.16860192,0.026258646,0.0030864484,0.0005357753,0.0011403062,0.0010654016,0.6871254],"genre_scores_gemma":[0.6315744,0.021313444,0.066816114,0.0029915993,0.001068068,0.0005206271,0.0008802901,0.00057229126,0.27426317],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981646,0.00034523365,0.00008049164,0.00041293906,0.0007276341,0.00026915662],"domain_scores_gemma":[0.9966807,0.0011680385,0.00039840577,0.00044614816,0.0009945025,0.0003122843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019280693,0.0006223382,0.00050204684,0.001121554,0.0022548093,0.0062662754,0.001318808,0.0018860346,0.03329717],"category_scores_gemma":[0.0059815375,0.00055828784,0.00055953814,0.0009706245,0.0029321553,0.005598785,0.002441752,0.0020999198,0.012358417],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038709881,0.00038350112,0.007296479,0.002143296,0.00008460867,0.0009349214,0.010879672,0.00624607,0.02218116,0.3982451,0.060318843,0.4908993],"study_design_scores_gemma":[0.000055628443,0.00026533936,0.010487777,0.0013239806,0.00007297793,0.00082565285,0.008693955,0.004375844,0.012345996,0.14378642,0.8176409,0.00012544625],"about_ca_topic_score_codex":0.0028416205,"about_ca_topic_score_gemma":0.0030007872,"teacher_disagreement_score":0.03329717,"about_ca_system_score_codex":0.002118769,"about_ca_system_score_gemma":0.0027209762,"threshold_uncertainty_score":0.11139017},"labels":[],"label_agreement":null},{"id":"W5579926","doi":"","title":"VIRTUAL PROTOTYPING OF REACTIVE SYSTEMS IN SIDE","year":2010,"lang":"en","type":"article","venue":"Archivum chirurgicum neerlandicum","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Virtual prototyping; Human–computer interaction; Simulation","score_opus":0.006206715483644568,"score_gpt":0.22937391475949578,"score_spread":0.2231671992758512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5579926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024561333,0.0001876678,0.9592936,0.00010103211,0.000096419666,0.00009077792,0.000071516886,0.004466333,0.011131273],"genre_scores_gemma":[0.41518024,0.00045085495,0.57271427,0.0001181774,0.00007694659,0.00044804014,0.00027885227,0.0013354773,0.009397206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903536,0.0003251803,0.000043509135,0.00010301662,0.00038581697,0.00010713198],"domain_scores_gemma":[0.9979522,0.0011769413,0.00013212119,0.00038432085,0.00020958357,0.0001447376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010571263,0.0007077287,0.00056254125,0.00041900313,0.00041513625,0.0011456814,0.0013603475,0.00073772133,0.009024926],"category_scores_gemma":[0.0024850836,0.0005982781,0.00081002334,0.00026967603,0.0011958521,0.001115842,0.0022067346,0.0011234286,0.0015695594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006727164,0.00023706867,0.001834696,0.00078345335,0.000095605945,0.0016149014,0.0016332752,0.47560862,0.17424655,0.17882013,0.0066574104,0.15779553],"study_design_scores_gemma":[0.0001559174,0.00049181725,0.00058794144,0.00010160244,0.000036822465,0.0007313471,0.000082490486,0.7997168,0.08902088,0.033636823,0.075359456,0.00007820021],"about_ca_topic_score_codex":0.00032921985,"about_ca_topic_score_gemma":0.00030998216,"teacher_disagreement_score":0.009024926,"about_ca_system_score_codex":0.00026001112,"about_ca_system_score_gemma":0.00033327623,"threshold_uncertainty_score":0.030191362},"labels":[],"label_agreement":null},{"id":"W585328639","doi":"10.1007/0-387-28393-5","title":"S+Functional Data Analysis: User's Manual for Windows ®","year":2005,"lang":"en","type":"book","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Functional principal component analysis; Functional data analysis; Functional dependency; Computer science; Smoothing; Principal component analysis; Functional analysis; Linear form; Basis (linear algebra); Functional design; Data mining; Mathematics; Artificial intelligence; Computer vision; Machine learning; Programming language; Mathematical analysis","score_opus":0.03967875262521185,"score_gpt":0.2952646748685062,"score_spread":0.25558592224329435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585328639","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016421332,0.0008247335,0.43776655,0.00034830082,0.0007866214,0.0006485995,0.09321256,0.42639586,0.0383746],"genre_scores_gemma":[0.009205077,0.0014276402,0.50437456,0.0008443393,0.00034953942,0.0034769445,0.12830284,0.22761795,0.12440119],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986625,0.00016676316,0.00023008509,0.00022972988,0.0005755949,0.00013527411],"domain_scores_gemma":[0.9957652,0.0016559295,0.0002024773,0.0008376714,0.001384011,0.00015470409],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002226962,0.0038475792,0.0028651296,0.0032408515,0.00097234524,0.0025456836,0.00375551,0.0007315205,0.32221296],"category_scores_gemma":[0.0072449115,0.0027270606,0.0020040877,0.0033705842,0.00043277195,0.0033158958,0.001895809,0.0032152734,0.29894254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023528273,0.0000628186,0.00041326336,0.00083809753,0.000061181105,0.00011583037,0.00010853967,0.00050059694,0.0068496293,0.0027566596,0.8012861,0.1867721],"study_design_scores_gemma":[0.00017090191,0.0000997621,0.0022585222,0.00041020958,0.00016108059,0.00085877255,0.00008391347,0.0090114875,0.03981519,0.0098422,0.9371266,0.00016133672],"about_ca_topic_score_codex":0.0020763068,"about_ca_topic_score_gemma":0.0028691455,"teacher_disagreement_score":0.32221296,"about_ca_system_score_codex":0.0005625785,"about_ca_system_score_gemma":0.0017385829,"threshold_uncertainty_score":0.96678096},"labels":[],"label_agreement":null},{"id":"W6922449833","doi":"10.11575/prism/34285","title":"Energy and Utility Regulation in Alberta: Like Oil and Water?","year":2009,"lang":"en","type":"other","venue":"Open MIND","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Energy (signal processing); Production (economics); Energy consumption; Work (physics); Renewable energy; Energy expenditure; Energy source","score_opus":0.014102134306266143,"score_gpt":0.2578263294621809,"score_spread":0.24372419515591479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6922449833","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060197677,0.059280664,0.009181045,0.23117429,0.0029309327,0.000060562146,0.0003632637,0.00019740908,0.63661426],"genre_scores_gemma":[0.7468035,0.03401338,0.0048067668,0.036691625,0.0007142632,0.00003053421,0.00032521127,0.000070175825,0.17654453],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958978,0.00045856618,0.00006874725,0.00043092092,0.0017563372,0.0013875935],"domain_scores_gemma":[0.99851555,0.00031633585,0.00012676619,0.00007170614,0.0006672624,0.0003023462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002478948,0.00033412152,0.0002635663,0.0014023634,0.010720128,0.013197728,0.0023898317,0.004231867,0.004104597],"category_scores_gemma":[0.0028305962,0.00028219787,0.0004315476,0.0039292346,0.01725647,0.003486859,0.0016502277,0.0038777923,0.00035774228],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003097638,0.0000202103,0.00468434,0.00013200131,0.00001237675,0.0005103076,0.0083915265,0.0014206955,0.00047818944,0.91229093,0.028629461,0.043399084],"study_design_scores_gemma":[0.00001109421,0.00002681517,0.015965253,0.0004477444,0.000026330497,0.00017968337,0.014917813,0.0009677146,0.00055827264,0.05547074,0.9113426,0.00008586323],"about_ca_topic_score_codex":0.97291505,"about_ca_topic_score_gemma":0.9822521,"teacher_disagreement_score":0.106994964,"about_ca_system_score_codex":0.106994964,"about_ca_system_score_gemma":0.13762404,"threshold_uncertainty_score":0.77630645},"labels":[],"label_agreement":null},{"id":"W6930801930","doi":"10.5281/zenodo.13788210","title":"SFPsimulations","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Drosophila melanogaster; Melanogaster; Gene; Drosophila pseudoobscura; Drosophila (subgenus)","score_opus":0.02864492249051208,"score_gpt":0.26257062576418405,"score_spread":0.23392570327367196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930801930","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08605896,0.0019672664,0.6037365,0.002189585,0.004034502,0.001476197,0.07589149,0.10386674,0.12077878],"genre_scores_gemma":[0.35452107,0.0020938818,0.4309581,0.0018084161,0.0005995126,0.0049115815,0.103453115,0.019217102,0.082437195],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992079,0.00014656634,0.000061725645,0.00021643424,0.00020260477,0.00016483203],"domain_scores_gemma":[0.9992623,0.00023079263,0.00005689281,0.00019566355,0.00016697617,0.00008728678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010316024,0.0013187935,0.00075454626,0.00083052414,0.00063092046,0.0012414326,0.002216376,0.0010894472,0.06154183],"category_scores_gemma":[0.0041545904,0.00046265256,0.0013714352,0.00064573076,0.00040003998,0.0013814126,0.002122746,0.0013979318,0.025581665],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023768689,0.0006434629,0.010033659,0.0024342097,0.00041541262,0.0011229,0.00060056633,0.07678105,0.05478408,0.15534858,0.42047945,0.27497968],"study_design_scores_gemma":[0.00047862926,0.00034766932,0.0018430458,0.00023040973,0.000165419,0.00049055007,0.0002062912,0.34834278,0.04933746,0.056070067,0.5423832,0.00010443537],"about_ca_topic_score_codex":0.0009320746,"about_ca_topic_score_gemma":0.0010687094,"teacher_disagreement_score":0.06154183,"about_ca_system_score_codex":0.00047537778,"about_ca_system_score_gemma":0.00095025,"threshold_uncertainty_score":0.2058779},"labels":[],"label_agreement":null},{"id":"W6958572635","doi":"10.6084/m9.figshare.27176843","title":"Additional file 1 of An evaluation of the preprints produced at the beginning of the 2022 mpox public health emergency","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Public Health Agency of Canada","funders":"","keywords":"Public health; Public access; MEDLINE; Emergency response; Health care","score_opus":0.09341718432749237,"score_gpt":0.325949581572729,"score_spread":0.23253239724523667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958572635","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024261756,0.000019604748,0.00062603113,0.00036047393,0.00013817711,0.00053943734,0.99233925,0.00081783155,0.00491662],"genre_scores_gemma":[0.013332057,0.00025063526,0.009479574,0.0019059954,0.0004959484,0.009629273,0.90827954,0.0045375787,0.05208938],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99753165,0.00069090526,0.00046461046,0.00038557753,0.000682383,0.00024486825],"domain_scores_gemma":[0.92984015,0.05118199,0.0021244888,0.0041208332,0.011440473,0.0012920283],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004682238,0.0009918299,0.00089017034,0.0030715652,0.0016999162,0.002369298,0.0014828983,0.001389808,0.9118294],"category_scores_gemma":[0.076741554,0.0006444689,0.0007937669,0.004116172,0.0005701271,0.0020391054,0.0017467039,0.0012325753,0.3380754],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026442553,0.000040232975,0.00048276997,0.0007699563,0.000010141281,0.000028491984,0.00006792466,0.00011174293,0.000059312555,0.0005087366,0.9924426,0.00521361],"study_design_scores_gemma":[0.0018014718,0.0001976505,0.012273272,0.0025409851,0.000078397425,0.0002338375,0.0007997784,0.00068891014,0.00079919206,0.008486288,0.9719775,0.00012271639],"about_ca_topic_score_codex":0.010761908,"about_ca_topic_score_gemma":0.0119103175,"teacher_disagreement_score":0.99531776,"about_ca_system_score_codex":0.0016757294,"about_ca_system_score_gemma":0.0038855467,"threshold_uncertainty_score":0.12576467},"labels":[],"label_agreement":null},{"id":"W6967548046","doi":"10.5281/zenodo.11864885","title":"reinventing prosperity pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prosperity; Poverty; Club; Climate change; Global warming; Inequality; Economic inequality; Environmental degradation","score_opus":0.023451109100235436,"score_gpt":0.24671120248851172,"score_spread":0.2232600933882763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967548046","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049431995,0.001035354,0.00035150384,0.00916208,0.010692878,0.00017361648,0.0013859965,0.0012794995,0.9754249],"genre_scores_gemma":[0.003508333,0.001208587,0.0006859771,0.0035533258,0.0034508856,0.00013241735,0.0020556257,0.00094193185,0.984463],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99604994,0.00031084268,0.00014712338,0.00026801528,0.002533406,0.0006907305],"domain_scores_gemma":[0.9910184,0.0005932227,0.00032339324,0.0010678782,0.0031913472,0.0038057584],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0027971456,0.0009583018,0.00087276415,0.003462094,0.004577969,0.01891488,0.0016491864,0.004250155,0.59535867],"category_scores_gemma":[0.012305317,0.0007371679,0.001117237,0.0022483072,0.0015819148,0.009521959,0.008744483,0.0050052805,0.46226025],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053320987,0.000018740406,0.00006781512,0.000034754798,0.0000011262199,0.000031058597,0.000047865164,0.000010483672,0.00004108898,0.0031438319,0.97378427,0.022813693],"study_design_scores_gemma":[0.0000042288684,0.000005885043,0.00026615898,0.00004618102,7.7695563e-7,0.000026141917,0.00005045488,0.000008740269,0.00003350133,0.00032830128,0.999225,0.0000046171485],"about_ca_topic_score_codex":0.005251037,"about_ca_topic_score_gemma":0.010423546,"teacher_disagreement_score":0.40464133,"about_ca_system_score_codex":0.0022270957,"about_ca_system_score_gemma":0.005474462,"threshold_uncertainty_score":0.57717174},"labels":[],"label_agreement":null},{"id":"W6967768083","doi":"10.5281/zenodo.13822173","title":"Hoe bevordert Nexalyn Nederland het algehele welzijn?","year":2024,"lang":"nl","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Subpoena; Headline; Gloom","score_opus":0.024418427854045327,"score_gpt":0.2558492705151969,"score_spread":0.23143084266115158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967768083","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003022858,0.007463605,0.0017003464,0.0072999783,0.006169045,0.00004969524,0.0038640623,0.0010179172,0.96941245],"genre_scores_gemma":[0.015897723,0.0061904914,0.0023994546,0.0030796784,0.00064825045,0.000047208494,0.002210869,0.002000881,0.96752554],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928916,0.0000913681,0.00003584225,0.0001202314,0.00035500788,0.00010836095],"domain_scores_gemma":[0.99966204,0.00005260137,0.000021481688,0.00003892485,0.00011477367,0.00011019103],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00066516356,0.000756599,0.00089094165,0.00075490476,0.0020977056,0.00862693,0.0008676924,0.002101428,0.4917352],"category_scores_gemma":[0.0015231407,0.00047408225,0.00045774007,0.0010930919,0.0011867061,0.0047475244,0.0028519824,0.0034391629,0.30903754],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023901276,0.000092936716,0.00034677098,0.0008309782,0.000021214419,0.00042530158,0.0016496606,0.0001248154,0.002718966,0.041127946,0.8242428,0.12817973],"study_design_scores_gemma":[0.000004818594,0.0000032732594,0.00007516079,0.00006471286,0.0000021790388,0.00004592625,0.00022149482,0.000009390714,0.00020158506,0.00068092503,0.99868697,0.0000037173859],"about_ca_topic_score_codex":0.0073472178,"about_ca_topic_score_gemma":0.017385624,"teacher_disagreement_score":0.4917352,"about_ca_system_score_codex":0.0014611126,"about_ca_system_score_gemma":0.0021839018,"threshold_uncertainty_score":0.7249781},"labels":[],"label_agreement":null},{"id":"W6967961354","doi":"10.5281/zenodo.11993581","title":"Iso 19113 pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"XML; Metadata; Data quality; Quality (philosophy); International standard; Spatial analysis; Set (abstract data type); Disclaimer; Standardization","score_opus":0.02076989612892256,"score_gpt":0.24592403704792876,"score_spread":0.2251541409190062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967961354","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007331402,0.0011346604,0.025064237,0.0020942285,0.0033862272,0.0014749576,0.030054547,0.0072236415,0.9288344],"genre_scores_gemma":[0.00618091,0.0025145046,0.035576656,0.0028976623,0.0007651693,0.0014182767,0.10314036,0.0056110355,0.8418955],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99094725,0.0008152913,0.0006541754,0.00047379406,0.0064992234,0.0006102722],"domain_scores_gemma":[0.98931026,0.00045284934,0.00029012622,0.0010123185,0.008709836,0.00022466003],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004513264,0.002112805,0.0009884269,0.00801202,0.0020007514,0.0065432778,0.0037967977,0.004162303,0.35250935],"category_scores_gemma":[0.010815725,0.0011001518,0.0016779718,0.007332185,0.0011673394,0.0061197034,0.0029331285,0.0028963496,0.39755714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039431165,0.00006596157,0.0001714276,0.00033560756,0.000007968402,0.00006613523,0.00007732722,0.00044052728,0.00093703327,0.015287957,0.898306,0.084264524],"study_design_scores_gemma":[0.0000050734707,0.000009892665,0.00025855112,0.00009474527,0.0000032639286,0.000038075785,0.000030114137,0.000070637856,0.0003834435,0.0010540824,0.9980421,0.000009987308],"about_ca_topic_score_codex":0.026331974,"about_ca_topic_score_gemma":0.014396409,"teacher_disagreement_score":0.6474906,"about_ca_system_score_codex":0.0037610605,"about_ca_system_score_gemma":0.008297521,"threshold_uncertainty_score":0.9235668},"labels":[],"label_agreement":null},{"id":"W6976369894","doi":"10.60692/396wj-aps77","title":"Farm production diversity and women's dietary diversity: Evidence from central Tunisia","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Dietary diversity; Agriculture; Diversification (marketing strategy); Diversity (politics); Context (archaeology); Production (economics); Agricultural diversification; Agricultural productivity","score_opus":0.039094924512592624,"score_gpt":0.1950290244457563,"score_spread":0.15593409993316365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976369894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965161,0.001959244,0.000053338634,0.00034431953,0.000007732708,0.000005445295,0.00033875383,0.0000012515844,0.0007737543],"genre_scores_gemma":[0.99835265,0.0010726438,0.000056094967,0.000116171745,0.000014055715,0.0000074622935,0.00016346043,0.0000011565451,0.00021630735],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925524,0.00032648246,0.000055499822,0.00014572263,0.00007605659,0.0001409697],"domain_scores_gemma":[0.9974191,0.00095250655,0.0010536194,0.00014169289,0.00018327888,0.00024991503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018766981,0.00036581783,0.0004404805,0.0013967761,0.0009138127,0.0009291162,0.0005278599,0.00046062542,0.0032337217],"category_scores_gemma":[0.0028430908,0.00029667185,0.0004924327,0.0031526033,0.0011591171,0.0004935591,0.00091413147,0.0004238518,0.00019652939],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023647776,0.000067102934,0.98679173,0.00015852902,0.00038410572,0.0002891868,0.002910175,0.00005492835,0.00026465065,0.00021333223,0.00030382856,0.008325951],"study_design_scores_gemma":[0.000013323289,0.000055709566,0.9963038,0.00010557078,0.00009602113,0.000071134666,0.002155384,0.000064527165,0.000045607965,0.000040862178,0.0010436593,0.0000043864206],"about_ca_topic_score_codex":0.12557293,"about_ca_topic_score_gemma":0.14582665,"teacher_disagreement_score":0.12557293,"about_ca_system_score_codex":0.0011678399,"about_ca_system_score_gemma":0.000966922,"threshold_uncertainty_score":0.24968404},"labels":[],"label_agreement":null},{"id":"W7014909669","doi":"","title":"Research viewpoints by IRC: South African team develops new tool to analyze HVAC control systems","year":2000,"lang":"en","type":"article","venue":"NPARC","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Viewpoints; Control (management); HVAC; Control system; Automation","score_opus":0.02014059016238168,"score_gpt":0.29104968213362886,"score_spread":0.2709090919712472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014909669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13223952,0.0032454084,0.6750902,0.017214362,0.0029622407,0.0011670825,0.003920351,0.0047287787,0.15943207],"genre_scores_gemma":[0.3570128,0.0037749729,0.47290945,0.0012553721,0.0005726158,0.00046997098,0.0026634154,0.001581773,0.15975958],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99807906,0.00037193135,0.00009334952,0.0003855362,0.00093147124,0.00013857165],"domain_scores_gemma":[0.9936413,0.0011273714,0.00028548297,0.0007314771,0.0038300606,0.00038435313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003541906,0.00080241886,0.0005644172,0.0028286858,0.0016449098,0.0033594237,0.00077589805,0.00094535464,0.03083324],"category_scores_gemma":[0.0065316525,0.0005334317,0.0007559003,0.0018594406,0.00076403783,0.0024730442,0.0011942879,0.0015527384,0.0060455617],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049394683,0.00030501204,0.022253435,0.0012871969,0.00022356569,0.0010452648,0.0052271215,0.02315321,0.13452843,0.085143164,0.0911438,0.6351959],"study_design_scores_gemma":[0.00019984935,0.0004533404,0.019414684,0.0010214786,0.0003173455,0.00095430034,0.0065314868,0.1470552,0.1943894,0.017109064,0.6124108,0.00014304373],"about_ca_topic_score_codex":0.016135149,"about_ca_topic_score_gemma":0.017731141,"teacher_disagreement_score":0.03083324,"about_ca_system_score_codex":0.002527703,"about_ca_system_score_gemma":0.0051361187,"threshold_uncertainty_score":0.10314751},"labels":[],"label_agreement":null},{"id":"W7039120260","doi":"","title":"Laïcité et humanisme","year":2017,"lang":"fr","type":"book","venue":"OpenEdition (OpenEdition)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mysticism; Perspective (graphical); Identity (music); Meaning (existential); Omniscience","score_opus":0.030373569161644864,"score_gpt":0.28685037300071037,"score_spread":0.2564768038390655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039120260","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010774777,0.09350221,0.0068087745,0.051589098,0.0023048378,0.000047790465,0.0003685806,0.0002834389,0.8343205],"genre_scores_gemma":[0.28302297,0.032766063,0.0028219111,0.00559282,0.0011995495,0.00008530186,0.00033121524,0.00027425756,0.6739059],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988814,0.00029080207,0.000024280778,0.00013135318,0.00046319535,0.00020898],"domain_scores_gemma":[0.99885726,0.0003701897,0.000076694516,0.00013787777,0.0004433858,0.00011456235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061087,0.00050934777,0.00040618668,0.0017629938,0.006150902,0.007642618,0.0007721077,0.001572761,0.015491273],"category_scores_gemma":[0.0026229925,0.00022053841,0.00027063186,0.0040734406,0.018369293,0.0037340769,0.0015077185,0.003013949,0.0018293303],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015752434,0.000006675531,0.00027172395,0.0001660141,0.0000070387996,0.000087433735,0.014507417,0.00021606608,0.00017606838,0.831728,0.11518894,0.03762899],"study_design_scores_gemma":[0.0000042205384,0.0000045778656,0.001321807,0.00024822415,0.000003995857,0.000084583466,0.003406145,0.00016508649,0.000101679,0.044509277,0.9501364,0.000014107828],"about_ca_topic_score_codex":0.6303747,"about_ca_topic_score_gemma":0.6455038,"teacher_disagreement_score":0.6303747,"about_ca_system_score_codex":0.040799577,"about_ca_system_score_gemma":0.026662758,"threshold_uncertainty_score":0.7436041},"labels":[],"label_agreement":null},{"id":"W7125371118","doi":"10.18280/mmep.121225","title":"Innovation Technology of Engineering Evaporation in the Accelerated Process into Old Brine with an Adaptive Fuzzy Logic Control","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Fuzzy logic; Process control; Fuzzy control system; Evaporation; Brine; Control system; Control (management)","score_opus":0.026279939351090652,"score_gpt":0.2622394021626134,"score_spread":0.23595946281152272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125371118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16871563,0.00079652044,0.82137465,0.00021184002,0.00012914962,0.000086805165,0.00003650842,0.00047101988,0.008177947],"genre_scores_gemma":[0.9419413,0.00036598928,0.05431983,0.000047217156,0.000019785439,0.00005075473,0.000023847751,0.000011651043,0.0032196217],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985933,0.000014631423,0.000010416317,0.000043300526,0.000059595433,0.000012774113],"domain_scores_gemma":[0.9999211,0.000018094135,0.00001623452,0.000009555858,0.000029616182,0.0000054291777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023212575,0.0002585743,0.00020979026,0.0002375646,0.00026258884,0.0004167161,0.0004131477,0.0002727601,0.0006667458],"category_scores_gemma":[0.00023226059,0.00012768623,0.00034547562,0.00018598212,0.000314707,0.0004366088,0.0003273764,0.00031614202,0.0001165829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025837682,0.00015031871,0.0049203425,0.0005316114,0.00005582033,0.00034645404,0.00035349219,0.110935636,0.62793964,0.011101369,0.0010653092,0.24234165],"study_design_scores_gemma":[0.00004942091,0.00041712334,0.0037827985,0.000036164354,0.00006897267,0.00023127039,0.00007611889,0.7867417,0.1963686,0.0027027058,0.009476882,0.00004827603],"about_ca_topic_score_codex":0.0013310284,"about_ca_topic_score_gemma":0.0013321572,"teacher_disagreement_score":0.0013310284,"about_ca_system_score_codex":0.00030050977,"about_ca_system_score_gemma":0.00031032754,"threshold_uncertainty_score":0.0026465654},"labels":[],"label_agreement":null},{"id":"W761468145","doi":"","title":"ON THE STABILIZATION OF NONLINEAR CONTROL SYSTEMS SUBJECT TO STOCHASTIC DISTURBANCES AND INPUT CONSTRAINTS","year":2015,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Nonlinear system; Control theory (sociology); Subject (documents); Control (management); Control engineering; Computer science; Mathematics; Engineering; Artificial intelligence; Physics; Library science","score_opus":0.010888632486916036,"score_gpt":0.21767024766218807,"score_spread":0.20678161517527202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W761468145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05785895,0.008996203,0.87113285,0.0025707595,0.00037365966,0.000117875476,0.00020272529,0.0002288611,0.05851814],"genre_scores_gemma":[0.8687284,0.032759365,0.053765718,0.00055348343,0.0010251268,0.0003791859,0.00035509307,0.000172759,0.04226084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99971527,0.00007091827,0.000012258434,0.000051962958,0.000118522716,0.00003093826],"domain_scores_gemma":[0.99947363,0.00038847298,0.000034410154,0.000024228906,0.00006745513,0.000011845084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054353237,0.00075194705,0.0005258311,0.000463628,0.00030530203,0.00086200103,0.0003053696,0.00047120513,0.0028501265],"category_scores_gemma":[0.00172038,0.00013224258,0.0003112235,0.00060278684,0.0013215785,0.0004913899,0.00076179643,0.0008612535,0.00034983925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015761306,0.00009469603,0.00037462614,0.0007180297,0.0000753304,0.00019163963,0.00045901447,0.530404,0.020719798,0.3145771,0.005339581,0.12688857],"study_design_scores_gemma":[0.000049335955,0.00021470495,0.0009132038,0.0001965546,0.000036673897,0.00005845942,0.000117223026,0.7807309,0.009262354,0.19372566,0.014661483,0.000033428987],"about_ca_topic_score_codex":0.0029759551,"about_ca_topic_score_gemma":0.0017553873,"teacher_disagreement_score":0.0029759551,"about_ca_system_score_codex":0.0005816929,"about_ca_system_score_gemma":0.0008406061,"threshold_uncertainty_score":0.009534597},"labels":[],"label_agreement":null}]}