{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":4,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":4,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"905a8c1557df","filters":{"venue":"2022 IEEE World AI IoT Congress (AIIoT)"}},"results":[{"id":"W4285101290","doi":"10.1109/aiiot54504.2022.9817236","title":"Detection of Faults in Electro-Hydrostatic Actuators Using Feature Extraction Methods and an Artificial Neural Network","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Actuator; Leakage (economics); Fault detection and isolation; Feature extraction; Artificial neural network; Computer science; Control theory (sociology); Fault (geology); Sensitivity (control systems); Artificial intelligence; Pattern recognition (psychology); Engineering; Electronic engineering","authors":[{"name":"Maryam Ghanbari","is_ca":true},{"name":"Witold Kinsner","is_ca":true},{"name":"Nariman Sepehri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01761959790551748,"gpt":0.3172959730188769,"spread":0.2996763751133594,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004403944,0.0006975769,0.0003942709,0.00093999,0.0002108662,0.0004626759,0.0003662013,0.0006484517,0.0004630507],"category_scores_gemma":[0.001323059,0.0001654562,0.0003462607,0.000433781,0.0002231313,0.0006229628,0.0002720247,0.0003560968,0.0001282947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004092545,"about_ca_system_score_gemma":0.0002568259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001186987,"about_ca_topic_score_gemma":0.001198725,"domain_scores_codex":[0.9997113,0.00004395779,0.00003431981,0.00007288766,0.0001134824,0.00002402679],"domain_scores_gemma":[0.999429,0.0002407274,0.0001146892,0.00003464226,0.0001657092,0.0000150871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006560202,0.0003479463,0.01011045,0.0002926326,0.0001347909,0.0003637247,0.0001395825,0.1100473,0.1791219,0.001253226,0.000923573,0.6966089],"study_design_scores_gemma":[0.00001154849,0.0002299004,0.008354296,0.00001932201,0.00003470146,0.0001892183,0.00003235118,0.9445351,0.04553174,0.0005580449,0.0004848114,0.00001894961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2165163,0.0005243391,0.780149,0.00015657,0.00008373639,0.00008033022,0.00009890299,0.001115782,0.001275073],"genre_scores_gemma":[0.855198,0.0001984227,0.143231,0.00004724946,0.00002203259,0.00007812848,0.0001112817,0.00001767627,0.001096104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001186987,"threshold_uncertainty_score":0.002969384,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285101299","doi":"10.1109/aiiot54504.2022.9817370","title":"An Approach for Automatic Discovery of Rules Based on ECG Data Using Learning Classifier Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Classifier (UML); Artificial intelligence; Component (thermodynamics); Personalized medicine; Decision support system; Association rule learning; Data mining; Bioinformatics","authors":[{"name":"Muthana Zouri","is_ca":true},{"name":"Alexander Ferworn","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05134884789720748,"gpt":0.3044739133264933,"spread":0.2531250654292858,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003307894,0.0008482691,0.001067012,0.00290003,0.0007951576,0.002536472,0.002211505,0.001616891,0.001974518],"category_scores_gemma":[0.01465712,0.0005496591,0.001059096,0.001855621,0.0007050716,0.001837178,0.0009898674,0.001851372,0.00103816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009784508,"about_ca_system_score_gemma":0.001861535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006789932,"about_ca_topic_score_gemma":0.005834321,"domain_scores_codex":[0.9965064,0.0006590805,0.0004132492,0.0008113128,0.001454978,0.0001549637],"domain_scores_gemma":[0.992755,0.003613002,0.0004370951,0.0009423352,0.00213134,0.0001213746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001771354,0.0003406767,0.007080456,0.0002731095,0.0002063191,0.0006048954,0.000437669,0.08213249,0.01823972,0.01963983,0.004930289,0.8659375],"study_design_scores_gemma":[0.00003405931,0.0001127261,0.001720272,0.00008265295,0.00008988577,0.0003116079,0.0000983387,0.9487567,0.01938633,0.01826111,0.01109658,0.00004963935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007721289,0.0001850673,0.9877113,0.0002876629,0.0000574127,0.0002539502,0.0001929902,0.002328523,0.001261838],"genre_scores_gemma":[0.1014596,0.00020225,0.89588,0.0001824124,0.00004663498,0.000254611,0.0005022769,0.0001028065,0.001369515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006789932,"threshold_uncertainty_score":0.01749402,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285101200","doi":"10.1109/aiiot54504.2022.9817232","title":"Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"NIST; Support vector machine; Logistic regression; Computer science; Artificial intelligence; Machine learning; Work (physics); Regression analysis; Fire safety; Training (meteorology); Aeronautics; Environmental science; Forensic engineering; Statistics; Engineering; Meteorology; Mathematics; Geography","authors":[{"name":"Adenrele A. Ishola","is_ca":false},{"name":"Damian Valles","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0232803376877445,"gpt":0.2698443647249787,"spread":0.2465640270372342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001837672,0.0009587791,0.0005520212,0.001754828,0.0002725626,0.0009947921,0.0005641833,0.0007829816,0.0007398722],"category_scores_gemma":[0.005890884,0.0002617977,0.0007426805,0.001013257,0.0002529711,0.001199715,0.0003903958,0.0008373074,0.0005290519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005445755,"about_ca_system_score_gemma":0.0005748763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009094297,"about_ca_topic_score_gemma":0.00768224,"domain_scores_codex":[0.9991624,0.0003022462,0.00007334741,0.0001678414,0.0002115795,0.00008254594],"domain_scores_gemma":[0.996986,0.001918715,0.0003769603,0.0001919537,0.0004613398,0.00006500156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003473185,0.0006966229,0.1756233,0.0001043472,0.0002777185,0.000167699,0.0002212789,0.5697105,0.007348499,0.0008550241,0.001077941,0.2435697],"study_design_scores_gemma":[0.000004982996,0.00008513446,0.01114791,0.00001083966,0.00001479156,0.0000276369,0.00008651135,0.9860564,0.002012167,0.0003807011,0.000157631,0.00001524672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8151076,0.0003554611,0.1813601,0.0002367755,0.00006755635,0.00007701005,0.0002897908,0.0008410552,0.001664724],"genre_scores_gemma":[0.963649,0.0001169846,0.03495471,0.00002532851,0.00001609772,0.00003663002,0.0003397538,0.00002135995,0.0008400358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009094297,"threshold_uncertainty_score":0.01808274,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285101237","doi":"10.1109/aiiot54504.2022.9817191","title":"Analysis of the Financial Efficiency of Companies in the Industrial Sector during COVID-19: Case Study in Peru","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Productivity; Revenue; Business; Equity (law); Financial crisis; Investment (military); Quarter (Canadian coin); Index (typography); Finance; Economics; Economic growth; Computer science","authors":[{"name":"Lucero Sovero Rivera","is_ca":false},{"name":"Nicole Ninamango Origuela","is_ca":false},{"name":"Grimaldo Quispe","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1043567045910751,"gpt":0.3805726327403521,"spread":0.276215928149277,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123749,0.0003340132,0.0002758279,0.002057716,0.0006395046,0.001170642,0.0004987774,0.000808245,0.0009951238],"category_scores_gemma":[0.003585348,0.0001745725,0.0004464234,0.00240514,0.0006327019,0.0007847789,0.001063929,0.0003986797,0.0001008028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002436722,"about_ca_system_score_gemma":0.0006723989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0235423,"about_ca_topic_score_gemma":0.01893006,"domain_scores_codex":[0.9991699,0.0003783857,0.00004914154,0.00006867343,0.0001406397,0.0001931452],"domain_scores_gemma":[0.9983057,0.0009495916,0.0003208763,0.00006450094,0.0002710877,0.00008827525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006798416,0.001247228,0.7819831,0.0005657528,0.0003185761,0.01911969,0.007984648,0.1119099,0.003905337,0.008817278,0.002267942,0.06120078],"study_design_scores_gemma":[0.00005932299,0.0008837852,0.792976,0.0001606043,0.0001322922,0.00228871,0.03307361,0.1596197,0.00280257,0.002090916,0.005827414,0.00008515685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997597,0.0001771237,0.0006006126,0.000130672,9.511412e-7,0.00002100739,0.0001122709,0.000006302042,0.001353949],"genre_scores_gemma":[0.9991487,0.0001581541,0.0003147784,0.000007431498,0.000002091964,0.00001244708,0.0001148344,0.000002023134,0.0002394616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0235423,"threshold_uncertainty_score":0.04681051,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}