{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007662768,0.0002394294,0.0004173529,0.0003272467,0.0002169634,0.00004210469,0.0001373592,0.00008050873,0.00008532738],"category_scores_gemma":[0.00002751447,0.000264541,0.00006561535,0.000936556,0.00003924828,0.0001741211,0.00003341829,0.0006920649,0.000001013228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329524,"about_ca_system_score_gemma":0.00002900823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001975096,"about_ca_topic_score_gemma":0.0009233208,"domain_scores_codex":[0.9980055,0.0005412877,0.0004762401,0.0002978049,0.0002827744,0.0003964113],"domain_scores_gemma":[0.9992459,0.0002250561,0.0001589781,0.0002505305,0.00002561607,0.00009389089],"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":[0.00008667845,0.00003658954,0.0009095384,0.0001099317,0.00005443874,0.00002013057,0.001015928,0.7129183,0.2321352,0.00003711564,0.0003786143,0.05229755],"study_design_scores_gemma":[0.0003120364,0.00009282435,0.0008018566,0.00004600714,0.00004171112,0.00006483198,0.0003485888,0.9716306,0.0235502,0.0002085164,0.002620787,0.0002820644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823737,0.0006883027,0.01222993,0.00005037646,0.003952133,0.0004188122,0.00001312133,0.0001272982,0.0001463846],"genre_scores_gemma":[0.9980238,0.000010422,0.001310611,0.000050992,0.0003475294,0.00005871891,0.00001223304,0.00005458745,0.000131108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2587123,"threshold_uncertainty_score":0.9999807,"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008350111,0.0002142851,0.0003348986,0.0003142831,0.0008769973,0.0002721927,0.00212767,0.0000374169,0.0000263598],"category_scores_gemma":[0.00002794154,0.0002156758,0.00009272652,0.0007604407,0.00009123681,0.0006260668,0.0004564913,0.0003929193,0.000002472954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001652067,"about_ca_system_score_gemma":0.0002612029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008414604,"about_ca_topic_score_gemma":0.00000645919,"domain_scores_codex":[0.9974476,0.0003282956,0.0004558775,0.0008063299,0.0006148331,0.0003470537],"domain_scores_gemma":[0.9974745,0.0003385885,0.0003505912,0.001665935,0.00008097169,0.00008940496],"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":[0.00002495067,0.0007372818,0.0009942099,0.0001869896,0.00005838838,0.000005752368,0.0001166306,0.9577243,0.002435341,0.02752802,0.005342979,0.004845149],"study_design_scores_gemma":[0.0004378795,0.000109968,0.000366105,0.00004240035,0.00002810061,0.000007858667,0.0001570601,0.9919536,0.0001281067,0.0001405863,0.006370208,0.0002581128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01809992,0.0002299397,0.9780691,0.0003987849,0.001219982,0.0008751908,0.0005165018,0.0001989974,0.0003916357],"genre_scores_gemma":[0.8748832,0.000002439516,0.1211963,0.0002412414,0.0002604284,0.0005128584,0.0006125621,0.00004600626,0.00224493],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8568727,"threshold_uncertainty_score":0.8795006,"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005415955,0.0002684953,0.000483786,0.001195536,0.0004123712,0.0001079789,0.0001277081,0.0000628276,0.0004347756],"category_scores_gemma":[0.00004567666,0.0002752335,0.00008292554,0.001873488,0.00003617484,0.000107291,0.0001763851,0.0008223928,0.000003409652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002188301,"about_ca_system_score_gemma":0.00001417891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002184536,"about_ca_topic_score_gemma":0.001499316,"domain_scores_codex":[0.9981785,0.0002534555,0.0004201357,0.0004347881,0.0003559645,0.0003571667],"domain_scores_gemma":[0.9993736,0.0001431588,0.00008952519,0.0002023405,0.00002814958,0.000163216],"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":[0.0001052398,0.00003485371,0.3466346,0.0001679238,0.0005413647,0.0001621426,0.002410651,0.5348409,0.1039823,0.00003126336,0.001266371,0.009822411],"study_design_scores_gemma":[0.0005923044,0.0000495911,0.02072485,0.00009090205,0.000119287,0.0000337214,0.0003828062,0.9378362,0.001326477,0.000005317029,0.03845355,0.0003849409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942076,0.002680939,0.0007440394,0.0002436552,0.001000922,0.0002921233,0.00005868435,0.0002545705,0.0005174711],"genre_scores_gemma":[0.9963335,0.00003763513,0.0002822263,0.0001064803,0.000107741,0.00004846438,0.00001959106,0.000052452,0.003011955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4029954,"threshold_uncertainty_score":0.99997,"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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01084253,0.0003033035,0.00118259,0.003740147,0.0009797183,0.0001503,0.00316631,0.00006933904,0.001382925],"category_scores_gemma":[0.005187475,0.0001961385,0.0005927135,0.02927137,0.0005452307,0.0001168247,0.0007727392,0.0011409,0.000003035876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003806605,"about_ca_system_score_gemma":0.0007379205,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006430237,"about_ca_topic_score_gemma":0.1045304,"domain_scores_codex":[0.9885314,0.004364869,0.002070331,0.0009254343,0.003608765,0.0004991765],"domain_scores_gemma":[0.9926707,0.004141043,0.001140527,0.00174536,0.0002025575,0.00009978978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001489269,0.001077425,0.5578911,0.000006784972,0.0001160906,0.0006367779,0.01649499,0.4226159,0.0002064211,0.0001040593,0.0005080493,0.0001935364],"study_design_scores_gemma":[0.008111305,0.0006188158,0.605875,0.00005652028,0.001932412,0.000211135,0.1453618,0.2320165,0.0007899117,0.0003632406,0.003534629,0.001128784],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964145,0.0001825298,0.00002695943,0.001041131,0.001220444,0.0008411097,0.0001178487,0.00001498514,0.000140523],"genre_scores_gemma":[0.9989628,0.000001160776,0.000005200561,0.0004050685,0.00007541019,0.00007998637,0.000002548374,0.00001319303,0.0004546586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1905994,"threshold_uncertainty_score":0.99953,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}