{"id":"W4312708109","doi":"10.1109/tfuzz.2022.3222025","title":"Choquet Integral-Based Aggregation for the Analysis of Anomalies Occurrence in Sustainable Transportation Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Choquet integral; Computer science; Quality (philosophy); Data quality; Data mining; Anomaly detection; Overexploitation; Data aggregator; Risk analysis (engineering); Artificial intelligence; Economics; Business; Wireless sensor network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006816156,0.0001382092,0.0002809612,0.0008382214,0.0005094795,0.00009536291,0.000550939,0.0000508781,0.000007388504],"category_scores_gemma":[0.000003684123,0.000119939,0.0002201429,0.003283464,0.00004319826,0.0002260009,0.000001477196,0.0001846125,8.487225e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002230065,"about_ca_system_score_gemma":0.0001070554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050443,"about_ca_topic_score_gemma":0.000183259,"domain_scores_codex":[0.9984345,0.0001451343,0.0005337503,0.0003413116,0.0003271374,0.0002181688],"domain_scores_gemma":[0.9986182,0.0003327933,0.0002793153,0.000542475,0.0001915574,0.00003570159],"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":[0.00002922019,0.0001531227,0.0001170628,0.00008875194,0.000110303,0.000001023069,0.0004688455,0.9749823,0.0001638618,0.02078987,0.0001125893,0.002983067],"study_design_scores_gemma":[0.0003902461,0.0002535173,0.0007029014,0.00002969021,0.0002298154,0.00000240006,0.003107191,0.986549,0.002874808,0.0001335027,0.005515906,0.0002110506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009627836,0.0001806974,0.9876436,0.0001987922,0.0003770413,0.00140951,0.0003619007,0.0001439071,0.0000567255],"genre_scores_gemma":[0.9946119,0.00001315067,0.0004731584,0.00002480272,0.000009533705,0.004443988,0.00002994103,0.000007825476,0.0003856565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9871705,"threshold_uncertainty_score":0.4890969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612562250454843,"score_gpt":0.2518415483564244,"score_spread":0.235715925851876,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}