{"id":"W2997167991","doi":"10.1609/aaai.v34i04.5712","title":"Detecting Semantic Anomalies","year":2020,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Generalization; Relevance (law); Context (archaeology); Task (project management); Anomaly detection; Set (abstract data type); Artificial intelligence; Natural language processing; Object (grammar); Machine learning; Data science; Information retrieval; Programming language; Epistemology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00003426519,0.0000447929,0.000047889,0.00001785911,0.00009258615,0.00006797212,0.0003057728,0.0000179762,0.00003964752],"category_scores_gemma":[0.00001156862,0.00003999795,0.00002880834,0.00028305,0.000009758489,0.0001497972,0.0001169286,0.00004770005,0.0001347295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004697291,"about_ca_system_score_gemma":0.000008070295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008719092,"about_ca_topic_score_gemma":0.00000134554,"domain_scores_codex":[0.9995958,0.000007258771,0.00008420421,0.0001676054,0.00005883332,0.0000862612],"domain_scores_gemma":[0.999721,0.00001507461,0.00002445527,0.0001640399,0.00001900997,0.00005643548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002240827,0.00003411249,0.00155658,0.00002529783,0.00001530376,0.000008147881,0.0009928298,0.00005017011,0.04279202,0.633154,0.005485965,0.3158834],"study_design_scores_gemma":[0.0001848885,0.0002407213,0.00225675,0.00000663415,0.000005984975,0.00003680108,0.0001639827,0.4598306,0.4286157,0.0108078,0.09740546,0.000444686],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007257508,0.00001132465,0.9740163,0.005224972,0.00001545121,0.00006105533,1.206036e-7,0.0009591726,0.01245408],"genre_scores_gemma":[0.895409,0.00000236621,0.1027585,0.001616988,0.00003399704,0.00001211618,8.55309e-8,0.000002801767,0.000164181],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8881515,"threshold_uncertainty_score":0.173172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317581135509868,"score_gpt":0.2364524755749352,"score_spread":0.2132766642198365,"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."}}