{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009525801,0.001525449,0.001259497,0.005163054,0.001504823,0.003543444,0.002460591,0.002392497,0.001956468],"category_scores_gemma":[0.0585653,0.0002311915,0.001022395,0.004157701,0.002020662,0.005030965,0.004297296,0.002378816,0.001196625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001872441,"about_ca_system_score_gemma":0.001649597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003043907,"about_ca_topic_score_gemma":0.004083475,"domain_scores_codex":[0.9890645,0.00305247,0.0007146394,0.002170241,0.00414363,0.0008544978],"domain_scores_gemma":[0.9595562,0.02144405,0.002494192,0.007672898,0.007576111,0.001256528],"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":[0.001289867,0.0007111899,0.1064436,0.00120573,0.0003850015,0.001051883,0.001068228,0.06461117,0.01780604,0.04206183,0.05533879,0.7080266],"study_design_scores_gemma":[0.00006870034,0.0004845301,0.040122,0.0002866327,0.0001571956,0.003089897,0.001730552,0.6600865,0.05186951,0.1869344,0.05500958,0.0001604933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3757488,0.007575028,0.5718232,0.007905127,0.001577826,0.000360176,0.00628618,0.01146776,0.01725594],"genre_scores_gemma":[0.8395464,0.0009064635,0.1459172,0.001085414,0.0003844267,0.0001452541,0.009346137,0.0007542554,0.001914472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009525801,"threshold_uncertainty_score":0.05037791,"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."}}