{"id":"W4382317916","doi":"10.1609/aaai.v37i13.27088","title":"AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Anomaly detection; Visualization; Computer science; Rendering (computer graphics); Multivariate statistics; Series (stratigraphy); Data mining; Anomaly (physics); Data visualization; Visual inspection; Time series; Artificial intelligence; Geology; Machine learning; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001756944,0.001274704,0.0006397235,0.004864014,0.0004734599,0.001977433,0.001064272,0.0007002425,0.0116255],"category_scores_gemma":[0.008915297,0.0003802164,0.0008171636,0.001764006,0.0003239821,0.002898207,0.00212755,0.001461653,0.001510022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003429206,"about_ca_system_score_gemma":0.0007240867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321419,"about_ca_topic_score_gemma":0.003255619,"domain_scores_codex":[0.9994044,0.0001324387,0.00005993358,0.00009166939,0.0002591246,0.00005236371],"domain_scores_gemma":[0.996047,0.002287662,0.0005014811,0.000398494,0.0005934576,0.0001719987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001750781,0.0002532232,0.01762325,0.001898753,0.0004099691,0.001892561,0.004275094,0.04424864,0.05639799,0.04850223,0.1749338,0.6478136],"study_design_scores_gemma":[0.0002280987,0.00028267,0.01639899,0.0005005841,0.0001694102,0.001852935,0.001096019,0.6084169,0.03937945,0.07854174,0.2527566,0.0003766007],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.02698942,0.0006364743,0.8473354,0.0009874415,0.0003034152,0.0001549789,0.007984209,0.1104415,0.005167115],"genre_scores_gemma":[0.3165823,0.001310187,0.6563989,0.0004406983,0.0003100827,0.0003851338,0.01006698,0.00961663,0.004889084],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0116255,"threshold_uncertainty_score":0.0388912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05910017496436031,"score_gpt":0.3253141772000305,"score_spread":0.2662140022356702,"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."}}