{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000473278,0.0001393343,0.0002239394,0.0003025545,0.00008776998,0.0001368489,0.0009563678,0.0000604895,0.00003501855],"category_scores_gemma":[0.0004544164,0.0001123104,0.00006305695,0.001870298,0.000194747,0.0006574466,0.0003684609,0.0001214067,0.0001296092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002629842,"about_ca_system_score_gemma":0.00007700586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004943744,"about_ca_topic_score_gemma":0.0000146335,"domain_scores_codex":[0.998575,0.00001851918,0.0005292079,0.0003088787,0.0003518691,0.0002165012],"domain_scores_gemma":[0.99905,0.00005503503,0.0003023278,0.0001783242,0.0003822793,0.00003207851],"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":[0.00004492072,0.0001511398,0.001153545,0.00004802916,0.000009783861,6.410728e-7,0.001789915,0.0001377628,0.0939927,0.8878852,0.0001670974,0.01461927],"study_design_scores_gemma":[0.00003481782,0.0001550731,0.002529374,0.0001768875,0.000004851027,0.000001574836,0.0006087178,0.5182999,0.430858,0.04710534,0.00006830697,0.0001571175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9467094,0.000009396301,0.04054745,0.003678389,0.0005772494,0.000656138,0.00003038836,0.0005099219,0.007281704],"genre_scores_gemma":[0.9985164,0.00002867244,0.0009564364,0.0000567409,0.00002315269,0.000008868715,0.000002123368,0.000007577253,0.0004000538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8407798,"threshold_uncertainty_score":0.4579885,"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."}}