{"id":"W2996467117","doi":"10.1101/2019.12.15.876730","title":"Nonparametric Anomaly Detection on Time Series of Graphs","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Anomaly detection; Univariate; Nonparametric statistics; Change detection; Snapshot (computer storage); Test statistic; Statistic; Series (stratigraphy); Data mining; Statistical hypothesis testing; Artificial intelligence; Theoretical computer science; Machine learning; Multivariate statistics; Econometrics; Mathematics; Statistics","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":[0.002768504,0.000519368,0.000816225,0.003401486,0.0003823559,0.001055385,0.001518792,0.0009361878,0.0009147967],"category_scores_gemma":[0.02343218,0.0003079802,0.0005168193,0.001989458,0.00154199,0.001916518,0.001048757,0.001283536,0.0001681114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000916704,"about_ca_system_score_gemma":0.0004632205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002769779,"about_ca_topic_score_gemma":0.001940356,"domain_scores_codex":[0.9984145,0.0007346931,0.00006438948,0.0003742385,0.000309514,0.0001025441],"domain_scores_gemma":[0.9836215,0.01149469,0.001901439,0.001304533,0.001257509,0.0004203892],"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":[0.0002780696,0.0001057767,0.02057605,0.0001357442,0.0001877947,0.0003164085,0.0002151445,0.8024658,0.005580831,0.07403938,0.001988304,0.09411073],"study_design_scores_gemma":[0.000002807982,0.000009555369,0.001271757,0.000002826084,0.000002862079,0.00002230298,0.00001148899,0.9773676,0.0003483442,0.02078768,0.0001684994,0.000004287517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1590656,0.0001726894,0.8389591,0.0003149377,0.00003303444,0.00003264254,0.0002450419,0.0005623903,0.0006145484],"genre_scores_gemma":[0.939887,0.0001300573,0.05850369,0.00004725912,0.00006639489,0.00005388583,0.0005060179,0.00006338352,0.0007423412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003401486,"threshold_uncertainty_score":0.0146414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008078320505200744,"score_gpt":0.2091046997054682,"score_spread":0.2010263792002674,"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."}}