{"id":"W3140302147","doi":"10.1016/j.cag.2021.03.004","title":"ProSeCo: Visual analysis of class separation measures and dataset characteristics","year":2021,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Österreichische Forschungsförderungsgesellschaft; NÖ Forschungs- und Bildungsges.m.b.H.; Deutsche Forschungsgemeinschaft","keywords":"Separation (statistics); Computer science; Skewness; Class (philosophy); Visualization; Dimensionality reduction; Artificial intelligence; Visual analytics; Pattern recognition (psychology); Data mining; Machine learning; Statistics; Mathematics","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":[0.0001560072,0.0000947321,0.0002122689,0.0002475691,0.0001114589,0.0001165444,0.0002492454,0.00006030297,0.000003120252],"category_scores_gemma":[0.00001163419,0.00009923772,0.00007800279,0.001594543,0.00006754291,0.0001738769,0.0001927139,0.00009075724,0.000001314707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009429449,"about_ca_system_score_gemma":0.00004066135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001007909,"about_ca_topic_score_gemma":0.00001590034,"domain_scores_codex":[0.9991003,0.00005103292,0.0002540405,0.0003140701,0.0001720779,0.0001084549],"domain_scores_gemma":[0.9991186,0.00006018532,0.000145302,0.0004360978,0.0001789659,0.00006080314],"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":[0.00002361315,0.0009101761,0.037596,0.0001515002,0.002187063,0.00004430275,0.00120484,0.000374707,0.01757081,0.6394488,0.01520758,0.2852806],"study_design_scores_gemma":[0.0001937759,0.0001260888,0.1210467,0.00001777702,0.0003463054,0.00002110712,0.00001804096,0.814141,0.008676636,0.001917247,0.0531652,0.0003301525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05226993,0.00007453612,0.9469829,0.0002863115,0.00006960965,0.00008283042,0.0001327516,0.00007252744,0.00002861646],"genre_scores_gemma":[0.9743857,0.0001610141,0.02447763,0.0004515928,0.00002440714,0.00001407636,0.0004682479,0.000004840114,0.00001246123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9225053,"threshold_uncertainty_score":0.4046797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852406985079736,"score_gpt":0.3002955657759921,"score_spread":0.2817714959251947,"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."}}