{"id":"W3030491320","doi":"10.1007/978-3-030-47358-7_23","title":"Anomaly Detection and Prototype Selection Using Polyhedron Curvature","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Computer science; Curvature; Vertex (graph theory); Polyhedron; CAD; Anomaly (physics); Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Projection (relational algebra); Algorithm; Mathematics; Theoretical computer science; Graph; Combinatorics; Geometry; 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":[0.001265922,0.001288207,0.002415034,0.002836448,0.0008229873,0.002680885,0.002912375,0.001546463,0.004229611],"category_scores_gemma":[0.00557425,0.0008097112,0.001644395,0.002361563,0.0009585877,0.002144828,0.002142473,0.001814389,0.002266867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008059715,"about_ca_system_score_gemma":0.0008852003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002589905,"about_ca_topic_score_gemma":0.002136085,"domain_scores_codex":[0.9978393,0.0002942528,0.0001116057,0.0005938407,0.0009731773,0.0001878486],"domain_scores_gemma":[0.99723,0.0007580482,0.000195231,0.0005543008,0.001133698,0.0001286597],"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":[0.0005147471,0.0001605952,0.002927908,0.0002099375,0.0001099731,0.0003093686,0.0001352009,0.0502539,0.05834098,0.009849929,0.008039351,0.8691481],"study_design_scores_gemma":[0.00001601142,0.0001025028,0.0009231522,0.00001225928,0.0000246405,0.000409651,0.00004433054,0.9721468,0.0185279,0.005562016,0.002202013,0.00002869886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01444296,0.0001977082,0.982077,0.00008402431,0.00006479541,0.00008060712,0.00008400324,0.002057643,0.000911225],"genre_scores_gemma":[0.2497054,0.0002713065,0.7446074,0.00008785608,0.00008350794,0.0001382382,0.0008011229,0.0006276689,0.003677489],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004229611,"threshold_uncertainty_score":0.01414943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705565521274891,"score_gpt":0.2494896249901361,"score_spread":0.2324339697773872,"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."}}