{"id":"W3039137796","doi":"10.1145/3394053","title":"Internal Evaluation of Unsupervised Outlier Detection","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Outlier; Anomaly detection; Computer science; Cluster analysis; Data mining; Artificial intelligence; Domain (mathematical analysis); Pattern recognition (psychology); Binary number; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002969287,0.0001534508,0.0001739446,0.0001006733,0.0001392987,0.0001162039,0.002318597,0.00007887397,0.000122459],"category_scores_gemma":[0.00007509893,0.0001515665,0.00009180734,0.0005632376,0.00004761198,0.001528809,0.0001327994,0.0002097652,0.0001211563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006210299,"about_ca_system_score_gemma":0.0001159603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001415572,"about_ca_topic_score_gemma":0.0001075203,"domain_scores_codex":[0.9984031,0.0001248339,0.0003400802,0.0006422192,0.0003548666,0.0001349506],"domain_scores_gemma":[0.9973517,0.0001240356,0.0001119491,0.00214071,0.0001810349,0.0000905405],"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":[0.00005553005,0.0004450823,0.00005806295,0.00001732877,0.0001186204,5.280273e-7,0.0008607258,0.0004566259,0.02280034,0.0004733574,0.0005580741,0.9741557],"study_design_scores_gemma":[0.001273032,0.0003459083,0.002381554,0.00006201524,0.0002579307,0.000003723004,0.0002320781,0.6556272,0.324532,0.006137423,0.008700502,0.0004466434],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01930838,0.0001076833,0.9780458,0.0007170492,0.0002364153,0.0003209901,0.0005167158,0.0001998348,0.0005471258],"genre_scores_gemma":[0.986863,0.00003857337,0.01263024,0.0001312207,0.00008374235,0.000073489,0.0001066259,0.00001428828,0.00005885168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9737091,"threshold_uncertainty_score":0.6180704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100711224031969,"score_gpt":0.3275591643988292,"score_spread":0.2174880419956323,"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."}}