{"id":"W77960026","doi":"10.1609/aaai.v27i1.8553","title":"Towards Cohesive Anomaly Mining","year":2013,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Office of International Science and Engineering; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Cluster analysis; Computer science; Task (project management); Data mining; Set (abstract data type); Anomaly detection; Anomaly (physics); Data set; Big data; Artificial intelligence; Engineering; Physics","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":[0.0002529046,0.0001790794,0.000197334,0.00008945903,0.0002070411,0.0004580119,0.002539183,0.00006397749,0.0001244813],"category_scores_gemma":[0.0002331999,0.0001313572,0.00008399993,0.0006361335,0.0002083366,0.0006655394,0.0005065596,0.0001762365,0.000346082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000277361,"about_ca_system_score_gemma":0.00009207987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001691888,"about_ca_topic_score_gemma":0.000004100386,"domain_scores_codex":[0.998444,0.000008560461,0.0004143845,0.0004532009,0.0003703765,0.0003094736],"domain_scores_gemma":[0.998495,0.00006530479,0.0002900189,0.0003817067,0.0006691584,0.00009885181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002587249,0.00007010172,0.0001087324,0.000009802112,0.000008266858,1.270905e-7,0.0008480198,0.000004215403,0.01351255,0.6844893,0.000979754,0.2999665],"study_design_scores_gemma":[0.00003044363,0.0002219894,0.002346931,0.0001994003,0.00001377933,0.000009140629,0.001473169,0.222512,0.5602335,0.2119085,0.0006519139,0.0003993167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.646346,0.00005248806,0.2080365,0.03100269,0.00097956,0.001741219,0.00003014864,0.0004724199,0.111339],"genre_scores_gemma":[0.9532444,0.00001256657,0.0459003,0.0002945375,0.00005843819,0.00009774297,6.342208e-7,0.00000916404,0.0003821957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5467209,"threshold_uncertainty_score":0.5356591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06691446556035674,"score_gpt":0.2859611338256471,"score_spread":0.2190466682652903,"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."}}