{"id":"W2144182447","doi":"10.1145/335191.335388","title":"LOF","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5181,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Local outlier factor; Computer science; Object (grammar); Anomaly detection; Degree (music); Data mining; Property (philosophy); Binary number; Theoretical computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002694256,0.00142554,0.001187727,0.004829217,0.001813795,0.003678647,0.002673462,0.002494426,0.07034544],"category_scores_gemma":[0.01388616,0.0004784772,0.00161999,0.004101695,0.001114177,0.005126272,0.002714814,0.002174135,0.05008679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200262,"about_ca_system_score_gemma":0.001771736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005218883,"about_ca_topic_score_gemma":0.006562405,"domain_scores_codex":[0.9971035,0.0003382367,0.0002085655,0.0006498533,0.001344405,0.0003553829],"domain_scores_gemma":[0.9959002,0.00110259,0.0002957609,0.001333119,0.001224075,0.0001442127],"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":[0.000356629,0.0002025341,0.003929155,0.0004179866,0.00008228906,0.0003197007,0.0002148739,0.01175134,0.004819777,0.06885535,0.1355728,0.7734776],"study_design_scores_gemma":[0.0001250805,0.00029257,0.003178155,0.0002346709,0.00008453245,0.002024675,0.0003771056,0.2003885,0.01350558,0.1527022,0.6269196,0.0001672388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00476077,0.001572126,0.9304909,0.001195126,0.0007168557,0.0003816389,0.004744658,0.01923711,0.03690088],"genre_scores_gemma":[0.1447886,0.002122269,0.735836,0.002439995,0.001199064,0.001146068,0.02564131,0.004455762,0.0823709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07034544,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01295973480496317,"score_gpt":0.2442732101353005,"score_spread":0.2313134753303373,"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."}}