{"id":"W1507654104","doi":"10.1007/11596448_113","title":"Grid-ODF: Detecting Outliers Effectively and Efficiently in Large Multi-dimensional Databases","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Outlier; Computer science; Grid; Anomaly detection; Data mining; Local outlier factor; Rank (graph theory); Database; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001063666,0.0004501287,0.0004271185,0.0009764578,0.0003963695,0.0002512625,0.001271471,0.000208413,0.000009338881],"category_scores_gemma":[0.0001022528,0.0004299915,0.0000860339,0.0007210755,0.0004116697,0.0005026597,0.001471212,0.0008540085,0.00001860045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002879086,"about_ca_system_score_gemma":0.0002017468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005642319,"about_ca_topic_score_gemma":0.0003040672,"domain_scores_codex":[0.9966913,0.00004346438,0.0004773244,0.001613169,0.0005471332,0.0006276172],"domain_scores_gemma":[0.9980844,0.0005109371,0.0002442331,0.0008726314,0.0001273728,0.0001604548],"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":[0.00001075611,0.0001601597,0.0007483583,0.00005050052,0.00001143135,0.00006587034,0.0006237208,0.02077471,0.0008307319,0.0126932,0.00001696695,0.9640136],"study_design_scores_gemma":[0.0006392384,0.0001552048,0.002681719,0.0003943773,0.000007553002,0.0001193143,5.658466e-7,0.9836573,0.004893082,0.004039329,0.002602017,0.0008103247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002063422,0.0003544271,0.9957484,0.0002260361,0.0004342136,0.0006106763,0.00002004306,0.0002447597,0.0002980123],"genre_scores_gemma":[0.3786279,0.00003115012,0.6199729,0.0008821121,0.0002560928,0.00004285303,0.000006715794,0.00003313966,0.000147072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9632033,"threshold_uncertainty_score":0.9998152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891582833929225,"score_gpt":0.2710187547318289,"score_spread":0.2521029263925367,"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."}}