{"id":"W2169629580","doi":"10.1109/ictai.2008.128","title":"Oracle Clustering: Dynamic Partitioning Based on Random Observations","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Cluster analysis; Computer science; Dynamism; Oracle; Stability (learning theory); Data mining; Function (biology); Sampling (signal processing); Algorithm; Artificial intelligence; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0001832998,0.0001143798,0.0001263192,0.0001253774,0.0004155101,0.00008303489,0.0005627144,0.00003701484,0.00004579668],"category_scores_gemma":[0.0001210488,0.0001067743,0.00005281092,0.0005120499,0.0000576134,0.0004673777,0.0001882389,0.0001574809,0.0001420081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003148,"about_ca_system_score_gemma":0.0000885085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002158724,"about_ca_topic_score_gemma":0.00002133213,"domain_scores_codex":[0.9986633,0.00006221553,0.0001882415,0.0003496949,0.0004026419,0.0003338723],"domain_scores_gemma":[0.9988883,0.0002551402,0.00004018787,0.0006043128,0.0001002415,0.0001118232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008430912,0.0002975719,0.001159328,0.00002975808,0.00001867361,0.0002086148,0.0004766347,0.9555004,0.00178173,0.003982165,0.0007927433,0.03566802],"study_design_scores_gemma":[0.0009783785,0.00007791891,0.005684749,0.00001657435,7.12355e-7,0.00001772114,0.000007203875,0.9912887,0.0004080251,0.0003274512,0.001060944,0.0001316747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005946544,0.000009963794,0.9886259,0.001764344,0.0001419121,0.0001616694,0.000001377721,0.0004144446,0.002933806],"genre_scores_gemma":[0.6142136,0.000007099409,0.383054,0.0005449589,0.00002188514,0.0000497619,0.000004980534,0.00001233134,0.00209137],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6082671,"threshold_uncertainty_score":0.4354128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06051568217962652,"score_gpt":0.3047927912108204,"score_spread":0.2442771090311939,"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."}}