{"id":"W2096198554","doi":"10.7551/mitpress/1120.003.0097","title":"Fast, Large-Scale Transformation-Invariant Clustering","year":2002,"lang":"en","type":"book-chapter","venue":"The MIT Press eBooks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Transformation (genetics); Scale invariance; Invariant (physics); Computer science; Scale (ratio); Mathematics; Artificial intelligence; Geography; Statistics; Cartography; Mathematical physics; Biology","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":[0.001690941,0.001615431,0.001742237,0.001835862,0.0009332658,0.001540302,0.002929183,0.001534759,0.005034121],"category_scores_gemma":[0.003524418,0.0009983886,0.001608715,0.003497005,0.001025273,0.002526757,0.002038563,0.002048735,0.006029749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184651,"about_ca_system_score_gemma":0.001202868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005159364,"about_ca_topic_score_gemma":0.008031893,"domain_scores_codex":[0.9984621,0.0002932108,0.00005990209,0.0004318721,0.0006714559,0.00008158143],"domain_scores_gemma":[0.9986987,0.0004013417,0.00006439012,0.0004341263,0.0003655067,0.00003594879],"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":[0.0001110909,0.00006520077,0.000310295,0.0002414412,0.000156426,0.0001036898,0.000188102,0.2050472,0.0121605,0.04818651,0.02103557,0.712394],"study_design_scores_gemma":[0.00001326533,0.00001303334,0.0003386125,0.0000139416,0.00001533724,0.00009762327,0.00002784275,0.9374186,0.005700868,0.04488287,0.01144575,0.00003215782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007030978,0.0002359055,0.9965148,0.00004668931,0.00002404162,0.00002029505,0.00006675255,0.001631188,0.0007571452],"genre_scores_gemma":[0.0315095,0.0004635337,0.9607918,0.00008798928,0.00004565943,0.0001108542,0.0009827685,0.0008577439,0.00515023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005159364,"threshold_uncertainty_score":0.01684082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03534195537640138,"score_gpt":0.2416083965492608,"score_spread":0.2062664411728594,"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."}}