{"id":"W2051644092","doi":"10.1002/cjs.10082","title":"Model‐based linear clustering","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Humanities; Cluster analysis; Maximum likelihood; Mathematics; Statistics; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003959166,0.00008351754,0.000137744,0.0001765851,0.00009308931,0.0001012899,0.0005527223,0.00005937096,0.00002534569],"category_scores_gemma":[0.0001523224,0.00007681734,0.00003831987,0.0001108957,0.00004893987,0.00015614,0.00001399177,0.0003866997,0.000004219299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003094975,"about_ca_system_score_gemma":0.001757832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002684134,"about_ca_topic_score_gemma":0.0115816,"domain_scores_codex":[0.9992637,0.00002837488,0.0002477892,0.00009320058,0.0001321368,0.0002348148],"domain_scores_gemma":[0.9986602,0.000070509,0.000135942,0.0002290737,0.0002521488,0.0006521244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005787672,0.00001717091,0.0002684744,0.00003316512,0.00002454819,0.000884972,0.0008373726,0.01143607,0.001856616,0.5958217,0.01149192,0.3773222],"study_design_scores_gemma":[0.0001666426,0.00004076212,0.0001026623,0.00001311006,0.000007087314,0.00009923991,0.000001764465,0.9495432,0.0001797083,0.04574053,0.004007528,0.00009771599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005328801,0.0000264215,0.997364,0.0005127059,0.000858928,0.00002834804,0.00004293225,0.000005623378,0.0006281899],"genre_scores_gemma":[0.1786748,0.000001518233,0.8206097,0.0005064628,0.0001112131,2.670301e-7,6.332487e-7,0.000007609412,0.0000877887],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9381072,"threshold_uncertainty_score":0.6462803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02886596827548563,"score_gpt":0.2641297409475343,"score_spread":0.2352637726720487,"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."}}