{"id":"W2031212901","doi":"10.1002/cjs.10047","title":"Model‐based clustering of longitudinal data","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Bayesian information criterion; Cluster analysis; Information Criteria; Model selection; Covariance; Convergence (economics); Computer science; Statistical model; Bayesian probability; Exponential family; Expectation–maximization algorithm; Mathematics; Data mining; Statistics; Maximum likelihood","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":[0.0006256946,0.00008184599,0.0001826861,0.0001772934,0.00006030583,0.00007338684,0.001373337,0.00004975659,0.0000234646],"category_scores_gemma":[0.0002215398,0.00007561273,0.00002626573,0.0001218004,0.00007980759,0.0002660644,0.00005843075,0.0003026571,9.541334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002073489,"about_ca_system_score_gemma":0.001967372,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006173212,"about_ca_topic_score_gemma":0.02157757,"domain_scores_codex":[0.9991363,0.00003126355,0.0003310634,0.0001302753,0.0001665322,0.0002045213],"domain_scores_gemma":[0.9983247,0.00009628603,0.0002392785,0.0006014713,0.0002771275,0.0004611425],"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":[0.00001531585,0.00004298363,0.002397558,0.0001212717,0.00007321832,0.0009016725,0.0006887466,0.008561687,0.002311429,0.5728716,0.027976,0.3840385],"study_design_scores_gemma":[0.0001812348,0.00004629264,0.0006669075,0.0000257304,0.00001640465,0.000103712,0.000002143827,0.9689576,0.0001303815,0.02881806,0.0009627321,0.00008877712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005987664,0.00004995086,0.9977868,0.0002762499,0.0006123403,0.00002887947,0.0002953907,0.000002658211,0.0003489454],"genre_scores_gemma":[0.2895901,0.000002153959,0.7102405,0.00008669934,0.00005320163,1.042749e-7,0.000003212693,0.000005348157,0.00001875827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9603959,"threshold_uncertainty_score":0.9962761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09939433401187582,"score_gpt":0.2985575540033773,"score_spread":0.1991632199915015,"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."}}