{"id":"W4399721808","doi":"10.1002/sim.10151","title":"A sparse factor model for clustering high‐dimensional longitudinal data","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Categorical variable; Dirichlet process; Curse of dimensionality; Gibbs sampling; Clustering high-dimensional data; Data mining; Bayesian probability; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007534227,0.0001490194,0.0002525354,0.0001477889,0.00004835337,0.00005212087,0.0007274289,0.00005210515,0.00002741803],"category_scores_gemma":[0.0002666165,0.0001155076,0.0000141916,0.0001977187,0.00007122735,0.0002143865,0.0003910934,0.0001975161,0.000005310759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000425242,"about_ca_system_score_gemma":0.0001300741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004737197,"about_ca_topic_score_gemma":0.0001047517,"domain_scores_codex":[0.9985048,0.00003693063,0.000308171,0.0005796279,0.0002959949,0.0002744349],"domain_scores_gemma":[0.9986306,0.0004944107,0.0000359958,0.0006839758,0.00005745897,0.00009755045],"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":[0.00001566162,0.00002361768,0.00001456944,0.0002009792,0.00002490119,0.0001600151,0.0007683227,0.001100598,0.0001904594,0.6845121,0.02963815,0.2833507],"study_design_scores_gemma":[0.0002851498,0.00005596165,0.00009157783,0.0001583405,0.00001298302,0.00001483037,0.000001878675,0.7746806,0.000007175301,0.2239055,0.00068587,0.0001001777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007418408,0.0005516554,0.9956971,0.001499326,0.001178393,0.0002006494,0.0006365794,0.00006695118,0.00009516774],"genre_scores_gemma":[0.05481521,0.00003601329,0.9440892,0.0002972986,0.0002399664,0.00001409823,0.00011675,0.00001571691,0.0003757666],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.77358,"threshold_uncertainty_score":0.4710262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1298754081797527,"score_gpt":0.39157382235535,"score_spread":0.2616984141755973,"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."}}