{"id":"W2739069932","doi":"10.1002/sim.7397","title":"Dynamic classification using credible intervals in longitudinal discriminant analysis","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council Canada; Belgian Federal Science Policy Office; Institut National de la Santé et de la Recherche Médicale; Medical Research Council; National Institute for Health and Care Research","keywords":"Linear discriminant analysis; False positive paradox; Bayesian probability; Multivariate statistics; Context (archaeology); Time point; Discriminant; Computer science; Confidence interval; Bayes' theorem; Statistics; Data mining; Set (abstract data type); Data set; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330844,0.0001295245,0.0003958727,0.0004661513,0.0001222384,0.0000830653,0.000806802,0.00005444393,0.00002024235],"category_scores_gemma":[0.0006065754,0.0001044565,0.00002982637,0.0004065167,0.0001862979,0.0002294564,0.0001686994,0.0002014016,0.000001859593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201203,"about_ca_system_score_gemma":0.00004906019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008287589,"about_ca_topic_score_gemma":0.001777798,"domain_scores_codex":[0.9985307,0.0001283055,0.0004267891,0.0003981151,0.0002643542,0.0002516773],"domain_scores_gemma":[0.9985445,0.000155284,0.0002513126,0.000908399,0.00007014612,0.0000704116],"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":[0.0000254128,0.0001513949,0.09679378,0.0001133886,0.0001168852,0.0003925785,0.003729735,0.0003159747,0.00253248,0.6537408,0.0003817854,0.2417058],"study_design_scores_gemma":[0.0002585501,0.0000323311,0.3293081,0.00008657495,0.00005893004,0.000003280878,0.00003334937,0.5999577,0.000009926328,0.07015875,0.00001326762,0.00007927174],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008819102,0.0001145181,0.9889868,0.000955573,0.0003605447,0.0001036802,0.00001328384,0.00001178135,0.0006347434],"genre_scores_gemma":[0.5086474,0.00004873907,0.4911885,0.0000315101,0.00001975323,0.000003842767,0.000007149501,0.00000399341,0.00004907484],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5996417,"threshold_uncertainty_score":0.4259613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017098413890321,"score_gpt":0.4232495001494327,"score_spread":0.3215396587604007,"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."}}