{"id":"W2160798324","doi":"10.1002/asmb.463","title":"A generalized multinomial discriminant procedure with applications","year":2002,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Multinomial distribution; Discriminant; Classifier (UML); Linear discriminant analysis; Mathematics; Applied mathematics; Artificial intelligence; Computer science; Mathematical optimization; Pattern recognition (psychology); Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001366348,0.0002508103,0.0002942518,0.000128055,0.0001491997,0.0001158019,0.0003940345,0.0002903349,0.000009617984],"category_scores_gemma":[0.00000666269,0.0001925688,0.00001810682,0.000613059,0.0001039195,0.0002928385,0.0001566872,0.0004335625,0.000002718665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002673926,"about_ca_system_score_gemma":0.0000504926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004184535,"about_ca_topic_score_gemma":0.00001139012,"domain_scores_codex":[0.9985449,0.00001966402,0.0002646597,0.0006132358,0.0001940973,0.0003634169],"domain_scores_gemma":[0.9992207,0.00003965463,0.00009398891,0.000449063,0.00006821749,0.0001283367],"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":[0.00002489716,0.0001739711,0.00001510243,0.00005301588,0.00001068573,0.000007645481,0.0007868197,0.01661259,0.000228162,0.8907846,0.0001279557,0.09117458],"study_design_scores_gemma":[0.002074214,0.00002647041,0.0004829892,0.00008498338,0.00002245006,0.00007673432,0.00004665145,0.9275401,0.00004849089,0.06893911,0.0001503167,0.0005075217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009983297,0.0001742229,0.985952,0.000670854,0.00003907581,0.0006280581,0.000003614148,0.00007213666,0.002476701],"genre_scores_gemma":[0.7536882,0.00001702039,0.2452342,0.0002265262,0.00008966677,0.0005950856,0.000002343375,0.00001777589,0.0001291738],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9109275,"threshold_uncertainty_score":0.7852729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03144942670678059,"score_gpt":0.2408952657679715,"score_spread":0.2094458390611909,"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."}}