{"id":"W2110615995","doi":"10.1007/s00180-009-0165-9","title":"Penalized multinomial mixture logit model","year":2009,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; McMaster University","funders":"","keywords":"Multinomial logistic regression; Linear discriminant analysis; Multinomial probit; Statistics; Mathematics; Mixture model; Logistic regression; Discriminant; Pattern recognition (psychology); Logit; Artificial intelligence; Optimal discriminant analysis; Feature (linguistics); Dimensionality reduction; Multivariate normal distribution; Multivariate statistics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004176537,0.0006848195,0.001313716,0.001391397,0.0004137468,0.001650082,0.00300023,0.001376267,0.005940489],"category_scores_gemma":[0.01120922,0.0004975108,0.0009809157,0.001420299,0.0009205933,0.00186596,0.001177854,0.001691501,0.001546098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009140809,"about_ca_system_score_gemma":0.0006529248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00297913,"about_ca_topic_score_gemma":0.002567338,"domain_scores_codex":[0.9967952,0.0019195,0.0001143311,0.0004437496,0.0005518452,0.0001753642],"domain_scores_gemma":[0.9959875,0.002700116,0.0003393587,0.0003313148,0.0005482718,0.00009346873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004703922,0.0001724257,0.004905554,0.0002349961,0.000209444,0.000541167,0.0002557572,0.6772783,0.001965547,0.1947411,0.00461889,0.1146064],"study_design_scores_gemma":[0.00001211799,0.0000151536,0.0003611301,0.00000763111,0.00001150863,0.00007869523,0.000009077338,0.9793894,0.0001632419,0.01902045,0.0009198286,0.00001180789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01595687,0.0002585295,0.9814341,0.0002815409,0.00004966337,0.00006658978,0.0001599943,0.0002358929,0.001556647],"genre_scores_gemma":[0.6896304,0.0007961594,0.2862344,0.0002571808,0.0001467387,0.0005150343,0.0007461924,0.0001275041,0.02154641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005940489,"threshold_uncertainty_score":0.02208793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147472188777151,"score_gpt":0.2951340797064518,"score_spread":0.2736593578186803,"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."}}