{"id":"W2147583488","doi":"10.1080/02664761003758976","title":"Classification with discrete and continuous variables via general mixed-data models","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Categorical variable; Extension (predicate logic); Ordinal data; Linear discriminant analysis; Continuous variable; Ordinal regression; Generalized linear mixed model; Discriminant function analysis; Mathematics; Data mining; Statistics; Pattern recognition (psychology); 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":[0.0006391011,0.0001845431,0.0004551691,0.00006249073,0.00008075452,0.00003320528,0.0002695929,0.00007162322,0.00001909471],"category_scores_gemma":[0.00022711,0.0001315248,0.00001688115,0.00007012152,0.0001507616,0.0001997409,0.0000843311,0.0002506952,5.248674e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002453881,"about_ca_system_score_gemma":0.00005600377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006402781,"about_ca_topic_score_gemma":0.000008829481,"domain_scores_codex":[0.9985631,0.00005639253,0.0006128527,0.0002275769,0.0003157059,0.0002244487],"domain_scores_gemma":[0.9977272,0.0006448519,0.0008369384,0.0003774227,0.0002365317,0.0001770358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003246989,0.00008947638,0.00001670324,0.00006354489,0.00008894752,0.00003401374,0.0004015675,0.00008584579,0.0004437544,0.9662549,0.00109288,0.03110367],"study_design_scores_gemma":[0.0006593781,0.0002099348,0.0001726188,0.00003021669,0.0002368912,0.00007133332,0.0002038954,0.07745796,0.0001364782,0.9204775,0.0001678814,0.0001758743],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002062744,0.00003283244,0.9940212,0.00001657597,0.00007714477,0.0001648021,0.0004295679,0.0000138316,0.003181237],"genre_scores_gemma":[0.153442,0.00006877485,0.8462743,0.00003278004,0.0000783139,0.000004105959,0.00001712145,0.00003221627,0.00005041702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1513793,"threshold_uncertainty_score":0.5363424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1643262084466867,"score_gpt":0.3663746482197034,"score_spread":0.2020484397730167,"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."}}