{"id":"W2475637399","doi":"10.1111/stan.12092","title":"A Skew‐normal copula‐driven GLMM","year":2016,"lang":"en","type":"article","venue":"Statistica Neerlandica","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège de Maisonneuve; McGill University","funders":"","keywords":"Copula (linguistics); Skew; Bivariate analysis; Mathematics; Monte Carlo method; Econometrics; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001448443,0.0001830962,0.0002406672,0.0000681306,0.0001846883,0.00004072893,0.0002045681,0.0001166332,0.006219153],"category_scores_gemma":[0.001890998,0.0001259958,0.00005914812,0.000191806,0.0002190663,0.00008313706,0.00004979465,0.0001404252,0.001505374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007833863,"about_ca_system_score_gemma":0.000070367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009332148,"about_ca_topic_score_gemma":0.000009602935,"domain_scores_codex":[0.9984668,0.0000644922,0.0004285583,0.000295369,0.0003653366,0.0003793654],"domain_scores_gemma":[0.9976302,0.001484373,0.0001303097,0.0003576376,0.000120109,0.0002773555],"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":[0.00001716744,0.00008585296,0.0002394147,0.00002184463,0.00002087582,0.000009715551,0.00003784807,1.938809e-7,0.000439983,0.8962443,0.09332937,0.009553403],"study_design_scores_gemma":[0.001670141,0.000123775,0.01757865,0.00009056017,0.0001000002,0.00003686768,0.000033909,0.001472733,0.0003778655,0.9252453,0.05280986,0.0004603086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00184977,0.000007758209,0.9812385,0.002433819,0.00008622859,0.0002682593,0.002872547,0.0002540544,0.01098902],"genre_scores_gemma":[0.8886039,0.00001336174,0.1082619,0.0002410241,0.00009075941,0.0001440704,0.0001541486,0.00003327429,0.00245758],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8867541,"threshold_uncertainty_score":0.999272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04996951293043424,"score_gpt":0.3537019899318107,"score_spread":0.3037324770013765,"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."}}