{"id":"W2122066903","doi":"10.1017/s0515036100014963","title":"A Primer on Copulas for Count Data","year":2007,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":310,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Eidgenössische Technische Hochschule Zürich","keywords":"Copula (linguistics); Inference; Transposition (logic); Mathematics; Econometrics; Statistical physics; Mathematical economics; Computer science; Calculus (dental); Applied mathematics; Artificial intelligence; Medicine; Physics; Geometry","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":[0.0114707,0.002222192,0.002489445,0.005773577,0.001199709,0.005821168,0.00349843,0.003595288,0.01169164],"category_scores_gemma":[0.03448007,0.001606525,0.003175768,0.006583042,0.004792974,0.01302848,0.002898948,0.01169742,0.005575337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002411552,"about_ca_system_score_gemma":0.002103878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002053617,"about_ca_topic_score_gemma":0.001505975,"domain_scores_codex":[0.9935508,0.003871878,0.0005833043,0.0006424846,0.001177404,0.0001741241],"domain_scores_gemma":[0.9653027,0.03028353,0.000890694,0.00163626,0.001520656,0.0003661684],"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":[0.00001094192,0.00004233708,0.0002806639,0.0003328134,0.00008255125,0.0002413531,0.0004594724,0.003799341,0.0001785809,0.8861316,0.0575615,0.05087882],"study_design_scores_gemma":[0.000007728626,0.00001711701,0.0002276244,0.0002859778,0.00001340409,0.0002216979,0.00007227108,0.007208542,0.0001156648,0.8176386,0.1741596,0.0000317033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006067976,0.04127889,0.8969222,0.03145174,0.004454677,0.0001299182,0.0005995283,0.0008262057,0.02373004],"genre_scores_gemma":[0.05579024,0.112932,0.729434,0.03789159,0.02876864,0.001538965,0.00150343,0.002666561,0.0294745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01169164,"threshold_uncertainty_score":0.06066364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3237179698178067,"score_gpt":0.4493690857354275,"score_spread":0.1256511159176208,"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."}}