{"id":"W2095967715","doi":"","title":"EVALUATION OF INFERENCE METHODS IN GLMMS FOR ECOLOGICAL MODELING","year":2011,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Computer science; Generalized linear mixed model; Consistency (knowledge bases); Statistical inference; Predictive inference; Focus (optics); Count data; Poisson distribution; Econometrics; Data science; Statistics; Machine learning; Artificial intelligence; Frequentist inference; Mathematics; Bayesian inference; Bayesian probability","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001844075,0.00009312465,0.0002427709,0.00001784499,0.00003323039,0.000003611178,0.0001216753,0.00002556878,0.00006354803],"category_scores_gemma":[0.0002913128,0.00007930461,0.00001793619,0.00004996299,0.00004324779,0.00009983568,0.00005558029,0.00006755917,1.999088e-10],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006717243,"about_ca_system_score_gemma":0.0009212149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008812688,"about_ca_topic_score_gemma":0.008457744,"domain_scores_codex":[0.9982404,0.0002714402,0.0003454307,0.000145522,0.0008392034,0.0001580704],"domain_scores_gemma":[0.9977572,0.001920192,0.0001254286,0.0001132836,0.00000291757,0.00008100236],"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":[0.0001173535,0.0000473078,0.002803996,0.0001263949,0.0000190958,0.000002002411,0.0001186292,0.00003511523,0.002472919,0.9072974,0.000009835063,0.08694996],"study_design_scores_gemma":[0.0002497503,0.00006722786,0.009481941,0.00006168954,0.00003510021,4.30976e-7,0.0004982959,0.2395744,0.02196254,0.7279719,0.00001356946,0.00008321539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05477057,0.00005311866,0.8639199,0.0001498206,0.0000822666,0.0003450956,0.00008922915,0.00000427386,0.08058577],"genre_scores_gemma":[0.5086211,0.000007417702,0.4912822,0.00003978033,0.000005009366,0.00001512609,4.590242e-7,0.000004060947,0.00002485218],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4538505,"threshold_uncertainty_score":0.4719619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1025558480739886,"score_gpt":0.3276280198600511,"score_spread":0.2250721717860625,"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."}}