{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2356057,0.00291012,0.002527225,0.004662889,0.002324993,0.005222139,0.006194883,0.004094172,0.009252016],"category_scores_gemma":[0.5918299,0.001625752,0.00492459,0.0054677,0.00340265,0.007342357,0.005041901,0.008690384,0.001989246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004637737,"about_ca_system_score_gemma":0.006378458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01100596,"about_ca_topic_score_gemma":0.01233279,"domain_scores_codex":[0.7881089,0.1897328,0.00560415,0.004762923,0.01099957,0.0007916749],"domain_scores_gemma":[0.3280633,0.6342076,0.006382503,0.01737357,0.01309436,0.0008787638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001113856,0.000333582,0.0148873,0.003231499,0.00324364,0.0003664999,0.001823888,0.1814179,0.001433872,0.3103274,0.01548181,0.4663388],"study_design_scores_gemma":[0.0003614553,0.0004322499,0.003972892,0.001272869,0.000446225,0.0002818208,0.0004639736,0.7125242,0.002916445,0.2621374,0.01501028,0.0001802183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003612404,0.000860553,0.9916527,0.0009422374,0.0001529747,0.0003449234,0.0002816041,0.0009215013,0.001231203],"genre_scores_gemma":[0.03112006,0.0005122297,0.9654717,0.0002859144,0.00009439054,0.00100214,0.000392654,0.000739917,0.0003809669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2356057,"threshold_uncertainty_score":0.9426342,"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."}}