{"id":"W2115966971","doi":"10.1139/f03-149","title":"A simulation study of impacts of error structure on modeling stockrecruitment data using generalized linear models","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; International Social Science Council","keywords":"Outlier; Statistics; Mathematics; Log-normal distribution; Linear model; Generalized linear model; Computer science; Algorithm; Econometrics; Applied mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01624832,0.000870785,0.0009134061,0.001282144,0.0007658573,0.001087854,0.001148869,0.001415203,0.0009987626],"category_scores_gemma":[0.03946779,0.000561403,0.001506289,0.001550265,0.0008527384,0.00144155,0.000952852,0.00167035,0.0001059416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002978881,"about_ca_system_score_gemma":0.001767249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04897659,"about_ca_topic_score_gemma":0.02855046,"domain_scores_codex":[0.9950263,0.003763896,0.0002008601,0.0002941195,0.0003733286,0.0003415739],"domain_scores_gemma":[0.8763752,0.1128449,0.003727694,0.002325315,0.004048205,0.000678737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001364777,0.00008442108,0.006514454,0.00002975091,0.00004397562,0.00006347866,0.00005996024,0.9892886,0.0001586683,0.001267817,0.00008907353,0.002263395],"study_design_scores_gemma":[0.00001999661,0.0001473689,0.001149366,0.000008629584,0.0000214652,0.00001200167,0.00004091901,0.9977313,0.0002591869,0.0005392878,0.00005784025,0.00001261033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577591,0.0002033482,0.03997477,0.0002307252,0.00001545304,0.0001104394,0.0002409666,0.0001047263,0.001360511],"genre_scores_gemma":[0.9828787,0.0001202826,0.01621592,0.00002796431,0.000003945855,0.0001056932,0.0002313853,0.00001401911,0.0004021056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04897659,"threshold_uncertainty_score":0.09738302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5681736802477407,"score_gpt":0.4708458043947748,"score_spread":0.09732787585296593,"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."}}