{"id":"W2152598152","doi":"10.1139/f08-141","title":"Models and model selection uncertainty in estimating growth rates of endangered freshwater mussel populations","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey","keywords":"Endangered species; Population; Statistics; Model selection; Deviance information criterion; Goodness of fit; Selection (genetic algorithm); Bayesian inference; Weighting; Deviance (statistics); Population model; Akaike information criterion; Ecology; Population growth; Fishery; Bayesian probability; Econometrics; Geography; Biology; Mathematics; Computer science; Machine learning; Habitat; Demography","routes":{"ca_aff":false,"ca_fund":false,"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":[0.02739047,0.0008886263,0.0009911484,0.001967174,0.0007948671,0.001752232,0.001711051,0.001121562,0.0003373462],"category_scores_gemma":[0.08415605,0.0006157931,0.001186883,0.001250395,0.001287821,0.002371302,0.001512068,0.001549089,0.00006231651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001640003,"about_ca_system_score_gemma":0.001178678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426478,"about_ca_topic_score_gemma":0.01592098,"domain_scores_codex":[0.9889897,0.008375689,0.0004854153,0.0008587333,0.00102317,0.0002673194],"domain_scores_gemma":[0.9412035,0.05276794,0.002953504,0.001549151,0.001266674,0.0002592394],"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":[0.0001072418,0.00002997308,0.0252173,0.000049738,0.0002820554,0.0001285571,0.0002580956,0.9496057,0.0005385143,0.01004822,0.0001577394,0.01357678],"study_design_scores_gemma":[0.00001444856,0.0000499953,0.0050025,0.00002883292,0.00007272532,0.00007171276,0.0000755967,0.9736061,0.0003756272,0.02043499,0.0002271311,0.00004045632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5183918,0.0007520816,0.4782394,0.0006225422,0.00003562524,0.00007219174,0.0001605274,0.0002440895,0.001481757],"genre_scores_gemma":[0.9350029,0.0002125232,0.06418884,0.00007146224,0.00002352441,0.00008193148,0.0001871591,0.00003425348,0.0001975591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02739047,"threshold_uncertainty_score":0.1448563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04430069597843944,"score_gpt":0.2397674566423216,"score_spread":0.1954667606638821,"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."}}