{"id":"W2580729662","doi":"10.1139/cjfas-2016-0240","title":"Understanding inter-reach variation in brown trout (<i>Salmo trutta</i>) mortality rates using a hierarchical Bayesian state-space model","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Electricité de France; Office National de l’Eau et des Milieux Aquatiques; Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture","keywords":"Salmo; Brown trout; Trout; Environmental science; Habitat; Ecology; Occupancy; Juvenile; Abundance (ecology); Climate change; Petromyzon; Bayesian probability; Salmonidae; Population; Biology; Fishery; Statistics; Mathematics; Fish <Actinopterygii>; 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":[0.001406178,0.0003527842,0.0003503543,0.0005273568,0.000252488,0.000623763,0.0007823783,0.0005333592,0.0008473348],"category_scores_gemma":[0.00231072,0.0003777717,0.0006333116,0.000329493,0.0005013993,0.0007680287,0.0004557595,0.0004640635,0.0001417336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187981,"about_ca_system_score_gemma":0.0009274251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05418809,"about_ca_topic_score_gemma":0.06058408,"domain_scores_codex":[0.9997494,0.0000940052,0.00001235976,0.00007462795,0.00002490619,0.00004465027],"domain_scores_gemma":[0.9993163,0.0003885827,0.0001556834,0.00003850797,0.0000615625,0.00003935272],"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":[0.00005129114,0.0000575415,0.02359007,0.00001211124,0.0000722429,0.00002454727,0.00006326335,0.965197,0.0008369911,0.004623706,0.0003061827,0.005165115],"study_design_scores_gemma":[0.000004166623,0.00001436429,0.005532471,0.0000019503,0.00001083537,0.000004855432,0.00001021855,0.99268,0.00005207103,0.001622939,0.00006100083,0.000005204545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8728997,0.000083033,0.1249882,0.000235887,0.000009285469,0.00003333723,0.0005032957,0.0001477142,0.001099586],"genre_scores_gemma":[0.9915638,0.00004937564,0.00684022,0.0000238466,0.000006822613,0.00003415978,0.0003731584,0.00001588761,0.00109272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05418809,"threshold_uncertainty_score":0.1077453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08462987630041925,"score_gpt":0.2783031362290657,"score_spread":0.1936732599286464,"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."}}