{"id":"W4225959154","doi":"10.3354/esr01186","title":"Incorporating environmental covariates into a Bayesian stock production model for the endangered Cumberland Sound beluga population","year":2022,"lang":"en","type":"article","venue":"Endangered Species Research","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Fisheries and Oceans Canada","funders":"","keywords":"Beluga Whale; Beluga; Population; Population model; Fishery; Geography; Stock assessment; Environmental science; Arctic; Ecology; Biology; Demography; Fishing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002139263,0.0002153663,0.0002228495,0.00008751476,0.00357456,0.0001116285,0.0005982927,0.00004717845,0.003043221],"category_scores_gemma":[0.0002983586,0.0001805749,0.0001091111,0.0005334631,0.0003820749,0.0002648811,0.002087855,0.0005447898,0.00004388472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001757738,"about_ca_system_score_gemma":0.00002481648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00367362,"about_ca_topic_score_gemma":0.009360475,"domain_scores_codex":[0.9965909,0.0003537715,0.0003543418,0.0006696882,0.001447139,0.0005841274],"domain_scores_gemma":[0.9988658,0.0003674517,0.0001523923,0.0004994508,0.00001901964,0.00009583617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001343242,0.001145789,0.6136491,0.0002110625,0.0003426632,0.00002411705,0.02645426,0.1198053,0.03889109,0.003648737,0.08354753,0.1109371],"study_design_scores_gemma":[0.001384352,0.0007477836,0.4523222,0.00001733856,0.00007421142,0.0000366061,0.008604752,0.3901593,0.0007808041,0.03033904,0.1146031,0.0009305185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775124,0.001022654,0.006477311,0.004586285,0.000463882,0.004503767,0.0002500557,0.0001185841,0.005065099],"genre_scores_gemma":[0.9885667,0.00007844302,0.002540396,0.00007450557,0.0002659828,0.001215573,0.0001289497,0.00004464249,0.007084832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.270354,"threshold_uncertainty_score":0.9978681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07640591451408603,"score_gpt":0.3213176427424512,"score_spread":0.2449117282283652,"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."}}