{"id":"W4412619945","doi":"10.1139/cjfas-2024-0321","title":"Estimating diadromous fish abundance during their migration by mark–recapture: remodeling combined with sequential Bayesian inference","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Université de Pau et des Pays de l'Adour","keywords":"Fish migration; Mark and recapture; Abundance (ecology); Biology; Fish <Actinopterygii>; Bayesian probability; Inference; Fishery; Ecology; Bayesian inference; Statistics; Mathematics; Computer science; Population; Artificial intelligence; 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":[0.006314457,0.0006385019,0.0008368935,0.001080176,0.0004463451,0.0006845588,0.001649022,0.000695838,0.001051481],"category_scores_gemma":[0.01460026,0.00109158,0.0008822036,0.0009692703,0.0006072012,0.001224599,0.001206595,0.0009991251,0.0002889074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005283328,"about_ca_system_score_gemma":0.0008728532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01357185,"about_ca_topic_score_gemma":0.02477971,"domain_scores_codex":[0.9983637,0.0008210775,0.0001039816,0.0004701924,0.000163403,0.00007763333],"domain_scores_gemma":[0.9908229,0.006965132,0.0008910655,0.0007586183,0.0004018345,0.0001604078],"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":[0.0004818015,0.0003132093,0.1175902,0.0002119213,0.0006427813,0.0003143979,0.0006214553,0.5646729,0.009354179,0.0136267,0.0008163335,0.291354],"study_design_scores_gemma":[0.00002360149,0.00007533629,0.00903999,0.00001270815,0.00006297429,0.00008668409,0.00002916622,0.9782977,0.0009305244,0.01093925,0.0004736846,0.0000284245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09006195,0.0001202395,0.9091341,0.00008634787,0.00001213805,0.00005234796,0.0001022298,0.0001667463,0.0002639162],"genre_scores_gemma":[0.608901,0.0001689702,0.3888516,0.00006479108,0.00005791716,0.0001859035,0.0006591914,0.00006925394,0.00104131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01357185,"threshold_uncertainty_score":0.03339446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009210081739961733,"score_gpt":0.2123712184229773,"score_spread":0.2031611366830155,"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."}}