{"id":"W2960801124","doi":"10.1139/cjfas-2019-0037","title":"State-space modeling of multidecadal mark–recapture data reveals low adult dispersal in a nursery-dependent fish metapopulation","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Direction des Pêches Maritimes et de l'Aquaculture; Institut Français de Recherche pour l'Exploitation de la Mer","keywords":"Metapopulation; Biological dispersal; Mark and recapture; Fishery; Population; Geography; Stock (firearms); Population model; Ecology; Biology; 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":[0.000972891,0.0002911593,0.0003573051,0.0003155807,0.0003111007,0.0006880108,0.0006358603,0.0004356134,0.0005734051],"category_scores_gemma":[0.002091284,0.0003116734,0.0006044264,0.0002676203,0.0004859843,0.0006902767,0.0004714936,0.0005186576,0.00009963876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007580551,"about_ca_system_score_gemma":0.000551308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04887484,"about_ca_topic_score_gemma":0.04042182,"domain_scores_codex":[0.9998091,0.00006970578,0.0000120527,0.00006346111,0.00001423111,0.00003149202],"domain_scores_gemma":[0.9990751,0.0005158036,0.0002275204,0.0000719341,0.00005720917,0.00005251806],"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":[0.00007504535,0.00004490245,0.05123061,0.00001544952,0.0001007645,0.00005152148,0.0000871755,0.9436752,0.0005657421,0.001344707,0.0001572588,0.002651608],"study_design_scores_gemma":[0.000004692093,0.00001854133,0.006365312,0.000001728914,0.000009525116,0.000006258424,0.00001393226,0.9931727,0.00004578121,0.0003170073,0.0000408967,0.000003603348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871633,0.0000594516,0.01223499,0.00008264726,0.00000428818,0.000004762838,0.00009942208,0.00003956095,0.0003116592],"genre_scores_gemma":[0.9982734,0.00003018913,0.001260914,0.000008208764,0.000002467084,0.000005098304,0.0001057194,0.000005455038,0.0003086194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04887484,"threshold_uncertainty_score":0.09718072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01871853066664969,"score_gpt":0.2285890527538446,"score_spread":0.2098705220871949,"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."}}