{"id":"W2951869266","doi":"10.1111/eva.12765","title":"Combining population genomics and forward simulations to investigate stocking impacts: A case study of Muskellunge ( <i>Esox masquinongy</i> ) from the St. Lawrence River basin","year":2019,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Ministère des Forêts, de la Faune et des Parcs; Ministry of Natural Resources; Ontario Ministry of Natural Resources and Forestry; New York State Department of Environmental Conservation","keywords":"Stocking; Tributary; Biology; Population; Ecology; Genetic diversity; Esox; Electrofishing; Effective population size; Fishery; Pike; Geography; Fish <Actinopterygii>; Abundance (ecology)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001528468,0.0001134279,0.0001333777,0.00003696462,0.0005494555,0.00001304524,0.0001639052,0.00003768119,0.0001475326],"category_scores_gemma":[0.00002515524,0.0001020866,0.0000211323,0.0003074995,0.0001485241,0.0002187956,0.0004097867,0.00009426938,0.0000688748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118208,"about_ca_system_score_gemma":0.000007912909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004594542,"about_ca_topic_score_gemma":0.006142371,"domain_scores_codex":[0.9990711,0.00007152212,0.0002419335,0.0003163724,0.0001433017,0.0001558035],"domain_scores_gemma":[0.9991693,0.0002794472,0.0001262909,0.0003481503,0.00001597338,0.00006076768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007904584,0.0001541032,0.968936,0.000004053825,0.00003769789,0.000002250837,0.002989134,0.02535818,0.0001430674,0.0004631854,0.001679186,0.0002252332],"study_design_scores_gemma":[0.0003436448,0.00009208964,0.9864253,0.000006767645,0.00006486047,0.000007505239,0.002636926,0.005528987,0.000003430292,0.002225513,0.002547123,0.0001178241],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954268,0.00002995812,0.00147661,0.0007011627,0.00003720447,0.001814538,0.0001018959,0.00003107274,0.0003807042],"genre_scores_gemma":[0.997303,0.000008262289,0.001819429,0.000499315,0.00002000134,0.0002135855,0.00005117567,0.000009256107,0.00007593822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01982919,"threshold_uncertainty_score":0.6945604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204975392210725,"score_gpt":0.2340732300750997,"score_spread":0.2220234761529925,"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."}}