{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001202344,0.0004935942,0.0005538142,0.0005883006,0.0008888528,0.0007624813,0.001038624,0.001031697,0.000998566],"category_scores_gemma":[0.002134849,0.0003751905,0.0006571268,0.0006313963,0.0005473316,0.0005034063,0.0004895637,0.0006985891,0.00009876979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002024604,"about_ca_system_score_gemma":0.001717068,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2741159,"about_ca_topic_score_gemma":0.3475272,"domain_scores_codex":[0.9998323,0.0000797163,0.000006918316,0.00002958307,0.00002245598,0.0000290569],"domain_scores_gemma":[0.9987223,0.000897749,0.00006088425,0.00006229492,0.0001791649,0.00007768463],"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":[0.00007199193,0.000185303,0.06763545,0.00002578333,0.0001074825,0.0003743418,0.0001641265,0.9249839,0.0008927858,0.0008445873,0.0004660683,0.004248148],"study_design_scores_gemma":[0.00002739691,0.00004656836,0.006742083,0.000004186885,0.00002203248,0.00001785225,0.0001611787,0.9921528,0.0001934421,0.0003763648,0.0002446415,0.00001138192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964545,0.00002970467,0.002047756,0.0001523356,0.000007255241,0.00001909647,0.0001910351,0.00005235479,0.001045992],"genre_scores_gemma":[0.9933003,0.00002747939,0.005837445,0.00005349588,0.000004427018,0.0000263236,0.0002744751,0.00001418192,0.0004618629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7258841,"threshold_uncertainty_score":0.5450408,"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."}}