{"id":"W2039262398","doi":"10.1139/f09-147","title":"Spatiotemporal dynamics of the Atlantic salmon (Salmo salar) Greenland fishery inferred from mixed-stock analysis","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Ministère des Ressources naturelles et des Forêts; Ministère des Ressources naturelles et des Forêts (Québec)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Salmo; Fishery; Stock (firearms); Geography; Fisheries management; Stock assessment; Productivity; Oceanography; Fish <Actinopterygii>; Biology; Fishing; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0004289723,0.00012384,0.0001910236,0.0009380303,0.0002214096,0.0003169015,0.0002505336,0.000132061,0.0006173336],"category_scores_gemma":[0.0008540595,0.00009854701,0.0002246,0.0009918143,0.0001785348,0.0001633603,0.0002635967,0.0001120921,0.00009030872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100107,"about_ca_system_score_gemma":0.0005584006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3065347,"about_ca_topic_score_gemma":0.5598265,"domain_scores_codex":[0.999854,0.00002331256,0.00001110193,0.00005047008,0.00002281147,0.00003833651],"domain_scores_gemma":[0.9992544,0.00009405082,0.0003018166,0.00005741442,0.000196517,0.00009576268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002458842,0.000003721314,0.9967663,0.000004376996,0.00004099718,0.00003185892,0.0001139318,0.0001821379,0.001158381,0.00001631505,0.00006856281,0.001588771],"study_design_scores_gemma":[3.825722e-7,0.000004367906,0.9992448,0.000002134932,0.00000777945,0.00001316475,0.00006803107,0.0005475986,0.00005254367,0.000005541967,0.00005214417,0.000001492609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994209,0.00004955661,0.00009154614,0.000008175098,5.887524e-7,0.000002025942,0.0003027719,0.000002836498,0.0001216751],"genre_scores_gemma":[0.9994158,0.00002131872,0.000118787,0.000003622998,5.928545e-7,0.000002062287,0.0003613115,0.00000106914,0.00007537584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3065347,"threshold_uncertainty_score":0.6095009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142671851793704,"score_gpt":0.2013946518496981,"score_spread":0.189967933331761,"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."}}