{"id":"W2098098727","doi":"10.1890/14-1771","title":"Supply–demand equilibria and the size–number trade‐off in spatially structured recreational fisheries","year":2016,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Freshwater Fisheries Society of BC; Ministry of Forests; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Freshwater Fisheries Society of British Columbia; University of Calgary","keywords":"Fishing; Fishery; Recreational fishing; Foraging; Recreation; Catch and release; Abundance (ecology); Geography; Population; Trout; Predation; Ecology; Fish <Actinopterygii>; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007665532,0.0001596924,0.0002452923,0.0005872943,0.000476733,0.001212078,0.0003838112,0.0004592998,0.002071592],"category_scores_gemma":[0.004444369,0.0002805596,0.0003353348,0.0004143971,0.00123918,0.0009516812,0.0008267435,0.0003064309,0.00009658686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00212798,"about_ca_system_score_gemma":0.0003090521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04133784,"about_ca_topic_score_gemma":0.0745055,"domain_scores_codex":[0.9996552,0.0001115481,0.00002598647,0.0001052553,0.00003986084,0.00006211927],"domain_scores_gemma":[0.9983979,0.0007327751,0.000509371,0.0001210567,0.0001092992,0.0001295761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003014308,0.0001106807,0.9319168,0.00004531329,0.0001723198,0.0003348445,0.001149437,0.04177766,0.006643114,0.008780312,0.000231595,0.008536401],"study_design_scores_gemma":[0.00001255438,0.0000575475,0.9119961,0.000008926976,0.00002656308,0.00009825909,0.001079712,0.07846286,0.0003401796,0.007646422,0.000251627,0.00001924606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997885,0.00001777217,0.00145286,0.00003295373,4.043891e-7,0.000005186167,0.00003637315,0.000003607799,0.0005658548],"genre_scores_gemma":[0.9995316,0.000006645941,0.0002899412,0.000005406157,6.174099e-7,0.000003416219,0.00002509147,0.000001263788,0.0001360823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04133784,"threshold_uncertainty_score":0.08219445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006913340818210327,"score_gpt":0.211783203942139,"score_spread":0.2048698631239287,"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."}}