{"id":"W4212946307","doi":"10.1111/faf.12649","title":"Managing fisheries for maximum nutrient yield","year":2022,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Aquaculture Nutrition and Growth","field":"Agricultural and Biological Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"H2020 European Research Council; Australian Research Council; Royal Society; Leverhulme Trust","keywords":"Fishery; Fishing; Nutrient; Nutrient management; Fisheries management; Food security; Maximum sustainable yield; Overfishing; Business; Environmental science; Biology; Ecology; Agriculture","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001200529,0.0001104331,0.0001335333,0.000008017414,0.0007928813,0.0001090255,0.0001290691,0.00003844881,0.001354582],"category_scores_gemma":[0.00003366013,0.00005101578,0.0000708514,0.0001445223,0.00006476002,0.0001361244,0.0001137218,0.0001004975,0.000001453353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001297289,"about_ca_system_score_gemma":0.000002814309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005960654,"about_ca_topic_score_gemma":0.0002420124,"domain_scores_codex":[0.9992626,0.00002889065,0.0001291056,0.0002290749,0.0001302991,0.0002200285],"domain_scores_gemma":[0.9996987,0.0001268997,0.00004518129,0.00003487129,0.00002490218,0.00006944619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003494526,0.0001911705,0.02058661,0.00006324615,0.00003245796,0.00001182342,0.0009163455,7.331786e-7,0.007794654,0.002720629,0.8829141,0.08441875],"study_design_scores_gemma":[0.0001657949,0.0003503673,0.01249237,0.000005262685,0.000007863641,0.00001301197,0.003105999,0.00001811904,0.000564829,0.01267829,0.9704299,0.0001681846],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8427461,0.0004506462,0.0001359853,0.1369347,0.0004498407,0.0008019057,0.00117861,0.0002990847,0.01700318],"genre_scores_gemma":[0.9839935,0.0001668342,0.0002294995,0.01000548,0.0003910882,0.0003970995,0.0004362762,0.000002599241,0.004377594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1412474,"threshold_uncertainty_score":0.9995583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02449238481340445,"score_gpt":0.1969170113397803,"score_spread":0.1724246265263758,"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."}}