{"id":"W3096638438","doi":"10.1101/2020.11.06.369785","title":"Implementing the precautionary approach into fisheries management: Making the case for probability-based harvest control rules","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"European Maritime and Fisheries Fund; School of Aquatic and Fishery Sciences; University of Washington","keywords":"Precautionary principle; Stock (firearms); Uncertainty; Fisheries management; Fish stock; Risk management; Operations research; Computer science; Economics; Actuarial science; Environmental resource management; Fish <Actinopterygii>; Fishery; Mathematics; Ecology; Statistics; Geography; Fishing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0162997,0.0005019483,0.000614676,0.0009308849,0.0005286033,0.002728123,0.001958638,0.001787592,0.0009475758],"category_scores_gemma":[0.04565703,0.0004476942,0.0006689787,0.0004940498,0.002169198,0.003019194,0.002044661,0.002462087,0.0001358514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001291527,"about_ca_system_score_gemma":0.002282879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003978536,"about_ca_topic_score_gemma":0.003172089,"domain_scores_codex":[0.9918301,0.004760198,0.0003530662,0.0005599658,0.002151775,0.0003449616],"domain_scores_gemma":[0.9602366,0.03064452,0.003180188,0.002392904,0.00302212,0.000523611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002117581,0.0003078445,0.009968308,0.0001941548,0.0001975042,0.0002962956,0.0003096258,0.7538671,0.003038106,0.1387806,0.001557184,0.09127152],"study_design_scores_gemma":[0.00005600716,0.0002219152,0.002286248,0.0001273168,0.00004928399,0.00007314274,0.0001217564,0.9032614,0.001856298,0.08989977,0.001996784,0.00005006469],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1712857,0.001016723,0.8096017,0.007639359,0.0001884039,0.0001611514,0.00005576597,0.000233374,0.009817892],"genre_scores_gemma":[0.8509577,0.0003272937,0.1474464,0.0003602245,0.00009243939,0.00007563681,0.00002084184,0.00003536381,0.0006840864],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0162997,"threshold_uncertainty_score":0.08620214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587122033024097,"score_gpt":0.2395381443082473,"score_spread":0.2136669239780064,"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."}}