{"id":"W2508552079","doi":"10.1111/faf.12177","title":"Transform high seas management to build climate resilience in marine seafood supply","year":2016,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Climate change; Exclusive economic zone; Fishery; Fishing; Sustainability; International waters; Fish stock; Fisheries management; Resilience (materials science); Psychological resilience; Geography; Environmental science; Environmental resource management; Business; Ecology; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002094004,0.0001606233,0.0001675642,0.00005557855,0.0001103499,0.00006962335,0.0002756435,0.0000567512,0.01138279],"category_scores_gemma":[0.00002709236,0.0001173888,0.00002815883,0.000302478,0.0002225443,0.0004234508,0.000649307,0.00008962865,0.0001073526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007871167,"about_ca_system_score_gemma":0.000005015925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008481604,"about_ca_topic_score_gemma":0.005235728,"domain_scores_codex":[0.9984497,0.00002829943,0.000206538,0.0004252601,0.0003169082,0.0005733354],"domain_scores_gemma":[0.9994488,0.00004175024,0.00001958081,0.0002793264,0.000006494279,0.0002040573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001640111,0.00003952291,0.6970499,0.00003219527,0.000005104921,0.00004347223,0.0001689133,9.310431e-7,0.0001343555,0.0002647274,0.008414187,0.2936827],"study_design_scores_gemma":[0.0004945891,0.0001881502,0.6475978,0.00001359473,0.000003311689,0.000004844815,0.0001513785,0.000005837541,0.0003762704,0.0007891741,0.350187,0.0001880848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6528133,0.000001411002,0.00004569284,0.01516093,0.00005377779,0.0004094035,0.00004506572,0.00003906941,0.3314314],"genre_scores_gemma":[0.9775865,0.0009264136,0.001303253,0.001631522,0.00004016778,0.0001757146,0.00001359528,0.00002403625,0.01829877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3417728,"threshold_uncertainty_score":0.989521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006206149980076718,"score_gpt":0.2076498873745258,"score_spread":0.2014437373944491,"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."}}