{"id":"W2789136174","doi":"10.3389/fmars.2018.00026","title":"Impacts of Ocean Warming on China's Fisheries Catches: An Application of “Mean Temperature of the Catch” Concept","year":2018,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"National Natural Science Foundation of China-Shandong Joint Fund; China Scholarship Council; National Natural Science Foundation of China; Marisla Foundation; Oak Foundation","keywords":"Fishery; Effects of global warming on oceans; Global warming; Environmental science; Warming up; Oceanography; China; Climate change; Geography; Biology; Geology","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":[0.0009562466,0.0004389652,0.0002870396,0.0008821377,0.0004349057,0.0008511831,0.0004352752,0.0004387246,0.00170235],"category_scores_gemma":[0.00136571,0.0001057465,0.00109358,0.00108142,0.0007190522,0.0007084335,0.0009645114,0.0005240461,0.00007033638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009364193,"about_ca_system_score_gemma":0.001026346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02148359,"about_ca_topic_score_gemma":0.0137793,"domain_scores_codex":[0.9996836,0.00009500135,0.00002028232,0.00007942446,0.00005818891,0.00006358181],"domain_scores_gemma":[0.9993318,0.0002602576,0.0001947498,0.00004934516,0.0001001067,0.00006370835],"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":[0.0001150563,0.00007960744,0.8666479,0.0003197954,0.0006365856,0.001187787,0.0009176826,0.06742685,0.003625949,0.0180621,0.001612998,0.03936766],"study_design_scores_gemma":[0.00001369196,0.0001714818,0.8839169,0.00007929879,0.0003127556,0.0001714575,0.001180253,0.09883373,0.001021765,0.009825841,0.004412864,0.00005998805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820606,0.00169776,0.006865678,0.001202371,0.00008934388,0.00002563989,0.0004331103,0.00004911302,0.007576492],"genre_scores_gemma":[0.9986898,0.0003975479,0.0004987432,0.0000439541,0.0000324274,0.000009216672,0.00007881231,0.000005270336,0.0002442925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02148359,"threshold_uncertainty_score":0.0427171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005950160405110362,"score_gpt":0.2364559963413159,"score_spread":0.2305058359362055,"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."}}