{"id":"W2077981612","doi":"10.1111/faf.12109","title":"Masked, diluted and drowned out: how global seafood trade weakens signals from marine ecosystems","year":2015,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Svenska Forskningsrådet Formas; Royal Swedish Academy of Sciences; Stiftelsen för Miljöstrategisk Forskning","keywords":"Sustainability; Business; Fishery; Publicity; Marine ecosystem; Corporate governance; Ecosystem; Natural resource economics; Economics; Marketing; Ecology; Finance","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":[0.001147889,0.0002549284,0.0003258465,0.0006251545,0.0008036508,0.002846662,0.0005508938,0.001215902,0.007616202],"category_scores_gemma":[0.006102161,0.0002475367,0.0003388126,0.0004532974,0.002922588,0.002714708,0.002053465,0.0008001038,0.0005096712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009335228,"about_ca_system_score_gemma":0.0006695738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008861826,"about_ca_topic_score_gemma":0.007505432,"domain_scores_codex":[0.9995472,0.000177409,0.00001896124,0.0001087013,0.00006507507,0.00008264036],"domain_scores_gemma":[0.9980844,0.00078081,0.0004694252,0.000264339,0.0002107832,0.000190248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001351684,0.0004981098,0.5552238,0.000638817,0.000482502,0.003250897,0.02762205,0.02189173,0.05769772,0.1336595,0.008183952,0.1894993],"study_design_scores_gemma":[0.0001597232,0.00110326,0.557469,0.0004875045,0.0006109279,0.001441707,0.05806912,0.05782249,0.01356876,0.2734885,0.03544385,0.0003350982],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612954,0.0002457016,0.004103074,0.002525299,0.00004252792,0.00001400854,0.00007760085,0.00004756788,0.03164877],"genre_scores_gemma":[0.9982983,0.00007253665,0.0004727147,0.000220355,0.000004763086,0.000003257235,0.00001512284,0.000007928925,0.0009050735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008861826,"threshold_uncertainty_score":0.02547872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648668752792549,"score_gpt":0.218812844164075,"score_spread":0.1923261566361495,"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."}}