{"id":"W4311633536","doi":"10.3389/fhumd.2022.1044321","title":"Subsidies and allocation: A legacy of distortion and intergenerational loss","year":2022,"lang":"en","type":"article","venue":"Frontiers in Human Dynamics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Social Sciences and Humanities Research Council of Canada; University of Washington; Ocean Nexus Center, EarthLab, University of Washington; Waterloo Foundation; EarthLab, University of Washington; Oak Foundation; Mitacs; Pew Charitable Trusts","keywords":"Subsidy; Fishing; Tuna; Transparency (behavior); Fishery; Business; Negotiation; Corporate governance; Commission; Natural resource economics; Economics; Public economics; Political science; Finance","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":[],"consensus_categories":[],"category_scores_codex":[0.0001239582,0.00004058768,0.00006489759,0.00003929112,0.0001226658,0.00001360144,0.00007010659,0.00001274592,0.0006080318],"category_scores_gemma":[0.000007382263,0.00004516274,0.000008671557,0.00008271127,0.0001993825,0.0001296321,0.0002686493,0.00007984167,1.384116e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691116,"about_ca_system_score_gemma":0.000004487028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002839665,"about_ca_topic_score_gemma":0.0008923215,"domain_scores_codex":[0.9995248,0.00003084134,0.0001088533,0.0001188026,0.0001438541,0.00007280859],"domain_scores_gemma":[0.9998741,0.000004992049,0.00002876158,0.00006853477,0.000003378123,0.00002029844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001556142,0.00002806839,0.9862506,0.000009119144,0.000004028409,0.000002053934,0.0004717014,0.0003598953,0.00005102675,0.001536029,0.001350839,0.009921025],"study_design_scores_gemma":[0.0006878873,0.0002727203,0.7120945,0.000003957343,0.000009167522,0.00001506639,0.0030442,0.2563466,0.00004267293,0.009777531,0.01743713,0.000268612],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893933,0.00003614999,0.004089687,0.0001824167,0.00009004032,0.00008035648,0.000009302923,0.000004045477,0.006114692],"genre_scores_gemma":[0.9975696,0.00003798386,0.0009642987,0.00001668215,0.000007910945,0.00002115871,0.00006019404,0.000003899742,0.001318285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2741562,"threshold_uncertainty_score":0.6657522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006854938523460628,"score_gpt":0.2197237766869014,"score_spread":0.2128688381634407,"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."}}