{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007161809,0.0002053927,0.0004738225,0.0008725916,0.001915644,0.00257717,0.0006985878,0.0005358778,0.004359995],"category_scores_gemma":[0.03027,0.0001830074,0.0002359128,0.001700898,0.005581021,0.002569365,0.003696395,0.001230642,0.0002008095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004934273,"about_ca_system_score_gemma":0.002437875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03504029,"about_ca_topic_score_gemma":0.03957352,"domain_scores_codex":[0.9948249,0.002475944,0.0002175642,0.0009038683,0.0009566474,0.0006210626],"domain_scores_gemma":[0.9887663,0.004479679,0.002707277,0.002676134,0.001051774,0.0003189065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001909531,0.00005639939,0.3155973,0.0001050791,0.0002025557,0.0004784612,0.01479206,0.00656882,0.0006838128,0.5239594,0.003144864,0.1342202],"study_design_scores_gemma":[0.00003969152,0.0001102232,0.3710305,0.0004243342,0.0001225915,0.0006329956,0.01192751,0.01006121,0.001100742,0.5407034,0.06375257,0.00009424269],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9096656,0.001714051,0.02778616,0.01217704,0.000114045,0.0000598683,0.0005937123,0.00004998827,0.04783938],"genre_scores_gemma":[0.9967688,0.0001876218,0.000974753,0.0003132103,0.00002017487,0.00001721532,0.00004230578,0.000009921305,0.001666039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03504029,"threshold_uncertainty_score":0.06967264,"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."}}