{"id":"W4384103479","doi":"10.1016/j.marpol.2023.105763","title":"Revisiting fuel tax concessions (FTCs): The economic implications of fuel subsidies for the commercial fishing fleet of the United Kingdom","year":2023,"lang":"en","type":"article","venue":"Marine Policy","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"Natural Environment Research Council; Social Sciences and Humanities Research Council of Canada; UK Research and Innovation","keywords":"Subsidy; Fuel tax; Fishing; Business; Profitability index; Diesel fuel; Natural resource economics; Economics; Fishery; Finance; Waste management; Engineering; Market economy; Revenue","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":[0.0005402274,0.00009561889,0.000141743,0.00005427855,0.0004575463,0.00003996103,0.0008807043,0.00003557618,0.0007998758],"category_scores_gemma":[0.0005492224,0.0000504821,0.0001090083,0.0007171957,0.0006185223,0.00007856361,0.001740958,0.000159864,0.0000170253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009563762,"about_ca_system_score_gemma":0.00006282154,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04874787,"about_ca_topic_score_gemma":0.005303179,"domain_scores_codex":[0.9990527,0.00009236365,0.0002861672,0.0001469706,0.0001438845,0.0002779093],"domain_scores_gemma":[0.998152,0.001064737,0.0001722644,0.0005529925,0.00002074159,0.00003728244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003920675,0.00002404563,0.8647828,0.0001152932,0.00006242022,2.216439e-7,0.001637906,0.001493738,0.0006854868,0.03835639,0.02500757,0.06779496],"study_design_scores_gemma":[0.0002042627,0.00001819928,0.7573944,0.000008787437,0.00001863012,0.000001265277,0.0003806002,0.003460019,0.0001243065,0.004570066,0.2337432,0.00007625122],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.629571,0.00001477179,0.0001104973,0.1791761,0.0001657793,0.001415747,0.0003734503,0.00005968737,0.189113],"genre_scores_gemma":[0.9967852,0.0001140139,0.00004460277,0.0005658517,0.0002189522,0.0001040756,0.00003045676,0.00001405656,0.00212282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3672142,"threshold_uncertainty_score":0.9575866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0666271298393614,"score_gpt":0.3410104893241914,"score_spread":0.2743833594848299,"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."}}