{"id":"W2901218484","doi":"10.1371/journal.pone.0207768","title":"Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Entrust (Canada); University of British Columbia; Fisheries and Oceans Canada","funders":"Social Sciences and Humanities Research Council of Canada; Paul M. Angell Family Foundation; MAVA Foundation; University of British Columbia; Marisla Foundation; Bloomberg Family Foundation; Oak Foundation; David and Lucile Packard Foundation","keywords":"Fishing; Revenue; Fishery; Business; Profit (economics); Sustainability; Fisheries management; Net profit; Engraulis; Fish stock; Natural resource economics; Fish <Actinopterygii>; Economics; Anchovy; Finance; Ecology; Microeconomics; Biology","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":[0.005873566,0.001838216,0.001517126,0.009675277,0.0009248181,0.006371339,0.002241626,0.001576433,0.004134082],"category_scores_gemma":[0.02179377,0.0009815327,0.00166643,0.006688804,0.001191481,0.005107316,0.002972431,0.002264627,0.001188733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002705742,"about_ca_system_score_gemma":0.001765249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00911707,"about_ca_topic_score_gemma":0.01272927,"domain_scores_codex":[0.9960526,0.001172024,0.0002585614,0.0007357143,0.001396132,0.0003848309],"domain_scores_gemma":[0.9866507,0.007229609,0.002074657,0.001085797,0.002563131,0.0003960503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000276194,0.001017434,0.3914285,0.0006986239,0.001096405,0.001356035,0.001232661,0.2011237,0.003148127,0.05899347,0.005113366,0.3345154],"study_design_scores_gemma":[0.00002890048,0.0004152939,0.1496283,0.0003790561,0.0004277735,0.0004169076,0.002586542,0.7524623,0.004383848,0.07969718,0.009400195,0.0001736399],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3509728,0.00134725,0.5870222,0.001747855,0.0001714628,0.0013035,0.003568118,0.0007168679,0.05314989],"genre_scores_gemma":[0.8551295,0.000503473,0.1376365,0.0001392556,0.0001045616,0.0004025867,0.001487702,0.00012303,0.004473446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009675277,"threshold_uncertainty_score":0.03106272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048766060757538,"score_gpt":0.2448158870293478,"score_spread":0.1399392809535941,"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."}}