{"id":"W2886333017","doi":"10.3389/fmars.2018.00266","title":"Reconstructed Russian Fisheries Catches in the Barents Sea: 1950-2014","year":2018,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Paul M. Angell Family Foundation; University of British Columbia; Marisla Foundation; Bloomberg Philanthropies; MAVA Foundation; Oak Foundation; David and Lucile Packard Foundation","keywords":"Discards; Fishery; Overexploitation; Geography; Fish stock; European union; Marine conservation; Stock (firearms); Fisheries management; Fishing; Subsistence agriculture; Business; Agriculture; Biology; International trade","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001386898,0.0001472141,0.0001641091,0.0001754339,0.0002961521,0.0001646813,0.001566907,0.00005310837,0.004557451],"category_scores_gemma":[0.0002315165,0.0001073964,0.00002821054,0.002009922,0.004007651,0.0006720367,0.001238365,0.0002910316,0.0001137015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440971,"about_ca_system_score_gemma":0.00007849587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003231036,"about_ca_topic_score_gemma":0.00294599,"domain_scores_codex":[0.9977289,0.0001078508,0.0002504276,0.0005250101,0.0007180109,0.0006698205],"domain_scores_gemma":[0.9992542,0.0000329008,0.00004965549,0.0005418158,0.000013712,0.0001076988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001971322,0.00002915708,0.9041162,0.000003088354,9.771684e-7,0.000009649401,0.0007232881,0.000001591343,0.00005047674,0.00004571975,0.0111947,0.08380544],"study_design_scores_gemma":[0.000216902,0.00007749126,0.957566,0.000004046895,0.000001305493,0.000008801555,0.0009024207,0.001018052,0.0001840682,0.003213459,0.03664933,0.0001581596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5659746,0.000001911809,0.00009931267,0.001244558,0.0005182981,0.0002349272,0.000002339523,0.00001752501,0.4319066],"genre_scores_gemma":[0.9770422,0.00003568205,0.02048184,0.0003836672,0.00007406312,0.00003821759,0.000005087745,0.000009663015,0.00192957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.429977,"threshold_uncertainty_score":0.9987029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016603388251434,"score_gpt":0.2321439513461859,"score_spread":0.2219779174636715,"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."}}