{"id":"W4414446886","doi":"10.1016/j.ocecoaman.2025.107908","title":"Fisheries decision-makers’ perspectives on the use of historical data to inform assessment and management","year":2025,"lang":"en","type":"article","venue":"Ocean & Coastal Management","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Fisheries management; Fisheries science; Fisheries law; Marine fisheries; Government (linguistics); Fisheries Research; Historical ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.07448825,0.0004404872,0.0004077744,0.00381556,0.006758056,0.01126622,0.001560394,0.003099573,0.002102757],"category_scores_gemma":[0.0759983,0.0005291203,0.0006042488,0.003350933,0.01446189,0.01464939,0.006158432,0.00496302,0.0001910001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007407928,"about_ca_system_score_gemma":0.008839126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01883526,"about_ca_topic_score_gemma":0.01935086,"domain_scores_codex":[0.9598949,0.02977564,0.002025939,0.001329915,0.004852579,0.00212103],"domain_scores_gemma":[0.9048756,0.07226568,0.005379997,0.002878428,0.01173832,0.002861846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001576131,0.0001316661,0.06806792,0.0008186407,0.0001441663,0.00279807,0.6059908,0.003691807,0.00244731,0.1897858,0.01307085,0.1128953],"study_design_scores_gemma":[0.00003185585,0.0001342529,0.0221583,0.00273697,0.0001064381,0.0006807873,0.7073411,0.003112216,0.002060353,0.09450086,0.1668977,0.0002392098],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.594772,0.007196555,0.03888097,0.2343791,0.0006745912,0.0001832947,0.0006300114,0.00004493694,0.1232385],"genre_scores_gemma":[0.9875009,0.002159775,0.006418465,0.002694097,0.0000703469,0.00005139882,0.0000823393,0.000009718261,0.001012855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07448825,"threshold_uncertainty_score":0.3939362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0634778240377435,"score_gpt":0.30301970138512,"score_spread":0.2395418773473765,"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."}}