{"id":"W4249866500","doi":"10.31230/osf.io/ba5wt","title":"ECONOMIC AND SOCIAL BENEFITS OF FISHERIES REBUILDING: SIX CANADIAN CASE STUDIES","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishery; Fishing; Fish stock; Stock (firearms); Fisheries management; Geography; Population; Stock assessment; Herring; Fish <Actinopterygii>; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00139695,0.0007639294,0.0004334941,0.002522985,0.005893533,0.001767854,0.001980321,0.00137409,0.002688724],"category_scores_gemma":[0.002525886,0.0002959272,0.001243033,0.00578996,0.001830465,0.0008060103,0.001712772,0.001443279,0.0001258295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05630732,"about_ca_system_score_gemma":0.01990312,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9837829,"about_ca_topic_score_gemma":0.9924115,"domain_scores_codex":[0.998769,0.000270353,0.00002966851,0.00005831136,0.0002938104,0.0005788812],"domain_scores_gemma":[0.9983402,0.0005661359,0.0001524735,0.00007198931,0.0005577148,0.0003114249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00123827,0.002884102,0.5126132,0.001047032,0.0008088478,0.01558702,0.01531553,0.2739681,0.003398567,0.06674615,0.02749135,0.07890184],"study_design_scores_gemma":[0.0004329777,0.001084652,0.5617169,0.0006183636,0.0008966396,0.001936925,0.1369705,0.2295169,0.002168702,0.01145558,0.05273568,0.0004661675],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801185,0.0005563896,0.0005233605,0.0009802417,0.00001145883,0.0002265863,0.001455411,0.00001122896,0.01611678],"genre_scores_gemma":[0.9909088,0.001601819,0.001860857,0.0001624655,0.000007589279,0.000106778,0.0007357859,0.000006459792,0.004609511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05630732,"threshold_uncertainty_score":0.4085401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04087959268619261,"score_gpt":0.2764744512664946,"score_spread":0.235594858580302,"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."}}