{"id":"W3134242164","doi":"10.14288/1.0394806","title":"What has Canada caught, and how much is left? Reconstructing and assessing fisheries in three oceans","year":2020,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishery; Geography; Oceanography; Geology; 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":[0.00185287,0.0003222822,0.0004165624,0.003730083,0.004300227,0.004118733,0.0009007282,0.0003715953,0.002405636],"category_scores_gemma":[0.0057198,0.0001723292,0.0004198842,0.008381599,0.002113421,0.001590882,0.00134481,0.0007410541,0.0002862934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04952202,"about_ca_system_score_gemma":0.06628262,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9952165,"about_ca_topic_score_gemma":0.9977229,"domain_scores_codex":[0.9986556,0.000102658,0.00008303878,0.0001854133,0.0005673426,0.0004058766],"domain_scores_gemma":[0.9960757,0.0002987199,0.0003448047,0.0001563704,0.002707168,0.0004172324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009932627,0.0000168033,0.7835774,0.0004529657,0.0002979102,0.0003906051,0.009637444,0.002223645,0.0006890165,0.006763522,0.01950131,0.17635],"study_design_scores_gemma":[0.000003575438,0.00002167223,0.9072964,0.0008197915,0.0001732665,0.0001126825,0.03399763,0.00162601,0.000677627,0.001281878,0.05389472,0.00009463404],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.904826,0.01025859,0.002778306,0.01217316,0.0001897813,0.00006406551,0.01529184,0.00009259545,0.05432577],"genre_scores_gemma":[0.9807761,0.006562053,0.002998549,0.0006296496,0.00002086998,0.00001814897,0.004284701,0.00004365231,0.004666291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04952202,"threshold_uncertainty_score":0.3593091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859153591089195,"score_gpt":0.171092919561914,"score_spread":0.152501383651022,"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."}}