{"id":"W4229485305","doi":"10.31230/osf.io/q38ez","title":"Fishery Audit 2019","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishery; Bycatch; Overfishing; Fish stock; Business; Stock (firearms); Fishing; Audit; Fisheries management; Stock assessment; Geography; Accounting; 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":[0.004578634,0.0007193423,0.0005415672,0.004674928,0.003106247,0.004480451,0.001575498,0.003388468,0.2352695],"category_scores_gemma":[0.01407841,0.0005980896,0.0006376903,0.003615314,0.0008539871,0.00215112,0.002313147,0.003097455,0.1237556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00753546,"about_ca_system_score_gemma":0.02734561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2160733,"about_ca_topic_score_gemma":0.2676433,"domain_scores_codex":[0.9925417,0.0004914104,0.0005349692,0.0005180038,0.004731628,0.001182407],"domain_scores_gemma":[0.9811031,0.001037998,0.0008708124,0.0009764702,0.01413862,0.001872956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00007459124,0.00002994577,0.001413909,0.0001208153,0.000003576878,0.00006663908,0.0000314679,0.00006249676,0.0001454307,0.001973195,0.9625423,0.03353566],"study_design_scores_gemma":[0.00001635563,0.00001580571,0.004275599,0.00008739599,0.000002412314,0.00002414604,0.0000562137,0.00007737564,0.0001019713,0.0003303775,0.9949965,0.00001570567],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00565831,0.001746066,0.001684326,0.02696117,0.01322473,0.001788737,0.05508318,0.003518765,0.8903347],"genre_scores_gemma":[0.0170623,0.001196944,0.001774078,0.01144254,0.0009074057,0.000544235,0.03137165,0.0006306052,0.9350702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2352695,"threshold_uncertainty_score":0.7870548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726119677616214,"score_gpt":0.2493289229829026,"score_spread":0.2320677262067404,"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."}}