{"id":"W3141376062","doi":"10.3389/fmars.2021.619190","title":"Beyond Post-release Mortality: Inferences on Recovery Periods and Natural Mortality From Electronic Tagging Data for Discarded Lamnid Sharks","year":2021,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Ichthyology and Marine Biology","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"Fundação para a Ciência e a Tecnologia; Fundação de Amparo à Pesquisa do Estado de São Paulo; European Commission","keywords":"Bycatch; Fishing; Fishery; Covariate; Biology; Pelagic zone; Psychological resilience; Ecology; Statistics","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":[0.003578161,0.0003590704,0.0001996016,0.001320011,0.0002313397,0.0004741528,0.000550646,0.00032397,0.0004806073],"category_scores_gemma":[0.009480638,0.0001834302,0.0004553072,0.0006934791,0.0002686378,0.0005996235,0.0005757526,0.0003038456,0.0002080794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000359919,"about_ca_system_score_gemma":0.0002716678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007033266,"about_ca_topic_score_gemma":0.01251996,"domain_scores_codex":[0.9990147,0.0003041601,0.0001465174,0.000277817,0.0001364999,0.0001202585],"domain_scores_gemma":[0.9929215,0.002548297,0.003034544,0.0006210779,0.0006349495,0.0002396888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001359736,0.0000329814,0.9895399,0.00002708579,0.00005529559,0.00006629639,0.0002264675,0.0009537881,0.0008943153,0.00004660483,0.0001129804,0.007908222],"study_design_scores_gemma":[0.000003616342,0.0001021003,0.9917212,0.00001893061,0.00004553833,0.00006528635,0.0001999718,0.007155555,0.0003939637,0.00005436588,0.0002312522,0.000008359872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979991,0.00005583747,0.001314781,0.00002266382,0.000004008759,0.00000843722,0.0003963626,0.00001032134,0.0001883692],"genre_scores_gemma":[0.9970397,0.00003944223,0.001185789,0.00002256039,0.00001178887,0.00001809034,0.001547573,0.000005853598,0.0001292516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007033266,"threshold_uncertainty_score":0.01892334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138273502030263,"score_gpt":0.2644900386700104,"score_spread":0.2531073036497078,"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."}}