{"id":"W2891258474","doi":"10.1111/faf.12322","title":"A synthesis to understand responses to capture stressors among fish discarded from commercial fisheries and options for mitigating their severity","year":2018,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Fisheries and Oceans Canada; Carleton University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fishing; Context (archaeology); Stressor; Fish <Actinopterygii>; Fishery; Business; Fisheries management; Task (project management); Risk analysis (engineering); Environmental science; Environmental resource management; Computer science; Biology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001671076,0.0002176689,0.0002785207,0.00003445535,0.001015168,0.0001490696,0.0001506838,0.0001066856,0.0004865502],"category_scores_gemma":[0.0005714371,0.0001963026,0.00004159034,0.0001300853,0.0008810458,0.0003327679,0.0004638984,0.00008536789,0.000005497428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004324219,"about_ca_system_score_gemma":0.00000575213,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007096695,"about_ca_topic_score_gemma":0.1073091,"domain_scores_codex":[0.9988507,0.00007532984,0.0001814978,0.0004434301,0.0001088759,0.0003401922],"domain_scores_gemma":[0.9990703,0.0005156742,0.00005399791,0.0001888938,0.0000182144,0.0001529272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002628695,0.00002555283,0.5790679,0.0000175901,0.00006555019,0.000001979955,0.01108539,0.000001121932,0.00002436365,0.00002034939,0.4080666,0.001360779],"study_design_scores_gemma":[0.0001767687,0.0001682712,0.9495542,0.00003820355,0.00004767941,7.944955e-7,0.01200259,0.00002714212,0.0002459224,0.0007584466,0.03671861,0.0002613422],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682931,0.000009279298,0.000266023,0.02622594,0.0001497577,0.0005281095,0.000961412,0.00006174578,0.003504585],"genre_scores_gemma":[0.9902468,0.00006029447,0.001760722,0.006522154,0.000106832,0.0001858961,0.00002126331,0.00001902908,0.001076975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.371348,"threshold_uncertainty_score":0.9089802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960435627126247,"score_gpt":0.2228825960181697,"score_spread":0.2032782397469072,"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."}}