{"id":"W3155065536","doi":"10.3389/fmars.2021.646174","title":"How Can Information Contribute to Management? Value of Information (VOI) Analysis on Indian Ocean Striped Marlin (Kajikia audax)","year":2021,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"Shanghai Ocean University; Ministry of Agriculture and Rural Affairs of the People's Republic of China; University of British Columbia","keywords":"Predictability; Fisheries management; Fishery; Yield (engineering); Value (mathematics); Statistics; Environmental resource management; Environmental science; Mathematics; Biology; Fishing","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":[0.0009821727,0.0001433123,0.0002500624,0.0009246708,0.0001542791,0.0003029551,0.0007013234,0.00005209379,0.0004558989],"category_scores_gemma":[0.0003794251,0.0001420936,0.00006696781,0.006467141,0.0003047458,0.002089191,0.001220841,0.0001753026,0.00002410822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004650749,"about_ca_system_score_gemma":0.0000768409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006257817,"about_ca_topic_score_gemma":0.0002108911,"domain_scores_codex":[0.997642,0.00005809786,0.0003790568,0.0002748003,0.001154504,0.0004915809],"domain_scores_gemma":[0.9990535,0.0000176698,0.000148852,0.0004942227,0.00007814045,0.0002075668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005606172,0.00003808069,0.7691089,0.00002985584,0.00003902648,0.00001095722,0.0007353519,0.002803638,0.00004465894,0.0005362825,0.004071788,0.2225254],"study_design_scores_gemma":[0.0007601224,0.0001366437,0.9345827,0.00001095247,0.00004420499,0.000001308469,0.003059131,0.01124073,0.001862976,0.0007887582,0.04720196,0.000310542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7332998,0.000001503764,0.0312435,0.00402505,0.0006892221,0.001014891,0.00009438721,0.00004643943,0.2295852],"genre_scores_gemma":[0.9525908,0.00003402182,0.04340553,0.0007895756,0.0000134216,0.00001989426,0.0001791986,0.000005152341,0.002962427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2266228,"threshold_uncertainty_score":0.579441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003786674537250965,"score_gpt":0.2074606757787446,"score_spread":0.2036740012414936,"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."}}