{"id":"W4317565775","doi":"10.1139/cjfas-2022-0110","title":"Identifying mature fish aggregation areas during spawning season by combining catch declarations and scientific survey data","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Biomass (ecology); Bay; Sampling (signal processing); Whiting; Fishery; Habitat; Fish <Actinopterygii>; Environmental science; Ecology; Geography; Biology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001599565,0.0004158975,0.0003947481,0.001938537,0.0003244232,0.0008621759,0.0006937572,0.0004123942,0.0009119014],"category_scores_gemma":[0.003563691,0.0003978938,0.0006331333,0.001928794,0.0003406342,0.00082692,0.001036364,0.0004533268,0.0002032451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007153572,"about_ca_system_score_gemma":0.001271403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05811007,"about_ca_topic_score_gemma":0.1129295,"domain_scores_codex":[0.9995461,0.0001091354,0.00003966576,0.0001895969,0.00006079516,0.00005463496],"domain_scores_gemma":[0.9983748,0.0006220662,0.0005109414,0.0002141069,0.0001847337,0.00009338956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007125556,0.00006872823,0.7098163,0.00007682353,0.0002105538,0.00015414,0.0003083739,0.2323337,0.001753666,0.001901288,0.0006709988,0.05263418],"study_design_scores_gemma":[0.000007597631,0.00003387193,0.2109583,0.00002431592,0.00004694917,0.00006249913,0.0001702889,0.7846373,0.000663973,0.002297115,0.001072805,0.00002499415],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7741717,0.0002671122,0.2194309,0.0002353252,0.00002127549,0.00008249599,0.002790418,0.000597458,0.002403297],"genre_scores_gemma":[0.9509609,0.0001007038,0.04611118,0.0000168975,0.00001217615,0.00004205793,0.001949313,0.00003172471,0.0007750454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9418899,"threshold_uncertainty_score":0.1155437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06813140901376274,"score_gpt":0.2785137426613458,"score_spread":0.210382333647583,"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."}}