{"id":"W2898173664","doi":"10.1139/cjfas-2018-0148","title":"Predicting species distribution from fishers’ local ecological knowledge: a new alternative for data-poor management","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Alfred P. Sloan Foundation","keywords":"Grouper; Endangered species; Geography; Marine protected area; Fishery; Range (aeronautics); Distribution (mathematics); Ecology; Vulnerable species; Harbour; Environmental resource management; Fish <Actinopterygii>; Biology; Habitat; Environmental science; 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":[0.01480676,0.0009924079,0.001690741,0.00451688,0.00134691,0.003669654,0.002356875,0.001086254,0.00245356],"category_scores_gemma":[0.0467847,0.0007417705,0.001203313,0.00443653,0.001791597,0.007515267,0.002977544,0.001874109,0.0003764638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503951,"about_ca_system_score_gemma":0.002252202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0228516,"about_ca_topic_score_gemma":0.03694866,"domain_scores_codex":[0.9940361,0.003881814,0.000398272,0.0009920168,0.0005330358,0.0001588012],"domain_scores_gemma":[0.9708173,0.01928899,0.002709268,0.004473403,0.002065598,0.0006454861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004207903,0.0004467732,0.4016548,0.0005266374,0.001096765,0.0003733743,0.002372567,0.1416795,0.0009737349,0.02384032,0.006220434,0.4203943],"study_design_scores_gemma":[0.0001388541,0.0003970441,0.08560713,0.0007795684,0.0003261435,0.0003766613,0.002898233,0.7480914,0.001415257,0.1442282,0.01540144,0.0003400869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3254884,0.003812384,0.6350077,0.01720949,0.000183632,0.0002983917,0.004012879,0.001524797,0.0124623],"genre_scores_gemma":[0.8495347,0.0006640706,0.1471842,0.0004526491,0.00009913887,0.0001471236,0.001120732,0.00006027538,0.0007370401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0228516,"threshold_uncertainty_score":0.07830656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648106247516089,"score_gpt":0.2469465805480831,"score_spread":0.2004655180729222,"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."}}