{"id":"W2405361473","doi":"10.1371/journal.pone.0155655","title":"More than Anecdotes: Fishers’ Ecological Knowledge Can Fill Gaps for Ecosystem Modeling","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundação de Apoio à Pesquisa do Rio Grande do Norte; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Ecosystem; Theoretical ecology; Ecosystem model; Ecology; Environmental resource management; Computer science; Data science; Geography; Biology; Environmental science; Medicine; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02898396,0.0005734141,0.0004122847,0.003143665,0.002935457,0.004260553,0.0009593479,0.002348649,0.005819877],"category_scores_gemma":[0.1215923,0.0003172256,0.0004030614,0.001640521,0.01618086,0.0186516,0.006831832,0.005070358,0.0004629084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003926824,"about_ca_system_score_gemma":0.004068787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003908733,"about_ca_topic_score_gemma":0.004291069,"domain_scores_codex":[0.9773122,0.0160661,0.001191635,0.0007916858,0.003721504,0.0009168813],"domain_scores_gemma":[0.8060299,0.1628123,0.009275054,0.006445631,0.01303981,0.002397397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002301765,0.0001398408,0.02683281,0.003164455,0.0001032631,0.005206855,0.4769357,0.001009703,0.001329476,0.2527227,0.03980872,0.1925162],"study_design_scores_gemma":[0.00002872108,0.0001527415,0.012345,0.01033841,0.0001351956,0.002953214,0.5394889,0.00176514,0.001095815,0.2188108,0.212724,0.0001620866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2920676,0.02640757,0.07810526,0.3914464,0.004568357,0.0003174298,0.0005089476,0.0001560417,0.2064223],"genre_scores_gemma":[0.9734611,0.007271692,0.006300028,0.009445649,0.0004905888,0.0001021086,0.00008610948,0.00003182201,0.002810898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02898396,"threshold_uncertainty_score":0.1532836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0758735970860324,"score_gpt":0.253161277216945,"score_spread":0.1772876801309126,"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."}}