{"id":"W3096760278","doi":"10.1371/journal.pone.0236540","title":"Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Memorial University of Newfoundland; Fisheries and Oceans Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Centro de Excelencia en Geotermia de Los Andes; Genome Atlantic; Canada First Research Excellence Fund; ArcticNet; Atlantic Canada Opportunities Agency; Ocean Frontier Institute; Petroleum Research Newfoundland and Labrador; Fisheries and Oceans Canada; Genome Canada","keywords":"Environmental DNA; Biology; Biome; Sampling (signal processing); Deep sea; Primer (cosmetics); Habitat; Ecology; Fishery; Environmental science; Biodiversity; Ecosystem","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":[0.002534962,0.0008548301,0.0005177512,0.001398061,0.0003896915,0.001213074,0.0006028773,0.0009555889,0.0004925221],"category_scores_gemma":[0.004173027,0.0005160209,0.0005792881,0.0008445398,0.0007224745,0.001109124,0.001112829,0.0007487155,0.0004538686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002551643,"about_ca_system_score_gemma":0.0006277867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070042,"about_ca_topic_score_gemma":0.004227922,"domain_scores_codex":[0.9977255,0.0007056789,0.0002080815,0.0007149685,0.0005386538,0.0001071391],"domain_scores_gemma":[0.9974412,0.001198539,0.0005203082,0.0002003197,0.0005376884,0.0001019052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008165684,0.00003595398,0.01777094,0.0004935314,0.00009713373,0.00008582148,0.0002610857,0.0006611497,0.928791,0.0002099972,0.00009214802,0.05141963],"study_design_scores_gemma":[0.00002058024,0.0007794577,0.07195766,0.0002463562,0.0004346983,0.0009275607,0.0004634518,0.009229572,0.9040235,0.001082855,0.01072358,0.000110587],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6716642,0.007433053,0.314032,0.0008787802,0.0001795774,0.000363003,0.001265668,0.0006497444,0.003533878],"genre_scores_gemma":[0.3868068,0.003934482,0.6053032,0.0007488383,0.0000550223,0.0002180265,0.00129009,0.0001015968,0.001542018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002534962,"threshold_uncertainty_score":0.01340628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04855952037841105,"score_gpt":0.2040383443568122,"score_spread":0.1554788239784011,"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."}}