{"id":"W2743058423","doi":"10.1139/cjfas-2017-0114","title":"At the forefront: evidence of the applicability of using environmental DNA to quantify the abundance of fish populations in natural lentic waters with additional sampling considerations","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey; U.S. Fish and Wildlife Service; Utah State University; National Science Foundation","keywords":"Salvelinus; Environmental DNA; Lake ecosystem; Abundance (ecology); Sampling (signal processing); Ecology; Relative species abundance; Environmental science; Arctic char; Population; Biology; Aquatic ecosystem; Ecosystem; Fishery; Biodiversity; Fish <Actinopterygii>; Trout","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":[0.009395661,0.0004464635,0.0002520879,0.001857071,0.0006602917,0.001866539,0.001428799,0.001116475,0.001577261],"category_scores_gemma":[0.03347516,0.0003452793,0.0003930302,0.001807328,0.004980825,0.001853567,0.001319355,0.0006709488,0.0005200702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042977,"about_ca_system_score_gemma":0.0009175062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02112456,"about_ca_topic_score_gemma":0.0297183,"domain_scores_codex":[0.994371,0.002168427,0.0002798029,0.001297939,0.001735678,0.0001472727],"domain_scores_gemma":[0.9627413,0.02021997,0.006746649,0.002554925,0.006952972,0.0007843135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002578103,0.00008277791,0.8771846,0.000709877,0.0004335424,0.000210806,0.001420124,0.0009401128,0.01025796,0.001592975,0.0004176094,0.1064917],"study_design_scores_gemma":[0.00001307098,0.0004705189,0.9873258,0.0002244122,0.00010434,0.000405017,0.0009934072,0.001796025,0.004420531,0.001420552,0.002789345,0.00003706781],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597035,0.01286935,0.01049505,0.001877644,0.00007564069,0.00006482218,0.0004270613,0.00006865548,0.0144183],"genre_scores_gemma":[0.9892664,0.002790486,0.0059856,0.000997232,0.00008778399,0.00002170543,0.0001357288,0.00001673177,0.0006983372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9788755,"threshold_uncertainty_score":0.04968959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08509716912011717,"score_gpt":0.2663708273439775,"score_spread":0.1812736582238603,"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."}}