{"id":"W2020200409","doi":"10.1371/journal.pone.0017497","title":"Environmental Barcoding: A Next-Generation Sequencing Approach for Biomonitoring Applications Using River Benthos","year":2011,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":597,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Guelph","funders":"Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"DNA barcoding; Pyrosequencing; Biomonitoring; Biology; Biodiversity; Environmental DNA; DNA sequencing; Bioindicator; Barcode; Identification (biology); Taxon; Evolutionary biology; Ecology; Computational biology; Computer science; DNA; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001177552,0.0001781014,0.0001574775,0.00003887877,0.0005490347,0.00002312147,0.0002228905,0.00007042108,0.0003001835],"category_scores_gemma":[0.000007437921,0.0001982989,0.00006402699,0.00009219911,0.0003079224,0.00038935,0.0003061399,0.00008270049,0.0001612088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007872253,"about_ca_system_score_gemma":0.000002202245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001368076,"about_ca_topic_score_gemma":0.000001801014,"domain_scores_codex":[0.9986964,0.00002471706,0.0001839498,0.0004569922,0.0003333043,0.0003045682],"domain_scores_gemma":[0.9995426,0.00001683162,0.00009455926,0.0002481847,0.0000025534,0.00009526905],"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":[0.000006739347,0.0006443063,0.2031684,0.00001765458,0.00007114725,5.77813e-7,0.001328988,0.00009667074,0.7940615,0.000009682788,0.00002111917,0.00057324],"study_design_scores_gemma":[0.0006178394,0.0001441771,0.05803144,0.00002564434,0.0004223801,0.000003467839,0.00201587,0.03009779,0.9077101,0.0001083582,0.0001579104,0.0006649581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815119,0.0000680576,0.01604743,0.000006998619,0.00002446984,0.00102454,0.000054766,0.00005174605,0.001210078],"genre_scores_gemma":[0.6843595,0.00004353033,0.3151845,0.00003261382,0.00009322887,0.0001530846,0.00003117511,0.00001609867,0.00008628266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2991371,"threshold_uncertainty_score":0.8086396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2569149886628772,"score_gpt":0.2368372294095626,"score_spread":0.02007775925331459,"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."}}