{"id":"W2171100005","doi":"10.1111/mec.13428","title":"Next‐generation monitoring of aquatic biodiversity using environmental <scp>DNA</scp> metabarcoding","year":2015,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1271,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"Office National de l’Eau et des Milieux Aquatiques; Électricité de France; Stichting Toegepast Onderzoek Waterbeheer; Lundbeckfonden","keywords":"Biology; Environmental DNA; Biodiversity; Ecology; Computational biology; Evolutionary biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001793256,0.0003327175,0.0003403182,0.002225821,0.0003173469,0.0006200612,0.0005824535,0.0007005135,0.0008386069],"category_scores_gemma":[0.002114504,0.0001695609,0.0003946851,0.001428383,0.0003400208,0.0008502359,0.0006976185,0.000453324,0.0004098506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003336612,"about_ca_system_score_gemma":0.0004758436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001783383,"about_ca_topic_score_gemma":0.005122181,"domain_scores_codex":[0.9988117,0.0003923606,0.000068313,0.0003888471,0.000263001,0.0000757032],"domain_scores_gemma":[0.9980478,0.0004611793,0.0007626648,0.0001628169,0.0004592836,0.0001062698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002841885,0.0001410902,0.3735856,0.0007820253,0.0003992993,0.0003979349,0.0004533491,0.003477324,0.3890567,0.002425133,0.001035668,0.2279617],"study_design_scores_gemma":[0.00002927535,0.0007010716,0.6897938,0.000307021,0.0006296028,0.001993981,0.0008402128,0.02128618,0.2410726,0.003880504,0.03933519,0.0001306685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.768355,0.006948403,0.2056131,0.0007630909,0.0001203661,0.0002497207,0.007183833,0.0005592633,0.01020731],"genre_scores_gemma":[0.6785458,0.002475161,0.3101124,0.0007623156,0.00006290213,0.000192033,0.005177501,0.00005017607,0.002621742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002225821,"threshold_uncertainty_score":0.009483755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05715350533372067,"score_gpt":0.2319246818130597,"score_spread":0.174771176479339,"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."}}