{"id":"W2885256052","doi":"10.1111/eva.12694","title":"Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; McGill University","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministerio de Economía y Competitividad; Compute Canada","keywords":"Biology; Barcode; Primer (cosmetics); DNA barcoding; Genetic marker; Evolutionary biology; Molecular marker; Biodiversity; Environmental DNA; Computational biology; Genetics; Ecology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002214782,0.0009233631,0.000764193,0.001519823,0.0004248551,0.001246501,0.0005905916,0.0008259569,0.0008578892],"category_scores_gemma":[0.004085824,0.0005856574,0.0005450514,0.0008232198,0.000468833,0.001444823,0.00110099,0.0007168883,0.0005480478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003599227,"about_ca_system_score_gemma":0.0004935586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009598781,"about_ca_topic_score_gemma":0.003097174,"domain_scores_codex":[0.9981976,0.0004685863,0.0001604629,0.0006611586,0.0004090787,0.0001031842],"domain_scores_gemma":[0.9974198,0.0008556976,0.0007438831,0.0002702947,0.0005251848,0.0001851751],"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":[0.0001076967,0.00005884701,0.009300225,0.0002060362,0.00007053753,0.00005737297,0.0001725412,0.0006777954,0.9562935,0.0001759404,0.0001416103,0.03273788],"study_design_scores_gemma":[0.00002832987,0.00075047,0.06538208,0.000132336,0.000278832,0.0006744103,0.0002130507,0.02338553,0.8995037,0.0008190222,0.008724271,0.0001079784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7237106,0.002380116,0.2680831,0.0004487866,0.0001080096,0.0003111523,0.001143399,0.001768664,0.002046117],"genre_scores_gemma":[0.4663102,0.001449411,0.5284038,0.0003593694,0.00004608441,0.0002722274,0.001102399,0.0001838169,0.001872741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002214782,"threshold_uncertainty_score":0.01171309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0431017646503335,"score_gpt":0.246051187304924,"score_spread":0.2029494226545905,"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."}}