{"id":"W2057536116","doi":"10.1111/1755-0998.12236","title":"Next‐generation <scp>DNA</scp> barcoding: using next‐generation sequencing to enhance and accelerate <scp>DNA</scp> barcode capture from single specimens","year":2014,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":238,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Genomics Institute; Guanacaste Dry Forest Conservation Fund; Ontario Genomics; Genome Canada; Forest Conservation Fund; JRS Biodiversity Foundation; Government of Canada; Wege Foundation; National Science Foundation","keywords":"Biology; Sanger sequencing; Barcode; Amplicon; DNA barcoding; Heteroplasmy; DNA sequencing; Pyrosequencing; Massive parallel sequencing; Genetics; Computational biology; DNA sequencer; Deep sequencing; DNA; Mitochondrial DNA; Polymerase chain reaction; Evolutionary biology; Gene; Genome; Computer science","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":[0.001002182,0.00058282,0.000469552,0.000696464,0.0004432854,0.0007925349,0.0005676191,0.001185646,0.001232369],"category_scores_gemma":[0.001195479,0.0003801724,0.0005422553,0.0006022373,0.0007905535,0.0008579322,0.0007596285,0.00127061,0.001216034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004001582,"about_ca_system_score_gemma":0.0006570403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001135145,"about_ca_topic_score_gemma":0.00313984,"domain_scores_codex":[0.9993007,0.0001238501,0.00004043018,0.0001901255,0.0002664893,0.00007841834],"domain_scores_gemma":[0.9994496,0.0001975619,0.0001084828,0.00008658013,0.0001116653,0.00004612203],"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":[0.00004041666,0.00003085406,0.0009778959,0.0001828668,0.00002200454,0.0001262395,0.00009917728,0.0008137017,0.9733718,0.0007492605,0.0005266053,0.02305914],"study_design_scores_gemma":[0.000007256027,0.0001014161,0.00335668,0.00003142627,0.00003056263,0.0004424423,0.000039844,0.01032226,0.9657421,0.0007510683,0.01913365,0.00004134776],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2237852,0.003643068,0.7594346,0.0008976209,0.0003944979,0.0004890027,0.002326325,0.002971958,0.006057847],"genre_scores_gemma":[0.2061595,0.0023736,0.7818171,0.0009351909,0.00008606797,0.0003272061,0.003080363,0.0003573326,0.004863633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001232369,"threshold_uncertainty_score":0.005300105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04204901374841892,"score_gpt":0.2288819342532397,"score_spread":0.1868329205048208,"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."}}