{"id":"W2051884088","doi":"10.1111/j.1755-0998.2009.02630.x","title":"Express barcodes: racing from specimen to identification","year":2009,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Genomics Institute; Genome Canada","keywords":"Barcode; Biology; DNA extraction; Computational biology; Polymerase chain reaction; Identification (biology); DNA barcoding; Workflow; DNA; Microfluidics; DNA sequencing; Genetics; Computer science; Database; Nanotechnology; Evolutionary biology; 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":[0.004368467,0.0008375356,0.0008583316,0.001355135,0.001217809,0.002066213,0.002222396,0.001653627,0.01002569],"category_scores_gemma":[0.008856588,0.001231036,0.0005806429,0.001005215,0.001741372,0.003514274,0.002684008,0.002807682,0.01013267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006144029,"about_ca_system_score_gemma":0.001620991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008131066,"about_ca_topic_score_gemma":0.001964474,"domain_scores_codex":[0.9964017,0.0006973968,0.0003186508,0.0006745903,0.001653566,0.0002540656],"domain_scores_gemma":[0.9945849,0.001400494,0.001022581,0.001650596,0.0009941172,0.0003472589],"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":[0.0007561093,0.0001959359,0.00420774,0.0006728982,0.00006931776,0.0003699114,0.0008760409,0.001207626,0.6436397,0.01913862,0.02625254,0.3026136],"study_design_scores_gemma":[0.00003622342,0.00025701,0.002718746,0.0001856342,0.00005167462,0.0009984982,0.0001691401,0.005683317,0.7420964,0.004065462,0.2435723,0.0001657331],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03615967,0.001270826,0.9330297,0.002035621,0.0009862086,0.0008823689,0.001662866,0.01316139,0.0108113],"genre_scores_gemma":[0.05009808,0.001204917,0.9140095,0.001321249,0.0002327403,0.0009172948,0.003082329,0.002586872,0.02654705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01002569,"threshold_uncertainty_score":0.03353924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006065495508507684,"score_gpt":0.2042042126403168,"score_spread":0.1981387171318091,"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."}}