{"id":"W2014694676","doi":"10.1038/srep09687","title":"Massively parallel multiplex DNA sequencing for specimen identification using an Illumina MiSeq platform","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":275,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; 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":"Massive parallel sequencing; Multiplex; DNA sequencing; Massively parallel; Computational biology; Identification (biology); Illumina dye sequencing; Computer science; Biology; DNA; Bioinformatics; Genetics; Parallel computing","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":[0.001703929,0.001180758,0.0009375986,0.001639643,0.0009276939,0.000915114,0.0008869999,0.0005870176,0.005523897],"category_scores_gemma":[0.001734322,0.0005715338,0.0008146394,0.001039124,0.0003809106,0.0006217791,0.0009216667,0.001131435,0.004669318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000397472,"about_ca_system_score_gemma":0.0009992237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009150321,"about_ca_topic_score_gemma":0.003060632,"domain_scores_codex":[0.9980604,0.0003203603,0.0001590198,0.0005711829,0.0007875479,0.0001014689],"domain_scores_gemma":[0.9992235,0.0001749519,0.0001093842,0.0001574375,0.0002404226,0.00009440888],"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":[0.0001660738,0.0001196583,0.001700938,0.00033779,0.000095226,0.0001369266,0.000128719,0.0009465198,0.9228188,0.000801854,0.003764107,0.06898344],"study_design_scores_gemma":[0.0001028674,0.001053366,0.02141634,0.0001183226,0.0002311911,0.001231679,0.0001375034,0.05647354,0.7900721,0.003089351,0.1258422,0.0002315095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06945356,0.001342432,0.9057719,0.0002746441,0.000304271,0.001645299,0.008860309,0.006159832,0.006187755],"genre_scores_gemma":[0.0435034,0.0006615046,0.9418913,0.0002990188,0.00008802898,0.001416099,0.007715445,0.0003493318,0.004075842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005523897,"threshold_uncertainty_score":0.01847923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09356488269976289,"score_gpt":0.2983565634396871,"score_spread":0.2047916807399243,"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."}}