{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353811,0.0001652971,0.0001756247,0.00006600578,0.0003149919,0.0001846267,0.0001679815,0.00009589413,0.000005114558],"category_scores_gemma":[0.0002230656,0.0001574531,0.0001036252,0.0001036887,0.000176691,0.000006816195,0.0001023545,0.00003550691,0.000004175547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007224986,"about_ca_system_score_gemma":0.0002935652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003495621,"about_ca_topic_score_gemma":0.00005862989,"domain_scores_codex":[0.9980866,0.00002458797,0.0005124779,0.0008114643,0.0002527035,0.0003121587],"domain_scores_gemma":[0.9982144,0.000007057958,0.0003786467,0.0007970992,0.0004313399,0.0001714773],"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":[0.0000242462,0.00003387553,0.001536819,0.000009544389,0.00003232485,0.00001178693,0.0004120064,0.001906899,0.9929172,0.00002030954,0.002470146,0.0006248774],"study_design_scores_gemma":[0.0006710203,0.0002022812,0.003227157,0.00001448287,0.00006676454,0.0002311955,0.00209556,0.005516481,0.8992661,0.006770792,0.08137287,0.0005652689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915828,0.0002862322,0.004494814,0.00002757126,0.00274994,0.000484533,0.00001682981,0.000007049317,0.0003501923],"genre_scores_gemma":[0.9861652,0.000004762827,0.01130168,0.0000240684,0.0002967068,0.00003684613,0.0002590904,0.00002403075,0.001887606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09365103,"threshold_uncertainty_score":0.642075,"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."}}