{"id":"W2052896743","doi":"10.1038/nmeth.3094","title":"SeqControl: process control for DNA sequencing","year":2014,"lang":"en","type":"article","venue":"Nature Methods","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"DNA sequencing; Computer science; Throughput; Software; Data mining; Quality (philosophy); Process (computing); Computational biology; Set (abstract data type); Multivariate statistics; Biology; Genetics; DNA; Machine learning; Wireless","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009687457,0.0001525935,0.0002265593,0.00002465332,0.0001029731,0.00001663738,0.0001727157,0.0003118345,0.00000243878],"category_scores_gemma":[0.0007837226,0.0001288462,0.0001206115,0.00004534667,0.00003846461,4.498912e-7,0.00002951152,0.0001399152,7.995008e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001051781,"about_ca_system_score_gemma":0.00004978101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000140036,"about_ca_topic_score_gemma":0.000004685466,"domain_scores_codex":[0.9990526,0.0001554962,0.0001529336,0.0003277926,0.00006513691,0.0002460544],"domain_scores_gemma":[0.999316,0.0001239132,0.00007584541,0.0002554984,0.0001725383,0.00005618869],"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":[0.0000878483,0.000008755847,0.0004678608,0.00003065752,0.000102945,1.246102e-7,0.00004652699,0.0001419221,0.9808237,0.000712679,0.0004097754,0.01716718],"study_design_scores_gemma":[0.002177938,0.0004001227,0.001421,0.000008738878,0.00009875357,0.000009085888,0.00007709116,0.002056744,0.7638096,0.007603111,0.2219863,0.0003514646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3176129,0.005497706,0.6713836,0.0005439776,0.0008331962,0.000640757,0.0000387926,0.00001367746,0.003435332],"genre_scores_gemma":[0.8922921,0.00002155068,0.1051417,0.001519096,0.0007002228,0.00008863728,0.00001320306,0.0000241239,0.0001993339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5746792,"threshold_uncertainty_score":0.5254196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224557252622056,"score_gpt":0.3484191359062002,"score_spread":0.3361735633799797,"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."}}