{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008974874,0.004654092,0.002409618,0.00306139,0.001783913,0.003841586,0.004309741,0.002606212,0.05143455],"category_scores_gemma":[0.01683282,0.003207626,0.00216995,0.001996359,0.002457089,0.002296216,0.002272698,0.00643078,0.02631983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002016508,"about_ca_system_score_gemma":0.004258267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004052569,"about_ca_topic_score_gemma":0.007246297,"domain_scores_codex":[0.9919716,0.001294944,0.0007854803,0.00285967,0.002379136,0.00070924],"domain_scores_gemma":[0.9901618,0.004964316,0.0006756525,0.002151261,0.001562727,0.000484317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004116595,0.000637123,0.005943348,0.002867605,0.0008595032,0.0006825135,0.001578406,0.005609918,0.3683984,0.027733,0.3857059,0.1958677],"study_design_scores_gemma":[0.0006464613,0.000273136,0.00355563,0.0003132804,0.0001794028,0.0003752814,0.0001338091,0.08641306,0.6614321,0.02523043,0.2208931,0.0005541721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003896035,0.0004566526,0.6401464,0.000335263,0.0006879964,0.0009232791,0.02209744,0.3258543,0.005602596],"genre_scores_gemma":[0.04634796,0.0006793741,0.7277165,0.003387316,0.0004393113,0.01001814,0.04652634,0.1395857,0.02529933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05143455,"threshold_uncertainty_score":0.1720657,"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."}}