{"id":"W2985175171","doi":"10.1111/ahg.12364","title":"Improved assembly and variant detection of a haploid human genome using single‐molecule, high‐fidelity long reads","year":2019,"lang":"en","type":"article","venue":"Annals of Human Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Human Genome Research Institute; H2020 European Research Council; Howard Hughes Medical Institute; National Institutes of Health; National Science Foundation","keywords":"Genome; Human genome; Structural variation; Sequence assembly; Segmental duplication; Hybrid genome assembly; Computational biology; Biology; Tandem repeat; Fidelity; DNA sequencing; Gene duplication; Sequence (biology); Genetics; Gene; Computer science; Gene family","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":[0.00144392,0.0003703663,0.0005257357,0.0005187078,0.0002967134,0.0007243248,0.0004408378,0.0004545501,0.001301378],"category_scores_gemma":[0.003249625,0.0004639726,0.0005322838,0.0004419991,0.0002099564,0.0004020804,0.0005076639,0.0006859524,0.0007015403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003999525,"about_ca_system_score_gemma":0.0004429914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001634723,"about_ca_topic_score_gemma":0.003373432,"domain_scores_codex":[0.9991584,0.0002146646,0.0000681585,0.0003100132,0.0002000876,0.00004871886],"domain_scores_gemma":[0.9988077,0.0004843723,0.0001737072,0.0002950177,0.0001866194,0.00005261067],"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":[0.0002954938,0.00003181924,0.006972118,0.0002558808,0.00009109165,0.0002184625,0.0003369604,0.005585178,0.9472387,0.001551326,0.0008154703,0.03660735],"study_design_scores_gemma":[0.00005025465,0.0002660688,0.03915552,0.00005233023,0.0001074162,0.001112203,0.0001474215,0.08019391,0.860237,0.001087341,0.0175083,0.00008234115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5202287,0.001020027,0.4676079,0.0002754435,0.00008230085,0.0001340245,0.005506651,0.003265883,0.001879162],"genre_scores_gemma":[0.4632772,0.0003700093,0.5248575,0.00009572061,0.00001962132,0.00007862717,0.00924892,0.0004902804,0.001562146],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001634723,"threshold_uncertainty_score":0.007636309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03786177443747611,"score_gpt":0.2881192018732974,"score_spread":0.2502574274358213,"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."}}