{"id":"W4319348988","doi":"10.33612/diss.570000248","title":"Smaller, denser, smarter: improved Strand-seq library preparation, inversion genotyping, and phasing","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Terry Fox Research Institute; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"DNA sequencing; Genetics; DNA; Genotyping; Computational biology; Biology; Computer science; Algorithm; Gene; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001850681,0.001221518,0.001262901,0.00108748,0.0006005808,0.001643295,0.00140507,0.0008295003,0.009764493],"category_scores_gemma":[0.001847991,0.001265563,0.00127093,0.000840947,0.0004563118,0.001024459,0.001395892,0.003671396,0.01060923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819214,"about_ca_system_score_gemma":0.001440368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008554832,"about_ca_topic_score_gemma":0.002720097,"domain_scores_codex":[0.9987512,0.000160972,0.0001062167,0.0004180715,0.0004752843,0.00008823315],"domain_scores_gemma":[0.9990056,0.0002449767,0.0001030641,0.0003029663,0.0002185492,0.0001249603],"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":[0.0002453138,0.0002430262,0.0007765444,0.0005263854,0.00008077803,0.0001197811,0.0002938905,0.00303998,0.8572201,0.003564441,0.01471639,0.1191734],"study_design_scores_gemma":[0.0001351705,0.0004130382,0.002381947,0.00009676841,0.00009028353,0.0004729292,0.00008595351,0.01739151,0.8112222,0.003311481,0.1642345,0.0001641506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06840932,0.001813884,0.8869516,0.001059026,0.0009273863,0.001434095,0.01147589,0.01606345,0.01186533],"genre_scores_gemma":[0.02881812,0.001389186,0.9321349,0.0007555665,0.00009669936,0.001010864,0.01522637,0.002413978,0.01815432],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009764493,"threshold_uncertainty_score":0.03266549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006971116877373549,"score_gpt":0.2281510865222801,"score_spread":0.2211799696449065,"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."}}