{"id":"W3182375185","doi":"10.1128/msystems.00552-21","title":"Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing","year":2021,"lang":"en","type":"article","venue":"mSystems","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Bristol-Myers Squibb Canada; Genentech; Astellas Pharma; Mirati Therapeutics; Ontario Genomics; Array BioPharma; MorphoSys; Celgene; Symphogen; Bristol-Myers Squibb; AstraZeneca; Princess Margaret Cancer Foundation; Amgen; Pfizer; Agios Pharmaceuticals; GlaxoSmithKline","keywords":"Shotgun sequencing; Shotgun; Biology; Computational biology; Deep sequencing; Context (archaeology); Metagenomics; DNA sequencing; Genetics; Gene; Genome","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":[0.0001233447,0.0001175812,0.0001695665,0.00003130131,0.0001020362,0.00004611159,0.0001008202,0.00009355182,0.00002221686],"category_scores_gemma":[0.00002073789,0.0001192372,0.00008265299,0.00008708501,0.00001436244,0.000006321307,0.00006363591,0.00005202067,0.0000213563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002881572,"about_ca_system_score_gemma":0.0002276057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002168621,"about_ca_topic_score_gemma":0.00004407462,"domain_scores_codex":[0.9990619,0.00008182257,0.0002146893,0.0003345115,0.00006542981,0.0002416553],"domain_scores_gemma":[0.9995201,0.00001858028,0.00005588895,0.0002173399,0.0000769253,0.0001111867],"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":[0.00006796388,0.00002512354,0.001738542,0.0001636308,0.00003779823,0.000006935722,0.0001820565,0.000185377,0.9882132,0.001027299,0.008312082,0.00003992824],"study_design_scores_gemma":[0.001305371,0.0008008115,0.001635733,0.0002691521,0.00002820129,0.00009387498,0.001078971,0.001302391,0.8554577,0.00009277301,0.1375276,0.0004074785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971406,0.001711805,0.02084941,0.0009040446,0.0006626283,0.0007541472,0.000433946,0.00004829923,0.003229701],"genre_scores_gemma":[0.9825493,0.00002330791,0.01322179,0.00111263,0.0003885969,0.00006645296,0.001346015,0.0000313239,0.00126058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1327556,"threshold_uncertainty_score":0.4862352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566419085240844,"score_gpt":0.2891813582569878,"score_spread":0.2635171674045793,"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."}}