{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004384686,0.001897099,0.001159908,0.001253825,0.0006035115,0.001374706,0.001380224,0.00140863,0.002722145],"category_scores_gemma":[0.0078485,0.001171073,0.001407804,0.0009974175,0.0007351699,0.001077584,0.001080976,0.002324915,0.002335837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316538,"about_ca_system_score_gemma":0.001591962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002255836,"about_ca_topic_score_gemma":0.004330922,"domain_scores_codex":[0.9975945,0.0005500257,0.0001881288,0.00078099,0.000735762,0.000150463],"domain_scores_gemma":[0.997207,0.001600142,0.000338532,0.0003576721,0.0004147558,0.00008186021],"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":[0.0004376337,0.0005710679,0.0122137,0.0004921792,0.0002004233,0.0001304198,0.0002089311,0.08779864,0.8068027,0.005449856,0.001893977,0.08380045],"study_design_scores_gemma":[0.00002588455,0.0002507294,0.002424059,0.00003276882,0.00004812454,0.00008261069,0.00003668405,0.7153147,0.2741776,0.004178061,0.003350641,0.00007818941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02153673,0.0001063966,0.9750295,0.00007315625,0.00003967125,0.0001962844,0.0007373375,0.001900191,0.0003806884],"genre_scores_gemma":[0.1655143,0.0002341287,0.8287033,0.0002418535,0.0000223623,0.001345074,0.001535709,0.0006147886,0.001788524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004384686,"threshold_uncertainty_score":0.02318871,"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."}}