{"id":"W3141699805","doi":"10.17504/protocols.io.uppevmn","title":"High molecular weight DNA extraction for long read sequencing v1","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"DNA sequencing; Extraction (chemistry); DNA; DNA extraction; Computational biology; Computer science; Chemistry; Biology; Chromatography; Genetics; Polymerase chain reaction; Gene","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":[0.003482666,0.005784726,0.004378384,0.004499828,0.002920456,0.002354515,0.00372325,0.002706859,0.1067721],"category_scores_gemma":[0.007093936,0.003062534,0.003600622,0.003925086,0.001756123,0.00154392,0.002543079,0.007612124,0.1450056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008266703,"about_ca_system_score_gemma":0.002348805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001731067,"about_ca_topic_score_gemma":0.005917334,"domain_scores_codex":[0.9952558,0.0007306748,0.0005421439,0.002228249,0.0006902091,0.0005529601],"domain_scores_gemma":[0.9970298,0.0006478747,0.0001556285,0.001362832,0.0005423044,0.0002615789],"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":[0.001229659,0.0005378434,0.001063517,0.004556129,0.0004543218,0.0007875708,0.0009615274,0.001061496,0.8273214,0.006998821,0.08628161,0.06874624],"study_design_scores_gemma":[0.0006101496,0.0008349485,0.004164614,0.0007481312,0.0006001063,0.001175072,0.0001722145,0.00603475,0.4691105,0.008776402,0.5073039,0.0004692091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01497849,0.002952164,0.8013233,0.0005741445,0.001371331,0.01215303,0.1017386,0.04912051,0.01578845],"genre_scores_gemma":[0.02044995,0.002255675,0.6872475,0.001353097,0.0003771071,0.0155166,0.1984165,0.02400473,0.0503789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1067721,"threshold_uncertainty_score":0.3571883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01904212710740832,"score_gpt":0.2698447617691559,"score_spread":0.2508026346617476,"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."}}