{"id":"W3207455988","doi":"10.1016/j.jviromet.2021.114339","title":"Optimization of magnetic bead-based nucleic acid extraction for SARS-CoV-2 testing using readily available reagents","year":2021,"lang":"en","type":"article","venue":"Journal of Virological Methods","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Provincial Health Services Authority; BC Centre for Disease Control; BC Cancer Agency; University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Magnetic bead; Nucleic acid; Biology; Reagent; Extraction (chemistry); Bead; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Chromatography; Virology; Computational biology; Materials science; Chemistry; Biochemistry; Pathology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002185763,0.0001637535,0.0005776599,0.0002144713,0.0001082239,0.00002872825,0.00008382938,0.000218444,0.00009864209],"category_scores_gemma":[0.01318031,0.0001345469,0.0002428824,0.00054686,0.00007076824,0.0001406436,0.00002606059,0.0003940109,0.000002355902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082113,"about_ca_system_score_gemma":0.0001958346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007638465,"about_ca_topic_score_gemma":4.786264e-7,"domain_scores_codex":[0.9978022,0.000521302,0.0009195961,0.0002336057,0.0002832579,0.0002400582],"domain_scores_gemma":[0.9965985,0.00134862,0.0009026446,0.0001849285,0.0009008418,0.00006443344],"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":[0.0004662924,0.0002639742,0.001237715,0.00008492349,0.00003832256,0.00008545318,0.00001477214,0.002607759,0.972309,0.000003698165,0.00004831733,0.02283976],"study_design_scores_gemma":[0.001549056,0.001606704,0.001033832,0.0001985136,0.0002991194,0.001057094,0.00002799476,0.1928146,0.8006311,0.0001545204,0.0005312935,0.00009615742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5534872,0.000427704,0.4451435,0.00009194843,0.0002714587,0.0001423842,0.000001601082,0.0000243707,0.0004097961],"genre_scores_gemma":[0.1645177,0.000009005242,0.8342036,0.001028287,0.0001958938,0.000003292339,0.000001512438,0.00002388219,0.00001679808],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3890601,"threshold_uncertainty_score":0.9951321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2541336991762186,"score_gpt":0.4458062475803536,"score_spread":0.1916725484041349,"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."}}