{"id":"W4281961566","doi":"10.3390/ijtm2020017","title":"CRISPR-Based Diagnostics for Point-of-Care Viral Detection","year":2022,"lang":"en","type":"article","venue":"International Journal of Translational Medicine","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"CRISPR; Point-of-care testing; Point of care; Molecular diagnostics; Computational biology; Computer science; Multiplex; Risk analysis (engineering); Standardization; Nanotechnology; Biochemical engineering; Data science; Biology; Bioinformatics; Medicine; Engineering; Genetics; Immunology; Materials science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002020821,0.00006979502,0.0001105205,0.0001339164,0.00003835168,0.000003031462,0.0001897702,0.00002829607,0.00007229608],"category_scores_gemma":[0.0001734736,0.00006611876,0.0001180612,0.00004099699,0.0000340701,0.000004114396,0.00001364964,0.00008604563,1.202976e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002111234,"about_ca_system_score_gemma":0.00006621059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003261431,"about_ca_topic_score_gemma":0.000005174891,"domain_scores_codex":[0.9990955,0.00002170792,0.0003383296,0.00008126094,0.0003963119,0.00006687168],"domain_scores_gemma":[0.9991806,0.00009022663,0.0001597996,0.0000558803,0.0004777513,0.00003579938],"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":[0.002567903,0.0001800996,0.003518912,0.00008243195,0.0004029912,0.00001591131,0.0006342522,0.1781935,0.7655905,0.0005243188,0.00238083,0.04590831],"study_design_scores_gemma":[0.01217721,0.00751958,0.01655236,0.0001774158,0.0002630157,0.0003185248,0.001172129,0.007470347,0.766122,0.001920757,0.1859177,0.0003889598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1766798,0.004123496,0.813346,0.003126464,0.002283947,0.0001663373,0.0001381901,0.000003861948,0.0001319126],"genre_scores_gemma":[0.995962,0.00005995138,0.002812528,0.0001898023,0.0008109863,0.00001056446,0.0001287472,0.00001099019,0.00001443498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8192822,"threshold_uncertainty_score":0.2696245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008042945079267658,"score_gpt":0.3257786854037178,"score_spread":0.3177357403244502,"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."}}