{"id":"W4309842452","doi":"10.3389/fmats.2022.1039247","title":"Using nanomaterials to address SARS-CoV-2 variants through development of vaccines and therapeutics","year":2022,"lang":"en","type":"article","venue":"Frontiers in Materials","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Victoria","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Nanotechnology; 2019-20 coronavirus outbreak; Disease; Risk analysis (engineering); Medicine; Computer science; Virology; Infectious disease (medical specialty); Materials science; Outbreak","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":[0.0009023159,0.0002030531,0.0006857064,0.000277187,0.0001430453,0.00005014496,0.0002222609,0.00007992247,0.00004772938],"category_scores_gemma":[0.00005460602,0.0001891665,0.00003103064,0.000310587,0.00004618526,0.00009717946,0.0003652925,0.00007619031,0.000003708824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054456,"about_ca_system_score_gemma":0.0002961588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001272463,"about_ca_topic_score_gemma":0.000005088117,"domain_scores_codex":[0.9979836,0.0002255808,0.0006479981,0.0003565934,0.0003981783,0.0003881014],"domain_scores_gemma":[0.9994401,0.00002895411,0.0001414502,0.0003013729,0.00006853991,0.00001951976],"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":[0.001074079,0.000122207,0.008441704,0.0002283801,0.0001095372,0.00004116913,0.002807723,0.000001120855,0.985705,0.00002827992,0.0005376804,0.0009030786],"study_design_scores_gemma":[0.001339272,0.0001338283,0.003909215,0.00008697686,0.00003868578,0.00003427297,0.0005747276,0.00001974166,0.9572918,0.0003186071,0.03606496,0.0001878923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959633,0.0004159273,0.0009298104,0.00007859341,0.001552114,0.0008061082,0.00006618135,0.00002919897,0.0001587773],"genre_scores_gemma":[0.9439969,0.00001074084,0.04715838,0.008493934,0.0001217278,0.0001418987,0.000009700275,0.00005290088,0.00001380295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05196637,"threshold_uncertainty_score":0.7713988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09847238530797932,"score_gpt":0.3769221496306131,"score_spread":0.2784497643226338,"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."}}