{"id":"W4200051436","doi":"10.1101/2021.12.20.473401","title":"Arsenal of Nanobodies for Broad-Spectrum Countermeasures against Current and Future SARS-CoV-2 Variants of Concerns","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Ottawa; National Research Council Canada","funders":"National Research Council Canada; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Neutralization; Computational biology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Heterologous; Broad spectrum; Epitope; Virology; Antibody; Biology; Coronavirus disease 2019 (COVID-19); Chemistry; Nanotechnology; Combinatorial chemistry; Medicine; Immunology; Genetics; Infectious disease (medical specialty); Materials science","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":[0.0003979328,0.0004133102,0.0002896222,0.0003157327,0.0001790502,0.0004924749,0.0002532396,0.0003428991,0.001150093],"category_scores_gemma":[0.0001136224,0.0001435169,0.0004145848,0.0001702121,0.0002123317,0.0002610086,0.0003702803,0.000491185,0.0004213698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004353869,"about_ca_system_score_gemma":0.0002413145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003409162,"about_ca_topic_score_gemma":0.0004909224,"domain_scores_codex":[0.9998069,0.00003210309,0.00001505641,0.00004001915,0.00007478992,0.00003105488],"domain_scores_gemma":[0.9999392,0.0000100665,0.00001320212,0.00001221319,0.00001290675,0.00001244663],"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":[0.00005445093,0.00003931387,0.0006755367,0.00007074093,0.00001849429,0.00005060123,0.00002731174,0.0007078258,0.9809333,0.0005135498,0.000229488,0.01667933],"study_design_scores_gemma":[0.00002784296,0.0007530246,0.003649002,0.00003528122,0.00006223375,0.0006519483,0.00005527188,0.003114798,0.9621413,0.0004131743,0.02908289,0.00001314992],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336374,0.01541413,0.03807631,0.0006281108,0.0001010998,0.0001769001,0.0006155492,0.0004155707,0.01093498],"genre_scores_gemma":[0.9687825,0.004983995,0.02009021,0.0003529395,0.00003096172,0.0001036841,0.0008167022,0.00004251621,0.004796582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001150093,"threshold_uncertainty_score":0.00384748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04313352194251244,"score_gpt":0.3136909863718837,"score_spread":0.2705574644293712,"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."}}