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Sensitivity of SARS-CoV-2 B.1.1.7 to mRNA vaccine-elicited antibodies
Dami A. Collier, Anna De Marco, Isabella A.T.M. Ferreira, Bo Meng, Rawlings Datir, Alexandra C. Walls, Steven A. Kemp, Jessica Bassi, Dora Pinto, Chiara Silacci-Fregni, Siro Bianchi, M. Alejandra Tortorici, John E. Bowen, Katja Culap, Stefano Jaconi, Elisabetta Cameroni, Gyorgy Snell, Matteo Samuele Pizzuto, Alessandra Franzetti-Pellanda, Christian Garzoni, Agostino Riva, Stephen Baker, Gordon Dougan, Christoph Hess, Nathalie Kingston, Paul J. Lehner, Paul Lyons, Nicholas J. Matheson, Willem H. Owehand, Caroline Saunders, Charlotte Summers, James Thaventhiran, Mark Toshner, Michael P. Weekes, Ashlea Bucke, Jo Calder, Laura Canna, Jason Domingo, Anne Elmer, Stewart Fuller, Julie Harris, Sarah Hewitt, Jane Kennet, Sherly Jose, Jenny Kourampa, Anne Meadows, Criona O’Brien, Jane Price, Cherry Publico, Rebecca Rastall, Carla M. S. Ribeiro, Jane Rowlands, Valentina Ruffolo, Hugo Tordesillas, Ben Bullman, Benjamin J. 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Bibliographic record
VenueNature · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersNIHR BioResourceDirectorate for Biological SciencesNational Institutes of HealthUniversity Hospitals Coventry and Warwickshire NHS TrustWarwick Medical SchoolQuadram Institute BioscienceOxford University Hospitals NHS Foundation TrustCambridge University HospitalsPublic Health AgencySwansea UniversityPublic Health EnglandQueen's University BelfastUniversity of BirminghamUniversity of WarwickUniversity of OxfordUniversity of SouthamptonWellcome TrustCancer Research UKUniversity College LondonQueen's UniversityUniversity of East AngliaNational Institute for Health and Care ResearchUniversity of CambridgeNIHR Cambridge Biomedical Research CentreAcademy of Medical SciencesFast GrantsNational Institute of Allergy and Infectious DiseasesUniversity Hospital Southampton NHS Foundation TrustKing's College Hospital NHS Foundation TrustUniversity of ExeterNorthumbria UniversityNational Institute of General Medical SciencesPublic Health WalesBurroughs Wellcome FundBill and Melinda Gates FoundationFordham UniversityMedical Research CouncilDepartment of Health and Social CareVir BiotechnologyKing's College LondonUniversity of Portsmouth
KeywordsVirologyAntibodyMonoclonal antibodyNeutralizationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ReceptorBiologyNeutralizing antibodySpike ProteinMessenger RNAVirusCoronavirus disease 2019 (COVID-19)MedicineGeneImmunologyGeneticsInfectious disease (medical specialty)
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.033
GPT teacher head0.374
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations716
Published2021
Admission routes1
Has abstractno