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Record W3150460481 · doi:10.21203/rs.3.rs-226857/v2

Two doses of SARS-CoV-2 vaccination induce more robust immune responses to emerging SARS-CoV-2 variants of concern than does natural infection.

2021· preprint· en· W3150460481 on OpenAlexfundno aff
Donal Skelly, Adam Harding, Javier Gilbert‐Jaramillo, Michael L. Knight, Stéphanie Longet, Anthony Brown, Sandra Adele, Emily Adland, Helen Brown, Lizzie Stafford, Síle A. Johnson, Ali Amini, Tiong Kit Tan, Lisa Schimanski, Kuan-Ying A. Huang, Pramila Rijal, PITCH Study Group, CMORE PHOSP-C Group, John Frater, Philip Goulder, Christopher P. Conlon, Katie Jeffery, Christina Dold, Andrew J. Pollard, Alex Sigal, Túlio de Oliveira, Alain Townsend, Paul Klenerman, Susanna Dunachie, Eleanor Barnes, Miles W. Carroll, William James

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersNIHR Oxford Biomedical Research CentreMedical Research CouncilNational Institutes of HealthHamilton Health Sciences FoundationUniversity of OxfordPublic Health EnglandDepartment of Health and Social CareBritish Heart FoundationNational Institute for Health and Care ResearchNational Institute for Health Research Health Protection Research UnitUK Coronavirus Immunology ConsortiumBiotechnology and Biological Sciences Research CouncilHuo Family FoundationUK Research and Innovation
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)VaccinationImmune systemVirology2019-20 coronavirus outbreakMedicineImmunologyBetacoronavirusOutbreakInfectious disease (medical specialty)DiseaseInternal medicine

Abstract

fetched live from OpenAlex
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 imitation

Not 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.133
GPT teacher head0.464
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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 designObservational
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

Citations24
Published2021
Admission routes1
Has abstractno

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