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Record W4386723880 · doi:10.1038/s41467-023-41366-8

Author Correction: National surveillance data analysis of COVID-19 vaccine uptake in England by women of reproductive age

2023· erratum· en· W4386723880 on OpenAlexaff
Laura A. Magee, Erika Molteni, Vicky Bowyer, Jeffrey N. Bone, Harriet Boulding, Asma Khalil, Hiten D. Mistry, Lucilla Poston, Sergio A. Silverio, Ingrid Wolfe, Emma L. Duncan, Peter von Dadelszen, Debra Bick, Abigail Easter, Julia Fox‐Rushby, Eugene Nelson, Mary Newburn, Paul T. Seed, Marina Soley‐Bori, Aricca D. Van Citters, Sara L. White

Bibliographic record

VenueNature Communications · 2023
Typeerratum
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsUniversity of British Columbia
FundersNational Institute for Health and Care Research
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyMedicineDemographyGeographyInternal medicineSociologyOutbreak

Abstract

fetched live from OpenAlex

The original version of this Article omitted a statement in the Acknowledgements recognising the UK Health Security Agency for providing data. This has been added to the Acknowledgements in both the PDF and HTML versions of the Article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.007
metaresearch head score (Gemma)0.125
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: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.125
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0370.021

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 head0.081
GPT teacher head0.419
Teacher spread0.338 · 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
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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