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Record W4413893105 · doi:10.1016/j.vaccine.2025.127686

Corrigendum to “Comparative study of humoral and cellular immunity against SARS-CoV-2 induced by different COVID-19 vaccine types: Insights into protection against wildtype, Delta and JN.1 omicron strains” [Vaccine 59 (2025) 127270]

2025· erratum· en· W4413893105 on OpenAlexaff
Zahra Hasan, Kiran Iqbal Masood, Shama Qaiser, Akbar Kanji, Fridah Mwendwa, Marta Alenquer, Junaid Iqbal, Filipe Ferreira, Yaqub Wassan, Sadaf Balouch, Shahneel Hussain, Kehkashan Begum, Khalid Feroz, Sajid Muhammad, Ayesha Sadiqa, Mishgan Akhtar, Atif Habib, Syed Muhammad Areeb Ahmed, Afsar Mian, Rabia Hussain, Maria João Amorim, Zulfiqar A Bhutta

Bibliographic record

VenueVaccine · 2025
Typeerratum
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsVirologyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakBiologyImmunityHumoral immunityImmunologyMedicineImmune systemOutbreakDiseaseInfectious disease (medical specialty)

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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.1660.088

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 head0.069
GPT teacher head0.350
Teacher spread0.282 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractno

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