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Record W4291624945 · doi:10.1038/s41592-022-01578-0

VDJdb in the pandemic era: a compendium of T cell receptors specific for SARS-CoV-2

2022· letter· en· W4291624945 on OpenAlexfundno aff
Mikhail Goncharov, Dmitry Bagaev, D. P. Shcherbinin, Ivan V. Zvyagin, Dmitriy A. Bolotin, Paul G. Thomas, Anastasia A. Minervina, Mikhail V. Pogorelyy, Kristin Ladell, James E. McLaren, David A. Price, Thi H. O. Nguyen, Louise C. Rowntree, E. Bridie Clemens, Katherine Kedzierska, Garry Dolton, Cristina Rius, Andrew K. Sewell, Jerome Samir, Fabio Luciani, Ksenia V. Zornikova, Alexandra Khmelevskaya, Saveliy A. Sheetikov, Grigory A. Efimov, Dmitriy M. Chudakov, Mikhail Shugay

Bibliographic record

VenueNature Methods · 2022
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersInstitute of Infection and ImmunityRussian Academy of SciencesNational Health and Medical Research CouncilUniversity of New South WalesPirogov Russian National Research Medical UniversityMinistry of Science and Higher Education of the Russian FederationSkolkovo Institute of Science and TechnologyCardiff UniversityMedical Research CouncilRussian Science FoundationWellcome TrustTechnische Universiteit EindhovenSt. Jude Children's Research Hospital
KeywordsCompendiumPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakVirologyReceptorBiologyComputational biologyMedicineGeneticsInfectious disease (medical specialty)HistoryDisease

Abstract

fetched live from OpenAlex

To the Editor — Here, we report the VDJdb database ( https://vdjdb.cdr3.net ) update prepared between 2019 and 2022, marked by the emergence of SARS-CoV-2, the causative agent of COVID-19.

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)0.006
metaresearch head score (Gemma)0.013
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: Dataset · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0240.022
Insufficient payload (model declined to judge)0.0020.003

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 head0.093
GPT teacher head0.445
Teacher spread0.352 · 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
GenreDataset

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

Citations230
Published2022
Admission routes1
Has abstractno

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