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Record W4306810879 · doi:10.1038/s41586-022-05349-x

Evolution of immune genes is associated with the Black Death

2022· article· en· W4306810879 on OpenAlexafffund
Jennifer Klunk, Tauras P. Vilgalys, Christian E. Demeure, Xiaoheng Cheng, Mari Shiratori, Julien Madej, Rémi Beau, Derek Elli, Maria I. Patino, Rebecca Redfern, Sharon N. DeWitte, Julia A. Gamble, Jesper L. Boldsen, Ann G. Carmichael, Nükhet Varlık, Katherine Eaton, Jean‐Christophe Grenier, G. Brian Golding, Alison Devault, Jean‐Marie Rouillard, Vania Yotova, Renata Sindeaux, Chun Ye, Matin Bikaran, Anne Dumaine, Jessica F. Brinkworth, Dominique Missiakas, Guy A. Rouleau, Matthias Steinrücken, Javier Pizarro‐Cerdá, Hendrik N. Poinar, Luis B. Barreiro

Bibliographic record

VenueNature · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsMcMaster University Medical CentreCanadian Institute for Advanced ResearchMcGill UniversityMontreal Neurological Institute and HospitalUniversité de MontréalUniversity of ManitobaMontreal Heart InstituteCentre Hospitalier Universitaire Sainte-JustineMcMaster University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesSocial Sciences and Humanities Research Council of CanadaNational Institute of Allergy and Infectious DiseasesMcMaster UniversityNational Institutes of HealthUniversity of ChicagoNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchWenner-Gren Foundation
KeywordsGeneBiologyImmune systemEvolutionary biologyGenetics

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.257
Teacher spread0.250 · 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

Citations248
Published2022
Admission routes2
Has abstractno

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