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Record W2613468526 · doi:10.1002/eji.201747038

Murine thymic NK cells: A case of identity

2017· letter· en· W2613468526 on OpenAlexaff
Victoria Male, Hugh J.M. Brady

Bibliographic record

VenueEuropean Journal of Immunology · 2017
Typeletter
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsInstitute of Infection and Immunity
FundersRoyal SocietyCancer Research UKWellcome TrustWellcome
KeywordsBiologyInnate lymphoid cellInterleukin 21Interleukin 12ImmunologyTranscription factorJanus kinase 3Cell biologyCytotoxic T cellInnate immune systemGeneT cellImmune systemGeneticsIn vitro

Abstract

fetched live from OpenAlex

Just over a decade ago, it was established that NK cells in the thymus do not follow precisely the same developmental pathway as conventional NK cells that develop in the bone marrow. Subsequently, it has emerged that NK cells are one branch of a family of innate lymphoid cells (ILCs). ILC1s and thymic NK cells have, however, sufficient similarities such that questions have been raised about how distinctive each cell type is from the other. In this issue of European Journal of Immunology, Gabrielli et al. [Eur. J. Immunol. 2017. 47: 800-805] make a detailed study of the transcription factor requirements of murine thymic NK cells. They provide a valuable insight into the distinctive identity of thymic NK cells with regard to Tbet, Nfil3, Id2, and Ets1. In addition, they clarify the nature of DX5 expression on NK cells and ILC-like cells in the murine thymus.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.006
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.238
Teacher spread0.217 · 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 designCase report
Domainnot available
GenreCommentary

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

Citations3
Published2017
Admission routes1
Has abstractyes

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