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Record W4206666463 · doi:10.1038/s41588-021-00998-6

Author Correction: Gain-of-function variants in SYK cause immune dysregulation and systemic inflammation in humans and mice

2022· erratum· en· W4206666463 on OpenAlexaff
Lin Wang, Dominik Aschenbrenner, Zhiyang Zeng, Xiya Cao, Daniel Mayr, Meera Mehta, Melania Capitani, Neil Warner, Jie Pan, Liren Wang, Li Qi, Tao Zuo, Sarit Cohen‐Kedar, Jiawei Lu, Rico Chandra Ardy, Daniel J. Mulder, Dilan Dissanayake, Kaiyue Peng, Zhiheng Huang, Xiaoqin Li, Yuesheng Wang, Xiaobing Wang, Shuchao Li, Samuel J. Bullers, Anís N. Gammage, Klaus Warnatz, Ana‐Iris Schiefer, Gergely Kriván, Vera Goda, Walter H.A. Kahr, Mathieu Lemaire, Helen Griffin, Sophie Hambleton, Chien-Yi Lu, Iram Siddiqui, Michael G. Surette, Daniel Kotlarz, Karin R. Engelhardt, Robert Rottapel, Hélène Decaluwe, Ronald M. Laxer, Michele Proietti, Suzanne Elcombe, Conghui Guo, Bodo Grimbacher, Iris Dotan, Siew C. Ng, Spencer A. Freeman, Scott B. Snapper, Christoph Klein, Kaan Boztuǧ, Ying Huang, Dali Li, Holm H. Uhlig, Aleixo M. Muise

Bibliographic record

VenueNature Genetics · 2022
Typeerratum
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsOntario Institute for Cancer ResearchUniversité de MontréalPrincess Margaret Cancer CentreSt. Michael's HospitalHospital for Sick ChildrenCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityUniversity Health NetworkUniversity of TorontoSickKids Foundation
FundersWellcome Trust
KeywordsSystemic inflammationBiologyInflammationImmune systemImmune dysregulationSykGain of functionFunction (biology)ImmunologyGeneticsMutationGeneSignal transduction

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)0.033
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 score0.045
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0450.023

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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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

Citations2
Published2022
Admission routes1
Has abstractno

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