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Record W2753715211 · doi:10.3389/fncel.2017.00256

Corrigendum: Divergent Neuroinflammatory Regulation of Microglial TREM Expression and Involvement of NF-κB

2017· erratum· en· W2753715211 on OpenAlexaff
Rosie Owens, Kathleen Grabert, Claire L. Davies, Alessio Alfieri, Jack P. Antel, Luke M. Healy, Barry W. McColl

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2017
Typeerratum
Languageen
FieldImmunology and Microbiology
TopicInflammation biomarkers and pathways
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersBiotechnology and Biological Sciences Research CouncilMedical Research Council
KeywordsNeuroinflammationMicrogliaNF-κBNeuroscienceExpression (computer science)Cell biologyMedicineBiologyImmunologyInflammationComputer scienceSignal transduction

Abstract

fetched live from OpenAlex

Corrigendum: In the published article, there was an error regarding the affiliation[s] for Barry W McColl. As well as having Affiliation 1, they should also have the affiliation UK Dementia Research Institute, University of Edinburgh, Edinburgh Medical School, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.

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)0.003
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 score0.066
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0660.052

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 head0.022
GPT teacher head0.216
Teacher spread0.194 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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