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ISDN2014_0027: REMOVED: Identification of a unique molecular and functional microglia signature in health and disease

2015· article· en· W2749670473 on OpenAlexaff
Oleg Butovsky, Mark P. Jedrychowski, Craig S. Moore, Ron Cialic, Amanda J. Lanser, Galina Gabriely, Thomas Koeglsperger, Ben Dake, Pauline Wu, Camille Doykan, Zain Fanek, Liping Liu, Zhuoxun Chen, Jeffrey D. Rothstein, Richard M. Ransohoff, Steven P. Gygi, Jack P. Antel, Howard L. Weiner

Bibliographic record

VenueInternational Journal of Developmental Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsIdentification (biology)Signature (topology)MicrogliaDiseaseMedicinePolitical scienceBiologyPathologyImmunologyMathematics

Abstract

fetched live from OpenAlex

This article has been removed: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our‐business/policies/article‐withdrawal ) This meeting abstract has been removed by the Publisher. Due to an administrative error, abstracts that were not presented at the ISDN 2014 meeting were inadvertently published in the meeting's abstract supplement. The Publisher apologizes to the authors and readers for this error.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1490.077

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 head0.033
GPT teacher head0.280
Teacher spread0.247 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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