MétaCan
Menu
Back to cohort
Record W4402452071 · doi:10.1007/978-3-031-68513-2_12

Infection, Neuroinflammation and Interventions for Healthy Brain and Longevity

2024· book-chapter· en· W4402452071 on OpenAlexafffund
Tamàs Fülöp, Charles Ramassamy, Guy Lacombe, Éric Frost, Alan A. Cohen, Serafim Rodrigues, Mathieu Desroches, Katsuiku Hirokawa, Benoît Laurent, Jacek M. Witkowski

Bibliographic record

VenueHealthy ageing and longevity · 2024
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanInstitut National de la Recherche ScientifiqueUniversité de SherbrookeActive Aging CanadaHealth and Social Services Centre University Institute of Geriatrics of Sherbrooke
FundersAgencia Estatal de InvestigaciónFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchMinisterio de Ciencia e InnovaciónBasque Center for Applied MathematicsEuropean Regional Development FundEusko JaurlaritzaUniversité de Sherbrooke
KeywordsNeuroinflammationLongevityPsychological interventionMedicinePsychologyGerontologyPsychiatryInternal medicineInflammation

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.006

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 head0.089
GPT teacher head0.336
Teacher spread0.248 · 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
Published2024
Admission routes2
Has abstractno

Explore more

Same venueHealthy ageing and longevitySame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207