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Record W2946021441 · doi:10.1186/s12979-019-0148-9

Research on immunity and ageing comes of age

2019· editorial· en· W2946021441 on OpenAlexaff
Nan‐ping Weng, Graham Pawelec

Bibliographic record

VenueImmunity & Ageing · 2019
Typeeditorial
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsHealth Sciences North
FundersNational Institutes of Health
KeywordsAgeingImmunityMedicineImmunologyGerontologyImmune system

Abstract

fetched live from OpenAlex

Ageing has a profound detrimental impact on almost all living organisms. Immune systems play a particularly important role in protection against external challenges (pathogens) and internal insults (cancer) but their protective capacity commonly wanes with advancing age. With the rapid increase in the numbers of older people around the world, research in the field of immunity and ageing is becoming increasingly important. This realization, together with recent and ongoing technical advances in analytical capabilities, is facilitating rapid progress towards a better understanding of immunity and ageing and the resulting anticipated improved application of this knowledge to medical treatments in the years ahead.

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)0.007
metaresearch head score (Gemma)0.019
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.001
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0120.022
Insufficient payload (model declined to judge)0.0080.008

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 head0.068
GPT teacher head0.364
Teacher spread0.296 · 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
GenreEditorial

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

Citations5
Published2019
Admission routes1
Has abstractyes

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