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Record W4403911789 · doi:10.1101/2024.10.29.620898

Markers, Mechanisms and Metrics of Biological Aging: A Scoping Review

2024· review· en· W4403911789 on OpenAlexaff
Alison Ziesel, Jennifer Reeves, Αναστασία Μαλλίδου, Lorelei Newton, Ryan E. Rhodes, Jie Zhang, Theone Paterson, Hosna Jabbari

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of VictoriaUniversity of Alberta
Fundersnot available
KeywordsComputational biologyComputer scienceData scienceBiology

Abstract

fetched live from OpenAlex

Abstract Biological aging is a rapidly growing area of research, which entails characterizing the rate of aging independent of an individual’s chronological age. In this review, we analyze the results of biological aging research in 435 papers published in a twelve year window, revealing changing patterns of molecular markers of biological aging use over time, and the development of novel metrics of biological aging. We further identify consistent and discordant research findings, as well as areas of potential future research focusing on questions of measurement with methylation or biomarker-based assessment and other variables relevant to the study of biological age.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0110.010
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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