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Record W3177318699 · doi:10.3389/fnetp.2021.712430

Network Physiology in Aging and Frailty: The Grand Challenge of Physiological Reserve in Older Adults

2021· editorial· en· W3177318699 on OpenAlexaff
Román Romero‐Ortuño, Nicolás Martínez‐Velilla, Richard Sutton, Andrea Ungar, Artur Fedorowski, Rose Galvin, Olga Theou, Andrew Davies, Richard B. Reilly, Jurgen A.H.R. Claassen, Aine Marie Kelly, Plamen Ch. Ivanov

Bibliographic record

VenueFrontiers in Network Physiology · 2021
Typeeditorial
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsDalhousie University
FundersEuropean Regional Development FundFundación Bancaria Caixa d'Estalvis i Pensions de BarcelonaNederlandse Organisatie voor Wetenschappelijk OnderzoekScience Foundation IrelandZonMwW. M. Keck Foundation
KeywordsGerontologySpecialtyMedicinePsychiatry

Abstract

fetched live from OpenAlex

In this Specialty Grand Challenge, we outline our vision of the current challenges in the field of Network Physiology as applied to aging and frailty. An expected development in this field for the 21st century is the modelling of the widely used (but still poorly understood) concept of ‘physiological reserve’ in relation to the wide heterogeneity in health status that exists between older adults of the same chronological 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)0.007
metaresearch head score (Gemma)0.020
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 score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0030.002
Research integrity0.0120.029
Insufficient payload (model declined to judge)0.0050.004

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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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

Citations53
Published2021
Admission routes1
Has abstractyes

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