MétaCan
Menu
Back to cohort
Record W6892114756 · doi:10.5061/dryad.4qrfj6qgz

Data for: Caloric restriction extends lifespan in a clonal plant

2024· dataset· en· W6892114756 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCaloric theoryPhotosynthesisLight intensityAdaptation (eye)SenescenceEnergy expenditure

Abstract

fetched live from OpenAlex

When subjected to dietary caloric restriction (CR), individual animals often outlive well-fed conspecifics. Here, we address whether CR also extends lifespan in plants. Whereas caloric intake in animals comes from ingestion, in plants it derives from photosynthesis. Thus, factors that reduce photosynthesis, such as reduced light intensity, can induce CR. In two lab experiments investigating the aquatic macrophyte Lemna minor, we tracked hundreds of individuals longitudinally, with light intensity – and hence, CR – manipulated using neutral-density filters. In both experiments, CR dramatically increased lifespan through a process of temporal scaling. Moreover, the magnitude of lifespan extension accorded with the assumptions that (a) light intensity positively relates to photosynthesis following Michaelis-Menten kinetics, and (b) photosynthesis negatively relates to lifespan via a power law. Our results emphasize that CR-mediated lifespan extension applies to autotrophs as well as heterotrophs, and suggest that variation in light intensity has quantitatively predictable effects on plant aging trajectories.

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)0.005
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.099
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0990.086

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 head0.355
Teacher spread0.267 · 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
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDRYADFrench-language works237,207