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Record W2146712430 · doi:10.1111/1365-2435.12359

Senescence in duckweed: age‐related declines in survival, reproduction and offspring quality

2014· article· en· W2146712430 on OpenAlexafffund
Patrick Barks, Robert A. Laird

Bibliographic record

VenueFunctional Ecology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOffspringBiologyReproductionSenescenceLongevityEcologyZoologyFecundityDemographyGeneticsPopulation

Abstract

fetched live from OpenAlex

Summary As they grow old, most organisms experience progressive physiological deterioration resulting in declining rates of survival and reproduction – a seemingly maladaptive phenomenon known as senescence. Although senescence is usually defined with respect only to survival and reproduction, a third component of fitness, offspring quality, may also decline with age. Few studies, however, have assessed age‐related changes in offspring quality using measures that truly reflect fitness. In a controlled environment, we tested for age‐related declines in three demographic components of fitness (survival, reproduction and offspring quality) in Lemna minor , a small aquatic plant in the subfamily Lemnoideae (the duckweeds) with a short life span and rapid rate of asexual reproduction. Our primary measure of offspring quality, the intrinsic rate of increase, more closely approximates fitness than measures used in previous studies such as size, life span and total reproductive output. We observed strong age‐related declines in all three components of fitness: old plants had lower rates of survival and reproduction, and produced lower‐quality offspring than younger plants. Theoretical and empirical research on the evolutionary biology of senescence should devote more attention to offspring quality. This often unrecognized component of fitness may change with age – as we have shown in L. minor – and may be shaped by, and feed back into, the same evolutionary forces that give rise to senescence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations60
Published2014
Admission routes2
Has abstractyes

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