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Record W2604563812 · doi:10.1002/ecy.1851

Age and years to death disparately influence reproductive allocation in a short‐lived bird

2017· article· en· W2604563812 on OpenAlexafffund
Corey E. Tarwater, Peter Arcese

Bibliographic record

VenueEcology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaNational Science Foundation
KeywordsSenescenceReproductionDemographyBiologyPopulationEcologyTime allocationGerontologyMedicineSociology

Abstract

fetched live from OpenAlex

Theory predicts that reproduction will change as individuals near the end of their lives by either increasing reproductive allocation (terminal allocation hypothesis) or decreasing allocation (senescence hypothesis) toward the end of life. Although senescence has received more support, few studies examine how both age and years to death influence late-life reproduction. We used a 37-yr study of Song Sparrows (Melospiza melodia) to ask how age and years to death influenced reproductive allocation late in life. We observed both senescence and terminal allocation, and that age and years to death interacted to influence individual variation in allocation tactics. In particular, we observed a decline in allocation in older individuals (senescence), but only when comparing individuals with the same number of years to death. Likewise, we observed terminal allocation, but only in young females. Reproductive tactics were most variable in young females, with many exerting high effort but living 1-2 yr, and others exerting low effort annually and living longer. Our results suggest that late-life reproductive tactics are influenced by both chronological age and years to death, and that short- and long-lived females with differing reproductive tactics exist within this population of Song Sparrows.

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)0.000
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 score0.309
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.048
GPT teacher head0.293
Teacher spread0.245 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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