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Record W2092870988 · doi:10.1111/jeb.12256

The oxidative costs of territory quality and offspring provisioning

2013· article· en· W2092870988 on OpenAlexafffund
Sarah Guindre‐Parker, Sarah Baldo, H. Grant Gilchrist, CA MacDonald, Christopher M. Harris, Oliver P. Love

Bibliographic record

VenueJournal of Evolutionary Biology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton UniversityUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyProvisioningOffspringQuality (philosophy)Evolutionary biologyZoologyEcologyGeneticsPregnancyEngineering

Abstract

fetched live from OpenAlex

The costs of reproduction are an important constraint that shapes the evolution of life histories, yet our understanding of the proximate mechanism(s) leading to such life-history trade-offs is not well understood. Oxidative stress is a strong candidate measure thought to mediate the costs of reproduction, yet empirical evidence supporting that increased reproductive investment leads to oxidative stress is equivocal. We investigated whether territory quality and offspring provisioning increase oxidative stress in male snow buntings (Plectrophenax nivalis) using a repeated sampling design. We show that arrival oxidative stress is not a constraint on territory quality or the number of offspring fledged. Nevertheless, owners of higher-quality territories experienced an oxidative cost, with this cost increasing more rapidly in younger males. Males that provisioned offspring at a high rate also experienced increased oxidative stress. Together, these findings support the potential role of oxidative stress in mediating life-history trade-offs. Future work should consider that reproductive workload is not limited to offspring care, and other activities - including territory defence - may contribute significantly to the costs of reproduction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.125

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 head0.029
GPT teacher head0.279
Teacher spread0.250 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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