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Record W6948405373 · doi:10.5061/dryad.t76hdr87h

Data from: Effects of home range size and burrow fidelity on survival and reproduction in eastern chipmunks (Tamias striatus) across different environmental contexts

2024· dataset· en· W6948405373 on OpenAlexaffabout

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsUniversité du Québec à MontréalBishop's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsHome rangeBurrowFidelityReproductionPopulationHabitatPhilopatryContext (archaeology)

Abstract

fetched live from OpenAlex

Survival and reproductive success are greatly influenced by individual habitat selection and space use, which can both differ across spatial scales. To better understand habitat-fitness relationships of animals, it is essential to study space use at multiple spatial scales. Here we used 13 years of capture-mark-recapture and burrow location data to investigate how two different aspects of space use influence the survival and female reproduction in a wild population of eastern chipmunks (Tamias striatus) in southern Québec. We quantified home range size and site fidelity in a population experiencing massive inter-annual variations in food availability due to the masting of American beech trees (Fagus grandifolia). We found that site fidelity increased the probability of reproduction but that this effect was strongly dependent on the context of beech seed production: probability of reproduction was higher for females that were faithful from a mast year to the following non-mast year. Site fidelity was not related to survival and we found no significant effect of home range size on either fitness trait. Our results indicate that different aspects of space use do not affect fitness traits in the same way in our study system and highlight the importance of examining multiple spatial scales in related analyses.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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.

Study designNot applicable
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

Citations0
Published2024
Admission routes2
Has abstractyes

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