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Record W7071996163

Variation in resource acquisition in a food-caching mammal, the North American red squirrel (Tamiasciurus hudsonicus)

2023· dissertation· en· W7071996163 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsResource Acquisition Is InitializationResource (disambiguation)Variation (astronomy)ForagingTrade-offSelection (genetic algorithm)CacheLitter
DOInot available

Abstract

fetched live from OpenAlex

Life history theory predicts that organisms will allocate their limited energy budgets towards growth, survival, and reproduction in such a way to maximize their fitness. As organisms use energy to fuel biological processes, they must also replenish their energy levels. Given the benefits of increased energetic resources such as enhanced survival and reproductive success, why there remains a high degree of variation observed in resource acquisition remains an important question in ecology and evolution. North American red squirrels (Tamiasciurus hudsonicus) in the southwest Yukon harvest and cache white spruce (Picea glauca) cones in conspicuous larders within exclusively defended individual territories, allowing individual resource acquisition to be quantified and linked to a single individual. The seasonal nature of cone availability, compounded with high interannual variability in cone crop size makes for a fluctuating resource environment for squirrels to manage through foraging and caching efforts. Previous work shows sex-specific selection acting on total cached resources accumulated in autumn, but it was unknown as to what caused variation in cache size. Using field, laboratory, and analytical tools, I investigated hypothesized behavioural, physiological, ecological, and evolutionary drivers of observed variation in resource acquisition. Despite evidence of selection acting on caching effort, I found little evidence that phenotypic variation is attributable to heritable genetic differences, leaving a large environmental component of caching success to explore. Contributing to this remaining variation were age, sex, local resource availability in the environment, days since the birth of the most recent litter (for females, specifically in years of high resource abundance), and how much food an individual has stored off-body prior to the caching season. Interestingly, neither body fat nor body mass were associated with variation in caching success, but correlations from long-term historic data revealed males with greater body mass gain between summer and autumn cached more cones. The research I present in this thesis demonstrates the roles that sex, age, environment, and interactions thereof can play in the acquisition (or not) of a key component of the life history equation: energy.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.151
Teacher spread0.146 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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