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Record W2095839182 · doi:10.1093/conphys/cou021

Short-term episodes of imposed fasting have a greater effect on young northern fur seals (Callorhinus ursinus) in summer than in winter

2014· article· en· W2095839182 on OpenAlexaff
David A. S. Rosen, Beth L. Volpov, Andrew W. Trites

Bibliographic record

VenueConservation Physiology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersNational Oceanic and Atmospheric Administration
KeywordsBiologyFur sealTerm (time)ZoologyEcology

Abstract

fetched live from OpenAlex

An unexpected shortage of food may affect wildlife in a different way depending on the time of year when it occurs. We imposed 48 h fasts on six female northern fur seals (Callorhinus ursinus; ages 6-24 months) to identify times of year when they might be particularly sensitive to interruptions in food supply. We monitored changes in their resting metabolic rates and their metabolic response to thermal challenges, and also examined potential bioenergetic causes for seasonal differences in body mass loss. The pre-fast metabolism of the fur seals while in ambient air or submerged in water at 4°C was higher during summer (June to Sepember) than winter (November to March), and submergence did not significantly increase metabolism, indicating a lack of additional thermoregulatory costs. There was no evidence of metabolic depression following the fasting periods, nor did metabolism increase during the post-fast thermal challenge, suggesting that mass loss did not negatively impact thermoregulatory capacity. However, the fur seals lost mass at greater rates while fasting during the summer months, when metabolism is normally high to facilitate faster growth rates (which would ordinarily have been supported by higher food intake levels). Our findings suggest that summer is a more critical time of year than winter for young northern fur seals to obtain adequate nutrition.

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 score0.049
Threshold uncertainty score0.967

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 head0.025
GPT teacher head0.259
Teacher spread0.234 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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