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Record W2316459572 · doi:10.1139/cjz-2013-0175

Warm-season heat stress in moose (<i>Alces</i><i>alces</i>)

2013· article· en· W2316459572 on OpenAlexvenueno aff
Nicholas P. McCann, Ron Moen, Thomas R. Harris

Bibliographic record

VenueCanadian Journal of Zoology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric Administration
KeywordsHeat stressBiologyRange (aeronautics)EcologyAnimal scienceEnvironmental science

Abstract

fetched live from OpenAlex

Understanding how moose (Alces alces (L., 1758)) are affected by temperature is critical for determining why populations have recently declined at the southern extent of their North American range. Warm-season heat-stress thresholds of 14 and 20 °C are commonly used to study moose, but the variable response of free-ranging moose to temperatures above these thresholds suggests that moose may be more tolerant to heat. We studied zoo-managed cow and bull moose to identify factors that influence warm-season heat stress. We found clear behavioral and physiological responses to thermal conditions. Moose selected shade, indicating solar radiation affects heat stress. Temperature and wind influenced respiration rates. Heat-stress thresholds for moose occurred at 17 °C when bedded under calm conditions and 24 °C when bedded under wind, demonstrating that the onset of heat stress is sensitive to wind and incorporating wind velocity into analyses would improve investigations of heat stress. Moose showing symptoms of gastrointestinal illness selected wind at lower temperatures than healthy moose, suggesting the effects of climate change will be compounded for health-compromised moose. Determining why moose are declining at the southern extent of their range may require understanding how temperature interacts with wind, moose health, and other factors.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.999

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)0.0020.000

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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 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

Citations71
Published2013
Admission routes1
Has abstractyes

Explore more

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