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Record W4414897721 · doi:10.2460/ajvr.25.06.0228

Evaluation of methods to reduce exercise-induced heat stress in working Labrador Retrievers

2025· article· en· W4414897721 on OpenAlexaboutno aff
Robert Gillette, José Matías Alves, Sarah Shull

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2025
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
Fundersnot available
KeywordsHeat stressStress (linguistics)Fight-or-flight response

Abstract

fetched live from OpenAlex

Objective: To compare different methods for cooling dogs in the field following a heat stress event. Methods: In this experimental study, animals were in a conditioning program 5 of 7 days per week. For the test, dogs ran on a treadmill for 30 minutes at 12.5 km/h and a 2.5% incline, with room temperature maintained between 21 and 22 °C and the relative humidity maintained between 64% and 65.6%. A wet bulb globe thermometer was used to evaluate the immediate environment. In the first test, 6 method groups were assessed based on the cooling method implemented. The groups were no cooling, ingestion of ice water, cooling blanket use, use of a fan, or the application of alcohol or water to glabrous skin areas. In this test, rectal temperature, core temperature, heart rate, and respiratory rate were measured. In a second test, a fan was added to the water and alcohol methods from test 1, and the values were compared to the test 1 values. Results: The sample included 12 Labrador Retrievers. In the first test, ingestion of ice water was the only treatment to reduce core temperature. In test 2, tap water + fan and alcohol + fan were more effective from the first evaluation moment postexercise. Both approaches showed similar results to each other. Conclusions: Applying alcohol or water to glabrous areas, in combination with increased airflow, effectively reduced core temperature from a very early stage. Clinical Relevance: This study describes an approach to reduce the risk of heat-related damage.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.314
GPT teacher head0.563
Teacher spread0.249 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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