Evaluation of methods to reduce exercise-induced heat stress in working Labrador Retrievers
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".