Water requirements of canine athletes during multi-day exercise
Bibliographic record
Abstract
BACKGROUND: Exercise increases water requirements, but there is little information regarding water loss in dogs performing multi-day exercise OBJECTIVES: Quantify the daily water turnover of working dogs during multi-day exercise and establish the suitability of SC administration of tracer to determine water turnover. ANIMALS: Fifteen privately owned Labrador retrievers trained for explosive detection duties and 16 privately owned Alaskan Huskies conditioned for mid-distance racing. METHODS: O/kg body weight by IV infusion, gavage, or SC injection before the start of a multi-day exercise challenge. Explosive detection dogs conducted 5 days of simulated off-leash explosive detection activity. Alaskan sled dogs completed a mid-distance stage race totaling 222 km in 2 days. Total body water (TBW) and daily water turnover were calculated using both indicator dilution and elimination regression techniques. RESULTS: Total body water (% of body weight) varied from 60% ± 8.6% in minimally conditioned Labrador retrievers to 74% ± 4.5% in highly conditioned Labrador retrievers. Daily water turnover was as high as 45% of TBW during exercise in cold conditions. There was no effect of sex or speed on daily water turnover. There was good agreement between results calculated using the indicator dilution approach and those calculated using a semilog linear regression approach when indicator isotope was administered IV or SC. CONCLUSIONS AND CLINICAL IMPORTANCE: Water requirements are influenced primarily by the amount of work done. SC administration of isotope-labeled water offers a simple and accurate alternative method for metabolic studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".