Energy requirements for racing endurance sled dogs
Bibliographic record
Abstract
Endurance sled dogs have unique dietary energy requirements. At present, there is disparity in the literature regarding energy expenditure and thus energy requirements of these dogs. We sought to further elucidate energy requirements for endurance sled dogs under field conditions. Three sled dog teams completing the 2011 Yukon Quest volunteered to provide diet history. Nutritional content was evaluated and a mock meal was analysed for each team. Race data were obtained from www.yukonquest.com. Dogs were weighed at the start of the race in Whitehorse Yukon (WH), a mid-way checkpoint in Dawson Yukon (DS) and at the finish in Fairbanks Alaska. Data are average value per dog or per dog per d. Linear regression compared average weight loss to average kcal/dog consumed daily. Diets and feeding regimes were similar for all three teams. The average daily energy intake and nutrient content was similar for all diets. During leg one (WH to DS), team 1 gained weight overall, whereas the other two teams experienced weight loss. Linear regression revealed 37 638 kJ/dog/d (8995 kcal/dog/d) was required for weight maintenance. During leg two (DS to Fairbanks Alaska), average weights decreased for all three teams. The extrapolated kcal requirement was approximately 57 734 kJ/dog/d (13 799 kcal/dog/d). The carbohydrate contents of these diets also suggest that presumed fat intake for endurance sled dogs may be slightly less than previously thought. Finally, these data support the concept that dietary energy requirements vary substantially with additional variables such as load pulled, terrain and ambient temperature.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".