881 Assessment of current musher practices across the sled dog industry
Bibliographic record
Abstract
A great deal of variation exists within the sled dog industry as a result of differences in training regimes, nutritional programs, and environmental management of these high-demand canine athletes. It is likely that these differences result in variable health and well-being. This study aimed to assess the current habits and practices in the sled dog industry using an online questionnaire circulated throughout the mushing community and anonymously accessed by 72 respondents. The questionnaire was developed based on guidelines set forth by mushing organizations established to facilitate communication and education among recreationalists, professional racers, and sled dog breeders. The questionnaire covered topics pertaining to the management of sled dogs, including nutritional programs, housing and training practices, the dogs' health and well-being, and the owners' overall knowledge of mushing practices. Nutritional programs for sled dogs remain a controversial area, with differences arising between mushers and researchers in terms of adequate dietary requirements. Although we have collected all the survey data, this report focuses on evaluating nutrition programs used among mushers and dietary management differences between the racing and off-seasons. The food type provided differed among mushers (P = 0.01) with combination diets (commercial food brand combined with homemade diet or raw meat) accounting for 62% of responses. The number of mushers feeding a combination diet was 27% greater during the racing season versus the off-season. The source of these homemade diet recipes differed (P < 0.0001), with 50% of mushers sourcing their diet recipes from experienced mushers rather than data-based scientific consultants (e.g., animal nutritionists, 16%; veterinarians, 6%; peer-reviewed journals, 3%). When ranking dietary nutrients based on importance, 85 and 95% of respondents considered fat and protein, respectively, to be the most important nutrients. A majority of mushers provided the same amount of feed (1–2 cups) and water (1–5 L) during the racing and off-seasons, even though dogs' nutrient demands and hydration requirements increase when racing. These results suggest that alterations in food and nutrient supply aimed at maximizing the health, well-being, and performance of sporting dogs should be examined. The data indicates that nutrition programs are largely tailored towards high-protein and -fat diet formulations, with too little attention paid towards nutritional balance of macro- and micronutrients and adequate hydration. This survey successfully identified a number of areas that require further controlled research to demonstrate potential benefits of individualized nutrition as well as environmental and athletic management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".