The activity patterns of nonworking and working sled dogs
Bibliographic record
Abstract
There are limited studies investigating the combined effects of biological, environmental, and human factors on the activity of the domestic dog. Sled dogs offer a unique opportunity to examine these factors due to their close relationship with handlers and exposure to the outdoors. Here, we used accelerometers to measure the activity of 52 sled dogs over 30 days from two locations in Canada. The two locations differ in the working demands of dogs, therefore we used linear mixed effects models to assess how different factors impact daytime and nighttime activity of working versus nonworking dogs. During the daytime, we found that males were more active than females among nonworking dogs and younger dogs were more active than older dogs among working dogs. Alaskan huskies had higher activity levels than non-Alaskan husky breeds in working sled dogs during the day. Nonworking dogs were slightly more active during colder weather, but temperature had no effect on working dogs' activity. The strongest predictor of daytime activity in working dogs was work schedule. These results indicate that the influence of biological factors on activity varied depending on dogs' physical demands and human activity was the most powerful driver of activity in working dogs.
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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".