Identifying habitual sled-pulling in dogs through the study of entheseal changes
Bibliographic record
Abstract
Sled dogs are among the most iconic animals of the North, and their efforts in pulling sleds facilitated trade and subsistence practices that sustained many Indigenous groups for thousands of years. Unfortunately, the history of dog sledding is difficult to trace in archaeology. The identification of dog sledding in the past has been mostly addressed through the association of dog skeletal remains with material parts of sleds and harnessing equipment. However, there is currently no method for identifying sled-pulling activity directly from canid remains. This article introduces a new visual scoring manual for entheseal changes to address this gap in knowledge. Entheseal changes are morphological variations to entheses , which are muscle, tendon, and ligament attachment sites on bone. They have been used to reconstruct past activity in humans and, more recently, reindeer and equid remains, but never in canids. This method was developed for thirteen entheses on the forelimb and hindlimb using 74 working sled dogs, non-working pet dogs, and wild canids. Visual scores were compared to examine the effect of activity on entheseal changes, but also confounding biological factors such as age, sex, and body size . Observer error tests were also conducted to determine the method's precision and repeatability. The results show that sled dogs have significantly higher scores than non-working canids, especially for seven attachments. This suggests that entheses are morphologically sensitive to habitual sled-pulling, though some attachments are better indicators of working activity than others. Overall, these findings demonstrate that the method can differentiate sled dogs from pet dogs and wild canids and is a useful tool for identifying sled-pulling activity in archaeological canid remains. Furthermore, this method will help to better understand the history and development of human-dog relationships in the North.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".