Ageing dogs and wolves using x-ray micro-computed tomography (μ-CT): an application to canid remains from the Junction Site, Alberta, Canada
Bibliographic record
Abstract
Evidence for relationships between people, dogs, and wolves can be found across much of the world. Methods for studying archaeological canid remains, particularly where sex or age is concerned, are limited. Reliable estimations of age at death could provide insights into the long and complex relationships people had with canids, including how they vary geographically, temporally, and by species. This study uses X-ray micro-computed tomography (μ-CT) to examine mandibular 1st molar root cross-sections for age estimation. Cementum is a reliable indicator of age in canids because it accumulates incrementally and resists remodelling. The proportion of cross-sectional area comprised of cementum (%C) is assessed for its correlation with age in 13 cementum aged modern wolves and 11 known-aged modern dogs. This study demonstrates that %C, visualized using μ-CT techniques, is an effective tool for determining the age at death for dogs and wolves (combined as a single group), with an error margin of 1.65 years. Taxon-specific %C regressions offer higher resolution, with error margins of 1.68 years for dogs and 1.45 years for wolves; however, sample sizes are small. The method is then applied to archaeological canids from the Junction site (DkPi-2) in Alberta, Canada. These techniques indicate that a few dogs and many wolves of all ages were present at the site, including juvenile/young adult, prime-age adults, and senescent individuals. Explaining this pattern is challenging, but it likely involved a combination of natural deaths, culling, predation, and some wolf-human conflict.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".