A refined guide for aging muskoxen (Ovibos moschatus) based on mandibular examination
Bibliographic record
Abstract
Accurately aging wildlife is essential for effective management and conservation efforts because it supports the estimation of demographic parameters used to model population dynamics and determine harvest quotas. Currently, accurately aging muskoxen is limited by the lack of validated and standardized protocols specific to this species. We investigated three methods for aging muskoxen: tooth eruption pattern, mandible morphometrics, and cementum annuli analysis (CAA). We examined 260 mandibles from community-harvested muskoxen with known harvest dates from Nunavut and the Northwest Territories and radiographed 89 of these mandibles with erupting teeth to track eruption stages. From these data, we developed a key to estimate age of muskoxen from newborn until all permanent teeth have completely erupted (60 months). Next, we assessed the relationship between muskox mandible morphometrics and age using 178 archived mandibles from Banks Island, Northwest Territories. Caudal mandible length was the strongest predictor of age in months (Adjusted R2 = 0.917) up to 5 years old (60 months), after which growth was negligible. This regression model included linear, quadratic, and interaction terms for caudal mandible length with sex. To evaluate accuracy of CAA for aging, we compared cementum results from incisors of 14 captive muskoxen to their known age using linear regression (Adjusted R2 = 0.847). We applied this model, fitted to captive muskox data, to predict the age of 32 community harvested adult muskoxen over 60 months old (5 yo+) using cementum age results. While there was a tendency to underestimate age, this method provided a more informative estimate than classifying all animals as adults once all teeth have erupted. Integrating these methods, we developed a decision tree to guide aging based on the putative age class and available sample type. This framework improves age estimation accuracy for harvested muskoxen, supporting population models and enabling more effective management and conservation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.031 |
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".