Age Estimation of Live Arctic Foxes <i>Vulpes lagopus</i> Based on Teeth Condition
Bibliographic record
Abstract
Aging individuals is a prerequisite to many studies in wildlife biology. In mammals, the most accurate methods are based on cement lines analysis and require tooth extraction. Here, we adapted a method based on tooth wear assessment in live individuals and calibrated against cement lines counts on dead individuals. We developed and tested this less invasive method in arctic foxes Vulpes lagopus. We photographed dentition of live individuals captured in 2006–2013 and of skulls collected opportunistically in 2003–2013. Five observers assessed twice the tooth wear of 245 sets of pictures (left, frontal, and right views of the dentition for 179 captures and 66 skulls, with a mean of 7.2 pictures per set) to obtain a teeth condition index (TCI) for each set. TCI was highly repeatable, with an intraclass correlation coefficient averaging 0.89 and 0.83 within and between observers, respectively. We then used 44 known‐aged individuals to predict fox age from TCI through linear regression (age = 0.62 × TCI + 1.04, r 2 = 0.64). We tested through jackknife validation the ability of this model to accurately age foxes. The rate of correct classification of exact age was 39%, while 80% and 100% of predicted ages were within one and two years of the true age, respectively. The model predicted correctly the age class (young, prime‐age adult, old) of individuals 75% of the time. We conclude that our proposed method, which is much less invasive than those based on tooth extraction, can allow progress in the study of demography of arctic foxes, and potentially other mammal species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".