Assessing current visual tooth wear age estimation methods for Rangifer tarandus using a known age sample from Canada
Bibliographic record
Abstract
Age estimation is crucial for investigating animal populations in the past and present. Visual examination of tooth wear and eruption is one of the most common ageing methods in zooarchaeology, wildlife management, palaeontology, and veterinary research. Such approaches are particularly advantageous because they are non-destructive, can be completed using photographs, and do not require specialized training. Several tooth wear and eruption methods have been developed for Rangifer tarandus, a widely distributed and long-utilized species in the North. This paper evaluates the practicality and effectiveness of three existing visual tooth wear and eruption methods for this species using a large known-age sample from several caribou populations in northern Canada (Bluenose East, Bluenose West, Dolphin-Union, Qamanirjuaq, and Beverly herds). These methods are evaluated based on: (1) the amount of error and bias between estimated and actual ages, (2) suitable and interpretable results, (3) user-friendly and unambiguous procedures, and (4) which teeth and visual features of those teeth are used to record wear and eruption status. This study finds that the three evaluated methods all have variable errors and biases, and two show extensive biases when applied to older individuals. Demographic data is simpler to generate and more flexible to report when methods allow age to be estimated as a continuous or discrete variable, rather than as age ranges. The dentition samples used by two of the previously developed methods impact their applicability to other populations of Rangifer. In one existing method, individuals were unavailable from some age ranges leaving gaps when assigning ages. For another Rangifer-ageing method, the population utilized was too distinct in morphology or diet to be used with the Canadian caribou analyzed here. Additional refinement of tooth wear and eruption ageing methods will benefit zooarchaeological research on reindeer and caribou remains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".