An improved age estimation method for caribou and reindeer using tooth eruption and wear
Bibliographic record
Abstract
Dental age estimation based on tooth eruption schedules and wear is a useful analytical tool in zooarchaeology for developing demographic profiles for animal skeletal remains, particularly those from ruminants. While tooth eruption schedules are applicable only to younger individuals, tooth wear can be used for older animals as the heights of their crowns shorten over their lifetime, creating recognizable visual changes to tooth occlusal surfaces. This study presents a novel tooth eruption and wear age estimation method for Rangifer tarandus , a key species of the Circumpolar North. The method was created using a sample of over 600 mandibles from known-age caribou and reindeer from several populations. These are Qamanirjuaq, Beverly, Dolphin-Union, Bluenose East, and Bluenose West caribou herds from Canada and forest reindeer from Finland. The method provides a user-friendly manual featuring tooth wear illustrations of premolars and molars created using frequency of occurrence data of easily recognizable visual wear traits. This method can be applied to modern and archaeological Rangifer dentition to estimate age and can be utilized with complete or fragmentary mandibles, including isolated teeth. • A novel tooth wear age estimation method is developed for caribou and reindeer. • The method is built using a large sample of mandibles from several herds. • It includes a user-friendly manual with caribou teeth wear illustrations. • The method can be applied to complete mandibles and isolated teeth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".