Bibliographic record
Abstract
The teeth of ruminants (cud-chewing herbivores) can be used to estimate age. Tooth wear age estimation is an especially valuable method in archaeological research because it is non-destructive, efficient, and is adaptable to multiple species, which provides effective results. The objective of this paper is to review tooth wear age estimation approaches taken with a focus on cervid (deer) and caprine (sheep and goat) mandibles. I discuss the process of dental attrition involving ruminant chewing, digestion, and feeding behaviour, as well as factors that affect the rate of wear including individual and population variance. The approaches to tooth wear age estimation have been divided into three overarching categories: the Crown Height Method, the Visual Wear Pattern Method, and the Wear Trait Scoring Method. These approaches are all non-destructive and require similar assumptions about the regularities of tooth wear. Each involves different levels of accuracy, ease of use, efficiency, and applicability to archaeological mandibles. This paper highlights the strengths and weaknesses for these approaches and explains that these various methods reviewed are each better suited to different research situations. Taken together, tooth wear age estimation is a valuable tool that zooarchaeologists employ to reconstruct age-based demographic profiles of animal remains recovered from archaeological sites, illustrating how people interacted with and used them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".