Age estimation by tooth cementum annulation: perspectives of a new validation study
Bibliographic record
Abstract
Introduction Discussions during the Rostock paleodemography workshops have shown two areas that clearly suffer from severe problems in transforming a skeletal sample into an historical population. First, more effort was needed to establish a reliable mortality pattern for skeletal samples. Second, although the Rostock Manifesto concentrated on the modeling of individual age and sex data, discussions frequently criticized the basic data, especially the insufficiently large age ranges in individual age estimations obtained from established morphological age estimation techniques. The group therefore drew attention to the search for methods that are able to determine age with higher accuracy. Owing to recent significant improvements, a promising method for age estimation is the evaluation of tooth cementum annulation (TCA). The proposed procedure reduces time-consuming, and therefore costly, preparation steps, so that larger samples can be observed with less expense. If a few simple guidelines are followed, the method may be among the best and most reliable of those used for age estimation of skeletal samples. To validate this method for frequent use, a standard protocol and use of confidence intervals are required, and these may be established by studying a known-age reference sample. The aim of this chapter is to introduce the concept and the methodological bases of an ongoing validation study, as well as the preliminary results. Morphological age estimation methods versus tooth cementum annulation Almost all established methods for age estimation in the skeleton suffer from severe problems (see Kemkes-Grottenthaler, Chapter 4, this volume).
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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 | 0.060 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".