How old are you now? A new ageing method for nonadults based on dental wear
Bibliographic record
Abstract
Abstract The main aim of this study is to present a novel method of nonadult (ca. 1–19 years) age‐at‐death estimation using the dental wear of deciduous, mixed deciduous‐permanent, and permanent dentitions, including the incisors, canines, premolars, and first and second molars. The stage‐based method is derived from degrees of dental wear in known‐age ( n = 39) and estimated‐age ( n = 11) nonadults containing 951 teeth from the predominately 19th century cemetery of Middenbeemster, The Netherlands. The need for such a method is warranted in cases where dental development and/or eruption cannot be assessed for age‐at‐death estimation. As well, by establishing a baseline for normal age‐related nonadult tooth wear, users may better document wear that could be due to extramasticatory behaviours. The regression analysis reveals a strong quadratic correlation— F (2, 47) = 555.1, p < .001, R 2 = .95, standard error of the estimate = 1.14, residual sum of squares (RSS) = 68.89, predicted residual error sum of squares (PRESS) = 77.67—between age and wear and multivariate adaptive regression splines ( R 2 = .95, generalised cross validation = 1.67, RSS = 67.68, PRESS = 89.34), which are used to develop an R‐package that users may employ to estimate age‐at‐death from dental wear. The accuracy of this method (78–98%) is evaluated using leave‐one‐out cross‐validation. Analyses of males versus females, deciduous versus permanent, upper versus lower, and anterior versus posterior teeth revealed no apparent reason to warrant separate methods for these groups of separated dentitions. This method fills a disciplinary gap in the understudied area of deciduous and nonadult dental wear and hopes to stimulate much future research. With the R‐package, we also provide the foundation and framework for the development of additional reference populations across different spatiotemporal contexts, to make the method more widely applicable.
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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 | 0.003 |
| 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".