A Reappraisal of Developing Permanent Tooth Length as an Estimate of Age in Human Immature Skeletal Remains
Bibliographic record
Abstract
This study expands on existing juvenile age prediction models from tooth length by increasing sample size and using classical calibration. A sample of 178 individuals from two European known sex and age skeletal samples was used to calculate prediction formulae for each tooth for each sex separately and combined. Prediction errors, residuals, and percentage of individuals whose real age fell within the 95% prediction interval were calculated. An ANCOVA was used to test sex and sample differences. Tooth length for age does not differ between the samples except for the canine and second premolar, and no statistically significant sex differences were detected. The least prediction error was found in the incisors and the first molar, and the highest prediction error was found in the third molar. Age prediction formulae provided here can be easily used in a variety of contexts where tooth length is measured from any isolated tooth.
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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 | 0.001 | 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 | 0.021 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".