An assessment and comparison of third molar development in relation to chronological age in a Western Australian and a South Indian population
Bibliographic record
Abstract
[Truncated abstract] In a forensic investigation the estimation of age at death is an important step towards the identification of unknown human skeletal remains. An accurate estimation of age will significantly narrow the field of possible matching identities. In order to achieve this, there are many skeletal methods available to the forensic odontologist and anthropologist, including assessment of skeletal and dental maturation (in the juvenile age range). However, the rate of skeletal maturation can be affected by environmental factors that include poor nutrition and illness. Dental development, however, is under strict genetic control and is strongly correlated to chronological age. This makes teeth a reliable age marker for assessment in forensic investigations. There are many published methods for evaluating and quantifying dental maturation in order to estimate personal age. One of the more widely applied methods was first described in 1973 by Demirjian and Goldstein, who studied French-Canadian children. The present study applies a modification of that method to statistically quantify the timing of third molar mineralization in a Western Australian and South Indian population. The primary aim is to evaluate how accurately age can be estimated using the third molars, to assess ethnic differences in mineralization rates, and to formulate population specific standards for age estimation using this tooth. Comparisons between sexes, upper and lower arches and side differences (within and between populations) are made to provide statistically usable reference data of mineralization rates in the third molars specific to Western Australia and South India. In addition, the degree of third molar agenesis is assessed in both populations...
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".