Evaluation of Age Estimation Technique: Testing Traits of the Acetabulum To Estimate Age at Death in Adult Males*
Bibliographic record
Abstract
This study evaluates the accuracy and precision of a skeletal age estimation method, using the acetabulum of 100 male ossa coxae from the Grant Collection (GRO) at the University of Toronto, Canada. Age at death was obtained using Bayesian inference and a computational application (IDADE2) that requires a reference population, close in geographic and temporal distribution to the target case, to calibrate age ranges from scores generated by the technique. The inaccuracy of this method is 8 years. The direction of bias indicates the acetabulum technique tends to underestimate age. The categories 46-65 and 76-90 years exhibit the smallest inaccuracy (0.2), suggesting that this method may be appropriate for individuals over 40 years. Eighty-three percent of age estimates were ±12 years of known age; 79% were ±10 years of known age; and 62% were ±5 years of known age. Identifying a suitable reference population is the most significant limitation of this technique for forensic applications.
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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 | 0.002 | 0.001 |
| 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 | 0.008 |
| 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".