New More Generic and Inclusive Regression Formulae for the Estimation of Stature from Long Bone Lengths in Children
Bibliographic record
Abstract
Existing child stature estimation methods have a number of disadvantages. This paper addresses some of these limitations by developing regression-based stature estimation formulae that are more generic and inclusive. A sample of 142 individuals under 12 years of age from the Hamann—Todd Human Osteological Collection and the New Mexico Decedent Images Database were used to generate five least squares linear regression formulae to estimate stature from the diaphyseal length of long bones. All models showed excellent fits to the data (R2 close to or at 0.98), and internal validation confirmed the stability and accuracy of model parameters. External validation was performed using a sample of 14 individuals from the Lisbon Collection and the Victoria Institute of Forensic Medicine. Overall, the humerus provides the most accurate estimate of stature, but the femur and tibia showed the greatest coverage. These formulae can be used in a variety of contexts and are not dependent on group affiliation, including sex.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".