<scp>A</scp>ge estimation of immature human skeletal remains from the metaphyseal and epiphyseal widths of the long bones in the post‐natal period
Bibliographic record
Abstract
OBJECTIVES: This study provides regression and classical calibration models for the estimation of age from the dimensions of the metaphyses and epiphyses of the six long bones. METHODS: A sample of 148 known sex and age individuals (56 females and 92 males), aged between birth and 12 years of age, selected from Portuguese and English skeletal collections. Age estimation models were obtained separately for each sex and for the sexes combined, for the entire sample and for the sample divided into two subsamples at the age of 2 years. RESULTS: The best performing epiphysis is the proximal epiphysis of the tibia. For the metaphyses, the distal metaphysis of the tibia and the proximal metaphysis of the femur are best for individuals below and above 2 years of age, respectively. Growth of the metaphyses is similar to that of the diaphysis. Results suggest that age can be as accurately estimated from the width of the metaphyses and epiphyses as from the length of the diaphyses, or as from the length of the deciduous and permanent teeth. CONCLUSIONS: These models may be useful for fragmentary material in both archaeological and forensic contexts. However, due to the background of the samples, the models would be most applicable to individuals exposed to adverse environmental conditions during growth and development. Metaphyseal and epiphyseal widths may be less affected by ecological conditions than diaphyseal length.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".