Age, sex and the life course: population variability in human ageing and implications for bioarchaeology
Bibliographic record
Abstract
Sex and age identification of human skeletal remains is essential in forensic anthropology, bioarchaeology and palaeodemography, and estimations rely on the use of proven methods. Many methods exist and are generally applied to skeletons from all time periods and geographic locations, despite studies suggesting that there are differences in the expression of traits characteristic of males and females and that ageing rates vary within and between populations. The aim of this project was to study variation in ageing and sexual dimorphism in six documented collections from different geographic locations and/or time periods. Age and sex methods were tested on adult skeletal remains dating from the 17th to 20th century from Canada, England, South Africa, and Portugal. Ageing methods used were focused on the fourth rib’s sternal end, cranial sutures, pubic symphysis and auricular surface. A more subjective age estimate for each individual was also produced, using informal skeletal age indicators alongside formal methods. Sex determinations were based on pelvic and skull morphology, and metrical analysis. Differences were found between some collections in terms of the distribution of age phases and mean ages per phase. Similarly, distributions of sexually dimorphic traits were found to differ between some of the collections. In terms of overall age estimates, the subjective age estimates were significantly better than estimates based only on formal ageing methods, and intraobserver error tests suggest that user experience was important. The magnitude of such differences and their implications for bioarchaeology, forensic anthropology and palaeodemography are discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".