Age estimation biases based on body size: the differential impacts of soft tissue on skeletal ageing
Bibliographic record
Abstract
Abstract The aim of this research project is to explore the differential impacts of soft tissues on skeletal ageing and apply these findings to skeletal age estimation methods. Computed tomography (CT) scans of 412 size‐selected individuals from Ontario, Canada, were assessed using an adapted pubic symphysis age estimation method. Individuals ranged from 20 to 79 years of age (mean = 49.46 years), with 208 males and 204 females. Almost 80 per cent of the sample was assigned to the correct age phase; those not correctly aged followed a similar pattern. Individuals with higher body mass, body mass index (BMI), circumference, and total fat area were over‐aged and those with lower body mass, BMI, circumference, and total fat area were under‐aged. High amounts of adipose tissue led to increased skeletal degeneration, but high amounts of muscle tissue did not have a protective effect. Skeletal elements were not reliable proxies for body mass; however, other morphological features may help identify individuals with high body mass from skeletal remains.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".