Biological mortality bias in diaphyseal growth of contemporary children: Implications for paleoauxology
Bibliographic record
Abstract
Abstract Objectives Biological mortality bias is the idea that individuals who comprise skeletal samples (non‐survivors) are a specific subset of the overall population, who may have been exposed to greater stress during life. Because of this, it is possible that studying growth in a skeletal population misrepresents the growth and health of survivors in that population. Using a modern sample, this study investigates whether biological mortality bias in growth may be present in archaeological skeletal samples. Materials and methods Postmortem computed tomography scans of 206 children aged under 13 years were collected from two institutions in the United States and Australia. The sample was separated into children who died from natural causes as proxies for non‐survivors and from accidental causes as proxies for survivors. Differences in long bone length for age were assessed through analysis of covariance (ANCOVA) and z‐score analysis, and these results were compared with studies linking anthropometrics and mortality risk in nonindustrialized societies. Results Differences in growth favoring survivors were greater for girls than for boys and seemed to increase over age. The effect in nonindustrialized societies was 1.5 to 5 times the magnitude of that in our contemporary sample. Conclusions A greater growth delay in girls than in boys has been documented in historical identified collections, and skeletal samples consistently become more stunted relative to modern standards over the course of growth. Our findings on biological mortality bias could explain part of these growth delays and impact interpretations of past ontogenetic environments.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".