Lack of biological mortality bias in the timing of dental formation in contemporary children: Implications for the study of past populations
Bibliographic record
Abstract
OBJECTIVES: Biological mortality bias is the idea that individuals who perish (non-survivors) are biologically distinct from those who survive (survivors). If biological mortality bias is large enough, bioarchaeological studies of nonsurvivors (skeletal samples) cannot accurately represent the experiences of the survivors of that population. This effect is particularly problematic for the study of juvenile individuals, as growth is particularly sensitive to environmental insults. In this study, we test whether biological mortality bias exists in one dimension of growth, namely dental development. MATERIALS AND METHODS: Postmortem computed tomography scans of 206 children aged 12 years and younger at death were collected from two institutions in the United States and Australia. The sample was separated into children dying from natural causes as proxies for non-survivors and from accidental causes as proxies for survivors. Differences in the timing of dental development were assessed using sequential logistic regressions between dental formation stages and residual analysis of dental minus chronological age. RESULTS: No consistent delay in age of attainment of dental stages was documented between survivors and non-survivors. Delays between survivors and non-survivors in dental relative to chronological age were greatest for infants, and were greater for females than for males. DISCUSSION: Lack of biological mortality bias in dental development reinforces confidence in juvenile age estimates and therefore in skeletal growth profiles and growth studies. As dental development is known to be less environmentally sensitive than skeletal growth and development, further studies should examine biological mortality bias in long bone 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 | 0.015 | 0.047 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".