Health of the non‐elites at Tombos: Nutritional and disease stress in New Kingdom Nubia
Bibliographic record
Abstract
During the New Kingdom period, Egypt succeeded in occupying most of Nubia. Colonial towns were built, which served as centers of government and redistribution. This paper uses a bioarchaeological approach to address the effects of this cultural contact on non-elites. Skeletal remains from the site of Tombos (N = 100), a cemetery in Upper Nubia dating to this important time, are analyzed, in addition to 1,082 individuals from contemporaneous Egyptian and Nubian sites, in order to shed light on the social, political, and economic processes at play and to determine how the people at Tombos were affected during this transitional period. In many ways, the Tombos population appears to have been affected by similar stressors as the other populations under study. However, a few small differences in the subadult frequencies of pathological lesions, especially remodeling rates, are significant in the overall picture of health at Tombos. These analyses suggest that, although the people of Tombos may have been integrated into the Egyptian colonial network, the additional resources they may have obtained could not protect them from nutritional and disease stress. A lower childhood survival through bouts of ill health at Tombos is suggested. While status may have played a role in the differences seen in the comparative populations, it is likely that parasites and/or other infections led to childhood illness and death.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".