Biological and cultural drivers of oral microbiota in Medieval and Post-Medieval London, UK
Bibliographic record
Abstract
Abstract The trillions of microorganisms that live in association with the human body (microbiota) are critical for human health and disease, but there is a limited understanding of how cultural and environmental factors shaped our microbiota diversity through time. However, biomolecular remnants of the human oral microbiota - recovered from the calcified dental plaque (calculus) of our long-dead ancestors - are providing a new means of exploring this key relationship of our evolutionary history. Here, we correlate extensive experimental, archaeological, and biological metadata with 128 ancient dental calculus specimens from Medieval and Post-Medieval London, UK (1066 – 1853 CE). We identify a significant association between microbiota and oral geography ( i.e. tooth type and tooth surface), which has confounded ancient microbiota studies to date. By controlling for oral geography, however, we identify the first associations between ancient microbiota and cultural and environmental signatures. We find significant links between ancient British microbiota structure and health, including skeletal markers of stress that may reflect low socioeconomic status. Furthermore, this study provides baseline data to explore factors that drive microbiota differentiation within and between ancient populations and highlights the potential of ancient microbiota to infer detailed health and sociocultural information about the past.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".