Age, water source, and sex do not significantly affect the microbiome of the Hadza people of Tanzania
Bibliographic record
Abstract
Very little is currently known about the factors affecting the gut and skin microbiomes of the Hadza people of Tanzania. Most of the current microbiome research has been conducted on industrialized populations, with age, sex, water, and diet identified as some of the factors influencing microbiome composition and variation. Societies like the Hadzabe are increasingly being studied, as their hunter-gatherer lifestyle may give insight into the pre-industrial microbiome. The purpose of our analyses was to use knowledge of the Hadza lifestyle to try to determine whether age, sex, and water source, which are important determinants of industrialized microbiomes, also influence the Hadza microbiome. We investigated the effects of age and sex on the volatility and composition of the Hadza gut microbiome, respectively. We also observed the composition of the gut microbiome of individuals using water sources with distinct microbial compositions. Additionally, we looked at the effects of sex on the skin microbiome of Hadza individuals. From the results of these analyses, we observed no significant impact of age, sex, or water source on either the volatility or composition of the gut and skin microbiomes. Our study provides further insight into the unique lifestyle of the Hadza people and its effect on their microbiomes, which contributes to bridging the knowledge gap between industrialized and non-industrialized populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".