Cultural Values and the Coliform Bacterial Load of “Masato,” an Amazon Indigenous Beverage
Bibliographic record
Abstract
Access to safe drinking water is limited in many isolated areas, such as the Amazon where Indigenous peoples frequently reside. Identifying safe forms of drinking water accepted by the communities could have positive health benefits for Indigenous peoples. Many Amazon Indigenous peoples traditionally prepare and consume a fermented beverage called masato, which is frequently the only form of water consumption. Despite its widespread consumption and evidence of the health benefits of fermentation, masato remains poorly investigated. We partnered with a Shawi Indigenous community in the Peruvian Amazon to conduct participatory photography to research masato preparation, and to characterize key cultural features and to assess the presence of total and fecal coliform bacteria by using a membrane filter technique. Pictures show that masato preparation is a key part of cultural practices and that there are clear gender roles in the preparation process. We found that 100% of communal water sources (26/26) were contaminated with coliform bacteria; by contrast, fewer, 18% of masato samples (2/11), were positive for coliform. This exploratory study suggests that fermented beverages like masato merit further investigation as they represent an Indigenous method to improve water quality in Amazonian communities where water safety cannot be assured.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".