Arsenic in Drinking Water Toxicological Risk Assessment in the North Region of Burkina Faso
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human health risks assessment were estimated by determining the nature and probability of adverse health effects in the North region’s populations who are now exposed to arsenic from drinking water or will be exposed in the future. Several questions were addressed in this study: what types of health problems may be caused by arsenic from drinking water? What is the chance that people will experience health problems when exposed to different levels of arsenic? What arsenic level are people exposed to and for how long? To answers these questions we have first identified the hazard by evaluating arsenic concentration in thirty-four (34) bore-hole water points among the region based on the assumption of clinical cases related to drinking water. Arsenic concentration ranged from 0 up to 87.8 micrograms per liter. Next we assessed the dose-response of exposure to arsenic. Dose-response relationship describes how the likelihood and severity of adverse health effects are related to the amount and condition of exposure to arsenic. This required us to choose toxicity reference values (TRVs) above which adverse effects may occur for noncarcinogenic and for carcinogenic effects. Exposure factors have been calculated in two scenarios: people from 0 to 14 years old and people from 15 to 70 years. Exposure has been estimated indirectly through consideration of measured concentrations of arsenic in drinking water. This study show that people in the Yatenga, Zondoma and Passore provinces are at very high risk for developing several pathologies such as hyper pigmentation, keratosis, cancer, etc. due by chronic exposure to arsenic in drinking water.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it