Spatial modelling of individual arsenic exposure via well water: evaluation of arsenic in urine, main water source and influence of neighbourhood water sources in rural Bangladesh
Bibliographic record
Abstract
Arsenic concentrations in well water often vary even within limited geographic areas. This makes it difficult to obtain valid estimates of the actual exposure, as people may take their drinking water from different wells. We evaluated a spatial model for estimation of the influence of multiple neighbourhood water sources on the actual exposure, as assessed by concentrations in urine in a population in rural Bangladesh. In total 1307 individuals (one per bari, group of families) were randomly selected. Arsenic concentrations of urine and water were analysed. Simple average and inverse distance weighted average of arsenic concentrations in the five nearest water sources were calculated for each individual. Spatial autocorrelation was evaluated using Moran's I statistics, and spatial regression models were employed to account for spatial autocorrelation. The average distance from a household to the nearest tube-well was 32 metres (Inter-Quartile Range 1-49 metres). Water arsenic concentrations of the reported main water sources were significantly correlated with concentrations in urine (R(2) = 0.41, rho < 0.0001, R(2) for women = 0.45 and for men = 0.36). General model fit improved only slightly after spatial adjustment for neighbouring water sources (pseudo-R(2) = 0.53, spatial lag model), compared to covariate adjusted regression coefficient (R(2) = 0.46). Arsenic concentration in urine was higher than arsenic in main water source with an intercept of 57 microg L(-1), indicating exposure from food. A suitable way of estimating an individual's past exposure to arsenic in this rural setting, where influence of neighbouring water sources was minimal, was to consider the reported main source of drinking water.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".