Temporal and seasonal variability of arsenic in drinking water wells in Matlab, southeastern Bangladesh: A preliminary evaluation on the basis of a 4 year study
Why this work is in the frame
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Bibliographic record
Abstract
Temporal and seasonal variability of As concentrations in groundwater were evaluated in As-affected areas of Matlab, southeastern Bangladesh. Groundwater samples from 61 randomly selected tubewells were analyzed for As concentrations over a period of three years and four months (from July 2002 to November 2005) and monitored seasonally (three times a year). The mean As concentrations in the sampled tubewells decreased from 153 to 123 μg/L during July 2002 to November 2005. Such changes were pronounced in tubewells with As concentration >50 μg/L than those with As concentrations <50 μg/L. Similarly, individual wells revealed temporal variability, for example some wells indicated a decreasing trend, while some other wells indicated stable As concentration during the monitoring period. The mean As concentrations were significantly higher in Matlab North compared with Matlab South. The spatial variations in the mean As concentrations may be due to the differences in local geological conditions and groundwater flow patterns. The variations in mean As concentrations were also observed in shallow (<40 m) and deep (>40 m) wells. However, to adequately evaluate temporal and seasonal variability of As concentration, it is imperative to monitor As concentrations in tubewells over a longer period of time. Such long-term monitoring will provide important information for the assessment of human health risk and the sustainability of safe drinking water supplies.
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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 | 0.007 | 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