Sediment color tool for targeting arsenic-safe aquifers for the installation of shallow drinking water tubewells
Bibliographic record
Abstract
In rural Bangladesh, drinking water supply mostly comes from shallow hand tubewells installed manually by the local drillers, the main driving force in tubewell installation. This study was aimed at developing a sediment color tool on the basis of local driller's perception of sediment color, arsenic (As) concentration of tubewell waters and respective color of aquifer sediments. Laboratory analysis of 521 groundwater samples collected from 144 wells during 2009 to 2011 indicate that As concentrations in groundwater were generally higher in the black colored sediments with an average of 239 μg/L. All 39 wells producing water from red sediments provide safe water following the Bangladesh drinking water standard for As (50 μg/L) where mean and median values were less than the WHO guideline value of 10 μg/L. Observations for off-white sediments were also quite similar. White sediments were rare and seemed to be less important for well installations at shallow depths. A total of 2240 sediment samples were collected at intervals of 1.5m down to depths of 100 m at 15 locations spread over a 410 km(2) area in Matlab, Bangladesh and compared with the Munsell Color Chart with the purpose of direct comparison of sediment color in a consistent manner. All samples were assigned with Munsell Color and Munsell Code, which eventually led to identify 60 color shade varieties which were narrowed to four colors (black, white, off-white and red) as perceived and used by the local drillers. During the process of color grouping, participatory approach was considered taking the opinions of local drillers, technicians, and geologists into account. This simplified sediment color tool can be used conveniently during shallow tubewell installation and thus shows the potential for educating local drillers to target safe aquifers on the basis of the color characteristics of the sediments.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".