Water Resources and Water Quality Assessment, Central Bamyan, Afghanistan
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
We surveyed and selectively sampled the major water sources in Bamyan city and the surrounding area to assess the water quality. Water quality measurements were taken in situ and more samples were collected for laboratory analysis from canals, rivers, springs, wells, and water supply systems. In urban areas, water supply systems provide 36% of the drinking water, but in rural areas, this source accounts for only 7% of drinking water supplies. Wells comprise 33% and 15% of urban and rural water supplies, respectively, while canals and rivers are modest water sources for Bamyan communities. Basic water quality parameters, such as pH, EC, and TDS, were variable with high values in some areas. Most of the samples fall in the range of potable water, but some had a high TDS and EC indicating that there is the potential of contamination. Values of pH were mostly were mostly in the range of drinking water (6.5–9.5). A Drinking Water Quality Index (DWQI) was calculated to better understand the water quality issues for the potable water supplies. Subsets of representative samples were analyzed for 17 selected chemical elements and other constituents. Barium (Ba) was detected in almost all of the water samples, while arsenic (As) was detected in about 9% of the analyzed samples, and this was mostly associated with thermal springs. Concentrations of Mn and Cu in some samples exceeded that of the water quality standards, while Zn concentrations were below tolerable limits in all of the samples. Most of the analyzed water samples were hard, and several samples showed evidence of microbial pollution in urban areas. Rivers originating from snow and glacier melting had excellent quality for drinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.001 |
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