THE USE OF WATER QUALITY INDICES IN EVALUATING THE TAP WATER QUALITY: THE CASE OF AL-BAQA’A REFUGEE CAMP, JORDAN
Bibliographic record
Abstract
Many people find tap water unsatisfactory for their daily consumption, despite the fact that suppliers are required to provide high-quality water to the public. Water quality indices that were developed to assess the water quality of water bodies are sometimes used to assess water in distribution networks. The use of the water quality indices Weighted Arithmetic (WA) and the Canadian Council of Ministers of the Environment (CCME) methods in describing the tap water quality status at Al-Baqa’a camp is evaluated in this study. Forty samples were collected over four rounds from ten households. The study concluded that the water supplied to the study area was safe but was subjected to a later irregular localized biological contamination. The analysis of water constituents revealed that both indices provide generalized descriptions that do not accurately represent the current state of tap water quality. The study proposed using a two-index system comprised of WQI at the network pumping location and a contamination threat index to study tap water quality. Applying the two indices system at Al-Baqa’a Camp tap water showed that water supplied to the area is safe but it has a localized biological contamination potential of 23.7%
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".