Hydrogeochemical and hydrochemical evaluation of surface water: insights into water quality for drinking use – case of Babar Dam in northeastern Algeria
Bibliographic record
Abstract
The quality of surface water from the Babar Dam in Khenchela province, northeastern Algeria, was assessed using monthly physicochemical data collected from July 2018 to June 2019. This study aimed to evaluate the water's suitability for both drinking and agricultural purposes. For the drinking water assessment, two water quality indices, the Water Quality Index (WQI) and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), were employed. The WQI results indicated that 99% of the monitoring stations consistently had good quality water, while only 1% showed permissible quality throughout the year, meeting the standard criteria for drinking water. In contrast, the CCME-WQI classified the water as marginal at all stations, suggesting that while the water met the basic standards for human consumption, certain parameters such as conductivity and specific ion concentrations fell outside the ideal range, potentially requiring treatment for improved quality. These findings highlight the overall suitability of the water for consumption but also emphasize the need for continued monitoring and possible intervention to ensure water quality remains consistently safe for all uses.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".