Water quality, heavy metals, and microbial assessments of <scp>Nile River</scp> water at <scp>Beni‐Suef</scp> governorate, <scp>Egypt</scp>
Bibliographic record
Abstract
Abstract This study investigates the assessment of the water quality and microbial quality of the Nile River water within the Beni‐Suef governorate in Egypt. Twenty water samples were collected seasonally from Autumn 2023 to Summer 2024. The findings showed a significant increase in nitrate, ammonium, and orthophosphate concentrations, especially in the vicinity of agricultural drainage points. Place, season, and inter‐group interactions were attributed to the significant differences seen in the analysis of variance for all bacterial groups. Canadian Council of Ministers of the Environment‐water quality index was used to assess each location's overall water quality. The majority of physicochemical characteristics were found to be within allowable bounds for irrigation, drinking water, and the protection of aquatic life. Extreme caution is advised in the Beni‐Suef governorate's River Nile, even though the levels of water contamination are below WHO‐acceptable thresholds. To preserve this essential water resource, recommendations include implementing sustainable agricultural practices, enforcing stronger regulations on polluting sources, and launching public awareness initiatives. These precautions include limiting the excessive use of organic fertilizers, establishing pollutant industries, and avoiding the use of wastewater and sewage sludge in agriculture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".