WASTEWATER REUSE FOR IRRIGATION ON THE DESERT SANDY SOIL OF EGYPT: LONG-TERM EFFECT
Bibliographic record
Abstract
Abstract In Egypt, the reuse of sewage water on the sandy soil of El-Gabal El-Asfar Farm took place since 1923. The present work discusses the physical and chemical characteristic of this water. Different soil samples that were irrigated by sewage for different increasing periods were collected to investigate the changes in the soil texture as well as accumulation of heavy metals by the soils according to the period of irrigation (7, 12, 23, 40, 50 and 75 years). The overall results revealed that the longer period of irrigation demonstrated higher level of metal accumulation in the soil. The progressive increase of metals in the soil represents serious risk to the cultivated plant (as a food cycle). It is; therefore; recommended to decrease the level of heavy metals in sewage water via further simple wastewater treatment. For this purpose, a laboratory attempts were carried out to decrease the level of metals in the sewage water. Lime, as coagulant, and/or the dried leaves of Water Hyacinth plant were examined. Remarkable elimination of metals was demonstrated. It was also recommended to use the given sewage water for cultivating the woody or Lumber trees (for high economic revenue) to reduce any expected metal hazard in the food chain.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".