Wastewater reuse in Jordan and its potential as an adaptation measure to climate change
Bibliographic record
Abstract
Reclaiming wastewater for reuse in agriculture is increasingly adopted by many countries around the globe. This is particularly true for Jordan, which is characterised as a water-scarce country where the per-capita share from renewable water resources is less than 100 (m3/capita)/year. The Third National Jordanian Communication report on climate change has estimated a significant decrease in precipitation of 1·2 mm/year and an increase in the mean air temperature by 0·02°C/year, which will be adversely reflected on the water resources potential. To bridge the gap between water resources, supply potential and the demand, Jordan utilises non-conventional water resources, such as wastewater reuse in irrigation, where agriculture accounted for 52% of the water use in the country in 2017. The main objective of the present paper is to update the Jordanian experience in wastewater reuse and to explore its potential as an adaptive measure to climate change. The analysis revealed that 92% of the treated wastewater has been reused either directly or indirectly, mainly for irrigation. Treated wastewater can be considered as an adaptation measure to climate change that is capable of reducing the deficit between demand and water resource potential up to 48% by the year 2025.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".