Sustainable wastewater reuse for agriculture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective management of water resources is crucial for global food security and sustainable development. In this Review, we explore the potential benefits and challenges associated with treated wastewater (TW) reuse for irrigation. Currently, 400 km3 yr−1 of wastewater is generated globally, but <20% is treated, and of that TW, only 2–15% is reused for irrigation depending on region. The main limitation of TW for irrigation is the inability of current treatment technologies to completely remove all micropollutants and contaminants of emerging concern, some of which have unknown impacts on crops, environment and health. However, advanced water treatment and reuse schemes, supported by water quality monitoring and regulations, can provide a stable water supply for agricultural production, as demonstrated in regions such as the USA and Israel. Such schemes could potentially serve a net energy source, as the embedded energy in wastewater exceeds treatment needs by 9 to 10 times. Agriculturally useful nutrients such as nitrogen, phosphorus and potassium could be also recovered and reused. TW reuse for irrigation could act as a major contributor to a circular economy and sustainable development, but the first steps will be funding and implementation of advanced and sustainable treatment technologies and social acceptance. Treated wastewater (TW) reuse for irrigation could alleviate water imbalances and boost food production in water-scarce regions, thus promoting global food and water security. This Review discusses the potential and challenges of widespread TW reuse for agriculture in a circular economy framework.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.014 |
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 it