Contraintes non pathogenes liees a l?irrigation avec des eaux usees. In FrenchNon-pathogenic trade-offs of wastewater irrigation
Bibliographic record
Abstract
The volume and extent of urban wastewater generated by domestic, industrial and commercial water use has increased with population, urbanization, industrialization, improved living conditions and economic development. Most developing-country governments do not have sufficient resources to treat wastewater. Therefore, despite official restrictions and potential health implications, farmers in many developing countries use wastewater in diluted, untreated or partly treated forms with a large range of associated benefits. Aside from microbiological hazards, the practice can pose a variety of other potential risks: excessive and often imbalanced addition of nutrients to the soil; build-up of salts in the soils (depending on the source water, especially sodium salts); increased concentrations of metals and metalloids (particularly where industries are present) reaching phytotoxic levels over the long term; and accumulation of emerging contaminants, like residual pharmaceuticals. As these possible trade-offs of wastewater use vary significantly between sites and regions, it is necessary to carefully monitor wastewater quality, its sources and use for location-specific risk assessment and risk reduction.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".