Discuter les approches conventionnelles de gestion de l?utilisation des eaux usees en agriculture. In FrenchChallenging conventional approaches to managing wastewater use in agriculture
Bibliographic record
Abstract
In developing countries urban wastewater management often fails to cope with increasing wastewater generation. Financial, technical and institutional limitations force authorities to discharge substantial amounts of untreated or partially treated wastewater into surface waters. Consequently, uncontrolled use of polluted water is increasingly common in the downstream peri-urban areas. Although wastewater use bears a significant risk on human health, such use is also productive and an asset for many. Agricultural use of wastewater is a strong manifestation of the urban-rural connection and transfers a waterborne risk from the wastewater disposal system to the food chain, requiring a paradigm shift in the approaches applied to risk minimization. Conventional models for urban wastewater treatment and management are based on top-down, mechanically driven approaches that do not, or do not sufficiently, consider the links between the social, economic and health aspects. This situation is understandable from historical and technological points of view, but does not provide innovative solutions to current problems in developing country cities. A different approach is required, one that rethinks conventional wastewater system design and management. By adopting a systems approach to analysing both the water and food chains, one discovers the interactions of different stakeholders that treat and use (or abuse) water, the impacts on overall productivity and the risks. Governance systems to manage wastewater use in agriculture must incorporate decentralization to accommodate thinking at the bottom layer, encourage stakeholder engagement and provide coordination and policy cohesion for managing risks jointly from both the water and food chains.
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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".