Bibliographic record
Abstract
To confront water scarcity and support food security, the concept of virtual water is used. As defined by Allan (1997) virtual water is “the water embedded in key water-intensive commodities such as wheat” or “the water required for the production of commodities”. The importance of this concept is related to its potential contribution for saving water, especially in water short regions like Tunisia. This research study tries to evaluate the strategic importance of polluted or gray water, which is a component of virtual water. Reduction of virtual water for strategic agricultural products can be obtained by the gray water reduction. The latter is defined as “water required diluting polluted water to reach the normalized quality, different with countries”. Water pollution is especially related to use of chemical products (fertilizers, pesticides, etc.) for some crops like vegetables. Besides having a lower opportunity cost, the use of green water for crop production has generally less negative environmental externalities than the use of blue water (irrigation with water abstracted from ground or surface water systems). Tunisia exports some crops and gray water volumes in exports have rarely been estimated. Thus, estimation of gray water plays a role in ensuring water and water-dependent food security and avoiding further potential damage to the water environments in both importing and exporting countries. In this context, Tunisian semi-arid region is chosen because the presence of a long period of dry and shiny, occurring after a cold and rainy one, useful for vegetables crops and family food security. The aim of this study is to present: Methodologies which can be used to reduce virtual water for some strategic vegetables crops in Tunisian semi-arid region, based on irrigation techniques improvements and the control of runoff and leaching water; Resources management practices that can be used to improve family income, especially women and children and target food security.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".