Agriculture sustainability in arid lands of southern Tunisia: Ecological impacts of irrigation water quality and human practices*
Bibliographic record
Abstract
Abstract Groundwater resources in southwestern Tunisia are facing serious quantity and quality degradation. Given that they are the only source used for oasis irrigation, serious impacts on the soil of the region are expected. Accordingly, the main objective of this study is to evaluate the suitability of the sampled water from the Complex Terminal aquifer for irrigation purposes and to determine the potential impacts of their long‐term use within the present agricultural practices on soil properties. The calculated ionic ratios indicate different classes ranging from permissible to unsuitable waters with respect to alkalinity standards (sodium adsorption ratio from 3.68 to 9.34, %Na from 36.7 to 61.2, and rill erodibility from 0.48 to 1.43). Water samples present high values of electrical conductivity, total dissolved solids, and potential salinity, about 54.5, 29.4, and 32.2%, respectively, which indicates serious salinity hazard. All the sampled waters may induce corrosion and clogging problems as deduced from the calculated light dynamic penetrometer and total hardness indices. The weighted water quality indices results (Canadian Water Quality Index and the irrigation water quality index) confirm the poor quality of the used irrigation water. The results of this study coupled with field investigations indicate that the cumulative impacts of low water quality and poor agricultural practices have damaged the total oasean ecosystem.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".