Water Supply Management in the Semi-Arid Areas of Algeria: A Case Study of Djelfa City — Balancing Supply and Demand
Bibliographic record
Abstract
Due to its geographical location in the southern Mediterranean, Algeria, like other countries, suffers from water scarcity that increases year after year, as it is characterized by its semi-arid and arid climate. The city of Djelfa is considered the most important urban population center in the central steppes (accelerated urban growth and an increase in population), where the increasing demands for water have become a challenge for the concerned authorities. The steppes have various surface and groundwater resources, as the hydrographic network is very simple and the flow is intermittent due to the growing phenomenon of drought. In addition to this, rainfall is limited in the winter and non-existent in summer, with moderate and varying amounts falling in the fall and spring seasons. The region also depends mainly on managing its water needs using groundwater resources. Achieving these resources and exploiting them in a sustainable manner is considered the most important step forward and the greatest challenge for actors over the medium and long term, while preserving the steppe environment with all its fragile components. Controlling supply and demand is also viewed as an essential element in the social and economic balance, as it enables us to overcome the state of water scarcity in a way that ensures water saturation. The increasing needs of various uses (domestic, institutional and industrial) are crucial.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".