A HYDRO-SPATIAL HIERARCHICAL METHOD FOR SITING WATER HARVESTING RESERVOIRS IN DRY AREAS
Bibliographic record
Abstract
Water availability is the main limiting factor in dry-land agriculture, throughout arid and semi-arid regions,due to low annual rainfall depth and its non-uniform temporal and spatial distribution. Water harvesting has been usedfor thousands of years to supplement scarce water resources in dry areas. Surface reservoirs are used to collect and storeprecipitation surface runoff so that stored water can be used for supplemental irrigation during long dry seasons. Thisarticle presents Hydro-Spatial AHP, a method for siting small water harvesting reservoirs. This method is used to rankpotential sites for such reservoirs based on a Reservoir Suitability Index (RSI) determined for each one of these sites. TheRSI is calculated using Geographic Information Systems (GIS) along with hydrologic modeling and the AnalyticHierarchy Process (AHP). This method was applied to Irsal, a dry-land agricultural region in Lebanon. Results revealthat Hydro-Spatial AHP works well in that area. The article also shows the flexibility of the method with respect to thecriteria used for ranking the candidate sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".