Spate Irrigation Potential Assessment for Ethiopian Watershed
Bibliographic record
Abstract
In the low lands of Logia sub basin, Ethiopia, because of shortage of rain to fully grow crops, irrigation is an obligation in general and the presence of seasonal rivers flowing in the region in particular makes flood utilization ideal for spate irrigation. The subjects of the present study were to assess the spate irrigation potential of Logiya watershed that has been brought under irrigation on the basis of flood water availability and land suitability. A GIS based technique combined with analytical hierarchy process (AHP) was applied to access the potential of the watershed for spate irrigation development. Potentially suitable sites for spate irrigation development were assessed for Maize, Sorghum and Tomato crops. Spate irrigation area was evaluated based on land use/cover, slope and soil suitability. CROPWAT software was used to estimate the reference crop evapotranspiration, effective rainfall, net irrigation and gross irrigation water requirement. The suitability model developed shows that only 26.15% of the total area falls under marginally to highly suitable categories for spate irrigation development. The Logiya seasonal river flow from July to October was 301.64 Mm3. However, the annual flood water available from the river was less than the total GIWR by 8.77 Mm3 during growing period. The surplus water available from the river before July might be stored and used for irrigation during water deficit period during growing seasons.
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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 | 0.000 | 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".