Modeling Optimal Canal Conveyance Capacity for the Ahero Irrigation Scheme using the Hydrologic Engineering Centre River Analysis System (HEC-RAS)
Bibliographic record
Abstract
Irrigation plays a critical role in addressing food security as envisaged in Kenya’s development blueprint, the Big Four Agenda. However, the performance of any open channel irrigation system is a function of its canal conveyance efficiency, among other factors. To overcome challenges with irrigation water conveyance at the Ahero Irrigation Scheme, a Hydrologic Engineering Centre River Analysis System (HEC-RAS) model was used to simulate the flow characteristics at the tail-end section of the canal network, covering a total length of 2.6 km. The study also consisted of a comparative review of an FAO-CROPWAT model estimation water requirement for rice. The manual estimation of the canal capacity in its unmaintained state revealed a discharge capacity of 0.228 m3/s, which was significantly lower than the minimum crop water demand requirement estimation of 0.3166 m3/s (a 28% water deficit). The simulated characteristics projected an optimal flow capacity of 0.583 m3/s. The study recommends canal maintenance (levelling bed undulations, dredging, and smooth concrete lining) to attain the optimal flow capacity at the tail end of the network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".