Performance evaluation of hydraulic ram pumping systems for small-scale farmers: a case study of West Pokot county, Kenya
Bibliographic record
Abstract
Abstract Hydraulic ram (hydram) pump has been in existence for more than two centuries. However, these pumps have been on the verge of extinction since the invention of motorized pumps, which are more powerful and efficient. Unfortunately, motorized pumps are expensive to acquire, operate, and maintain. Their contribution to climate change and environmental degradation has steered the need for an alternative pumping technology. Therefore, as the world's technology shifts to green energy, hydram pumps need to be re-invented. In the late twentieth century, studies on hydram pumps have been revived with the aim of making them more efficient and economically competitive. Small-scale farmers in West Pokot County, Kenya, have embraced the hydram technology, but due to low technical capacity; installed low-performing hydram that operated under low efficiencies of less than 30%, with the majority having operational failure due to inadequate designs. Hence, this study investigated the design and operation of these pumps. Thereafter, designed and assembled a hydram pump, using locally available materials, to supply water for domestic and small-scale agricultural use. The optimum efficiency achieved by the pump was 54%, with an optimum delivery flow rate of about 13 L/min.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".