Rational design of an on-farm reservoir
Bibliographic record
Abstract
There is increasing concern to store water in on-farm reservoirs (OFRs) for meeting the supplemental irrigation requirements and (or) improving the irrigation efficiency of irrigation systems. The provision of OFRs leads to loss of a certain portion of agricultural land, but the economic viability of providing OFRs has been justified. As widely prevalent among designers, the volume of various shapes of OFRs is usually computed manually or using computer programs based on the trapezoidal or pyramidal formula. However, it has been found that the trapezoidal or pyramidal formula cannot be used for irregular geometries. For such cases, a method of superimposition has been proposed to compute volume accurately. A simplified approach is presented in this article for optimum design of OFRs. The relations between length and breadth are also developed for a variety of OFR shapes to guide farmers for minimum lining.Key words: on-farm reservoir, trapezoidal formula, pyramidal formula, superimposition, optimization.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".