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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".