Hydraulic design of irrigation networks with optimization methods
Bibliographic record
Abstract
Agricultural sustainability is an issue of great concern worldwide, for the period between 2005 and 2050 will require a significant increase of 60% to 110% in food production.There is an urgent demand for efficient irrigation systems that meet design specifications.The network must guarantee adequate delivery of demand to users and sufficient pressure for the correct operation of the network's emitters.On the other hand, the implementation of an irrigation system requires a large economic investment, in this study optimization algorithms are implemented that will allow mitigating the design costs.This research is developed in a community of irrigators in a local area.The research begins with the agronomic design, then using the Clment model, the circulating flows of the irrigation network operating on demand are determined.The methods for the hydraulic design of the irrigation network were the conventional method, the Granados method, and the genetic algorithms.The results indicate that it is possible to obtain a significant investment cost reduction in piping, which varies considering one or the other method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".