Optimal cultivation rules in multi‐crop irrigation areas
Bibliographic record
Abstract
Abstract A linear programming model is developed for annual cultivation rules of multi‐crop irrigation areas in a reservoir–irrigation system. The objective is to maximize the annual benefit of the system by assigning annual irrigation areas as well as monthly irrigation schedules over the planning horizon. The annual irrigation areas are considered to be a linear function of both total volume of storage at the end of the last operating year and the average inflow rate of the current year. The methodology is applied to a previously analyzed problem, without considering operational rules. Results are compared with those of a linearized modeling of the problem and the advantages of the proposed approach are discussed. Furthermore, results indicate that although there is a 40% decrease in the value of the objective function when using cultivation rules, the model is nonetheless a helpful tool for planners and/or stakeholders to decide at the beginning of each year how much and which type of product should be cultivated. This has been verified by applying the extracted rules with a generated five‐year inflow time series. Results show the robustness of the rules facing the uncertainty of model parameters. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".