Optimal cultivation rules in multi‐crop irrigation areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 it