Inexact <i>De Novo</i> Programming for Agricultural Irrigation System Planning
Bibliographic record
Abstract
Rapid population growth and economic development have led to increasing reliance on water resources. For agricultural irrigation systems, reasonable water resource allocation is necessary to support a significant increase in food demand during the next decade. The de novo programming method was effective for seeking a portfolio of resource levels to deal with optimal design problems by allocating a budget. In this study, an inexact agriculture irrigation de novo model was developed to obtain optimal water-allocation strategies for agricultural irrigation systems through the design of optimal agricultural irrigation management systems under uncertainty. This model has the advantages in constructing optimal agricultural irrigation system design via an ideal system by introducing the flexibility toward the available resources in the system constraints. The inexact agriculture irrigation de novo model was then applied to a regional agricultural management problem to design an optimization agricultural irrigation management system under limited budget, instead of finding the optimum in a given system with fixed resources in an agricultural irrigation planning case. The model was taken account into conjunctive use of surface and groundwater resources and some other necessary resources input. Results demonstrate that the developed model efficiently produced stable solutions under different objectives during the planning period. Obtained results can help decision makers identify desired all kinds of resource input for agricultural sustainability within a given budget under uncertainty.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".