Factorial Two-Stage Irrigation System Optimization Model
Bibliographic record
Abstract
This study proposes a factorial two-stage irrigation system optimization model (FTIM) for supporting agricultural irrigation water-resource management under uncertainty. The FTIM incorporates fractional factorial design, two-stage stochastic programming (TSP), interval linear programming (ILP), and interval probability and is applied to agricultural water allocation. The FTIM can take full advantage of conventional two-stage optimization approaches to tackle uncertainties presented as intervals, to investigate potential interactions among input parameters and their influences on system performance, and to enhance applicability to dual uncertainties expressed as interval probabilities. The proposed FTIM approach is for the first time applied to a hypothetical case study of water resource allocation in an agricultural irrigation problem. The results indicate that the effects of parameters on the objective function are evaluated quantitatively, which can help decision makers screen out significant parameters, analyze their interactions in model response, and identify possible schemes with maximized net system benefit. Especially for the study problem, the most positive significant factor affecting total net benefits is water quality at a medium flow; penalties resulting from undelivered water and benefit rates of onion farms in both periods have negative effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".