Zero-One Programming Model for Daily Operation Scheduling of Irrigation Canal
Bibliographic record
Abstract
Irrigation scheduling is one of the important managerial activities that aim at effective and efficient utilization of water.A number of scheduling techniques are available today. Despite this, irrigation scheduling is only at inception level inmost of developing countries. In India also there are many methods of irrigation scheduling to canals are available. Thedrawback of this method of operation of laterals is highlighted in this paper. Further, operations of the laterals are to besimple so that the system can be managed easily. In addition, the supply to laterals should match with the day supply inthe canal and total supply for the period. In this paper a Mixed Linear Integer Programming model is described, whichaims at daily scheduling of laterals from the canal considering the constraints of the system. It is proposed to run thelaterals, (except a lateral which is proposed to operate at variable discharge) either full/half or closed condition formaking the laterals operation simple. This Zero-One Mixed Linear Integer Programming model is applied to a fieldproblem to derive daily operation scheduling of laterals of the system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".