Integrated System for Multi-Usage Reservoir Management in Sri Lanka
Bibliographic record
Abstract
The Mahaweli Authority of Sri Lanka (MASL) is responsible for planning the water allocation across five major River systems in Sri Lanka. This includes providing water to the 15 major hydro plants and over 32 irrigation areas. MASL has been using computer models since the1980’s to meet the various water demands over the entire system and establish the right balance between the multiple stakeholders as part of the calculation of a seasonal plan (SOP). This includes an evaluation of the risk associated with water shortages for irrigation. As part of an on-going modernization effort, a fully integrated system for multi-use reservoir management (Vista DSSTM) has been developed and implemented to help produce the seasonal plan on an operational basis. It includes a multi objective long and shortterm optimization model, extensive data acquisition capability as well as inflow and irrigation water demand forecasting. The system has been designed to dynamically address changes in conveyance maximum capacities due to outages or other unplanned maintenance activities, along with changes in water supply due to expected near-term rain events. The new implementation has been in operation over the last 12 months and is expected to provide greater flexibility in conducting the various analyses, promote higher data integration, and further optimize the use of the country’s water resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".