A goal programming algorithm to incorporate the Columbia river non-power flow requirements in the Columbia River treaty model
Bibliographic record
Abstract
Canada built and operates three large dams and the U.S. built and operates one dam in the Columbia River based on the Columbia River Treaty (CRT) which was signed in 1961 and ratified in 1964. Annual Operating Plan developed by two Entities does not include non-power requirements, unless they are mutually agreed upon by both Entities. Supplemental Operating Agreements (SOA) have been negotiated and implemented since the 1990s to meet the U.S. and the Canadian power, fish, wildlife and/or recreation needs. The objective of this research was to develop a multi-objective optimization model to deal with multiple and conflicting objectives. The Columbia River Treaty Model (CRTM) developed by BC Hydro was modified by this research to incorporate three non-power requirements that are agreed upon by both Entities. The new model, which utilized the Goal Programming technique to solve the multi-objective reservoir optimization problem, was used to perform a number of case studies in order to investigate the impacts of incorporating different non-power requirements onto the BC Hydro system. Specific minimum outflow at the border in January affects the level of fulfillment of three non-power requirements. The first requirement is the Flow Augmentation requirement to aid in the downstream migration of Salmon in the U.S. The second requirement is the flow requirements below the Arrow reservoir to protect Whitefish eggs during the spawning and hatching periods. The third requirement is the specific flow requirement to provide enough water cover for the Trout spawning downstream of Arrow. The model uses three prioritized objectives of which the Flow Augmentation and Whitefish are of highest priority followed by the Trout Spawning and the maximization of BC Hydro revenues. Four Arrow minimum flow scenarios were compared with the Treaty operation. The study results show that lowering the minimum Arrow flow limit in January increases the satisfaction level of the Flow Augmentation and Whitefish non-power requirements. However, it may be in conflict with the power requirement of meeting Pacific North-west winter peak loads. Unless additional flow is required for Flow Augmentation during April, it has no effect on Trout spawning.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| 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".