Developing a New Operations Plan for the Bow River Basin Using Collaborative Modeling for Decision Support
Bibliographic record
Abstract
Abstract The Bow River Basin is a cornerstone of Alberta's development. In 2010, stakeholders representing interests from agriculture, municipalities, environment, and more formed the Bow River Project Research Consortium to help determine the potential for improving the operations in the basin. At present, upstream reservoirs are operated primarily for hydropower, whereas downstream reservoirs are operated for irrigation. Through Collaborative Modeling for Decision Support the stakeholders were able to develop a new method for operating the system that would dramatically improve environmental performance. The main components of the new operating strategy called for: purchase or setting aside of a small amount of storage volume in the power reservoirs; a set of rules for releases from that storage; an agreement by the major irrigation districts with the largest water licenses to utilize their ability to shift deliveries to and from their large offstream storage reservoirs to allow for increased instream flows, and to allow junior water license holders (mainly municipal and industrial supplies) an uninterrupted water supply; limitations of reservoir fluctuations to improve inreservoir habitat for fisheries; and increased minimum flows throughout the system leading to improved environmental outcomes. Costs of this strategy were minimal, impacts on power revenue were estimated at
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".