Designing Technical Upstream Fishways Capable of Adapting to Changing Environmental Conditions
Bibliographic record
Abstract
Hydroelectric dams serve an important role in attaining independence from fossil fuel and are a key component of the renewable energy portfolio. When dam removal is not feasible or when new dams are being built, fish passage must be considered and designed with adaptability in mind for the long-term use. This paper covers the need to design technical upstream fishways capable of adapting to changing conditions. Changing conditions may be environmental and influenced by climate change, such as different water quality, temperature, or flow rates. Changing conditions may also be stakeholder imposed such as the need to increase efficiency to a greater range of species, increase the operation window, and/or increase the low and high tailwater elevations. Addressing these changes may prove difficult with a concrete structure that offers minute flexibility. This paper argues that the flexibility for adaptive management should be built into the design with consideration for unexpected changes. This argument is reinforced through a case study to demonstrate the planning, construction, and operation of two upstream fishways, one temporary and one permanent, as part of the construction of a third hydroelectric dam on the Peace River in northeast British Columbia, Canada (Site C Clean Energy Project). The paper focuses on lessons learned from the temporary facility and how they informed the design and construction of the permanent facility.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".