Using a fish entrainment model assistant in a reservoir operation in China
Bibliographic record
Abstract
Abstract Individual fish are vulnerable in hydropower reservoirs due to spillway and intake operations. It is essential to understand how reservoir forebay fish ecosystems respond to water levels, intake, and spillway regulation. This study aims to explore the fish entrainment risk of the Dawei Reservoir operation on the Dadu River in Sichuan, China, accounting for both intake and spillway operations under wet, normal, and dry seasonal reservoir water levels. Hydrodynamic variables, reservoir operation scenarios, and two fish species were used as indexes to analyze the fish entrainment risk. The simulation results showed that the fish entrainment risk was low under the Dawei intake operation schemes ranging from 0.84 × 103 to 5.97 × 103 m2. The results also showed that the fish entrainment risk was very high under the Dawei spillway operation in fish entrainment areas ranging from 3.90 × 104 to 2.08 × 105 m2. Based on the simulation results, the lowest fish entrainment risk happened with two intakes open and the reservoir water level at 2,640 m. The highest fish entrainment risk happened with five intakes open and the reservoir water level at 2,670 m. The results indicate that the long-term Dawei Reservoir regulation would not modify the fish entrainment risk at significant levels under the Dawei Reservoir operation schemes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".