Case Study of Total Dissolved Gas Transfer and Degasification in a Prototype Ski-Jump Spillway
Bibliographic record
Abstract
Gas transfer in dam spillways often leads to supersaturation of total dissolved gases (TDGs) that can cause fish mortality. Quantifying TDG associated with this process is crucial in the development of operating alternatives to minimize environmental risks to downstream aquatic habitats. In this study, the transfer of TDG in a spillway was evaluated through field observations at the Seven Mile Dam on the Pend d’Oreille River in British Columbia, Canada. The dam degasses high-TDG water due to a ski-jump design where aerated flows are generated from a flip bucket at the end of the spillway chute. A simplified mathematical formulation, incorporating physical processes related to air entrainment, bubble characteristics, and mass transfer across free surface and bubbles, was tested and verified for prototype flows to partition gas transfer in the spillway face, free jet, and plunge pool supported by extensive field measurements. Due to gas exchange dominated by bubble-mediated transfer, substantial degassing of TDG was observed during spill operations, with degassing in the free jet being considerably higher compared to the spillway face and plunge pool. The gas transfer efficiency was high when pre-aeration (air entrainment on the spillway face) occurred. Practical relationships were proposed to estimate degassing in the jet and assess overall gas transfer efficiency. Results from this study can help inform water management decisions during periods of elevated TDG, particularly in cascading hydropower systems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".