Hydropower turbine-generated total dissolved gas supersaturation during synchronous condense operations
Bibliographic record
Abstract
Total dissolved gas (TDG) supersaturation in rivers is a well-documented environmental concern, as exposure to levels above 110 % can increase mortality risk for fish and other aquatic organisms. While supersaturated TDG is primarily generated during spillway releases where entrained air dissolves in plunge pools, hydropower turbines operating in synchronous condense mode may also contribute to TDG generation. In this study, field measurements were conducted at the Seven Mile Dam on the Pend d'Oreille River, Canada, under different synchronous condense scenarios to evaluate the magnitude and mechanisms of turbine-driven TDG supersaturation. A simplified mathematical formulation was developed to describe mass transfer across the draft tube free surface and simulate TDG transport. The influence of operational parameters including compressed air pressure, cooling water discharge, mass transfer coefficient, and operation duration on TDG generation was examined. TDG levels up to 146 % were observed downstream of the unit, demonstrating that turbines operating in synchronous condense mode can generate biologically significant supersaturation. Results showed that operating synchronous condense mode in conjunction with generation flow reduced TDG levels by about 20 %, and that supersaturation effects were largely confined within 0.5 km downstream of the turbine. These findings highlight the importance of turbine operation as a potential source of TDG supersaturation and its associated ecological risks, and provide guidance for operational strategies to mitigate impacts on river ecosystems. • Hydro turbine synchronous condense operations generate supersaturated dissolved gas. • Operational factors like air pressure and discharge strongly affect dissolved gas levels. • Total dissolved gas risks mitigated by optimized operations to protect aquatic life.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".