Methods to prevent or mitigate total dissolved gas supersaturation in the waterways downstream hydropower plants
Bibliographic record
Abstract
Abstract Downstream hydropower plants, a change in water chemistry can lead to the occurrence of a widely unknown problem: total dissolved gas (TDG) supersaturation. It takes place when air is entrained in a water body and exposed to high pressures, which leads to gas dissolution in the water. Re-exposure to atmospheric pressure downstream the power plant results in TDG supersaturation. This is a potential danger for the aquatic environment living in these waters, as the increased saturation poses the risk of experiencing gas bubble disease (GBD). Studies about TDG supersaturation are found in North America (USA and Canada), China, Brazil, and Norway (minor studies include Austria, Germany, and Sweden). Yet, knowledge about the risk of the problem is not widespread, which leads to the repetition of mistakes. Moreover, shifting precipitation patterns induced by climate change are expected to lead to an increase in TDG supersaturation occurrences, as those are associated with flooding. An overview of methods to either prevent or mitigate the problem of TDG supersaturation downstream hydropower plants is presented and recent study results are disseminated. These include civil engineering, operational, and technical methods. Where hydropower plants are in a planning phase, this can contribute to preventing the occurrence of TDG supersaturation in the first place, while existing hydropower plants can implement different measures to reduce the risk of producing TDG supersaturation in the downstream waterways. This helps maintain the aquatic environment as well as local habitat for fish and invertebrates, and therefore counts towards hydropower taxonomy and increases social acceptance of hydropower.
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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 | 0.002 | 0.001 |
| 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) | 0.003 | 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".