Potential benefits to the endemic fish habitat in the highland deeply incised high-energy river responding to hydropower development
Bibliographic record
Abstract
The implementation of hydropower development in the Yarlung Tsangpo River (YTR), one of the largest rivers originating on the Qinghai Tibetan Plateau, has the potential to contribute to carbon neutrality. This study aimed to investigate the potential ecological impacts of upcoming hydropower development on the endemic fish habitats in YTR and to explore a possible balance between power generation benefits and fish habitats protection. A systematic field investigation was conducted on the logistically-challenging and data-lacking YTR, and accurate digital channel topography was generated. The potential changes in habitat of the endemic fish species Schizopygopsis younghusbandi were simulated under the different scenarios of coming hydropower operation modes, i.e., maximum energy mode (ME) and flood peak weakened mode (FW). The results showed that the habitats for non-spawning adults could be improved during hydropower operation throughout the whole year, especially under the ME mode that weighted useable area for fish (WUA) was increased by 21.3% on average. The spawning area could be increased by 2% on average under both modes. However, the habitats for juveniles could be reduced during spring and winter under both FW and ME operation modes. Following the principle of minimizing negative impacts on key life stages of the fish, a seasonally mixed operation mode of FW and ME is recommended for future to achieve both power generation benefits and aquatic ecology protection. This study extends the understanding of sustainable hydropower development in extremely high energy rivers and will benefit nature-based solutions for deeply incised rivers over the world.
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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) | 0.002 | 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".