Application of a fish habitat model to assess habitat fragmentation using high flow and sediment transport in the Rumei Dam in Lancang River (China)
Bibliographic record
Abstract
Abstract Dam construction and operation can result in serious disturbances to the downstream flow regime and fluvial process, river morphology and the river's ecological condition worldwide. To understand the effects of discharge and sedimentation on fish habitats and ecosystems, an ecohydraulic approach was applied to one of the mega hydropower schemes in the downstream Lancang River (Tibet). The approach comprised a dam operation module with high flow and sediment transport and the application of a dynamic fish habitat model for assessing habitat fragmentation of two targeted fish species: Schizothorax prenanti (S. prenanti) and Schizothorax davidi (S. davidi). The hydrodynamics of the river system and the assessed fish habitat show a significant impact of dam construction and operation on the downstream riverine ecosystem, in which fish habitats were found rapidly deteriorated with the destruction of feeding and reproductive grounds. To improve fish habitat after dam construction, the sediment supplement was applied to the river and shown to be a useful restoration strategy, which recovers the habitat fragmentation of target fish. Our model not only is useful to predict dam operation impacts on the Lancang River's ecological status but also shows great potential in mitigating hydropower‐induced environmental impacts and developing river conservation guidelines worldwide.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".