Enhancing ecological success in Yangtze River channel construction: A numerical modelling approach
Bibliographic record
Abstract
Abstract Channel constructions significantly impact river hydrodynamics, subsequently influencing river ecosystems. To mitigate the negative influence of channel construction and protect fish habitat, it is essential to evaluate fish habitat suitability through the integration of hydrodynamic and habitat models. This study models channel constructions on both the left and right riverbanks to evaluate habitat suitability for Four Major Chinese Carps and the Chinese sturgeon. Initially, flow velocity, water depth and grain size are simulated, followed by an assessment of habitat conditions using the Habitat Suitability Index (HSI) for three distinct construction strategies. Results reveal that constructing channels on the left bank mitigates adverse effects on fish habitat, while constructions on the right bank and both riverbanks lead to habitat degradation. Long‐term effects on riverbed elevation and fish habitat suitability post‐construction were also investigated. Notably, the Four Major Chinese Carps consistently demonstrate greater suitability for the studied river, regardless of flow rate or construction strategies, in comparison to the Chinese sturgeon. These findings underscore diverse responses to channel construction, providing valuable insights for identifying critical habitat areas for endemic fish conservation. This research presents a robust framework for assessing fish ecology in river systems, offering support for conservation decision‐makers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".