Numerical modelling for ecologically successful spawning-site restoration in Chin-sha River, China
Bibliographic record
Abstract
Context The construction of dams on the Chin-sha River will affect fish spawning sites, leading to a decline in fish species. Aims This paper presents a model to evaluate the ecological status of restoration strategies aimed at fish species living at a spawning site. Methods The model comprises hydro-morphodynamic and habitat modules. The modelling approach was applied with two restoration strategies (side-channel addition and riverbank reconstruction) and their corresponding post-restoration effects. Key results Three indicators were utilised to assess the ecological status of the spawning site. Modelling results showed poor ecological status under current hydrological conditions, with weighted usable area and overall suitability index values of 1.07 × 106 m2 and 0.41. Without implementing a restoration strategy, the ecological status would continue to fragment and deteriorate. Conclusions The weighted usable area can be recovered to 2.86 × 106 and 1.67 × 106 m2 in scenarios of side-channel and bank construction respectively. The overall suitability index values increase to 0.67 and 0.63 respectively. Implications It is also noted that the ecological restoration strategy (side-channel addition) can considerably enhance the freshwater Reeves shad’s habitat status. Additionally, the restoration strategy illustrated the feasibility of the side-channel addition restoration strategy.
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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) | 0.003 | 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".