Reconstructing Single Side of Riverbanks Minimizes Adverse Effects on Sailfin Suckers’ Habitat
Bibliographic record
Abstract
ABSTRACT River reconstructions, encompassing flood and erosion control along with the restoration of fish habitat, can alter hydrodynamics within the constructed channel, thereby impacting fish habitat. This study aimed to assess the influence of specific river reconstruction practices, particularly spur dikes, on both hydrodynamics and fish habitat, to optimize the design of spur dike constructions for fish habitat restoration. The study utilized ecohydraulic modeling to examine the design of river reconstruction strategies and provide valuable insights on their efficacy, with a focus on refining these practices to enhance the suitability of habitat for Myxocyprinus asiaticus (Chinese Sailfin Sucker) in the Yangtze River. We used various flow rates and time periods to test hydrodynamic and habitat responses and long-term stability of the restoration schemes. The results suggested that the habitat quality of all the tested cases increased with flow rates and then decreased. Modelling results indicated a ranking of the habitat status from high to low in the following scenarios: baseline channel without riverbank constructions, with straight or T-shaped spur dikes installed on a single riverbank, and with straight or T-shaped spur dikes installed on both riverbanks. For post-effects of the restoration strategies, each approach would scour the riverbed. Installing spur dikes on both riverbanks would severely degrade the fish habitat suitability level, with the overall suitability index decreasing over time. In summary, installation of spur dikes along a single riverbank minimized the impact on the suitability of fish habitats and was the preferred scenarios to optimize habitat suitability for Chinese Sailfin Sucker.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".