The Influence of Riparian Vegetation on the Sinuosity and Lateral Stability of Meandering Channels
Bibliographic record
Abstract
Abstract Floodplains of meandering rivers are colonized with various plant species that differ in how they stabilize streambanks, modulating sinuosity evolution. Here, we compile observations of meander migration from North and South America, categorizing channels based on the riparian vegetation as cropland, forest, grassland, and rainforest. Our analysis reveals that the most stable meanders are those developed in rainforests and the reason is likely related to their established root systems and clay‐rich soils. The most unstable meanders were found in cropland areas, which is explained by the lack of vegetation cover and the frequent land disturbances associated with cultivation. Rivers in grassland and North American forest environments have intermediate migration rates. We found that meanders in grasslands have lower migration rates than those in North American forests. The reason for this may lie in the fact that grassland meanders in general have higher sinuosity, lower gradients, and layered banks.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".