A predictive model for overland flow velocity on vegetated slopes considering various environmental factors
Bibliographic record
Abstract
Accurately predicting the arithmetic mean velocity of overland flows on vegetated slopes is essential for developing hydraulic erosion models. However, there exists a significant challenge in predicting this velocity in various vegetation conditions. This study proposed a new predictive model based on the principle of resistance superposition, which accounted for a wide range of environmental factors—e.g., vegetation coverage, slope angle, and flow discharge. The model was validated against a comprehensive database with 4168 datasets established from published sources, showing 83.3% of the calculated R squared values in excess of 0.750. The model was also compared with the existing models, demonstrating superior applicability and reliability at various test conditions. After validation and comparison, parametric analysis was conducted to assess the effects of the environmental factors on the velocity. The results highlighted that the velocity decreased with increasing vegetation coverage until reaching a limit and the strong interactive effects of these environmental factors on the velocity. These findings provide valuable insights into how environmental factors influence flow velocity, offering a theoretical foundation for erosion control on vegetated slopes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".