Effectiveness of Stream Restoration in Reducing Stream Bank Erosion: The Case of Batavia Kill Stream Restoration Projects, New York
Bibliographic record
Abstract
The number of stream restoration projects has increased dramatically during the last decade, especially in the New York City watershed region, where stream management to improve water quality is a high priority, and where the NYC Department of Environmental Protection and the Greene County Soil and Water Conservation District have partnered to develop a set of restoration demonstration projects. In this paper, the effectiveness of stream restoration projects in reducing stream bank erosion in the Batavia Kill watershed (Greene County, New York) is evaluated. This evaluation is based on a multivariate regression model to relate stream bank erosion rates to various explanatory variables including instruments representing geomorphological characteristics, flow conditions, rainfall conditions, temperature, the vegetation index, soil erodibility, and sediment characteristics. The general to specific approach is used to specify the regression model. A range of statistical tests is applied to check the model accuracy and the validity of the regression model. The results of these tests show that the stepwise regression model accurately predicts stream bank erosion rates on the Batavia Kill stream. The regression model is then applied on the project reaches, assuming there was no stream restoration to predict the stream bank erosion. It is found that the measured erosion on the restored reaches is much smaller than predicted erosion in the "without restoration" case, which means that the effectiveness of stream restoration in reducing bank erosion in the Batavia Kill watershed is significant.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".