Urban Erosion Potential Risk Mapping with GIS
Bibliographic record
Abstract
With increased regulatory focus on eroded sediment and its bound pollutants, methods are needed to predict areas with high erosive potential (EP) in urbanized areas. Using EP to prioritize urban areas for maintenance, implementation of Stormwater Control Measures (SCMs), stream restoration or monitoring is crucial. This study utilizes commonly available geospatial layers in conjunction with a computational procedure for prioritizing the contribution of site specific- and transport-erosion to compute relative EP risk throughout a target urban watershed. Factors that contribute to erosion were evaluated: local cell slope, soil erodibility, land cover, runoff volume, distance and slope to nearest stormwater conveyance point along a surface flow travel path. A case study of the developed methodology was performed on a 1.6 square kilometer urban watershed in Blacksburg, VA, to generate EP risk maps. Results of the study indicate areas of erosive potential within the target watershed and provide a methodology for creating erosion potential risk maps for use by MS4 planners, engineers and other individuals that manage erosion control programs.
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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 | 0.002 | 0.002 |
| 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) | 0.006 | 0.001 |
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".