Getting Community Support for Large-Scale Urban Flood Mitigation Improvements: The Blounts Creek Watershed Story
Bibliographic record
Abstract
The City of Fayetteville has developed a Watershed Master Plan program that uses 1D and 2D riverine and collection system modeling to proactively identify and develop flood mitigation projects across the city. The multi-million-dollar Russell-Person Street Bridge and Stream Improvements project was a recommended result of this modeling work. The project mitigates flooding for a large, marginalized area of downtown Fayetteville, includes nature-based solutions, and serves as a capstone project for the Blounts Creek Watershed as well as the entire Watershed Master Plan program. Local leaders have supported grant initiatives and actively backed funding for the project. They understand that a comprehensive project and program that supports resiliency and equitably uses City resources must have inter-governmental and cross-departmental collaboration and public support.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".