Assessment of and Solutions to the Stormwater Management System of Auburn University Campus in Auburn, Alabama
Bibliographic record
Abstract
Stormwater management needs attention as it causes surface flooding and pollution of nearby waterbodies. Parkerson Mill Creek in Auburn University, which gets polluted through surface runoff, is an example of this. In this study, a Personal Computer Stormwater Management Model (PCSWMM) was used to determine the susceptibility of the existing stormwater network to flooding on the Auburn University campus. Maximum water velocity mapping was used to identify areas associated with 3 categories of velocity (high, medium, and low) to find areas of potential erosion. Among the various sustainable stormwater management initiatives, it was found through a literature review that bioretention cells had the greatest potential to improve stormwater quality by screening pollutants from runoff water as well as minimizing erosion by reducing surface water velocity. Suitability analysis for bioretention cells identified 8 areas on the campus where bioretention cell could be installed for the most effective stormwater management. This study highlights the usability of PCSWMM models and techniques in increasing the efficiency of the stormwater system in any locality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".