Implementation of a Program to Prevent Sanitary Sewer Overflows by Reduction of Stormwater Inflow
Bibliographic record
Abstract
The City of Ann Arbor, Michigan suffered fi"om recurring episodes ofbasement flooding from sanitary sewer backups during major rainfall events.An investigation was conducted to identify the circumstances controlling the flooding and to evaluate alternatives for mitigation of the flooding.Five major areas subjected to flooding were identified and a monitoring program was conducted in these areas.Due to the distributed nature of the flooding occurrences, a number of system deficiencies were identified.In addition, it was observed that system flows increased rapidly after the commencement of rainfall, leading to the conclusion that contributions from footing drains in older residences \vere a major contribution to the increased system inflows.Options investigated for mitigation of the basement flooding included system capacity increase by construction of relief sewers and pipe bursting, provision of underground storage facilities, and implementation of a footing drain disconnect program.Disconnection of footing drains was indicated to be the least cost alternative in most of the areas although uncertainties existed in the exact contribution of footing drains to system inflows due to the limited number of homes investigated during the field study.Additional considerations such as periodic overflmvs at the wastewater treatlnent plant during high rainfall events and pending sanitary sewer overflow regulations led to the decision to implement the footing drain disconnect program city-wide.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".