Determining a Consistent Peak Flow Level of Control for a Wet Weather Management Plan
Bibliographic record
Abstract
The City of Columbus, Ohio, completed a comprehensive Wet Weather Management Plan (WWMP) to mitigate hydraulic deficiencies in the City's main trunk sewers and to address sanitary sewer overflow (SSO) into waterways and water-in-basements occurrence (WIBs).One of the main problems we overcame was to determine the desired level of service (LOS), or what is more commonly called the most reasonable level of control (LOC).The concept, restated, answers the question, "With what frequency should deficiency (problem, emergency) thresholds be reached?"That is, how often (on average) should SSOs and WIBs occur?At the outset of Columbus' WWMP production, the design team was well aware of the high sensitivity of the model to hydraulic and hydrologic parameters such as storm recurrence intervals and durations, antecedent moisture conditions, and rainfall distributions.Federal guidelines do not mandate specifics on these.And the impact these would have had on the program costs made determining a reasonable LOC one of the most important questions we faced.To minimize the impact of these sensitivities, and to ensure that all of the City of Columbus' main trunk sewers meet or exceed the LOC, the design team used peak-flow events or a maximum
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".