Modeling Approach using PCSWMM to Support Infiltration/Inflow Remediation Area Studies
Bibliographic record
Abstract
Launching a series of sewer system inflow and infiltration (I/I) remediation projects, the City of Columbus, Ohio structured the project schedules so that the first I/I project would set the technical approach "cornerstone" of the projects to follow.In particular, it was important that the initial project develop a set of modeling tools and application approaches designed to streamline all the hydrologic and hydraulic (H/H) models and provide a consistent approach for the entire series of projects.The first I/I project is known as the Livingston/James Sewer System I/I Remediation Project.To ensure consistency and compatibility, the City established PCSWMM as the platform for all the City's I/I remediation projects.In developing the modeling approach for the first project, the City of Columbus, CDM Inc., and CHI worked together as a team to enhance and apply PCSWMM and the SWMM 4.4h computational engine to model the subject sewer system.This chapter discusses the lessons learned and the solutions that the project team developed, including several SWMM code revisions and the development of new PCSWMM routines.Each of these solutions represents valuable developments potentially applicable to other PCSWMM modeling projects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".