A Model Maintenance Tool - Moving Forward with an Investment in a System-Wide Model
Bibliographic record
Abstract
In 2000, the Metropolitan Sewer District of Greater Cincinnati (MSDGC) initiated the development of a system-wide computer model (SWM) of their wastewater collection system to assist the agency in the assessment of the hydraulic performance of its system and in the prioritization of short-and long-term system improvements.The 42,000-node SWM was developed and calibrated, using EPA SWMM 4 (Huber, 1988), over a three-year period and represents over 1,500 miles of pipe (CDM, 2003).In response to Consent Decree requirements (United States of America, 2002America, & 2003)), the SWM was used to perform a comprehensive hydraulic capacity assessment of the wastewater collection system under both dry-and wet-weather flow conditions and is currently being applied to find solutions to assure system capacity.The SWM has been integrated into the agency operations and is being applied to meet a variety of objectives, some of which will extend well into the future.The SWM was built primarily with data from the Cincinnati Area Geographic Information System (CAGIS), a consortium of public and private entities with the goal of developing and maintaining infrastructure inventories in a common framework, in which MSDGC has participated since 1989.The inventory of sewer data in CAGIS has been diligently
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".