Modeling as a Tool for Economic Analysis of Basement Flood Relief Projects
Bibliographic record
Abstract
The City of Winnipeg, with a population of 630,000, is centrally located in the southern part of the Province of Manitoba, Canada.Combined sewer systems service approximately 50% of the City of Winnipeg.These systems were installed from the early 1900s up until1960 and primarily service the older core areas of the City.The original designs reflected the site conditions at the time; however, changes in land use and new development have resulted in dramatic increases in the volume and rate of stormwater runoff.The increased stormwatcr results in frequent sewer surcharge and backup into basements with associated property damages.The City ofWinnipeg has been carrying out an extensive basement flood relief program since 1977.The program is designed to upgrade basement flood protection to a minimum five-year level.The development and prioritization of proposed relief works is dependent on a comparison of the benefits of installing relief works to the implementation costs.SWMM modelling is used extensively in the program to develop relief options and to provide data for the economic analysis.This chapter details the full development of a basement flood relief plan for the Dumoulin Combined Sewer District in the City of Winnipeg.It includes descriptions of model development, calibration and verification, assessment of existing levels ofbasement flood protection and relief alternatives to upgrade the combined sewer system, and the economic analysis of proposed relief works.---------
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".