Quantifying Rainfall-Derived Inflow from Private Foundation Drains in Sanitary Sewers: Case Study in London, Ontario, Canada
Bibliographic record
Abstract
Rainfall-derived infiltration and inflow (RDII) is a major issue causing surcharge flows in many municipal sanitary sewer systems. This paper demonstrates a statistical method for characterizing the rainfall-derived inflow (RDI) originating from residential foundation drains (also referred to as weeping tiles), as well as the importance of having site-specific data. The results differentiating the contribution of RDI from residential weeping tiles (WT) to total RDII are demonstrated for a case study site in a residential subdivision in London, Ontario, Canada. This research used statistical linear regression analyses with bootstrapping methods to quantify the RDI and its flow duration. It was found that the RDI from WT contributed up to 85% of the total RDII in the sanitary sewer. By disconnecting WT at this site, the RDI generated as a result of rainfall events was reduced by a minimum of 78% in volume and 32% in flow duration. Thus, this paper presents a novel method to quantify RDI and its duration from statistical perspectives, which provides better supporting evidence and guidance for RDI projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".