Bibliographic record
Abstract
Increasing urbanization and climate change frequently overwhelm the capacity of combined sewer overflow (CSO) tanks, posing significant risks to the environment, public health, and the economy. This study applied a discrete-time Markov chain (DTMC) model to analyze the stochastic behavior of monthly CSO tank overflow volumes, incorporating uncertainties such as rainfall variability and operational conditions. We used a decade of data (2013–2022) from two CSO tanks (HCS01 and HCS04) in Hamilton, Canada, and categorized the monthly overflow data into five states—no overflow, minor overflow, moderate overflow, major overflow, and severe overflow—using the elbow method and silhouette score. Our results show distinct overflow dynamics between the tanks: HCS04 demonstrated robust resilience, with an 84% probability of no overflow following months of no or minor overflow, and only a 4% chance of severe overflow, suggesting that it was more stable. In contrast, HCS01 exhibited greater instability with a 65% probability of no overflow and a higher probability of severe overflow, particularly after major overflow events. These findings emphasize the need for tailored management strategies for each tank. This study’s novel application of the DTMC model, combined with data uncertainty incorporation, provides valuable probabilistic insights into CSO dynamics, informing better infrastructure planning and overflow risk mitigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".