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 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 | 0.001 | 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".