Strategic prioritization of sewersheds to mitigate combined sewer overflows under climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• CSOs pose significant environmental and public health challenges. • Increased precipitation under climate change must be considered in CSO management. • A predictive model was used to predict CSOs under climate change. • CSOs will increase exponentially over time under climate change. • Results inform municipalities on prioritizing sewersheds to mitigate future CSOs. The impact of combined sewer overflows (CSOs) on water bodies is well documented: they pose severe threats to water quality, ecosystems, and public health. Exposure to contaminants from overflows can lead to waterborne diseases, emphasizing the critical need for effective stormwater management. Mitigating the effects of CSOs can be achieved through various solutions, including blue-green infrastructure (BGI). However, the implementation of these solutions often occurs opportunistically rather than strategically, depending on the opportunities that arise. In addition, simulations under climate change predict a surge in extreme events, necessitating adaptation in urban planning and infrastructure design. This paper proposes a prioritization index to support the location choice for mitigation measures under current conditions and projected climate scenarios. The model's effectiveness is validated, and simulated precipitations generated by the Canadian Regional Climate Model version 5 (CRCM5) are used, revealing an exponential increase in CSO events over time due to climate change. The importance of spatial location in prioritizing urban catchments for mitigation measures implementation is emphasized, providing valuable insights for urban planners to navigate climate-induced challenges and protect water bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it