Water Management Modelling in the Simulation of Water Systems in Coastal Communities
Bibliographic record
Abstract
It is no longer a question of scientific debate that research declares our climate is changing. One of the most important and visible impacts of this phenomenon is sea level rise which has impacts on coastal cities and island communities. Sea level rise also magnifies storm surges which can have severely damaging impacts on different human made infrastructure facilities near the shorelines in coastal zones. In this research we are concerned about the proximity of water systems as one of the most vulnerable infrastructures in the coastal zones because of the impact of stormwater combining with sewage water. In Canada, the government has plans to address these issues, but to date, there needs to be further attention to stormwater management in coastal zones across the country. This research discusses the impacts of severe environmental events, e.g., hurricanes and storm surge, on the water systems of selected coastal communities in Canada. The purpose of this research is to model coastal zone water systems using the open source StormWater Management Modelling (SWMM) software in order to manage stormwater and system response to storms and storm surge on water treatment plants in these areas. Arichat on Isle Madame, Cape Breton, one of the most sensitive coastal zones in Canada, is the focal point case study for this research as part of the C-Change International Community-University Research Alliance (ICURA) 2009-2015 project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".