Overview of Vulnerabilities of Coastally-Influenced Conveyance and Treatment Infrastructure in Greater Vancouver to Climate Change: Identification of Adaptive Responses
Bibliographic record
Abstract
The Greater Vancouver Regional District (GVRD) and its member municipalities own and operate a variety of infrastructure including: combined, sanitary and storm sewers; creek and ditch drainage systems; and wastewater treatment plants (WWTPs). A significant portion of this infrastructure discharges either directly or indirectly to tidally-influenced receiving waters. Thus, both this infrastructure and its functionality can be impacted by coastal conditions, especially sea levels. The GVRD has developed a variety of long-range plans that provide a foundation to manage core infrastructure so as to satisfy various criteria, including protection of the receiving water ecosystem. The Liquid Waste Management Plan (LWMP) outlines a strategy to protect regional sustainability by managing liquid waste and addressing issues such as combined sewer overflows (CSOs), sanitary sewer overflows (SSOs), wastewater treatment upgrading and stormwater management. Climate change, along with sea level rise (SLR), has the potential to impact the functionality of this infrastructure over the time frame of long-range planning (LRP). The specific vulnerabilities of coastally-influenced conveyance and treatment infrastructure to the impacts of climate change and SLR have not been widely evaluated in available research. This paper is intended to provide an overview understanding by reviewing some relevant research, identifying and classifying some of the potential vulnerabilities of this infrastructure to climate change and SLR and then discussing possible adaptive strategies for this infrastructure. The field of research into adaptive strategies for infrastructure is expanding, along with a better understanding of these issues. A basic and preliminary methodology to evaluate regional vulnerabilities as part of LRP has been developed for consideration for ultimate refinement and implementation. The magnitude and timing of SLR and climate change is uncertain, but has a large impact on the vulnerability of infrastructure and the scope of appropriate adaptive strategies. Thus, maintenance of maximum system flexibility as reasonable may be an appropriate response for infrastructure planning to respond to a changing climate. Periodic study of developing climate change trends will assist in improving the understanding of both infrastructure vulnerabilities and appropriate adaptive strategies.
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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".