Engineering Meets Public Participation on the Coast in Surrey, British Columbia, Canada
Bibliographic record
Abstract
City of Surrey is a coastal community located within the Greater Vancouver area of British Columbia, Canada. It has a population of approximately 550,000, with an average annual growth rate of 2% over the last 10 years. Since early European settlement in the 19th century, the community has managed flooding, having over 60 sq. km. of land located within coastal floodplains containing significant critical infrastructure such as highways, water, sewer, power and rail lines. To tackle the hard questions about sea level rise, City of Surrey embarked on an innovative public planning process that incorporated engineering analysis and built on extensive coastal, riverine and hydrologic modelling. By engaging residents, stakeholders and partners on long-term adaptation approaches, priority near-term infrastructure investments were developed that were consistent with long-term needs. A coastal flood resilience and adaptation project valued at C$187 million was developed to increase resilience of critical infrastructure, while also reduces cumulative socio-economic damages and provides important community benefits. Approximately 10% of the investment is in green infrastructure that offsets biodiversity and recreational impacts of sea level rise and minimizes necessary grey infrastructure demands through wave attenuation and flood storage. The case study demonstrates the benefit of engineers collaborating with other professions (including planners, landscape architects, teachers and communication experts) to engage the public when seeking clear and confident decision-making around complex coastal engineering challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.006 |
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".