A Climate Change Adaptation Planning Process for Low-Lying, Communities Vulnerable to Sea Level Rise
Bibliographic record
Abstract
While the province of British Columbia (BC), Canada, provides guidelines for flood risk management, it is local governments’ responsibility to delineate their own flood vulnerability, assess their risk, and integrate these with planning policies to implement adaptive action. However, barriers such as the lack of locally specific data and public perceptions about adaptation options mean that local governments must address the need for adaptation planning within a context of scientific uncertainty, while building public support for difficult choices on flood-related climate policy and action. This research demonstrates a process to model, visualize and evaluate potential flood impacts and adaptation options for the community of Delta, in Metro Vancouver, across economic, social and environmental perspectives. Visualizations in 2D and 3D, based on hydrological modeling of breach events for existing dike infrastructure, future sea level rise and storm surges, are generated collaboratively, together with future adaptation scenarios assessed against quantitative and qualitative indicators. This ‘visioning package’ is being used with staff and a citizens’ Working Group to assess the performance, policy implications and social acceptability of the adaptation strategies. Recommendations based on the experience of the initiative are provided that can facilitate sustainable future adaptation actions and decision-making in Delta and other jurisdictions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".