An agent-based modeling approach to household adaptation for flooding and coastal erosion at Channel-Port aux Basque
Bibliographic record
Abstract
Climate change poses a significant threat to coastal communities, particularly those reliant on coastal infrastructure. Rising sea levels and increasingly severe weather events endanger coastlines across Canada, a nation with over 243,000 km of coastline. Indigenous communities, with their long history of coastal resource utilization, are especially vulnerable. This study investigates the social, economic, and environmental impacts of coastal erosion and flooding in Channel-Port aux Basques, Newfoundland. Hurricane Fiona’s devastating impact on the community in 2022 underscores the urgency of adaptation strategies. The research employed a multi-pronged approach: data analysis from weather and census sources, a literature review, a vulnerability assessment using established frameworks, and agent-based modeling using NetLogo software. Findings reveal significant social impacts, including displacement, emotional distress, and loss of public spaces. While existing adaptation measures like shoreline armoring offer some protection, their effectiveness in the face of stronger storms and projected sea level rise remains questionable. The agent-based model predicts increased coastal erosion, with households within 8 meters experiencing erosion within the first year at current sea level rise rates. The study recommends developing a comprehensive plan to mitigate coastal erosion risks. Potential solutions include sea walls and promoting sustainable land-use practices that prioritize coastal ecosystems and managed retreat strategies. The research concludes by emphasizing the need for context-specific, participatory adaptation strategies to enhance coastal community resilience in the face of a changing climate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".