Assessing the social risks of flooding for coastal societies: a case study for Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract With the worldwide growing threat of flooding, assessing flood risks for human societies and the associated social vulnerability has become a necessary but challenging task. Earlier research indicates that islands usually face heightened flood risks due to higher population density, isolation, and oceanic activities, while there is an existing lack of experience in assessing the island-focused flood risk under complex interactions between geography and socioeconomics. In this context, our study employs high-resolution flood hazard data and the principal component analysis (PCA) method to comprehensively assess the social risk of flood exposure and social vulnerability in Prince Edward Island (PEI), Canada, where limited research has been delivered on flood risk assessments. The findings reveal that exposed populations are closely related to the distribution of flood areas, with increasingly severe impact from current to future climate conditions, especially on the island’s north shore. Exposed buildings exhibit a concentrated distribution at different levels of community centers, with climate change projected to significantly worsen building exposure compared to population, possibly due to the urban agglomeration effect. The most populated cities and towns show the highest social vulnerabilities in PEI, and the results reflect a relatively less complex economic structure of islands. Recommendations for research and management in the coming stage include the necessity of particular climate actions, recognizing community centers as critical sites for flood hazard responses, and incorporating flood hazards into urban planning and management to mitigate the impacts of continuous urbanization on ecosystem services for flood prevention.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".