Adaptation Decision Support: An Application of System Dynamics Modeling in Coastal Communities
Bibliographic record
Abstract
This research develops and applies a system dynamics (SD) model for the strategic evaluation of environmental adaptation options for coastal communities. The article defines and estimates asset-based measures for community vulnerability, resilience, and adaptive capacity with respect to the environmental, economic, social, and cultural pillars of the coastal community under threat. The SD model simulates the annual multidimensional dynamic impacts of severe coastal storms and storm surges on the community pillars under alternative adaptation strategies. The calculation of the quantitative measures provides valuable information for decision makers for evaluating the alternative strategies. The adaptation strategies are designed model results illustrated for the specific context of the coastal community of Charlottetown, Prince Edward Island, Canada. The dynamic trend of the measures and model sensitivity analyses for Charlottetown—facing increased frequency of severe storms, storm surges, and sea-level rise—provide impetus for enhanced community strategic planning for the changing coastal environment. This research is presented as part of the International Community-University Research Alliance C-Change project “Managing Adaptation to Environmental Change in Coastal Communities: Canada and the Caribbean” sponsored by the Social Science and Humanities Research Council of Canada and the International Development Resource Centre.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".