Physical basis of coastal adaptation on tropical small islands
Bibliographic record
Abstract
Small tropical islands are widely recognized as having high exposure and vulnerability to climate change and other natural hazards. Ocean warming and acidification, changing storm patterns and intensity, and accelerated sea-level rise pose challenges that compound the intrinsic vulnerability of small, remote, island communities. Sustainable development requires robust guidance on the risks associated with natural hazards and climate change, including the potential for island coasts and reefs to keep pace with rising sea levels. Here we review these issues with special attention to their implications for climate-change vulnerability, adaptation, and disaster risk reduction in various island settings. We present new projections for 2010–2100 local sea-level rise (SLR) at 18 island sites, incorporating crustal motion and gravitational fingerprinting, for a range of Intergovernmental Panel on Climate Change global projections and a semi-empirical model. Projected 90-year SLR for the upper limit A1FI scenario with enhanced glacier drawdown ranges from 0.56 to 1.01 m for islands with a measured range of vertical motion from −0.29 to +0.10 m. We classify tropical small islands into four broad groups comprising continental fragments, volcanic islands, near-atolls and atolls, and high carbonate islands including raised atolls. Because exposure to coastal forcing and hazards varies with island form, this provides a framework for consideration of vulnerability and adaptation strategies. Nevertheless, appropriate measures to adjust for climate change and to mitigate disaster risk depend on a place-based understanding of island landscapes and of processes operating in the coastal biophysical system of individual islands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".