Risks to future atoll habitability from climate‐driven environmental changes
Bibliographic record
Abstract
Abstract Recent assessments of future risk to atoll habitability have focused on island erosion and submergence, and have overlooked the effects of other climate‐related drivers, as well as differences between ocean basins and island types. Here we investigate the cumulative risk arising from multiple drivers (sea‐level rise; changes in rainfall, ocean–atmosphere oscillations and tropical cyclone intensity; ocean warming and acidification) to five Habitability Pillars: Land, Freshwater supply, Food supply, Settlements and infrastructure, and Economic activities. Risk is assessed for urban and rural islands of the Pacific and Indian Oceans, under RCP2.6 and RCP8.5, in 2050 and 2090, and considering a moderate adaptation scenario. Risks will be highest in the Western Pacific which will experience increased island destabilization together with a high threat to freshwater, and decreased land‐based and marine food supply from reef‐dependent fish and tuna and tuna‐like resources. Risk accumulation will occur at a lower rate in the Central Pacific (lower pressure on land, with more limited cascading effects on other Habitability Pillars; increase in pelagic fish stocks) and the Central Indian Ocean (mostly experiencing increased land destabilization and reef degradation). Risk levels will vary significantly between urban islands, depending on geomorphology and local shoreline disturbances. Rural islands will experience less contrasting risk levels, but higher risks than urban islands in the second half of the century. This article is categorized under: Trans‐Disciplinary Perspectives > Regional Reviews
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".