Health Impacts of Climate Change in the Solomon Islands: An Assessment and Adaptation Action Plan
Bibliographic record
Abstract
The Pacific island countries are particularly vulnerable to the environmental changes wrought by global climate change such as sea level rise, more frequent and intense extreme weather events and increasing temperatures. The potential biophysical changes likely to affect these countries have been identified and it is important that consideration be given to the implications of these changes on the health of their citizens. The potential health impacts of climatic changes on the population of the Solomon Islands were assessed through the use of a Health Impact Assessment framework. The process used a collaborative and consultative approach with local experts to identify the impacts to health that could arise from local environmental changes, considered the risks associated with these and proposed appropriate potential adaptive responses. Participants included knowledgeable representatives from the biophysical, socio-economic, infrastructure, environmental diseases and food sectors. The risk assessments considered both the likelihood and consequences of the health impacts occurring using a qualitative process. To mitigate the adverse effects of the health impacts, an extensive range of potential adaptation strategies were developed. The overall process provided an approach that could be used for further assessments as well as an extensive range of responses which could be used by sectors and to assist future decision making associated with the Solomon Islands' responses to climate change.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".