Priority climate and health modelling needs
Bibliographic record
Abstract
Climate and health modelling is necessary for improving understanding of the current and future distribution and timing of climate-related health risks. However, underinvestment in this area has limited the understanding required to inform policies that enable multisectoral interventions to safeguard health. We synthesised insights from a survey of 65 global climate and health modelling experts and 36 participants in a hybrid meeting to identify priority strategies for enhancing the validity, utility, and policy relevance of climate and health models. Foundational investments to support modelling included strengthening research capacity, establishing a network of multinational centres of excellence for transdisciplinary research and capacity building, improving data collection and sharing infrastructure, investing in scenario development and quantitative elaboration, assessing adaptation effectiveness, and committing to intermodel comparisons and interdisciplinary modelling activities. Specific recommendations included updating the 2014 WHO Quantitative Risk Assessment to cover a wider range of causal pathways and health endpoints, using interdisciplinary methods that facilitate model intercomparisons. Additional recommendations included supporting modelling of a broader set of climate-health outcomes, developing models to support early warning systems and investments in their implementation, evaluation, and maintenance, and improving health system capacity for modelling in low-resource settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".