Conscience July 2024 Policy Brief - Climate Change, Dengue Fever, and Vaccine Shortages
Bibliographic record
Abstract
Climate Change and Dengue Fever: Urgent Need for New Solutions Amidst Rising Cases and Limited Vaccine Supply Climate change has exacerbated the spread of multiple tropical diseases through various mechanisms while research into treatment and prevention of these diseases does not reflect their expected impact on life expectancy. Amongst those diseases, recently increased temperatures have expanded the geographical scope of mosquitoes carrying dengue fever. The virus traditionally affected countries near the Equator, but new cases have been reported as far as Europe. This Policy Brief explores the problem, some resulting solutions, and lessons learned
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.059 | 0.026 |
| Insufficient payload (model declined to judge) | 0.061 | 0.027 |
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".