Climate factors related to the dengue incidence in Costa Rica and future projections under scenario SSP5-8.5.
Bibliographic record
Abstract
This article has three objectives: 1) modeling the climate-dengue relationship at the smallest administrative division (districts) using high-resolution data; 2) use of an objective algorithm for the selection of predictors that results in parsimonious models, cross-validated to prevent overfitting; and 3) using estimates from CMIP6 climate models to provide mid-century (2035-2065) potential dengue incidence projections under a pessimistic scenario (SSP5-8.5) with seasonal windows actionable by region in Costa Rica to guide preparedness decisions. Results show that temperature and precipitation data are significantly related to dengue incidence. Projections of dengue cases for mid-century show increments of up to 42 more cases in some districts compared to the historical scenario. It should be remembered that ultimately dengue variations and change are related to climatic and non-climatic factors and the results presented here represent a future potential increase of the dissemination of the disease, based on the projected climate change of the most pessimistic scenario.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".