Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai China
Bibliographic record
Abstract
Objective: To predict the distribution of dengue vector Aedes ( Ae .) albopictus and identify high-risk areas for dengue fever transmission. Methods: Data on Ae. albopictus occurrences were collected from electronic databases. Ensemble models were developed to assess the impacts of climate, vegetation, and human activity on Ae. albopictus . The optimal ensemble model was then used to identify the distribution of suitable areas for Ae. albopictus . Results: After removing duplicate sites and retaining only one location per 100 m × 100 m grid, 189 Ae. albopictus breeding sites were identified. The optimal ensemble model revealed that Ae. albopictus exhibited higher breeding suitability in Shanghai under specific conditions: a normalized difference vegetation index of 0.1 to 0.6, maximum precipitation in the warmest month ranging from 400 mm to 470 mm, maximum temperature in the warmest month between 30.0 °C and 31.0 °C, and proximity to waterways within 0.5 km. The most suitable habitats for Ae. albopictus were primarily concentrated in Shanghai’s central urban areas and scattered across the inner suburban districts. Conclusions: The high-risk areas of Ae. albopictus are widely distributed throughout the central urban area and scattered across the inner suburban district of Shanghai, creating conditions conducive to the outbreak of dengue fever. It is essential to enhance targeted control measures for Ae. albopictus in the identified risk areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".