Composite function and Biomod2 for evaluating the influence of climate change on the distribution of Aedes aegypti and Aedes albopictus in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vector-borne diseases transmitted by Aedes, including dengue fever, Chikungunya fever, Zika virus, and yellow fever, represent major global public health threats. This study utilized the Biomod2 modeling framework, incorporating 19 bioclimatic variables, to simulate the current and future geographical distributions of Aedes aegypti and Aedes albopictus in China under climate change scenarios (SSP2–4.5 and SSP5–8.5). The results indicated that under future climate scenarios, highly suitable regions for both Aedes would decrease in area, while moderately suitable regions would expand. The co-presence probability analysis revealed that highly suitable regions for both species would concentrate in southern and southeastern China, with notable areas in Yunnan, Guangxi, Guangdong, and Hainan. From current to 2090s, the centroid would shift to northeast under SSP2–4.5 and SSP5–8.5. For Ae. aegypti , the most important variables were isothermality (bio3, 44.05 %), precipitation of the wettest quarter (bio16, 27.87 %), and mean temperature of the coldest quarter (bio11, 22.4 %). For Ae. albopictus , the mean temperature of the coldest quarter (bio11, 54.12 %), annual precipitation (bio12, 22.76 %), and precipitation of the coldest quarter (bio19, 13.47 %) were most significant. These findings highlight the potential impacts of climate change on the distribution dynamics of dengue vectors and provide a basis for developing targeted surveillance and control strategies to mitigate future transmission risks.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it