IMPACT OF CLIMATE CHANGE ON ABUNDANCE, DISTRIBUTION, AND SURVIVAL OF AEDES SPECIES: SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction: Aedes species is a common vector that causes various types of infection. One of the factors that can affect their distribution is the climate change. Identifying the components of climate change that can affect this distribution and how they affect it can aid in predicting and controlling the Aedes species distribution. Methods: Systematic search on articles related to the impact of climate change on Aedes species distribution was conducted using four databases namely Cochrane Library, PubMed, Ovid Medline and Science Direct. All the articles which were published within year 2014 till 2019, was then assesses by using the PRISMA checklist 2009 guided by the inclusion and exclusion criteria set. Results: Ultimately, 19 articles inclusive of six cross-sectional studies, six modelling and seven ecological studies were subjected to narrative and objective quality analysis using Newcastle- Ottawa Scale. Each component of climate change – rainfall, temperature, humidity and wind velocity were examined on its relational impact towards vector Aedes species distribution and survival. All studied climate components showed a unidirectional effect on the distribution and survival of Aedes species Temperature range 3.4oC-34.2oC, humidity <70%, post rainfall (<70mm) and low wind velocity related to increased vector Aedes species distribution, abundance and survival. Quality assessment yielded 17 high quality articles and two moderate quality. Conclusion: Climate change affects the Aedes species distribution and survival. By incorporating the knowledge on the effects of each component of climate change Aedes species vector control effort, a more objective and effective mitigation can be achieved.
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 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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".