Meteorological Parameters and Mosquito Abundance in Pashan Area of Pune, India during South West Monsoon and Post-Monsoon Seasons in 2016
Bibliographic record
Abstract
The resurgence of vector borne diseases over the last decade has raised concerns about the role of climatic factors. Rapid urbanization due to expansion of Indian cities like Pune over the last decade has altered local land-use and environment. The present study was undertaken to investigate the composition and seasonal abundance of mosquito population in Pashan area of urban Pune, which was urbanized rapidly during 2001-2005. Mosquitoes were trapped and identified to determine the species composition and abundance. Association of meteorological parameters like temperature, humidity and rainfall with mosquito abundance was also analyzed from June to November 2016. Raw meteorological data was obtained and analyzed mathematically to determine derived parameters like diurnal temperatures and fortnightly averages of all parameters. A total of 21 species of mosquitoes were observed across four genera viz. Aedes , Anopheles , Culex and Armigerus . Mosquito abundance (M) peaked during South West (SW) Monsoon and correlated positively with maximum and minimum relative humidity and rainfall. In post-monsoon season mosquito abundance decreased alongwith relative humidity. Interestingly, the mosquito abundance is modulated by diurnal temperature range (DTR). During SW monsoon, low DTR corresponded to high mosquito abundance. The trend was reversed in the post-monsoon season as DTR increased by ~4 folds in comparison to SW monsoon and mosquito abundance decreased sharply. Mosquito population in the study area showed diversity and seasonal variability, influenced by meteorological parameters. DTR seemed to be the major factor affecting seasonal variability in mosquito abundance.
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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 | 0.000 |
| 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".