Meteorological Parameters and Seasonal Variability of Mosquito Population in Pune Urban Zone, India: A Year-round Study, 2017
Bibliographic record
Abstract
The re-emergence of vector-borne viral diseases in an emerging climate change scenario has raised considerable public health concern in rapidly expanding cities of India where altered land-use and environmental factors have resulted in increased mosquito breeding and occurrences of dengue and chikungunya in the recent years. The present year-round study was undertaken to investigate the seasonal variability and demographic diversity of mosquito population in the Pune urban-zone, western India, (18.52°N / 73.85°E), which is located on the eastern slope of the Western Ghats mountain range with considerable green cover. Mosquitoes were trapped from different localities (fixed trapping sites) representative of the urban-zone throughout the year covering all the four seasons. Specimens were identified, determined the demographic diversity, mosquito abundance and the association of the latter to meteorological parameters. Meteorological parameters were recorded daily and analyzed mathematically to obtain the derived parameters and fortnightly averages. Thirteen species of mosquitoes were found across the Pune urban-zone covering 4 genera , i.e., Aedes , Anopheles , Armigeres and Culex. Culex spp. was abundant throughout the year, while spurt in Aedes population was seen only during South West Monsoon (SW). Overall, mosquito abundance increased during the SW Monsoon due to low diurnal temperature range (DTR) along with increased rainfall and humidity, but decreased during winter followed by a slight increase during the Pre-Monsoon season. Seasonal variability of mosquito abundance and demographic diversity was observed in the study area which may form a basis for prospective systematic surveys and control measures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".