Meteorological Parameters and Mosquito Species Diversity and Abundance along the Arabian Sea Coastline of Alappuzha District, India: A Year-round Study (2017-18)
Bibliographic record
Abstract
Tropical countries like India has a huge burden of vector-borne diseases, necessitating studies on mosquito demographics for effective control. In the present paper, we summarize the findings of a 12-month entomological survey conducted to determine the diversity of mosquitoes in human settlements located along the Arabian Sea shoreline in Alappuzha district, Kerala, India. Adult mosquitoes were sampled using CDC light-traps operated in dusk to dawn operations at ten trapping sites. Captured mosquitoes were transported to laboratory and identified using standard entomological keys. Shannon’s diversity and evenness were calculated to evaluate the richness and diversity of mosquito species. A total of 20 species were identified across five genera. Culex tritaeniorhynchus is the eudominant species followed by Culex quinquefasciatus and Culex gelidus. The seasonal variability of Cx. tritaeniorhynchus and Cx. gelidus , the two principal vectors for West Nile and Japanese Encephalitis viruses, were studied. The present study provided valuable information about the mosquito demographics and seasonal variability of abundance in human settlements along the Arabian Sea shoreline in Alappuzha, India. Considering the venerability of the area to vector-borne diseases due to ecology and presence of migratory birds, future studies may be necessitated to determine the association between vector biodiversity and risk of viral disease transmission to humans.
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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".