Microbiota variations in<i>Culex nigripalpus</i>disease vector mosquito of West Nile virus and Saint Louis Encephalitis from different geographic origins
Bibliographic record
Abstract
Although mosquito microbiota are known to influence reproduction, nutrition, disease transmission, and pesticide resistance, the relationship between host-associated microbial community composition and geographical location is poorly understood. To begin addressing this knowledge gap, we characterized microbiota associated with adult females ofCulex nigripalpusmosquito vectors of Saint Louis Encephalitis and West Nile viruses sampled from three locations in Florida (Vero Beach, Palmetto Inland, and Palmetto Coast). High-throughput sequencing of PCR-amplified 16S rRNA genes demonstrated significant differences among microbial communities of mosquitoes sampled from the three locations. Mosquitoes from Vero Beach (east coast Florida) were dominated by uncultivatedAsaiasp. (Alphaproteobacteria), whereas microbiota associated with mosquitoes collected from two mosquito populations at Palmetto (west coast Florida) sites were dominated by unculturedSpironema culicis(Spirochaetes),Salinisphaera hydrothermalis(Gammaproteobacteria),Spiroplasma(Mollicutes), unculturedEnterobacteriaceae, Candidatus Megaira (Alphaproteobacteria;Rickettsiae), andZymobacter(Gammaproteobacteria). The variation in taxonomic profiles ofCx. nigripalpusgut microbial communities, especially with respect to dominating taxa, is a potentially critical factor in understanding disease transmission and mosquito susceptibility to insecticides among different mosquito populations.
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 | 0.000 | 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) | 0.001 | 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".