Development of Three Additional Culex Species-Specific Polymerase Chain Reaction Primers and Their Application in West Nile Virus Surveillance in Canada
Bibliographic record
Abstract
In 2002, more than 17,000 mosquito pools collected in Canada (Quebec, Ontario, and Manitoba) were tested at the National Microbiology Laboratory in Winnipeg, Manitoba, for infection with West Nile virus (WNV). Using real-time reverse-transcriptase polymerase chain reaction (RT-PCR), 558 mosquito pools (86% Culex species and 14% other species) had evidence of infection with WNV. Only 30% of the Culex specimens, however, were identified to the species level. In this study, Culex species-specific PCR primers were designed to identify individual mosquitoes and mixed pools of Culex mosquitoes to species. In addition, pools of non-Culex mosquitoes that tested positive for WNV were also screened for Culex DNA to determine the frequency of cross-contamination among mosquitoes of different species. All DNA extracts from 121 Culex and 51 non-Culex pools, previously positive for WNV, were screened, and Culex DNA was detected in approximately 6% of non-Culex pools. This study demonstrates that contamination among mosquito species can occur and emphasizes that precautions should be taken to minimize this potentially confounding effect.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".