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Record W2065199367 · doi:10.2987/11-6169.1

Potential for Canadian Mosquitoes To Transmit Rift Valley Fever Virus<sup>1</sup>

2011· article· en· W2065199367 on OpenAlexafffundabout
Mahmood Iranpour, Michael J. Turell, L. Robbin Lindsay

Bibliographic record

VenueJournal of the American Mosquito Control Association · 2011
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health Agency of Canada
KeywordsRift Valley feverArbovirusBiologyPhlebovirusVirologyVector (molecular biology)OutbreakCulexWest Nile virusCulex pipiensBunyaviridaeYellow feverVirusEcologyLarva

Abstract

fetched live from OpenAlex

The rapid spread of West Nile viral activity across North America since its discovery in 1999 illustrates the potential for an exotic arbovirus to be introduced and become widely established across North America. Rift Valley fever virus (RVFV) has been responsible for large outbreaks in Africa that have resulted in hundreds of thousands of human infections and major economic disruption due to loss of livestock and to trade restrictions. However, little is known about the potential for mosquitoes in Canada to transmit this virus, should it be introduced into North America. Therefore, we evaluated mosquito species captured near Winnipeg, Manitoba, Canada, for their ability to serve as potential vectors for RVFV. Mosquitoes were exposed to RVFV by allowing them to feed on adult hamsters inoculated the previous day with RVFV. These mosquitoes were tested for infection, dissemination, and the ability to transmit RVFV after incubation at 25 degrees C for 14-18 days. Based on the detection of virus in saliva collected in capillary tubes, individual Culex tarsalis, Aedes sticticus, and Coquillettidia perturbans were able to transmit RVFV under laboratory conditions. These preliminary results suggest that these 3 species may be able to transmit RVFV, should this virus be introduced into Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.244
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2011
Admission routes3
Has abstractyes

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