MétaCan
Menu
Back to cohort
Record W2115258270 · doi:10.3201/eid1608.100483

West Nile Virus Range Expansion into British Columbia

2010· article· en· W2115258270 on OpenAlexafffundabout
David Roth, Bonnie Henry, Sunny Mak, Mieke Fraser, Marsha Taylor, Min Li, Allen Furnell, Quantine Wong, Muhammad Morshed

Bibliographic record

VenueEmerging infectious diseases · 2010
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of British ColumbiaBC Centre for Disease Control
FundersCanadian Institutes of Health ResearchHealth CanadaBritish Columbia Centre for Disease ControlUniversity of British ColumbiaMichael Smith Health Research BC
KeywordsWest Nile virusCulexVirologyRange (aeronautics)BiologyTransmission (telecommunications)Japanese encephalitisEncephalitisVirusFlavivirusVeterinary medicineLimitingEcologyMedicineLarva

Abstract

fetched live from OpenAlex

In 2009, an expansion of West Nile virus (WNV) into the Canadian province of British Columbia was detected. Two locally acquired cases of infection in humans and 3 cases of infection in horses were detected by ELISA and plaque-reduction neutralization tests. Ten positive mosquito pools were detected by reverse transcription PCR. Most WNV activity in British Columbia in 2009 occurred in the hot and dry southern Okanagan Valley. Virus establishment and amplification in this region was likely facilitated by above average nightly temperatures and a rapid accumulation of degree-days in late summer. Estimated exposure dates for humans and initial detection of WNV-positive mosquitoes occurred concurrently with a late summer increase in Culex tarsalis mosquitoes (which spread western equine encephalitis) in the southern Okanagan Valley. The conditions present during this range expansion suggest that temperature and Cx. tarsalis mosquito abundance may be limiting factors for WNV transmission in this portion of the Pacific Northwest.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0080.000

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 head0.003
GPT teacher head0.237
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.

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

Citations46
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueEmerging infectious diseasesSame topicMosquito-borne diseases and controlFrench-language works237,207