MétaCan
Menu
Back to cohort

The Features of West Nile Fever Epidemiological Situation in the World and Russia in 2013 and Prognosis of Its Development in 2014

2014· article· en· W2770914615 on OpenAlexaboutno aff
E. V. Putintseva, В. А. Антонов, V. P. Smelyanskiy, Н. Д. Пакскина, O. N. Skudareva, Д. В. Викторов, Г. А. Ткаченко, V. A. Pak, К. В. Жуков, M. V. Monastirskiy, N. V. Boroday, V. V. Manankov, N. I. Pogasiy, И. М. Шпак, С. С. Савченко, L. V. Lemasova, О. С. Бондарева, Т. V. Zamarina, I. A. Barkova

Bibliographic record

VenueProblems of Particularly Dangerous Infections · 2014
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
Fundersnot available
KeywordsWest Nile virusOutbreakRussian federationGeographyEpidemiologyHydrometeorologySocioeconomicsEnvironmental protectionVirologyMeteorologyBiologyMedicineVirus

Abstract

fetched live from OpenAlex

Epidemiological situation on West Nile Fever (WNF) in Europe in 2013 was characterized by a notable rise of morbidity rate primarily due to the outbreak of WNF in Serbia (302 cases registered). In the North America, in the United States and Canada, WNF manifestations in 2013 were characterized by the lower intensity compared to previous epidemic season. 192 cases were registered in 16 constituent entities of the Russian Federation in 2013. It was revealed, that genotype 2 West Nile Virus (WNV) circulated in the territory of the Volgograd and Saratov regions, the same as in Serbia, Greece and Italy, and genotype 1 WNV in the Astrakhan region. According to the data obtained from the Reference Center for monitoring over WNV pathogen, WNV markers were detected in the territory of 61 constituent entities of the Russian Federation throughout the period of observation in 1999-2013 which testified to the existence of potential risk of human exposure during epidemic season in most of the parts of country. According to Federal Service for Hydrometeorology and Environmental Monitoring forecast, climatic conditions in Russia for the next 5-10 years will stick to global warming trend which will contribute to further spread of WNV onto the northern areas.

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)0.000
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 score0.023
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.288
Teacher spread0.262 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProblems of Particularly Dangerous InfectionsSame topicViral Infections and VectorsFrench-language works237,207