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FMD EPIDEMIC PROCESS CHARACTERISTICS IN RUSSIA IN 2010 – FIRST QUARTER OF 2019

2019· article· en· W2978022780 on OpenAlexaboutno aff
В. П. Семакина, Т. П. Акимова, А. К. Караулов

Bibliographic record

VenueVeterinary Science Today · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakRussian federationGeographyFoot-and-mouth diseaseChinaQuarter (Canadian coin)LivestockSocioeconomicsHuman settlementDemographyVeterinary medicineEnvironmental healthMedicineVirologyForestryArchaeology

Abstract

fetched live from OpenAlex

Foot-and-mouth disease is a highly contagious viral disease of cloven-hoofed livestock and wild animals. Europe, North America and Oceania have long been FMD free; at the same time, the virus is widely spread in Asian and African countries. In the period from 2010 to March 2019, FMD was notified to the World Organisation for Animal Health (OIE) by 89 countries of the world. Local FMD outbreaks were also reported in several regions of the Russian Federation throughout this period. The research was aimed at studying some characteristics of the FMD epidemic process in the Russian Federation. The undertaken epidemiological analysis covers the Russian Federation regions where FMD outbreaks were reported between 2010 and March 2019. During the period under consideration, FMD cases were reported in 9 regions of the Russian Federation, mainly in the settlements located in close proximity to the Russia-China and Russia-Mongolia international borders. Most of the outbreaks were caused by serotype O and A FMD viruses. In most cases, FMD was reported in cattle and pigs and, less frequently, in sheep and goats. The analysis of the FMD epidemic situation in the Russian Federation Subjects was performed through epidemic process assessment based on the following estimates: the proportion of infected settlements, epidemic, contagiousness and morbidity rates. The Zabaykalsky and Primorsky Krais have a lead in the number of infected settlements. The highest morbidity rate in pigs was recorded in the Primorsky Krai, in cattle – in the Amur Oblast. The epidemic rate was the highest in the Primorsky and Zabaykalsky Krais. The Primorsky Krai also accounted for the highest contagiousness rates in 2014 and 2019 when FMD occurred on several large pig farms.

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 score0.086
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.264
Teacher spread0.240 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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