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Record W2162126337 · doi:10.3390/ani3030843

Characteristics of a Canine Distemper Virus Outbreak in Dichato, Chile Following the February 2010 Earthquake

2013· article· en· W2162126337 on OpenAlexaff
Elena Garde, Guillermo E. Pérez, Gerardo Acosta‐Jamett, Mark Bronsvoort

Bibliographic record

VenueAnimals · 2013
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsEngineers Without Borders Canada
FundersBiotechnology and Biological Sciences Research CouncilDisney Conservation FundDisney Worldwide Conservation FundWildlife Conservation Society
KeywordsCanine distemperOutbreakMedicineTiterVirologyVirus

Abstract

fetched live from OpenAlex

Following the earthquake and tsunami disaster in Chile in February 2010, residents of Dichato reported high morbidity and mortality in dogs, descriptions of which resembled canine distemper virus (CDV). To assess the situation, free vaccine clinics were offered in April and May. Owner information, dog history and signalment were gathered; dogs received physical examinations and vaccines protecting against CDV, and other common canine pathogens. Blood was collected to screen for IgM antibodies to CDV. In total, 208 dogs received physical exams and vaccines were given to 177. IgM antibody titres to CDV were obtained for 104 dogs. Fifty-four dogs (51.9%) tested positive for CDV at the cut off titre of >1:50, but a total of 91.4% of dogs had a detectable titre >1:10. Most of the positive test results were in dogs less than 2 years of age; 33.5% had been previously vaccinated against CDV, and owners of 84 dogs (42.2%) reported clinical signs characteristic of CDV in their dogs following the disaster. The presence of endemic diseases in dog populations together with poor pre-disaster free-roaming dog management results in a potential for widespread negative effects following disasters. Creation of preparedness plans that include animal welfare, disease prevention and mitigation should be developed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.671

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)0.0010.000

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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