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Prevalence of the Bacterium <i>Coxiella burnetii</i> in Wild Rodents from a Canadian Natural Environment Park

2012· article· en· W2136056755 on OpenAlexaffabout
Michele Thompson, Nadia Mykytczuk, Krista L. Gooderham, Albrecht I. Schulte‐Hostedde

Bibliographic record

VenueZoonoses and Public Health · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsMcGill UniversityLaurentian University
Fundersnot available
KeywordsWildlifeCoxiella burnetiiBiologyDisease reservoirQ feverTransmission (telecommunications)ZoologyWildlife diseaseZoonosisVeterinary medicineEcologyVirologyMedicine

Abstract

fetched live from OpenAlex

Zoonotic diseases impact both wild and domestic animal populations and can be transmitted to humans through close contact with animal species. Reservoir species acting as vectors are major traffickers of disease. Rodents contribute to the transmission of Coxiella burnetii although little is known about its prevalence in wild animal populations. DNA was extracted from genital swabs collected from woodland jumping mice, deer mice, Southern red-backed voles, Eastern chipmunks, North American red squirrels, as well as Southern and Northern flying squirrels collected from Algonquin Park, Canada. The presence of C. burnetii was determined through real-time PCR. All species sampled had some prevalence of infection, except Eastern chipmunks, indicating wild rodents in Algonquin Park are reservoirs for C. burnetii. Emerging zoonotic diseases are linked to increasing globalization. Contact amongst individuals increases as crowding, habitat loss and fragmentation increase within wild spaces. Parks often act as a last refuge for wildlife but may also be an important transmission zone of wildlife disease to humans. Investigations that attempt to discover wild reservoir species of zoonotic disease are critically important to understanding the risk of pathogen exchange between wild and human populations.

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 score0.181
Threshold uncertainty score0.981

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 head0.012
GPT teacher head0.221
Teacher spread0.210 · 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

Citations24
Published2012
Admission routes2
Has abstractyes

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