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Record W2148915753 · doi:10.7589/2014-06-159

Septicemic Listeriosis in Wild Hares from Saskatchewan, Canada

2015· article· en· W2148915753 on OpenAlexaffabout
Jamie L. Rothenburger, Katarina R. Bennett, Lorraine Bryan, Trent K. Bollinger

Bibliographic record

VenueJournal of Wildlife Diseases · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
Fundersnot available
KeywordsListeria monocytogenesBiologyZoologyVeterinary medicineEncephalitisVirologyBacteriaMedicineVirus

Abstract

fetched live from OpenAlex

The bacterium Listeria monocytogenes causes disease in a wide variety of mammals including rabbits and hares. We describe naturally acquired metritis and septicemic listeriosis in wild female hares from Saskatchewan, Canada. Between April 2012 and July 2013, two white-tailed jackrabbits (Lepus townsendii) and a snowshoe hare (Lepus americanus) were presented to the Veterinary Medical Centre at the Western College of Veterinary Medicine, Saskatoon, Saskatchewan, Canada with nonspecific neurologic signs. The hares were euthanized and autopsied. Necrotizing fibrinosuppurative metritis was present in all. Additional findings in individual hares included fetal maceration, multifocal necrotizing myocarditis, multifocal hepatic necrosis, and nonsuppurative encephalitis. Listeria monocytogenes was cultured from multiple tissues in each hare. Although listeriosis in pregnant domestic rabbits has been studied, this is the first detailed description in wild North American hares. The epidemiology of listeriosis, including prevalence and the role of environmental sources and coprophagy in transmission among hares, requires further investigation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.075
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.040
GPT teacher head0.284
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2015
Admission routes2
Has abstractyes

Explore more

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