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Record W2028948282 · doi:10.7589/0090-3558-47.3.745

Prevalence of Trichinella spp. in Black Bears, Grizzly Bears, and Wolves in the Dehcho Region, Northwest Territories, Canada, Including the First Report of T. nativa in a Grizzly Bear from Canada

2011· article· en· W2028948282 on OpenAlexaffabout
Nicholas C. Larter, Lorry B. Forbes, Brett Elkin, Danny G. Allaire

Bibliographic record

VenueJournal of Wildlife Diseases · 2011
Typearticle
Languageen
FieldMedicine
TopicParasitic Diseases Research and Treatment
Canadian institutionsCanadian Food Inspection AgencyGovernment of Northwest Territories
Fundersnot available
KeywordsUrsusGrizzly BearsTrichinellaCanisBiologyGeographyZoologyEcologyHelminthsPopulationEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Samples of muscle from 120 black bears (Ursus americanus), 11 grizzly bears (Ursus arctos), and 27 wolves (Canis lupus) collected in the Dehcho Region of the Northwest Territories from 2001 to 2010 were examined for the presence of Trichinella spp. larvae using a pepsin-HCl digestion assay. Trichinella spp. larvae were found in eight of 11 (73%) grizzly bears, 14 of 27 (52%) wolves, and seven of 120 (5.8%) black bears. The average age of positive grizzly bears, black bears, and wolves was 13.5, 9.9, and approximately 4 yr, respectively. Larvae from 11 wolves, six black bears, and seven grizzly bears were genotyped. Six wolves were infected with T. nativa and five with Trichinella T6, four black bears were infected with T. nativa and two with Trichinella T6, and all seven grizzly bears were infected with Trichinella T6 and one of them had a coinfection with T. nativa. This is the first report of T. nativa in a grizzly bear from Canada. Bears have been linked to trichinellosis outbreaks in humans in Canada, and black bears are a subsistence food source for residents of the Dehcho region. In order to assess food safety risk it is important to monitor the prevalence of Trichinella spp. in both species of bear and their cohabiting mammalian food sources.

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)0.001
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 score0.008
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.269
Teacher spread0.243 · 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

Citations23
Published2011
Admission routes2
Has abstractyes

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