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Record W3143396709 · doi:10.7589/jwd-d-20-00135

High Prevalence and Intensity of Trichinella Infection in Yukon American Black (Ursus americanus) and Grizzly (Ursus arctos) Bears

2021· article· en· W3143396709 on OpenAlexaffabout
N. Jane Harms, Meghan Larivee, Brad Scandrett, Don Russell

Bibliographic record

VenueJournal of Wildlife Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicParasitic Diseases Research and Treatment
Canadian institutionsCanadian Food Inspection AgencyYukon Department of Environment
Fundersnot available
KeywordsUrsusGrizzly BearsBiologyTrichinellaVeterinary medicineZoologyEcologyLarvaMedicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Trichinella spp. nematodes are commonly found in bear species (Ursidae) and can pose severe health risks to humans when infective first-stage larvae are ingested in meat. Samples of tongue or masseter muscle from 22 male and 11 female American black bears (Ursus americanus; mean age 6.5 yr, range 1-16 yr) and 22 male, eight female, and one unknown sex grizzly bears (Ursus arctos; mean age 8.8 yr, range 2-28 yr), from Yukon, Canada, were tested to determine prevalence and intensity of Trichinella spp. infection. For black bears, prevalence was 20% and mean intensity was 401 larvae per gram of tissue (LPG), whereas for grizzly bears, prevalence was 71%, and mean infection intensity was 35 LPG. Isolates from all positive samples were identified as genotype Trichinella-T6 by multiplex PCR. For black bears, prevalence is the highest reported in Canada and infection intensity the highest recorded in North America. One black bear had a larval burden of 1,173 LPG, the second highest recorded in any host species. The prevalence in grizzly bears was the highest reported in Canada for this host. In total, 90% (27 of 30) of infected bears had infection burdens above the human food safety threshold of ≥1 LPG, reinforcing the importance of communicating the health risks to people consuming bear meat.

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)0.002
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 score0.011
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.304
Teacher spread0.291 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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