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Record W2907658868 · doi:10.1017/s0022149x1800113x

Helminth parasites and zoonotic risk associated with urban coyotes (<i>Canis latrans</i>) in Alberta, Canada

2018· article· en· W2907658868 on OpenAlexaffabout
Lien T. Luong, Jackson Chambers, A. Moizis, T. M. Stock, Colleen Cassady St. Clair

Bibliographic record

VenueJournal of Helminthology · 2018
Typearticle
Languageen
FieldMedicine
TopicParasitic infections in humans and animals
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsEchinococcus multilocularisBiologyCanisToxocara canisTaeniaHelminthsZoonosisVeterinary medicineZoologyEcologyEchinococcosisImmunology

Abstract

fetched live from OpenAlex

Coyotes (Canis latrans) are resilient, adaptable, cosmopolitan omnivores that are increasingly prevalent in urban environments, where they interact with both humans and domestic dogs. Coyotes potentially transmit zoonotic parasites, including the tapeworm Echinococcus multilocularis, which appears to be increasing in prevalence in western North America. In this study, we analysed the carcasses of 23 urban coyotes in Edmonton, Alberta, Canada. Focusing primarily on the helminth community, we recovered three tapeworm species (E. multilocularis, Taenia pisiformis, T. serialis), four nematodes (Toxascaris leonina, Uncinaria stenocephala, Capillaria sp., Physaloptera sp.), and two trematodes (Alaria arisaemoides and A. americana). Compared to previous studies of urban coyotes conducted in North America, we report one of the highest levels of E. multilocularis infection in North America: 65.2% infection prevalence. These results amplify concerns expressed by others about the increasing prevalence of this zoonotic parasite and the role coyotes may play in parasite transmission. More research is needed to better understand how various ecological factors, urbanization and wildlife management practices influence the transmission of potentially zoonotic parasites such as E. multilocularis.

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 score0.586
Threshold uncertainty score0.918

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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