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Record W7098810261

INTERNATIONAL POLAR YEAR DISPATCHES Dogs as Sources and Sentinels of Parasites in Humans and

2013· article· en· W7098810261 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeRabiesOutbreakVaccinationOne HealthDiseaseTaenia soliumEchinococcusEchinococcosis
DOInot available

Abstract

fetched live from OpenAlex

A minimum of 11 genera of parasites, including 7 known or suspected to cause zoonoses, were detected in dogs in 2 northern Canadian communities. Dogs in remote settlements receive minimal veterinary care and may serve as sources and sentinels for parasites in persons and wildlife, and as parasite bridges between wildlife and humans. Throughout their long history of domestication, dogs have been sources of zoonotic parasites and have served as a link for parasite exchange among livestock, wildlife, and humans (1). Globally, dogs remain an important source of emerging disease in humans (e.g., eosinophilic enteritis caused by Ancylostoma caninum), a bridge for reemerging infections (Echinococcus multilocularis), and a source of parasites for immunocompromised persons (1). Human disease and parasite infections in dogs in northern Canada have been recognized for some time (2– 5). Historically, attention was focused on rabies virus, parvovirus, and canine distemper virus. However, dogs were also recognized as sources of zoonotic parasites such as Echinococcus spp. and as a possible bridge for rabies between wildlife and humans (4,5). Today, in many northern communities, veterinary services are absent or restricted, and disease surveillance programs and routine preventive health measures such as vaccination and parasite control are rare. These conditions have limited our understanding of disease interactions at the dog-human-wildlife interface and our ability to detect and respond to emerging diseases. Northern environments and socioeconomic systems are changing rapidly and altering interactions among humans,

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.371
Threshold uncertainty score0.738

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0390.010

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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