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The Pathogenesis of Foot-and-Mouth Disease I: Viral Pathways in Cattle

2011· review· en· W1757744415 on OpenAlexaff
Jonathan Arzt, Nicholas Juleff, Zhidong Zhang, Luis L. Rodrı́guez

Bibliographic record

VenueTransboundary and Emerging Diseases · 2011
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Food Inspection Agency
FundersBiotechnology and Biological Sciences Research Council
KeywordsPathogenesisFoot-and-mouth diseaseVirologyDiseaseConvalescenceBiologyImmunologyViral replicationVirusViral pathogenesisMedicinePathology

Abstract

fetched live from OpenAlex

In 1898, foot-and-mouth disease (FMD) earned a place in history as the first disease of animals shown to be caused by a virus. Yet, despite over a century of active investigation and elucidation of many aspects of FMD pathogenesis, critical knowledge about the virus-host interactions is still lacking. The aim of this review is to provide a comprehensive overview of FMD pathogenesis in cattle spanning from the earliest studies to recently acquired insights emphasizing works which describe animals infected by methodologies most closely resembling natural infection (predominantly aerosol or direct/indirect contact). The three basic phases of FMD pathogenesis in vivo will be dissected and characterized as: (i) pre-viraemia characterized by infection and replication at the primary replication site(s), (ii) sustained viraemia with generalization and vesiculation at secondary infection sites and (iii) post-viraemia/convalescence including resolution of clinical disease that may result in long-term persistent infection. Critical evaluation of the current status of understanding will be used to identify knowledge gaps to guide future research efforts.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.273
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations277
Published2011
Admission routes1
Has abstractyes

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