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Record W2989810596 · doi:10.3390/v11121105

Yersinia Phages and Food Safety

2019· review· en· W2989810596 on OpenAlexaff
Carlos G. Leon-Velarde, Jin Woo Jun, Mikael Skurnik

Bibliographic record

VenueViruses · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsUniversity of Guelph
FundersJane ja Aatos Erkon SäätiöAcademy of FinlandHelsingin Yliopisto
KeywordsYersinia enterocoliticaYersiniaYersiniosisBiologyReactive arthritisMicrobiologyPathogenic bacteriaBacteriaFood safetyEnterobacteriaceaeFood scienceEscherichia coliArthritisImmunology

Abstract

fetched live from OpenAlex

One of the human- and animal-pathogenic species in genus Yersinia is Yersinia enterocolitica, a food-borne zoonotic pathogen that causes enteric infections, mesenteric lymphadenitis, and sometimes sequelae such as reactive arthritis and erythema nodosum. Y. enterocolitica is able to proliferate at 4 °C, making it dangerous if contaminated food products are stored under refrigeration. The most common source of Y. enterocolitica is raw pork meat. Microbiological detection of the bacteria from food products is hampered by its slow growth rate as other bacteria overgrow it. Bacteriophages can be exploited in several ways to increase food safety with regards to contamination by Y. enterocolitica. For example, Yersinia phages could be useful in keeping the contamination of food products under control, or, alternatively, the specificity of the phages could be exploited in developing rapid and sensitive diagnostic tools for the identification of the bacteria in food products. In this review, we will discuss the present state of the research on these topics.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.086
GPT teacher head0.379
Teacher spread0.293 · 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

Citations40
Published2019
Admission routes1
Has abstractyes

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