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Record W2621042636 · doi:10.3389/fmicb.2017.00996

A Syst-OMICS Approach to Ensuring Food Safety and Reducing the Economic Burden of Salmonellosis

2017· article· en· W2621042636 on OpenAlexafffund
Jean-Guillaume Emond-Rhéault, Julie Jeukens, Luca Freschi, Irena Kukavica‐Ibrulj, Brian Boyle, Marie-Josée Dupont, Anna Colavecchio, Virginie Barrère, Brigitte Cadieux, Gitanjali Arya, Sadjia Békal, Chrystal Berry, Elton Burnett, Camille Cavestri, Travis K. Chapin, Alanna Crouse, France Daigle, Michelle D. Danyluk, Pascal Delaquis, Ken Dewar, Florence Doualla‐Bell, Ismaı̈l Fliss, Karen Fong, Éric Fournier, Eelco Franz, Rafael A. Garduño, Alexander Gill, Samantha Gruenheid, Linda J. Harris, Carol Huang, Hongsheng Huang, Roger P. Johnson, Yann Joly, Maud Kerhoas, Nguyet Kong, Gisèle LaPointe, Line Larivière, Stéphanie Loignon, Danielle Malo, Sylvain Moineau, Walid Mottawea, Kakali Mukhopadhyay, Céline Nadon, John J. Nash, Ida Ngueng Feze, Dele Ogunremi, Ann Perets, Ana Victoria C. Pilar, Aleisha Reimer, James A. Robertson, John R. Rohde, Kenneth E. Sanderson, Lingqiao Song, Roger Stephan, Sandeep Tamber, Paul J. Thomassin, Denise M. Tremblay, Valentine Usongo, Caroline Vincent, Siyun Wang, Joel T. Weadge, Martin Wiedmann, Lucas M. Wijnands, Emily D. Wilson, Thomas E. Wittum, Catherine Yoshida, Khadija Youfsi, Bart C. Weimer, Lawrence Goodridge, Roger C. Lévesque

Bibliographic record

VenueFrontiers in Microbiology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsWilfrid Laurier UniversityUniversity of CalgaryDalhousie UniversityUniversity of GuelphHealth CanadaUniversité de MontréalCanadian Food Inspection AgencyGenome CanadaUniversity of British ColumbiaMcGill University and Génome Québec Innovation CentreAgriculture and Agri-Food CanadaUniversité LavalSte. Anne's HospitalPublic Health Agency of CanadaMcGill University
FundersOntario Ministry of Research and InnovationGenome British ColumbiaGénome QuébecGenome Canada
KeywordsSalmonellaOmicsBiologyGenomeVirulenceMetadataGenomicsComputational biologyBiotechnologyFood safetyAntibiotic resistanceFood microbiologyGeneticsGeneBacteriaComputer scienceWorld Wide WebFood science

Abstract

fetched live from OpenAlex

The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance.

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)0.024
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.010
Science and technology studies0.0020.002
Scholarly communication0.0100.007
Open science0.0030.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.003

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 head0.019
GPT teacher head0.213
Teacher spread0.194 · 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 designTheoretical or conceptual
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

Citations42
Published2017
Admission routes2
Has abstractyes

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Same venueFrontiers in MicrobiologySame topicSalmonella and Campylobacter epidemiologyFrench-language works237,207