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Record W2593960886 · doi:10.1128/jcm.00017-17

Whole-Genome Sequencing in Epidemiology of Campylobacter jejuni Infections

2017· review· en· W2593960886 on OpenAlex
Ann‐Katrin Llarena, Eduardo N. Taboada, Mirko Rossi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Clinical Microbiology · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsCampylobacter jejuniOutbreakWhole genome sequencingEpidemiologyCampylobacterBiologyMolecular epidemiologyPublic healthGenomeMedicineVirologyGeneticsBacteriaGenotypeGene

Abstract

fetched live from OpenAlex

ABSTRACT This review describes the current state of knowledge regarding the application of whole-genome sequencing (WGS) in the epidemiology of Campylobacter jejuni , the leading cause of bacterial gastroenteritis worldwide. We describe how WGS has increased our understanding of the evolutionary and epidemiological dynamics of this pathogen and how WGS has the potential to improve surveillance and outbreak detection. We have identified hurdles to the full implementation of WGS in public health settings. Despite these challenges, we think that ample evidence is available to support the benefits of integrating WGS into the routine monitoring of C. jejuni infections and outbreak investigations.

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.

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)0.010
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.399
GPT teacher head0.471
Teacher spread0.072 · 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