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Record W2096791273

Prevalence and Risk Factor Investigation of Campylobacter Species in Retail Ground Beef from Alberta, Canada.

2009· article· en· W2096791273 on OpenAlexaboutno aff
Sherry J. Hannon, G. Douglas Inglis, Brenda Allan, Cheryl Waldner, Margaret L. Russell, Andrew Potter, Lorne A. Babiuk, Hugh G.G. Townsend

Bibliographic record

VenueFood Protection Trends · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCampylobacteriosisCampylobacterVeterinary medicineSalmonellaCampylobacter jejuniBiosecurityBiologyFood scienceMedicineBacteriaEcology
DOInot available

Abstract

fetched live from OpenAlex

Campylobacteriosis is the most commonly reported (notifiable) bacterial enteric disease in Alberta, Canada. The purpose of this study was to assess the prevalence of Campylobacter species in retail ground beef based on a survey of 60 stores (four supermarket chains, three cities) in southern Alberta. None of the 1,200 retail lean and regular ground beef packages were culture positive. Direct PCR results from a subset of samples (n = 142) indicated that 46% of packages tested were positive for Campylobacter DNA. By species, 14.8% (21/142), 26.8% (38/142) and 1.4% (2/142) of packages were PCR positive for C. jejuni, C. coli and C. hyointestinalis DNA, respectively. The presence of campylobacters varied depending on the dates of collection. However, type of package (regular or lean), whether the store cut/packaged poultry in the meat department, type of meat used as the beef source (market trim, coarse grind tubes or a combination of these), whether meat portions were previously frozen, and package weight were not associated with the odds of finding Campylobacter spp. DNA by use of PCR. The high levels of Campylobacter DNA in the beef suggest that breaks in food safety protocols within slaughter plants, processors or grocery stores could have potentially important public health repercussions.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.202
Teacher spread0.171 · 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 teacher head, 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

Citations9
Published2009
Admission routes1
Has abstractyes

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