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Record W4386304213 · doi:10.4315/fpt-23-005

Salmonella Prevalence is Low in Deep Tissue Lymph Nodes of Hog Carcasses from a Pork Processing Plant in Alberta, Canada

2023· article· en· W4386304213 on OpenAlexaboutno aff

Bibliographic record

VenueFood Protection Trends · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsSalmonellaAntimicrobialContaminationBiologyLymphVeterinary medicineFood scienceAntibiotic resistanceFood contaminantFood microbiologyMicrobiologyMedicineBacteriaAntibiotics

Abstract

fetched live from OpenAlex

Deep tissue lymph nodes (DTLNs) could be an important source of Salmonella in pork because carcass decontamination strategies have no effect on Salmonella cells that are deeply embedded and protected. The objective of this study was to determine the prevalence, concentration, and antimicrobial resistance of Salmonella in DTLNs in chilled hog carcasses as well as in ground pork. A total of 400 DTLNs were collected over a 10-month period from a commercial pork processing plant. Salmonella was detected in 2 (0.5%) of 400 DTLNs; Salmonella Uganda was detected in a DTLN from the belly and Salmonella Bovismorbificans in one DTLN from the shoulder. Salmonella Uganda was also detected in one ground pork sample. The three Salmonella isolates were susceptible to all antimicrobials tested, and no clinically significant antimicrobial resistance genes were detected in these genomes after sequencing. The prevalence of Salmonella in DTLNs in pork tissues intended for human consumption is very low and could be a minor source of contamination in the production of ground pork. These findings are important for the pork industry to assess the risks and benefits of removing DTLNs from pork cuts and trimmings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.228
Teacher spread0.204 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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