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Growth Model of a Plasmid‐Bearing Virulent Strain of <i>Yersinia pseudotuberculosis</i> in Raw Ground Beef

2009· article· en· W1937257177 on OpenAlexfundno aff
Saumya Bhaduri, John G. Phillips

Bibliographic record

VenueZoonoses and Public Health · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
FundersPublic Health Agency of Canada
KeywordsVirulenceYersinia pseudotuberculosisStrain (injury)MicrobiologyPopulationPlasmidBiologyVeterinary medicineFood scienceMedicine

Abstract

fetched live from OpenAlex

The growth kinetics of virulence plasmid-bearing Yersinia pseudotuberculosis (YPST) in sterile ground beef were studied at temperatures ranging from 0 to 30°C. In irradiated sterile ground beef, YPST replicated from 0 to 30°C, with corresponding growth rates (GR) ranging from 0.023 to 0.622 log CFU/h at 0-25°C, and the GR was 0.236 log CFU/h at 30°C. The maximum population densities (MPD) ranged from 8.7 to 11.0 log CFU/g. The growth and MPD of YPST were reduced significantly at 30°C. Models for GR and MPD of YPST in raw ground beef (RGB) as a function of storage temperatures were produced and displayed acceptable bias and accuracy. The models were validated with rifampicin-resistant YPST (rif-YPST) in sterile ground beef stored at 4, 10 and 25°C. The observed GR and MPD were within 95% of the predicted values. When compared to non-sterile retail ground beef, the growth of rif-YPST was not inhibited and displayed similar GR at 0, 10 and 25°C and MPDs as sterile ground beef at 10 and 25°C. Moreover, there was no loss of virulence plasmid in YPST during its growth in ground beef indicating that RGB contaminated with virulence plasmid-bearing YPST could cause disease due to refrigeration failure, temperature (10-25°C) abuse, and if the meat was not properly cooked.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.299
Teacher spread0.265 · 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 designBench or experimental
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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