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Record W4411466239 · doi:10.1016/j.idairyj.2025.106332

Characterization of microorganisms following dairy keeping quality tests in Québec

2025· article· en· W4411466239 on OpenAlexafffundabout
Laurie Sanschagrin, Antoine Labrie, Anhely Carolina Sanchez Martinez, Éric Jubinville, Valérie Goulet-Beaulieu, Simon Dufour, Steve Labrie, Julie Jean

Bibliographic record

VenueInternational Dairy Journal · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversité de MontréalUniversité LavalMinistère de l'Agriculture, des Pêcheries et de l'AlimentationFonds de Recherche du Québec – Nature et Technologies
FundersMitacsMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsMicroorganismQuality (philosophy)Dairy industryCharacterization (materials science)Environmental scienceBusinessBiotechnologyBiologyFood scienceBacteriaNanotechnologyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

: Despite best efforts of the dairy industry, premature spoiling and non-compliance of products remain problematic and cause economic loss. In this study, 190 dairy products/samples of the Québec province were analyzed using governmental quality tests and keeping quality tests, such as the Virginia Tech. procedure, the Moseley test and a Paenibacillus test, to allow identification, by 16S sequencing, and characterization of the microorganisms causing non-compliance. We found that the Paenibacillus test isolated mostly Bacillus and Paenibacillus whereas the other tests isolated primarily Pseudomonas . Tests in 96-well assay plates showed that Pseudomonas had the most moderate to strong biofilm forming ability producers. Except for four isolates of Pseudomonas and one isolate of Stenotrophomonas chelatiphaga , biofilm producers were sensitive to both sodium hypochlorite and peracetic acid at concentrations typically used to disinfect dairy equipment. This study will help in the development of control strategies targeting problematic bacterial contaminants in the dairy sector.

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 score0.920
Threshold uncertainty score0.302

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 head0.019
GPT teacher head0.276
Teacher spread0.258 · 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
Published2025
Admission routes3
Has abstractyes

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