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Record W4313308210 · doi:10.1016/j.jfp.2022.11.010

Practice and Progress: Updates on Outbreaks, Advances in Research, and Processing Technologies for Low-moisture Food Safety

2022· review· en· W4313308210 on OpenAlex

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 Food Protection · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFood safetyOutbreakGovernment (linguistics)BusinessFoodborne pathogenBiosafetyEnvironmental healthRisk analysis (engineering)BiotechnologyBiologyMedicineFood science

Abstract

fetched live from OpenAlex

Large, renowned outbreaks associated with low-moisture foods (LMFs) bring to light some of the potential, inherent risks that accompany foods with long shelf lives if pathogen contamination occurs. Subsequently, in 2013, Beuchat et al. (2013) noted the increased concern regarding these foods, specifically noting examples of persistence and resistance of pathogens in low-water activity foods (LWAFs), prevalence of pathogens in LWAF processing environments, and sources of and preventive measures for contamination of LWAFs. For the last decade, the body of knowledge related to LMF safety has exponentially expanded. This growing field and interest in LMF safety have led researchers to delve into survival and persistence studies, revealing that some foodborne pathogens can survive in LWAFs for months to years. Research has also uncovered many complications of working with foodborne pathogens in desiccated states, such as inoculation methods and molecular mechanisms that can impact pathogen survival and persistence. Moreover, outbreaks, recalls, and developments in LMF safety research have created a cascading feedback loop of pushing the field forward, which has also led to increased attention on how industry can improve LMF safety and raise safety standards. Scientists across academia, government agencies, and industry have partnered to develop and evaluate innovate thermal and nonthermal technologies to use on LMFs, which are described in the presented review. The objective of this review was to describe aspects of the extensive progress made by researchers and industry members in LMF safety, including lessons-learned about outbreaks and recalls, expansion of knowledge base about pathogens that contaminate LMFs, and mitigation strategies currently employed or in development to reduce food safety risks associated with LMFs.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.434
Teacher spread0.273 · 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