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Record W4380291867 · doi:10.1080/09603123.2023.2223487

Descriptive analysis of the most common types of food safety infractions at ready-to-eat meat processing plants in Ontario, Canada

2023· article· en· W4380291867 on OpenAlexafffundabout
Jiin Jung, Ian Young, Fatih Şekercioğlu

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsToronto Metropolitan University
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsFood safetyFood scienceEngineeringBusinessEnvironmental healthAdvertisingMedicineBiology

Abstract

fetched live from OpenAlex

Food safety inspections of meat processing plants and abattoirs that process ready-to-eat (RTE) meats have identified a lack of compliance with good manufacturing practices. This study was undertaken to identify common food safety infractions in the RTE meat processing sector in Ontario through an analysis of historical audit records. A total of 376,457 audit item results were evaluated across 912 unique audits of 204 different RTE meat plants. A nearly two-thirds overall item pass rate (64.4%; n = 242,478) was identified. Across all other risk categories, the highest rates of infractions were observed in the “maintenance of premises, equipment and utensils” (56.7%; n = 750). The overall item pass rate was higher in free-standing meat processing plants than abattoirs, while pass rates gradually decreased across the study period. The results of this study have identified key areas for improvement in future inspection, audit and outreach with RTE meat processing plants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.108
GPT teacher head0.341
Teacher spread0.233 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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