Evaluation and prioritization of food safety risks in the Nigerian red meat industry
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.
Bibliographic record
Abstract
Food safety is a global concern, particularly in developing countries like Nigeria. Hence, this study aims to identify and rank food safety priorities in the red meat industry in Ilorin, Northcentral Nigeria, as a first step towards targeting interventions and resource allocation. A cross-sectional study involved 496 participants in various roles within the red meat industry, including butchers, meat traders, veterinarians, and others. Data were collected through a structured questionnaire administered over eight months in ten slaughterhouses and slaughter slabs in Ilorin. The study assessed knowledge about major concerns on food safety and ranked these concerns based on perceived importance by the participants. The study revealed that 89.5% of 496 participants were aware of food safety, with less than 40.0% having received formal training. However, >85% of participants were aware of contamination risks during carcass processing, and sanitation practices needed more consistency. Participants ranked antemortem and postmortem inspections as the most critical concerns (48.8 and 26.7%, respectively) and meat handling by retailers (0.42%) as the least important concerns. Socio-demographic factors such as age, gender, years of experience, level of education, and role within the industry significantly influenced participants' knowledge and prioritization of food safety issues. The findings indicate a need for a comprehensive training program tailored to the diverse roles within the red meat industry. Improvements in sanitation, transportation, storage, and regular inspections are recommended to enhance food safety standards. These efforts aim to mitigate the risks associated with foodborne diseases while improving red meat products' quality. However, the gap between intent and actual outcomes underscores the need for effective implementation and continuous monitoring of food safety practices.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it