Quality and safety of cheese shipped to the United Arab Emirates
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
Abstract During an examination of 3299 cheeses imported into the United Arab Emirates (UAE) from 2017 to 2021 for compliance with regulations regarding moisture and fat content, microbial quality, acidity, the presence of quinoline (a non-permitted colorant), sorbic acid, and the presence of rust discoloration, it was found that 91% of cheeses were compliant with UAE legislation. However, 9% were in violation of one or more of the mandated quality parameters, suggesting that adulteration had occurred. Within product categories the greatest level of non-conformity at 13% was noted for processed cheese, primarily due to violations caused by high moisture and low-fat content. This is important because moisture levels in processed cheese can influence its texture and shelf life. The microbial assessment of cheese showed that 85.7% of semi-hard and 77.5% of soft cheeses had non-compliant levels of E. coli . It was notable that 21.8% of non-compliant products originated from Turkey. Cheeses from Germany had the lowest level of non-conformity at 0.6%. This study illustrates the need for border scrutiny to include physicochemical examinations of cheese samples. The current initiative aims to promote the need for equity in global trade and to prevent the marketing of adulterated food items. Graphical Abstract
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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