Hygienic Conditions in Butcher Shops at the City of Navirai, Brazil—An Applied Case Study
Bibliographic record
Abstract
This study comprises the evaluation of the hygienic-sanitary conditions of butcher shops in the city of Naviraí, state of Mato Grosso do Sul, Brazil. Raw meat and meat products are foods widely consumed by Brazilian citizens, which is also widely exported overseas to several regions, including Europe. Due to its composition, high water content and almost neutral pH, which favors the growth of microorganisms, meat handling requires strict hygienic and sanitary guidelines. The city of Naviraí is located in the Central region of Brazil, where beef cattle and beef and meat products are one of the main activities, both for domestic consumption as well as aiming exports. We surveyed two butcher shops by applying a checklist based on the Resolution RDC 216 of September 15, 2004, issued by the Brazilian Health Ministry. The scores were given from 0% to 100% and each surveyed item was classified as Satisfactory; Satisfactory with Restriction; or Unsatisfactory. After that, all nonconformities were pointed out and owners were instructed on how to fix them. Several items for both butcher shops were in disagreement with the cited legislation, being classified as unsatisfactory and satisfactory with restrictions. It was evidenced the need for most intensive inspection by the Sanitary Vigilance Department of the Brazilian Health Ministry, not only by applying fines and other penalties, but also with guidelines to employees and owners, since the lack of information, awareness and commitment is notorious.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".