Traceability, transparency and assurance (TTA) systems implementation by the Brazilian exporter pork meat chain compared with other countries
Bibliographic record
Abstract
In the last decade, Brazil has been characterized as a new and important player in the world pork meat market. Enlarging its participation and confirming the status of important world competitor will be necessary to observe the international quality standards and to assure the food safety as demanded by foreign consumers, which became still more critical in the last time, after the events related with food contaminations. In that context, the present study aims to measure the levels of availability and effective implementation of programs related to the traceability, transparency and assurance systems (TTA Systems) and to compare the Brazilian results with other important countries, like Europeans, United States and Australia. To reach the objective, a survey research was accomplished with the main actors of the Brazilian exporter pork meat chain (BEPMC), applying the obtained data to the Liddell and Bailey’s Model. Results show that Brazil and Australia/New Zealand are in an intermediary position when compared with top ranked European countries as United Kingdom and Denmark. On the other hand, Brazil obtained a higher score than United States, Canada and Japan. The main conclusion is that, although Brazil possesses a reasonable level of availability of TTA Systems, there is a lot to be done by the BEPMC actors in the sense of implementing those programs throughout the pork meat chain as a way to properly assure food safety and enlarge its market share by accessing countries with higher quality and safety standards. Key words: TTA systems, food safety, Brazilian export pork meat chain, pork meat.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".