COMPETITIVIDADE NO MERCADO MUNDIAL DE CARNE SUÍNA: 2007-2017
Bibliographic record
Abstract
This article aims to study the performance of the competitiveness in the world pork market, total and by subgroups from 2007 at 2017.The methodology is based on analysis of traditional competitiveness indicators and an indicator of global competitiveness, with data from the United Nations and OECD.From the analysis of global competitiveness, it was concluded that Denmark, Spain and Brazil are highly competitive countries in this sector.Belgium, Canada and the Netherlands have medium competitiveness and the United States has low competitiveness.Furthermore, there are differences in competitiveness between swine meat subgroups.The European Union has hegemony in the subgroup of fresh or chilled swine meat, while Brazil leads the frozen segment.Sanitary and phytosanitary barriers; animal welfare and health; traceability and environmental issues are challenges to Brazil in the exports to Asia, EU and Russia markets.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.047 |
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; both teacher heads agree on what is shown here.
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".