Az agrárium versenyképessége az Európai Unióban: fókuszban a tejipar
Bibliographic record
Abstract
The European Union produces 15% of its agricultural production in the dairy industry. Furthermore, one quarter of global milk production is produced in this region, and European milk consumption is three times higher than the world average. In 2017, total milk production for the EU28 was 170.1 million tonnes, the vast majority of which is cows’ milk production, 164.8 million tonnes. Consumption of fresh dairy products in EU Member States totalled 30.7 million tonnes of milk and 15.6 million tonnes of other fresh dairy products (Eurostat, 2017). The article focuses on the European Union’s and Hungary’s dairy export and analyses it with Balassa’s (Revealed Comparative Advantage, RCA) index. Our aim is to explore the foundations of the region’s competitiveness and the role and opportunities of Hungarian dairy sector. The analysis is based on EU dairy export data for the period 2000–2017. The main result of the analysis is that in terms of competitiveness, the order of the countries, export performance (the largest ones: Denmark, France, Ireland and Belgium) is not fully in line with the order of dairy producing and processing (the largest ones: Germany, France, the United Kingdom, the Netherlands) or dairy export (the largest exporters: Germany, Netherlands, France and Belgium). The reason is that the highest customer value can be achieved through the production of high-end products, and the most competitive countries specialise in the production of one or a few of these products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".