El viñedo y el vino entre 1995 y 2019: veinticinco años de cambios en la producción, mercado y consumo de vino en el mundo
Bibliographic record
Abstract
Wine production consistently exceeds wine consumption on a global scale, which explains why the wine market is highly demanding, competitive and increasingly international. This paper quantifies the changes (between 1995 and 2019) in wine consumption, production and the international wine market. Traditionally, the international wine sector was managed from Europe, mainly by a group of countries (Spain, France, Italy and Portugal) that concentrated most of the vineyards, production and international trade. Since the end of the 20th century, several countries (USA, Canada, Chile, Argentina, South Africa, Australia and New Zealand) have made strong inroads into the sector, increasing their vineyard surface area and having a significant and growing presence in international markets. On the other hand, other countries (China, Russia, Brazil, India, the Netherlands, Japan, etc.) are emerging in wine consumption. All this has changed the geographical distribution of vineyards and wine production, moving progressively from the "Old World" to the "New World". In 1961, Europe accounted for almost 69% of the world's vineyards, whereas in 2019 it will account for only 50%. On the other hand, wine imports and exports doubled between 1995 and 2019, while the number of wine-producing and wine-consuming countries is increasing. The globalisation of the wine market has driven changes in wine production and winemaking systems, as well as the regulation of international trade to take into account competition from non-EU wines, issues that simply discussed in this paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".