Bibliographic record
Abstract
Old traditional winemaking countries are now ahead of new winemaking countries. In addition to countries with European and Middle Eastern viticultural traditions, wine production has begun on a large scale in Canada, Australia, Argentina, Chile, Mexico, New Zealand, South Africa, Australia and the Americas. The top wine exporters in 2019 are dominated by international wine trade in Italy, Spain and France - the total of 57.1 million hectoliters, which is 54% of the global market. Germany, the United Kingdom and the United States were the largest importers - a total of 40.4 million hectoliters, accounting for 38% of the global market. These three countries account for 39% of the total value of world wine imports, amounting to 11.9 billion euros. The US is the largest consumer of wine in the world, with a record level of 33.0 million hectoliters in 2019. Georgia has a serious potential to establish itself in the world markets with its uniqueness, with the introduction of innovative digital technologies. Keywords: Old World wines, New World wines, Wine Export-Import, Global Wine Market, Wine Economy, Viticulture, Harmonized customs system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.017 |
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".