ROMANIA’S VITICULTURAL IDENTITY WHILE JOINING THE EUROPEAN UNION
Bibliographic record
Abstract
Romania, a country with a long lasting tradition in viticulture and wine-making, which is worldwide acknowledged, while joining the European Union, will contribute to the sorts variety in the market of grapes, wines and high quality distillations with many COD wines. The kind of table grapes with precocious maturation ”VICTORIA” (owning a Romanian OSIM patent) has already been adopted by some viticulture Mediterranean countries (Greece, Italy, Spain etc.). The native kinds: Feteasca alba, Feteasca regala, Selected Cramposia, Romanian Tamaioasa, Grasa de Cotnari , Busuioaca de Bohotin, Feteasca neagra, Cadarca, Novac, are the basis from which are obtained the famous types of wine:white, aromatic and red, as well as some fine distillations DOC-CIB and DOC-CT, that have entered the international trade circuit longtime ago ( Germany, Austria, Great Britain, USA, Canada, Japan, etc.). To these could be joined the worshipped wines COD-CIB and COD-CT, issued from kinds belonging to the European patrimony like: Sauvignon de Dragasani, Traminer de Blaj, Cabernet Sauvignon de Samburesti, Pinot noir de Valea Calugareasca, Chardonnay de Murfatlar, Muscat Ottonel de Jidvei, Muscat Frontignan de Segarcea, where they have found a second homeland. This work presents the parameters of quality attended by the mentioned kinds in some viticultural centers from Romania and from Europe.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".