Аналіз сучасного стану та історичних коренів виробництва крижаного вина
Bibliographic record
Abstract
Number of ice wine companies is increasing every year due to unique chemical composition and consumer demand. Unfortunately, processes associated with outputs of exclusive wines are examined only by Canadian winemakers in scientific literature. Despite of the strict requirements in non-classic technology that largely influence on obtaining of frozen grapes in winter season, other companies and wineries have included ice wine to their product offering.This study surveyed comprehensive information related to first ice wine producers and present definition of rare wine production that are the objectives of current research. Thus, data about primary frozen vintages, wine regions in each producing country and famous winemakers had influenced the expanding of dessert special styles were described; differences in the titles wines were shown. After the experience of the Germans in winter technology, other countries also began to introduce the wine of the premium segment. The current state of production and the wineries, which produce special wine, were reviewed and enterprises developed winter technologies were highlighted in the North America, Europe and Asia. Placement of the wineries, their number and ranges of ice wine were represented and argued according to agro-climatic conditions of each producing lands.The entire world situation referring the existing of ice wine producers in the main regions of European and other territories supplemented special beverages to their profiles suggesting by our study afford an opportunity to fully understand the significant centres of production and changes in wine market supply. Furthermore, current research can conduce to the further publications of ice wine data compositions from different producing areas to evaluating of sensory and physical-chemical parameters and, perhaps, to find the solutions of challenges in ice wine production considering the experiences of each countries.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".