Analysis of alternative methods and price politic of icewine production
Bibliographic record
Abstract
The artificial methods of must concentration were discussed in current study: the microwave vacuum dehydration, reverse osmosis and cryoextraction. The main factor of using of alternative ways is deficiently low temperatures in winter period that are necessary for freezing grapes on vine according to the classical technology. The benefits and disadvantages of using of non-classic processes to obtain sweet musts were shown. The physical, chemical and sensory characteristics of wine made from grapes previously frozen by alternative and natural ways were analyzed. Indicators influencing on price of icewines and dessert wines bottle including agricultural climatic, technological and marketing factors were determined. Detailed indicators highlight specificity of used technology and represent consumer preferences. Producers of winemaking regions of Argentina, New Zealand, Israel, Ukraine and Australia adhere to provisions that are inconsistent with the standards of Canada and the European countries regarding the icewine output. These instruments determine the processing of grapes and parameters reflect on parameters of the finished product.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".