Advances in the Quality Improvement of Fruit Wines: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fruit wines have gained great interest in recent years due to the increasingly diverse demands of consumers for different fruit wines with different colors, flavors, and nutritional values. Some fruits such as blueberry and strawberry are perishable and have a short shelf life. The production of fruit wine reduces fruit losses after harvest and enhances fruit utilization. The production of fruit wine with premium quality is determined by both intrinsic (i.e., genetic background) and extrinsic factors (e.g., yeast and fermentation protocol). This article provides an updated overview on the strategies and technologies aiming to improve the quality of fruit wines. Recent progress in improving fruit wine quality by variety selection, post-harvest treatments, yeast selection, fermentation protocols, fermentation conditions, and aging technologies has been comprehensively reviewed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it