The Market Potential of Grape Waste Alternatives
Bibliographic record
Abstract
During wine production, approximately 25% of the grape weight results in by-product/waste (termed ‘pomace’ which is comprised of skins and seeds). Currently, most pomace is being composted to be reintroduced into the vineyards to complete the carbon cycle. Due to the increasing consumer demand for the use of natural over synthetic compounds, and because of increased attention to sustainability of agricultural practices (Fontana, Antoniolli, & Bottini, 2013), there is a vast array of applications for grape pomacebioactives including: functional foods (dietary fiber + polyphenols), food processing (biosurfactants), cosmetics (grapeseed oil + antioxidants), pharmaceutical/biomedical (pullulan) and supplements (grape pomace powder). To date, there has been no assessment as to the market potential for value-added usage of grape pomace. This paper seeks to address this gap. The annual production of grape pomace along with its multitude of applications, create an opportunity to discover an unexploited market with great commercial potential.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".