The Market Potential of a Grape Pomace Microemulsion
Bibliographic record
Abstract
Canada’s food waste reached $31 billion in 2014. 95% of this waste ended up in landfills across the country, being a severe burden both economically and environmentally. By implementing sustainable agriculture projects, alternative uses can be found for food waste that produce positive income for companies, and alleviate stresses on the environment. Grape pomace, a food waste produced through the process of wine-making, currently ends up in landfills after wine-production. However, this agricultural by-product holds great market potential for the production of chemical microemulsions. These microemulsion systems show great potential in the food, pharmaceutical and cosmeceutical industries. The market potential was calculated by determining the volume of grape seed oil that could be extracted from grape pomace. The current market value of microemulsion surfactants were then obtained and a value was calculated based on the oil yield. Grape pomace microemulsions had the highest market potential as pharmaceutical raw ingredients, followed respectively by food additive and cosmeceutical raw ingredients. The purpose of this paper is to measure the market potential for grape pomace microemulsions in each of these industries and to provoke further investigations into the production of value added products from agricultural waste.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".