Technological prospecting of the use of vegetables in the development of gluten-free foods
Bibliographic record
Abstract
The growing demand for gluten-free foods, by people seeking more healthiness or who have dietary restrictions, has led to the acquisition of gluten-free foods. However, the development of gluten-free foods is a challenge due to the reduced nutritional value, requiring enrichment from other plant sources. A technological prospection study was carried out on the use of vegetables in the development of gluten-free food products, from October 10 to 18, 2020, by surveying technological information available in national and international patent databases, INPI and ESPACENET, respectively. Search strategies were defined using the association of keywords and international codes relevant to the topic. The results obtained in the international patent base differed by 490% in the period from 2001 to 2020, when compared with the national database. China stands out as a technology-dominated country, followed by the United States, Canada and Japan. Prospecting based on the number of patent filings revealed a 298% growth trend for gluten-free products, from 2001 to 2020, according to the international patent base, which emerges as an innovative alternative to meet the trends of the food market for the coming years.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".