Technological prospecting of the use of vegetables in the development of gluten-free foods
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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