Bibliographic record
Abstract
Patent trends on foundation technique and application technologies of rapeseed by-products were analyzed for major producing countries including Korea, USA, Japan, China and Europe, to determine the usability and economical efficiency. To date, patents related to these by-products have been increasing steadily, since the first patent application in 1973. Patent applications in China are overwhelmingly active but those of Korea are steadily increasing. Japan and Europe unions have been slow in applying patents since the mid-2000s. The most number of application is the MB company in Canada, which is actively, doing research to develop technologies related to extraction and purification of useful substances from rapeseed. Most applicants were focusing on one or two of the major field technologies, depending on the company’s main products. Agricultural material section category accounted for more than half of the patent applications followed by technology for separation and purification of useful substances for cosmetics. In the early years of technology development, patent applications were mainly related to technologies on functional protein foods, agricultural materials and separation and purification of useful substances. In recent years, research had shifted on various fields such as cosmetics and medicines technology development is being attempted. In terms of section category, the USA has applied for the most number of patents in all fields except agricultural materials, where China is currently leading, Agricultural development efforts is well supported and encouraged by the Chinese government’s policies. In conclusion, it is suggested that technology development pertaining to separation and purification of useful substances from rapeseed by-products for cosmetics, functional protein foods, medicines and biotic pesticides to enhance value-added utilization of rapeseed by-products.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".