Canola/Rapeseed Protein: Future Opportunities and Directions—Workshop Proceedings of IRC 2015
Bibliographic record
Abstract
At present, canola meal is primarily streamlined into the animal feed market where it is a competitive animal feed source owing to its high protein value. Beyond animal feed lies a potential game-changer with regards to the value of canola meal, and its opportunity as a high quality food protein source. An economic and sustainable source of protein with high bioavailability and digestibility is essential to human health and well-being. Population pressures, ecological considerations, and production efficiency underscore the importance of highly bioavailable plant proteins, both for the developed and developing world. Despite decades of research, several technologies being developed, and products being brought to large scale production, there are still no commercially available canola protein products. The workshop entitled "Canola/Rapeseed Protein-Future Opportunities and Directions" that was held on 8 July 2015 during the 14th International Rapeseed Congress (IRC 2015) addressed the current situation and issues surrounding canola meal protein from the technological, nutritional, regulatory and genomics/breeding perspective. Discussions with participants and experts in the field helped to identify economic barriers and research gaps that need to be addressed in both the short and long term for the benefit of canola industry.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".