Breeding Pea for Improved End-Use Quality
Bibliographic record
Abstract
As the global population continues to grow, a massive challenge facing humanity is providing healthy diets from sustainable food systems. Canada is the world’s leading producer and exporter of dry pea. While traditional uses of pea include consumption as whole or dehulled/split seeds in soups and dhal, or as a protein-rich animal feed supplement, modern techniques have allowed for an expanded use pattern through value added fractionation of seeds into protein, starch and fiber fractions, and the subsequent use of these fractions in food, feed, and industrial applications. Objectives of the pea breeding program at the Crop Development Centre include improving the agronomic performance of the crop, as well as its end-use quality. Over the past two decades we have placed emphasis on improved seed visual quality, improved nutritional quality, and improved utilization properties. This presentation will summarize our progress in these areas. Parallel presentations will focus on research related to pea protein concentration and quality, biofortification of pea for enhanced iron bioavailability, and use of genomic tools to facilitate pea breeding.
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".