Progress and opportunities in seed genomics research at the Plant Biotechnology InstituteThis paper is one of a selection of papers published in a Special Issue from the National Research Council of Canada – Plant Biotechnology Institute.
Bibliographic record
Abstract
Seed crops play a major role in the global food and feed supply industries. Cereals, oilseeds, and legumes are the predominant seed crops grown in Canada. Brassica napus L. (canola) is the most important oilseed, and currently contributes over $13 billion to the Canadian economy (Canola Council of Canada). The value of oilseed crops, canola in particular, is expected to grow owing to the increasing demand for food, feed, and bioproduct (including biodiesel) applications. In the last 6 years, the Plant Biotechnology Institute (PBI) of the National Research Council Canada, in partnership with several collaborators, has been involved in the study of oilseed crops genomics, in particular Brassica spp., to improve our understanding of this important crop. The research is providing insights into key gene function that can be applied to the improvement of crop performance, productivity, and quality, to meet the increased demand. PBI has focused its activities on two strategic areas. First, the generation of genomics resources that can be used for the study of B. napus and related species. The resources include the development of expressed sequence tags (ESTs), genomic DNA sequences, and the development of DNA arrays. Secondly, a systematic analysis of seed development and composition aimed at improving our understanding of the seed biology. Similar genomics tools developed in Brassica are now being developed in other crops including flax and legumes. Progressing from genomics to functional genomics, these research engagements will be a significant step towards understanding the molecular processes underlying seed composition, quality, yield, and stress resistance of plants thus facilitating the development of elite germplasm.
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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".