Study of Pea Accessions for Development of an Oilseed Pea
Bibliographic record
Abstract
Global interest in stable energy resources coupled with growing demand for bio-oils in various conventional and arising industries has renewed the importance of vegetable oil production. To address this global interest, oilseed production has been increased in recent decades by different approaches, such as extending the cultivation area of oil crops, or breeding and growing genetically modified plants. In this study, pea (Pisum sativum L.) accessions were screened for lipid content using a rapid extraction method. This method quantifies lipid concentration in pea seeds and was developed by assessing and comparing the results of existing extraction methods used for canola and soybean, the top two Canadian oilseeds. Seeds of 151 field pea accessions were grown to maturity in 2009 and 2010 at McGill University (Quebec, Canada). Overall, lipid concentration in pea seeds ranged from 0.9 to 5.0%. Among several seed characteristics, only seed shape (wrinkled verses round) had a significant effect on the total lipid production in the seeds. Peas are a valuable source of protein and starch, but the lipid concentration in their seeds has been undervalued. This research supports the idea of developing a novel dual-purpose oilseed pea that emulates the protein and oil production in soybean seeds while being conveniently adapted to a colder climate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".