Bibliographic record
Abstract
The global interest in vegetable oil is due to greater environmental concerns and increasing demand for renewable sources of energy in recent decades. In order to meet the growing demand for vegetable oil, oilseed production has increased globally, and needs to be further extended. In warm temperate regions of Canada, protein and vegetable oil are primarily produced by soybean, which is replaced by canola (Brassica napus) and field pea (Pisum sativum) in less temperate regions of western Canada. The objective of this research was to examine a variety of field pea accessions for the total lipid content in the seeds to create a comparable dual purpose (protein and oil) crop for western Canada. The research was initiated by validation of lipid extraction methods, and multiplication of 174 acquired pea accessions in 2009 and 2010 at McGill University (Quebec, Canada). Lipid extraction was carried out by the validated method (the butanol extraction procedure) presented in chapter 2 and applied to the seeds of pea accessions which were grown to maturity as presented in chapter 3. Lipid content ranged from 0.3 % to 6.3 % with the accession (p<0.0001), the year (p=0.0002) and the interaction of accession by year (p <0.0001) being significant factors on the total lipid production in pea seeds. Among the plant characteristics, which were investigated in the research, seed surface type (wrinkled as compared to smooth) had a significant effect (p= 0.001) on the total lipid production in the seeds. The data can contribute to the selective breeding of field pea accessions for specific traits suitable for lipid production
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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 | 0.001 | 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".