Composition and correlation between major seed constituents in selected lentil (<i>Lens culinaris.</i> Medik) genotypes
Bibliographic record
Abstract
Tahir, M., Lindeboom, N., Båga, M., Vandenberg, A. and Chibbar, R. N. 2011. Composition and correlation between major seed constituents in selected lentil ( Lens culinaris Medik.) genotypes. Can. J. Plant Sci. 91: 825–835. Development of lentil cultivars with increased seed amylose, protein and reduced concentration of anti-nutritional constituents are desired from the perspectives of lentil utilization and human health. In selected lentil genotypes, we studied seed weight, seed coat weight and color, seed composition and the association between major quality traits. Significant (P ≤ 0.05) variation existed for all traits except seed coat weight. The starch and protein concentrations ranged from 39.4 to 45.3 g and from 23.8 to 29.3 g 100 g−1 flour DM whereas the amylose concentration ranged from 29.8 to 34.0 g 100 g−1 starch. Glucose, sucrose and raffinose family oligosaccharides (RFO) concentrations of lentil genotypes ranged from 0.04 to 0.08 g, from 0.7 to 2.4 g, and from 4.6 to 6.6 mmoles 100 g−1 flour DM, respectively. Raffinose, stachyose and verbascose concentrations varied from 1.6 to 2.4 g, from 1.7 to 2.9 g, and from 1.2 to 1.9 g 100 g−1 flour DM, respectively. A significant (P ≤ 0.05) positive correlation existed between 1000-seed weight and starch, 1000-seed weight and RFO and sucrose concentration. Similarly, a significant (P ≤ 0 .05) negative correlation was found between starch and protein concentration, 1000-seed weight and protein concentration, and 1000-seed weight and amylose concentration. The lack of a significant correlation between RFO and other quality traits indicates that selection for low RFO concentration may not affect other important quality traits in lentil seeds.
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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 | 0.000 |
| 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".