Composition and correlation between major seed constituents in selected lentil (<i>Lens culinaris.</i> Medik) genotypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it