Evaluating the breeding potential of cultivated lentils for increasing protein and amino acid concentration in the Northern Great Plains
Bibliographic record
Abstract
ABSTRACT The rising demand for plant-based proteins has intensified interest in pulse crops due to their high protein concentration. However, few studies have evaluated protein and amino acid composition/variability in cultivated lentil ( Lens culinaris Medik.). We evaluated protein and amino acid composition using near-infrared reflectance spectroscopy (NIRS) in a diversity panel grown in four site-years in Saskatchewan, Canada, followed by genome-wide association analyses with phenology-related traits as covariates. We found little correlation between days from sowing to flowering, region of origin, cotyledon color, or seed size, and protein concentration. Reproductive period was correlated with protein concentration. We also observed large variability between environments and more variability within market classes than among them. Our results demonstrate the potential for breeders to identify germplasm and select for increased protein and amino acid concentration and quality using a high-throughput NIRS method. We were able to identify numerous molecular markers for use in marker-assisted breeding. Our approach could be replicated by breeders from other regions or with other pulse crops to help meet the demand for plant-based protein and improvements in protein quality.
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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 | 0.001 | 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) | 0.000 | 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 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".