Genetic stability and genotype × environment interaction analysis for seed protein content and protein yield of lentil
Bibliographic record
Abstract
Abstract Lentil ( Lens culinaris Medik.) seeds are an excellent source of staple dietary protein that can be a viable plant‐based alternative protein source to food processing industries. Understanding of the basis of genotype × environment (G × E) interaction is fundamental knowledge for plant breeding. We explored genetic stability and G × E for seed protein content and protein yield for 34 lentil genotypes using AMMI (additive main effects and multiplicative interaction) and SREG (site regression) models. Genotypes were evaluated under field conditions in five locations at western Canada during 2017–2018. Protein content and protein yield were 21.6–26.9% of seed dry weight and 156.8–1113.0 kg ha −1 , respectively, across 10 environments. Environment and G × E had fewer effects on protein content than protein yield. Higher seed protein content was observed in the extra‐small red market class. Based on both models, genotypes IBC 1235, 3923‐9, 3674‐17, IBC 929R, and 4371‐4 with stable protein productivity would be useful genetic resources for the development of protein‐rich varieties in lentil breeding programs. Our results suggest genetic improvement of protein and protein yield together is possible for lentil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".