Breaking Seed Dormancy in Wild Lentil Species Using an Abscisic Acid Antagonist to Explore and Utilize Wild Relatives as Genetic Resources
Bibliographic record
Abstract
ABSTRACT Wild lentil genotypes are a valuable resource for genetic diversity, but their scientific study and utilization in breeding programs have been hampered by traits such as crossability barriers and seed dormancy. To make wild lentil genotypes more accessible for further exploration, we investigated the effect of different seed scarification techniques plus the abscisic acid (ABA) analog, ABA‐1019 on breaking dormancy and seed germination release in seven lentil genotypes and 54 accessions (lines). Dormancy was alleviated by scarification with or without the addition of chemical substances but germination release was dependent on lentil genotypes/accessions and seed coat manipulations. ABA‐1019 functions as an antagonist to native ABA, thereby blocking the ABA‐induced delay in germination. ABA‐1019 significantly increased germination and seedling emergence rates compared to the water and dimethyl sulfoxide (DMSO) controls. DMSO increased germination rates and indices in some lentil genotypes compared to water control. ABA‐1019 also had a positive effect on shortening the time to germination, emergence, and flowering, which is of particular interest when developing inbred populations or advancing lines to increase homogeneity. Our results indicate that combinational (physical and physiological) dormancy exist in some lentil accessions.
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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 | 0.000 | 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) | 0.001 | 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".