Pod Shattering: A Homologous Series of Variation Underlying Domestication and an Avenue for Crop Improvement
Bibliographic record
Abstract
In wild habitats, fruit dehiscence is a critical strategy for seed dispersal; however, in cultivated crops it is one of the major sources of yield loss. Therefore, indehiscence of fruits, pods, etc., was likely to be one of the first traits strongly selected in crop domestication. Even with the historical selection against dehiscence in early domesticates, it is a trait still targeted in many breeding programs, particularly in minor or underutilized crops. Here, we review dehiscence in pulse (grain legume) crops, which are of growing importance as a source of protein in human and livestock diets, and which have received less attention than cereal crops and the model plant Arabidopsis thaliana. We specifically focus on the (i) history of indehiscence in domestication across legumes, (ii) structures and the mechanisms involved in shattering, (iii) the molecular pathways underlying this important trait, (iv) an overview of the extent of crop losses due to shattering, and the effects of environmental factors on shattering, and, (v) efforts to reduce shattering in crops. While our focus is mainly pulse crops, we also included comparisons to crucifers and cereals because there is extensive research on shattering in these taxa.
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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 | 0.001 |
| 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) | 0.002 | 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".