Analysis of pea mutants reveals the conserved role of <i>FRUITFULL</i> controlling the end of flowering and its potential to boost yield
Bibliographic record
Abstract
Monocarpic plants have a single reproductive phase in their life. Therefore, flower and fruit production are restricted to the length of this period. This reproductive strategy involves the regulation of flowering cessation by a coordinated arrest of the growth of the inflorescence meristems, optimizing resource allocation to ensure seed filling. Flowering cessation appears to be a regulated phenomenon in all monocarpic plants. Early studies in several species identified seed production as a major factor triggering inflorescence proliferative arrest. Recently, genetic factors controlling inflorescence arrest, in parallel to the putative signals elicited by seed production, have started to be uncovered in Arabidopsis, with the MADS-box gene FRUITFULL (FUL) playing a central role in the process. However, whether the genetic network regulating arrest is also at play in other species is completely unknown. Here, we show that this role of FUL is not restricted to Arabidopsis but is conserved in another monocarpic species with a different inflorescence structure, field pea, strongly suggesting that the network controlling the end of flowering is common to other plants. Moreover, field trials with lines carrying mutations in pea FUL genes show that they could be used to boost crop yield.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".