Conservation vs divergence in <i>LEAFY</i> and <i>APETALA1</i> functions between <i>Arabidopsis thaliana</i> and <i>Cardamine hirsuta</i>
Bibliographic record
Abstract
Summary A conserved genetic toolkit underlies the development of diverse floral forms among angiosperms. However, the degree of conservation vs divergence in the configuration of these gene regulatory networks is less clear. We addressed this question in a parallel genetic study between the closely related species Arabidopsis thaliana and Cardamine hirsuta . We identified leafy ( lfy ) and apetala1 ( ap1 ) alleles in a mutant screen for floral regulators in C. hirsuta . C. hirsuta lfy mutants showed a complete homeotic conversion of flowers to leafy shoots, mimicking lfy ap1 double mutants in A. thaliana . Through genetic and molecular experiments, we showed that AP 1 activation is fully dependent on LFY in C. hirsuta , by contrast to A. thaliana . Additionally, we found that LFY influences heteroblasty in C. hirsuta , such that loss or gain of LFY function affects its progression. Overexpression of UNUSUAL FLORAL ORGANS also alters C. hirsuta leaf shape in an LFY ‐dependent manner. We found that LFY and AP 1 are conserved floral regulators that act nonredundantly in C. hirsuta , such that LFY has more obvious roles in floral and leaf development in C. hirsuta than in A. thaliana .
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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".