Alteration in flowering time causes accelerated or decelerated progression through<i>Arabidopsis</i>vegetative phases
Bibliographic record
Abstract
We have identified three phases within the wild-type Arabidopsis thaliana (L.) Heynh. rosette, based on significant differences in leaf shape, size, vascular pattern, and presence of abaxial trichomes. To test the hypothesis that a single, central mechanism controls the progression through all plant phases and that conditions that alter the time to flowering will also alter the progression through vegetative phases, we analysed the rosette phases under such conditions. In support of our hypothesis, we determined that those conditions (loss of LEAFY activity, short days) that decelerate time to flowering show decelerated progression through the rosette phases, while those conditions (loss of TERMINAL FLOWER, overexpression of LEAFY, low light) that accelerate time to flowering show accelerated progression through the rosette phases. In all conditions except short days, the length of the first phase was unaffected, indicating that this phase is less susceptible to influences of the central mechanism. Progression through the subsequent two rosette phases was accelerated differentially, such that the second phase was affected more strongly than the first. This supports the idea that, in the rosette, as in the inflorescence, the inhibition of phase transition by the central mechanism is gradually decreasing.Key words: phase change, flowering time, Arabidopsis thaliana, LEAFY, TERMINAL FLOWER, heteroblasty.
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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".