Crosstalk and abscisic acid: the roles of terpenoid hormones in coordinating development
Bibliographic record
Abstract
The sesquiterpenoid hormone abscisic acid (ABA) regulates many aspects of plant growth and development. It has been difficult, however, to understand how this hormone functions in a myriad of events. Genetic analysis, particularly in Arabidopsis, has identified genes that modulate ABA responsiveness, but a molecular framework has not been developed to explain how these genes direct ABA‐mediated developmental events. Certainly, some of the diversity of processes influenced by ABA is a result of crosstalk with other signalling pathways. In other cases, the complex development of a multicellular organism with different cell types and growth conditions throughout its life cycle also increases the possible output signals of ABA action. In this article, we touch on some of these issues in the context of ABA signalling during embryogenesis. On a more speculative level, we propose that a developmental and molecular framework of ABA action in the embryo may be gained from two chemically related terpenoid signalling hormones in animals: juvenile hormone (JH) and retinoic acid (RA). Many of the developmental issues with regard to ABA action in plant embryos are mirrored in JH studies from invertebrates, and the molecular action of RA in vertebrates suggests that transcriptional regulation is a direct output of RA addition. Both of these systems may be useful in furthering our developmental and molecular understanding of ABA action in plants.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".