Growth Control by Ethylene: Adjusting Phenotypes to the Environment
Bibliographic record
Abstract
Plants phenotypically adjust to environmental challenges, and the gaseous plant hormone ethylene modulates many of these growth adjustments. Ethylene can be involved in environmentally induced growth inhibition as well as growth stimulation. Still, ethylene has long been considered a growth inhibitory hormone. There is, however, accumulating evidence indicating that growth promotion is a common feature in ethylene responses. This is evident in environmental challenges, such as flooding and competition, where the resulting avoidance responses can help plants avoid adversity. To show how ethylene-mediated growth enhancement can facilitate plant performance under adverse conditions, we explored a number of these examples. To escape adversity, plants can optimize growth and thereby tolerate abiotic stresses such as drought, and this response can also involve ethylene. In this article we indicate how opposing effects of ethylene on plant growth can be brought about, by discussing a unifying, biphasic ethylene response model. To understand the mechanistic basis for this multitude of ethylene-mediated growth responses, the involvement of ethylene in processes that control cell expansion is also reviewed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".