Persistence of Abscisic Acid Analogs in Plants: Chemical Control of Plant Growth and Physiology
Bibliographic record
Abstract
Abscisic acid (ABA) is a plant hormone that regulates numerous plant processes, including plant growth, development, and stress physiology. ABA plays an important role in enhancing plant stress tolerance. This involves the ABA-mediated control of gene expression to increase antioxidant activities for scavenging reactive oxygen species (ROS). ABA is a fragile molecule that is rapidly isomerized by ultraviolet (UV) light and catabolized in plants. This makes it challenging to apply as a plant growth substance. ABA analogs are synthetic derivatives of ABA that alter ABA's functions to modulate plant growth and stress physiology. Modifying functional group(s) in ABA analogs alters the potency, selectivity to receptors, and mode of action (i.e., either agonists or antagonists). Despite current advances in developing ABA analogs with high affinity to ABA receptors, it remains under investigation for its persistence in plants. The persistence of ABA analogs depends on their tolerance to catabolic and xenobiotic enzymes and light. Accumulated studies have demonstrated that the persistence of ABA analogs impacts the potency of its effect in plants. Thus, evaluating the persistence of these chemicals is a possible scheme for a better prediction of their functionality and potency in plants. Moreover, optimizing chemical administration protocols and biochemical characterization is also critical in validating the function of chemicals. Lastly, the development of chemical and genetic controls is required to acquire the stress tolerance of plants for multiple different uses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".