Evidence for involvement of jasmonic acid in the induction of leaf senescence by potassium deficiency in <i>Arabidopsis</i>
Bibliographic record
Abstract
Potassium (K+) deficiency induces leaf senescence, and jasmonic acid (JA) plays a role in the regulation of leaf senescence; however, there is no direct evidence that JA has a role in the induction of leaf senescence by K+ deficiency. Here, we determined that JA is involved in the induction of leaf senescence by K+ deficiency. Leaf senescence was induced by K+ deficiency, as indicated by both the induction of expression of two senescence-associated genes, SAG12 and SAG13, and a decline in chlorophyll concentration; whereas inhibitors of JA biosynthesis, aspirin and salicylate, abolished the induction of leaf senescence by K+ deficiency. The JA concentration was threefold higher in the leaves of plants with K+ deficiency than it was in the leaves of control plants. In addition, transcript levels of two JA-responsive genes, PDF1.2 and Thi2.1, were higher in the leaves of plants with K+ deficiency than in the leaves of control plants. Our studies provide evidence that K+ deficiency induces leaf senescence, at least in part, via a JA-dependent pathway.
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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".