Water stress effects on the content of low molecular weight carbohydrates and phenolic acids in <i>Ctenanthe setosa</i> (Rosc.) Eichler
Bibliographic record
Abstract
Morphological and biochemical changes in plant cells are known as important events for adaptation to stress. In this study, changes in carbohydrate and phenolic acid concentrations during leaf rolling under water stress were investigated. Leaves of vegetatively propagated Ctenanthe setosa (Rosc.) Eichler plants started to roll after a 28-d water deficit. After approximately 33–35 d, the leaves were tightly rolled. Water stress significantly increased the dry weight of rolled leaves. Low molecular dry weight carbohydrate components identified in unrolled and rolled leaves were fructose, glucose, inositol and sucrose. Leaves of stressed plants tended to accumulate more carbohydrates of low molecular weight. The same sugars (except inositol) were also identified in liquid and crystal forms of exudates, which appeared on the abaxial surface of the leaves during leaf rolling. The phenolic acids identified in unrolled and rolled leaves were from the benzoic group (benzoic, salicylic, 4-hydroxybenzoic, vanillic, 3,4-dihydroxybenzoic, syringic acids), and the cinnamic group (ferulic and caffeic acids both in free and methyl ester form and cis- and trans-p-coumaric acids). All phenolic acid concentrations (except for salicylic acid) in the phenolic group increased in rolled leaves in comparison with unrolled leaves. In the cinnamic group, the amounts of cis- and trans-p-coumaric and caffeic acids were greater in rolled leaves than in unrolled leaves. Key words: Ctenanthe setosa, exudate, crystal, leaf rolling, sugar, phenolic acid
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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) | 0.000 | 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 | 0.001 |
| 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".