Comparative profiles of the cuticular chemicals and transpiration barrier properties in various organs of Chinese flowering cabbage and Chinese kale
Bibliographic record
Abstract
Abstract Plant cuticle as hydrophobic barrier covers almost all aerial plant organs. Herein the cuticular chemical components and the transpiration of various organs of Chinese flowering cabbage (CFC) and Chinese kale (CK) were comprehensively characterized. Numerous species‐ and organ‐specific differences in morphological, chemical, and physiological levels were found. The various organs were relatively smooth in surface for CFC but glaucous with hollow tube‐ and plate‐type crystals for CK. The chemical composition of cuticular waxes were very‐long chain n‐alkanes, ketones, secondary alcohols with a prominent carbon chain of C29 in CK, primary alcohols dominated by C26, and aldehydes prominently C30 in CFC. Cutin monomers accumulated with similar levels as waxes and were dominated by α,ω‐dicarboxylic acids and fatty acids without added groups. The minimum water conductance differed considerably among species and various organs ranging between 8.9 × 10−5 (CK leaf) and 3.7 × 10−4 m s−1 (CFC leaf petiole). These differences in transpiration properties were proposed to be largely related to the cuticular chemicals in various organs and species. The presented results provide further insights to link the transpiration barrier functions with surface characteristics and cuticular chemicals.
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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 | 0.000 |
| 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".