Changes in elastic modulus, leaf tensity and leaf density during dehydration of detached leaves in two plant species of Moraceae
Bibliographic record
Abstract
The quick adjustments of leaf traits are critical for keeping the survival of plants under dehydration.In this study, we examined the hypotheses that plants would adapt to dehydration by shrinking its mesophyll cells to offset the water loss, or triggering water regulation mechanism caused by enzymes.Leaf structure, elastic modulus (Em), leaf tensity (LT) and leaf density (LD) were determined with detached leaves of Broussonetia papyrifera (L.) Vent.and Morus alba L. at each water loss moment (0, 1, 2, 3, 4 and 5 h).The coupling model between gripping force and LT was established using the Gibbs free energy equation, and the initial LT was determined.The intracellular water availability of M. alba decreased at 4 h, which was earlier than that of B. papyrifera.The intracellular water availability of M. alba was more sensitive than B. papyrifera.Broussonetia papyrifera adapted to dehydration by shrinking its mesophyll cells to offset the water loss, or triggering water regulation mechanism caused by enzymes, i.e., carbonic anhydrase.The sponge parenchyma of B. papyrifera at 3 h decreased by 25.73% of that at control.Morus alba maintained intracellular water availability just by changing the leaf structure.The offset effects through shrinking cells differed between B. papyrifera and M. alba, because the elastic-plastic behavior of their leaves and cells were different.The Em of M. alba was over five-fold higher than that of B. papyrifera.The investigations of water status were more accurate in terms of leaf physical traits instead of water content.
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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) | 0.000 | 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".