Mechanisms for minimizing height‐related stomatal conductance declines in tall vines
Bibliographic record
Abstract
Abstract The ability to transport water through tall stems hydraulically limits stomatal conductance (gs), thereby constraining photosynthesis and growth. However, some plants are able to minimize this height‐related decrease in gs, regardless of path length. We hypothesized that kudzu (Pueraria lobata) prevents strong declines in gs with height through appreciable structural and hydraulic compensative alterations. We observed only a 12% decline in maximum gs along 15‐m‐long stems and were able to model this empirical trend. Increasing resistance with transport distance was not compensated by increasing sapwood‐to‐leaf‐area ratio. Compensating for increasing leaf area by adjusting the driving force would require water potential reaching −1.9 MPa, far below the wilting point (−1.2 MPa). The negative effect of stem length was compensated for by decreasing petiole hydraulic resistance and by increasing stem sapwood area and water storage, with capacitive discharge representing 8–12% of the water flux. In addition, large lateral (petiole, leaves) relative to axial hydraulic resistance helped improve water flow distribution to top leaves. These results indicate that gs of distal leaves can be similar to that of basal leaves, provided that resistance is highest in petioles, and sufficient amounts of water storage can be used to subsidize the transpiration stream.
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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".