Factors controlling plasticity of leaf morphology in <i>Robinia pseudoacacia</i>: III. biophysical constraints on leaf expansion under long‐term water stress
Bibliographic record
Abstract
In this article, we measured the relative growth rate (RGR) of leaves of Robinia pseudoacacia seedlings under well-watered and water-stressed conditions (mid-day Ψ(w) = leaf water potential estimated with a pressure bomb of -0.48 and -0.98 MPa, respectively). Pressure-volume (PV) curves were done on growing leaves at 25, 50 and 95% of the mature size (growth stage) in order to compute solute potential (Ψ) and turgor pressure (Ψ(P) ) as a function of Ψ(w) . The PV curves and diurnal measurements of Ψ(w) and RGR allowed us to evaluate the parameters (cell wall extensibility m and growth turgor threshold Y) of the Lockhart equation, RGR = m(Ψ(P)-Y), at each growth stage. Our data showed that m and Y did change with leaf age, but the changes were slow enough to evaluate m and Y on any given day. We believe this is the first study to provide evidence that the Lockhart equation adequately quantifies leaf growth of trees over a range of time domains. The value of m linearly declined and Y linearly increased with growth stage. Also, mild drought stress caused a decline in m and increase in Y relative to controls. Although water stress caused an osmotic adjustment which, in turn, increased Ψ(P) in stressed plants relative to controls, the RGR and final leaf sizes were reduced in water-stressed plants because of the impact of water stress on decreased m and increased Y.
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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".