The impact of long‐term water stress on relative growth rate and morphology of needles and shoots of <i>Metasequoia glyptostroboides</i> seedlings: research toward identifying mechanistic models
Bibliographic record
Abstract
Leaf morphology in the upper canopy of trees tends to be different from that lower down. The effect of long-term water stress on leaf growth and morphology was studied in seedlings of Metasequoia glyptostroboides to understand how tree height might affect leaf morphology in larger trees. Tree height increases water stress on growing leaves through increased hydraulic resistance to water flow and increased gravitational potential, hence we assume that water stress imposed by soil dehydration will have an effect equivalent to stress induced by height. Seedlings were subjected to well-watered and two constant levels of long-term water stress treatments. Drought treatment significantly reduced final needle count, area and mass per area (leaf mass area, LMA) and increased needle density. Needles from water-stressed plants had lower maximum volumetric elastic modulus (ε(max)), osmotic potential at full turgor (Ψ¹⁰⁰(π)) (and at zero turgor (Ψ⁰(π)) (than those from well-watered plants. Palisade and spongy mesophyll cell size and upper epidermal cell size decreased significantly in drought treatments. Needle relative growth rate, needle length and cell sizes were linear functions of the daily average water potential at the time of leaf growth (r² 0.88-0.999). We conclude that water stress alone does mimic the direction and magnitude of changes in leaf morphology observed in tall trees. The results are discussed in terms of various models for leaf growth rate.
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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".