An ecophysiological and developmental perspective on variation in vessel diameter
Bibliographic record
Abstract
Abstract Variation in xylem vessel diameter is one of the most important parameters when evaluating plant water relations. This review provides a synthesis of the ecophysiological implications of variation in lumen diameter together with a summary of our current understanding of vessel development and its endogenous regulation. We analyzed inter‐specific variation of the mean hydraulic vessel diameter (Dv) across biomes, intra‐specific variation of Dv under natural and controlled conditions, and intra‐plant variation. We found that the Dv measured in young branches tends to stay below 30 µm in regions experiencing winter frost, whereas it is highly variable in the tropical rainforest. Within a plant, the widest vessels are often found in the trunk and in large roots; smaller diameters have been reported for leaves and small lateral roots. Dv varies in response to environmental factors and is not only a function of plant size. Despite the wealth of data on vessel diameter variation, the regulation of diameter is poorly understood. Polar auxin transport through the vascular cambium is a key regulator linking foliar and xylem development. Limited evidence suggests that auxin transport is also a determinant of vessel diameter. The role of auxin in cell expansion and in establishing longitudinal continuity during secondary growth deserve further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".