A global analysis of xylem vessel length in woody plants
Bibliographic record
Abstract
PREMISE OF THE STUDY: Vessels are the chief conduit for long-distance water transport in the majority of flowering plants. Vessel length is a key trait that determines plant hydraulic efficiency and safety, yet relatively little is known about this xylem feature. • METHODS: We used previously published studies to generate a new global data set of vessel length in woody plants. These data were used to examine how evolutionary history, plant habit, environment, and growth ring porosity influenced vessel length. We also examined the relationship between mean vessel length and mean vessel diameter and maximum vessel length. • KEY RESULTS: Data on mean vessel length were available for stems of 130 species and on maximum vessel length for stems of 91 species. A phylogenetic analysis indicated that vessel length did not exhibit significant phylogenetic signal. Liana species had longer vessel lengths than in tree or shrub species. Vessel diameter was not predictive of mean vessel length, but maximum vessel length strongly predicted mean vessel length. Vessel length did not vary between species that differed in growth ring porosity. • CONCLUSIONS: Many traits often assumed to be linked to vessel length, including growth ring porosity and vessel diameter, are not associated with vessel length when compared interspecifically. Sampling for vessel length has been nonrandom, e.g., there are virtually no data available for roots, and sampling for environment has been confounded with sampling for habit. Increased knowledge of vessel length is key to understanding the structure and function of the plant hydraulic pathway.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".