Wood anatomical correlates with theoretical conductivity and wood density across China: evolutionary evidence of the functional differentiation of axial and radial parenchyma
Bibliographic record
Abstract
BACKGROUND AND AIMS: In recent years considerable effort has focused on linking wood anatomy and key ecological traits. Studies analysing large databases have described how these ecological traits vary as a function of wood anatomical traits related to conduction and support, but have not considered how these functions interact with cells involved in storage of water and carbohydrates (i.e. parenchyma cells). METHODS: We analyzed, in a phylogenetic context, the functional relationship between cell types performing each of the three xylem functions (conduction, support and storage) and wood density and theoretical conductivity using a sample of approx. 800 tree species from China. KEY RESULTS: Axial parenchyma and rays had distinct evolutionary correlation patterns. An evolutionary link was found between high conduction capacity and larger amounts of axial parenchyma that is probably related to water storage capacity and embolism repair, while larger amounts of ray tissue have evolved with increased mechanical support and reduced hydraulic capacity. In a phylogenetic principal component analysis this association of axial parenchyma with increased conduction capacity and rays with wood density represented orthogonal axes of variation. In multivariate space, however, the proportion of rays might be positively associated with conductance and negatively with wood density, indicating flexibility in these axes in species with wide rays. CONCLUSIONS: The findings suggest that parenchyma types may differ in function. The functional axes represented by different cell types were conserved across lineages, suggesting a significant role in the ecological strategies of the angiosperms.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".