A global analysis of parenchyma tissue fractions in secondary xylem of seed plants
Bibliographic record
Abstract
Parenchyma is an important tissue in secondary xylem of seed plants, with functions ranging from storage to defence and with effects on the physical and mechanical properties of wood. Currently, we lack a large-scale quantitative analysis of ray parenchyma (RP) and axial parenchyma (AP) tissue fractions. Here, we use data from the literature on AP and RP fractions to investigate the potential relationships of climate and growth form with total ray and axial parenchyma fractions (RAP). We found a 29-fold variation in RAP fraction, which was more strongly related to temperature than with precipitation. Stem succulents had the highest RAP values (mean ± SD: 70.2 ± 22.0%), followed by lianas (50.1 ± 16.3%), angiosperm trees and shrubs (26.3 ± 12.4%), and conifers (7.6 ± 2.6%). Differences in RAP fraction between temperate and tropical angiosperm trees (21.1 ± 7.9% vs 36.2 ± 13.4%, respectively) are due to differences in the AP fraction, which is typically three times higher in tropical than in temperate trees, but not in RP fraction. Our results illustrate that both temperature and growth form are important drivers of RAP fractions. These findings should help pave the way to better understand the various functions of RAP in plants.
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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 | 0.001 | 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".