Crown structural properties, wood density, and liana load: influence on growth and mortality in subtropical forests
Bibliographic record
Abstract
Wood density (WD) and other wood mechanical and structural properties may have a strong functional relationship with demographic patterns and allometry of trees. We analyzed the influence of WD, structural properties, architectural traits, and community-level attributes on growth rates (GRs) and mortality modes of canopy tree species in a subtropical forest of Argentina. Stem WD and the WD, strength, stiffness, toughness, and hardness of branches were measured in 10 canopy species. Architectural traits and liana load were also determined. Strength and hardness of branches were linearly correlated to branch WD, and GRs were linearly correlated to stem WD across species. At the individual level, trees with greater hardness and toughness in branches died mostly uprooted, and trees with greater branch stiffness and susceptibility to colonization by lianas were mostly broken. At the community level, the suppressed trees died mostly broken. The dominant trees with high local tree density died mostly broken, whereas more isolated trees died mostly uprooted. Mortality modes were determined not only by mechanical properties, but also by community properties such as liana load, crown canopy position, and number of neighboring trees. Other biophysical traits besides WD are important explanatory variables when dry wood is used to describe functional characteristics of trees.
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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".