The relationship between wood density and mortality in a global tropical forest data set
Bibliographic record
Abstract
Wood density is thought to be an important indicator of plant life history because it is coupled to many aspects of whole-plant form and function. We used a hierarchical Bayesian approach to explain variation in mortality rates with wood density, drawing on data for 765,500 trees from 1639 species at 10 sites located across the Old and New World tropics. Mortality rates declined with increasing wood density at five of 10 sites. Similar negative trends were detected at four additional sites, while one site showed no relationship. Our model explained 40% of variation in mortality on average. Both wood density and mortality rates show a high degree of phylogenetic conservatism. Grouping species by family across sites in a second analysis, we found considerable variation in the relationship between wood density and mortality, with 10 of 27 families demonstrating a strong negative relationship. Our results highlight the importance of wood density as a functional trait in tropical forests, as it is strongly linked to variation in survival. However, the relationship varied among families, plots, and even census intervals within sites, indicating that the factors responsible for the relationship between wood density and mortality vary spatially, taxonomically and temporally.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".