A global map of wood density
Bibliographic record
Abstract
Abstract Wood density influences how quickly woody plants grow, how long they live and how much carbon they store, yet its global variation remains poorly mapped. Here we combined 109,626 wood density measurements from 16,829 species with 300,949 vegetation plots to produce a km-scale map of community-weighted wood density for every woody biome. Our model led to a prediction accuracy 32–51 % higher than previous global products, and a 1.8–3.7-fold wider wood density range (0.28–1.00 g cm −3 ; global mean: 0.57 g cm −3 ) than previously assumed. Spatial cross-validation showed low bias (±2.5 % of the mean), and uncertainties decreased from 20% in poorly sampled drylands and boreal regions to 5% in data-rich temperate forests. Mean annual temperature was the best predictor of community-weighted mean wood density, increasing by 0.01 g cm −3 for every 1°C change. We deliver a low-bias, high-resolution wood density layer for Earth system models, together with spatially explicit error maps. This study represents a major step forward for carbon accounting and trait-based forecasts of vegetation change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".