Bibliographic record
Abstract
Accurate knowledge of carbon (C) content in live wood is essential for quantifying tropical forest C stocks, yet generic assumptions (such as biomass consisting of 50% carbon on a weight/weight basis) remain widely used despite being supported by little chemical analysis. Empirical data from stem cores of 59 Panamanian rainforest tree species demonstrate that wood C content is highly variable among co-occurring species, with an average (47.4±2.51% S.D.) significantly lower than widely assumed values. Prior published values have neglected to account for volatile C content of tropical woods. By comparing freeze- and oven-dried wood samples, we show that volatile C is non-negligible, and excluding the volatile fraction underestimates wood C content by 2.48±1.28% (S.D.) on average. Wood C content varied substantially among species (from 41.9-51.6%), but was neither strongly phylogenetically conserved, nor correlated to ecological (i.e. wood density, maximum tree height) or demographic traits (i.e. relative growth rate, mortality rate). Overall, assuming generic C fractions in tropical wood overestimates forest C stocks by ∼3.3-5.3%, a non-trivial margin of error leading to overestimates of 4.1-6.8 Mg C ha(-1) in a 50-ha forest dynamics plot on Barro Colorado Island, Panama. In addition to addressing other sources of error in tropical forest C accounting, such as uncertainties in allometric models and belowground biomass, compilation and use of species-specific C fractions for tropical tree species would substantially improve both local and global estimates of terrestrial C stocks and fluxes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".