The importance of the volatile carbon fraction in estimating deadwood carbon concentrations
Bibliographic record
Abstract
The volatile carbon concentration or fraction (VCF) of wood—i.e., the proportion of woody tissue which is composed of heat-sensitive volatile organic compounds (VOCs), which are lost during sample preparation—is an important contributor to wood C concentrations. Studies of live wood have shown that failure to account for the VCF of wood may result in significant errors in forest C stock estimates. However, while studies have shown that deadwood C concentrations differ from those in live wood, no study has explicitly quantified the VCF in deadwood. Here, we quantify the VCF in deadwood for the first time, using n = 400 individual deadwood samples obtained from 13 species, multiple decay classes (DC), and two primary woody tissue types (i.e., stem wood and bark), in a temperate forest. The VCF in deadwood is non-trivial, averaging ∼0.9 % and ranging widely across species and decay classes. Across both taxonomic divisions (gymnosperms vs. angiosperms) VCF is largest (1.73 %) in DC 1 but declines to 0 % in DC 5. Overall, stem wood exhibits higher VCF (1.06 %) than bark (0.64 %). Lastly, deadwood VCF appears systematically lower than that in live wood, indicating that live wood VCFs may not be good approximators of the VCF in deadwood. Our results suggest that failing to account for the VCF of deadwood in forest C estimation studies, especially in the early stages of decay, results in errors in deadwood C stock estimates of ∼0.9 % on average. Future studies focused on tree- and forest-scale C estimation should therefore account for the VCF in their analyses, in order to improve the accuracy of C stock estimates. • The volatile carbon fraction (VCF) in deadwood was quantified for the first time. • The VCF ranges widely and averages ∼0.9 % across 13 temperate species. • VCF generally decreases as decomposition increases. • Decay class explains some of the variation in VCF.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".