Variation in carbon and nitrogen concentration among major woody tissue types in temperate trees
Bibliographic record
Abstract
Quantifying variation in the wood chemical traits of trees is critical for parameterizing forest biogeochemical budgets and models. Available data on wood chemical traits are based largely on samples taken from main stems; few studies have evaluated how wood chemical traits vary among major woody tissue types. We examined variation in wood carbon (C) and nitrogen (N) concentrations in 17 temperate tree species across five woody tissue types: sapwood, heartwood, small branches, coarse roots, and bark; analyses were corrected for losses of volatile C. Both C and N varied significantly among tissue types, but differences were driven mainly by high C and N in bark, a pattern observed for nearly all species. Among nonbark tissue types, bivariate correlations among sapwood, heartwood, small branches, and coarse roots were highly significant and positive for wood C (r = 0.88–0.98) and N (r = 0.66–0.95) concentrations. We suggest that intraspecific variation in C across tissue types is less important than interspecific variation for assessment and modeling of forest-level C dynamics. In contrast, differences in N among tissue types were larger and appeared to be more important to incorporate into forest-level nutrient assessments and models. Our results suggest that, with the exception of bark, wood chemical trait values derived from stemwood can be used to accurately represent whole-tree trait values in models of forest C and N stocks and fluxes, at least for temperate species.
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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 | 0.001 |
| 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) | 0.000 | 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".