Carbon concentration in the world's trees across climatic gradients
Bibliographic record
Abstract
Wood carbon (C) concentration is a key wood trait that varies widely among tree species, but our understanding of the factors governing this trait is limited, despite reason to hypothesize that wood C varies systematically across environmental gradients. We compiled a novel database of 1145 geo-referenced wood C observations from 415 species, to elucidate climate correlates of wood C concentrations, and test if these relationships differ across tissue types and major taxonomic divisions (i.e. angiosperms vs gymnosperms). Climate variables, including mean annual temperature (MAT) and precipitation and temperature seasonality, are significantly correlated with wood C concentrations. Relationships between wood C and these variables differ across tissue types and taxonomic divisions, yet there is a negative relationship between wood C and MAT that exists across all tissues and species groups. Wood C concentrations in trees are influenced by climate, with experimental evidence (albeit scant) indicating that climate-driven changes in lignin concentrations likely govern these relationships. Our study presents among the first lines of evidence indicating that wood C concentrations are correlated with environmental conditions, thereby enhancing our understanding of the potential adaptive significance of wood C variation in trees.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".