Plant traits and wood fates across the globe: rotted, burned, or consumed?
Bibliographic record
Abstract
Abstract Wood represents the defining feature of forest systems, and often the carbon in woody debris has a long residence time. Globally, coarse dead wood contains 36–72 Pg C, and understanding what controls the fate of this C is important for predicting C cycle responses to global change. The fate of a piece of wood may include one or more of the following: microbial decomposition, combustion, consumption by insects, and physical degradation. The probability of each fate is a function of both the abiotic environment and the wood traits of the species. The wood produced by different species varies substantially in chemical, micro‐ and macro‐morphological traits; many of these characteristics of living species have ‘afterlife’ effects on the fate and turnover rate of dead wood. The colonization of dead wood by microbes and their activity depends on a large suite of wood chemical and anatomical traits, as well as whole‐plant traits such as stem‐diameter distributions. Fire consumption is driven by a slightly narrower range of traits with little dependence on wood anatomy. Wood turnover due to insects mainly depends on wood density and secondary chemistry. Physical degradation is a relatively minor loss pathway for most systems, which depends on wood chemistry and environmental conditions. We conclude that information about the traits of woody plants could be extremely useful for modeling and predicting rates of wood turnover across ecosystems. We demonstrate how this trait‐based approach is currently limited by oversimplified treatment of dead wood pools in several leading global C models and by a lack of quantitative empirical data linking woody plant traits with the probability and rate of each turnover pathway. Explicitly including plant traits and woody debris pools in global vegetation climate models would improve predictions of wood turnover and its feedback to climate.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".