Decomposition rates of American chestnut (<i>Castanea dentata</i>) wood and implications for coarse woody debris pools
Bibliographic record
Abstract
Observations of the rapid growth and slow decomposition of American chestnut (Castanea dentata (Marsh.) Borkh.) suggest that its reintroduction could enhance terrestrial carbon (C) sequestration. A suite of decomposition models was fit with decomposition data from coarse woody debris (CWD) sampled in Wisconsin and Virginia, U.S. The optimal (two-component exponential) model was integrated with generic growth curves and documented longevity and typical stem density to evaluate how CWD and biomass pools relate to decomposition. CWD decomposed faster in Wisconsin (4.3% ± 0.3% per year) than in Virginia (0.7% ± 0.01% per year), and downed dead wood decomposed faster (8.1% ± 1.9% per year) than standing dead wood (0.7% ± 0.0% per year). We predicted considerably smaller CWD pools in Wisconsin (maximum 41 ± 23 Mg C·ha–1) than in Virginia (maximum 98 ± 23 Mg C·ha–1); the predicted biomass pool was larger in the faster growing Wisconsin trees (maximum 542 ± 58 Mg C·ha–1) compared with slower growing trees in Virginia (maximum 385 ± 51 Mg C·ha–1). Sensitivity analysis indicated that accurate estimates of decomposition rates are more urgent in fertile locations where growth and decomposition are rapid. We conclude that the American chestnut wood is intermediate in resistance to decomposition. Due to the interrelatedness of growth and decomposition rates, CWD pool sizes likely do not depend on species alone but on how the growth and decomposition of individual species vary in response to site productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".