In situ decomposition of northern hardwood tree boles: decay rates and nutrient dynamics in wood and bark
Bibliographic record
Abstract
The decomposition of coarse woody debris contributes to forest nutrient sustainability and carbon (C) balances, yet few field studies have been undertaken to investigate these relationships in northern hardwood forests. We used a paired-sample approach to study the decomposition of sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Erhr.), and yellow birch (Betula alleghaniensis Britt.) boles at the Hubbard Brook Experimental Forest in New Hampshire. Mass loss over 16 years followed a first-order exponential decay pattern with half-lives ranging from 4.9 to 9.4 years in bark and from 7.3 to 10.9 years in wood. Nitrogen (N) and phosphorus (P) concentrations increased significantly during decomposition, resulting in sharp decreases in C:N and C:P ratios. We did not, however, observe significant net increases in the amount of N or P stored in decomposing boles, as reported in some other studies. Calcium (Ca) concentration decreased by up to 50% in bark but more than doubled in wood of all species. The retention of Ca in decomposing wood helps maintain Ca pools in this base-poor ecosystem. Together, the exponential model for mass loss and a combined power-exponential model for changes in nutrient concentrations were able to simulate nutrient dynamics in decomposing boles after clear-cutting in an adjacent watershed.
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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 | 0.000 | 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".