Fine-root decomposition and N dynamics in coniferous forests of the Pacific Northwest, U.S.A.
Bibliographic record
Abstract
We examined the effects of species, initial substrate quality, and site differences (including temperature, precipitation, and soil N availability) on fine-root (<2 mm diameter) decomposition in litter bags and its N dynamics in Sitka spruce (Picea sitchensis (Bong) Carrière), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) forests in Oregon, U.S.A. Species significantly influenced fine-root mass loss during the first 2 years of decomposition. Over the same period, site differences had little impact on decomposition of fine roots. The percentage of initial mass remaining of decomposing fine roots fitted a single-exponential model. The decomposition rate constant (k) for all 15 species examined ranged from 0.172 year1 for Engelmann spruce (Picea engelmanni Parry ex Engelm.) to 0.386 year1 for Oregon ash (Fraxinus latifolia Benth.). Initial C quality indices (e.g., cellulose concentration, lignin concentration) of fine roots were correlated with fine-root decomposition rates. In contrast, initial N concentration and soil N availability were not correlated with fine-root decomposition rates. The rate of N released from decomposing roots was positively correlated with the initial N concentration of the fine roots. The data suggest that decomposing fine roots could release at least 20 kg N/ha annually in mature Douglas-fir forests of the Pacific Northwest.
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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 | 0.000 | 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".