Decomposition of Small Woody Debris of California Red Fir
Bibliographic record
Abstract
Abies forests cover the subalpine region from latitude 36°N in Sierra Nevada, California, to 55°N in British Columbia, Canada. Because of slow nutrient cycling, Abies forests usually are nutrient‐stressed. This study determined the decomposition rates, changes in the C/N ratio, and the dynamics of N, P, and Mn of bark and wood of small diameter debris of California red fir ( Abies magnifica A. Murr.). Triplicate 0.06‐ha plots were established with four combinations of N‐fertilization and thinning treatments: Control (16 687 trees ha −1 ), Thinned (1141 trees ha −1 ), N fertilized (300 kg N ha −1 , as urea), and Thinned + N fertilized (1141 trees ha −1 , plus 300 kg N ha −1 ). Individually labeled samples, 0.5‐ to 5‐cm diameter and 15‐ to 30‐cm length, were scattered in the plots and retrieved after 3 and 17 yr. Bark and wood were then separated and analyzed for total dry mass, and the concentration and mass of N, P, and Mn. For bark there were few notable effects of the treatments at 3 or 17 yr. Over the 17‐yr period, total dry mass of bark decreased by 62% (because of decomposition and sloughing); the C/N ratio dropped from 72/1 to 48/1; concentrations of N and Mn increased, but P concentration decreased; masses of N and P decreased, but Mn mass increased. For wood, there were a few significant effects of the treatments at 3 yr and none at 17 yr. At 3 yr, thinning reduced the C/N ratio; all the treatments lost more total dry mass than the Control; N and P mass losses differed slightly between treatments, but Mn mass loss did not differ between treatments. Over the 17‐yr period, total dry mass of wood decreased by 37%; the C/N ratio dropped from 217/1 to 177/1; concentrations of N and Mn increased, but P concentration decreased; masses of N and P decreased, but mass of Mn remained the same. White‐rot fungi require Mn to decompose lignin, and maintain relatively high levels of Mn in decomposing bark and wood.
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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) | 0.000 | 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".