Decomposition of oak leaf litter is related to initial litter Mn concentrations
Bibliographic record
Abstract
The factors determining the quantity of litter being incorporated into stable organic matter were examined as part of a broader study investigating carbon (C) sequestration in forest ecosystems. Litter was collected from 20 common oak (Quercus robur L.) stands in Wales (UK) and placed in litter-decomposition bags. These bags were installed in an oak stand for 3, 6, 12, 21, and 31 months to study the effect of litter quality on decomposition (mass loss) rates and the limit value for a broad-leaf species. Results indicate that the initial decomposition rate is highly correlated with the manganese content of the litter (P = 0.007, R2 = 0.34). In the final stages of decomposition, limit values ranged between 57% and 95% of initial litter mass. These estimated limit values were not significantly correlated with initial concentrations of other nutrients. However, Ca concentrations gave a significance level of P = 0.067. Estimated rates of C sequestration in soil ranged from 0.93 to 80.22 g C·m–2·year–1.
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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 | 0.000 | 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".