Litter decomposition in a transect of Norway spruce forests: substrate quality and climate control
Bibliographic record
Abstract
We used a climatic transect of 14 stands of Norway spruce (Picea abies (L.) Karst.) at which locally collected needle litters was incubated. Our purpose was to show that climate is not necessarily the main rate-regulating factor even in a long climatic transect. The sites are found in Sweden from 56 to 66°N. There was virtually no relationship between climate (AET ranging between 371 and 545 mm) and first-year mass loss (range 19.4-32.8%). Instead, substrate quality (litter Mn concentration) explained 27% of the site-to-site variation in first-year mass loss. For the later stages of decomposition (second to fifth year), the sites could be divided into two groups; one in which lignin concentration regulated litter mass-loss rates, and one in which lignin concentration was not an important control. In this latter group, Mn concentrations were the component best correlated with litter mass loss. When combining all data, Mn concentration gave the best linear relationship. We repeated this procedure using first- to fifth-year mass-loss values and found the same pattern. We concluded that litter Mn concentrations is a key factor for Norway spruce litter decomposition because of its influence on lignin degradation and that the very early stage is short or nonexistant.
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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 | 0.001 | 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".