Tree species mediated effects on leaf litter dynamics in pure and mixed stands of oak and beech
Bibliographic record
Abstract
This study aimed to evaluate the relative importance of the factors whereby tree species composition can influence leaf litter dynamics. Leaf litter production and chemical composition were measured in pure and mixed stands of oak ( Quercus petraea Liebl.) and beech ( Fagus sylvatica L.). Pure and mixed leaf litter of both species were incubated in each stand type to assess separately the environmental, litter quality, and litter mixture effects on decomposition. To better understand the environmental effects, ground climate was measured in the different stands and the effects of soil water content on decomposition were evaluated using roofs to simulate drought conditions. Although total leaf litter amounts were not affected by stand composition, leaf decomposition varied with litter quality and with the environmental conditions. In the same environment, oak leaf litter disappeared on average 1.7 times faster than beech leaf litter. Decomposition of oak leaves increased significantly in the mixed-species litterbags. In contrast, the overall mass loss of the mixed litter tallied with the mass loss estimated by examining the decomposition of the component litter separately (additive-effect hypothesis). The effects of stand type appeared in the third year of incubation: leaf mass loss of both species was greater in the beech stand. In addition, soil water content affected leaf decomposition: the oak and beach leaf mass losses dropped by 24% and 17%, respectively, in the dry modality.
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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".