Effect of Litter Quality on Leaf-Litter Decomposition in the Context of Home-Field Advantage and Non-Additive Effects in Temperate Forests in China
Why this work is in the frame
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Bibliographic record
Abstract
Litter quality is often considered the main driver of rates of decomposition. Litter decomposes faster in its home environment than in any other environment, which is called the home-field advantage (HFA). However, evidence for this phenomenon has not been universal. In addition, litter mixtures of different species can induce a non-additive effect (NAE) on decomposition processes. However, the direction and magnitude of NAE vary and underlying mechanisms remain unclear. The aim of our study was to assess the effect of litter quality on leaf-litter decomposition in the context of HFA and NAEs in temperate forests in China. Litterbags containing aspen (Populus davidiana), birch (Betula platyphylla), and oak (Quercus liaotungensis) litter were incubated in situ in pure aspen and broadleaved mixed forests in Chinese temperate forests for 360 days. The main results were:
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it