Interaction between decomposing litter and soil fauna of the<i>Betula ermanii</i>forest floor of the Changbai Mountains, China
Bibliographic record
Abstract
Soil fauna play a key role in litter decomposition as they influence the litter mass loss rate in terrestrial ecosystems. However, the interaction between decomposing litter and soil fauna has not been adequately addressed. We examine the interaction between different types of decomposing litter and soil fauna on the Betula ermanii Cham. (BE) forest floor of the Changbai Mountains, China, by measuring the mass loss of six litter species groups using litterbags with two sizes of mesh (4 mm and 0.01 mm) during a yearlong experiment. Soil fauna were identified at the order level. We found that soil fauna have a limited effect on litter mass loss at the initial stage of the experiment. Its positive effect became apparent at month 12 of the experiment. After 1 year, soil fauna increased the litter mass loss rate of the high-quality litter of Parasenecio komarovianus (Pojark.) Y.L. Chen (PK) by 7.02% and of the low-quality litter of Rhododendron aureum Georgi (RA) by 25.26%. BE + PK litter was associated with a significantly higher abundance of soil fauna at months 8 and 10 of the experiment and also with a significantly higher richness of soil fauna at month 10 of the experiment. At the end of the experiment, however, the Shannon–Wiener diversity index of soil fauna was not necessarily higher in mixed-species litter. Litter mixing did promote the abundance, richness, and diversity of soil fauna during the warm season in the high-quality litter substrate of BE + PK. Our results illustrate that the impact of soil fauna on the litter mass loss of both single- and mixed-species litterbags ranges from a limited impact to a positive impact as litter mass loss advances. The soil fauna contribute more to the litter mass loss of the low-quality litter with higher C to N ratios than to those with a low C to N ratio. The promoting effect of litter mixing on the soil faunal community composition is only short term and is dependent on substrate quality.
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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".