Soil fauna affects the optical properties in alkaline solutions extracted (humic acid-like) from forest litters during different phenological periods
Bibliographic record
Abstract
The formation of soil organic matter via humification of plant litter is important for long-term carbon sequestration in forests; however, whether soil fauna affects litter humification is unclear. In this study, we quantified the effects of soil fauna on the optical properties (i.e., ΔlogK and E4/E6) of the alkaline-extracted humic acid-like solutions of four foliar litters by removing soil fauna via litterbags with different mesh sizes in two subtropical evergreen broad-leaved forests. Litterbags were collected at the leaf falling, budding, expanding, maturation, and senescence stages from November 2013 to October 2015 to assess whether the effects of soil fauna on litter humification vary in different plant phenology periods. The results showed that soil fauna significantly reduced the ΔlogK and E4/E6 values in the leaf expanding stage of oak litter and in the leaf falling stage of camphor and fir litters. The richness index of soil fauna explained 21%, 55%, 19%, and 45% of the variations in the E4/E6 values for oak, fir, camphor, and pine litters, respectively. The effects of litter water content on these optical properties were greater than that of temperature. These results indicated that soil fauna plays a key role in litter humification in the leaf expanding and falling stages and are potentially involved in soil carbon sequestration in these subtropical forests.
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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".