Variations of soil microbial biomass across four different plant communities along an elevation gradient in Wuyi Mountains,China
Bibliographic record
Abstract
Soil microbial biomass carbon is an important component in carbon cycle of the terrestrial ecosystem. We chose Evergreen Broadleaf Forest (EBF),Coniferous Forest (CF),Dwarf Forest (DF) and Alpine Meadow (AM) as experimental sites along an elevation gradient in the national natural preserve of Wuyi Mountains,and to examine the variations of soil microbial biomass and its influencing factors from June,2005 to April,2006. The results showed that:(1)At 0-10cm depth of soil layer,the annual mean value of soil microbial biomass was increasing with increasing elevation. The soil microbial biomass of AM was 4.07 g·kg-1,and which was 2.06,3.21 and 3.91 times higher than that of DF,CF and EBF,respectively (p0.01),and the annual mean value of soil microbial biomass of DF was significantly higher than that of EBF,CF,respectively (p0.05). However,the annual mean values of soil microbial biomass between DF and CF were not significantly different (p0.05). Variations of soil microbial biomass at 10-25cm depth of soil layer was the same as that at 0-10cm depth of soil layer. (2)At 0-10cm soil layer,the annual mean values of soil microbial biomass were significantly correlated to soil organic carbon,total nitrogen,total sulphur and soil moisture,respectively. At 10-25cm depth of soil layer,the annual mean values of soil microbial biomass were significantly correlated to soil organic carbon,total nitrogen,respectively. Our research showed that soil microbial biomass were increasing along the elevation gradient in subtropical forest in Wuyi Mountain,and soil organic carbon,total nitrogen,total sulfur or soil moisture might be the major factors controlling soil microbial biomass.
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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 | 0.001 | 0.001 |
| 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".