Vegetation Degradation of Guanshan Grassland Suppresses the Microbial Biomass and Activity of Soil
Bibliographic record
Abstract
Changes in vegetation influence the function of grassland ecosystems. A degradation of the vegetation type has been found from high to low altitudes in Guanshan grassland in the order of forest grassland (FG) < shrub grassland (SG) < herb grassland (HG). However, there is poor information regarding the effect of vegetation degradation on soil microbes in Guanshan grassland. Therefore, our study evaluated the impact of vegetation degradation on the microbial parameters of soil, as well as the mechanisms responsible for these variations. Soils were sampled from 0 to 30 cm under the FG, SG, and HG in Guanshan grassland for determining the microbial biomass, enzymatic activities, basal respiration (BR), and metabolic quotient (qCO2) in April and July 2017. The results showed that vegetation types are important factors that obviously influence the above-mentioned soil microbial properties. The FG and SG had significantly higher soil microbial biomass, enzymatic activities, and BR than those of the HG, but markedly lower qCO2 (p < 0.05). Soil pH, available nitrogen (AN), organic carbon (SOC), total phosphorus (TP), available P (AP), and total N (TN) were key factors in the decline in the soil microbial biomass and microbial activities of the degraded vegetation. Moreover, slope aspects also affected the soil microbial properties, with the east slope having higher soil microbial biomass, enzymatic activities, and BR and lower qCO2 than the west slope. Conclusively, vegetation degradation has led to a decline in the soil microbial biomass and microbial activities, indicating the degradation of the Guanshan grassland ecosystem.
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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) | 0.001 | 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".