Response of Soil Bacterial Community Structure to Land-use Conversion of Natural Forests in Maoershan National Forest Park, China
Bibliographic record
Abstract
To compare and evaluate the soil bacterial community composition in Maoershan National Forest Park, we analyzed soil samples from four replicated land-use types (hardwood forests, cultivated lands, settlement and slash lands) using a denaturing gradient gel electrophoresis (DGGE) method based on 16s rRNA gene fragments. Forty-two DGGE bands were successfully excised for sequencing. Our results revealed that the conversion of natural forest to other land-use types had a significant effect on the soil bacterial community. Bacteroidetes was absent in forest soils. Beta-Proteobacteria was unique to settlement soils, whereas Cyanobacteria and Verrucomicrobia were absent in agricultural soils. Additionally, Acidobacteria and Proteobacteria (α, β, γ, δ classes) were the dominant bacterial communities in all soils. Thus, conversion of the forest land into other land-use types resulted in changes in the bacterial communities which might affect the productivity of the soil ecosystem. Together these results suggested that the utility of using sequence-based approaches to analyze bacterial communities provides detailed information on individual bacterial community composition and permit the robust assessment of the biogeographical patterns.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".