Grassland use impacts on soil properties and microbial communities: A case study from the Qinghai-Tibetan plateau
Bibliographic record
Abstract
Different grazing practices can have varying impacts on soil properties and soil microbial communities, which are critical for maintaining productivity and functions of grasslands and the overall ecosystem. The Qinghai-Tibetan plateau (QTP) is the largest high-altitude grazing region on earth, and has three different grazing practices, including seasonal grazing (SG), continuous grazing (CG), and exclosure grazing (EG) for 10 years. Vegetation, soil properties from two different depths (0-0.15 and 0.15-0.30 m) and soil microbial communitiesin the surface soil layer (0-0.15 m) were measured in triplicate plots within each grazing practice. The soil conditions in the SG site were the best, the CG site was the worst, while the EG site was intermediate. Dry aboveground biomass, soil organic carbon, total nitrogen, and total phosphorus content under SG were 838 g/m2, 20.73g/kg, 1.74 g/kg, and 0.20 g/kg, respectively, and under CG were 8.80 g/m2, 8.07g/kg, 1.07 g/kg, 0.16 g/kg, respectively. There was no significant difference in the α-diversity of soil bacteria and fungi among the three grazing practices. However, the bacterial communities were significantly different from each other; only the fungal community under EG was significantly different from the other grazing practices. While the relative abundance of Basidiomycota under SG was significantly higher than that under EG, no difference was observed in the relative abundance of Ascomycota, Zygomycota, and unclassified_k_Fungi among the three grazing practices. Compared to SG, CG and EG significantly increased the relative abundance of Actinobacteria, Gemmatimonadetes, Verrucomicrobia, and Nitrospirae, but decreased the relative abundance of Proteobacteria and Bacteroidetes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".