Grassland use impacts on soil ciliate communities on the Qinghai-Tibetan Plateau
Bibliographic record
Abstract
：Soil ciliates, crucial components of grassland ecosystems, serve as sensitive bioindicators of soil health and disturbance. This study investigates ciliate communities across four grassland use types on the Qinghai-Tibetan Plateau (QTP): seasonal and continuous grazing in natural grasslands (SGG and CGG), artificial perennial Elymus nutans Griseb. grasslands (PEG), and artificial annual Avena grasslands (AAG). Using live observation techniques, we identified 114 ciliate species from 10 classes and 21 orders, with Haptorida and Sporadotrichia emerging as dominant groups. Our findings reveal that grazing grasslands harbor greater endemic ciliate species richness compared to AAG. SGG and CGG exhibited significantly higher ciliate diversity (Shannon, Simpson, and Margalef indices) than other use types, while PEG and SGG supported higher ciliate abundances. Low Jaccard similarity indices between grassland types indicate distinct ciliate communities, reflecting management-induced environmental heterogeneity. Redundancy analysis identified above-ground biomass and soil pH as primary drivers of ciliate community structure. Notably, seasonal grazing (SGG) promoted the highest ciliate diversity, suggesting its potential as a sustainable management practice for maintaining soil health on the QTP. This research provides crucial insights into the relationship between land management and soil microbial diversity in high-altitude grasslands. Our findings support the implementation of moderate grazing practices to enhance soil quality and ecosystem resilience on the QTP, with implications for sustainable management of similar ecosystems worldwide.
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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 | 0.001 | 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".