Exploring the optimal grazing intensity in desert steppe based on soil nematode community and function
Bibliographic record
Abstract
Abstract Grazing is a key regulator of the biodiversity of the desert steppe in Inner Mongolia and has important ecological significance for the sustainable development of underground ecosystems. In a 14‐year grazing intensity experiment, we systematically explored the changes in soil nematode communities in desert steppe soils and comprehensively evaluated the optimal grazing intensity for the sustainability of the desert steppe underground ecosystem. Using high‐throughput sequencing, we analyzed the soil nematode communities and their relationships with environmental factors. The 14‐year grazing experiment revealed a significant impact on the diversity and composition of the soil nematode community in the surface layer (0–10 cm) and on the soil nematode community in the whole soil layer (0–20 cm). Based on LEfSe multilevel discriminant analysis, we found that the relative abundances of Acrobeles, Cephalobus, Filenchus, Aphelenchus, Longidorella, Amplimerlinius, Aporcelaimellus, Acrobeloides, Dorylaimellus, Hemicycliophora, Thonus, Alaimus, and Oxydirus changed significantly under different grazing treatments. Considering the number and function of soil nematode communities, long‐term light grazing was found to significantly promote an increase in soil nematode diversity and helped maintain soil nematode community stability. We determined that the most suitable grazing intensity for the sustainability of the soil underground ecosystem of the desert steppe in Inner Mongolia is light grazing (0.91 sheep hm‐2 0.5 yr‐1). We have, thus, provided a tool for determining and evaluating optimal grazing intensities for sustainable soil underground ecosystems.
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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) | 0.000 | 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".