Soil properties and microbiome of annual and perennial cultivated grasslands on the<scp>Qinghai–Tibetan</scp>Plateau
Bibliographic record
Abstract
Abstract Efforts to enhance food, fibre, and forage yields and achieve global food security have included the conversion of natural grasslands to cultivated grasslands. The quantitative effects of a global shift in grassland management on soil properties and microbial communities critical to ecosystem function have remained largely unexplored, particularly on China's Qinghai–Tibetan Plateau. Accordingly, the distinct and contrasting effects of annualAvenagrasslands (AAG) versus perennialElymus nutansGriseb. cultivated grasslands (PEG) on the region's soil properties and microbiome were investigated in an effort to examine their contribution to maintaining soil carbon and nutrients. Across three sites per grassland type, soil moisture content (45.55%), soil organic carbon (48.97 g kg−1), soil total nitrogen (5.13 g kg−1), and soil ammonium nitrogen (298.32 mg kg−1) were 19%–32% greater at PEG sites than AAG sites, whereas soil pH (7.81), soil total phosphorus (0.44 g kg−1), and soil available phosphorus (0.81 mg kg−1) were 2%–31% lower. The AAG and PEG site soils had different bacterial and fungal β‐diversities but similar α‐diversities. The relative combined abundance ofGemmatimonadetesandChytridiomycota, and that ofRozellomycotaindividually, were, respectively, higher and lower in AAG versus PEG site soils. This suggests that, on the Qinghai–Tibetan Plateau, contrasting grassland cultivation practices affect soil properties and microbes differently. Given the strong interaction between a soil and its microbiome, changes in a soil's microbial community structure can be expected to substantially alter soil function. This will have important ecological service implications, particularly in terms of carbon storage and water conservation in this ecologically fragile region.
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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".