Changes in the soil fungal communities of steppe grasslands at varying degradation levels in North China
Bibliographic record
Abstract
The grasslands in North China are rich in fungal resources. However, the knowledge of the structure and function of fungal communities and the role of microbial communities in vegetation restoration and succession are limited. Thus, we used an Illumina HiSeq PE250 high-throughput sequencing platform to study the changing characteristics of soil fungal communities in degraded grasslands, which were categorized as non-degraded (ND), lightly degraded, moderately degraded, and severely degraded (SD). Moreover, a correlation analysis between soil physical and chemical properties and fungal communities was completed. The results showed that the number of plant species, vegetation coverage, aboveground biomass, and diversity index decreased significantly with increasing degradation, and there were significant differences in the physical and chemical properties of the soil among the different degraded grasslands. The dominant fungal phyla in the degraded grassland were as follows: Ascomycota, 44.88%–65.03%; Basidiomycota, 12.68%–29.91%; and unclassified, 5.51%–16.91%. The dominant fungi were as follows: Mortierella, 6.50%–11.41%; Chaetomium, 6.71%–11.58%; others, 25.95%–36.14%; and unclassified, 25.56%–53.0%. There were significant differences in the microbial Shannon–Wiener and Chao1 indices between the ND and degraded meadows, and the composition and diversity of the soil fungal community differed significantly as the meadows continued to deteriorate. The results showed that pH was the most critical factor affecting soil microbial and fungal communities in SD grasslands, whereas soil microbial and fungal communities in ND grasslands were mainly affected by water content and other environmental factors.
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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 | 0.000 | 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".