Rhizospheric soil fungal community patterns of <i>Duchesnea indica</i> in response to altitude gradient in Yunnan, southwest China
Bibliographic record
Abstract
The magnitude of the impact of altitude gradient on microbial community and diversity has been studied in recent decades. Whereas bacteria have been the focus of most studies, fungi have been given relatively less attention. As a vital part of the macro- and microscopic ecosystem, rhizosphere fungi play a key role in organic matter decomposition and relative abundance of plant species and have an impact on plant growth and development. Using Duchesnea indica as the host plant, we examined the rhizosphere soil fungal community patterns across the altitude gradient in 15 sites of Yunnan province by sequencing the fungal ITS2 region with the Illumina MiSeq platform. We determined the fungal community composition and structure. We found that, surprisingly, rhizosphere soil fungal diversity of D. indica increased with altitudinal gradient. There was a slight difference in diversity between samples from high- and medium-altitude sites, with medium-altitude sites having the greater diversity. Furthermore, the rhizosphere soil fungal community composition and structure kept changing along the altitudinal gradient. Taxonomic results showed that the extent of phylum diversity was greatest at high-altitude sites, with Ascomycota, Basidiomycota, Zygomycota, and Glomeromycota as the most dominant fungal phyla.
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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".