Mudflat reclamation causes change in the composition of fungal communities under long-term rice cultivation
Bibliographic record
Abstract
Fungi play a critical role in farmland ecosystems, especially in improving soil fertility; however, little is known about the changes in fungal communities caused by mudflat reclamation under rice cultivation. In this study, mudflats located in Yancheng, China, which were divided into nine plots with 0, 11, and 20 years of successive rice cultivation histories, were sampled to determine the fungal community composition by using Illumina MiSeq sequencing. Results show that the Shannon diversity of the fungal communities did not change significantly but the species richness increased under mudflat reclamation with long-term rice cultivation. Ascomycota was the dominant phylum throughout the reclaimed mudflats samples, while Sordariomycetes was the dominant class. Fungal functional prediction found that the relative abundance of saprotrophs gradually increased with mudflat reclamation and mainly belonged to Ascomycota after 20 years of successive reclamation. Redundancy analysis showed that electrical conductivity, organic matter, and total nitrogen were the main factors affecting the composition and ecological function of the fungal community during mudflat reclamation. In short, a fungal community dominated by Ascomycota was established during mudflat reclamation under rice cultivation, which is more conducive to promoting soil fertility because of the higher proportion of saprotrophic fungi in Ascomycota.
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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".