Variation of Soil Bacterial Communities in a Chronosequence of Rubber Tree (Hevea brasiliensis) Plantations
Bibliographic record
Abstract
Regarding rubber tree plantations, researchers lack a basic understanding of soil microbial communities; specifically, little is known about whether or not soil microbial variation is correlated with succession in these plantations. In this paper, we used high-throughput sequencing of the 16S rRNA gene to investigate the diversity and composition of the soil bacterial communities in a chronosequence of rubber tree plantations that were 5, 10, 13, 18, 25 and 30 years old. We determined that: (1) Soil bacterial diversity and composition show changes over the succession stages of rubber tree plantations. The diversity of soil bacteria were highest in 10, 13 and 18 year-old rubber tree plantations, followed by 30 year-old rubber tree plantations, whereas 5 and 25 year-old rubber tree plantations had the lowest values for diversity . A total of 438,870 16S rDNA sequences were detected in 18 soil samples from six rubber tree plantations, found in 28 phyla, 66 classes, 139 orders, 245 families, 355 genera and 645 species, with 1.01% sequences from unclassified bacteria. The dominant phyla were Acidobacteria, Proteobacteria, Chloroflexi, Actinobacteria and Verrucomicrobia (relative abundance large than 3%). There were differences in soil bacterial communities among different succession stages of rubber tree plantation. (2) Soil bacteria diversity and composition in the different stages was closely related to pH, vegetation, soil nutrient and altitude, of which pH and vegetation were the main drivers.
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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 | 0.000 |
| 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".