Effects of simulated warming on bacterial diversity and abundance in tropical soils from East Malaysia using open top chambers
Bibliographic record
Abstract
The effects of global warming are increasingly evident, where global surface temperatures and atmospheric concentration of carbon dioxide have increased in past decades. Given the role of terrestrial bacteria in various ecological functions, it is important to understand how terrestrial bacteria would respond towards higher environmental temperatures. This study aims to determine soil bacterial diversity in the tropics and their response towards in situ warming using an open-top chamber (OTC). OTCs were set up in areas exposed to sunlight throughout the year in the tropical region in Malaysia. Soil samples were collected every 3 months to monitor changes in bacterial diversity using V3–V4 16S rDNA amplicon sequencing inside the OTCs (treatment plots) and outside the OTCs (control plots). After 12 months of simulated warming, an average increase of 0.81 to 1.15 °C was recorded in treatment plots. Significant changes in the relative abundance of bacterial phyla such as Bacteroidetes and Chloroflexi were reported. Increases in the relative abundance of Actinobacteria were also observed in treatment plots after 12 months. Substantial changes were observed at the genus level, where most bacterial genera decreased in relative abundance after 12 months. This study demonstrated that warming can alter soil bacteria in tropical soils from Kota Kinabalu.
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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".