Soil pH modulates the activity of low-affinity methane oxidation in soils from the Amazon region
Bibliographic record
Abstract
AIMS: In the Amazon region, pastures are the main land use subsequent to deforestation and this change can result in soil acidification and degradation. Liming is a management practice to increase soil pH, important to recover degraded lands and increase soil fertility, but its impacts on soil methane cycling in tropical soils are unknown. Here we investigate the role of soil pH on methane uptake under high concentrations of the gas, manipulating pasture and forest soils pH by liming and evaluating the active methane cycling microbial community. METHODS AND RESULTS: Top layer of forest and pasture soils were subjected to liming treatment and incubated with ∼10 000 ppm of 13CH4. Soil DNA was evaluated with Stable Isotopic Probing (SIP-DNA), methanotrophic abundance was quantified (pmoA gene), and high throughput sequencing of 16S rRNA was performed. Liming increased the methane uptake in both forest (∼10%) and pasture (∼25%) soils. Methanotrophs Methylocaldum spp. (type I) and potential methanotrophs in Beijerinckiaceae (type II) were identified to actively incorporate carbon from methane in limed pasture soils. In limed forest soils, Nitrososphaeraceae were identified as 13C-enriched taxa, indicating that ammonia oxidizers can oxidize methane in these soils. CONCLUSIONS: Liming Amazonian pasture soils not only contributes to the fertility and recovery of degraded areas but also has the potential to improve the oxidation of methane at high concentrations of this gas.
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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) | 0.001 | 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".