Biological H<sub>2</sub> and CO oxidation activities are sensitive to compositional change of soil microbial communities
Bibliographic record
Abstract
Trace gas uptake by microorganisms controls the oxidative capacity of the troposphere, but little is known about how this important function is affected by changes in soil microbial diversity. This article bridges that knowledge gap by examining the response of the microbial community-level physiological profiles (CLPPs), carbon dioxide (CO2) production, and molecular hydrogen (H2) and carbon monoxide (CO) oxidation activities to manipulation of microbial diversity in soil microcosms. Microbial diversity was manipulated by mixing nonsterile and sterile soil with and without the addition of antibiotics. Nonsterile soil without antibiotics was used as a reference. Species composition changed significantly in soil microcosms as a result of dilution and antibiotic treatments, but there was no difference in species richness, according to PCR amplicon sequencing of the bacterial 16S rRNA gene. The CLPP was 15% higher in all dilution and antibiotic treatments than in reference microcosms, but the dilution treatment had no effect on CO2 production. Soil microcosms with dilution treatments had 58%–98% less H2 oxidation and 54%–99% lower CO oxidation, relative to reference microcosms, but did not differ among the antibiotic treatments. These results indicate that H2 and CO oxidation activities respond to compositional changes of microbial community in soil.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".