Functional potential differences between <i>Firmicutes</i> and <i>Proteobacteria</i> in response to manure amendment in a reclaimed soil
Bibliographic record
Abstract
Manure amendment generally bolsters soil organisms but not all bacteria equally. To understand why different taxa respond differently, we used shotgun metagenomic approaches to profile functional potentials and correlate them with taxon abundances. A soil originally unproductive was reclaimed using commercial manure and finally became productive. The abundance of Firmicutes in the soil decreased, whereas that of Bacteroidetes and Proteobacteria increased after manure addition. Thirty-nine KEGG modules were significantly different across fertilizer treatments. These modules were mainly associated with the phosphoenolpyruvate-dependent phosphotransferase system (PTS), ATP-binding cassette (ABC) transporters, and two-component signal transduction systems. The Proteobacteria and Firmicutes mainly contributed to these modules. Correlation between the abundances of phyla and orthologs showed two distinctive patterns. One linked the Firmicutes to cell wall biosynthesis, PTS, and ABC transporters, and the other linked the Betaproteobacteria, Bacteroidetes, and Verrucomicrobia to lipopolysaccharide biosynthesis, bacterial motility, and carbon metabolism. Correlation between the abundances of phyla and Carbohydrate-Active Enzyme Database families also showed two distinctive patterns, one of them linking the Betaproteobacteria, Bacteroidetes, and Verrucomicrobia to very high abundances of glycosyltransferases and glycoside hydrolases. Overall, the Proteobacteria and Firmicutes were main drivers of functional potential differences across fertilizer treatments. The Firmicutes were enriched with genes associated with cell wall biosynthesis and membrane transports, while Proteobacteria with lipopolysaccharide biosynthesis and carbohydrate metabolism, which supports our hypothesis that the Firmicutes have a lower potential for utilizing manure-derived carbohydrates, while Proteobacteria have a higher potential. This explains why the Proteobacteria and Firmicutes responded to manure differently.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".