Bulk soil bacterial community structure and function respond to long-term organic and conventional agricultural management
Bibliographic record
Abstract
Understanding how soil microbiomes respond to management is essential to maximizing soil health. We contrasted microbiomes in bulk soil under long-term organic and conventional management in a grain production setting. Management category significantly impacted the relative abundances of 17% of the most abundant taxa. Both conventional and organic management favored particular taxa, but these effects were not reflected in summary richness and diversity indices. Management systems also lead to differences in soil edaphic properties, including pH and nutrient status; this may have been the mechanism by which change in the prokaryote community was enacted. Community change between years of sampling was less pronounced, with only 6 taxa differentially abundant among years. Management category also impacted the abundance of functional genes related to the production and consumption of greenhouse gases. Particulate methane monooxygenase genes were more frequent in soil under organic management, while soluble methane monooxygenase genes were more frequent in soil under conventional management in 1 of 2 years. Nitrous oxide reductase genes were significantly less abundant in soils under second-year alfalfa than in soils under corn. This work highlights the ability of agricultural management to enact broad rearrangements to the structure of bulk soil bacterial communities.
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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 | 0.001 | 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".