Chemical fumigation and biofumigation alter soil bacterial community diversity and composition
Bibliographic record
Abstract
Chemical fumigation and biofumigation are used to reduce soil-borne diseases in agricultural production systems; however, non-targeted soil microorganisms may also be affected. This study compared the effects of chemical fumigation, either used alone or combined with an organic amendment, and biofumigation on soil bacterial community diversity and composition under controlled conditions over 160 days. Treatments included: fumigation with chloropicrin (CP), fumigation with metam sodium used alone (MS) or combined with barley plant residues (MSBR); biofumigation with mustard plant residues; addition of barley plant residues; and untreated control. Biofumigation had a greater impact on bacterial diversity at early time points, transiently decreasing species evenness and yielding the most dissimilar β-diversity after 3 days. MS fumigation did not affect bacterial diversity indices; however, MSBR transiently decreased species evenness after 8 days. CP-treated soil had decreased species evenness that did not recover over time and had the most dissimilar β-diversity at the end of the incubation compared to all other treatments. This study demonstrated that CP fumigation had the greatest and most persistent impact on bacterial diversity, whereas MS fumigation and biofumigation led to transient decreases in bacterial diversity.
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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".