Insight into the correlation between biochar amendment and shifts in bacterial community 4 years after a single incorporation in soybean- and maize-planted soils in northeastern China
Bibliographic record
Abstract
To date, there have been few reports examining the correlation between biochar treatments, crop species, and microbiome shifts. In this study, shifts in the soil bacterial community were investigated 4 years after a single incorporation of biochar in soils planted with soybeans and maize. Clear changes in the bacterial community composition and structure were detected in the soybean-planted soil amended with low-titer biochar (7.89 t/ha), whereas such changes in the maize-planted soil were not observed at the same biochar amendment rate, suggesting a more sensitive influence on the bacterial community in the soybean-planted soil than that in the maize-planted soil. Bacterial abundance in the maize-planted soil was reduced significantly with increasing biochar addition (15.78 and 47.34 t/ha), which was probably due to the inhibitory substances originating from biochar. Both the bacterial community and biomarkers in soil under biochar amendment varied with planted crops, bacterial communities responding differently to biochar amendment. All these results suggested that biochar might influence the bacterial community in maize- and soybean-growing soils under different mechanisms. Our findings should be valuable for an in-depth understanding of the potential mechanism of soil microbiome changes following biochar incorporation and for biochar application in agriculture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".