Effect of rapeseed straw-derived biochar on soil bacterial community structure at tillering stage of <i>Oryza Sativa</i>
Bibliographic record
Abstract
Numerous studies have reported the dynamics of microbes when biochar was applied, whereas the information on the alterations of bacterial community after application of rapeseed straw-derived biochar is limited. A pot experiment with two rapeseed straw-derived biochar application treatments (with biochar application at the rate of 200 g/pot, C1, and without biochar application, 0 g/pot, C0) was conducted. No significant differences were observed in the number of operational taxonomic units, observed species, Shannon index, Simpson index, Chao1, ACE, and phylogenetic diversity whole tree between the C1 and C0 treatments. Taxonomic analysis at the phylum level showed that the abundances of Bacteroidetes and Parcubacteria were higher in the C1 treatment compared to the C0 treatment, while Acidobacteria, Chloroflexi, Rokubacteria, Berkelbacteria, and Latescibacteria were observed with higher abundance in the C0 treatment compared to the C1 treatment. Taxonomic analysis at the genus level showed that the abundances of Gracilibacter, Lentimicrobium, unidentified Rikenellaceae, Hydrogenophaga, and Bacillus were higher in the C1 treatment compared to the C0 treatment, while Candidatus Solibacter, Candidatus Koribacter, and Lutispora abundances were found to be higher in the C0 treatment compared to the C1 treatment. Obvious clusters were observed between the C1 and C0 treatments in both principal component analysis and nonmetric multidimensional scaling. These results indicate that soil bacterial community was altered after rapeseed straw-derived biochar was applied.
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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".