Response of soil microbial communities to site preparation before afforestation
Bibliographic record
Abstract
Site preparation is a common practice before afforestation that can increase soil carbon (C) release by changing the soil microbial community. This study examined changes in soil microbial communities at two different times (the 7th and 98th days) after site clearing (brush clearing vs. controlled burning), followed by soil preparation (overall soil preparation (OP), spot soil preparation (SP), and no soil preparation (NP)). Results showed that total, bacterial, fungal, and actinomycetic phospholipid fatty acids (PLFAs) increased in controlled burning plots compared with brush clearing plots at the two sampling times. Within brush clearing plots, OP significantly reduced the total, bacterial, fungal, and actinomycetic PLFAs, whereas SP showed a significant increase in these groups compared with NP. In addition, soil microbial community showed obvious seasonal variation in brush clearing plots. Within controlled burning plots, OP significantly decreased the total, bacterial, fungal, and actinomycetic PLFAs. The variations in microbial community composition significantly correlated with soil organic C, total nitrogen (N), dissolved organic C, and C–N ratios. Our results suggest that controlled burning and spot soil preparation can create some soil conditions more conducive to soil microbial communities in the short term, but the long-term effects merit further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".