Effects of Vermicompost on Soil Enzyme Activity and Bacterial Diversity of Black Soil in Greenhouse
Bibliographic record
Abstract
The effects of various amounts of vermicompost applied on soil enzyme activity, bacteria number and diversity were conducted. The experiment set up five treatments: Treatment 1, control(CK), nil fertilizer; Treatment 2,vermicompost 7. 5 t hm-2; Treatment 3,vermicompost 15 t hm-2; Treatment 4,vermicompost 30 t hm-2; Treatment 5,vermicompost 60 t hm-2.The results showed that vermicompost promoted the activities of soil catalase, urease,invertase and phosphatase, which being slightly different by different enzyme types. With the increase of vermicompost application rate, the soil bacteria increased at first and then decreased, but the soil bacteria community diversity showed an increasing trend. The effects of all treatments on soil bacteria richness and diversity indices were significantly higher than the control, but all treatments were not different among the evenness indexes. Cluster analysis showed that DGGE profile similarity of soil bacteria by treatment 3(vermicompost 15 t hm-2) and treatment 4(vermicompost 30 t hm-2) reached 93.8%, and gathered into a cluster; bacterial species of treatment 3 were most similar to treatment 4. There were no significant difference between treatment 3 and treatment 4 on soil bacteria diversity indexes and evenness indexes, but the number of bands of treatment 3 significantly increased. Similarity of treatment 4 and treatment 5(vermicompost 60 t hm-2) reached 78.1%, similarity of treatment 1 and treatment 2(vermicompost 7.5 t hm-2) only reached 57.3%.
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) | 0.001 | 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".