Forage mixed planting can effectively improve soil enzyme activity and microbial community structure and diversity in agro-pastoral interlacing arid zone
Bibliographic record
Abstract
Aiming at the problems of serious soil desertification and continuous reduction of effective soil nutrients in the agricultural and animal husbandry interlaced arid area in Inner Mongolia, this study used Aohan alfalfa, old awn wheat, and fodder oats at the Siziwang Banner, Ulanchabu City, Inner Mongolia Autonomous Region. There were a total of five treatments, namely, single seeding alfalfa, single seeding old awn wheat, single seeding fodder oats, alfalfa and old awn wheat mixed sowing, and control. The results showed that the urease activity and microbial biomass nitrogen content of mixed planting were higher than other treatments. With the advancement of the growth period, the bacteria α-diversity index showed an upward trend; β-diversity analysis showed that planting method was the main factor affecting bacteria diversity and sampling time was the main factor affecting fungi diversity. In summary, mixed planting treatment was more helpful to improve soil enzyme activity, microbial biomass, and enrich soil microbial diversity, which was of great significance to maintain the balance of soil ecosystem. It is hoped that this study will contribute to the theoretical basis and practical experiences for efficient utilization of microbial resources in the field of soil improvement.
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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) | 0.000 | 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".