Anthropic Impacts on Microbiota and Chemical Properties of Cerrado Soil Through Soybean Cultivation
Bibliographic record
Abstract
Population growth and improved gross domestic product may increase food consumption. Soybean is the main source of protein, lipids and mineral salts for human and domestic animals’ foods. Brazil is responsible of most of the soybeans produced in the world. However, soybean production in Tocantins/Brazil state caused a decrease in the Cerrado’s biome. Therefore, the aim of this study was to evaluate the anthropic impact of planting of soybean on microbial and physical-chemical properties of Cerrado’s soil. Soil samples were collected in three soybean farms (SF) of the Tocantins/Brazil state. They were collected in the soybean field, in native vegetation field, and in anthropogenic fragmentation area in the dry and wet seasons. The diversity of arbuscular mycorrhizal fungi (AMF) and nitrogen-fixing bacteria (NFB) were analyzed by denaturing gradient gel electrophoresis (DGGE). Regardless of the SF, physico-chemical indicators did not present significant differences between the seasons. The DGGE profiles of NFB and AMF genes were different between the soybean field and native vegetation field in both seasons. The viable cells counts and NFBs and AMFs diversity were influenced by the substitution of native vegetation for soybean. The increase of the agricultural production in Cerrado soil is worrisome, due to the endemic microorganisms that was observed in this study. In addition, anthropic action on the microbial community was more effective in the soybean field during the dry season, which showed the importance of maintaining an environmental reserve area within agricultural production units.
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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 | 0.001 |
| 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".