Effects of Management Practices and Land Use on Biological and Enzymatic Attributes of an Agricultural Area
Bibliographic record
Abstract
A series of anthropogenic approaches, including burning practices and soil disturbances as soil cover removal, plowing and harrowing were experimentally undertaken to mimic land conversion for agricultural production in northern Amazonia. These manipulations led to changes in soil biological and biochemical properties. To reduce knowledge gaps concerning land conversion in the Amazon, the study objective was to evaluate the influence of land use and management practices on the biological attributes and enzymatic activity of the soil in Tepequem, a settlement in north of the Amazon, Brazil. Tepequem was chosen for being highly representative in terms of land use and management patterns in the region. Microbial biomass carbon (MBC), soil basal respiration (SBR), metabolic quotient (qCO2) and enzymatic activity were analyzed. Land use changes resulted in alterations to soil quality. The spontaneous plants found on degraded pasture ensured system diversification, protection and organic contribution, facilitating resumption of ecological balancing of the soil. Good soil quality in managed pasture was attributed to the maintenance of soil cover, provided by grasses, and the absence of soil rotation. Burning, soil disturbances and lack of cover negatively influenced the biological and enzymatic activity in sites that were preparation, deforested and burnt. Chemical attributes are significant factors influencing soil quality and health at subsistance plantation. MBC, qMIC and qCO2, acid phosphatase, Beta-glucosidase and urease were the most sensitive parameters of differentiation of sites in preparation from those of native vegetation and pastures.
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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 | 0.001 |
| 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".