Impact of Management on the Physical Attributes of a Dystrophic Yellow Latosol
Bibliographic record
Abstract
Soil use and management systems aim to create conditions that are favorable to crop growth. The hypothesis is that areas subject to intensive use of agricultural machinery and animal trampling tend to have a soil structure that is altered by aggregate fragmentation, which causes soil compaction and consequently decreases the soil’s physical and hydraulic properties. The aim of this study was to assess and compare the physical and hydraulic parameters of a dystrophic yellow latosol in an area of Cerrado in the municipality of Chapadinha, Maranhão, Brazil under different use and management systems. The following five use and management systems were studied with five replicates: native forest (control), slash-and-burn agriculture, grassland, no-till crop production and conventional tillage. Data analysis was performed using a completely randomized experimental design. The soil’s density, macroporosity, microporosity, total porosity, hydraulic conductivity, infiltration, water retention curve, penetration resistance and Soil quality assessment index (S index) were assessed for all management systems. The soil use and management systems were found to have a significant effect on the penetration resistance and the water infiltration rate. The native forest and slash-and-burn agriculture areas provided the highest soil water infiltration rates and the lowest soil penetration resistance. A multivariate analysis identified the variables associated with each soil use and management system. The slash-and-burn agriculture area had the highest S index, which means it provided soil of the best physical quality.
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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".