Simulated Traffic Dynamic Loading on Physical Properties of a Red Latosol under No-Tillage
Bibliographic record
Abstract
The use of intensive mechanization in no-tillage areas can change soil physical conditions, mainly in the “Cerrado”, which has reduced cover ratio. This study aimed to evaluate physical and mechanical properties of a red latosol under no-tillage and subjected to simulated dynamic traffic in different surfaces conditions. For this, soil samples were collected, with dimensions of 0.2 × 0.2 × 0.3 m (height, width and length), in an area subjected to no-tillage in the last four years. Subsequently, samples were transported to the laboratory and subjected to different traffic levels in a simulator. Shortly after, a completely randomized experimental design was set up, in factorial 2 × 5, with two covers (with and without haystack) and five traffic levels (zero, one, two, four and eight run overs) applied by the simulator. Assessed physical properties were superficial settlement, soil density, compaction degree and pre-consolidation pressure. Results showed that superficial settlement, soil density and compaction degree were significantly influenced by soil cover and traffic intensity. Pre-consolidation was not affected by cover, and had higher values when subjected to more traffic intensity.
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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) | 0.001 | 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".