Amostral Optimization of Mechanical Resistance to the Penetration of a Yellow Oxisol Under Pasture
Bibliographic record
Abstract
The degradation of pastures can be characterized by several factors, mainly due to the management adopted, so in view of the country’s territorial extension and the peculiarity of each region and soil type, it is essential to develop research to improve the monitoring of the system. The objective of this study was to evaluate the effect of different sample densities to establish a mesh that gives precision in maps of spatial variability of soil mechanical resistance to root penetration to pasture areas in the coastal tableland region of Northeast Brazil. In a pasture area, three sampling meshes were demarcated for georeferenced evaluation of soil mechanical resistance to root penetration: mesh 1 established in the dimensions of 50 × 50 m, mesh 2 of 100 × 100 m and mesh 3 of 150 × 150 m, totaling an area of 9 ha. The soil resistance to penetration was measured using an automated apparatus, coupled to a tractor. The variation found in the values of penetration resistance in subsurface can be related to the management adopted in the area, as well as the trampling of the animals. Data on soil penetration resistance in pasture showed that the most compacted zone was always below 30 cm depth by using different sample densities. The results allow us to conclude that the higher the density of the sampling mesh, the greater the accuracy of the data and that, independently of the sample mesh, it was possible to identify the layer of higher soil mechanical resistance to root penetration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".