Physical Attributes of an Ultisol Under Different Uses in the North of Espírito Santo
Bibliographic record
Abstract
The evaluation of the physical attributes of the soil is of fundamental importance for the understanding of the impacts caused by the different uses in the agricultural systems. In this sense, the objective of this work was to evaluate the changes in physical attributes of the soil in an area with different uses located in the north of Espírito Santo. The experiment followed a randomized block design (DBC), in a 4 × 2 factorial scheme, represented by 4 areas (coffee, fruit, pasture and native forest) and 2 depth classes (0-10 and 10-20 cm), resulting in a total of 8 treatments with 5 replications. The physical attributes evaluated were: texture, Ds (soil density); Dp (particle density); Ma (macroporosity); Mi (microporosity) and Pt (total porosity). The data obtained were submitted to analysis of variance and the comparison of means was performed using the Tukey test at 5%, using the statistical program R© 4.2. Then, the physical attributes data were grouped into a similarity dendrogram, using the Euclidean distance method. The area with native forest presented the best physical attributes of the soil, followed by: coffee, fruit and pasture, not differing in depth. As for the analysis by grouping, native forest was similar to coffee growing and fruitful showed the greatest dissimilarity between land uses, especially in relation to forest.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".