Characterization of Physical-Chemical and Structural Soil Attributes in the Semiarid Region of the Rio Grande do Norte State, Brazil
Bibliographic record
Abstract
The study of the soil characterization and the relation of its attributes allows a systematic proposal of the local particularities, leading to adequate practices for maintenance and/or preservation of its productive capacity. In this sense, the aim of this study was to evaluate the influence of structural attributes in association with physical and chemical soil classes, using the multivariate statistical techniques to differentiate environments. The research was carried out in the Moacir Lucena Project, located in the municipality of Apodi, RN, Brazil. Three representative environments were chosen as follows: Profile 1 (P1)-Red-yellow Latosol-Area in recovery (1AR), P2-Haplic Cambisol-Lake Area, (2AL) and P3-Eutrophic Yellow Latosol-Cashew Tree Area (3AC). The soil samples were collected in the horizons of the studied areas. Ten (10) samples were collected per horizons in volumetric rings and in soil blocks (aggregate analysis), which resulted in triplicates in the laboratory. Structural, physical and chemical attributes were evaluated. The data were analyzed using multivariate statistical techniques, with correlation matrix, clustering analysis and factorial analysis performed by the extraction of the factors into principal components. The use of clustering analysis allowed the formation of four groups for soil classes and attributes; the inorganic fractions were determinant for environmental differentiation, where the sand was discriminant for the Red-yellow Latosol and the Eutrophic Yellow Latosol, and the clay and silt for the Haplic Cambisol. Higher similarity was observed in the transition horizons of the Latosols Class. The physical and structural attributes were determinant in the dissimilarity for the Haplic Cambisol, reflecting in physical restrictions to the plant growth. The factor analysis revealed that the variables particle density (Dp), Ca2+, Mg2+, sum of bases (SB) and cation exchange capacity (CEC) for factor 1, followed by pH, P, K+, total Sand, Clay and soil density (Ds) for factor 2 are important soil attributes to distinguish the studied environments.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".