Physical Quality of an Oxisol in Different Agricultural Systems in Brazilian Cerrado
Bibliographic record
Abstract
The soil physical quality is a way of evaluating the current condition of forest plantations that is growing in the southeast region of Mato Grosso do Sul State. In this sense, this work aimed to evaluate the impact of the forest plantations on the physical quality of an Oxisol (Haplic Acrustox) in Cerrado. The experiment was conducted in the Experimental area of the Teaching and Research Farm, of the Engineering college of Ilha Solteira (UNESP), located in the city of Selvíria-MS, situated in the conditions of the Brazilian Cerrado. The soil samples were collected at depths of 0.00-0.10; 0.10-0.20; 0.20-0.30 and 0.30-0.40 m in three areas cultivated for 30 years: area (1) Pine forest (Pinus caribaea var. hondurensis); (2) Eucalyptus forest (Eucalyptus camaldulensis); (3) Reforested ciliary forest, being used a completely randomized design, with 25 replications and 3 treatments. The analyzed attributes of the soil was: macroporosity (Ma), microporosity (Mi), total porosity (TP), bulk density (BD), real particle (RP), soil resistance to penetration (PR), gravimetric moisture (GM), volumetric moisture (VM) and sand, silt and clay contents. The three evaluated areas presented macroporosity below the critical limit (0.100 m³ m-³), thereby impairing the root development. The three evaluated areas affected the physical quality of the soil. Being the physical attributes that most influenced in the reduction of the soil physical quality was the bulk density, total porosity, microporosity, macroporosity and soil resistance to penetration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".