Land-Use Changes the Chemical and Physical Properties of an Oxisol in the Brazilian Cerrado
Bibliographic record
Abstract
This research aimed to evaluate the effect of different land-uses on physical and chemical properties of Oxisols under cerrado conditions in central region of Goiás, Brazil. The data were analysed in a randomized experimental design in a 4 × 3 factorial arrangement with five replications. The land-uses were: 1-Annual crops, 2-Pasture, 3-Sugarcane and 4-Native forest (control). The second factor consisted of three sampling depths: 0-0.05, 0.1-0.2 and 0.2-0.3 m. The physical attributes analysed were: total clay (TC), natural clay (NC), bulk density (BD), particle density (PD), total porosity (TP), degree of flocculation (DF), soil resistance to penetration (SRP), gravimetric water content (%GWC). Chemicals attributes analized: pH in water (pH), calcium (Ca), magnesium (Mg), aluminium (Al), potential acidity (H+Al), potassium (K), phosphorus (P), soil organic matter (SOM), cation exchange capacity (T) and base saturation (V%). Annual crops showed higher K levels (0-0.3 m) and lower SOM values (0-0.3 m), Ca and Mg (0-0.05 m) affecting T in relation to the forest. The use sugarcane showed higher values of BD, PD and SRP, and smaller values of TP, SOM, P and T. On the other hand, the values of Ca and Mg are kept close to those of the control. The attribute V% is preserved for annual crops, sugarcane and pasture with values higher than those verified in native forest. The use pasture reduced TP and P and Ca, Mg and T. The land-uses studied reduced soil quality compared to the forest, in descending order were sugarcane > annual crops > pasture.
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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 | 0.000 | 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) | 0.001 | 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".