Soil Chemical Attributes under Crop-Livestock-Forest Integration System and in Different Land Uses in Mata dos Cocais Region
Bibliographic record
Abstract
The sustainability of ecosystems is closely linked with the assessment of soil properties that estimate their quality. This work proposes to evaluate soil chemical attributes as a function of the implantation of a crop-livestock-forest integration system (ICLF) in the region of Mata dos Cocais in the state of Maranhão, Northeast Brazil. The four different land uses evaluated were native vegetation with babassu, capoeira vegetation, degraded pasture and area under ICLF system (with marandu grass, maize and eucalyptus consortium). The samples were collected up to one meter deep, comprising seven layers: 0.00-0.10, 0.10-0.20, 0.20-0.30, 0.30-0.40, 0.40-0.60, 0.60-0.80 and 0.80-1.0 m. The chemical attributes evaluated were pH, Ca, Mg, Al, P, K and Na, potential acidity, base sum, base saturation and soil cation exchange capacity (CEC). The levels of P, in the 0.00-0.10 m layer, were higher in the ICLF system than those of the native forest with babassu. The levels of K in the ICLF system and degraded pasture were higher than the other land uses up to a depth of 0.40 m, ranging from 0.92 cmolc dm-3 to 0.62 cmolc dm-3 and 1.04 cmolc dm-3 and 0.67 cmolc dm-3, respectively. Base saturation was higher in soils under ICLF system and degraded pasture than those observed in native forest and capoeira vegetation. There was an effect in chemical attributes of the soil such as a function of land use and, in general, the highest values were found in areas with degraded pasture and ICLF.
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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 | 0.001 |
| 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 | 0.000 | 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".