Carbon Management Index and Carbon Stock of a Cohesive Oxisol Under Different in the Region Northeast of Brazil
Bibliographic record
Abstract
The objective of this study was to evaluate the soil organic carbon fractions and the carbon management index (CMI) in a cohesive oxisol under different uses. Conventional cassava planting (CC), pasture (PP), and 7- and 12-year agroforestry systems (AF7 and AF12, respectively), were tested against secondary forest (SF). Soil samples of these areas were physically fractionated in order to determine total organic matter (TOC) as well as the labile- (LOC) and non-labile (NOC) fractions of the soil organic matter, as well as carbon management index (CMI). Total organic C ranged from 14.17 to 27.20 g kg-1 of soil, showing no differences in the surface layer among the land uses as compared to the secondary forest. No differences were found in the surface layer for LOC as well, except for the AF12 where LOC was higher. This condition accounts for higher microbial activity and nutrient cycling in the soil. This system also showed higher CMI values, pointing to a better response of soil quality under long-term agroforestry system. This system improved soil organic matter, regardless of the depth. On the other hand, conventional cassava and planted pasture systems reduced carbon in soil. In conclusion, the agroforestry system is the best choice of farmers, when they seek for better soil quality.
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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 | 0.000 | 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".