Carbon Stocks and Soil Organic Matter Quality Under Different of Land Uses in the Maranhense Amazon
Bibliographic record
Abstract
In the face of the traditional model of succession of native environments in pastures or agricultural areas, followed by superpastejo and the concern with emissions of greenhouse gases in the Brazilian Amazon region, this work aims to determine the influence of different land uses on carbon sequestration and soil organic matter changes in the municipality of Pindaré-Mirim, in state of Maranhão. This study evaluated different uses of the soil: native forest; secondary vegetation (capoeira); degraded pasture and CLFI (Crop-Livestock-Forest Integration) system. The deformed and undisturbed samples were collected at depths: 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 0.80-1.00 m. Soil densities were determined by the volumetric ring method, the carbon stocks by the carbon content in the soil evaluating the dry combustion, and the accumulated carbon stocks were calculated in 1.00 m. The physical fractions of the organic matter were determined by means of the granulometric method. At depth 0.0-0.10 m, the soil density in the native forest (1.17 g cm-3) was lower than the average of degraded pasture (1.40 g cm-3). There was no difference in the carbon content between all the land uses up to 0.40 m depth. The accumulated carbon stocks up to 1.00 m ranged from 49.52 Mg ha-1 to 64.41 Mg ha-1 and were higher in the native forest compared to capoeira and the ICLF system. In relation to the accumulated carbon stock, the native forest and degraded pasture were the ones that obtained the highest levels, followed by the capoeira and the CLFI system.
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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) | 0.001 | 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".