Carbon and Nitrogen Content and Stock in No-Tillage and Crop-livestock Integration Systems in the Cerrado of Goias State, Brazil
Bibliographic record
Abstract
The objective of this study was to evaluate the total organic carbon (TOC) and nitrogen content, and quantify the TOC and nitrogen stocks in land use systems in the Cerrado region of Goiás State, Brazil. A crop-livestock integration system (CLIS) was evaluated - (corn+brachiaria/beans/cotton/soya beans) and a no-tillage system (NTS) - (sunflower/millet/soya beans/corn). The vegetal coverage of the natural Cerrado, adjacent to the NTS and CLIS, was considered as the original soil condition. Soil samples were collected at layer depths of 0.0-10.0 cm, 10.0-20.0 cm, 20.0-30.0 cm, 30.0-40.0 cm, 40.0-50.0 cm, 50.0-60.0 cm, 60.0-80.0 cm, and 80.0-100.0 cm, in a full random experimental design. The CLIS had higher contents of TOC and N than the NTS up to a level of 30.0 cm. Higher nitrogen stocks were observed in the Cerrado. In the CLIS, higher TOC contents were found up to 30.0 cm and nitrogen contents up to 20.0 cm. The sum of stocks up to a depth of 100 cm for TOC and 40 cm for nitrogen was greater in the CLIS when compared to the NTS. Under the CLIS, the stocks of TOC (0.0-30.0 cm) and nitrogen (0.0-20.0 cm) increased in relation to those of the NTS. The CLIS was more efficient in stock accumulation than the Cerrado for the sum of layers 0.0-40.0 cm and 0.0-60.0 cm.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".