Carbon Dioxide Emissions in Agricultural Systems in the Brazilian Savanna
Bibliographic record
Abstract
The objectives of this work were to evaluate the CO2 emission potential of an Oxisol under integrated crop-livestock and no-tillage systems in the Brasilian Savanna; and to analyze the impact of soil temperature and soil moisture as factors that regulate the seasonality and the emission of C-CO2 in these systems. Field-scale study was carried out at Fazenda Brejinho, in Pedro Afonso-TO, under Oxisol. The production systems studied were integrated crop-livestock and no-tillage under cropped with soybean and corn. The amount of carbon (C) released from the soil in the form of CO2 (C-CO2) was evaluated using cylindrical chambers installed in the field with a vial containing sodium hydroxide to collect CO2 released from the soil (soil emission) in an interval of 15 hours. Five evaluations were conducted over crops growing season, started in 01/26 through 06/16/2015. Data were tested to analysis of variance and the means were compared with Duncan test at 5%. The emission of C-CO2 differed between treatments in all evaluation periods. On average, the temperature ranged from 26.5 to 27.7 °C, the soil moisture ranged from 12.2 to 15.7% and the C-CO2 emission ranged from 87.4 to 119.9 mg m-2 h-1. Temperature, soil moisture and the production systems contributed to the emission and seasonality of carbon dioxide emissions. The integrated crop-livestock cropped with soybean/corn rotation was the system that had the lowest carbon dioxide emission.
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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".