Net carbon dioxide exchange in a hyperseasonal cattle pasture in the northern Pantanal wetland of Brazil
Bibliographic record
Abstract
The Pantanal is the largest seasonal wetland in the world with a landscape that consists of a mosaic of permanent aquatic habitats, and floodable and non-floodable savannas, pastures and forests. Drought events are expected to occur more frequently in the Pantanal biome under future climate conditions, but the effects of land management and hydrological extremes on pastures have been poorly studied at spatial scales relevant to local livestock. In this study, we measured CO2C fluxes using eddy covariance over a hydrological year on pastures within a cattle farm in the Brazilian Pantanal that experienced seasonal flooding. Our measurements show that seasonally flooded pastures were large emitters of CO2C, contributing 337 g CO2C m−2 year−1 to the atmosphere. During flooding, when the soils were anaerobic, and soil O2 was close to zero, the flooded pasture was a net sink of -18 g CO2C m−2, while during the aerobic phase (soil O2 > 15%) the pasture was a significant CO2 source to the atmosphere (301 g CO2C m−2). Transitions to and from anaerobic conditions corresponded to 54 g CO2C m−2. Our results indicate that the seasonally flooded cattle pastures in the Brazilian Pantanal may be an important regional source of CO2C for the atmosphere. Better management, and use of drought resistant grasses, may be a way to improve soil C stocks and limit emissions, especially as global climate change is anticipated to increase heating and drying for the Pantanal biome.
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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 | 0.000 | 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".