Soil CO<sub>2</sub>concentrations and efflux dynamics of a tree island in the Pantanal wetland
Bibliographic record
Abstract
Abstract The Pantanal is the largest tropical wetland on the planet, and yet little information is available on the biome's carbon cycle. We used an automatic station to measure soil CO2concentrations and oxidation‐reduction potential over the 2014 and 2015 flood cycles of a tree island in the Pantanal that is immune to inundation during the wetland's annual flooding. The soil CO2concentration profile was then used to estimate soil CO2efflux over the two periods. In 2014, subsurface soil saturation at 0.30 m depth created conditions in that layer that led to CO2buildup close to 200,000 ppm and soil oxidation‐reduction potential below −300 mV, conditions that were not repeated in 2015 due to annual variability in soil saturation at the site. Mean CO2efflux over the 2015 flood cycle was 0.023 ± 0.103 mg CO2‐C m−2 s−1representing a total annual efflux of 593 ± 2690 mg CO2‐C m−2 y−1. Unlike a nearby tree island site that experiences full inundation during the wet season, here the soil dried quickly following repeated rain events throughout the year, which led to the release of CO2pulses from the soil. This study highlights not only the complexity and heterogeneity in the Pantanal's carbon balance based on differences in topography, flood cycles, and vegetation but also the challenges of applying the gradient method in the Pantanal due to deviations from steady state conditions.
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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 | 0.000 |
| 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".