Methane and carbon dioxide evasion from a mosaic of Amazon lakes, river channels, and inundated forests
Bibliographic record
Abstract
Seasonally inundated forests are the largest type of wetland in the Amazon basin. Here we provide new data from inundated forests, a lake, and river channels during high water in the forested Anavilhanas archipelago (Negro River, Brazil). Evasion pathways of CH4 in flooded forests include tree trunks, diffusion from the water, and ebullition. Within flooded forest sites, diffusive CH4 fluxes were lowest (mean, 6.2 µmol m-2 h-1), ebullitive fluxes averaged 50 µmol m-2 h-1, and fluxes from the trees had the highest fluxes when expressed per inundated surface area (83 µmol m-2 h-1). The lake and river channels usually had higher CH4 and CO2 fluxes than inundated forests. Overall, mean CH4 fluxes from inundated forests in our study were lower than fluxes measured in nutrient-rich inundated forests. Our results contribute to understanding the heterogeneity of C fluxes from inundated forests. Water levels, currents and extent of inundated habitats contribute to variability in gas fluxes. Outgassing rates are expected to become more variable with projected periods of especially high and low water levels as the climate changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".