Plant controls over tropical wetland nitrous oxide dynamics: a review
Bibliographic record
Abstract
Tropical wetlands are an important global source of greenhouse gas emissions, including nitrous oxide, a potent and long-lasting greenhouse gas. Tropical wetland ecosystems can be highly heterogeneous, featuring a variety of vegetation types, from grasses through to palms and mangroves. While soil conditions (particularly soil moisture and pH) are essential for determining the formation of nitrous oxide in soils, plants have a central role in determining the balance of emissions. In this review, we summarise the importance of vegetation in regulating tropical wetland nitrous oxide dynamics. We show how a variety of plant-mediated processes can exert key controls over wetland plant-soil nitrogen transportation and transformations. Key mechanisms of plant regulation of dynamics include influencing substrate availability (carbon and nitrogen) through litter inputs, rhizodeposition, root turnover and plant nitrogen uptake, rhizosphere biology, and plant-mediated nitrous oxide transportation, all of which can vary between species and dominant vegetation types. We propose that there is a critical need to better quantify such processes across dominant wetland ecotypes, to support improved upscaling of emissions, and assess their sensitivity to future environmental change.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".