Greenhouse gas emissions from peatlands under manipulated warming, nitrogen addition, and vegetation composition change: a review and data synthesis
Bibliographic record
Abstract
Peatlands play an essential role in carbon cycling and global warming. However, the feedback of peatlands to global changes is still unclear. Here, we conducted a data synthesis of 236 observations from 52 field experiments to evaluate the effect of three important global changes (warming, nitrogen addition, and vegetation composition change) on three major greenhouse gas (GHG) fluxes: CO2, CH4, and N2O. The results showed that (i) GHG responses to warming varied among warming methods, between air temperature increase rates, and between warming durations; (ii) GHG responses to N addition varied between peatland types, between N forms, between N concentrations, and between experimental durations; (iii) the response rates of GHGs were associated with local environmental parameters (mean annual precipitation, MAP; and water table level, WTL); (iv) the global warming potential (GWP) considerably increased under these global changes, which indicates that cooling function of peatlands will be weakened. Overall, given these global changes occur simultaneously, the interaction of them on GHG fluxes should not be ignored. Our results highlight that a large number of studies in different locations are needed to comprehensively understand and accurately predict GHG emissions from peatlands.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".