Effects of nitrogen deposition and litter layer management on soil CO2, N2O, and CH4 emissions in a subtropical pine forestland
Bibliographic record
Abstract
Abstract Forestland soils play vital role in regulating global soil greenhouse gas (GHG) budgets, but the interactive effect of the litter layer management and simulated nitrogen (N) deposition on these GHG flux has not been elucidated clearly in subtropical forestland. A field trial was conducted to study these effects by using litter removal method under 0 and 40 kg N ha −1 yr −1 addition in a subtropical forestland in Yingtan, Jiangxi Province, China. Soil CO 2 emission was increased by N addition (18–24%) but decreased by litter removal (24–32%). Litter removal significantly ( P < 0.05) decreased cumulative N 2 O emission by 21% in treatments without N addition but only by 10% in treatments with 40 kg N ha −1 yr −1 addition. Moreover, litter-induced N 2 O emission under elevated N deposition (0.094 kg N 2 O-N ha −1 ) was almost the same as without N addition (0.088 kg N 2 O-N ha −1 ). Diffusion of atmospheric CH 4 into soil was facilitated by litter removal, which increased CH 4 uptake by 55%. Given that the increasing trend of atmospheric N deposition in future, which would reduce litterfall in subtropical N-rich forest, the effect of surface litter layer change on soil GHG emissions should be considered in assessing forest GHG budgets and future climate scenario modeling.
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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) | 0.000 | 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".