Foliar methane and nitrous oxide fluxes in Salix bebbiana respond to light and soil factors
Bibliographic record
Abstract
Foliar exchange of methane and nitrous oxide is a significant yet poorly understood component of global greenhouse gas budgets. To address this knowledge gap, we investigated foliar methane and nitrous oxide fluxes in Salix bebbiana, under varying light conditions (0–2000 μmol·m−2·s−1), soil aeration, and nitrogen availability, manipulated via biochar incorporation and nitrogen additions. Using rapid spectroscopic gas analysers, we observed consistent net foliar methane oxidation and nitrous oxide emission across all light conditions, demonstrating saturating light response patterns. Maximum flux rates were significantly more sensitive to soil conditions than carbon dioxide or water vapour exchange. Analysis revealed foliar methane and nitrous oxide fluxes overwhelmingly regulated by internal leaf processes like xylem transport, with modulation by external light intensity. These predictable light-response patterns provide a basis for scaling leaf-level methane and nitrous oxide fluxes, enhancing accuracy in predicting biogenic greenhouse gas fluxes within ecosystem and biosphere models. Xylem-mediated transport and light intensity on leaf surface primarily drive foliar methane oxidation and nitrous oxide emissions in Salix bebbiana plant, according to an analysis of an ecophysiological manipulation experiment with biochar incorporation and nitrogen addition.
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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 | 0.001 |
| 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".