A model-based analysis of foliar NO <sub> <i>x</i> </sub> deposition
Bibliographic record
Abstract
Foliar deposition of NO 2 removes a large fraction of the global soil-emitted NO x . Understanding the mechanisms of NO x foliar loss is important for constraining surface ozone, constraining NO x mixing ratios, and assessing the impacts of nitrogen inputs to ecosystems. We have constructed a 1-D multibox model with representations of chemistry and vertical transport to evaluate the impact of leaf-level processes on canopy-scale concentrations, lifetimes, and canopy fluxes of NO x . Our model is able to closely replicate canopy fluxes and above-canopy NO x daytime mixing ratios observed during two field campaigns, one in a western Sierra Nevada pine forest (BEARPEX-2009) and the other in a northern Michigan mixed hardwood forest (UMBS-2012). We present a conceptual argument for the importance of NO 2 dry deposition and demonstrate that NO 2 deposition can provide a mechanistic explanation for the canopy reduction of NO x . We show that foliar deposition can explain observations suggesting as much as ∼60 % of soil-emitted NO x is removed within forest canopies. Stomatal conductances greater than 0.1 cm s −1 result in modeled canopy reduction factors in the range of those used in global models, reconciling inferences of canopy NO x reduction with leaf-level deposition processes. We show that incorporating parameterizations for vapor pressure deficit and soil water potential has a substantial impact on predicted NO 2 deposition in our model, with the percent of soil NO x removed within one canopy increasing by ∼15 % in wet conditions compared to dry conditions. NO 2 foliar deposition was also found to have a significant impact on ozone and nitrogen budgets under both high- and low-NO x conditions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".