A model-based analysis of foliar NO <sub> <i>x</i> </sub> deposition
Bibliographic record
Abstract
Abstract. Foliar deposition of NO2 removes a large fraction of the global soil-emitted NOx. Understanding the mechanisms of NOx foliar loss is important for constraining surface ozone, NOx mixing ratios, and assessing the impacts of nitrogen inputs to ecosystems. We have constructed a 1D multi-box 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 NOx. Our model is able to closely replicate canopy fluxes and above-canopy NOx daytime mixing ratios observed during two field campaigns, one in a western Sierra Nevada pine forest (BEARPEX-2009) and the other a northern Michigan mixed hardwood forest (UMBS-2012). Our model demonstrates that NO2 deposition can provide a mechanistic explanation for canopy reduction factors (CRFs). We show that foliar deposition can explain observations suggesting as much as ~ 60 % of soil-emitted NOx is removed within forest canopies. Stomatal conductances greater than 0.1 cm s−1 result in modelled canopy reduction factors in the range of those used in global models, reconciling inferences of canopy NOx reduction with leaf-level deposition processes. We also show that incorporating parameterizations for vapor pressure deficit and soil water potential has a substantial impact on predicted NO2 deposition, with the percent of soil NOx removed within one canopy increasing by ~ 15 % in wet conditions compared to dry conditions. NO2 foliar deposition was found to have a significant impact on ozone and nitrogen budgets under both high and low NOx 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 | 0.000 | 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".