Can atmospheric chemistry deposition schemes reliably simulate stomatal ozone flux across global land covers and climates?
Bibliographic record
Abstract
Over the past few decades, ozone risk assessments for vegetation have evolved two methods based on stomatal O 3 flux. However, substantial uncertainties remain in accurately simulating these fluxes. Here, we investigate stomatal O 3 fluxes across various land cover types worldwide simulated by six established deposition models. Hourly O 3 concentration and meteorological data at nine sites were extracted from the Tropospheric Ozone Assessment Report (TOAR) database, a comprehensive global collection of measurements, for the model simulations. The models estimated reasonable O 3 deposition (0.5–0.8 cm s −1 in summer), which is mostly in agreement with the literature. Simulations of canopy conductance showed differences that varied by land cover type with correlation coefficients of 0.75, 0.80, and 0.85 for forests, crops, and grasslands among the models. Differences between models were primarily influenced by soil moisture and vapour pressure deficit, depending on each model's specific structure. Across models, the range of O 3 damage simulations at each site was most consistent for crops (6 to 11 mmol O 3 m −2 ), followed by forests (3 to 19.5 mmol O 3 m −2 ) and grasslands (7 to 33 mmol O 3 m −2 ). The median estimate across models aligns well with the literature at the sites most vulnerable to O 3 damage. Overall, this study represents a critical first step in developing and evaluating tools for broad-scale assessment of O 3 impacts on vegetation within the framework of TOAR phase II.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".