New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Ammonia surface flux is bidirectional; that is, net flux can be either upward or downward. In fertilized agricultural croplands and grasslands there is usually more emission than deposition especially in midday during warmer seasons. In North America, most of the ammonia emissions are from agriculture with a significant fraction of that coming from fertilizer. A new bidirectional ammonia flux modeling system has been developed in the Community Multiscale Air Quality (CMAQ) model, which has close linkages with the Environmental Policy Integrated Climate (EPIC) agricultural ecosystem model. Daily inputs from EPIC are used to calculate soil ammonia concentrations that are combined with air concentrations in CMAQ to calculate bidirectional surface flux. The model is evaluated against surface measurements of NH 3 concentrations, NH 4 + and SO 4 2− aerosol concentrations, NH 4 + wet deposition measurements, and satellite retrievals of NH 3 concentrations. The evaluation shows significant improvement over the base model without bidirectional ammonia flux. Comparisons to monthly average satellite retrievals show similar spatial distribution with the highest ammonia concentrations in the Central Valley of California (CA), the Snake River valley in Idaho, and the western High Plains. In most areas the model underestimates, but in the Central Valley of CA, it generally overestimates ammonia concentration. Case study analyses indicate that modeled high fluxes of ammonia in CA are often caused by anomalous high soil ammonia loading from EPIC for particular crop types. While further improvements to parameterizations in EPIC and CMAQ are recommended, this system is a significant advance over previous ammonia bidirectional surface flux models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it