*On assignment from the National Oceanic and Atmospheric Administration. Development of an Anthropogenic Emissions Inventory for Annual Nationwide Models-3/CMAQ Simulations of Ozone and Aerosols
Bibliographic record
Abstract
The U.S. EPA is undertaking a “Proof-of-Concept ” effort which includes applications of the Models-3/CMAQ (Community Multiscale Air Quality) modeling system for a domain covering all of the continental United States as well as adjacent portions of Canada and Mexico. The intent is to determine the feasibility of applying this modeling system for a full year over a nationwide domain and to extend our knowledge of the behavior of the model for simulating ozone and aerosols. This paper describes the modifications and updates made in processing anthropogenic emissions used for performing national annual CMAQ simulations for ozone and aerosols. The cornerstone of the U.S. anthropogenic emissions data base was the 1996 National Emissions Trends (NET) inventory Version 3.11. From this inventory, the primary emissions of VOC, NOX, CO, SO2, PM2.5, PM10, and NH3 were speciated into the chemical mechanism classes and directly emitted sulfate, nitrate, elemental carbon, organic aerosols, other fine particles <2.5 Fg/m3, and coarse particles (particle diameters between 2.5 and 10 Fg/m3). As part of this process a new methodology was developed and implemented for estimating gaseous sulfate emissions for certain combustion source categories based on SO2 emissions from these sources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.300 | 0.098 |
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".