Comment on egusphere-2023-855
Bibliographic record
Abstract
Abstract. Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States (U.S.). We update existing methods for calculating mobile source organic particle and vapor emissions in the U.S. with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from onroad vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of U.S. mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20–25 % of the IVOC concentrations and 4.4–21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the U.S. (4.3–11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.557 | 0.399 |
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".