Emission factor ratios, SOA mass yields, and the impact of vehicular emissions on SOA formation
Bibliographic record
Abstract
Abstract. The underprediction of ambient secondary organic aerosol (SOA) levels by atmospheric models in urban areas is well established, yet the cause of this underprediction remains elusive. Likewise, the relative contribution of emissions from gasoline- and diesel-fueled vehicles to the formation of SOA is generally unresolved. Here we address these two issues using data from the 2010 CalNex experiment carried out in the Los Angeles basin (Ryerson et al., 2013). We use gas-phase organic mass (GPOM) and CO emission factors in conjunction with measured enhancements in oxygenated organic aerosol (OOA) relative to CO to investigate the relative importance of gasoline vs. diesel emissions to organic aerosol formation. Two possible conclusions emerge from the analysis to yield consistency with the ambient data: (1) vehicular emissions are not a dominant source of anthropogenic fossil SOA in the Los Angeles basin, or (2) ambient SOA mass yields are substantially higher than those derived from laboratory chamber studies.
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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) | 0.002 | 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".