Bibliographic record
Abstract
The production of organic peroxy radicals from the reactions of the hydroxyl radical (OH) and organic compounds is frequently a rate-limiting step in the formation of ground-level ozone. OH reactivity, the inverse of the OH radical’s lifetime, gives a measure of the production rate of peroxy radicals. A custom-built instrument that can directly measure the total OH reactivity was developed at the Forschungszentrum Jülich and employed on the AEROMMA measurement campaign, organized by the US National Oceanic and Atmospheric Administration in the summer of 2023. The measurement campaign utilized a variety of aircraft and an array of advanced instrumentation to investigate the chemical composition of urban pollution outflows. It is expected that the importance of vehicle emissions is decreasing and that of other emissions, like volatile chemical products, are increasing. OH reactivity was measured over major urban areas, including New York City, Los Angeles, Chicago, and Toronto. A suite of instrumentation measured the OH reactants, such as inorganics, nitrogen oxides, alkanes/alkenes, aromatics, and biogenic organic compounds. In the presentation, the sum of OH reactivity from these species is compared to the measured reactivity, to explore the closure of reactivity budget. From the measurement campaign, the relative contributions of different emission sources to the total OH reactivity including sources for VCPs are analyzed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".