An overview of current issues in the uptake of atmospheric trace gases by aerosols and clouds
Bibliographic record
Abstract
Abstract. A workshop was held in the framework of the ACCENT (Atmospheric Composition Change – a European Network) Joint Research Programme on "Aerosols" and the Programme on "Access to Laboratory Data". The aim of the workshop was to hold "Gordon Conference" type discussion covering accommodation and reactive uptake of water vapour and trace pollutant gases on condensed phase atmospheric materials. The scope was to review and define the current state of knowledge of accommodation coefficients for water vapour on water droplet and ice surfaces, and uptake of trace gas species on a variety of different surfaces characteristic of the atmospheric condensed phase particulate matter and cloud droplets. Twenty-six scientists participated in this meeting through presentations, discussions and the development of a consensus review. In this review we present an analysis of the state of knowledge on the thermal and mass accommodation coefficient for water vapour on aqueous droplets and ice and a survey of current state-of the-art of reactive uptake of trace gases on a range of liquid and solid atmospheric droplets and particles. The review recommends consistent definitions of the various parameters that are needed for quantitative representation of the range of gas/condensed surface kinetic processes important for the atmosphere and identifies topics that require additional research.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".