In Situ Characterization of Cloud Condensation Nuclei, Interstitial, and Background Particles Using the Single Particle Mass Spectrometer, SPLAT II
Bibliographic record
Abstract
The aerosol indirect effect remains the most uncertain aspect of climate change modeling, calling for characterization of individual particles sizes and compositions with high spatial and temporal resolution. We present the first deployment of our single particle mass spectrometer (SPLAT II) operated in dual data acquisition mode to simultaneously measure particle number concentrations, density, asphericity, and individual particle size and quantitative composition, with temporal resolution better than 60 s, thus yielding all the required properties to definitively characterize the aerosol-cloud interaction in this exemplary case. We find that particles are composed of oxygenated organics, many mixed with sulfates, biomass burning particles, some with sulfates, and processed sea-salt. Cloud residuals are found to contain more sulfates than background particles, explaining their higher efficiency to serve as cloud condensation nuclei (CCN). Additionally, CCN sulfate content increased with time due to in-cloud droplet processing. A comparison between the size distributions of background, CCN, and interstitial particles shows that while nearly all CCN particles are larger than 100 nm, over 80% of interstitial particles are smaller than 100 nm. We conclude that for this cloud, particle size is the controlling factor on aerosol activation into cloud-droplets, with higher sulfate content playing a secondary role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".