Airborne characterization of subsaturated aerosol hygroscopicity and dry refractive index from the surface to 6.5km during the SEAC<sup>4</sup>RS campaign
Bibliographic record
Abstract
In situ aerosol particle measurements were conducted during 21 NASA DC-8 flights in the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys field campaign over the United States, Canada, Pacific Ocean, and Gulf of Mexico. For the first time, this study reports rapid, size-resolved hygroscopic growth and real refractive index (RI at 532 nm) data between the surface and upper troposphere in a variety of air masses including wildfires, agricultural fires, biogenic, marine, and urban outflow. The Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP) quantified size-resolved diameter growth factors (GF = D-p,D-wet/D-p,D-dry) that are used to infer the hygroscopicity parameter kappa. Thermokinetic simulations were conducted to estimate the impact of partial particle volatilization within the DASH-SP across a range of sampling conditions. Analyses of GF and RI data as a function of air mass origin, dry size, and altitude are reported, in addition to kappa values for the inorganic and organic fractions of aerosol. Average RI values are found to be fairly constant (1.52-1.54) for all air mass categories. An algorithm is used to compare size-resolved DASH-SP GF with bulk scattering f(RH=80%) data obtained from a pair of nephelometers, and the results show that the two can only be reconciled if GF is assumed to decrease with increasing dry size above 400 nm (i.e., beyond the upper bound of DASH-SP measurements). Individual case studies illustrate variations of hygroscopicity as a function of dry size, environmental conditions, altitude, and composition.
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 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) | 0.007 | 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".