Characterization of the Real Part of Dry Aerosol Refractive Index Over North America From the Surface to 12 km
Bibliographic record
Abstract
Abstract This study reports a characterization of the real part of dry particle refractive index (n) at 532 nm based on airborne measurements over the United States, Canada, the Pacific Ocean, and the Gulf of Mexico from the 2012 Deep Convective Clouds and Chemistry (DC3) and 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaigns. Effectivenvalues are reported, with the limitations and uncertainties discussed. Eight air mass types were identified based on criteria related to gas‐phase tracer concentrations, location, and altitude. Average values ofnfor these air types ranged from 1.50 to 1.53. Values ofnfor the organic aerosol (OA) fraction (nOA) were calculated using a linear mixing rule for each air mass type, with 1.52 shown to be a good approximation for all OA. Case studies detailing vertical structure revealed thatnandnOAincreased with altitude, simultaneous with enhancements in the mass fraction of OA. Values ofnOAwere positively (negatively) correlated with the O:C (H:C) ratio in the absence of biomass burning influence; in contrast, the cumulative data set revealed a slight decrease innOAas a function of the O:C ratio. The performance of parametric (multiple linear regression) and nonparametric (Gaussian process regression) methods in predictingnbased on aerosol composition data is discussed. It is shown that even small perturbations innvalues significantly impact aerosol optical depth retrievals, radiative forcing, and optical sizing instruments, emphasizing the importance of further improving the understanding of this important aerosol property.
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) | 0.000 | 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".