Chemical Imaging of Atmospheric Organic Particles in the Eastern North Atlantic Field Campaign Report
Bibliographic record
Abstract
The ability to reliably predict future climate is hindered, in major part, by an insufficient understanding of atmospheric aerosol-radiation interactions and aerosol-cloud interactions. (IPCC 2013; Bony and Dufresne 2005) Factors affecting marine stratocumulus clouds are particularly important given that they are the dominant cloud type globally. Global climate models frequently misrepresent ubiquitous marine stratocumulus clouds for a variety of reasons, including a lack of understanding of how these cloud properties change with aerosol cloud condensation nuclei (CCN) concentration and composition.(Nam et al. 2012) Limited information is available regarding the sources of CCN in remote regions where marine stratocumulus clouds dominate. Measurements in these marine locations, which are periodically influenced by anthropogenic emissions, provide additional opportunity to study the impact of humans on cloud characteristics. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Eastern North Atlantic (ENA) observatory on Graciosa Island in the Azores is well suited to study these problems. The ENA site is exposed to a multitude of air masses as well as a variety of cloud regimes, as documented by passive and active satellite cloud retrieval. (Tselioudis et al. 2013) Thus, placing detailed aerosol and cloud measurements at the ARM ENA site may help gain a better understanding of the CCN budget and cloud interactions in remote environments. To provide detailed information on aerosol morphology, composition, and microphysical properties, size- segregated sampling was carried out at the ENA observatory on Graciosa Island during two intensive operating periods (IOPs). IOP1 occurred from June 17 to July 18, 2017, and IOP2 occurred from January 9 to February 21, 2018. Daytime and nighttime sampling were carried out separately to investigate any diurnal differences of aerosol composition due to differing meteorology. For both IOPs, a micro-orifice uniform deposit impactor (MOUDI) was used to collect particles for microscopic and microphysical analysis. A variety of microscopic substrates (formvar coated copper grids, silicon chips, silicon nitride windows, or molybdenum substrates) were used. Microscopic chemical characterization was carried out using computer-controlled scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (CCSEM-EDX) and the synchrotron-based scanning transmission X-ray microscopy coupled with near-edge X-ray absorption spectroscopy (STXM-NEXAFS). (Laskin et al. 2006; Kilcoyne et al. 2003; Moffet et al. 2010) Two light sources were used for this analysis: 1) the Advanced Light Source at Lawrence Berkeley National Laboratory, and 2) The Canadian Light Source.
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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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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".