Airborne Observations and Modeling Comparison of Global Inorganic Aerosol Acidity
Bibliographic record
Abstract
This dataset provides observations collected during eleven airborne campaigns from 2006–2017 and associated input and output from nine widely used chemical transport models (CTMs). The airborne campaigns include ARCTAS-A, ARCTAS-B, ATom-1 and ATom-2, CalNex, DC3, INTEX-B, KORUS-AQ, MILAGRO, SEAC4RS, and WINTER, and they sampled mainly tropospheric air over the conterminous U.S. and the state of Alaska, Mexico, Canada, Greenland, and South Korea and remote areas over the Arctic, Pacific, Southern, and Atlantic Oceans. The CTMs are the AM4.1, CCSM4, GEOS-5, GEOS-Chem TOMAS, GEOS-Chem v10, GEOS-Chem v12, GISS-MATRIX, GISS-ModelE, and TM4-ECPL-F, and the output includes sulfate, nitrate, temperature, specific humidity, mixing ratio of ammonium, the volume mixing ratio of nitric acid, surface pressure, gas-phase ammonia, gas-phase nitric acid, pressure, total ammonium, etc. The observations were collected in-situ from a variety of instruments, including the Aerosol Microphysical Properties (AMP), HR Aerodyne Aerosol Mass Spectrometer (AMS), CIT Chemical Ionization Mass Spectrometer (CIMS), diode laser hygrometer (DLH), a mist chamber/ion chromatography system (MC/IC), Particle Analysis by Laser Mass Spectrometer (PALMS), Single Particle Soot Photometer (SP2), and UCI Whole Air Sampler (WAS). In-situ data also include latitude, longitude, and pressure. These observations were used to investigate how aerosol pH and ammonium balance change from polluted to remote regions, such as over oceans, and were compared to predictions from the CTMs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".