Comparison of advanced offline and in situ techniques of organic aerosol composition measurement during the CalNex campaign
Bibliographic record
Abstract
Abstract. Our understanding of formation processes, physical properties, and climate/health effects of organic aerosols is still limited in part due to limited knowledge of organic aerosol composition. We present speciated measurements of organic aerosol composition by two methods: in situ thermal-desorption proton-transfer-reaction mass spectrometry (TD-PTR-MS) and offline two-dimensional gas chromatography with a time-of-flight mass spectrometer (GC × GC/TOF-MS). Using the GC × GC/TOF-MS 153 compounds were identified, 123 of which were matched with 64 ions observed by the TD-PTR-MS. A reasonable overall correlation of 0.67 (r2) was found between the total matched TD-PTR-MS signal (sum of 64 ions) and the total matched GC × GC/TOF-MS signal (sum of 123 compounds) for the Los Angeles area. A reasonable quantitative agreement between the two methods was observed for most individual compounds with concentrations which were detected at levels above 2 ng m−3 using the GC × GC/TOF-MS. The analysis of monocarboxylic acids standards with TD-PTR-MS showed that alkanoic acids with molecular masses below 290 amu are detected well (recovery fractions above 60 %). However, the concentrations of these acids were consistently higher on quartz filters (quantified offline by GC × GC/TOF-MS) than those suggested by in situ TD-PTR-MS measurements, which is consistent with the semivolatile nature of the acids and corresponding positive filter sampling artifacts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".