Simultaneous factor analysis of organic particle and gas mass spectra: AMS and PTR-MS measurements at an urban site
Bibliographic record
Abstract
Abstract. During the winter component of the SPORT (Seasonal Particle Observations in the Region of Toronto) field campaign, particulate non-refractory chemical composition and concentration of selected volatile organic compounds (VOCs) were measured by an Aerodyne time-of-flight aerosol mass spectrometer (AMS) and a proton transfer reaction-mass spectrometer (PTR-MS), respectively. Sampling was performed in downtown Toronto ~15 m from a major road. The mass spectra from the AMS and PTR-MS were combined into a unified dataset, which was analyzed using positive matrix factorization (PMF). The two instruments were given equal weight in the PMF analysis by application of a scaling factor to the uncertainties of each instrument. A residual based metric, Δesc, was used to evaluate the relative weight. The PMF analysis yielded a 5-factor solution that included factors characteristic of regional transport, local traffic emissions, charbroiling, and oxidative processing. The unified dataset provides information on particle and VOC sources and atmospheric processing that cannot be obtained from the datasets of the individual instruments, such as apportionment of oxygenated VOCs to direct emission sources vs. secondary reaction products, improved correlation of oxygenated aerosol factors with photochemical age, and increased detail regarding the composition of oxygenated organic aerosol factors. This analysis represents the first application of PMF to a unified AMS/PTR-MS dataset.
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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 | 0.001 |
| 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) | 0.001 | 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".