Measuring aerosol size distributions with the aerodynamic aerosol classifier
Bibliographic record
Abstract
The Aerodynamic Aerosol Classifier (AAC) is a novel instrument that selects aerosol particles based on their relaxation time or aerodynamic diameter. Additional theory and characterization is required to allow the AAC to accurately measure an aerosol’s aerodynamic size distribution by stepping while connected to a particle counter (such as a Condensation Particle Counter, CPC). To achieve this goal, this study characterized the AAC transfer function (from 32 nm to 3 μm) using tandem AACs and comparing the experimental results to the theoretical tandem deconvolution. These results show that the AAC transmission efficiency is 2.6–5.1 times higher than a combined Krypton-85 radioactive neutralizer and Differential Mobility Analyzer (DMA), as the AAC classifies particles independent of their charge state. However, the AAC transfer function is 1.3–1.9 times broader than predicted by theory. Using this characterized transfer function, the theory to measure an aerosol’s aerodynamic size distribution using an AAC and particle counter was developed. The transfer function characterization and stepping deconvolution were validated by comparing the size distribution measured with an AAC-CPC system against parallel measurements taken with a Scanning Mobility Particle Sizer (SMPS), CPC, and Electrical Low Pressure Impactor (ELPI). The effects of changing AAC classifier conditions on the particle selected were also investigated and found to be small (<1.5%) within its operating range. Copyright © 2018 American Association for Aerosol Research
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".