Inter-Comparison of a Fast Mobility Particle Sizer and a Scanning Mobility Particle Sizer Incorporating an Ultrafine Water-Based Condensation Particle Counter
Bibliographic record
Abstract
An Ultrafine Water-based Condensation Particle Counter (UWCPC), a Scanning Mobility Particle Sizer (SMPS) incorporating an UWCPC, and a Fast Mobility Particle Sizer (FMPS) were deployed to determine the number and size distribution of ultrafine particles. Comparisons of particle number concentrations measured by the UWCPC, SMPS, and FMPS were conducted to evaluate the performance of the two particle sizers using ambient particles as well as lab generated artificial particles. The SMPS number concentration was substantially lower than the FMPS (FMPS/SMPS = 1.56) measurements mainly due to the diffusion losses of particles in the SMPS. The diffusion loss corrected SMPS (C-SMPS) number concentration was on average ∼ 15% higher than the FMPS data (FMPS/C-SMPS = 0.87). Good correlation between the C-SMPS and FMPS was also observed for the total particle number concentrations in the size range 6 nm to 100 nm measured at a road-side urban site (r2 = 0.91). However, the particle size distribution measured by the C-SMPS was quite different from the size distribution measured by the FMPS. An empirical correction factor for each size bin was obtained by comparing the FMPS data to size-segregated UWCPC number concentrations for atmospheric particles. The application of the correction factor to the FMPS data (C-FMPS) greatly improved the agreement of the C-SMPS and C-FMPS size distributions. The agreement of the total particle concentrations also improved to well within 10% (C-FMPS/C-SMPS = 0.95).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".