Monitoring strategy for characterization of airborne nanoparticles
Bibliographic record
Abstract
A major challenge in monitoring indoor exposures to nanoparticles is the selection and effective use of suitable instrumentation. Comparability, portability, response time, and reliability are important selection criteria in addition to reasonable cost. Amongst these criteria, instrument comparability is especially critical due to the requirement for multiple instruments in a single exposure assessment and the lack of reference standards for instrument calibration. Testing and verifying instrument comparability, therefore, is essential to ensure the reliability of exposure assessment data. In this study, a variety of portable and non-portable direct-reading instruments, including scanning mobility particle sizers, condensation particle counters, aerodynamic particle sizers, diffusion charger and aerosol mass monitors, were deployed simultaneously. Instrument performance was evaluated in a room-sized environmentally-controlled chamber with the goal of recommending a suite of instruments to provide particle number, surface area, particle size distribution and mass measurements with an acceptable level of uncertainty. The instrumental strategy was then applied to monitoring background aerosols in a typical workplace setting, where laser printers provided a point source for monitoring response time and comparing peak-to-background signals. The study also explored filter-based methods for collecting nanoparticles for subsequent elemental analysis using inductively-coupled plasma mass spectroscopy (ICP-MS).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".