Demonstration of the CPMA-Electrometer System for Calibrating Black Carbon Particulate Mass Instruments
Bibliographic record
Abstract
In an effort to improve regulations for particulate emissions from aircraft engines, the Aircraft Exhaust Emissions Measurement Committee, SAE E-31, is investigating instruments to measure black carbon mass concentration in real time. The current candidates are a laser-induced incandescence instrument (LII 300) and a photo-acoustic Micro-Soot Sensor (MSS). However, both of these instruments use indirect techniques, measuring parameters other than the actual mass of particulate in the exhaust, and therefore require calibration. Previously, it has been shown that a centrifugal particle mass analyzer (CPMA) can be used in conjunction with an aerosol electrometer to traceably generate an aerosol with known mass concentration. This system can be used to rapidly calibrate particle mass instruments (on the order of minutes), without the time-consuming process of filter sampling, which is often used for calibration and prone to sampling artifacts. Here, we demonstrate the feasibility of the CPMA-electrometer system for calibrating two LII 300 instruments and two MSS instruments, which were calibrated to the NIOSH 5040 standard. The correlations between the CPMA-electrometer system and the challenge instrument were highly linear for both the LII and the MSS, and agreed well with the previous calibration. All four instruments were found to correlate with the CPMA-electrometer system with R2 values of 0.993 to 0.999. The standard uncertainty in the CPMA-electrometer system averaged 4.3% and was as low as 2.6% for some measurements. With a simple improvement to the aerosol electrometer, we estimate this average uncertainty would be less than 3%. This lower uncertainty and much higher speed of measurement support the use of the CPMA-electrometer system as a mass measurement calibration method for black carbon instruments.Copyright 2015 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".