Advancements in Techniques for Calibration and Characterization of In Situ Optical Particle Measuring Probes, and Applications to the FSSP-100 Probe
Bibliographic record
Abstract
Abstract Advancements in techniques for the operational calibration and characterization of instrument performance of the Particle Measuring Systems, Inc. (PMS), forward scattering spectrometer probe (FSSP) and optical array probes (OAPs) are presented, which also can be used for most in situ particle-measuring optical probes on the market. These techniques include the determination of a distortion matrix to correct for instrumental broadening of the measured particle size distribution. A new version of a monodisperse droplet generator is introduced for absolute calibration in the size range between 10 and 100 μm. In addition, a high-speed technique was employed for the determination of airspeed influence on the sample volume and the sizing of particles. The calibration of a PMS FSSP with real water droplets may be significantly different from the usual calibration with glass beads. High-speed measurements simulate particles at speeds of up to about 250 m s−1. Particle undersizing and the decrease of the sample volume with increasing airspeed are described. The use of the modular tools, built for this work, is discussed for probe alignment, functionality checks, and general characterization and diagnostics both in laboratory and field environments.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".