On-line Analysis of Mass Spectra of Individual Aerosol Particles Using Fuzzy Clustering Algorithms
Bibliographic record
Abstract
This paper presents a general solution for data processing of large numbers of individual particle mass spectra in aerosol analysis.The method is based on immediate evaluation of unipolar laser desorption ionization mass spectra acquired in an on-line aerosol time-of-flight mass spectrometer.After automatic peak analysis of each newly acquired unipolar mass spectrum,the mass spectral information is statistically evaluated by a fuzzy clustering algorithm(fuzzy c-means,FCM),provided for an immediate attribution of the particle to predefined particle classes or particle class database.The particle distributions over these classes can be monitored as a function of time and particle size range.During this study,the data processing method has been successfully applied in on-line analysis of individual aerosol particles of dioctylphthalate(DOP) and CaCl2.A great amount of valuable data of the size and composition of individual particles has been obtained.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".