Aerosol Laser Ablation Mass Spectrometry of Suspended Powders from PM Sources and Its Implications to Receptor Modeling
Bibliographic record
Abstract
Primary sources of particulate matter (PM) were analyzed by suspending powdered samples into an aerosol laser ablation mass spectrometer (LAMS). PM sources studied included vehicle exhaust particulates, dust from a non-ferrous smelter, cement powder, incinerator fly ash, two coal fly ash samples, and two soils. Marker peaks signified certain PM source sectors: construction particles could be distinguished by abundant Ca and Ca compounds, fuel combustion was marked by elemental carbon clusters, and nonferrous industrial particles showed inorganic As, Cu, Pb, Zn, and SOx. In addition to the distinction between particles from these different source sectors, mass spectral results also showed that for a single source, different particle types existed, and among different sources within a sector, similar spectra were present. The aerosol LAMS results show the difficulty in differentiating among separate fly ash sources as well as among different soil samples. A particle class balance receptor model that measures the amount of specific particle types rather than the amount of a chemical component is suggested as a means of source apportionment when particle spectra with overlapping source possibilities occur. The assumptions and limitations of receptor modeling aerosol LAMS data are also described. In particular, methods need to be developed to account for the contribution of secondary sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".