Characterization of urban amine-containing particles in southwestern China: seasonal variation, source, and processing
Bibliographic record
Abstract
Amine-containing particles were characterized in an urban area of Chongqing during both summer and winter using a single-particle aerosol mass spectrometer (SPAMS). Among the collected particles, 12.7 % were amine-containing in winter and 8.3 % in summer. Amines were internally mixed with elemental carbon (EC), organic carbon (OC), sulfate, and nitrate. Diethylamine (DEA) was the most abundant among amine-containing particles. Wintertime amine-containing particles were mainly from the northwest direction where a forest park was located; in summer, they were from the northwest and southwest (traffic hub) directions. These origins suggest that vegetation and traffic were the primary sources of particulate amines. The average relative peak area of DEA depended strongly on humidity, indicating that the enhancement of DEA was possibly due to increasing aerosol water content and aerosol acidity. Using an adaptive resonance theory neural network (ART-2a) algorithm, four major types of amine-containing particles were clustered: amine–organic carbon (A-OC), A-OCEC, DEA-OC, and A-OCEC aged. The identified particle types implied that amines were taken up by particles produced from traffic and biomass burning. The knowledge gained in this study is useful to understand the atmospheric processing, origin, and sources of amine-containing particles in the urban area of Chongqing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".