Size‐Resolved Mixing States and Sources of Amine‐Containing Particles in the East China Sea
Bibliographic record
Abstract
Abstract Single‐particle aerosol mass spectrometry was employed during a cruise campaign from 3 to 27 June 2017 to investigate the mixing states and sources of amine‐containing particles in the East China Sea. A total of 271,160 particles were successfully identified, and 19,861 of these particles contained amines. Monomethylamine (MMA), trimethylamine (TMA), and diethylamine (DEA) were the most abundant amines. They accounted for 50%, 16%, and 29%, respectively, of the amine‐containing particles. The size distributions (mean ± geometric standard deviation) of the MMA‐, TMA‐, and DEA‐containing particles peaked at 0.58 ± 0.12, 0.56 ± 0.10, and 0.40 ± 0.10 μm, respectively. Using an adaptive resonance theory neural network algorithm, four major clusters of amine‐containing particles were categorized: carbon‐rich particles, K‐rich particles, Na‐rich particles, and metal‐rich particles. The MMA‐containing particles contained abundant elemental carbon‐, K‐, Mn‐, and Fe‐rich particles, which exhibit higher concentrations in land air masses, indicating that the MMA mainly originated from terrestrial anthropogenic sources. The TMA‐containing particles contained abundant organic and elemental carbon‐, EC‐, K‐, and V‐rich particles. High concentrations of TMA‐containing particles were measured near ports, indicating the significant contribution of the ports. The DEA‐containing particles contained abundant organic and elemental carbon‐, K‐, Mn‐, Cu‐, and Na‐rich particles, and high concentrations of DEA‐containing particles were observed in remote marine areas, suggesting that both terrestrial anthropogenic and marine sources are important contributors to DEA‐containing particles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".