Characterization and sources of regional‐scale transported carbonaceous and dust aerosols from different pathways in coastal and sandy land areas of China
Bibliographic record
Abstract
Concentrations of 12‐hour averaged organic carbon (OC), elemental carbon (EC), and other trace elements were determined from bulk aerosol samples at a coastal city of Lian Yun Gang (LYG) in east China from June to December 2003 and a sandy land site of Tong Liao in northeast China from June to August 2003. Regional transports from four main source areas accounted for ∼35–49% of the Asian dust and 16–18% of the carbonaceous particles for both sites. The regional mean concentrations of various species, especially EC, were comparable to or lower than those in urban areas of inland China, Korea, and Japan but tended to be higher than those in Hong Kong or rural sites in east Asia. At LYG, OC showed a clear seasonal pattern with a peak loading in winter (24 μg m−3) and a low in summer (10 μg m−3). Seasonality of EC was more pronounced than that of OC with a difference of approximately threefold (3.8 to 11 μg m−3). Three types of air masses with high particulate loadings were found to be responsible for the peak EC and low OC/EC ratios in winter. Clean air masses with more than 50% secondary organic carbon contents were largely of marine origins. Elemental concentrations (Ca, Fe, K, Mn, and Ti) were mainly associated with Asian dust aerosols with a certain fraction of K from biomass burning in mainland China characterized with a ratio of 1.3 for OC/K.
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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 | 0.001 | 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".