PM<sub>2.5</sub> characterization and source-receptor relations in South Carolina
Bibliographic record
Abstract
Ambient PM2.5 aerosol samples were collected at three sites at Clemson University during January and June of 2001. The PM2.5 mass concentrations averaged 12.2 μg/m3 in January and 21.2 μg/m3 in June. Chemical characterization of the samples identified an average of 99% of the fine particulate matter collected in January and 96% in June. The primary ingredients of the PM2.5 aerosol were organic carbon compounds (49% in January and 44% in June) and sulfates (21% in January and 33% in June). Total heavy metals on average contributed 0.3% of the fine aerosol mass. Source-receptor reconciliation was completed for the fine aerosol using a chemical mass balance model, Chemical Mass Balance v. 8 (CMB8). In January, approximately 105% of the measured mass was accounted for, and the major sources were soil dust, ammonium sulfate, and ammonium nitrate. In June, an average of 80% of the measured mass was accounted for, and the major sources were the same as in January, except that the contribution of ammonium nitrate was negligible. A composite source of organic compounds that includes gasoline vehicle exhaust, meat cooking, cigarette smoke, and wood burning, was one of the major sources for both June and January seasons. Key words: PM2.5, CMB8, source receptor modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".