Regional haze in Wisconsin: sources and the spatial distribution
Bibliographic record
Abstract
The atmospheric fine particulate matter (PM2.5) chemical composition and the light scattering coefficient were measured at a rural air monitoring site in Wisconsin between September 2001 and August 2002 to understand the source of regional haze in the area. The average fine particle mass concentration, was 10.8 µg/m3 over this time period and was 27.2% organic material, 2.4% elemental carbon, 34.2% ammonium sulfate, 31.6% ammonium nitrate, 3.7% soil, and 1.8% non-soil trace elements. Ammonium sulfate, organic matter, and soil were highest during the summer period while ammonium nitrate was highest during the winter. Elemental carbon peaked in the fall while the trace elements did not vary significantly. These trends were also observed at an urban Milwaukee site. A comparison of spring 2002 data between sites distributed across Wisconsin revealed a similar relative chemical composition, though absolute concentrations varied significantly. The average measured light scattering coefficient (bsp) at the rural sampling site was 53 ± 1 Mm–1. The PM2.5 mass and the measured bsp showed good correlation with an R2 of 0.84. The average chemical composition of the particulate matter was used to calculate bsp and the resulting values agree well with the measured bsp, though not as well as the PM2.5 mass, giving a regression slope of 1.06 and an R2 of 0.72. Key words: PM2.5 chemical speciation, light scattering coefficient, visibility.
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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 | 0.001 | 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".