Annual Variation of Polycyclic Aromatic Hydrocarbon Concentrations in Precipitation Collected near the Great Lakes
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbon (PAH) concentrations were measured in precipitation samples collected from 1997 to 2003 at seven sites near the Great Lakes as a part of the Integrated Atmospheric Deposition Network. The 28-day integrated concentrations of most PAHs showed significant seasonal trends with higher concentrations in the winter and lower concentrations in the summer. Long-term decreasing trends were observed for all PAHs measured in precipitation at Chicago. At the sites on Lakes Superior, Michigan,,and Erie, most PAHs did not show significant long-term trends. At the two Canadian sites on Lakes Huron and Ontario, lower molecular weight PAHs (e.g., fluorene to pyrene) showed long-term decreasing trends; however, no long-term trends were observed for higher molecular weight PAHs at these sites. Interestingly, retene, a marker for wood burning, showed increasing trends at the sites on Lakes Superior and Michigan. For all the other PAHs, precipitation collected at Chicago had by far the highest PAH concentrations followed by the site on Lake Erie. Generally, the Lake Superior sites had the lowest PAH concentrations. However, retene concentrations in precipitation collected at the Lake Superior site were higher compared to Lakes Michigan and Erie, which indicate more residential wood burning in the far north of the Great Lakes basin.
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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) | 0.000 | 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".