Sources and Deposition of Polycyclic Aromatic Hydrocarbons to Western U.S. National Parks
Bibliographic record
Abstract
Seasonal snowpack, lichens, and lake sediment cores were collected from fourteen lake catchments in eight western U.S. National Parks and analyzed for sixteen polycyclic aromatic hydrocarbons (PAHs) to determine their current and historical deposition, as well as to identify their potential sources. Seasonal snowpack was measured to determine the current wintertime atmospheric PAH deposition; lichens were measured to determine the long-term, year around deposition; and the temporal PAH deposition trends were reconstructed using lake sediment cores dated using (210)Pb and (137)Cs. The fourteen remote lake catchments ranged from low-latitude catchments (36.6 degrees N) at high elevation (2900 masl) in Sequoia National Park, CA to high-latitude catchments (68.4 degrees N) at low elevation (427 masl) in the Alaskan Arctic. Over 75% of the catchments demonstrated statistically significant temporal trends in SigmaPAH sediment flux, depending on catchment proximity to source regions and topographic barriers. The SigmaPAH concentrations and fluxes in seasonal snowpack, lichens, and surficial sediment were 3.6 to 60,000 times greater in the Snyder Lake catchment of Glacier National Park than the other 13 lake catchments. The PAH ratios measured in snow, lichen, and sediment were used to identify a local aluminum smelter as a major source of PAHs to the Snyder Lake catchment. These results suggest that topographic barriers influence the atmospheric transport and deposition of PAHs in high-elevation ecosystems and that PAH sources to these national park ecosystems range from local point sources to diffuse regional and global sources.
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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 | 0.000 | 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".