Polycyclic aromatic compounds in a northern freshwater ecosystem: Patterns, sources, and the influences of environmental factors
Bibliographic record
Abstract
Polycyclic aromatic compounds (PACs) - a large group of organic chemicals naturally present in petroleum deposits (i.e., petrogenic) or released into the environment by incomplete combustion of organic materials (i.e., pyrogenic) - represent a potential risk to the health of aquatic ecosystems. In high latitude freshwater ecosystems, concentrations of PACs may be increasing, yet there are limited studies in such systems to assess change and to understand threats. Using 10 years of contemporary data from passive samplers deployed across five regions (n = 43 sites) in the Mackenzie River Basin, we (i) describe baseline levels of PACs, (ii) assess spatiotemporal patterns, and (iii) evaluate the extent to which environmental factors (fire, snowmelt, and proximity to oil infrastructure) influence concentrations in this system. Measured concentrations were low, relative to those in more southern systems, with mixtures primarily being dominated by non-alkylated, low molecular weight compounds. Concentrations were spatially consistent, except for two sites near Norman Wells (an area of active oil extraction) with increased levels. Similarly, observed annual variation was minimal, with 2014 having generally higher levels of PACs. We did not detect effects of fire, snowmelt, or oil infrastructure on concentrations. Taken together, our findings suggest that PACs in the Mackenzie River are currently at low levels and are primarily petrogenic in origin. They further indicate that ongoing monitoring and testing of environmental drivers (especially at finer spatial scales) are needed to better predict how ecosystem change will influence PAC levels in the basin and in other northern systems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".