Thermokarst Disturbance Drives Concentration and Composition of Metals and Polycyclic Aromatic Compounds in Lakes of the Western Canadian Arctic
Bibliographic record
Abstract
Abstract When assessing the environmental impact of petroleum hydrocarbon exploitation, it can be challenging to differentiate anthropogenic from natural hydrocarbon sources. For example, areas underlain by permafrost may be affected by erosion of hydrocarbon‐rich deposits from thermokarst activity, complicating environmental assessments of human impacts from petroleum extraction. Here we examined polycyclic aromatic compounds (PACs) and metals in sediment cores from lakes affected to varying degrees by thawing permafrost. We used a paired‐lake design on lakes with pronounced shoreline retrogressive thaw slumps and compared them to nearby lakes in undisturbed (no retrogressive thaw slump) systems in the Mackenzie Delta uplands (Northwest Territories, Canada). Total organic carbon (TOC)‐normalized concentrations of parent and alkylated PACs were higher in surface sediments of slump‐affected lakes. Slump‐affected lakes were also enriched in metals related to local shale‐based, Quaternary deposits (e.g., Ca, Sr, and Mn) when compared to reference lakes where surficial materials were not exposed by thermokarst activity. Diagnostic ratios of specific PACs suggested that slump‐affected lakes also had a greater influence from petroleum‐based compounds, likely sourced from the local geology. Higher PAC concentrations and petrogenic composition were best explained as a combination of low TOC availability and increased inputs of previously bound hydrocarbons from the catchment due to permafrost erosion.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".