Spatial modelling of polycyclic aromatic hydrocarbon distribution in a Canadian ice wedge polygon tundra landscape
Bibliographic record
Abstract
Permafrost thaw due to climate change raises concerns about the potential remobilisation of organic pollutants such as Polycyclic Aromatic Hydrocarbons (PAHs) in Arctic environments. This study aims to identify environmental controls and create maps of PAH storage in two catchment-scale study areas, Komakuk Beach and Ptarmigan Bay, on the Canadian Beaufort coast (Yukon), while dealing with the lack of data concerning organic contaminants in Arctic soils. Soil samples were collected using a stratified random sampling design based on the catchment area and quaternary geology. The samples were analysed for 22 different PAHs, including 16 PAHs classified as priority pollutants by the US EPA, using accelerated solvent extraction and gas chromatography - triple quadrupole mass spectrometry. The dataset included measurements of PAHs, soil organic carbon content (SOC %), depth, and landform types from permafrost sediment and soil samples. Significant differences in PAH concentrations were found across soil depths and between low-centered polygon (LCP) and high-centered polygon (HCP) landforms, reflecting varying biogeochemical and hydrological dynamics in permafrost degradation forms. Random Forest models were used to predict spatial PAHs distributions in the study areas, divided by molecular weight and depth intervals, using SOC% data from digital soil mapping as an environmental support variable. The modelling approach showed promise for intermediate soil layers but faced challenges in the surface and deep layers owing to data limitations. This study addresses a critical knowledge gap and demonstrates the potential of upscaling PAH distribution data in Arctic permafrost regions using limited ground data. These findings highlight the complex relationships between PAHs, soil carbon, and permafrost landforms, emphasising the need to consider these factors when assessing contaminant dynamics in thawing permafrost. This study identifies spatial patterns and landscape-level factors for PAH upscaling, which can support further studies across Arctic permafrost regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".