Climate Change Impacts on Contaminant Transport and Active Layer Groundwater Dynamics in the High Arctic, Canada
Bibliographic record
Abstract
The Arctic is warming two to four times faster than the global average, leading to permafrost thaw and changes in groundwater flow due to alterations in the timing and the depth of the active layer. These changes may lead to previously immobilized contaminants being transported through the active layer; these new pathways for contaminant migration raises concerns, given that there are an estimated 13,000 to 20,000 industrially contaminated sites, many of which remain unremediated. Understanding how the mobilization of contaminants in a high-Arctic settings is crucial to understand to ensure the safety of northern community water resources. The objective of our research is to assess how changing active layer dynamics affects the transport behavior of contaminants in a continuous permafrost hillslope environment via a numerical modeling approach.For this study, we use SUTRA-solice, a version of the US Geological Survey SUTRA model that incorporates mass transport processes with groundwater flow and energy transport with dynamic freeze-thaw processes. We simulate a 280 m long, two-dimensional transect that terminates in a lake. The site has an unconsolidated overburden of 2 m over crystalline bedrock and contains continuous permafrost. The model results focus on seasonal contaminant migration through the active layer and discharge into the lake via groundwater flux. By untangling the relationship between climate change processes (increased temperature, precipitation, etc.) alongside contaminants migration, we are able to understand how constituent migration through evolving active layers will impact down-slope water 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".