Using Spatial Regression as a Tool for Permafrost Hazard Assessment: A Case Study of the Hudson Bay Railway
Bibliographic record
Abstract
The Hudson Bay Railway (HBR) has faced increasing instability and rising maintenance costs due to permafrost thaw, a process accelerated by climate change over the past three decades. Geotechnical investigations have identified the Herchmer Subdivision as the most severely impacted area, with historical and contemporary data revealing that previously stable ground is becoming unstable and that the permafrost boundary is shifting northward. As permafrost degradation continues, there is a pressing need for accurate predictions of thaw-related hazards to support infrastructure resilience and maintenance planning along the HBR. To address this challenge, we employed Multiscale Geographically Weighted Regression (MGWR) to identify the key variables contributing to sinkhole formation along the railway. This spatial modeling tool enables the assessment of multiple climatic and ecological factors influencing permafrost degradation while determining their statistical significance. In our MGWR model, sinkholes—measured by the number of surface depressions along the railway—served as the dependent variable. In contrast, the independent variables included ground ice abundance, snow depth, surface temperature, and organic carbon content. Regression coefficients derived from the MGWR model were used to calculate variable weights, which were then applied in a weighted sum analysis in ArcGIS to generate a hazard map. This hazard map incorporates projected datasets for the 2030–2039 period, offering insights into the evolving risk landscape along the railway. By illustrating the shifting permafrost boundaries, the map enhances our understanding of the railway’s vulnerability to damage caused by the thawing permafrost. The findings from this study will serve as a crucial tool for guiding resource allocation and mitigation strategies, ensuring the long-term stability of the HBR in the face of climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".