Integrating electrical resistivity tomography into predictive thermal modeling of permafrost beneath railway infrastructure: Case study of the Hudson Bay Railway
Bibliographic record
Abstract
This study investigates the integration of electrical resistivity tomography (ERT) data into predictive thermal modeling of permafrost conditions at three sites along the Hudson Bay Railway in northern Manitoba. The model was initially calibrated using borehole temperature data collected under undisturbed natural conditions, followed by calibration of the subsurface temperature regime beneath the railway embankment using ERT-derived resistivity fields. The calibrated model was then used to forecast the ground temperature evolution over a 30-year period, supporting the assessment of infrastructure stability and long-term maintenance planning. This integrated approach demonstrates the value of ERT in locations where conventional ground temperature monitoring is limited or infeasible. By improving the spatial resolution of initial model conditions, the methodology enhances predictive accuracy, supporting better-informed design strategies and mitigation measures for infrastructure projects in permafrost regions. • Use of ERT to correlate temperature profiles beneath railway infrastructure. • Application of resistivity-derived temperatures in permafrost thermal modeling. • Field-calibrated resistivity–temperature relationships for frozen mineral soils. • Integration of geophysical data with modeling to assess thaw near embankments. • Drone-mapped snow drifts used to refine surface boundary conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".