Monitoring Climate-Induced Changes in Northern Railway Infrastructure: An Innovative Approach Using Track Geometry Data
Bibliographic record
Abstract
The widespread presence of permafrost in high-latitude regions poses a significant threat to the viability of vital circumpolar infrastructure such as Manitoba’s Hudson Bay Railway. Completed in 1929 and underlain with diverse permafrost conditions, the nearly 1000 km long transportation corridor serves as an international connection to Canada’s west via the port of Churchill. In the context of accelerating climate warming, understanding the interaction between rail infrastructure and the underlying environment is critical due to the expected increase in the incidence rate of permafrost-related maintenance challenges. This thesis presents a novel methodology for generating surface deformation datasets from passively collected, regulatorily necessary, track geometry parameters. The objective of this work was to first develop, and field validate the workflow in order to track the evolution of individual track geometry defects in terms of their absolute surface profile, and second, to leverage this ability to employ the railway as a continuous linear deformation sensor, capturing recent climate change effects in the Hudson Bay Lowlands using an archived dataset spanning three years. Validation efforts demonstrate the ability of the workflow to accurately encapsulate the absolute shape of features with centimeter accuracy. Furthermore, rates of subsidence derived from calculated profiles were shown to be a reliable first-order approximation of the real rate of settlement as measured by a Shape Array. Finally, the tool is employed to explore the spatial variance of rates of settlement throughout the Herchmer Subdivision, making comparisons to current and historical rates of feature incidence, highlighting the capability of archived track geometry datasets to serve as a tool for understanding infrastructure movement through a climatic lens.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".