CHARACTERIZING RAIL EMBANKMENT STABILIZATION NEEDS ON THE HUDSON BAY RAILWAY
Bibliographic record
Abstract
The Hudson Bay Railway (HBR) is a 510 mile railway completed in 1929 in northern Manitoba, Canada. It connects domestic locations in North America with international destinations through the Port of Churchill. Permafrost was encountered during construction at milepost 136 in isolated peat bogs which continued in a gradual northward transition from discontinuous to continuous permafrost. Over the past 80 years, warming climate combined with poor engineering properties of the railway embankment material has resulted in further thawing of the discontinuous permafrost leading to differential settlement, termed ‘sinkholes’, along the rail embankment and high annual maintenance costs. This study incorporated geophysical investigations, track geometry data and remote sensing techniques to investigate the current condition of the underlying permafrost. Without employing the use of boreholes, two geophysical methods, electrical resistivity tomography (ERT) and ground penetrating radar (GPR) have proved to be effective in validating each other’s results. These were used to establish a baseline for future work in delineating the permafrost conditions along the entire 510-mile HBR route. A predictive model has been developed that shows a correlation between vegetation and surface water raster data and track geometry exceptions. A three-level severity rating scheme was also developed that classified the susceptibility of sections to permafrost degradation as low, moderate or high. A rating of 1 represents a low degradation susceptibility region in the lowest 10th percentile which is likely to develop a maximum of four track exceptions per year and hence can be inferred that they are less susceptible to permafrost degradation. A rating of 2 represents the section of track with a moderate susceptibility to permafrost degradation likely to develop at most eight exceptions per year. Finally, a rating of 3 represents the very critical sections of track whose values are above the highest 50th percentile are likely to develop more than eight exceptions every year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".