Optimizing Winter Speed Restrictions
Bibliographic record
Abstract
This article describes research conducted by the National Research Council of Canada's Centre for Surface Transportation Technology (NRC-CSTT) on optimizing cold weather speed restrictions for trains. Initiated by Canadian Pacific Railway (CPR), the study looked at the issue of limiting train speed when extremely cold weather would increase the probability of rail breakage. Researchers examined the issue from a versus perspective in which stress refers to the track components load resulting from environmental conditions and railway wheel contacts, while strength refers to the track and infrastructure's ability to endure forces, withstand damage, and avoid breakage. Using a stress model and Wheel Impact Load Detector (WILD) data, researchers analyzed nine track subdivisions. A strength model was developed using defect data representing different types of defects/service failures. A Stress versus Strength Model was then developed and incorporated into a software tool known as the Trains Highball All Winter ((THAW) meter which provides the maximum recommended speed for an individual train in cold weather. Factors taken into account by the THAW meter include the subdivision, ambient temperature, and train wheel condition.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".