The consideration of a changing climate for continuously welded rail
Bibliographic record
Abstract
Canada’s climate has been warming at a rate that is approximately double the global average between 1948 and 2016. Climate change is impacting Canada more than the world as a whole, with climate models predicting further warming during all four seasons, ranging from an increase of a low of 1.8 ⁰C to a high of 6.3 ⁰C depending on the emissions trajectory (or representative concentration pathway). Canada’s extensive rail network of 46,000 km traverses from coast to coast through different climate zones. Without expansion joints to release thermal stress buildup due to changes in ambient temperature, continuously welded rail (CWR) is vulnerable to climate change. The increase in magnitude and frequency of extreme temperature events projected under future climate will induce additional longitudinal stress in the rail. This paper examines how the projected changes in climate will impact CWR, latest climate data and models, as well as propose a method, along with a case study to incorporate future climate scenarios into CWR thermal stress management practices for specific sites.
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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".