Bibliographic record
Abstract
Tie plate icing (or ice jacking) is a cold weather railway phenomenon understood as a build up of ice and/or snow between the tie plate and the base of the rail, which causes a safety risk to railways in cold climates, and has caused derailments in the past. In addition to an investigation into root causes, this report sought to understand operational safety impacts, identify potential monitoring methods, and propose remedial actions to the phenomenon. Three field inspections occurring between January and April of 2022 were performed, measuring weather conditions and site and track characteristics to determine lateral track gage strength and widening. Conditions of concern were identified as areas where rail is not tightly seated to the tie plate, and inclement weather causes snow or ice to accumulate. Tie degradation, fastener deterioration, ballast fouling, high rail neutral temperature, hanging ties, and rail pumping were all risk-contributing factors. “Snow jacking” vs. “ice jacking” are also identified as separate weather conditions with jacking mechanics distinguishable from one another. Some measures identified to mitigate these conditions include improved drainage design, manual ice removal, geometry tightening, and degraded tie and fastener replacement. To better understand the mechanics of these conditions, a preliminary finite element model was developed including the variables of fastening conditions, jacking severity, and rail neutral temperature and curvature. Future work is suggested to validate this model through laboratory testing, along with a survey of railroad companies’ best practices for inspecting, identifying, and remediating the issue beyond those identified in this report.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".