Bibliographic record
Abstract
Hunting of rail vehicles refers to self-excited oscillation of truck and carbody. This dynamic instability is due to the conicity of the wheels, the wheel/rail creep forces and the action of the suspensions. Hunting increases wheel/rail wear, causes damage to sensitive lading and in extreme cases can throw the track out of geometry. Furthermore, severe hunting can create unsafe operating conditions that lead to derailments. Although it is widely recognized that truck hunting is not a good thing, it is a fact that many thousands of trucks do hunt on any given day but the number of derailments from hunting are few. So when is the hunting unsafe? Database and parametric studies of unsafe hunting are presented in this paper. The FRA database was used to study the hunting derailments by year, car type, track, speed, load condition, weather and temperature. Parametric studies of hunting and unsafe hunting of three-piece freight cars were conducted based on a large number of measured wheel profiles in combination with worn and new rail profiles. The vehicle, track and operational factors that have the major influence on unsafe hunting are analyzed and the conditions of unsafe hunting presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".