Bibliographic record
Abstract
Different codes and railways specify different maximum curvatures for their track systems. The curve-negotiating capability of the vehicle dictates the absolute minimum radius used on a track system; other factors may also be considered to decide the minimum radius. In this paper, a minimum radius labeled as the threshold radius is determined when a ballasted, continuously welded curved track would not move under the action of thermal load in an unladen track. Thus, the curve with the threshold or flatter radius would not require hot-weather or cold-weather patrolling or other measures to augment the lateral strength of the track. A formula is derived in this paper to determine the threshold value: the temperature limits at which hot-weather or cold-weather patrolling is to be enforced if the radius happens to be sharper than the threshold radius. Formulas are also used to determine the desirable temperature range for tamping. The paper also presents a review and analysis of the current literature to be able to choose a practical value for lateral resistance of monoblock concrete ties; it also presents a literature review on recently developed forms of concrete ties. A typical application is then presented to demonstrate the utility of the formulas. Finally, on the basis of the formulation and analyses, curves are then classified into three groups based on a maintenance point of view—Group I (least desirable), Group II (desirable), and Group III (most desirable). The research presented here will be useful for both design and maintenance engineers.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".