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Record W3013564037 · doi:10.1061/jtepbs.0000343

Heavy Haul Operation: Issues and Remedies

2020· article· en· W3013564037 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Transportation Engineering Part A Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsTrack (disk drive)WeldingEngineeringFailure rateForensic engineeringStructural engineeringReliability engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The paper investigates the causes of high-rate rail/weld failure on a heavy haul track using real life rail weld fracture data. The results show that despite the significant progress in rail manufacturing, and rail welding technology, the failure rate has not dropped significantly. Thus, it is postulated with an analysis that the failure rate may be linked with the current high value of allowable bending fatigue stress and current track maintenance standard; the analysis gives insights on the requirement of construction and maintenance of a heavy haul track to a high standard. The analysis would also be helpful to establish heavy haul track maintenance and operation strategy with rail wear and deteriorating track stiffness. The paper sheds light on some causes of rail/weld failure and suggests remedial measures. Finally, further research is suggested toward a new rail section/s for heavy haul operation.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.237
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it