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Record W2737936997 · doi:10.1139/cjce-2017-0076

Canadian main track derailment trends, 2001 to 2014

2017· article· en· W2737936997 on OpenAlexaffvenueabout
Eric M. Leishman, Michael T. Hendry, C. Derek Martin

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDerailmentTrack (disk drive)Forensic engineeringStock (firearms)EngineeringTransport engineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

The Transportation Safety Board of Canada (TSB) maintains the Rail Occurrence Database System (RODS). This database contains information on all types of rail occurrences including derailments that must be reported by all Canadian railway operators. This paper analyzes the derailments that occurred on Canadian main track network between 2001 and 2014. The results from the analysis show that between 2001 and 2014 there was an overall decreasing trend in the number and intensity of main track derailments, derailments involving dangerous goods cars, and the number of derailments resulting in the release of dangerous goods. The RODS data was further analyzed to evaluate the frequency of the differing causes of derailments and the severity of the resulting incidents. The most common and severe derailment causes resulted from rail breaks, track geometry, and environmental conditions. Derailment velocity was also found to have an impact on the severity, with higher velocities resulting in a greater number of derailed rolling stock.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0090.015
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2017
Admission routes3
Has abstractyes

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