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Record W3166546887 · doi:10.1115/jrc2021-58468

A Systems Approach for the Evaluation and Rebuilding of the Rogers Pass Systems on Canadian Pacific

2021· article· en· W3166546887 on OpenAlexaffabout
David F. Thurston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsCanadian Pacific Railway (Canada)
Fundersnot available
KeywordsInstallationPlan (archaeology)Process (computing)Reliability (semiconductor)Track (disk drive)Computer scienceOperations researchOperations managementReliability engineeringAeronauticsEngineeringTelecommunicationsTransport engineeringArchitectural engineeringOperating system

Abstract

fetched live from OpenAlex

Abstract In the 1980’s, Canadian Pacific (CP) constructed one of the most ambitious projects since the original completion of the railway in 1885. The Rogers Pass project was initiated at CP in the early 1980’s to allow for increased capacity and efficiency by installing a second main track within the Rogers Pass area. Completed in 1988, the Rogers Pass Project included the construction of a new line with significantly lower westbound grades and two tunnels with a combined length of over ten miles. Several other systems were required to complete the project that will be discussed I this paper. Recently, CP has started a new Multi-Year Plan to rebuild virtually all of the tunnel systems infrastructure that will not only prolong the life of these systems, but will introduce technology not known at the time of construction. These new systems will enable CP to greatly reduce maintenance cost while improving reliability. These systems include a high voltage transmission line that feed the ventilation house, a sophisticated ventilation system that allows fresh combustion air to reach the locomotives working the uphill grades, as well as process controllers that automate all of these systems. As all of the systems are reaching the end of their useful life, CP’s rebuilding will also increase overall system capacity.

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 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)0.001
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 score0.397
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.214
Teacher spread0.192 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2021
Admission routes2
Has abstractyes

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