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Record W2593905957 · doi:10.3141/2648-04

Redesigning Main Lines for Commuter Rail Electrification

2017· article· en· W2593905957 on OpenAlexaboutno aff
John G. Allen

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2017
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsElectrificationTrack (disk drive)Transport engineeringInstallationOverhead (engineering)EngineeringElectricityElectrical engineering

Abstract

fetched live from OpenAlex

After decades of relative inactivity, interest in commuter rail electrification is growing. Long limited to already electrified systems in New York City; Philadelphia, Pennsylvania; Chicago, Illinois; and Montreal, Quebec, Canada, commuter rail electrification is increasingly being recognized as a way to increase speed and train throughput on busier properties. Several commuter railroads are planning or implementing new electrification, which presents challenges as well as opportunities. Installing overhead wires and support structures will make track alignments essentially final for the foreseeable future. Therefore, railroads should make any proposed changes to track layout and elevation before electrification. Other right-of-way considerations are also noted. As interest in commuter rail electrification grows, best practices from early 20th-century projects will be relevant for future installations. Between the 1900s and the 1930s, railroads electrifying their suburban and intercity passenger operations found ways to accommodate different types of trains, meet the needs of peak-period service, and keep different types of trains out of each other’s way, to the maximum extent possible. Alternatives for track arrangements are examined in the context of operating and right-of-way needs of the railroads implementing each configuration.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.373
Teacher spread0.267 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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