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Record W4283716176 · doi:10.1115/jrc2022-77801

Power Over CTC, A Novel Way to Control Signal Power Supplies

2022· article· en· W4283716176 on OpenAlexaff
David F. Thurston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsCanadian Pacific Railway (Canada)
Fundersnot available
KeywordsRedundancy (engineering)SIGNAL (programming language)Electric power systemPower (physics)Computer scienceElectrical engineeringEngineeringReliability engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract The electrical energy that powers the signal systems for railways is typically provided by commercial services adjacent or near the enclosures housing the signal equipment. In remote or areas of challenging terrain, railways have installed their own signal power lines to maintain a high level of reliability while lowering the cost of energy supply. These power lines are typically fed from a commercial power source and fed to the railway at a lower voltage (< 1KV). These lines are controlled from local manipulation of fuse cutouts and do not provide for any redundancy. When there is trouble on the signal power line, the response requires railway staff to go to each site on the line to investigate the trouble and provide corrective or temporary measures to restore service. This paper proposes to utilize existing infrastructure to control and indicate the signal power lines that includes sectionalization, remote stop/start of standby generators, and other function. Most signal power lines are concentrated at Centralized Traffic Control (CTC) points. These locations have connectivity to the central dispatching office via “Code Line” that can be expanded to incorporate a separate controls and indications for the signal power systems. Just as Dispatchers have software to help manage traffic on the railway; the new separate controls for the power system can be created to mimic safety protocols for system 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.988

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)0.0130.000

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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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