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Record W566918620

How transatlantic technology has boosted the Docklands railway

2006· article· en· W566918620 on OpenAlexaboutno aff
David Crawford

Bibliographic record

VenueTraffic engineering & control · 2006
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsTrainAutomatic controlBlock (permutation group theory)Controller (irrigation)EngineeringDeskTelecommunicationsControl systemAutomatic train controlMinicomputerTrack circuitComputer scienceReal-time computingSimulationAutomotive engineeringElectrical engineeringOperating systemControl engineering
DOInot available

Abstract

fetched live from OpenAlex

The Docklands Railway in London, UK, opened in 1987 with driverless trains using an automatic train operation (ATO) system. The railway was upgraded with the opening of the new control centre at Poplar, reduced signal block lengths, the installation of more trackside and in-train equipment and the implementation of an automatic train supervision (ATS) system. Planning for the Beckton extension provided an opportunity for resignalling. In 1990 Alcatel was awarded a contract for the implementation of a moving block signalling system using Alcatel's Seltrac product, originally installed on Vancouver's Sky Train. The system consists of a central controller continuously communicating with all vehicles in the control area. The control area is divided into 6.25-m sections and the controller can follow trains to this degree of accuracy. Seltrac controls both automatic and manually driven modes and a restricted emergency shunt mode. In 1996, the moving block system was moved onto desk top computers from mini-computers. Each vehicle has a vehicle on-board controller communicating with the vehicle control centre. (A)

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 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 score0.197
Threshold uncertainty score0.996

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 head0.004
GPT teacher head0.144
Teacher spread0.140 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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