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Record W312540173

QNS&L Wheel-Rail Optimization Strategy

2006· article· en· W312540173 on OpenAlexvenueaboutno aff
Riopel Jose, Rob Caldwell

Bibliographic record

VenueNPARC · 2006
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringGrindingScheduleThird railTrack (disk drive)Plan (archaeology)WeldingTransport engineeringAutomotive engineeringMechanical engineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

QNS&L, Canada has undertaken a long-term plan to improve its track maintenance process including the development of a long-term wheel-rail interaction strategy. The plan included an assessment of the annual rail replacement requirements, better welding procedures, improved gauge-face lubrication, profile grinding of the rails, preventive reprofiling of wheels, and the development of a long-term wheel-rail interaction strategy. The company also adopted a maintenance-of-way strategic plan to improve track rehabilitation in 2003. The plan also included funds to contract-out the rail grinding program and to begin joint elimination and rail destressing programs. The main objective of the company's plan is to optimize the wheel-rail interaction through the management of wheel-rail friction and the introduction of wheel and rail profiles, which are specific to their system.

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: none
Teacher disagreement score0.929
Threshold uncertainty score0.426

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 head0.173
Teacher spread0.169 · 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 routes2
Has abstractyes

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