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Record W2805617879 · doi:10.1002/cepa.797

Development of condition‐based tamping process in railway engineering

2018· article· en· W2805617879 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuece/papers · 2018
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsBallastTrack (disk drive)CompactionLevellingEngineeringProcess (computing)StiffnessGeotechnical engineeringMarine engineeringStructural engineeringGeologyMechanical engineeringComputer scienceElectrical engineeringGeodesy

Abstract

fetched live from OpenAlex

Abstract Ballast, rails and sleepers form a quasi‐elastic track system. When the deformations exceed the elastic limit of the system and the track is no longer lying in its correct position, precautions have to be taken. During a technical track examination several parameters are measured. Should the operational tolerance values of these parameters be exceeded, track maintenance needs to be conducted. Track maintenance includes levelling, lifting, lining and tamping of the track, which is performed by a tamping machine, where the tamping tines penetrate the ballast and compact it beneath the sleeper. For the purpose of this research project, a tamping machine was equipped with a number of strategically positioned sensors in order to perform the in‐situ measurements required to describe the interaction of the tamping tines with the ballast and its compaction beneath the sleeper. With a special emphasis on the energy transferred into the ballast and alteration of ballast stiffness during compaction, conclusions concerning efficiency of the tamping process in different ballast conditions are made and presented.

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.

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 score0.044
Threshold uncertainty score0.758

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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