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Record W2610420599

Relationship Between Lifespan and Mechanical Performance of Railway Ballast Aggregate

2016· article· en· W2610420599 on OpenAlexaboutno aff
Vaidas Ramūnas, Audrius Vaitkus, Alfredas Laurinavičius, Donatas Čygas

Bibliographic record

VenueProceedings of the International Conference on Road and Rail Infrastructure CETRA · 2016
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBallastAbrasion (mechanical)TonnageAggregate (composite)Environmental scienceToughnessEngineeringMarine engineeringForensic engineeringGeologyMaterials scienceMechanical engineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

As the lifespan is main criterion for selection of the aggregate for ballast and for planning the maintenance of the railroad, it is important to define relationship between the particle load resistant characteristics and ballast‘s lifetime in structure. Assessment of the quality of the ballast particles under dynamic and static loading should reflect both, the toughness and hardness, and these can be identified with the values of Los Angeles Abrasion and microDeval Abrasion. In order to predict the amount of loads, expressed in cumulated tonnes, the model formerly developed by Canadian Pacific Railroads was adapted. A number of different aggregate mixtures were tested in the laboratory including dolomite and granite rocks. The results were used to assess the gross tonnage possible to transport during the lifetime of ballast until repair or reconstruction should be done. The outcome of this study is the possibility to classify the requirements for aggregates‘ Los Angeles abrasion and micro-Deval abrasion values attributing them to designed traffic volumes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.337

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueProceedings of the International Conference on Road and Rail Infrastructure CETRASame topicRailway Engineering and DynamicsFrench-language works237,207