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Record W7068158472

Nonlinear dynamics of railway wheelsets incorporating randomness / by Jing Yu.

2007· dissertation· en· W7068158472 on OpenAlexfundno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2007
Typedissertation
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRandomnessChaoticNonlinear systemParametric statisticsLyapunov exponentControl theory (sociology)Mathematical modelBifurcationVehicle dynamics
DOInot available

Abstract

fetched live from OpenAlex

"The thesis is to investigate the dynamic behavior of a single-axle railway wheelset and identify its chaotic behavior by means of time history, phase portrait, Lyapunov exponents, information dimension, bifurcation diagram and control strategy. The thesis has its analytical and computational components. For the analytical component, several mathematical models of a single-axle rail vehicle wheelset are presented in order to compare their features for similarities and differences. These models present different contact theories of creep force, and have different parametric values. In addition, Model III does not consider gravitational stiffnesses and gyroscopic couple. As a result, directly comparing simulation results of these models makes it difficult to interpret results and to draw conclusions. Therefore, these models need to be expanded so that issues can be isolated and investigated accordingly.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.058
GPT teacher head0.398
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), 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
Published2007
Admission routes1
Has abstractyes

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