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Record W4293764574 · doi:10.1061/9780784484371.002

Slip Temperature Differential and Usage: Ballasted and Direct Fixation Tracks

2022· article· en· W4293764574 on OpenAlexaff
Nazmul Hasan

Bibliographic record

VenueInternational Conference on Transportation and Development 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsSlip (aerodynamics)Materials scienceStructural engineeringComputer scienceGeologyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper analyzes the slip temperature differential in a railway track. The formula for the slip temperature differential in a ballasted track from Lichtberger was used to derive alternate formulas in terms of longitudinal resistance and the sectional area of a rail for both ballasted and direct fixation tracks. Additionally, the formula form Esveld for the maximum slip of a rail in a fastener under elastic shear resistance was rearranged and reformulated to obtain the slip temperature differential in terms of longitudinal resistance and the sectional area of a rail for both ballasted and direct fixation tracks. The aforementioned formulas were compared, and some interesting findings were drawn after their application. The slip temperature differential was used to compute the rail neutral temperature range for natural distressing. Finally, the Transit Cooperative Research Programs’ procedure for estimating the longitudinal resistance in a direct fixation track was analyzed, and a review was called for.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.240
Teacher spread0.226 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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