MétaCan
Menu
Back to cohort
Record W4293765145 · doi:10.1061/9780784484371.006

Rail Break Gap in a Frozen Ballasted Track

2022· article· en· W4293765145 on OpenAlexaff
Nazmul Hasan, Montasir Islam

Bibliographic record

VenueInternational Conference on Transportation and Development 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsTrack (disk drive)BreathingBreathing gasBallastPoint (geometry)Structural engineeringMechanicsEngineeringPhysicsElectrical engineeringMathematicsMechanical engineeringGeometryAnatomy

Abstract

fetched live from OpenAlex

In case of a single rail break, there are at least two known formulas to compute the breathing length, one uses half-tie resistance and the other uses full-tie resistance. As a result, the possible values of the breathing length and hence, the rail break gap, differ by 100%, thus warranting further research. Herein, we review these two calculations, plus a proposed method, to compute breathing length and rail break gap by using a more effective tie resistance value. The rail break gap in freezing temperatures under three contexts is computed, discussed, and summarised. The point at the end of the track where both rails are free to move and the axial force increases from zero to a temperature load of ΔT is typically referred to as the breathing length. The breathing length due to a single rail break in the non-breathing portion of a continuously-welded rail track may not be expected to have the same breathing length at the end of the track. Thus, breathing lengths under two scenarios are discussed. Further research is suggested to resolve the issue of breathing length definition and the two issues related to the effect of freezing temperatures on ballast resistance.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Conference on Transportation and Development 2022Same topicRailway Engineering and DynamicsFrench-language works237,207