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Record W636886358

Field Evaluation of Improved Rail Welding Methods

2014· article· en· W636886358 on OpenAlexaboutno aff
Daniel Gutscher, Martita Mullen

Bibliographic record

VenueRailway track and structures · 2014
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsThermiteWeldingOverlayEngineeringService (business)RevenueForensic engineeringMetallurgyMechanical engineeringMaterials scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Service tests of heat-affected-zone (HAZ) overlay treatments for thermite welds are being conducted by the Transportation Technology Center, Incorporated (TTCI) at the Facility for Accelerated Service Testing (FAST) in Pueblo, Colorado. Thermite weld running surfaces tend to degrade under service operating conditions, which results in additional maintenance and possible failure without intervention. This is especially true for thermite welds in high strength rails that are under heavy-axle-load (HAL) service conditions. This article presents laboratory tests and in-track observations that are being conducted in order to understand how and why thermite welds degrade. TTCI has been working with Canadian National (CN) to test HAZ overlay treatments in revenue service. The welds that FAST have accumulated 108 millon gross tons (mgt) while the welds in CN revenue service lines have accumulated from 51 to 56 mgt with no reported failures. The article discusses how HAZ treatments appear to be reducing the overall rate of thermite weld degradation.

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)0.004
metaresearch head score (Gemma)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.277
Teacher spread0.266 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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