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Record W170931844

AN UPDATE ON REVENUE SERVICE TESTS OF BAINITIC STEEL RAIL CROSSING DIAMONDS

2003· article· en· W170931844 on OpenAlexaboutno aff
Dave D. Davis, D Guillen, Charity Duran Sasaoka

Bibliographic record

VenueRailway track and structures · 2003
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringAxleRevenueService lifeService (business)Forensic engineeringMetallurgyStructural engineeringMaterials scienceMechanical engineeringFinanceEconomyBusiness
DOInot available

Abstract

fetched live from OpenAlex

The second year of testing bainitic steel rail crossing diamonds shows that they continue to perform well. Three are still in service at spots maintained by Canadian National/Illinois Central and Union Pacific. Reduced running surface deformation and less maintenance are reported. They are also projected to enjoy a longer service life, perhaps as much as 2 to 3 times as long as pearlitic rail diamonds. With railroads spending more than $250 million each year on repairs on these types of tracks, any advances could result in significant savings. All three are located on level open ground and are on mainlines in the Midwest. They have high crossing angles and were chosen for their severe service demands. Other elements being tested are standard AREMA versus thick-web running rail sections; whether traffic is main or crossing line, and traffic rates of 40 versus 110 mgt/yr. The bainitic steel used in the test sections is code named J6, a medium carbon bainitic microstructure steel developed by AAR. It has a surface hardness of 410 BHN and an internal hardness of 430 BHN. It has a fracture toughness three to five times as great as conventional rail steel at room temperature. It has proven to be durable for crossing diamonds in heavy axle load testing, though tests at more realistic sites are needed for this particular component.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.894

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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 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

Citations3
Published2003
Admission routes1
Has abstractyes

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