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Record W4293764601 · doi:10.1061/9780784484371.004

Use of Critical Temperature Differential in Railroad Engineering

2022· article· en· W4293764601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Conference on Transportation and Development 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsTrack (disk drive)Structural engineeringBallastTrack geometryConsolidation (business)Differential (mechanical device)Range (aeronautics)Computer scienceEnvironmental scienceEngineeringMechanical engineeringAerospace engineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this study the potential use of the critical temperature differential for buckling, ΔTC, of a track alone and in conjunction with a rail neutral temperature, RNT, was explored. A brief review on ΔTC was done for selecting an appropriate value of it. The value of ΔTC was used to classify a track and to determine the longitudinal test load of a bonded joint, allowable thermal stress, and RNT range for natural distressing of a continuously welded rail track. Formulas were derived in terms of ΔTC and RNT to determine cold and hot weather patrolling temperatures, the limiting temperature of tamping, and the ballasting of a skeleton track. An operational load was recommended for the consolidation of concrete and wood tie tracks, respectively, before opening for revenue traffic. The aforementioned formulas would be useful for the construction and maintenance of a track for any track condition, and geometry at any region.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.675
Threshold uncertainty score1.000

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)0.0010.000

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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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