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Record W1985600951 · doi:10.1177/0954409712459306

Wayside gauge face lubrication: How much do we really understand?

2012· article· en· W1985600951 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

VenueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · 2012
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsL.B. Foster Rail Technologies (Canada)
Fundersnot available
KeywordsGreaseLubricationLubricantGauge (firearms)Ranking (information retrieval)Face (sociological concept)Process (computing)Mechanical engineeringAutomotive engineeringProcess engineeringComputer scienceEngineeringPetroleum engineeringMaterials scienceComposite materialArtificial intelligenceMetallurgy

Abstract

fetched live from OpenAlex

Wayside gauge face lubrication is widely used to minimize rail wear. Scientific understanding of this process is limited; however, there have been significant recent improvements in application equipment. In this paper the process is analyzed in terms of a number of interacting sub-processes, and the factors thought to be important for lubricant and application equipment are reviewed. Wheel/rail contact conditions (pressure and temperature) are also identified as significant variables. Grease stability and retentivity are significant factors that affect lubricant performance; however, significant knowledge gaps exist about the factors that influence grease pick up and carry down especially at the extremes of operating temperatures. Laboratory (two-roller rig measurement of retentivity) and field evaluation (rail friction measurements of carry down) gave the same relative ranking for the tested grease samples. Areas for future research in the area are identified.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.191
Teacher spread0.179 · 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