MétaCan
Menu
Back to cohort
Record W4386568019 · doi:10.1016/j.wear.2023.205116

Investigating the effect of different adhesion materials on electrical resistance using a high pressure torsion rig

2023· article· en· W4386568019 on OpenAlex
William A. Skipper, Sadegh Nadimi, Dmitry V. Gutsulyak, Jeremy Butterfield, Thomasina V. Ball, Roger Lewis

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

VenueWear · 2023
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsL.B. Foster Rail Technologies (Canada)
FundersRoyal Academy of Engineering
KeywordsMaterials scienceTorsion (gastropod)Composite materialAngle of reposeElectrical conductorBreakageElectrical resistivity and conductivityAdhesionElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents an assessment of newly-developed conductive adhesion materials (Products A-E) in comparison to standard rail sand used in Britain. Current rail sand is an insulating material which can affect track circuits; newly-developed conductive materials could reduce the risk of this and allow for more material to be applied to further mitigate against low adhesion. The particles were characterised to determine their densities, and size and shape distributions. Bulk behaviour was assessed through three characteristics: angle of repose, bulk shear strength, and particle breakage index. Materials were then assessed using a high pressure torsion approach to measure their effects on adhesion and electrical resistance in dry, wet, and leaf contaminated conditions. It was found that all products produced better or equivalent conductivity compared to the currently used GB rail sand and that Product D and Product E should be considered for future field testing.

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 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 score0.194
Threshold uncertainty score0.333

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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