MétaCan
Menu
Back to cohort
Record W3136514598 · doi:10.1080/19236026.2020.1734407

Performance evaluation of ultra-class mining shovel track roller paths

2020· article· en· W3136514598 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

VenueCIM Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShovelTrack (disk drive)EngineeringCoal miningScale (ratio)Path (computing)Structural engineeringReliability engineeringComputer scienceSimulationAutomotive engineeringCoalMechanical engineering

Abstract

fetched live from OpenAlex

ABSTRACT This paper outlines a scale test approach for roller-roller path contact proportional to the field impact observed in the undercarriage of ultra-class mining shovels. The scale test is a cost-effective means to predict the degree of roller contact fatigue deterioration as a function of the number of field-measurable duty cycles. The proposed test method will potentially permit more cost-effective, small-scale development testing of roller path technology for specific mining conditions for ultra-class shovel designers. The preliminary data reported in this paper indicate that the proposed test configuration can infer performance to end-of-life roller-roller path combinations within the confines of a given set of field loading conditions (coal mine in this case). The results open up a future opportunity to verify the sensitivity of damage models, leading to the advancement of roller-roller path systems with greater operational longevity and reducing maintenance time and cost through avoidance of catastrophic failure.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.292

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 head0.040
GPT teacher head0.238
Teacher spread0.198 · 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