MétaCan
Menu
Back to cohort

Optical strain measurement for fault detection in haul-truck tires

2012· article· en· W2040012198 on OpenAlexafffund
Amanda Christine Kotchon, David S. Nobes, Michael Lipsett

Bibliographic record

VenueJournal of Physics Conference Series · 2012
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsTruckDigital image correlationAutomotive engineeringRoad surfaceFault detection and isolationStructural engineeringFault (geology)Deformation (meteorology)Computer scienceMaterials scienceEngineeringComposite materialArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

Tire condition is integral to the safe operation of heavy machinery, such as ultra-class haul trucks. A new approach to haul truck tire monitoring is being investigated based on optical strain measurement, which has the advantage of providing quantitative information from sensors that do not contact the tire. A laboratory-scale apparatus has been constructed to monitor a tire as it is subjected to various loads and pressures. Digital image correlation is used to calculate the deformation in the tire. Using this method, damage resulting from a horizontal and vertical cut created on the tire surface could be detected. A three-dimensional surface reconstruction of the tire was created to assist in the characterization of more complex damage types such as wear and fatigue. In addition to providing information for a possible industrial scale damage detection system, this apparatus will also further the understanding of damage mechanisms in tires.

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)0.001
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: none
Teacher disagreement score0.943
Threshold uncertainty score0.499

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 communication0.0000.002
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 head0.084
GPT teacher head0.291
Teacher spread0.206 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicOptical measurement and interference techniquesFrench-language works237,207