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Record W3099842690 · doi:10.1063/5.0024491

A wearing energy model

2020· article· en· W3099842690 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research CentersNatural Sciences and Engineering Research Council of Canada
KeywordsAsperity (geotechnical engineering)Rendering (computer graphics)Materials scienceMechanicsDeformation (meteorology)Energy consumptionWork (physics)Service lifeForensic engineeringComputer scienceMechanical engineeringEngineeringComposite materialPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The classic sliding wear model, represented by Archard's equation, has long been used to estimate the service life of equipment and guide selection and modification of tribo-materials. However, the model was developed based on the asperity contact geometry without directly dealing with the wearing energy, rendering it unable to precisely describe wear under some conditions, e.g., it fails to predict wear of strain-hardened materials, which has never been clarified. In this study, incorporating with the plastic deformation–electron work function relationship, we reexamined and modified the classic model by taking account of the deformation energy consumption during wear. The modified model, or termed a wearing-energy model, is verified with relevant experimental observations.

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: none
Teacher disagreement score0.976
Threshold uncertainty score0.233

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 head0.021
GPT teacher head0.196
Teacher spread0.175 · 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