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Record W4252538041 · doi:10.31399/asm.hb.v18.a0006382

Abrasive Wear

2017· book-chapter· en· W4252538041 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

VenueASM International eBooks · 2017
Typebook-chapter
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAbrasiveAbrasion (mechanical)Materials scienceMicrostructureComposite materialMetallurgyCeramicLubricationNatural rubberWear resistanceToughness

Abstract

fetched live from OpenAlex

Abstract Abrasive wear is a surface-damage process with material loss caused by hard asperities or abrasive particles occurring when two surfaces are sliding against each other. There are two types of abrasive wear: two-body abrasion and three-body abrasion. This article discusses the abrasive wear mechanism in ductile materials and commonly used testers for evaluating the resistance of materials to abrasive wear. The testers include pin-on-disk, block-on-ring, block-on-drum, and dry sand/rubber wheel abrasion tester. The article reviews the abrasion resistance of metallic materials, ceramic materials, and polymeric materials. It discusses factors that influence abrasive wear, including the environment, hardness, toughness, microstructure, and lubrication.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.770
Threshold uncertainty score1.000

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.005

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 head0.024
GPT teacher head0.288
Teacher spread0.264 · 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