MétaCan
Menu
Back to cohort
Record W2558112571 · doi:10.5539/mer.v6n2p48

The Effect of the Slippage Degree at Rolling with Slipping on the Wear Resistance of Contact Surfaces

2016· article· en· W2558112571 on OpenAlexvenueno aff
Tareq M. A. Al-Quraan, Oksana O. Mikosyanchik, Rudolph G. Mnatsakanov

Bibliographic record

VenueMechanical Engineering Research · 2016
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Coatings
Canadian institutionsnot available
Fundersnot available
KeywordsSlippingSlippageMaterials scienceLubricantMetallurgySlip (aerodynamics)Composite materialWear resistanceStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The regularities of the wear of the steel 1045 during cutting off the feed of a lubricant under the non–stationary friction conditions were established. The influence of the properties of the secondary structures, formed on the contact surfaces under conditions of rolling with a different degree of slippage, on the wear of advancing and lagging surfaces was determined. The influence of specific friction work, the degree of hardening – weakening of surface layers of metal and the intensity of saturation by active elements of near–surface layers of metal on the wear resistance of friction pairs is examined. The change is established in localization of the depth of spreading the stressed and deformed state of material of the contact surfaces with an increase in the slip rate from 0.315 m/s to 1.14 m/s at rolling with the slippage.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMechanical Engineering ResearchSame topicSurface Treatment and CoatingsFrench-language works237,207