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Record W4395704743 · doi:10.37394/23202.2024.23.18

Prediction of the Residual Resource of Pneumatic Tire Materials from Accumulation and Type of Damage

2024· article· en· W4395704743 on OpenAlexaff
Dmitry A. Dmitriev, Serhii Chursov, Serhii Rusanov

Bibliographic record

VenueWSEAS TRANSACTIONS ON SYSTEMS · 2024
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsTransport Canada
Fundersnot available
KeywordsResidualResource (disambiguation)Environmental scienceAutomotive engineeringForensic engineeringComputer scienceEngineeringAlgorithm

Abstract

fetched live from OpenAlex

The present paper examines the mechanical characteristics at the boundary of the distribution of rubber matrix and metal and fabric fibrous materials as a distinct area in the crack braking mechanism, and their impact on the durability of pneumatic tires in the event of damage accumulation during operation. Experimental studies were conducted on the delamination of the components of the tire material composition in samples obtained from diverse locations of the car tire. The strength of the rubber matrix fibers of the metal cord was determined, which makes it possible to assess the overall strength of the tire material as a composition of reinforcing elements and the matrix during the accumulation of damage created artificially during operation. The method of experimental research is reasonably stable. The nature and behavior of the sample rupture during the tests were evaluated.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.243
Teacher spread0.199 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueWSEAS TRANSACTIONS ON SYSTEMSSame topicFatigue and fracture mechanicsFrench-language works237,207