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Record W4388917117 · doi:10.4236/jamp.2023.1111224

Non-Pneumatic Tire Design and Modeling: An Overview of Research

2023· article· en· W4388917117 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

VenueJournal of Applied Mathematics and Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsComputer scienceSystems engineeringIndustrial engineeringBiochemical engineeringMechanical engineeringAutomotive engineeringEngineering

Abstract

fetched live from OpenAlex

The research provides valuable insights into the intricate world of Non-Pneumatic (NP) tire technology, covering various facets from modeling and validation to material properties, design optimization, and tire-soil interactions. It begins with an exploration of existing NP tire modeling techniques, emphasizing the importance of accurate and reliable models for NP tires, including static and dynamic validation methods, and demonstrating the influence of structural features and material properties on tire performance. The review emphasizes the challenges and prospects of NP tires and aims to support the development of innovative airless tire solutions. The reviewed papers collectively contribute to a deeper understanding of NP tires, their applications, and potential enhancements in performance and efficiency across various industries.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.320

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 communication0.0000.000
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 head0.169
GPT teacher head0.353
Teacher spread0.184 · 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