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Record W2108605796 · doi:10.5539/mas.v9n2p320

Method of Static Characteristics Calculation of Adjustable Two-Ring Type Full-Metal Vibroinsulators in a Non-Linear Approach

2015· article· en· W2108605796 on OpenAlexvenueno aff
Vladimir S. Melentjev, Alexander S. Gvozdev

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEnergy
TopicMechanical Systems and Engineering
Canadian institutionsnot available
FundersMinistry of Education and Science of the Russian Federation
KeywordsEllipseRigidity (electromagnetism)Finite element methodVibrationLinearityJoint (building)Structural engineeringComputer scienceControl theory (sociology)MathematicsGeometryAcousticsEngineeringPhysicsElectronic engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Full-metal vibroinsulators with circular elements are widely spread and effectively reduce a harmful impact of vibration in mechanical systems. However, in a contemporary literature an issue of a regulation of that kind of vibroinsulators and their study, considering non-linearity of the characteristics, is addressed insufficiently. In the presented study by a joint implementation of Mohr's method and finite element method the method of a calculation of loading characteristics of adjustable full-metal vibroinsulators with elastic elements in the form of an ellipse and a system of two ellipses was developed. Broad (in a few hundreds of times) capabilities to regulate their rigidity characteristics were demonstrated. The presence of zone of quasi-zero rigidity is detected. The method allows to accurately determine parameters of a vibroinsulator, which increases quality of final products.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.281
Teacher spread0.245 · 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 designSimulation or modeling
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

Citations4
Published2015
Admission routes1
Has abstractyes

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