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Record W1555781192

Sound package for a magnesium alloy dash panel

2011· article· en· W1555781192 on OpenAlexaffvenue
Aljosa Rakic, Raymond Panneton, Noureddine Atalla

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsStatistical energy analysisSound transmission classStiffnessMaterials scienceStructural engineeringDoorsTransmission lossSound energyDashAlloyVibrationAcousticsSound (geography)Finite element methodEngineeringComposite materialComputer science
DOInot available

Abstract

fetched live from OpenAlex

The automobile companies such as Ford and Volkswagen decided to use magnesium alloy to replace some steel parts of the car. The magnesium alloy has several advantages over steel. It is more malleable, more ductile, lighter, and it has a higher stiffness weight ratio. However, its low mass gives a poor sound transmission loss performance compared to steel. A 3D model of a car has been designed in the statistical energy analysis (SEA) software VA ONE to calculate the average sound absorption of the car cabin. This model takes into account the sound absorption of seats, roof, floor and doors. The first concept is composed of absorbers and insulators materials. A similar structure was used for the second concept. Only a small damping material layer was added to improve the transmission loss at low frequencies. The optimization was performed on 33 different materials considering the following criteria.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.395
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3950.126

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 head0.027
GPT teacher head0.185
Teacher spread0.158 · 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.

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

Citations0
Published2011
Admission routes2
Has abstractyes

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