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Record W2226868912 · doi:10.4271/2005-01-2279

Improving the Efficiency of Sealing Parts for Hollow Body Network

2005· article· en· W2226868912 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

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComputer scienceEngineering drawingEngineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Nowadays, expanding sealing parts in automotive hollow body networks are widely used. These parts are usually made up from expanding foams or an assembly of expanding foams and solid materials. The use of these sealing parts has demonstrated an influence on the noise inside the car. These findings proved the necessity of designing sealing parts especially to reduce the propagation of sound through the frame cavities and hollow bodies. In this work, experimental investigations have been conducted to characterize the acoustic performances (absorption, transmission loss) of the individual materials constituting the parts and their assembly. Some design rules have been extracted to improve their efficiencies. Also, to better understand the acoustic behavior of the expanding foams, existing theoretical models for closed or open foams have been tested and compared to measurements. The comparisons showed the importance of accounting for the resonant and non-resonant surface absorption of these closed-cell foams. A simplified modeling of the expanding foam consisting of an elastic core surrounded by a resistive layer is proposed and compared to measurements.</div>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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