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Record W2309388225 · doi:10.4271/2001-01-1559

Dynamic Characterization of Foam Materials and Its Application to Modeling of Automotive Seat-Passenger System

2001· article· en· W2309388225 on OpenAlexaff
Chang-Kook Chae, Suyon Song, Kwang-Joon Kim, Han-Kee Jang, Seung-Han Kim, Young-Ho Cho

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2001
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsAutomotive industryCharacterization (materials science)Car seatAutomotive engineeringComputer scienceMaterials scienceEngineeringAerospace engineeringNanotechnology

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Dynamic properties of flexible polyurethane foam materials for car seats are highly complicated. In this paper, characterizations of dynamic stiffness of several foam specimens based on static stiffness obtained from IFD(Indentation Force Deflection) curve measurements are presented. It is observed that dynamic stiffness and its drift with static loading duration in logarithmic scale are proportional to the static stiffness at a given static loading regardless of types and dimensions of the foam. A three degree-of-freedom model of seated human body based on apparent mass measurements together with the characteristics of foam materials were incorporated for transmissibility prediction of the passenger-seat system.</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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
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 score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.207
Teacher spread0.201 · 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 teacher head, 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

Citations1
Published2001
Admission routes1
Has abstractyes

Explore more

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