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Record W4366602768 · doi:10.1080/00140139.2023.2206073

Cross-cultural comparison of body dimensions and trends: an analysis of ANSUR and Size Korea datasets for automotive seating design

2023· article· en· W4366602768 on OpenAlexaff
Jiyeong Kang, Haein Jeon, Eun‐Sik Kim, Kyongwon Kim, Hayoung Jung, Chris Lee

Bibliographic record

VenueErgonomics · 2023
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Windsor
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of Korea
KeywordsAnthropometryDimension (graph theory)Product (mathematics)Automotive industryPopulationGeographyKorean populationEngineeringDemographyMathematicsSociologyMedicine

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the change in body dimensions over time in both Western (US) and Eastern (Korea) populations. In order to analyse the change of body dimension between the past and present and between western and eastern population, 13 body dimensions relating to automobile driver seat design were extracted from the ANSUR and Size Korea datasets at two time points, the past (ANSUR I: 1988, Size Korea: 1992) and the present (ANSUR II: 2012, Size Korea: 2012). Most of the dimensions differed significantly between past and present, as well as between the US and Korea. Overall, the data show an increasing trend of body dimensions over time for both genders. Based on the results, all countries should be encouraged to conduct periodic and national anthropometric research because body dimensions are continuously changing over time worldwide.Practitioner summary: This paper describes a study that investigates the changes in body dimensions over time in Western (US) and Eastern (Korean) populations. Findings indicate increasing trends in most dimensions for both populations, crucial for user-friendly product design and preventing hazards caused by faulty 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.052
GPT teacher head0.404
Teacher spread0.352 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2023
Admission routes1
Has abstractyes

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