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Record W29507013 · doi:10.4271/2005-22-0021

Evaluation of the ES-2re Dummy in Biofidelity, Component, and Full Vehicle Crash Tests

2005· article· en· W29507013 on OpenAlexaff
Aleta Sutterfield, Katie M. Pecoraro, Stephen W. Rouhana, Lan Xu, Joe Abramczyk, J. Berliner, Annette Irwin, Jack Jensen, Harold J. Mertz, Guy S. Nusholtz, H. Pietsch, Risa Scherer, Suzanne Tylko

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsTransport Canada
Fundersnot available
KeywordsOblique caseEngineeringCrashMathematicsComputer science

Abstract

fetched live from OpenAlex

This technical paper presents the results from tests conducted with the ES-2re, a version of the ES-2 side impact dummy that was modified by the National Highway Traffic Safety Administration (NHTSA) to improve its performance in crash tests. Through the series of biofidelity tests conducted on the ES-2re, described in International Standards Organization (ISO) Technical Report (TR)9790 (1999), the OSRP observed a final overall biofidelity ranking of 4.1 for the ES-2re, which corresponds to an ISO classification of "marginal." The biofidelity of the ES-2re is compared to that of the ES-2 and the WorldSID. Repeatability was also evaluated on the ES-2re based on the biofidelity test data. Additional pendulum tests were performed to assess the response of the dummy in oblique loading conditions, and results indicate that oblique loading from the front leads to significantly reduced rib deflections. To evaluate inconsistencies observed in the response of the ES-2, the OSRP analyzed the shoulder biofidelity via additional sled and drop tests. Due to the shoulder design of the ES-2 and ES-2re, the dummies appear to have significant sensitivity to initial conditions, potentially increasing variability in full vehicle tests. Finally, the responses of the ES-2re in full vehicle tests are compared to those of the ES-2 and the WorldSID.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.306
Teacher spread0.274 · 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 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

Citations11
Published2005
Admission routes1
Has abstractyes

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