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Record W2273646220 · doi:10.1115/1.4032804

Age Does Not Affect the Material Properties of Expanded Polystyrene Liners in Field-Used Bicycle Helmets

2016· article· en· W2273646220 on OpenAlexaff
Shannon G. Kroeker, Stephanie J. Bonin, Alyssa L. DeMarco, Craig A. Good, Gunter P. Siegmund

Bibliographic record

VenueJournal of Biomechanical Engineering · 2016
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceComposite materialCompression (physics)Plateau (mathematics)Expanded polystyreneModulusPolystyreneAttenuationStress (linguistics)Strain (injury)Yield (engineering)PolymerMathematicsMedicineOptics

Abstract

fetched live from OpenAlex

Bicycle helmet foam liners absorb energy during impacts. Our goal was to determine if the impact attenuation properties of expanded polystyrene (EPS) foam used in bicycle helmets change with age. Foam cores were extracted from 63 used and unused bicycle helmets from ten different models spanning an age range of 2-20 yrs. All cores were impact tested at a bulk strain rate of 195 s(-1). Six dependent variables were determined from the stress-strain curve derived from each impact (yield strain, yield stress, elastic modulus, plateau slope, energy at 65% compression, and stress at 65% compression), and a general linear model was used to assess the effect of age on each dependent variable with density as a covariate. Age did not affect any of the dependent variables; however, greater foam density, which varied from 58 to 100 kg/m(3), generated significant increases in all of the dependent variables except for yield strain. Higher density foam cores also exhibited lower strains at which densification began to occur, tended to stay within the plateau region of the stress-strain curve, and were not compressed as much compared with the lower density cores. Based on these data, the impact attenuation properties of EPS foam in field-used bicycle helmets do not degrade with the age.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

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