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Record W2936374367 · doi:10.1177/1754337119840848

Comparison of surrogate 50th percentile human headforms to an adult male sample using three-dimensional modeling and principal component analysis

2019· article· en· W2936374367 on OpenAlexaff
Kristie Liu, David J Greencorn, Daniel I Aponte, Shawn M. Robbins, David J. Pearsall

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology · 2019
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPercentilePrincipal component analysisHead (geology)Roundness (object)Hybrid IIIIce hockeySample (material)MathematicsStatisticsPoison controlPhysical medicine and rehabilitationMedicineGeometryBiologyPhysics

Abstract

fetched live from OpenAlex

Ice hockey helmets are required to be impact tested while mounted to a surrogate 50th percentile adult male headform. However, head shape and size can vary substantially from user to user. Furthermore, the contact area between a headform and helmet interior has been identified as an important factor affecting the protective capabilities of a helmet. The objective of this study was to compare quantitative measures of head shapes between three 50th percentile adult male headforms and a sample of adult human subjects who wore a medium-sized helmet. Using three-dimensional models of the human subjects and headforms, head shape was quantified by assessing radial distances in two transverse planes of the head and by using principal component analysis to determine the largest components of fit. Notable differences were found between the headforms and human subjects. The headforms were smaller than the human subjects, demonstrating smaller radial distances for the entire head. The principal components of head shape were overall size and roundness of the head. The results of this study demonstrate the headforms are not representative of the sample median.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.274
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations4
Published2019
Admission routes1
Has abstractyes

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