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Record W4280524108 · doi:10.1038/s41598-022-11559-0

Evaluation of two rotational helmet technologies to decrease peak rotational acceleration in cycling helmets

2022· article· en· W4280524108 on OpenAlexaff
T. Blaine Hoshizaki, Andrew Post, Carlos Zerpa, Elizabeth Legace, Michael D. Gilchrist

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsLakehead UniversityUniversity of OttawaUniversity of Waterloo
Fundersnot available
KeywordsAngular accelerationKinematicsRotational speedRotation around a fixed axisAccelerationRotation (mathematics)Angular velocityPoison controlSimulationComputer sciencePhysicsEngineeringMechanical engineeringArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The risk of brain trauma has been associated with the rotational kinematics leading to the development of helmets with a variety rotational management technologies. The purpose of this paper was to employ a rotation specific test protocol to evaluate the effectiveness of two of these technologies. Dynamic response of the head was measured to assess the performance of each technology. Three cycling helmets with identical construction were included in this study. One helmet with no rotational technology, an established, commercial technology and a novel helmet rotational technology designed and assembled by the authors were tested. A drop test onto a 45° anvil was used to measure the ability of each helmet to manage the dynamic response of the head form during a series of impacts. The results revealed both rotational helmet technologies resulted in lower peak rotational acceleration and brain strain, however each technology demonstrated unique performance characteristics depending on the impact condition.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.359
Teacher spread0.296 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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