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Development of injury probability function for the thorax of a pedestrian dummy based on two-dimensional evaluation of chest deflection using human and pedestrian dummy fe models

2010· article· en· W21893645 on OpenAlexfundno aff
Yu Knayama, Akihiko Akiyama, Masayoshi OKAMOTO, Yukou Takahashi

Bibliographic record

VenueProceedings of the International Research Council on the Biomechanics of Injury conference · 2010
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsPedestrianThorax (insect anatomy)Deflection (physics)Poison controlSide impactStructural engineeringEngineeringAnatomyMedicinePhysicsTransport engineeringMedical emergency

Abstract

fetched live from OpenAlex

This paper will discuss that in a pedestrian accident, the loading direction to the pedestrian thorax is affected by the posture of the pedestrian upon impact and a subsequent rotation of the upper body. Because of this, in order to predict pedestrian thoracic fractures accurately, injury criteria for the thoracic deflection formulated in one single loading direction (frontal or lateral) are not sufficient and evaluation of thoracic fractures due to loadings in different directions is required. This study investigated a technique for two-dimensional evaluation of thoracic deflection using human and dummy FE models and formulated injury probability functions for the thorax of a pedestrian dummy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.353
GPT teacher head0.412
Teacher spread0.058 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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