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Record W2022993501 · doi:10.1080/13588265.2012.661658

Development of granular-medium-based impact energy management system

2012· article· en· W2022993501 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Crashworthiness · 2012
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsnot available
FundersUniversity of SaskatchewanDurban University of Technology
KeywordsAutomotive industryImpact energyBravais latticeComputer scienceMechanical engineeringAutomotive engineeringSimulationEngineeringStructural engineeringAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract A granular-medium-based impact energy management system has been developed. The system was subjected to low-to-medium velocity regime impacts. Effects of lubrication of granules and defaulting of boundary conditions using Bravais cubic lattice structures have been investigated. Unlike traditional design platforms where heavy reliance is placed on the intrinsic properties of materials, experimental results indicate that the new system effectively relies on the underlying synergistic mechanisms to absorb and dissipate impact energy. Dynamic simulation results validate the system's practical relevance to the automotive industry and similar contexts. Keywords: bumpersgranular mediumimpact energyabsorptiondissipation. Acknowledgments Dynamic simulation was conducted by Qfinsoft (Pty) Ltd., South Africa. The information contained in this paper is protected by patents held by the Durban University of Technology.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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