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Record W2005972580 · doi:10.1115/imece2006-13330

Development of a Draft Heavy Vehicle Rear Underride Guard Specification

2006· article· en· W2005972580 on OpenAlexaboutno aff
Ali Osman Atahan, Guido Bonin, Moustafa El–Gindy, James Allen

Bibliographic record

VenueDesign Engineering and Computers and Information in Engineering, Parts A and B · 2006
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGuard (computer science)CrashTrailerCrash testEngineeringMotor vehicle crashAeronauticsAutomotive engineeringComputer sciencePoison controlHuman factors and ergonomics

Abstract

fetched live from OpenAlex

This paper summarizes results of a large research program intended to develop a draft rear underride guard specification for heavy vehicles. Results of a series of laboratory and full-scale crash tests performed at the Transport Canada Research Center were used in the development of these specifications. A total of eleven full-scale crash tests was carried out to evaluate the effectiveness of different underride guards. The first ten of these tests were performed on a simulated trailer attached guard. Four different underride guard designs were used in these ten full-scale crash tests. Three different vehicle models traveling at 48, 56 and 65 km/h speeds were used to impact underride guards head on. Results of the first ten crash tests show that the currently used US FMVSS 223 standard is far from adequate in preventing the occurrance of rear underride. Based on findings obtained from these crash tests, an improved guard design was developed and tested using a 16-meter trailer. This final crash test verified the effectiveness of improved guard design in reducing the undesirable effects of rear underride crashes. Based on the results, a draft heavy vehicle rear underride guard specification was developed.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.591

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 communication0.0000.001
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 head0.007
GPT teacher head0.166
Teacher spread0.159 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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Same venueDesign Engineering and Computers and Information in Engineering, Parts A and BSame topicTransportation Safety and Impact AnalysisFrench-language works237,207