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Record W2954490092 · doi:10.1201/9781315106595-24

Electronic Belt Fit Test Device-a Software Tool to Optimize and Evaluate Seat Belt Design

2018· book-chapter· en· W2954490092 on OpenAlexaboutno aff
Andreas Seidl

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)SoftwareSeat beltComputer scienceEngineeringEmbedded systemAutomotive engineeringGeologyOperating system

Abstract

fetched live from OpenAlex

In attempt to reduce the risk of accident injuries caused by inappropriate belt fitting, the Canadian Government (Transport Canada) developed a physical Belt Fit Test Device (BTD) and in 1995 proposed new vehicle safety regulations involving BTD test procedures. To overcome deviations of physical hardware tests and to enable review of belt design in early design phases, an electronic version of the BTD (eBTD) was proposed by the affected manufacturers. As a long-time supplier of ergonomic CAD software and digital human modelling technology, Human Solutions was assigned as the software development partner. The software simulation which incorporates anchor point kinematics and measures the belt position over clavicle, sternum and lap scales was accepted by Transport Canada as an official certification tool. In July 2006 a Memorandum of Understanding was signed with the affected manufacturers in which they commit to certify all vehicles sold in Canada using BTD/eBTD procedures. With the availability of the eBTD in the 3D human modelling software RAMSIS, vehicle designers are provided with an integral digital design environment in which ergonomics, safety and certification are combined seamlessly.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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 designSimulation or modeling
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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