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Record W2565774677

Energy attenuation performance of impact protection for motorcyclists

2016· article· en· W2565774677 on OpenAlexaff
Bianca Albanese, Lauren Meredith, Tom Whyte, Tom Gibson, Liz de Rome, Michael Fitzharris, Matthew Baldock, Julie Brown

Bibliographic record

VenueDeakin Research Online (Deakin University) · 2016
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsImpact
Fundersnot available
KeywordsAttenuationImpact energyForensic engineeringEngineeringStructural engineering
DOInot available

Abstract

fetched live from OpenAlex

Energy attenuation provided by motorcycle impact protectors (IPs) is a mechanism that can be used toreduce loads transferred to the body of motorcyclists. Impact protectors have been shown to reduce the overallinjury risk in motorcycle crashes [1] and severity of fracture injuries in laboratory tests [2‐3]. However, previousresearch shows little evidence that commonly used motorcycle IPs are effective in reducing the risk of fracturesin real‐world crashes. Motorcycle IPs usually comply with the European Standard EN1621‐1, which setsminimum energy attenuation requirements. This study aims to examine the effectiveness of IPs worn byAustralian riders in crashes, in terms of EN1621‐1 energy attenuation requirements and injury outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

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)0.0080.001

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 head0.047
GPT teacher head0.293
Teacher spread0.246 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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