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Record W2003403820 · doi:10.1115/sbc2011-53287

Evaluation of Energy Attenuating Floor Mats for Protection of Lower Limbs From Anti-Vehicular Landmines

2011· article· en· W2003403820 on OpenAlexafffund
Cheryl E. Quenneville, Cynthia E. Dunning

Bibliographic record

VenueASME 2011 Summer Bioengineering Conference, Parts A and B · 2011
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsWestern UniversityMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Dynamics Land Systems
KeywordsHullImpulse (physics)AccelerationEngineeringAutomotive engineeringMarine engineeringEnvironmental scienceForensic engineeringPhysics

Abstract

fetched live from OpenAlex

During an anti-vehicular (AV) landmine event a large impulse is applied to the underside of a vehicle, causing rapid deformation of the lower hull and floor. These deformations, as well as the global vehicle acceleration, have the potential to cause serious injuries to vehicle’s occupants. As the lower legs are often directly in contact with the floor, they are loaded rapidly and are particularly vulnerable to injury.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.243
Teacher spread0.168 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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Same venueASME 2011 Summer Bioengineering Conference, Parts A and BSame topicTransportation Safety and Impact AnalysisFrench-language works237,207