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Record W4200017048 · doi:10.5750/ijme.v156ia3.929

STEPPING RESPONSE DURING CONSTRAINED AND UNCONSTRAINED STANDING IN MOVING ENVIRONMENTS

2021· article· en· W4200017048 on OpenAlexaff
Carolyn A. Duncan, Wayne J. Albert, Robert Langlois, Scott N. MacKinnon

Bibliographic record

VenueThe International Journal of Maritime Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsCarleton UniversityUniversity of New BrunswickMemorial University of Newfoundland
Fundersnot available
KeywordsKinematicsStepping stoneMotion (physics)Task (project management)Time steppingDegrees of freedom (physics and chemistry)Computer scienceStanding waveSimulationMathematicsArtificial intelligenceEngineeringMathematical analysisPhysicsAcousticsClassical mechanicsEconomics

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the differences in human stepping response reaction between constrained and unconstrained standing while being exposed to simulated wave-induced platform motions. Twenty subjects (10 male and 10 female), with limited experience recreating or working in motion-rich environments, performed a constrained and an unconstrained standing task on a six-degrees-of-freedom motion bed while being exposed to two different simulated platform motion conditions. Stepping occurrence was greater during unconstrained standing than constrained standing during all three motion conditions. However, no significant differences in platform kinematics were found between stepping cases. These results suggest that stepping occurs more frequently than originally hypothesized. As such, stepping should not be considered as a last resource when all fixed-support options have been exhausted. This should be taken into consideration to ensure ecological validity when developing models to predict stepping occurrence.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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 science0.0000.000
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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Same venueThe International Journal of Maritime EngineeringSame topicEvacuation and Crowd DynamicsFrench-language works237,207