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Record W2596399712 · doi:10.1504/ijfe.2017.082967

Event dynamics and injury reconstruction of a zip-line incident using MADYMO software: a case study

2017· article· en· W2596399712 on OpenAlexaff
Geoffrey T. Desmoulin, Katerina Doslikova

Bibliographic record

VenueInternational Journal of Forensic Engineering · 2017
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsEvent (particle physics)KinematicsSensitivity (control systems)Computer scienceSoftwareLine (geometry)SimulationEngineeringMathematics

Abstract

fetched live from OpenAlex

GTD Engineering was retained to perform a biomechanical investigation of a zip-line incident involving head and neck injuries sustained by a female patron. Resources limited a full-scale reconstruction of the incident; hence a MADYMO (MAthematical DYnamic MOdels) software model was used in its place. The aim of the study was to assess the model's dynamics and injury responses as well as provide a description of its development. The model was validated using (a) a sensitivity analysis, (b) the actual injuries sustained during the incident as confirmed by medical records and (c) eyewitness accounts of the event. A description of key time points, how they were reconstructed and the likelihood of injuries sustained during each are provided. The model created using MADYMO proved to be an accurate tool to reconstruct the incident, including event kinematics, kinetics and injury responses.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.317
Teacher spread0.296 · 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 designCase report
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
Published2017
Admission routes1
Has abstractyes

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