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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

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)0.0000.000

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