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Record W2052207759 · doi:10.1080/10255842.2012.672563

Development of a finite element/multi-body model of a newborn infant for restraint analysis and design

2012· article· en· W2052207759 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2012
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsMcMaster UniversityUniversity of Windsor
FundersAUTO21 Network of Centres of Excellence
KeywordsTorsoFinite element methodBiomechanicsMedicinePhysical medicine and rehabilitationOrthodonticsStructural engineeringEngineeringAnatomy

Abstract

fetched live from OpenAlex

A finite element/multi-body model of a newborn infant has been developed by researchers at the University of Windsor. The geometry of this model is derived from a Nita newborn hospital training mannequin. It consists of 17 parts: eight upper and lower limb segments, the torso, head, and a seven-segment neck with seven translational and eight rotational joints. Anthropometry is consistent with hospital growth charts, measurements requested from health professionals and data from the open literature. The biomechanical properties of the model (i.e. joint stiffnesses) are implementations of data identified in the open literature. The model has been validated with respect to studies of the biomechanics of shaken baby syndrome, infant falls and the Q0 anthropomorphic testing device. A significant conclusion of this study is that the kinetics of the Q0 neck is not biofidelic. This model is currently used in an analysis of airway patency for infants in modern automotive child restraints.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.081
GPT teacher head0.369
Teacher spread0.288 · 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