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Record W4386966469 · doi:10.1080/13588265.2023.2258629

Identification of material parameters for the Vawter-Fung lung tissue constitutive model and assessment in human body model for impact loading

2023· article· en· W4386966469 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 Crashworthiness · 2023
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHyperelastic materialStiffnessExperimental dataConstitutive equationTension (geology)Structural engineeringMaterials scienceComputer scienceEngineeringFinite element methodMathematicsCompression (physics)Composite materialStatistics

Abstract

fetched live from OpenAlex

The widely used Vawter-Fung (VF) lung tissue constitutive model, originally developed to model respiration, was assessed for applicability to impact human body models (HBMs). A review of the mechanical properties of lung tissue demonstrated existing parameter sets for the VF model encompassed a wide range of stiffness relative to experimental data. Consistent experimental datasets of lung tissue for uniaxial and biaxial tension were identified, and new parameters were fit to the VF model. A thoracic pendulum impact using a contemporary HBM was used to assess existing literature parameter sets, and the new parameters. The VF model parameters presented in this study produced uniaxial and biaxial tension response with improved hyperelastic response compared to experimental data and previously reported parameters. The VF surface tension component did not contribute substantially to the lung response in impact. The proposed VF model parameters were numerically stable for impact simulations and use in HBMs.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.041
GPT teacher head0.414
Teacher spread0.373 · 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