MétaCan
Menu
Back to cohort
Record W2096573790 · doi:10.1109/ultsym.2015.0510

Sensitivity analysis of leaky Lamb modes to the thickness and material properties of cortical bone with soft tissue: A semi-analytical finite element based simulation study

2015· article· en· W2096573790 on OpenAlexaff
Tho N.H.T. Tran, Lawrence H. Le, Vu‐Hieu Nguyen, Kim-Cuong T. Nguyen, Mauricio D. Sacchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFinite element methodSensitivity (control systems)Cortical boneMaterials scienceAcousticsBiomedical engineeringStructural engineeringPhysicsElectronic engineeringEngineeringMedicineAnatomy

Abstract

fetched live from OpenAlex

Inversion of ultrasonic guided waves (UGW) for cortical bone properties is an important topic. The fundamental ultrasonic guided modes (UGM) are consistently observed in long bones in vitro and in vivo. The responses of UGM to the changes of cortical thickness, cortical elastic parameters, and thickness of the overlying soft tissues are not well understood. This study aims to investigate the sensitivity of leaky Lamb modes to the geometry and material characteristics of layered bone model by simulation. The analysis provides opportunity to study the responses of the UGM, to identify the sensitive regimes in the frequency-phase velocity domain, and to quantify the amount of sensitivity of each mode. The study is important as it offers guidance to the parameter inversion process about the optimal selection of UGM and regions of sensitivity for better inversion results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.027
GPT teacher head0.246
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicUltrasonics and Acoustic Wave PropagationFrench-language works237,207