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Record W2130622956 · doi:10.1109/icmens.2004.1508959

Thermally Induced Fiber Deformation Using High Frequency Magnetic Field

2006· article· en· W2130622956 on OpenAlexaff
Ya-jing Shang, Warren H. Finlay, Walied A. Moussa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceFiberDeformation (meteorology)Finite element methodEddy currentMagnetic fieldBendingHeat transferFerromagnetismThermalMechanicsComposite materialCondensed matter physicsStructural engineeringPhysicsElectrical engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

This paper presents a model to externally induce deformation of a fiber inside a human lung. The fiber contains ferromagnetic materials and has a cross-section of sub-micron size. The fiber heating is achieved by applying an external high frequency magnetic field to induce eddy currents in the ferromagnetic materials. Prediction of the thermal bending of the fiber requires multi-physics modeling. The electromagnetic energy deposition is calculated by constructing models using a finite element method. The deposited energy is coupled to a heat-transfer model to calculate the temperature rise. The distribution of temperature inside the fiber then allows prediction of fiber deformation. This is the first attempt to use thermal heating induced by a high frequency oscillatory magnetic field to cause deformation of a fiber.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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 designBench or experimental
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

Citations3
Published2006
Admission routes1
Has abstractyes

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