MétaCan
Menu
Back to cohort
Record W2051659360 · doi:10.1080/15599610701385487

Modeling of a Magnetic-Fluid Deformable Mirror for Retinal Imaging Adaptive Optics Systems

2007· article· en· W2051659360 on OpenAlexaff
Azhar Iqbal, Foued Ben Amara

Bibliographic record

VenueInternational Journal of Optomechatronics · 2007
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDeformable mirrorAdaptive opticsWavefrontComputer scienceSurface (topology)OpticsMagnetic fieldComputer visionArtificial intelligencePhysicsMathematics

Abstract

fetched live from OpenAlex

Magnetic-fluid deformable mirrors (MFDMs) offer a simple alternative to the costly yet inefficient wavefront correctors currently in use in adaptive optics (AO) systems. They have been found particularly suitable for AO systems used in ophthalmic applications, such as retinal imaging. However, their practical implementation in clinical devices is contingent on the development of effective methods to model and control their surface-shape. This article presents a model of the dynamic response of the surface of a MFDM to the applied magnetic field. The resulting model allows the development of high-performance shape control algorithms for the mirror surface.

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)0.001
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations20
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of OptomechatronicsSame topicOptical Coherence Tomography ApplicationsFrench-language works237,207