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Record W2049992109 · doi:10.1117/12.875907

Three-dimensional data acquisition with aberrations correction for video-rate microscopy

2011· article· en· W2049992109 on OpenAlexafffund
Masood Samim, Richard Cisek, Daaf Sandkuijl, Ian Tretyakov, Samuel C. Siu, S. F. Musikhin, Virginijus Barzda

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of ExcellenceOntario Centre of InnovationOntario Innovation Trust
KeywordsWavefrontOpticsDeformable mirrorAdaptive opticsFemtosecondWavefront sensorLaserDifferential interference contrast microscopyOptical microscopeFrame rateMicroscopyDiffractionComputer sciencePhysicsScanning electron microscope

Abstract

fetched live from OpenAlex

We demonstrate a multimodal, multifocal, differential nonlinear optical microscope, which is equipped with a pair of deformable mirrors and a Shack-Hartmann sensor for dynamic wavefront manipulation. The optical wavefronts of a home built Yb:KGW femtosecond (1028 nm) laser-beams are engineered to perform multidepth focusing in differential mode with simultaneous corrections for optical aberrations. The 39-actuator deformable mirrors provide fast reshaping of the wavefront and optical aberrations correction of the diffraction-limited focal volume allowing for fast axial scanning. Combination of ~200 frames per second lateral scanning with fast refocusing enables a three-dimensional video rate scanning capability, which is essential for studying rapid dynamics in biological organisms, such as blood flow, cardiac contractions, and motility of microorganisms in a three-dimensional volume.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.238
Teacher spread0.217 · 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
GenreMethods

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

Citations0
Published2011
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207