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Record W2058559875 · doi:10.1364/oe.19.025161

Vertical optical sectioning using a magnetically driven confocal microscanner aimed for in vivo clinical imaging

2011· article· en· W2058559875 on OpenAlexafffund
Hadi Mansoor, Haishan Zeng, Keqin Chen, Yingqiu Yu, Jianhua Zhao, Mu Chiao

Bibliographic record

VenueOptics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMaterials scienceOpticsConfocalMicrolensOptical sectioningConfocal microscopyLaserWaferOptical axisOptical fiberSurface micromachiningTransverse planeOptoelectronicsLens (geology)Physics

Abstract

fetched live from OpenAlex

This paper presents a confocal microscanner for direct vertical optical sectioning of biological samples. Confocal imaging is performed by transverse (X-axis) and axial (Z-axis) scanning of a focused laser beam using an optical fiber and a microlens respectively. The actuators are fabricated by laser micromachining techniques and are driven by electromagnetic forces. Optical and mechanical performance of the system is predicted by simulation software packages and characterized by experimental measurements. The scanner has lateral resolution of 3.87 µm and axial resolution of 10.68 µm with a field of view of 145 µm in X and 190 µm in Z directions. Confocal imaging of a polymer layer deposited on a silicon wafer and onion epidermal cells is demonstrated.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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

Citations18
Published2011
Admission routes2
Has abstractyes

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