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Record W2027800010 · doi:10.1063/1.3556787

Spectral-domain phase microscopy with improved sensitivity using two-dimensional detector arrays

2011· article· en· W2027800010 on OpenAlex
Kanwarpal Singh, C. Dion, Mark R. Lesk, T. Ozaki, Santiago Costantino

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsDetectorOpticsSensitivity (control systems)Signal-to-noise ratio (imaging)MicroscopyDisplacement (psychology)Materials sciencePhase (matter)Noise (video)PhysicsAxial symmetrySIGNAL (programming language)PixelComputer scienceElectronic engineeringArtificial intelligenceImage (mathematics)

Abstract

fetched live from OpenAlex

In this work we demonstrate the use of two-dimensional detectors to improve the signal-to-noise ratio (SNR) and sensitivity in spectral-domain phase microscopy for subnanometer accuracy measurements. We show that an increase in SNR can be obtained, from 82 dB to 105 dB, using 150 pixel lines of a low-cost CCD camera as compared to a single line, to compute an averaged axial scan. In optimal mechanical conditions, phase stability as small as 92 μrad, corresponding to 6 pm displacement accuracy, could be obtained. We also experimentally demonstrate the benefit of spatial-averaging in terms of the reduction of signal fading due to an axially moving sample. The applications of the improved system are illustrated by imaging live cells in culture.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.274
Teacher spread0.250 · 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