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Record W2999319217 · doi:10.1364/ol.382415

Reflective objective-based ultraviolet photoacoustic remote sensing virtual histopathology

2019· article· en· W2999319217 on OpenAlexafffund
Nathaniel J. M. Haven, Pradyumna Kedarisetti, Brendon S. Restall, Roger J. Zemp

Bibliographic record

VenueOptics Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsOpticsMaterials scienceNumerical apertureMicroscopyResolution (logic)Photoacoustic imaging in biomedicineOptical sectioningBiomedical engineeringOptoelectronicsComputer scienceMedicinePhysics

Abstract

fetched live from OpenAlex

Histopathological examination typically involves tissue resection or biopsy, fixation, sectioning, and staining protocols. A non-contact high-resolution photoacoustic remote sensing microscopy system is presented which is capable of depth-resolved imaging of cell nuclei in fixed and fresh tissues without the need for stains or labels. The reflection-mode system is based on a 0.5 numerical aperture reflective objective and enables fast optical scanning using a 600 kHz repetition rate fiber laser to produce histological-like images with 0.39 µm resolution and with close agreement to traditional H&E and fluorescence staining.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations47
Published2019
Admission routes2
Has abstractyes

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