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Record W2621047161 · doi:10.1038/lsa.2016.278

Non-interferometric photoacoustic remote sensing microscopy

2017· article· en· W2621047161 on OpenAlexafffund
Parsin Haji Reza, Wei Shi, Kevan Bell, Robert J. Paproski, Roger J. Zemp

Bibliographic record

VenueLight Science & Applications · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsIllumisonics (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesKillam TrustsAlberta Innovates - Health SolutionsMinistry of Advanced Education, Government of AlbertaUniversity of AlbertaCancer Research Institute
KeywordsOpticsMaterials scienceRefractive indexInterferometryMicroscopyMicroscopeWhite light interferometryPhotoacoustic Doppler effectPhotoacoustic effectOptoelectronicsLaserPhysics

Abstract

fetched live from OpenAlex

Abstract Elasto-optical refractive index modulation due to photoacoustic initial pressure transients produced significant reflection of a probe beam when the absorbing interface had an appreciable refractive index difference. This effect was harnessed in a new form of non-contact optical resolution photoacoustic microscopy called photoacoustic remote sensing microscopy. A non-interferometric system architecture with a low-coherence probe beam precludes detection of surface oscillations and other phase-modulation phenomenon. The probe beam was confocal with a scanned excitation beam to ensure detection of initial pressure-induced intensity reflections at the subsurface origin where pressures are largest. Phantom studies confirmed signal dependence on optical absorption, index contrast and excitation fluence. In vivo imaging of superficial microvasculature and melanoma tumors was demonstrated with ~2.7±0.5 μm lateral resolution.

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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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

Citations206
Published2017
Admission routes2
Has abstractyes

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