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Record W2064689588 · doi:10.1364/oe.16.016284

Determination of porcine corneal layers with high spatial resolution by simultaneous second and third harmonic generation microscopy

2008· article· en· W2064689588 on OpenAlexafffund
L. Jay, A. Brocas, Kanwarpal Singh, J. C. Kieffer, Isabelle Brunette, T. Ozaki

Bibliographic record

VenueOptics Express · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsHôpital Maisonneuve-RosemontInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsOpticsFemtosecondMaterials scienceCorneaSecond-harmonic generationLaserMicroscopyMicroscopeWavelengthResolution (logic)Image resolutionOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

We describe a dual, second harmonic generation (SHG) and third harmonic generation (THG) microscope, with the aim to obtain large-scale images of the cornea that can simultaneously resolve the micron-thick thin layers. We use an Ytterbium femtosecond laser as the laser source, the longer wavelength of which reduces scattering and allows simultaneous SHG and THG imaging. We measure one-dimensional SHG and THG profiles across the entire thickness of pig cornea, detected in both the forward and backward directions. These profiles allow us to clearly distinguish all the porcine corneal layers (epithelium, stroma, Descemet's membrane and endothelium). From these profiles, longitudinal cross sectional images of the corneal layers are generated, providing large scale topographic information with high-spatial resolution. The ability to obtain both SHG and THG signals in epi-detection on fresh eyes gives promising hopes for in vivo applications.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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

Citations58
Published2008
Admission routes2
Has abstractyes

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