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Record W1945056148 · doi:10.1039/9781849732826-00191

Fluorescence Imaging on the Nanoscale: Bioimaging Using Near-field Scanning Optical Microscopy

2011· book-chapter· en· W1945056148 on OpenAlex

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.

Bibliographic record

VenuePhotochemistry · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsSteacie Institute for Molecular Sciences
FundersRoyal Society of ChemistryRoyal Society
KeywordsMicroscopyNear-field scanning optical microscopeNanoscopic scalePhotoactivated localization microscopyOpticsDiffractionMaterials scienceResolution (logic)Image resolutionNanotechnologyOptical microscopeFluorescence-lifetime imaging microscopyScanning confocal electron microscopyFluorescence microscopeSuper-resolution microscopyFluorescenceComputer sciencePhysicsScanning electron microscopeArtificial intelligence

Abstract

fetched live from OpenAlex

Fluorescence microscopy is one of the most widely used tools for visualization of biological structures, despite the fact that diffraction of light limits the spatial resolution to several hundred nanometers for visible excitation. This review will focus on one method for overcoming the diffraction limit and achieving nanoscale spatial resolution in optical microscopy, namely near-field scanning optical microscopy. A brief overview of the technical details of various aperture and apertureless-based near field methods is presented, followed by examples that illustrate recent applications of near field techniques to cellular imaging. Finally, perspectives on new approaches and a comparison with recent developments in super-resolution fluorescence imaging are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.239
Teacher spread0.223 · 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