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Record W2148810568 · doi:10.1364/ao.41.005367

Digital in-line holography of microspheres

2002· article· en· W2148810568 on OpenAlexaff
Wenbo Xu, M. H. Jericho, Ian A. Meinertzhagen, H. J. Kreuzer

Bibliographic record

VenueApplied Optics · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDigital Holography and Microscopy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOpticsHolographyOpacityDigital holographyMaterials sciencePosition (finance)Particle (ecology)Resolution (logic)MicrometerLight intensityImage resolutionSPHERESWavelengthPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We have used digital in-line holography (DIH) with numerical reconstruction to image micrometer-sized latex spheres as well as ferrimagnetic beads suspended in gelatin. We have examined in detail theoretically and experimentally the conditions necessary to achieve submicrometer resolution of holographic reconstructions. We found that both transparent and opaque particles could be imaged with a resolution that was limited only by the wavelength of the light used. Simple inspection of intensity profiles through a particle allowed an estimate to be made of the particle's three position coordinates within an accuracy of a few hundred nanometers. When the derivative of a second-order polynomial fitted to the intensity profiles was taken, the X, Y, Z position coordinates of particles could be determined within +/-50 nm. More-accurate positional resolution should be possible with the help of more-advanced computer averaging techniques. Because a single hologram can give information about a large collection of distributed particles, DIH offers the prospect of a powerful new tool for three-dimensional tracking of particles.

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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.208
Teacher spread0.200 · 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

Citations181
Published2002
Admission routes1
Has abstractyes

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