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Oblique incidence reflection imaging of live cells: improving contrast and image quality with an objective mask

2007· article· en· W2010515126 on OpenAlexafffund
Sylvie Landry, L. H. DeGAGNE, P. L. McGhee, W. J. Keeler

Bibliographic record

VenueJournal of Microscopy · 2007
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsLakehead UniversityThunder Bay Regional Health Sciences Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContrast (vision)Reflection (computer programming)Image qualityQuality (philosophy)OpticsHigh contrastImage contrastOblique caseIncidence (geometry)Computer visionImage (mathematics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

Dry 40x and 60x microscope objectives were fitted with opaque black masks in order to eliminate reflection and scattering of light off the objective front lens assembly during oblique incidence reflection (OIR) microscopy. The reflection and scattering are shown to induce background glare that leads to degradation in the quality of the OIR images. Mask prototypes were designed and machined to snap onto the spring-loaded retractable front lens assembly of each objective. OIR images of live cells and normalized intensity line profiles are used to demonstrate that, if these alterations to the housing of the objective are implemented, background glare is significantly reduced with the 60x objective, and virtually eliminated with the 40x objective.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.330
Teacher spread0.320 · 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
GenreMethods

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

Citations0
Published2007
Admission routes2
Has abstractyes

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