MétaCan
Menu
Back to cohort
Record W2073689594 · doi:10.1117/12.841887

Development of optical phantoms for use in fluorescence-based imaging

2010· article· en· W2073689594 on OpenAlexaff
Isabelle Noiseux, Michel Fortin, S. Leclair, Jocelyne Osouf, Ozzy Mermut

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsInstitut National d'Optique
Fundersnot available
KeywordsMolar absorptivityFluorescenceAbsorbanceCyanineAlexa FluorMaterials sciencePolyurethaneExtinction (optical mineralogy)CastingAnalytical Chemistry (journal)PhotochemistryOpticsChemistryComposite materialChromatographyMineralogy

Abstract

fetched live from OpenAlex

We fabricated permanent solid polyurethane-based phantoms in which fluorophores were homogeneously incorporated. For this study, fluorophores of three different families were used: Cyanines, Alexa Fluor and Quantum Dots. The goal of this study was to evaluate the impact of casting the fluorophores in a polyurethane matrix on their optical properties, more specifically the absorbance, molecular extinction coefficient, emission of fluorescence and the resultant fluorescence intensity. All measurements were carried out with 5 concentrations of each fluorophores embedded in polyurethane and in solution. Stability over time was also monitored for a three months period. The casting of fluorophores affects the optical properties of the three dyes under study. The max absorbance, the fluorescence emission and intensity along with the molar extinction coefficient were all affected. Quantum dots behave differently to the cyanine and Alexa Fluor dyes. It was also observed that the incorporation of dyes enables long-term stability of the fluorescence signal.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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 teacher head, not a consensus.

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

Citations5
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207