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Record W2004798529 · doi:10.1364/ao.51.006343

Modeling of fiber-optic fluorescence probes for strongly absorbing samples

2012· article· en· W2004798529 on OpenAlexaff
Dorit Munzke, John Saunders, Hengameh Omrani, Oliver Reich, Hans‐Peter Loock

Bibliographic record

VenueApplied Optics · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsQueen's University
Fundersnot available
KeywordsOpticsMaterials scienceAbsorption (acoustics)FluorescenceOptical fiberExcitationFiberBeam (structure)Fiber optic sensorAttenuation coefficientPhysics

Abstract

fetched live from OpenAlex

The dynamic range of fiber-optic fluorescent probes such as single fibers and fiber bundles is calculated for strongly absorbing samples, such as process liquids, foodstuffs, and lubricants. The model assumes an excitation beam profile based on a Lambertian light source and uses analytical forms of the collection efficiency, followed by an Abel transformation and numerical integration. It is found that the effect of primary absorption of the excitation light and secondary absorption of the fluorescence is profound. For fiber bundles and bifurcated fiber probes, the upper accessible concentration limit is roughly given by the absorption length of the primary and secondary absorption. Fluorescence detectors that are placed at right angles to the excitation beam axis or collinear to the beam axis are equally strongly affected by secondary absorption. A probe in which the same fiber is used for excitation and for collection of the fluorescence emerges as the fiber probe with the largest accessible concentration range.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.244
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations20
Published2012
Admission routes1
Has abstractyes

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