Calibration of fluorescence reflectance reference phantoms
Bibliographic record
Abstract
Fluorescence intensity is often standardized by comparing the unknown sample signal to that of a reference solution with a known concentration of a reference fluorophore. To use this technique for in vivo fluorescence reflectance measurements standardization, a reference sample that also mimics the scattering and absorption properties of the tissue would need to be used. A simpler approach to fluorescence reflectance measurements standardization is to express the intensity of the measured fluorescence as a ratio of the excitation irradiance to the fluorescence radiance. This ratio of radiometric quantities can be measured by normalizing the measured fluorescence image to a reflectance image acquired with a reflection standard at the excitation wavelength (without the emission filter). Instruments could be calibrated to report their intensity results using this dimensionless ratio. The calibration could also be transferred from a reference calibrated instrument to other instruments through the use of fluorescence phantoms. Reporting fluorescence intensity measurements using this dimensionless ratio will ease instrument standardization and comparison of fluorescence reflectance results between instruments, vendors and applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".