A rapid excitation-emission matrix fluorometer utilizing supercontinuum white light and acousto-optic tunable filters
Bibliographic record
Abstract
Scanning speed and coupling efficiency of excitation light to optic fibres are two major technical challenges that limit the potential of fluorescence excitation-emission matrix (EEM) spectrometer for on-line applications and in vivo studies. In this paper, a novel EEM system, utilizing a supercontinuum white light source and acousto-optic tunable filters (AOTFs), was introduced and evaluated. The supercontinuum white light, generated by pumping a nonlinear photonic crystal fiber with an 800 nm femtosecond laser, was efficiently coupled into a bifurcated optic fiber bundle. High speed EEM spectral scanning was achieved using AOTFs both for selecting excitation wavelength and scanning emission spectra. Using calibration lamps (neon and mercury argon), wavelength deviations were determined to vary from 0.18 nm to -0.70 nm within the spectral range of 500-850 nm. Spectral bandwidth for filtered excitation light broadened by twofold compared to that measured with monochromatic light between 650 nm and 750 nm. The EEM spectra for methanol solutions of laser dyes were successfully acquired with this rapid fluorometer using an integration time of 5 s.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".