Evanescent-Wave Fiber-Optic Fluorometer Capable of Dense Channel Multiplexing, Signal Enhancement and Stray Excitation Light Suppression
Bibliographic record
Abstract
A modified evanescent-wave fiber-optic fluorometer capable of dense channel multiplexing, simultaneous signal enhancement and suppression of excitation stray light is examined. To achieve these features in combination, a multimode fiber is decladded at numerous positions to form channels, with each individual decladded section approximately 2 mm wide. The illuminating fiber is then aligned perpendicular to one of the channels covered by a sample droplet. When the fluorescent samples or water droplets are added to or removed randomly from other channels, interchannel crosstalk is found to be completely suppressed, confirming the ability of the proposed fluorometer to multiplex a large number of channels. The maximum acceptable channel length is also explored. Zero interchannel crosstalk is demonstrated with a channel separation of 1 mm or less, indicating the possibility of dense multiplexing of channels within a short fiber segment. This study established the foundation for an alternative approach to analysis of multiple samples located in separate channels that eliminates the need to consider either the sequence in which samples are added or removed, or the number of samples. In particular, signals from samples can be examined individually or assayed in group to form a single spectrum or more overlapped spectra, enabling online comparisons. In addition, all channels share a single extremely cost-effective detection system whose reliability is guaranteed by the aforementioned superior signal quality.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".