Discretization of Tilted FBGs Spectra for Sensing and Coding
Bibliographic record
Abstract
Tilted fiber Bragg gratings (TFBGs) give rise to a dense comb of cladding mode resonances for which they are appealing for high-resolution sensing. However, the interrogation of such gratings tends to be complex and expensive. As an alternative to conventional interrogation approaches, herein, we propose to discretize the TFBG spectra to form binary codes of “0” and “1” and to correlate such bits with different values of a target measurand. As an example, we demonstrate the measurements of water-alcohol mixtures with TFBGs excited with randomly polarized light and interrogated over a narrow (6 nm) wavelength range. We have found that for each mixture surrounding a TFBG, a unique set of bits can be generated. As bits are the language of digital technologies and are easy to process, we believe that the approach proposed here can pave the way for a new manner of interrogating TFBGs. Moreover, the binary codes generated by discretization of TFBG spectra may allow the generation of unique visual labels (barcodes) for the identification of liquids, thereby expanding the applications of TFBGs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".