Use of a hybrid ray-thin film interference model for the optimization of a FTIR FOEWS
Bibliographic record
Abstract
Certain designs for frustrated total internal reflection fiber optic evanescent wave sensors (FTIR FOEWS) include the partial removal of cladding along a finite length of the fiber optic that acts as the sensing region. This paper presents a model for a FTIR FOEWS that has a thin, partial cladding in the sensing region. Since the thickness of the cladding in the sensing region is in the 1 μm range, while the propagating light is on the order of 850 nm, commonly used ray optic modeling techniques fail to properly simulate the thin film interference effects. In this study, a modification to the usual ray model is performed by including thin film optic analysis at the thin film sensing interface. The resulting hybrid ray/thin film model maintains the efficiency of previously reported models, but also adds the ability to fully model the partial cladding of the sensing region. The intensity and angular distributions of light from a Lambertian LED source onto the fiber input face is also derived to discuss the effects of launching conditions on the sensor performance. Investigation of a variety of meridional propagation ray distributions into the fiber are performed by varying the distance and angle of the point source to the fiber input face. Optimal cladding thicknesses, LED distance and tilt angle are studied and used to draw conclusions about FTIR FOEWS performance based on launching conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".