Bending sensor combining multicore fiber with a mode-selective photonic lantern
Bibliographic record
Abstract
A bending sensor is demonstrated using the combination of a mode-selective photonic lantern (PL) and a multicore fiber. A short section of three-core fiber with strongly coupled cores is used as the bend sensitive element. The supermodes of this fiber are highly sensitive to the refractive index profiles of the cores. Small bend-induced changes result in drastic changes of the supermodes, their excitation, and interference. The multicore fiber is spliced to a few-mode fiber and excites bend dependent amounts of each of the six linearly polarized (LP) modes guided in the few-mode fiber. A mode selective PL is then used to demultiplex the modes of the few-mode fiber. Relative power measurements at the single-mode PL output ports reveal a high sensitivity to bending curvature and differential power distributions according to bending direction, without the need for spectral measurements. High direction sensitivity is demonstrated experimentally as well as in numerical simulations. Relative power shifts of up to 80% have been measured at radii of approximately 20 cm, and good sensitivity was observed with radii as large as 10 m, making this sensing system useful for applications requiring both large and small curvature measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".