Recent Developments in Micro-Structured Fiber Optic Sensors
Bibliographic record
Abstract
Recent developments in fiber-optic sensing have involved booming research in the design and manufacturing of novel micro-structured optical fiber devices. From the conventional tapered fiber architectures to the novel micro-machined devices by advanced laser systems, thousands of micro-structured fiber-optic sensors have been proposed and fabricated for applications in measuring temperature, strain, refractive index (RI), electric current, displacement, bending, acceleration, force, rotation, acoustic, and magnetic field. The renowned and unparalleled merits of sensors-based micro-machined optical fibers including small footprint, light weight, immunity to electromagnetic interferences, durability to harsh environment, capability of remote control, and flexibility of directly embedding into the structured system have placed them in highly demand for practical use in diverse industries. With the rapid advancement in micro-technology, micro-structured fiber sensors have benefitted from the trends of possessing high performance, versatilities and spatial miniaturization. Here, we comprehensively review the recent progress in the micro-structured fiber-optic sensors with a variety of architectures regarding their fabrications, waveguide properties and sensing applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".