IEEE Canadian Review — Spring / Printemps 20068 Optical Fiber Components Obtained by Refraction Index Modulation and Geographical Formulation
Bibliographic record
Abstract
ince their market introduction in 1995, the use of opticalFiber Bragg Gratings in commercial products has grownexponentially, largely in the fields of telecommunicationsand stress sensors. The demand for broadband is rapidlyincreasing. This demand for more bandwidth in telecommunication net-works has rapidly expanded the search and development of new opticalcomponents and devices (especially in Wavelength DivisionMultiplexers). Optical fiber components are key elements in WDM sys-tems (Figure 1). Today, the technology of Fiber Bragg gratings (FBG) and long periodfiber gratings (LPFG) has been recognized as one of the most significantenabling technologies for fiber optic communications due to its use inseveral applications such as gain equalization for Erbium-Doped FiberAmplifier (EDFA)4,22, specialized narrowband lasers19, wavelengthdivision multiplexing (WDM) narrowband and broadband tunable fil-ters7,20, dispersion compensators for long-distance telecommunicationnetworks18 and even sensors 8,9,17,23. The grating period L and the grat-ing length (L) are both important factors in building FBG & LPFG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.029 |
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".