Bibliographic record
Abstract
In this chapter we will discuss properties of the linear fibre, where refractive index does not depend on field intensity. We will concentrate on the following topics: • geometrical-optics description • wave propagation • dispersion in single-mode fibres • dispersion-induced limitations. Due to dispersion, the width of propagating pulse will increase. In multimode fibres, broadening of optical pulses is significant, about 10 ns km −1 . In the geometrical-optics description, such broadening is attributed to different paths followed by different rays. In a single mode fibre, intermodal dispersion is absent; all energy is transported by a single mode. However, pulse broadening exists. Different spectral components of the pulse travel at slightly different group velocities. This effect is known as group velocity dispersion (GVD). Geometrical-optics description Consider an optical fibre whose cross-section is shown in Fig. 5.1. The corresponding change in refractive index profiles is shown in Fig. 5.2. We illustrate two profiles: step index and graded index. In the following discussion we will assume validity of a geometrical optics description which holds in the limit λ ≪ a , where λ is the light wavelength, and a is the core radius [1], [2]. Numerical aperture (NA) To start our discussion, consider the propagation of rays entering a cylindrical fibre at different angles in the plane passing through the core centre, see Fig. 5.3. Ray B will travel in the cladding region and it is known as an unguided ray, whereas ray A will stay within core region, and it will form a guided ray.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".