Roadmap on optical communications
Bibliographic record
Abstract
Abstract The Covid-19 pandemic showed forcefully the fundamental importance broadband data communication and the internet has in our society. Optical communications forms the undisputable backbone of this critical infrastructure, and it is supported by an interdisciplinary research community striving to improve and develop it further. Since the first ‘Roadmap of optical communications’ was published in 2016, the field has seen significant progress in all areas, and time is ripe for an update of the research status. The optical communications area has become increasingly diverse, covering research in fundamental physics and materials science, high-speed electronics and photonics, signal processing and coding, and communication systems and networks. This roadmap describes state-of-the-art and future outlooks in the optical communications field. The article is divided into 20 sections on selected areas, each written by a leading expert in that area. The sections are thematically grouped into four parts with 4–6 sections each, covering, respectively, hardware, algorithms, networks and systems. Each section describes the current status, the future challenges, and development needed to meet said challenges in their area. As a whole, this roadmap provides a comprehensive and unprecedented overview of the contemporary optical communications research, and should be essential reading for researchers at any level active in this field.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.024 |
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".