Bibliographic record
Abstract
Canada has a long standing history of contributions to the field of photonics.Photonics research occurs at a number of universities across the country as well as in government research labs.The Canadian photonics industry includes small start-ups to large scale corporations, many of which are involved in research.It is estimated that photonics is a $6 billion industry in Canada that employs more than 24000 people.Photonics clusters can be found in Vancouver, Toronto, Ottawa, Montreal, and Quebec City-cities that are also home to universities where world renowned photonics research takes place.In this special issue, we present a highlight of recent research activities in Canada.Prominent researchers across the country have contributed papers on topics ranging from optical and wireless communications, fiber and integrated technologies, and quantum photonics.Prof. Xiupu Zhang from Concordia University reviews the development of broadband linearization techniques, both optical and electrical, for the fronthaul transmission technologies that are crucial to emerging 5G networking.Profs.Julian Cheng and Jonathan F. Holzman from the University of British Columbia, along with their colleagues, describe optical indoor positioning systems for visible light communications.Positioning systems are a critical aspect of optical-wireless or free-space optical communications.Prof. John C. Cartledge from Queen's University provides an overview of research on coherent optical fiber communications.In particular, he describes work aimed at compensation of transmission impairments and understanding limitations on the performance of systems operating at 1 Tb/s.Prof. Roberto Morandotti from the
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.122 | 0.042 |
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".