A special issue on Photonics Research in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it