Bringing together graduate students and companies to solve industry-related problems in optics and photonics
Bibliographic record
Abstract
For the last fifteen years, Universite Laval’s SPIE Student Chapter has been building strong links between academia and industry to better prepare its student members to face their future career and to guide them towards industry. With now over fifty companies working in the field of optics and photonics in the Quebec City area alone, this makes it one of the best places in the world for students to visit companies and learn about companies’ expertise, equipment and work environments. In 2017 and for the first time at Universite Laval, the Student Chapter organized a day-long workshop where students had to solve real-world industry-related problems presented by high-end optics-related companies, i.e. an industrial seminar. Now at its fourth edition, a retrospective picture investigating the success of this event can be drawn. Over the years, more than 20 companies from Quebec City’s rich optics and photonics area were invited to present their domain of expertise to students through conferences, product demonstrations and original problem scenarios encountered in the past. As a result, no fewer than 100 students were familiarized with the work of these technology companies. They also exchanged and shared ideas with expert engineers, physicists, chemists, etc., and were given real-world problems to solve. From this process, direct links were created between the employers and the future employees, and a clearer picture was drawn for graduates envisioning an industrial career. Consequently, this event has shown to be beneficial for both students and companies.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.013 |
| 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".