University of Toronto Institute for Optical Sciences collaborative program in optics
Bibliographic record
Abstract
We describe the activities of the Institute for Optical Sciences (IOS) at the University of Toronto towards the establishment of a Master’s Program in Optics. The IOS was formed as a collaboration between faculty members interested in optics from the four departments of Physics, Chemistry, Electrical and Computer Engineering and Materials Science and Engineering. One of its goals is to serve as unifying entity for graduate and undergraduate programs in optical sciences. The details of the proposed graduate program will be discussed. It will be set up in the form of a collaborative university program, where students must satisfy the requirements of one of the four home departments, as well as a set of IOS-specific requirements of the program. IOS-specific activities include attending the Distinguished Visiting Scientist Series, participation in a best-research-practice mini-course, where essential research skills are discussed, as well as participation in an annual internal conference. The benefits of this interdisciplinary program, for students, faculty and relevant industries are discussed. The students will benefit from a wider exposure and a more coherent curriculum. The IOS will also serve as local community within the campus to which students could belong and network. Faculty, on the other hand, will benefit from a reduced teaching load, as redundancies among the departments will be removed.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.018 |
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".