Outcome of an academic collaboration between Defence Scientists of the radiological and nuclear technology group at Defence Research and Development Canada – Suffield Research Centre and Toronto Metropolitan University
Bibliographic record
Abstract
<p>In Canada, defence security sciences are typically taught within specialized programs and institutions, such as the Royal Military College of Canada, and as such, graduates from programs outside these specializations tend to be unfamiliar with the world of defence science, its applicability and relevancy to their program. Such limitation was observed in an academic collaboration between defence scientists of the Radiological and Nuclear Technology group at Defence Research and Development Canada (DRDC) – Suffield Research Centre and the Physics department of Toronto Metropolitan University in Ontario (formerly called Ryerson University). The collaboration, which took place during the Winter 2021 semester involved the contribution of course material by DRDC – Suffield Research Centre defence scientists, Dr. Anna Rae Green and Dr. Helen Moise to the Physics graduate level course titled, “Radiation Protection and Dosimetry,” which is led and taught by course professor and Physics department chair, Dr. Ana Pejović-Milić. The outcome of this collaboration was a first for all parties involved and proved to be successful allowing students to utilize their knowledge to the challenging field of defence science—an opportunity that they have very likely never received as part of the regular science stream.</p>
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".