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
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
<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>
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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