Radiological training for the defence sciences: a unique playing field
Bibliographic record
Abstract
Defence Research and Development Canada (DRDC), part of the Department of National Defence, comprises seven research centres and forty-seven research and development (R&D) capabilities across Canada.Of these centres, the Suffield Research Centre, located in Alberta, provides training and expertise on radiological and nuclear technology through the Radiological and Nuclear Technologies Group (RNTG).Housed within the Canadian Forces Base, which includes a vast experimental proving ground facility, the RNTG is tasked with providing radiological training to various members and clients including Canadian Armed Forces Members, NATO allies, foreign nationals as arranged by Global Affairs Canada, and First Responders in the safe handling and remediation of radiological and nuclear material.The expansive inventory of various sources (in terms of activity (up to several TBq of material) and forms of ionising radiation), the Department of National Defence specific regulatory body, and the procurement and use of more novel isotopes, give it the unique capability to deliver specialised radiological training within the NATO partner nations.The RNTG's Radiological and Nuclear (RN) Defence program goes beyond field radiation training.Existing as a group of subject matter experts, the RNTG also conducts research and can provide expertise, advice and reach-back support to both Canadians and non-Canadian partners alike.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".