Interpretation of ALARA in the Canadian regulatory framework
Bibliographic record
Abstract
The Atomic Energy Control Board (AECB) is responsible for the regulation of all aspects of atomic energy in Canada. This includes the complete nuclear fuel cycle from uranium mining to long-term disposal of nuclear fuel, as well as the medical and industrial utilization of radioisotopes. Clearly, the regulatory approach will differ from practice to practice but, as far as possible, the AECB has attempted to minimize the degree of prescription of regulatory requirements. The traditional modus operandi of the AECB has been to have broad general principles enshrined in regulations with the requirement that licensees submit specific operating policies and procedures to the AECB for approval. In the large nuclear facilities with their sophisticated technical infrastructures, this policy has been largely successful although in a changing legal and political milieu the AECB is finding that a greater degree of proactive regulation is becoming necessary. With the smaller users, the AECB has for a long time found it necessary to have a greater degree of prescription in its regulatory function. Forthcoming General Amendments to the Atomic Energy Control Regulations will, amongst other things, formally incorporate the concept of ALARA into the Canadian regulatory framework. Within the broad range of practices licensed by the AECB it is not practical to provide detailed guidance on optimization that will be relevant and appropriate to all licensees, however the following general principles are proposed.
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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 | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.026 | 0.005 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".