Poster - Thur Eve - 02: Regulatory oversight of the robotic radiosurgery facilities
Bibliographic record
Abstract
Following a recent review of the Class II Nuclear Facilities and Prescribed Equipment Regulations and regulatory oversight of particle accelerators, the Canadian Nuclear Safety Commission (CNSC) has changed its policy concerning the regulation of particle accelerators. In November 2011, the CNSC began to exercise its regulatory authority with respect to all particle accelerators operating at a beam energy of 1 (one) MeV or greater. The CNSC already licences and inspects particle accelerators capable of operating at or above 10 MeV. The decision to now include low energy particle accelerators (i.e., those operating at or above 1 MeV) ensures adequate, uniform and consistent regulatory oversight for all Class II accelerators. The CNSC expects these facilities to comply with CNSC requirements by December 2013. Besides conventional linear accelerators of lower energy (6 MeV or below) typically found in cancer clinics, two types of equipment now fall under the CNSC's regulatory oversight as a result of the above change: robotic radiosurgery and tomotherapy equipment and facilities. A number of clinics in Canada already operates these types of equipment and facilities. The safety aspects of radiosurgery equipment differ slightly from those for conventional linear accelerators. This poster aims to present an approach taken by the CNSC to regulate robotic radiosurgery equipment and facilities. The presentation will explain how to meet regulatory requirements of the Class II Nuclear Facilities and Prescribed Equipment Regulations by licensees operating or planning to acquire these types of equipment and facilities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.022 |
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".