The role of standards laboratories in reducing uncertainty in clinical dosimetry: A Canadian perspective
Bibliographic record
Abstract
Abstract As a National Metrology Institute, the National Research Council Canada (NRC) provides confidence in measurement results, and data traceable to SI units. This paper outlines some of the ways that the NRC contributes to reducing measurement uncertainty in clinical medical physics dosimetry. These activities include, (i) the improvement of existing primary standards and traceability for established beam modalities, such as MV photon and electron beams; (ii) improving measurement accuracy for new beam modalities through the development of transportable systems which permit operation at the user’s facility; (iii) contributing to new dosimetry protocols, best practice reports and educational outreach; and (iv) supporting the verification of clinical implementation by offering dosimetry auditing capabilities through coordination with the clinical medical physics community.
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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 | 0.106 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".