Determination of the output of the linac-MR system, installed at the Cross Cancer Institute, using alanine dosimeters traceable to the Canadian primary standard of absorbed dose to water
Bibliographic record
Abstract
The National Research Council of Canada has developed a capability for using alanine dosimeters to measure absorbed dose to water at therapy dose levels in Co-60 and MV photon beams. The stated uncertainty in the determination of dose using such dosimeters in a clinical radiation beam is estimated to be less than 1 % for doses in the range 10 Gy to 100 Gy. This NRC capability was applied to the linac-MR installed at the Cross Cancer Institute to provide external validation of the dose measured by medical physicists at the centre using standard dosimetry protocols and ionization chambers. Control dosimeters were used to investigate any impact of shipping, no significant effect was seen. The dose was determined from the alanine measurements using a calibration in a Co-60 zero-magnetic field combined with correction factors for the actual irradiation field being a ~ 7 MV x-ray beam in a 0.5 T magnetic field (1.005 and 0.998 respectively). The agreement between stated and measured doses was better than 1 %, less than the combined k=1 uncertainty of the alanine and ion chamber dose measurements.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".