Comparison of dosimetry calibration factors at the NRCC and the NIST
Bibliographic record
Abstract
In early 1998, three transfer ionization chambers were used to compare the air-kerma and absorbed-dose-to-water calibration factors measured by the National Research Council of Canada (NRCC) and the National Institute of Standards and Technology (NIST). The ratios between the NRCC and NIST calibration factors are 0.9950 and 1.0061 in the case of the absorbed-dose-to-water and air-kerma standards, respectively. In the case of the standard of absorbed dose to water, the combined uncertainty of the ratio between the standards of the two laboratories is about 0.6% and consequently, the observed difference of 0.5% is not significant at the one sigma level. In the case of the standard of air kerma, the combined uncertainty of the ratio between the standards of the two laboratories is about 0.4%, and so the observed difference of 0.61% is significant at the one sigma level. However, this discrepancy is due to the known differences in the methods of assessing the wall correction factor at the two laboratories. Taking into account changes implemented in the standards that form the basis of the calibrations, the present results are consistent with those of the previous comparison done in 1990/91. As a direct result of these differences in the calibration factors, changing from an air-kerma based protocol following TG-21 to an absorbed-dose-to-water based protocol following TG-51, would alter the relationship between clinical dosimetry in Canada and the United States by about 1%. For clinical reference dosimetry, the change from TG-21 to TG-51 could result in an increase of up to 2% depending upon the ion chamber used, the details of the protocol followed and the source of traceability, either NRCC or NIST.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".