Po‐Poster ‐ 14: In‐house leak testing with a multi‐channel analyzer system
Bibliographic record
Abstract
The URSA‐II Multi‐Channel Analyzer (MCA) (URSA‐II Radiation Safety Associates, Inc. Hebron, CT) connected to a NaI well counter (PR‐4112, Ludlum Measurements, Inc. Sweetwater, TX) is an ideal instrument for performing accurate assays of samples with low levels of activity. Clinically this is important for assaying leak‐test samples taken from sealed sources. Leak testing is a Canadian Nuclear Safety Commission (CNSC) licensing requirement for all sealed sources with activities exceeding 50 MBq. The URSA‐II was evaluated by calibrating the system using a set of known Co‐60 and Cs‐137 sources and then assaying a second set of known Co‐60 and Cs‐137 test sources. The measured activities for the test sources set were within ±5% of the values listed on the certificate once corrected for decay. We found the minimum detectable activity (MDA) for this instrument is 7 Bq for Co‐60 and 3 Bq for Cs‐137 when using a fixed counting time of 60 seconds. These MDAs demonstrate this instrument would have no difficulties in determining if a leak test sample is under the 200 Bq action level as dictated by the CNSC in the CNSC regulatory document R‐116 for leak testing. The URSA‐II is a convenient and simple tool for performing in‐house leak testing as part of a standard license compliance program for class II clinical installations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".