Summing Coincidence Errors Using 152Eu Lungs to Calibrate a Lung-counting System: Are They Significant?
Bibliographic record
Abstract
The use of a lung phantom containing 152Eu/241 Am activity can provide a sufficient number of energy lines to generate an efficiency calibration for the in vivo measurements of radioactive materials in the lungs. However, due to the number of energy lines associated with 152Eu, coincidence summing occurs and can present a problem when using such a phantom for calibrating lung-counting systems. A Summing Peak Effect Study was conducted at three laboratories to determine the effect of using an efficiency calibration based on a 152Eu/241 Am lung phantom. The measurement data at all three laboratories showed the presence of sum peaks. While one of the laboratories found only small biases (< 5%) when using the 152Eu/241 Am calibration, the other facilities noted up to 30% positive bias in the 140 keV to 190 keV energy range that prevents the use of the 152Eu/241 Am lung phantom for routine calibrations. Although manufactured by different vendors, the three facilities use similar types of germanium detectors (38 cm2 by 25 mm thick or 38 cm2 by 30 mm thick) for counting. These results underscore the need to evaluate the coincidence summing effect, which appear system dependent, when using a nuclide such as 152Eu for the calibration of low-energy lung counting systems and highlight the problem of using a general calibration curve in place of specific nuclide calibration factors.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".