GEANT4 simulations of cosmic muon background in CEMRC BEGe lung detectors and detection sensitivity optimization of trans-uranic radionuclides
Bibliographic record
Abstract
In this work, we discuss two results from GEANT4 simulations of the broad-energy germanium (BEGe) lung counting system used by the Carlsbad Environmental Monitoring and Research Center (CEMRC) internal dosimetry laboratory. The first component of the presented research consists of the integration of the cosmic muon background into the environmental background spectra and updated calculations of the minimum-detectable activities (MDAs) that were previously published by the authors. This is followed by an investigation of methods to increase the detection sensitivity to low-energy γ rays produced by the trans-uranic radioisotopes of interest by reducing the number of background counts observed in the region-of-interest (ROI) of the measured γ ray spectra measured by the CEMRC in vivo counting system. Two methods were investigated for improving the MDAs: (1) a reduction in the thickness of the germanium detector crystals, and (2) the addition of a Compton-suppression detection system surrounding the germanium detectors, acting as a anti-coincidence veto counter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".