Software to Compensate Coincidence Losses in the Environmental Sample Analysis of a Digital Anti-Compton Spectrometer
Bibliographic record
Abstract
Anti-Compton spectroscopy is a useful approach to analyze low-level contaminants in environmental samples that would otherwise be obscured by the Compton signal of the natural radionuclides present in the sample matrix.An example of this is the sensitive identification of 137 Cs in soil samples.A challenge of the anti-Compton technique is that the anti-coincidence veto to remove Compton scattered photons also suppresses the identification of contaminant radionuclides that have cascade gamma emissions such as 60 Co.This is a major limitation of the technique in environmental sample analysis.The time stamped pulse analysis method presented in this paper recovers the undesired full-energy-peak (FEP) coincidence losses for cascade radionuclides that trigger the anti-Compton spectrometer veto while retains suppression of true Compton events to keep the improved detection limit, thus extending the usefulness of the anti-Compton spectroscopy.In this study, the software has been used to analyse the radionuclides with a relative simple decay scheme, such as 22 Na, 60 Co and 88 Y, and a complex decay scheme, such as 125 Sb, 134 Cs, 152 Eu and 154 Eu.The results have indicated that in the reconstruct anticoincidence spectra the FEP restoration efficiency, both for minor and major peaks, is near 100%.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".