Computational generation of tailored radionuclide libraries for alpha-particle and gamma-ray spectrometry
Bibliographic record
Abstract
Radionuclide identification is a radioanalytical method employed in various scientific disciplines that utilize alpha-particle or gamma-ray spectrometric assays, ranging from astrophysics to nuclear medicine. Radionuclide libraries in conventional radionuclide identification systems are crafted in a manual fashion, accompanying labor-intensive and error-prone user tasks and hindering library customization. This research presents a computational algorithm and the architecture of its dedicated software that can automatically generate tailored radionuclide libraries. Progenitor-progeny recurrence relations were modeled to enable recursive computation of radionuclide subsets. This theoretical concept was incorporated into open-source software called and validated against four actinide decay series and 12 radioactive substances, including a uranium-glazed legacy Fiestaware, natural uranium and thorium sources, a <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mmultiscripts> <a:mi>Ra</a:mi> <a:mprescripts/> <a:none/> <a:mn>226</a:mn> </a:mmultiscripts> </a:math> sample, and the medical radionuclides <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"> <b:mmultiscripts> <b:mi>Ac</b:mi> <b:mprescripts/> <b:none/> <b:mn>225</b:mn> </b:mmultiscripts> <b:mo>,</b:mo> <b:mo> </b:mo> <b:mmultiscripts> <b:mi>Lu</b:mi> <b:mprescripts/> <b:none/> <b:mn>177</b:mn> </b:mmultiscripts> </b:math> , and <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:mmultiscripts> <c:mi>Tc</c:mi> <c:mprescripts/> <c:none/> <c:mrow> <c:mn>99</c:mn> <c:mi>m</c:mi> </c:mrow> </c:mmultiscripts> </c:math> . The developed algorithm yielded radionuclide libraries for all the tested specimens within minutes, demonstrating its efficiency and applicability across diverse scenarios. The proposed approach introduces a framework for computerized radionuclide library generation, thereby trivializing library-driven radionuclide identification and facilitating the spectral recognition of unregistered radionuclides in radiation spectrometry. Published by the American Physical Society 2024
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".