Improving the safety of a body composition analyser based on the PGNAA method
Bibliographic record
Abstract
The 252Cf radioisotope and 241Am-Be are intense neutron emitters that are readily encapsulated in compact, portable and sealed sources. Some features such as high flux of neutron emission and reliable neutron spectrum of these sources make them suitable for the prompt gamma neutron activation analysis (PGNAA) method. The PGNAA method can be used in medicine for neutron radiography and body chemical composition analysis. 252Cf and 241Am-Be sources generate not only neutrons but also are intense gamma emitters. Furthermore, the sample in medical treatments is a human body, so it may be exposed to the bombardments of these gamma-rays. Moreover, accumulations of these high-rate gamma-rays in the detector volume cause simultaneous pulses that can be piled up and distort the spectra in the region of interest (ROI). In order to remove these disadvantages in a practical way without being concerned about losing the thermal neutron flux, a gamma-ray filter made of Pb must be employed. The paper suggests a relatively safe body chemical composition analyser (BCCA) machine that uses a spherical Pb shield, enclosing the neutron source. Gamma-ray shielding effects and the optimum radius of the spherical Pb shield have been investigated, using the MCNP-4C code, and compared with the unfiltered case, the bare source. Finally, experimental results demonstrate that an optimised gamma-ray shield for the neutron source in a BCCA can reduce effectively the risk of exposure to the 252Cf and 241Am-Be sources.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".