Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 11: Commissioning of a system for the measurement of electron stopping powers
Bibliographic record
Abstract
Purpose: To develop the techniques required to experimentally determine electron stopping powers for application in primary standards and dosimetry protocols. Method and Materials: A large‐volume HPGe detector system (>80% efficiency) was commissioned for the measurement of high energy (5–35 MeV) electron beams. As a proof of principle the system was used with a Y‐90/Sr‐90 radioactive source. Thin plates of absorbing material (< 0.1 gcm‐2) were then placed between the source and detector and the emerging electron spectrum was acquired. The full experimental geometry was modelled using the EGSnrc package to validate the detector design, optimize the experimental setup and compare measured and calculated spectra. Results: The biggest challenge using a beta source was to identify a robust spectral parameter to determine for each measurement. An end‐point‐fitting routine was used to determine the maximum energy, Emax, of the beta spectrum for each absorber thickness t. The parameter dEmax/dt is related to the electron stopping power and the same routine was applied to both measured and simulated spectra. Although the standard uncertainty in dEmax/dt was of the order of 5 %, by taking the ratio of measured and Monte Carlo values for dEmax/dt the uncertainty of the fitting routine was eliminated and the uncertainty was reduced to less than 2 %. The agreement between measurement and simulation was within this uncertainty estimate. Conclusion: The investigation confirmed the experimental approach and demonstrated that EGSnrc could accurately determine correction factors that will be required for the final measurement setup in a linac beam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".