Development of a graphite probe calorimeter for absolute clinical dosimetry
Bibliographic record
Abstract
The aim of this work is to present the numerical design optimization, construction, and experimental proof of concept of a graphite probe calorimeter (GPC) conceived for dose measurement in the clinical environment (U.S. provisional patent 61∕652,540). A finite element method (FEM) based numerical heat transfer study was conducted using a commercial software package to explore the feasibility of the GPC and to optimize the shape, dimensions, and materials used in its design. A functioning prototype was constructed inhouse and used to perform dose to water measurements under a 6 MV photon beam at 400 and 1000 MU∕min, in a thermally insulated water phantom. Heat loss correction factors were determined using FEM analysis while the radiation field perturbation and the graphite to water absorbed dose conversion factors were calculated using Monte Carlo simulations. The difference in the average measured dose to water for the 400 and 1000 MU∕min runs using the TG-51 protocol and the GPC was 0.2% and 1.2%, respectively. Heat loss correction factors ranged from 1.001 to 1.002, while the product of the perturbation and dose conversion factors was calculated to be 1.130. The combined relative uncertainty was estimated to be 1.4%, with the largest contributors being the specific heat capacity of the graphite (type B, 0.8%) and the reproducibility, defined as the standard deviation of the mean measured dose (type A, 0.6%). By establishing the feasibility of using the GPC as a practical clinical absolute photon dosimeter, this work lays the foundation for further device enhancements, including the development of an isothermal mode of operation and an overall miniaturization, making it potentially suitable for use in small and composite radiation fields. It is anticipated that, through the incorporation of isothermal stabilization provided by temperature controllers, a subpercent overall uncertainty will be achieved.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".