Sci-Thur PM: YIS - 09: Development of a graphite probe calorimeter for absolute clinical dosimetry: Numerical design optimization, prototyping and experimental proof-of-concept
Bibliographic record
Abstract
In this work, the feasibility of absolute dose to water measurements using a small-scale graphite probe calorimeter (GPC) in a clinical environment is established. A numerical design optimization study was conducted by simulating the heat transfer in the GPC resulting from irradiation using a finite element method software package. The choice of device shape, dimensions and materials was made to minimize the heat loss in the sensitive volume of the GPC. The resulting design, which incorporates a novel aerogel-based thermal insulator, was built in-house. Absorbed dose to water measurements were made under standard conditions in a 6 MV 1000 MU/min photon beam and subsequently compared against TG-51 derived values. The average measured dose to water was 95.7 ±1.4 cGy/100 MU, as compared to an expected value of 96.6 cGy/100 MU. The Monte Carlo-calculated graphite to water dose conversion factor was 1.099, while the derived heat loss correction factors varied between 1.005 and 1.013. The most significant sources of uncertainty were the repeatability (type A, 1.4%) and thermistor calibration (type B, 2.1%). The contribution of these factors to the overall uncertainty is expected to decrease significantly upon the implementation of active thermal stabilization provided by a temperature controller and direct electrical calibration, respectively. This work demonstrates the feasibility of using the GPC as a practical clinical absolute photon dosimeter and will serve as the basis for a miniaturized version applicable to small and composite fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".