TH-C-19A-02: Characterization of An Actively Controlled Graphite Probe Calorimeter
Bibliographic record
Abstract
Purpose: To construct and experimentally evaluate the performance of a miniature probe-format graphite calorimeter (GPC) with built-in active control. The GPC is the first ever calorimeter designed specifically for routine clinical use. Methods: The original GPC design, developed as part of our previous work, was modified to accommodate a micro-thin, resistive heating element and sensing thermistors embedded in the outermost graphite layer. A software-based process control loop was written to maintain a predefined set point temperature throughout the detector by precisely modulating the current flowing through the resistive element. Continuous feedback is provided to the controller by the additional thermistors. A functioning prototype was constructed in-house and operated in adiabatic mode in a 6 MV photon beam. Ten sets of measurements of varying duration (10 to 60 seconds) and repetition rate (200 to 600 MU/min) were analyzed. Reproducibility, linearity and dose rate dependence were assessed. Results: A total of 47 individual measurements were performed using the active GPC. The reproducibility, defined here as the sample standard deviation weighted across all 10 measurement sets, was found to be 0.37%. Similarly, the standard error (type A uncertainty) was 0.17%. Linearity was quantified by plotting signal as a function of monitor units delivered at 600 MU/min. The adjusted R-square of the resulting linear fit was 0.9996. Dose rate dependence, evaluated as the change in normalized response (signal/MU) as a function of repetition rate, was found to be statistically insignificant. Conclusions: The incorporation of active control has resulted in a marked improvement in the GPC's reproducibility and it allows the user to now perform measurements within minutes of setup. With this refinement, it is estimated that the overall standard uncertainty in the absolute determination of dose to water is 1%, making the GPC comparable to a calibrated ionization chamber. This research has received financial support from Sun Nuclear Corporation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".