Poster — Thur Eve — 22: A water calorimeter for low‐energy particle beams
Bibliographic record
Abstract
In this work, the feasibility of absolute dose to water measurements in low‐energy electron beams using a water calorimeter specifically developed for shallow measurements is established. The calorimeter design consists of a cylindrical glass vessel encased in a block of expanded polystyrene. The vessel has a front window thickness of 1.1 mm, a 4 cm radius, and is 2.5 cm in depth. The vessel‐block assembly sits inside a thermally‐insulated box and is air‐cooled to an operating temperature of 4 °C. Radiation‐induced thermal gradients were simulated in a geometric model of the calorimeter using a finite element analysis software package. 52 absorbed dose to water measurements were performed in a 6 and 8 MeV electron beam ( z max of 1.32 and 1.76 cm, respectively) for 60 seconds at a repetition rate of 400 MU/min and an SSD of 120 cm. Within the vessel, the depth of measurement was set to 1.08 cm relative to the inner front window. The average measured dose to water was 59.6 ± 0.2 cGy/100 MU (6 MeV), and 63.7 ± 0.3 cGy/100 MU (8 MeV). The associated heat transfer corrections were determined to be 1.026 ± 0.003 and 1.017 ± 0.004 for the 6 and 8 MeV beams, respectively. The most significant source of uncertainty in this study was the repeatability (type A, 0.42%). It is expected that performing fewer consecutive measurements under higher dose rate conditions will improve the stability of the thermal background, thereby improving the repeatability and reducing the overall standard uncertainty.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".