First‐stage validation of a portable imageable MR‐compatible water calorimeter
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to design a water calorimeter with three goals in mind: (a) To be fully magnetic resonance (MR)-compatible; (b) To be imaged using kV cone beam computed tomography (CBCT), MV portal imaging or MRI for accurate positioning; (c) To accommodate both vertical and horizontal beam incidence, as well as volumetric deliveries or Gamma Knife®. Following this, the calorimeter performance will be measured using an accelerator-based high-energy photon beam. METHODS: A portable 4°C cooled stagnant water calorimeter was built using MR-compatible materials. The walls consist of layers of acrylic plastic, aerogel-based material acting as thermal insulation, as well as tubing for coolant to flow to keep the calorimeter temperature stable at 4°C. The lid contains additional pathways for coolant to flow through as well as two hydraulically driven stirrers. The water calorimeter was positioned in an Elekta Versa using kV CBCT imaging as well as orthogonal MV image pairs. Absolute absorbed dose to water was then determined under a 6 MV flattening filter-free (FFF) beam. This was compared against reference dosimetry results that were measured under identical conditions with an Exradin A1SL ionization chamber with a calibration coefficient directly traceable to the National Research Council Canada. RESULTS: The dose to water determined with the calorimeter (n = 30) agreed with the A1SL ionization chamber reference dose measurements (n = 15) to within 0.25%. The uncertainty associated with the water calorimeter absorbed dose measurement was estimated to be 0.54% (k = 1). CONCLUSIONS: An MR-compatible water calorimeter was successfully built and absolute absorbed dose to water under a conventional 6 MV FFF beam was determined successfully as a first-stage validation of the system.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".