Characterization of a multi‐axis ion chamber array
Bibliographic record
Abstract
PURPOSE: The aim of this work was to characterize a multi-axis ion chamber array (IC PROFILER; Sun Nuclear Corporation, Melbourne, FL, USA) that has the potential to simplify the acquisition of LINAC beam data. METHODS: The IC PROFILER (or panel) measurement response was characterized with respect to radiation beam properties, including dose, dose per pulse, pulse rate frequency (PRF), and energy. Panel properties were also studied, including detector-calibration stability, power-on time, backscatter dependence, and the panel's agreement with water tank measurements [profiles, fractional depth dose (FDD), and output factors]. RESULTS: The panel's relative deviation was typically within (+/-) 1% of an independent (or nominal) response for all properties that were tested. Notable results were (a) a detectable relative field shape change of approximately 1% with linear accelerator PRF changes; (b) a large range in backscatter thickness had a minimal effect on the measured dose distribution (typically less than 1%); (c) the error spread in profile comparison between the panel and scanning water tank (Blue Phantom, CC13; IBA Schwarzenbruck, DE) was approximately (+/-) 0.75%. CONCLUSIONS: The ability of the panel to accurately reproduce water tank profiles, FDDs, and output factors is an indication of its abilities as a dosimetry system. The benefits of using the panel versus a scanning water tank are less setup time and less error susceptibility. The same measurements (including device setup and breakdown) for both systems took 180 min with the water tank versus 30 min with the panel. The time-savings increase as the measurement load is increased.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".