Streamlined open-source gel dosimetry analysis in 3D slicer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three dimensional dosimetry is being used in an increasingly wide variety of clinical applications as more gel and radiochromic plastic dosimeters become available. However, accessible 3D dosimetry analysis tools have not kept pace. 3D dosimetry data analysis is time consuming and laborious, creating a barrier to entry for busy clinical environments. To help in the adoption of 3D dosimetry, we have produced a streamlined, open-source dosimetry analysis system by developing a custom extension in 3D Slicer, called the Gel Dosimetry Analysis slicelet, which enables rapid and accurate data analysis. To assist those interested in adopting 3D dosimetry in their clinic or those unfamiliar with what is involved in a 3D dosimeter experiment, we first present the workflow of a typical gel dosimetry experiment. This is followed by the results of experiments used to validate, step-wise, each component of our software. Overall, our software has made a full 3D gel dosimeter analysis roughly 20 times faster than previous analysis systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it