Modified optical computed tomography scanner for low temperature scanning
Bibliographic record
Abstract
Abstract Large radiochromic hydrogels, such as ferrous xylenol formulations require several hours to warm from the storage temperature of 278 K to 293 K for optical CT scanning and irradiation. This warm-up time can be avoided if gels are scanned at 278 K, resulting in a more practical 3D dose measurement. In this study a Vista 16 optical CT scanner was modified to allow scanning at 278 K, below the temperature where condensation forms on the aquarium windows. The refractive index matching liquid was first cooled to 276K in order to cool the aquarium as it was filled. The modification involved pumping cool, dry air into the aquarium scanner compartment to provide a positive pressure, preventing condensation on the aquarium windows. A warming rate of 2 K per hour was achieved with passive cooling which relied on the liquid-filled aquarium’s thermal mass. The radiochromic reaction rate decreased with temperature, requiring an increased post-irradiation wait time from 1 hour at 293 K to 1.5 hours at 278 K. In addition, initial sample transmission was greater by avoiding the auto-darkening that occurred as the samples were warmed from 278 K to 293 K over several hours. This increase in transmission resulted in a larger dynamic range for the dosimeter system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".