Poster — Wed Eve—23: Electron Energy Spectral Analyses on Electron Backscatter in Solid Water Using Low‐Energy Electron Beams
Bibliographic record
Abstract
Electron energy spectral analyses were carried out to evaluate the dosimetric performance of electron backscatter, when Solid Water was used to substitute water as phantom in electron radiotherapy using low‐energy electron beams (≤ 6 MeV). Monte Carlo simulation (EGSnrc‐based code) was used to predict electron energy spectra of Solid Water and water slabs above 0.3 mm of lead (Pb) layers, using the 4 and 6 MeV electron beams produced by a Varian 21 EX linear accelerator. For the simulation geometry, slabs of Solid Water and water on top of a piece of Pb layer were irradiated by electron beams ( applicator and cutout) with the source‐to‐surface distance equal to 100 cm. The thickness of Solid Water or water above the Pb layer was equal to 0.5 and 1 cm. Electron energy spectra were determined at scoring planes at depths of 0, 0.5 and 1 cm of the phantoms. For comparison, Monte Carlo simulations were repeated with Pb layers taken out from the phantoms with the same experimental configuration. Analyses of the electron energy spectra at different depths showed that deviation of energy spectra between Solid Water and water was more significant in the high‐energy range (i.e. close to the maximal electron energy) than lower range corresponding to the electron backscatter. The good agreement for the parts of the spectra representing the electron backscatter in Solid Water and water indicated that Solid Water performed well as a substitute of water for the electron backscatter when using low‐energy electron beams.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".