Electron slowing-down spectra in water for electron and photon sources calculated with the Geant4-DNA code
Bibliographic record
Abstract
Recently, a very low energy extension was added to the Monte Carlo simulation toolkit Geant4. It is intended for radiobiological modeling and is referred to as Geant4-DNA. Its performance, however, has not been systematically benchmarked in terms of transport characteristics. This study reports on the electron slowing-down spectra and mean energy per ion pair, the W-value, in water for monoenergetic electron and photon sources calculated with Geant4-DNA. These quantities depend on electron energy, but not on spatial or angular variables which makes them a good choice for testing the model of energy transfer processes. The spectra also have a scientific value for radiobiological modeling as they describe the energy distribution of electrons entering small volumes, such as the cell nucleus. Comparisons of Geant4-DNA results with previous studies showed overall good agreement. Some differences in slowing-down spectra between Geant4-DNA and previous studies were found at 100 eV and at approximately 500 eV that were attributed to approximations in models of vibrational excitations and atomic de-excitation after ionization by electron impact. We also found that the high-energy part of the Geant4-DNA spectrum for a 1 keV electron source was higher, and the asymptotic high-energy W-value was lower than previous studies reported.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".