SU‐E‐T‐05: Comparing DNA Strand Break Yields for Photons under Different Irradiation Conditions with Geant4‐DNA
Bibliographic record
Abstract
PURPOSE: To validate and scrutinize published DNA strand break data with Geant4-DNA and a probabilistic model. To study the impact of source size, electronic equilibrium and secondary electron tracking cutoff on direct relative biological effectiveness (DRBE). METHODS: density. The ROI was irradiated with mono-energetic photons, with a uniformly distributed volumetric isotropic source (0.28, 1.5 keV) or a plane beam (0.662, 1.25 MeV), of variable size. Electrons were tracked down to 50 or 10 eV, with G4-DNA processes and energy transfer greater than 10.79 eV was scored. Based on volume ratios, each scored event had a 0.0388 probability of happening on either DNA helix (break). Clusters of at least one break on each DNA helix within 3.4 nm were found using a DBSCAN algorithm and categorized as double strand breaks (DSB). All other events were categorized as single strand breaks (SSB). RESULTS: Geant4-DNA is able to reproduce strand break yields previously published. Homogeneous irradiation conditions should be present throughout the ROI for DRBE comparisons. SSB yields seem slightly dependent on the primary photon energy. DRBEs show a significant increasing trend for lower energy incident photons. A lower electron cutoff produces higher SSB yields, but decreases the SSB/DSB yields ratio. The probabilistic and geometrical DNA models can predict equivalent results. CONCLUSIONS: Using Geant4, we were able to reproduce previously published results on the direct strand break yields of photon and study the importance of irradiation conditions. We also show an ascending trend for DRBE with lower incident photon energies. A probabilistic model coupled with track structure analysis can be used to simulate strand break yields. NSERC, CIHR.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".