SU‐C‐BRE‐07: Sensitivity Analysis of the Threshold Energy for the Creation of Strand Breaks and of Single and Double Strand Break Clustering Conditions
Bibliographic record
Abstract
Purpose: To analyse the sensitivity of the creation of strand breaks (SB) to the threshold energy (Eth) and thresholding method and to quantify the impact of clustering conditions on single strand break (SSB) and double strand break (DSB) yields. Methods: Monte Carlo simulations using Geant4‐DNA were conducted for electron tracks of 280 eV to 220 keV in a geometrical DNA model composed of nucleosomes of 396 phospho‐diester groups (PDGs) each. A strand break was created inside a PDG when the sum of all energy deposits (method 1) or energy transfers (method 2) was higher than Eth or when at least one interaction deposited (method 3) or transferred (method 4) an energy higher than Eth. SBs were then clustered into SSBs and DSBs using clustering scoring criteria from the literature and compared to our own. Results: The total number of SBs decreases as Eth is increased. In addition, thresholding on the energy transfers (methods 2 and 4) produces a higher SB count than when thresholding on energy deposits (methods 1 and 3). Method 2 produces a step‐like function and should be avoided when attempting to optimize Eth. When SBs are grouped into damage patterns, clustering conditions can underestimated SSBs by up to 18 % and DSBs can be overestimated by up to 12 % compared to our own implementation. Conclusion: We show that two often underreported simulation parameters have a non‐negligible effect on overall DNA damage yields. First more SBs are counted when using energy transfers to the PDG rather than energy deposits. Also, SBs grouped according to different clustering conditions can influence reported SSB and DSB by as much as 20%. Careful handling of these parameters is required when trying to compare DNA damage yields from different authors. Research funding from the governments of Canada and Quebec. PP acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".