A library of proton lineal energy spectra spanning the full range of clinically relevant energies
Bibliographic record
Abstract
PURPOSE: ) based models. However, the development of microdosimetric spectrum-based biological effects models is hampered by the extreme computational difficulty of calculating microdosimetric spectra. Given a precomputed library of lineal energy spectra for monoenergetic protons, a weighted summation can be performed which yields the lineal energy spectrum of an arbitrary polyenergetic beam. Using this approach, lineal energy spectra can be rapidly calculated on a voxel-by-voxel level. ACQUISITION AND VALIDATION METHODS: Monoenergetic proton tracks generated using Geant4-DNA were imported into SuperTrack, a GPU-accelerated software for calculation of microdosimetric spectra. Libraries of proton lineal energy spectra which span the energy range of 0-300 MeV were computed. The libraries were validated by comparison to Monte Carlo calculations in the literature, as well as lineal energy spectra measured experimentally with a tissue equivalent proportional counter. DATA FORMAT AND USAGE NOTES: The lineal energy libraries have been made available in three data formats, two are plain-text, .csv and .les, and one binary encoded, root. Library files include the lineal energy bin abscissa in keV per micron and the unnormalized number of counts occurring within that bin. A computational technique for summation of the library files to yield the lineal energy of a polyenergetic beam is described in this work. POTENTIAL APPLICATIONS: The lineal energy libraries can be used to rapidly determine the lineal energy spectra at the location of cell cultures for in-vitro experiments and in each voxel of a treatment plan for in-vivo outcome modelling. These libraries have already been incorporated into RayStation 2023B-IonPG for lineal energy spectra calculation and we anticipate they will be incorporated into further dose calculation engines and Monte Carlo toolkits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".