An EGSnrc Monte Carlo‐calculated database of TG‐43 parameters
Bibliographic record
Abstract
Monte Carlo methods are used to calculate a complete TG-43 dosimetry parameter data set for 27 low-energy photon emitting brachytherapy sources (18 125I and 9 l03Pd). All Monte Carlo calculations are performed using the EGSnrc user-code BrachyDose. TG-43 dosimetry parameters, including dose rate constants, radial dose functions (with functional fitting parameters), and anisotropy data, are calculated with finer spatial resolution, greater range of distances, and smaller uncertainties than data currently available in the literature for many of these sources. In particular, for most of the seeds, this is the first time that anisotropy data have been tabulated at distances less than 0.5 cm from the source. These calculations employ the state-of-the-art XCOM photon cross sections, and detailed source geometries are modeled using Yegin's multigeometry package. This data set serves as a completely independent verification of the currently available dosimetry parameters calculated using other Monte Carlo codes, including MCNP and PTRAN. This report also describes the Carleton Laboratory for Radiotherapy Physics TG-43 Parameter Database, a publicly accessible web site (at http://www.physics.carleton.ca/clrp/seed_database/) through which all of the data calculated for this study can be accessed. Also available on the web site are descriptions of the methods and Monte Carlo models used in this study and comparisons of data calculated in this study with data calculated by other authors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".