Sci-Fri PM: Delivery - 07: Analysis of Systematic Uncertainties in Monte Carlo Calculated Beam Quality Conversion Factors
Bibliographic record
Abstract
The beam quality conversion factor, kQ, can be calculated directly using Monte Carlo simulations. In order to validate the use of these calculated values, an evaluation of the associated systematic uncertainties is required. In a Monte Carlo simulation, the relative statistical uncertainty can be reduced by increasing the number of histories at the expense of computing time. Other sources of uncertainty that must be considered originate from possible variations in photon cross-sections, stopping powers, chamber dimensions, the choice of source use for the simulation and variation of W/e with beam energy. In this study, these systematic uncertainties are quantified with Monte Carlo calculations using different methods with the EGSnrc code system. In most cases, it is possible to assign an uncertainty on a given quantity (e.g. photon cross-sections) based on information from the literature. The specific parameter is changed by one standard deviation and the change in kQ, ΔkQ, is calculated separately for each parameter, yielding an uncertainty in kQ from each source of uncertainty. The overall uncertainty in kQ is determined using well-known methods. Uncertainty due to the variation of photon cross-sections depends on whether or not the cross-section uncertainties are correlated. If correlated the total systematic uncertainty in kQ amounts to 0.64%, or 1.0% if one assumes uncorrelated photon cross-sections. The uncertainty in W/e (0.5%) is a major source of uncertainty which also affects all other calculations of kQ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.002 |
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".