Calculation of photon energy deposition kernels and electron dose point kernels in water
Bibliographic record
Abstract
Effects of changes in the physics of EGSnrc compared to EGS4/PRESTA on energy deposition kernels for monoenergetic photons and on dose point kernels for beta sources in water are investigated. In the diagnostic energy range, Compton binding corrections were found to increase the primary energy fraction up to 4.5% at 30 keV with a corresponding reduction of the scatter component of the kernels. Rayleigh scattered photons significantly increase the scatter component of the kernels and reduce the primary energy fraction with a maximum 12% reduction also at 30 keV where the Rayleigh cross section in water has its maximum value. Sampling the photo-electron angular distribution produces a redistribution of the energy deposited by primaries around the interaction site causing differences of up to 2.7 times in the backscattered energy fraction at 20 keV. Above the pair production threshold, the dose distribution versus angle of the primary dose component is significantly different from the EGS4 results. This is related to the more accurate angular sampling of the electron-positron pair direction in EGSnrc as opposed to using a fixed angle approximation in default EGS4. Total energy fractions for photon beams obtained with EGSnrc and EGS4 are almost the same within 0.2%. This fact suggests that the estimate of the total dose at a given point inside an infinite homogeneous water phantom irradiated by broad beams of photons will be very similar for kernels calculated with both codes. However, at interfaces or near boundaries results can be very different especially in the diagnostic energy range. EGSnrc calculated kernels for monoenergetic electrons (50 keV, 100 keV, and 1 MeV) and beta spectra (32P and 90Y) are in excellent agreement with reported EGS4 values except at 1 MeV where inclusion of spin effects in EGSnrc produces an increase of the effective range of electrons. Comparison at 1 MeV with an ETRAN calculation of the electron dose point kernel shows excellent agreement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".