Accurate determination of dose‐point‐kernel functions close to the origin using Monte Carlo simulations
Bibliographic record
Abstract
Dose-point-kernel (DPK) functions are used extensively for the dosimetry of gamma and beta emitters in many physical problems. These functions are usually obtained from Monte Carlo simulations where the energy deposited in concentric spherical shells around a point source is tallied. The energy scored in a spherical shell divided by the shell mass is taken as the dose at some effective radius R(eff) of the shell. The effective radius R(eff), defined as the distance of a hypothetical zero-thickness scoring region from the source, can be evaluated in different ways for a finite thickness scoring region. For a shell thickness that is very small compared to the distance from the origin, this exact evaluation method becomes unimportant and the arithmetic mean is usually an accurate estimator for R(eff). However, accurately determining R(eff) can be problematic for the innermost regions when the radial dose function D(r) varies considerably over the finite spherical shell thickness. In this work, a new method for determining R(eff) is introduced which yields consistent results for any shell thickness, thus improving on previous Monte Carlo calculations for DPKs at or near the origin. Dimensionless DPK functions for monoenergetic electrons were reevaluated using EGSnrc with an emphasis on accuracy and consistency near the origin using our new method for determining R(eff). These improved functions were implemented in a software code to calculate the DPKs for an exhaustive list of 546 beta emitters, thus extending the compilation from previous works.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".