Monte Carlo calculation of organ and effective doses due to photon and neutron point sources and typical X-ray examinations: Results of an international intercomparison exercise
Bibliographic record
Abstract
This paper summarizes the results of an intercomparison on the use of the ICRP Reference Computational Phantoms with radiation transport codes, which was organized by EURADOS working group 6. Three exercises are described: exposure to an anterior-posterior (AP) photon point source, exposure to an AP neutron point source, and exposure to two typical medical X-ray examinations. The three exercises received 17, 8 and 8 solutions, respectively. Participants originated from fifteen different countries, and used a wide range of Monte Carlo codes. Due to difficulties in defining the precise source location unambiguously in the exercise description, agreement to within ∼10% of the reference solution was considered satisfactory for a given participant's results. Although some participants provided initial solutions in good agreement with the reference solutions, differences of several tens of percent, or even several orders of magnitude, were exhibited for many of the others. Following feedback and suggestions from the organizers, revised solutions were submitted by some of the participants for the photon exercises; in general, agreement was improved. The overall observations from these three intercomparison exercises are summarized and discussed.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".