Application and Future Prospects of Computational Human Models in Conjunction with Monte Carlo Simulations in Radiation Dosimetry
Bibliographic record
Abstract
In the field of radiation dosimetry research, the combination of computer-based human models and Monte Carlo simulation methods has proven to be indispensable and enables a differentiated and detailed approach to studying radiation exposure and its effects. This article examines in depth the application of radiation dosimetry to computer-aided human models combined with Monte Carlo simulations in assessing ionizing radiation doses and illustrates its critical role in this field. This article systematically analyses three main techniques for assessing ionizing radiation dose: direct measurement, phantom model measurement and the advanced method using computer-aided human models with Monte Carlo simulations, highlighting the notable advantages of the latter method. In addition, this article introduces a variety of software tools and discusses the basic principles and wide-ranging applications of computational human models based on Monte Carlo simulations, highlighting their adaptability and effectiveness in this area. Finally, the paper provides a visionary outlook on the evolving landscape and future possibilities of integrating computer-based human models with Monte Carlo simulation methods for radiation dose estimation and predicts significant advances in the field.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".