Development of anthropomorphic computational phantoms at the UFPE
Bibliographic record
Abstract
To evaluate the amount of energy deposited in radiosensitive organs and tissues of the human body, when an anthropomorphic phantom is irradiated, researchers in numerical dosimetry use the so-called exposure computational models (ECMs). One can imagine an ECM as a virtual scene composed of a phantom in a mathematically defined position in relation to a radioactive source. The source in these ECMs produces the initial state of the simulation: the position, direction, and energy with which each particle enters the phantom are essential variables. For subsequent states of a particle history, robust Monte Carlo (MC) codes are used. For the subsequent states of a particle's history, robust Monte Carlo (MC) codes are used, which simulate the average free path that the particle performs without interacting, its interaction with the atoms in the medium and the amount of energy deposited per interaction. MC codes also evaluate normalization quantities, so the results are printed in text files in the form of conversion coefficients between the absorbed dose and the selected normalization quantity. From the 2000s, the authors have published ECMs where a voxel phantom is irradiated by photons in the environment of the MC code EGSnrc (EGS = Electron Gamma Shower; nrc = National Research Council Canada). The production of articles, dissertations and theses required the use of specific computational tools, such as the FANTOMAS, DIP (Digital Image Processing) and Monte Carlo applications, for the various steps of numerical dosimetry, which ranges from the preparation of input files to the execution from the ECM to the organization and graphical and numerical analysis of the results. This article reviews computational phantoms for dosimetry mainly those produced in DEN-UFPE dissertations and thesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".