Sci‐Thur AM: YIS – 06: A Monte Carlo study of macro‐ and microscopic dose descriptors and the microdosimetric spread using detailed cellular models
Bibliographic record
Abstract
Purpose: To develop Monte Carlo models of cell clusters to investigate the relationships between macro‐ and microscopic dose descriptors, quantify the microdosimetric spread in energy deposition for subcellular targets, and determine how these results depend on the computational model. Methods: Microscopic tissue structure is modelled as clusters of 13 to 150 cells, with cell (nuclear) radii between 5 and 10 microns (2 and 9 microns). Energy imparted per unit mass (specific energy or dose) is scored in the nucleus (Dnuc) and cytoplasm (Dcyt) for incident photon energies from 20 to 370 keV. Dose‐to‐water (Dw,m) and dose‐to‐medium (Dm,m) are compared to Dnuc and Dcyt. Single cells and single nuclear cavities are also simulated. Results: Dnuc and Dcyt are sensitive to the surrounding environment with deviations of up to 13% for a single nucleus/cell compared with a multicellular cluster. These dose descriptors vary with cell and nucleus size by up to 10%. Dnuc and Dcyt differ from Dw,m and Dm,m by up to 32%. The microdosimetric spread is sensitive to whether cells are arranged randomly or in a hexagonal lattice, and whether subcellular compartment sizes are sampled from a normal distribution or are constant throughout the cluster. Conclusions: Dnuc and Dcyt are sensitive to cell morphology, elemental composition and the presence of surrounding cells. The microdosimetric spread was investigated using realistic elemental compositions for the nucleus and cytoplasm, and depends strongly on subcellular compartment size, source energy and dose.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".