Bibliographic record
Abstract
Abstract Several 3D dosimeters are commercially available. However, there are many circumstances that require a customized 3D dosimeter. Examples include feasibility tests of non-standard treatment modalities, inhomogeneous tissue configurations, unique shapes and sizes and teaching. In this session, general approaches for preparing radiochromic dosimeters, Fricke and polymer gel dosimeters, micelle gel and silicone dosimeters were presented. Advise will be given to developers of new 3D dosimeters. For optical readout, light absorption and scatter can limit the practical size of dosimeters. Specifically, increasing from 5 to 15 cm diameter dosimeters is optically challenging. Strategies to maximize initial optical transmission were presented. For MRI readout, the dose resolution is determined by both the dosimeter sensitivity and the pulse sequence parameters and the accuracy is determined by the sensitivity of the dosimeter to temperature and dose rate, next to imaging performance. For X-ray CT imaging, the dose resolution is determined by the sensitivity of the dosimeter which largely depends on the polymer density that can be achieved. The importance of characterizing the dosimeter in terms of dose sensitivity and stability, spatial integrity, dose rate and fractionation dependence, oxygen and ambient light sensitivity, temperature sensitivity and thermal history were emphasized. The dosimeter requirements also dictate the types of vessels and scanners appropriate for readout. For example, the preferred dosimeter formulation may include a compound that is incompatible with the preferred vessel.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".