Dosimetry of biological irradiations using radiochromic films
Bibliographic record
Abstract
Delivering accurate radiation dose to blood specimens during biological irradiations is essential in quantifying damage of ionizing radiation. To estimate dose to blood samples as accurately as possible, pieces of EBT2 model GAFCHROMIC™ film were placed within an approximately 10 mm finely ground rice layer that was used to simulate test specimens inside 40 mL plastic flasks. Irradiations of flasks were carried out using an X-RAD 320 irradiator with a beam quality of 320 kVp and a measured half value layer of 1.12 mm Cu, in air and in a full scattering setup which consisted of either rice or Solid Water™ (SW) surrounding flasks, filled to the same level at top of the flasks, together with a 5 cm thick SW slab beneath them. Outputs, per cent depth doses and beam profiles at different depths were measured and compared between setups. For the same setting, the dose delivered to the middle flask under the full scattering setup is 22% larger than with the in-air setup at the depth of the specimen and 9.2% more homogeneous across the specimen thickness of 10 mm (2.3% variation in comparison to the surface). Rice showed a fairly similar performance to SW within 1% at the same depth of 10 mm. Experimental setup based on full scattering conditions was shown to provide faster, more homogenous and fairly uniform dose delivery to biological specimens in comparison to conventionally used in-air setups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".