A Comparison Study of the Solid Phantom and Water Phantom Using 6 MV and 15 MV Photon Energies, Depending on the Depth
Bibliographic record
Abstract
Water is such an environmental element that is considered the best human body tissue equivalent. In the field of dosimetry studies, water is frequently used. This comparison study is conducted by a solid phantom and a water phantom with 6 MV and 15 MV photon energies, respectively. A cylindrical-type ionization chamber is used to collect charge when beams are on. The distance between the ray source and the surface of the phantom was fixed at 100 cm i.e. to SSD (Source to Surface Distance) of during the experiment. Chamber travels 1 cm to 20 cm in both phantoms and an electrometer is attached in the experimental set-up to measure the charge. The field size was 10x10 cm2. The relative deviation ratio of the solid phantom to the water phantom was calculated. In the result, the maximum deviation was 0.64%, while the minimum deviation was 0%, corresponding to the depths of 1 cm and 2.5 cm, respectively, for 6 MV and at 15 MV, maximum deviation and minimum deviation were 1.90% and 0.167% respectively, corresponding to the depths of 1.5 cm and 13 cm. Therefore, it can be said that the solid phantom can overcome the disadvantages of installation time required for the water phantom and problems while water level changing for depth measurement, simultaneously can used to measure the radiological dose precisely. Bangladesh J. Nuclear Med. 27(1): 34-38, 2024
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".