SU‐C‐105‐05: Reference Dosimetry of High‐Energy Electron Beams with a Farmer‐Type Ionization Chamber
Bibliographic record
Abstract
Purpose: To investigate gradient effects and provide Monte Carlo calculated beam quality conversion factors to characterize the Farmer‐type NE2571 ion chamber for high‐energy reference dosimetry of clinical electron beams. Methods: The EGSnrc code system is used to calculate the absorbed dose to water and to the gas in a fully modeled NE2571 chamber as a function of depth in a water phantom. Electron beams incident on the surface of the phantom are modeled using realistic BEAMnrc accelerator simulations and electron beam spectra. Beam quality conversion factors are determined using calculated doses to water and to air in the chamber in high‐energy electron beams and in a cobalt‐60 reference field. Calculated water‐to‐air stopping power ratios are employed for investigation of the overall ion chamber perturbation factor. Results: An upstream shift of 0.3–0.4 multiplied by the chamber radius, r_cav, both minimizes the variation of the overall ion chamber perturbation factor with depth and reduces the difference between the beam quality specifier (R5 0) calculated using ion chamber simulations and that obtained with simulations of dose‐to‐water in the phantom. Beam quality conversion factors are obtained at the reference depth and gradient effects are optimized using a shift of 0.2r_cav. The photon‐electron conversion factor, k_ecal, amounts to 0.906 when gradient effects are minimized using the shift established here and 0.903 if no shift of the data is used. Systematic uncertainties in beam quality conversion factors are investigated and amount to between 0.4 to 1.1% depending on assumptions used. Conclusion: The calculations obtained in this work characterize the use of an NE2571 ion chamber for reference dosimetry of high‐energy electron beams. These results will be useful as the AAPM continues to review their reference dosimetry protocols.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".