Spectral sensitivity study of dose distributions for a commercial convolution/superposition algorithm
Bibliographic record
Abstract
The focus of this study is to validate whether the sensitivity of dose distribution following the interface of different media can be used to distinguish between small variations of photon energy spectra in the context of the convolution/superposition algorithm in the polyenergetic implementation (Philips Pinnacle3, ADAC Laboratories, Milpitas, CA). Calculations were performed in homogeneous water and heterogeneous lung/water phantoms. Spectra were generated, in which the weights of the low-, medium- and high-energy components were adjusted sequentially. The heterogeneity correction factor CFlung, the D20/D10 ratio for homogeneous water and logarithmic derivative in buildup region LDbuildup were assessed for their relative ability to discriminate between different spectra for various field sizes. In accordance with another study (Charland et al 2004), the superior discrimination ability of the CFlung and LDbuildup tests over the D20/D10 test was observed for changes in an energy component as small as 0.3% of the total weight in the energy spectrum. Furthermore, new tests utilizing transverse dose profile data for discriminating between spectra, Fringe Index (FI) and Penumbra Index (PI), were introduced. The discrimination ability of the PI and FI tests was superior when a medium containing interface effects was exploited to obtain the transverse profile data (water/lung phantom for PIhung and FIlung tests) as opposed to when a homogeneous water medium was used (PIwater and FIwater tests).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".