Bibliographic record
Abstract
The ability of the analytic anisotropic algorithm (AAA), a superposition- convolution algorithm implemented in the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system (TPS), to accurately account for the presence of inhomogeneities in simple geometries is examined. The goal of 2% accuracy, as set out by the American Association of Physicists in Medicine Task Group 65, serves as a useful benchmark against which to evaluate the inhomogeneity correction capabilities of this treatment planning algorithm. A planar geometry phantom consisting of upper and lower layers of Solid Water (Gammex rmi, Middleton, WI) separated by a heterogeneity region of variable thickness, is modeled within the Eclipse TPS. Results obtained with the AAA are compared with experimental measurements. Seven different materials, spanning the range from air to aluminum, constitute the inhomogeneity layer. In general, the AAA overpredicts dose beyond low-density regions and underpredicts dose distal to volumes of high density. In many cases, the deviation between the AAA and experimental results exceeds the Task Group 65 target of 2%. The source of these deviations appears to arise from an inability of the AAA to correctly account for altered attenuation along primary ray paths.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".