A model retrieving based method for bolus shaping
Why this work is in the frame
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Bibliographic record
Abstract
Bolus is a sheet of material commonly used in the treatment of superficial tumors for desired dose distributions. Existing methods of the bolus shaping cannot meet required accuracy to cover some irregular surfaces. This paper introduces a shape retrieving method to increase the bolus accuracy and process efficiency. Common human surfaces that need bolus in the treatment are pre-processed by segmentation based on the surface flattenability and deformation. The segmented surfaces are unfolded to form 2D shapes with the minimal deformation and saved in a model base. A bolus can then be quickly formed by retrieving the matched bolus model in the model base using the patient data captured by a Kinect motion sensor. To match the model in a high accuracy, features of patient's data are first extracted using the Laplacian matrix to build a feature space. The features are matched using an iterative closest point (ICP) method. An example of the human nose bolus is presented to show the proposed method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it