3D Rapid Prototyping for Synchrotron Application
Bibliographic record
Abstract
The Biomedical Imaging and Therapy (BMIT) Beamlines at the Canadian Light Source (CLS) is designed for the purpose of imaging and radiation therapy research both with animals ranging from mice to horses and humans. Being the only beamline in the world with this capacity, BMIT will be continuously encountering unique problems requiring innovative solutions. 3D rapid prototyping has proven to be one of the possible solutions. There are a variety of applications for 3D rapid prototyping (RP) in the Synchrotron environment. At BMIT, rapid prototyping will be the primary concentration for fabrication of unique and difficultly machined precision instruments and animal restraints. The designing and manufacturing process of such unique components includes: 1. Development of 3D model of part which could include an x-ray CT or MRI scan of animal. 2. Conversion of model or scanned data to CAD formats. 3. Fabrication of unique component using 3D printer. Conventional prototyping techniques are expensive, time consuming and dated. With late advancements in 3D printing, scanning, and CAD software, the task of 3D-RP is becoming more readily available, cost effective, accurate and provides quick turnaround. This paper describes the current and future technologies of each process, which will be implemented at the BMIT beamlines to ensure maximum resolution image quality, while improving efficiency and reducing stress on the animals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".