Sub-second and Dynamic Computed Tomography Development at the Canadian Light Source
Bibliographic record
Abstract
Dynamic CT is an emerging technique of uninterrupted acquisition of radiographic projections of a sample as it forms, deforms, or interacts to external conditions. However, a basic principle for correct tomographic reconstruction is that the sample remain unchanged during CT acquisition to avoid motion artefacts. To capture dynamic processes, either the sample stability is controlled above the limitations of the capture device, or tomographic data needs to be acquired faster. The former is used in dynamic CT joint studies through precisely controlled joint movements at clinical scanners. The Canadian Light Source (CLS) uses the latter approach as the high flux is several orders of magnitude greater than laboratory X-ray sources and well suited for sub-second acquisitions. The greater temporal resolution allows for tomographic reconstruction of an evolving sample, and the changing internal structures can be captured and visualized. Dedicated micro-CT systems are also capable of dynamic CT with scans on the order of 2 CTs/min. Computational and mechanical constraints limit dynamic CT studies to small samples for short periods of time. Research applications have been in material sciences and preliminary studies in small animals and medical implant design. In this abstract, dynamic CT was used to visualize the wet granulation process of pharmaceutical powders once in contact with water.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".