Interpretation of CT-Scan Sinograms to Study the Dynamic Flow Properties of a Fluidized Bed
Bibliographic record
Abstract
Fast moving objects typically produce poor quality images and motion‐artifacts in Computed Tomography (CT) scans. The imaging of bubbling gas‐solid fluidized beds, where many bubbles may pass through the sensing cross‐section in a tomographic measurement, gives such a problem. Previously, one method of dealing with this problem was to remove artifacts from the reconstructed image itself through low pass filtering. However, this method leads to lost information from the images. When the concentration of bubbles is low, and their shape is close to cylindrically symmetric, analysis of consecutive scans in the raw data sinogram can also provide valuable information about the frequency of bubbles passing, and about their individual size, shape and position. Then, utilizing the inverse Abel transform, this information can then also be used to contribute to time averaged properties of the bed’s behaviour such as voidage distribution, bubble phase area fraction and spatial bubble number distribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".