Reconstruction from truncated projections using mixed extrapolations of exponential and quadratic functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In computer tomography (CT), truncated projections are produced due to scanning large objects with a detector that is limited in width. Applying filtered back-projection(FBP) method directly to truncated projections, the reconstructed image will contain truncation artifacts – bright rings on the boundary of region of interest (ROI). Extrapolation algorithms can be used to reduce the truncation artifacts; however extrapolations are usually double the length of the projection data; resulting in an increased calculation time. This paper introduces mixed extrapolation, which is a combination of exponential and quadratic extrapolation. It is proven that doubling the length of the projection data for the mixed extrapolation can be avoided. The projections were extrapolated according to the boundary values and their derivatives. The algorithm achieves equivalence to the extrapolation approach with negligible increased calculation time. Supplementary functions are introduced in order to simplify the calculations. These functions can be calculated prior to extrapolation process, hence the calculation time is significantly reduced. The calculation times are compared between fast extrapolation introduced in this paper and normal extrapolation with doubling the length of projection data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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