Po‐Thur Eve General‐25: Development of a Tissue Sample Analysis System Using Diffraction Enhanced Imaging and Small and Wide Angle Scattering
Bibliographic record
Abstract
Current methods of x‐ray imaging, whether it is a dental x‐ray or an image of a broken bone, use absorption properties of the body for contrast in the image. This reliance on absorption to obtain an image creates problems when the soft tissues of the body are the subjects of interest. The difficulty in obtaining suitable contrast image is particularly challenging when the difference in absorption properties of two adjacent tissues are small. This challenge of obtaining a high contrast soft tissue image requires revisiting the processes of radiation interaction. The common x‐ray interactions within the diagnostic energy range are: 1) the photoelectric effect; 2) Compton scattering; and 3) Coherent scattering. For absorption x‐ray techniques the first process, the photoelectric effect, is responsible for the contrast in the image. The remaining processes of x‐ray interaction, scattering, degrade the absorption image and efforts are made to remove scattering from absorption images in the form of anti‐scatter grids and are somewhat effective. In recent years however techniques have emerged that are not degraded by scattering or even use scatter as a method of analysis and imaging. The first technique is diffraction enhanced imaging and uses x‐ray crystal diffraction as a method of removing scatter. The choice of the crystal diffraction plane will determine the level of scatter rejection. The second method is small and wide angle x‐ray scattering, (SAXS, WAXS). In this technique the unique scatter patterns from various tissues are used to form an image.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".