Bibliographic record
Abstract
All medical x-ray imaging today is done using the transmitted photons, i.e., those x-ray quanta which do not suffer any interaction within the patient. An alternative is to use the more plentiful scattered photons. Backscatter is almost entirely Compton (incoherent) scatter, which is principally sensitive to the number of electrons per unit volume. Forward scatter is dominated by coherent scatter, which is the basis of x-ray diffraction. Its cross section varies with angle and photon energy in a material-specific manner, even for amorphous materials. The dependence on Z and chemical structure allows it to be very useful in distinguishing tissues within the patient. Many workers have demonstrated utilization of both types of scatter in the lab, but it has been difficult to compare the performance of these systems with conventional transmission imaging. Therefore, we devised a semi-analytic model of scatter imaging. Our calculations predict that for some imaging tasks the contrast and signal-to-noise ratio achieved by collecting a portion of the scatter (in an annular cone) will be superior to that achieved by conventional transmission imaging, for the same number of photons incident on the patient. Our analysis is reliant on the limited published data for coherent scattering for biological materials.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".