A simulated comparison of turnstile and Poisson photons for x-ray imaging
Bibliographic record
Abstract
X-rays are used for diagnostic imaging because they produce images with excellent resolution in a cost-effective manner. Due to their carcinogenic potential, however, a trade-off must be made between the amount of x-rays used (dose) and image quality desired Some noise in x-ray images is attributed to the random nature in which x-rays are emitted from a normal x-ray source. So-called turnstile photon devices have been developed, which release photons in a controlled manner, one at a time. A simple Monte Carlo simulation was created to model specimen radiography, or x-ray of a thin slab. The simulation models two different types of x-ray emitters: regular (Poisson statistics) and turnstile. Projected images were produced for a wide range of doses. These images were compared using mean square error and entropy measures. Results show that image quality would be significantly improved or dose reduced if turnstile photon sources were developed to replace conventional x-ray sources.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".