Po‐Thur Eve General‐19: Energy dispersive x‐ray diffraction system: Analysis of noise at low momentum transfer arguments
Bibliographic record
Abstract
An energy dispersive x‐ray diffraction system has been built to measure the diffraction signals of breast tissue. The diffraction signals are a function of the momentum transfer argument, x=(E/hc)sin(θ/2), where E = photon energy and θ = scatter angle. The current system has noise problems for signals measured at x < 1.3 nm−1 where it is anticipated that most of the contrast exist for diagnosing normal breast tissue versus cancerous tissue. For a 5 mm thick 5 mm diameter water sample, the scatter signals at low x obtained at 9° with a 3 mm diameter 50 kV beam are about 70% higher than what are to be expected. Values at higher x values are however in good agreement with those in the literature. In this work we measured the scattered field from the pinhole since it could contribute to the large signals at low x values. The scattered count rates were measured at angles of 2 to 15 degrees in the usual way. For smaller angles including zero we used a 1.5 mm by 1.5 mm beam stopper centered at the bottom of the pinhole. A 100 μm diameter W aperture was placed above the detector and the field was sampled at various locations in the shadow of the beam stopper. The results suggest that the scatter from the pinhole could increase the diffraction signals at low x by 6%. Based on these findings we intend to revisit our measurement of the incident spectrum and the multiple scatter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".