Spektr: A computational tool for x‐ray spectral analysis and imaging system optimization
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
A set of computational tools are presented that allow convenient calculation of x-ray spectra, selection of elemental and compound filters, and calculation of beam quality characteristics, such as half-value layer, mR/mAs, and fluence per unit exposure. The TASMIP model of Boone and Seibert is adapted to a library of high-level language (Matlab) functions and shown to agree with experimental measurements across a wide range of kVp and beam filtration. Modeling of beam filtration is facilitated by a convenient, extensible database of mass and mass-energy attenuation coefficients compiled from the National Institute of Standards and Technology. The functions and database were integrated in a graphical user interface and made available online at http:// www.aip.org/epaps/epaps.html. The functionality of the toolset and potential for investigation of imaging system optimization was illustrated in theoretical calculations of imaging performance across a broad range of kVp, filter material type, and filter thickness for direct and indirect-detection flat-panel imagers. The calculations reveal a number of nontrivial effects in the energy response of such detectors that may not have been guessed from simple K-edge filter techniques, and point to a variety of compelling hypotheses regarding choice of beam filtration that warrant future investigation.
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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 | 0.000 |
| 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 it