Spektr: A computational tool for x‐ray spectral analysis and imaging system optimization
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".