The necessity of maximum information utilization in x‐ray analysis
Bibliographic record
Abstract
Abstract There are significant discrepancies in the experimental data needed in the analysis of x‐ray spectra. Examination of the data in detail shows that they often contradict simple logic, elemental arithmetic, even parity and angular momentum conservation laws. We have identified that the main source of the problems, other than the human factor, is rooted in the signal processing electronics. We have developed a line of fully digital signal processors that not only have excellent resolution and line shape but also allow proper accounting. We achieve this by processing all events and separating them into two or more spectra where the first spectrum is the accepted or good spectrum and the second spectrum is the rejected spectrum. It is not enough to know that an event was rejected, and increment the input counter—it is necessary to know what happened and why, whether it was pure noise, a noisy or disturbed event, a true event, or any pile up combination of the above in order to account properly for true event input rate and processor dead time. The data processing methodology cannot be reliably established on the partial and fractional information offered by other approaches. The availability of all the events allows one to see the other part of the spectrum. To our surprise the total information explains many of the shortcomings and contradictions of the x‐ray database. We call this a maximum information utilization approach in signal processing. Also a fundamental parameter x‐ray fluorescence analysis program (CSX‐XRF) has been developed to utilize all the information offered by the signal processor. The fundamental parameter method is only as good as the database it uses and the description of the x‐ray fluorescence analysis system. This latter poses significant difficulties, and to ease the demand we have developed an inverse fundamental parameter program package for x‐ray tube based equipment characterization. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".