Characterizing the detection system nonlinearity, internal inelastic background, and transmission function of an electron spectrometer for use in x-ray photoelectron spectroscopy
Bibliographic record
Abstract
We present a method for removing spectrometer specific contributions to x-ray photoelectron spectroscopy data. We consider the degree of linearity of the detection system, the strength of the internal analyzer inelastic background, and finally determine the spectrometer's transmission function. The procedures presented here are performed on a SPECS Phoibos 150 hemispherical analyzer with a two-dimensional detection system, but are applicable to a wide variety of different electron spectrometers. The spectrometer's detection system is found to deviate from linear behavior by a few percent over the whole intensity range studied. The size of the analyzer internal inelastic scattering has been measured, and we find that it can normally be neglected at large pass energies or high kinetic energies for most types of analysis (contributing less than 1% at 100 eV pass energy). Finally, we measure the transmission function of the analyzer and lens system for a variety of different settings with the preceding corrections applied, and find that the form of the transmission function is dependent on small changes in the system's settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".