Broadband models and their consequences on line shape analysis in vibrational sum-frequency spectroscopy
Bibliographic record
Abstract
Vibrational sum-frequency generation (SFG) spectroscopy can provide valuable qualitative and quantitative information about molecular species at surface and buried interfaces. For example, the resonance frequency of a particular chemical function group is revealing of the surface environment, especially when compared to what is observed in bulk IR absorption or Raman scattering spectra. Furthermore, the amplitude of the mode can be related to the molecular orientation, providing a detailed quantitative account of the surface structure. Each of these attributes, however, requires fitting the spectra to some vibrationally resonant line shape. This is particularly challenging when the modes of interest co-exist with broad resonance features, such as water O-H stretching. In this perspective, we examine the merits and consequences of different approaches to fitting homodyne SFG data. We illustrate that, while any model can provide a useful description of the data, no model can accurately and consistently provide even the relative phase deeply encoded in homodyne data without the use of additional information.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".