Inherent Complexities of Trace Detection by Surface‐Enhanced Raman Scattering
Why this work is in the frame
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Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) and surface-enhanced resonance Raman scattering (SERRS) are powerful optical scattering techniques used in such frontier areas of research as ultrasensitive chemical analysis, the characterization of nanostructures, and the detection of single molecules. However, measuring and, most importantly, interpreting SERS/SERRS spectra can be incredibly challenging. This is the result of modifications to the measured spectra that are due to of a variety of instabilities and contributions. These interferences and modifications arise from the nature of the enhancement itself, as well as the conditions used to attain SERS spectra. The present report is an attempt to collect in one place the analytical interferences that are most commonly found during the collection of SERS/SERRS spectra.
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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