Background removal from polarized Raman spectra of tooth enamel using the wavelet transform
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A wavelet transformation method is introduced to remove the large fluorescence background from polarized Raman spectra of stained tooth enamel. This method exploits the wavelet multiresolution decomposition where the experimental Raman spectrum is decomposed into signals with different frequency components, and where the lowest frequency background and highest frequency noise are removed. This method is optimized using a simulated collection of parallel‐polarized and cross‐polarized Raman spectra of the enamel and then applied to a set of experimental data. The results show that the wavelet transform technique can extract the pure spectra from background and noise, with the depolarization ratio used to discriminate between early dental caries and sound enamel preserved. Copyright © 2010 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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