Handling techniques for channel spectra in synchrotron-based Fourier transform spectra
Bibliographic record
Abstract
Recently, the high radiance of synchrotron sources has been used to enhance FTIR spectrometer performance. However, excessive channel spectra when synchrotron sources are used degrade the quality of retrieved spectral parameters. We have investigated seven different techniques for handling channel spectra. These techniques were used to reduce channel spectra for a test group of seven samples of CO 2 mixed with air recorded using the synchrotron source at the Canadian Light Source. The increases in signal to noise ratio (SNR) of spectra handled with each technique were calculated. SNR results showed that transmission spectra, produced using synthetic background spectra with simulated channel spectra, achieved the highest SNR improvement. However, when the spectra groups were fitted using a nonlinear least square fit algorithm, the technique using channel spectra fitting produced the smallest fitting residual. Moreover, the retrieved intensities and air broadening coefficients of 21 spectral lines showed that the spectral fitting technique produced the most accurate values as compared to the HITRAN 2008 database (Rothman et al. J. Quant. Spectrosc. Radiat. Transfer, 110, 533 (2009)). Although the spectral fitting technique was accurate in retrieving spectral line parameters, applying the technique at wider spectral ranges reduced this accuracy.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".