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 CO2 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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".