Analysis of Fourier transform spectra of N<sub>2</sub>O in the <i>ν</i><sub>3</sub> band for atmospheric composition retrievals
Bibliographic record
Abstract
We report measurement results for line positions, intensities, half-width, and pressure-induced shift coefficients and line mixing coefficients for N2O broadened by air in the ν3 band. The high signal-to-noise ratio spectra have been recorded at high resolution using the McMath–Pierce Fourier transform spectrometer formerly located at the National Solar Observatory on Kitt Peak, Ariz., USA. The spectra were analyzed using a multispectrum nonlinear least-squares curve-fitting technique employing the speed-dependent Voigt profile with a Rosenkranz (weak) line mixing component. The speed dependence parameters were calculated as suggested in the study of Kochanov (J. Quant. Spectrosc. Radiat. Transf. 189, 18 (2017). doi: 10.1016/j.jqsrt.2016.11.007 ). Several comparisons have been performed between the retrieved parameters and previously published results. For |m| ≤ 40, our results for line positions, broadening, and line mixing coefficients agree best with the results of Loos et al. (J. Quant. Spectrosc. Radiat. Transf. 151, 300 (2015). doi: 10.1016/j.jqsrt.2014.10.008 ). Also, we compared the obtained line positions and intensities with the corresponding values in HITRAN2016 and GEISA-2015 databases. No significant or systematic differences were noticed. The precision of our line positions was estimated to be 3 × 10−5 cm−1. The reported line positions, intensities, and air-broadening coefficients are accurate to better than 2%. The accuracy of air-pressure-induced line shifts and line mixing coefficients is better than 5%. The line mixing coefficients and air-broadening coefficients were also calculated using the exponential power gap scaling law, and these calculated values were found to be in good agreement with the experimental results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".