HIGH ACCURACY NEAR-INFRARED CARBON DIOXIDE INTENSITY MEASUREMENTS TO SUPPORT REMOTE SENSING
Bibliographic record
Abstract
We used two previously described [1,2] cavity ring-down spectroscopy systems to accurately measure line intensities in the following three $^{12}$C$^{16}$O$_{2}$ rovibrational bands near 1.6 $\\mu$m: (30012) $\\leftarrow$ (00001), (30013) $\\leftarrow$ (00001), and (30014) $\\leftarrow$ (00001). These bands are commonly used in remote sensing applications, including the Total Carbon Column Observing Network (TCCON) [3]. We estimate relative combined standard uncertainties for these band intensities of less than 0.1\\% and obtain percent-level deviations in the measured intensities relative to those in the literature and several spectroscopic databases. However, we find 0.1\\% level agreement with the (30013) and (30014) band intensities given in the HITRAN 2016 [4] database, which were calculated using ab initio dipole moment surfaces. Incorporation of the resulting line intensities into TCCON retrievals leads to significantly reduced biases in the (30012) and (30013) bands. These results indicate that refinements of spectroscopic databases are required to meet increasingly stringent remote sensing uncertainty targets.\n\n[1] Lin, H. et. al. J. Quant. Spectrosc. Radiat. Transfer, 161, 11-20.\n\n[2] Truong, G. W. et. al. (2013) Nat. Photonics, 7(7), 532-534.\n\n[3] Wunch, D. et. al. Philos. Trans. Royal Soc. A, 369(1943), 2087-211\n\n[4] Gordon, I. E., et al. (2017), J. Quant. Spectrosc. Radiat. Transfer, 203, 3-69.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".