Vertical sensitivity of satellite remote sensing of atmospheric carbon monoxide
Bibliographic record
Abstract
The vertical sensitivity of the new MOPITT version 5 (V5) data is examined in terms of the averaging kernels and the Degree of Freedom of Signals (DFS). The MOPITT's vertical sensitivity is enhanced in V5 NIR/TIR data, noticeably with elevated averaging kernels in the free troposphere, in addition to in the lower troposphere emphasized previously. DFS is most enhanced over land in the Northern Hemispheric middle to high latitudes (up to 1.5). DFS is higher in winter and spring than in summer and autumn, higher over land than over the oceans, higher in the tropics than in the high latitudes. Globally, the mean maximum DFS can reach 2.5, while individual profiles can contain over 2.5 pieces of independent information vertically. Although with limited vertical sensitivity, MOPIIT CO data can generally distinguish CO in the lower, middle, and upper troposphere, thus are useful in addressing scientific questions related to air quality and climate change.
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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 | 0.002 |
| 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".